vendor/libonnxruntime.so in onnxruntime-0.2.0 vs vendor/libonnxruntime.so in onnxruntime-0.2.1
- old
+ new
@@ -1,720 +1,772 @@
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_ZNSolsEi _ZNKSt15basic_stringbufIcSt11char_traitsIcESaIcEE3strEv _ZSt20__throw_out_of_rangePKc _ZNSt19basic_ostringstreamIcSt11char_traitsIcESaIcEED1Ev OrtGetApiBase _ZNSs4swapERSs _ZNSt9exceptionD2Ev _ZNSs6appendEPKcm __cxa_allocate_exception __cxa_throw __cxa_free_exception memmove _ZNSo9_M_insertImEERSoT_ memset _ZNSs6assignERKSs _ZSt20__throw_length_errorPKc _ZSt18_Rb_tree_incrementPKSt18_Rb_tree_node_base _ZNKSs7compareEPKc _ZSt29_Rb_tree_insert_and_rebalancebPSt18_Rb_tree_node_baseS0_RS_ _ZNKSs7compareERKSs _ZSt18_Rb_tree_decrementPSt18_Rb_tree_node_base _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 _ZNSt13runtime_errorC1ERKSs _ZNSt13runtime_errorD1Ev _ZTISt13runtime_error _ZNSs9_M_mutateEmmm _ZNSt11logic_errorD2Ev _ZNSs4_Rep9_S_createEmmRKSaIcE vsnprintf _ZNSs6insertEmPKc _ZNSs6appendEPKc 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fmodf fmod sin roundf rint __errno_location strtof _ZSt24__throw_invalid_argumentPKc strtoll tanh _ZNSs6assignEPKc _ZNSbIwSt11char_traitsIwESaIwEE4_Rep20_S_empty_rep_storageE _ZNSbIwSt11char_traitsIwESaIwEE4_Rep10_M_destroyERKS1_ iconv_open iconv _ZNSbIwSt11char_traitsIwESaIwEE6assignEPKwm iconv_close _ZNSbIwSt11char_traitsIwESaIwEE6assignERKS2_ _ZNSbIwSt11char_traitsIwESaIwEEC1ERKS2_ _ZNSbIwSt11char_traitsIwESaIwEE4_Rep10_M_disposeERKS1_ _ZNSt6localeC1EPKc wmemcmp _ZNSbIwSt11char_traitsIwESaIwEE12_M_leak_hardEv _ZSt9use_facetISt5ctypeIwEERKT_RKSt6locale _ZNSsD1Ev _ZNSbIwSt11char_traitsIwESaIwEEC1EPKwRKS1_ _ZNSbIwSt11char_traitsIwESaIwEED1Ev _ZNKSt9exception4whatEv _ZNSt15__exception_ptr13exception_ptraSERKS0_ _ZNSt18condition_variable10notify_allEv _ZTVSt11logic_error _ZSt17current_exceptionv _ZNSt15__exception_ptr13exception_ptr4swapERS0_ _ZNSt15__exception_ptr13exception_ptrD1Ev _ZNSt18condition_variableC1Ev _ZNSt15__exception_ptr13exception_ptrC1Ev _ZNSt18condition_variable4waitERSt11unique_lockISt5mutexE _ZNSt15__exception_ptreqERKNS_13exception_ptrES2_ _ZNSt15__exception_ptr13exception_ptrC1ERKS0_ _ZSt17rethrow_exceptionNSt15__exception_ptr13exception_ptrE _ZNSo3putEc _ZNSo5flushEv _ZNSo9_M_insertIdEERSoT_ _ZNKSt5ctypeIcE13_M_widen_initEv _ZSt16__throw_bad_castv _ZNSo9_M_insertIxEERSoT_ strtol strtoul _ZNSolsEs _ZNSo9_M_insertIbEERSoT_ strtoull strtod fesetround nearbyintf __udivti3 sysconf posix_memalign stderr fprintf pthread_cond_broadcast pthread_cond_wait pthread_join pthread_cond_destroy pthread_mutex_destroy pthread_cond_init pthread_mutex_init pthread_create lseek _ZNSo5writeEPKcl strerror _ZNSo9_M_insertIPKvEERSoT_ _ZTIl _ZTIf _ZTId _ZTIh _ZTIm _ZTIs _ZTIi _ZTIb _ZTIj _ZTIt _ZTIa _ZNSs6appendEmc strdup dirname _ZSt18_Rb_tree_decrementPKSt18_Rb_tree_node_base gmtime_r mktime difftime syscall _ZNSt9basic_iosIcSt11char_traitsIcEE5imbueERKSt6locale _ZNSt6localeC1ERKS_ _ZSt9use_facetISt8numpunctIcEERKT_RKSt6locale _ZNSt13basic_filebufIcSt11char_traitsIcEE5closeEv _ZNSt13basic_filebufIcSt11char_traitsIcEE4openEPKcSt13_Ios_Openmode _ZTVSt13basic_filebufIcSt11char_traitsIcEE _ZNSt12__basic_fileIcED1Ev _ZNSt6thread15_M_start_threadESt10shared_ptrINS_10_Impl_baseEE pthread_self getpid nanosleep dlerror dlsym dlclose strerror_r dlopen munmap mmap __fxstat pread _ZTVSt12system_error _ZSt15system_categoryv _ZNSt12system_errorD1Ev _ZTISt12system_error gettimeofday ldexpf _ZTVSt13runtime_error _ZTTSt14basic_ifstreamIcSt11char_traitsIcEE _ZTVSt14basic_ifstreamIcSt11char_traitsIcEE _ZTTSt13basic_fstreamIcSt11char_traitsIcEE _ZTVSt13basic_fstreamIcSt11char_traitsIcEE _ZNSt13basic_fstreamIcSt11char_traitsIcEED1Ev isspace _ZNSdC2Ev _ZNKSs4findEPKcmm _ZNSs7replaceEmmPKcm _ZNSs6resizeEmc fflush _ZNSs14_M_replace_auxEmmmc fwrite pthread_rwlock_init pthread_rwlock_unlock pthread_rwlock_wrlock strchr memchr pthread_rwlock_destroy pthread_rwlock_rdlock memrchr isalpha libdl.so.2 librt.so.1 libstdc++.so.6 get_nprocs usleep _ZNSt8__detail12__prime_listE floor _ZTISt14error_category _ZNKSt14error_category23default_error_conditionEi _ZNKSt14error_category10equivalentEiRKSt15error_condition _ZNKSt14error_category10equivalentERKSt10error_codei _ZTVSt14error_category libm.so.6 libgomp.so.1 libgcc_s.so.1 libpthread.so.0 libc.so.6 ld-linux-x86-64.so.2 libonnxruntime.so.1.0.0 $ORIGIN VERS_1.0.0 GLIBC_2.3 GLIBC_2.2.5 GCC_3.0 GOMP_1.0 GLIBC_2.3.2 GLIBCXX_3.4.10 GLIBCXX_3.4.9 GLIBCXX_3.4.11 CXXABI_1.3.3 CXXABI_1.3 GLIBCXX_3.4
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__cxa_pure_virtual __pthread_key_create _ZNSt13runtime_errorC1ERKSs _ZNSt13runtime_errorD1Ev _ZTISt13runtime_error _ZNSs9_M_mutateEmmm _ZNSt11logic_errorD2Ev _ZNSs6insertEmPKc _ZNSs6appendEPKc _ZNSt16invalid_argumentC1ERKSs _ZNSt16invalid_argumentD1Ev _ZTISt16invalid_argument _ZSt19__throw_logic_errorPKc _ZNSt11logic_errorC2ERKSs _ZNSs12_M_leak_hardEv _ZSt20__throw_system_errori _ZNKSt11logic_error4whatEv pthread_mutex_unlock pthread_mutex_lock __cxa_guard_acquire __cxa_guard_release __cxa_guard_abort _ZTVN10__cxxabiv121__vmi_class_type_infoE _ZNSt13runtime_errorD2Ev _ZTTSt18basic_stringstreamIcSt11char_traitsIcESaIcEE _ZTVSt18basic_stringstreamIcSt11char_traitsIcESaIcEE _ZNSt18basic_stringstreamIcSt11char_traitsIcESaIcEED1Ev _ZNSdD2Ev _ZNSt13runtime_errorC2ERKSs _ZNKSt13runtime_error4whatEv _ZSt18_Rb_tree_incrementPSt18_Rb_tree_node_base _ZNSt18basic_stringstreamIcSt11char_traitsIcESaIcEEC1ESt13_Ios_Openmode _ZStlsISt11char_traitsIcEERSt13basic_ostreamIcT_ES5_PKc _ZSt4endlIcSt11char_traitsIcEERSt13basic_ostreamIT_T0_ES6_ _ZSt4cerr _ZSt15__once_callable __tls_get_addr _ZSt11__once_call __once_proxy _ZTVN10__cxxabiv120__function_type_infoE _ZTVN10__cxxabiv119__pointer_type_infoE pthread_once _ZNSt18condition_variable10notify_oneEv _ZNSt6thread4joinEv _ZNSt18condition_variableD1Ev free _ZNSt11logic_errorC1ERKSs _ZNSt11logic_errorD1Ev __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 _ZNSsD1Ev _ZTVSt13runtime_error _ZNSs9push_backEc _ZSt28_Rb_tree_rebalance_for_erasePSt18_Rb_tree_node_baseRS_ __errno_location strtod strtoull strtoll localeconv _ZTISt12out_of_range _ZNSsC1EPKcmRKSaIcE _ZTIc sqrtf _ZdaPv sqrt tolower _Znam logf exp _ZTVSt9bad_alloc _ZTISt9bad_alloc _ZNSt9bad_allocD1Ev malloc omp_get_thread_num omp_get_num_threads omp_get_max_threads GOMP_parallel_start GOMP_parallel_end powf pow floorf atanhf acoshf asinhf atanf acosf asinf fmodf fmod sin roundf rint strtof _ZSt24__throw_invalid_argumentPKc tanh _ZNSs6assignEPKc _ZNSbIwSt11char_traitsIwESaIwEE4_Rep20_S_empty_rep_storageE _ZNSbIwSt11char_traitsIwESaIwEE4_Rep10_M_destroyERKS1_ iconv_open iconv _ZNSbIwSt11char_traitsIwESaIwEE6assignEPKwm iconv_close _ZNSbIwSt11char_traitsIwESaIwEE6assignERKS2_ _ZNSbIwSt11char_traitsIwESaIwEEC1ERKS2_ _ZNSbIwSt11char_traitsIwESaIwEE4_Rep10_M_disposeERKS1_ wmemcmp _ZNSt6localeC1EPKc _ZNSbIwSt11char_traitsIwESaIwEE12_M_leak_hardEv _ZSt9use_facetISt5ctypeIwEERKT_RKSt6locale _ZNSbIwSt11char_traitsIwESaIwEEC1EPKwRKS1_ _ZNSbIwSt11char_traitsIwESaIwEED1Ev _ZNKSt9exception4whatEv _ZNSt15__exception_ptr13exception_ptraSERKS0_ _ZNSt18condition_variable10notify_allEv _ZTVSt11logic_error _ZSt17current_exceptionv _ZNSt15__exception_ptr13exception_ptr4swapERS0_ _ZNSt15__exception_ptr13exception_ptrD1Ev _ZNSt18condition_variableC1Ev _ZNSt15__exception_ptr13exception_ptrC1Ev _ZNSt18condition_variable4waitERSt11unique_lockISt5mutexE _ZNSt15__exception_ptreqERKNS_13exception_ptrES2_ _ZNSt15__exception_ptr13exception_ptrC1ERKS0_ _ZSt17rethrow_exceptionNSt15__exception_ptr13exception_ptrE _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 sysconf posix_memalign stderr fprintf pthread_cond_broadcast pthread_cond_wait pthread_join pthread_cond_destroy pthread_mutex_destroy pthread_cond_init pthread_mutex_init pthread_create read lseek _ZNSo5writeEPKcl strerror _ZNSo9_M_insertIPKvEERSoT_ _ZTIf _ZTId _ZTIa _ZTIh _ZTIs _ZTIi _ZTIt _ZTIj _ZTIl _ZTIb _ZTIm _ZNSs6appendEmc strdup dirname _ZSt18_Rb_tree_decrementPKSt18_Rb_tree_node_base gmtime_r mktime difftime syscall _ZNSt9basic_iosIcSt11char_traitsIcEE5imbueERKSt6locale _ZNSt6localeC1ERKS_ _ZSt9use_facetISt8numpunctIcEERKT_RKSt6locale _ZNSt13basic_filebufIcSt11char_traitsIcEE5closeEv _ZNSt13basic_filebufIcSt11char_traitsIcEE4openEPKcSt13_Ios_Openmode _ZTVSt13basic_filebufIcSt11char_traitsIcEE _ZNSt12__basic_fileIcED1Ev _ZNSt6thread15_M_start_threadESt10shared_ptrINS_10_Impl_baseEE pthread_self getenv getpid nanosleep dlerror dlsym dlclose strerror_r dlopen munmap __fxstat mmap _ZTVSt12system_error _ZSt15system_categoryv _ZNSt12system_errorD1Ev _ZTISt12system_error gettimeofday ldexpf _ZTTSt14basic_ifstreamIcSt11char_traitsIcEE _ZTVSt14basic_ifstreamIcSt11char_traitsIcEE _ZTTSt13basic_fstreamIcSt11char_traitsIcEE _ZTVSt13basic_fstreamIcSt11char_traitsIcEE _ZNSt13basic_fstreamIcSt11char_traitsIcEED1Ev isspace _ZNSdC2Ev _ZNKSs4findEPKcmm _ZNSs7replaceEmmPKcm _ZNSs6resizeEmc fflush _ZNSs14_M_replace_auxEmmmc fwrite pthread_rwlock_init pthread_rwlock_unlock pthread_rwlock_wrlock strchr memchr pthread_rwlock_destroy pthread_rwlock_rdlock memrchr isalpha libdl.so.2 librt.so.1 libstdc++.so.6 get_nprocs usleep _ZNSt8__detail12__prime_listE floor _ZTISt14error_category _ZNKSt14error_category23default_error_conditionEi _ZNKSt14error_category10equivalentEiRKSt15error_condition _ZNKSt14error_category10equivalentERKSt10error_codei _ZTVSt14error_category libm.so.6 libgomp.so.1 libgcc_s.so.1 libpthread.so.0 libc.so.6 ld-linux-x86-64.so.2 libonnxruntime.so.1.1.0 $ORIGIN VERS_1.1.0 GLIBC_2.3 GLIBC_2.2.5 GCC_3.0 GOMP_1.0 GLIBC_2.3.2 GLIBCXX_3.4.10 GLIBCXX_3.4.9 GLIBCXX_3.4.11 CXXABI_1.3.3 CXXABI_1.3 GLIBCXX_3.4
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IfofofoA fs fHL9ffs fffs fwfoI9fofsfofofofs fffs fffs ff $H$t
ID HIHC H5 [ J6 Hxf D 1ɺ f ID IHHL9u Ll$`Ht$@Ly5 H|$0HL$Ht$ MLHD$ H} HD$0HD$0 HHE t:H|$0Ht+H|$`Ht 1H|$`HHtH|$@HtH,HH= HWt+(H萒LH@뫋PJHLL4! AWAVIAUATIULSLH HHL$HD$@ HD$H HD$P j HD$P HD$H A L9 Ll$`L謡H5W L(LL}H5W HHHH[H|$ Iu͐Ht$ Hl$0G6 HHD$ HHx腏H HPH@H$ Hg HT$`HHD$hH$ HxH;= Hq I}@HHD$hWH I}XHPH@HT$`HRHD`H HH$ NH|$@HtH H[]A\A]A^A_f. H H9l H IHH$H$I1HHD$@H HL$PH $ÍIGH $I9IEHL$HI9 I Lfo#W H1E1H4 D AoL
IfofofoA fs fHL9ffs fffs fwfoI9fofsfofofofs fffs fffs ff $H$t
ID HIH?H99 H5 [ E6 Hn @ 1ɺ ID IHHL9u Ll$`Ht$@LY5 H|$0HL$Ht$ MLHD$ H} HD$0HD$0 HHE tH|$0HtH|$`Ht̏1H|$`HHt賏H|$@Ht褏HHH=Ǖ HWt+(HPLH 뫋PJH L4 f. D AWAVIAUATIULSLH HHL$HD$@ HD$H HD$P j HD$P HD$H A L9 Ll$`L\H54
-W L؏LL-H51
-W HH趏HH H|$ Iu}Ht$ Hl$0C6 HHD$ HHx5HΓ HPH@H$ H HT$`HHD$hH$ HxH;= H! I}@HHD$hH I}XHPH@HT$`HRHD`H_ HH$ H|$@Ht蟍H H[]A\A]A^A_f. H H9l H IHH$薍H$I1HHD$@H HL$PH $sIGH $I9IEHL$HI9 I LfoY W H1E1H4 D AoL
IfofofoA fs fHL9ffs fffs fwfoI9fofsfofofofs fffs fffs ff $H$t
ID HIH H99 H5o[ tA6 Hn @ 1ɺ ID IHHL9u Ll$`Ht$@L^y5 H|$0HL$Ht$ MLHD$ ZH} HD$0HD$0 HHE t誗H|$0Ht蛗H|$`Ht|1H|$`HHtcH|$@HtTH蜑HH=w HWt+(H LH谒뫋PJH輅L4 f. D AWAVIAUATIULSLH HHL$HD$@ HD$H HD$P j HD$P HD$H A L9 Ll$`L H5W L舋LL݇H5W HHfHH軇H|$ Iu-Ht$ Hl$0Y?6 HHD$ HHxH~ HPH@H$ HǏ HT$`HHD$hH$ HxH;=f Hю I}@HHD$h跌H` I}XHPH@HT$`HRHD`H HH$ 讃H|$@HtOH H[]A\A]A^A_f. H H9l H IHH$FH$I1HHD$@H HL$PH $#IGH $I9IEHL$HI9 I Lfo W H1E1H4 D AoL
IfofofoA fs fHL9ffs fffs fwfoI9fofsfofofofs fffs fffs ff $H$t
ID HIH H99 H5 [ $=6 Hn @ 1ɺ ID IHHL9u Ll$`Ht$@Li5 H|$0HL$Ht$ MLHD$
-H} HD$0HD$0 HHE tZH|$0HtKH|$`Ht,1H|$`HHtH|$@HtHLHH=' HWt+(H谅LH`뫋PJHlL4 f. D AWIAVAUIATMULSH HHL$HD$@ HD$H HD$P Z HD$P HD$H H9} Lt$`L轔H5W L9HL莃H5W HHHHlH|$ IvރHt$ Hl$0
-;6 HHD$ HHx薂H/ HHPH@H$ Hu HT$`HHD$hH$ HxVH I~@HHD$hmH I~XHPH@HT$`HRHD`Hʼn HH$ dH|$@HtH H[]A\A]A^A_H H9 H IH
-H H1HHD$@H$HL$PHL$L$HL$I@HL$HI9IFI9L IB LfoW H11H4 @ Ao HfofofoA fs fHH9ffs fffs fwfoI9fofsfofofofs fffs fffs ff $H $IHIH9Lt$`Ht$@L5 H|$0HL$Ht$ MLHD$ I<$HD$0HD$0 HI$tgH|$0HtXH|$`Ht91 f1ɻ f IIHHL9uH|$`HHtH|$@HtH-LHrH}f D AWAVIAUATIULSLH HHL$HD$@ HD$H HD$P j HD$P HD$H A L9 Ll$`LH5V LhLLH5V HHFHHH|$ Iu
Ht$ Hl$0976 HHD$ HHx~H^ HPH@H$ H HT$`HHD$hH$ HxH;=F H I}@HHD$h藄H@ I}XHPH@HT$`HRHD`H HH$ {H|$@Ht/H H[]A\A]A^A_f. H H9l H IHH$&H$I1HHD$@H HL$PH $}IGH $I9IEHL$HI9 I LfoW H1E1H4 D AoL
IfofofoA fs fHL9ffs fffs fwfoI9fofsfofofofs fffs fffs ff $H$t
ID HIHC H5 Z 56 Hxf D 1ɺ f ID IHHL9u Ll$`Ht$@LI5 H|$0HL$Ht$ MLHD$ H} HD$0HD$0 HHE t:H|$0Ht+H|$`Ht 1H|$`HHt~H|$@Ht~H,HH= HWt+(H}LH@뫋PJHLyL4! AWAVIAUATIULSLH HHL$HD$@ HD$H HD$P j HD$P HD$H A L9 Ll$`L謌H5V L(LL}{H5V HHHH[{H|$ Iu{Ht$ Hl$026 HHD$ HHxzH HPH@H$ Hg HT$`HHD$hH$ HxH;= Hq I}@HHD$hWH I}XHPH@HT$`HRHD`H HH$ NwH|$@Ht|H H[]A\A]A^A_f. H H9l H IHH$|H$I1HHD$@H HL$PH $xIGH $I9IEHL$HI9 I LfoW H1E1H4 D AoL
IfofofoA fs fHL9ffs fffs fwfoI9fofsfofofofs fffs fffs ff $H$t
ID HIHC H5Z 06 Hxf D 1ɺ f ID IHHL9u Ll$`Ht$@L讶虎5 H|$0HL$Ht$ MLHD$ H} HD$0HD$0 HHE tH|$0HtH|$`Htz1H|$`HHtzH|$@HtzHHH=ǀ HWt+(HPyLH 뫋PJH uL4! AWAVIAUATIULSLH HHL$HD$@ HD$H HD$P j HD$P HD$H A L9 Ll$`LlH5DV LzLL=wH5AV HHzHHwH|$ IuwHt$ Hl$0.6 HHD$ HHxEvH~ HPH@H$ H' HT$`HHD$hH$ HxH;=} H1~ I}@HHD$h|H} I}XHPH@HT$`HRHD`Ho} HH$ sH|$@HtxH H[]A\A]A^A_f. H H9w H IHH$xH$I1HHD$@H HL$PH $tIGH $I9IEHL$HI9 I Lfoi
-W H1E1H4 D AoL
IfofofoA fs fHL9ffs fffs fwfoI9fofsfofofofs fffs fffs ff $H$t
ID HIH H9D H5Z ,6 Hn @ 1ɺ ID IHHL9u Ll$`Ht$@Ln5 H|$0HL$Ht$ MLHD$ jH] HD$0HD$0 HHE t HW8 HvH|$0Ht蠂H|$`Htv1H|$`HHthvH|$@HtYvH|HH=|| HWt+ HuLH}뫋PJHpL4 @ AWAVIAUATIULSLH HHL$HD$@ HD$H HD$P j HD$P HD$H A L9 Ll$`L H5V LvLLrH5V HHvvHHrH|$ Iu=sHt$ Hl$0i*6 HHD$ HHxqHz HPH@H$ Hz HT$`HHD$hH$ HxH;=vy Hy I}@HHD$hwHpy I}XHPH@HT$`HRHD`H y HH$ nH|$@Ht_tH H[]A\A]A^A_f. H H9l H IHH$VtH$I1HHD$@H HL$PH $3pIGH $I9IEHL$HI9 I LfoW H1E1H4 D AoL
IfofofoA fs fHL9ffs fffs fwfoI9fofsfofofofs fffs fffs ff $H$t
ID HIH?H99 H5/Z 4(6 Hn @ 1ɺ ID IHHL9u Ll$`Ht$@L Y5 H|$0HL$Ht$ MLHD$ H} HD$0HD$0 HHE tj~H|$0Ht[~H|$`Ht<r1H|$`HHt#rH|$@HtrH\xHH=7x HWt+(HpLHpy뫋PJH|lL4 f. D AWAVIAUATIULSLH HHL$HD$@ HD$H HD$P j HD$P HD$H A L9 Ll$`LH5V LHrLLnH5V HH&rHH{nH|$ IunHt$ Hl$0&6 HHD$ HHxmH>v HPH@H$ Hv HT$`HHD$hH$ HxH;=&u Hu I}@HHD$hwsH u I}XHPH@HT$`HRHD`Ht HH$ njH|$@HtpH H[]A\A]A^A_f. H H9l H IHH$pH$I1HHD$@H HL$PH $kIGH $I9IEHL$HI9 I LfoW H1E1H4 D AoL
IfofofoA fs fHL9ffs fffs fwfoI9fofsfofofofs fffs fffs ff $H$t
ID HIH H99 H5Z #6 Hn @ 1ɺ ID IHHL9u Ll$`Ht$@LΩ虀5 H|$0HL$Ht$ MLHD$ H} HD$0HD$0 HHE tzH|$0Ht zH|$`Htm1H|$`HHtmH|$@HtmH tHH=s HWt+(HplLH u뫋PJH,hL4 f. D AUATUSDH AHD$
HV EIIHLNc Lf D LL$ ILLu D HW H\$ HtHHD$耷8 HlHD$HĨ []A\A] D LL$ ILL LL$ ILL LL$ ILLx LL$ ILLMX LL$ ILLm8 LL$ ILL LL$ ILL LL$ ILL LL$ ILL LL$ ILL LL$ ILL-x LL$ ILL]X LL$ ILL8 Hl$@HyH5c*W H/lH%qH5FV " HlH|$0HuhHt$0 6 HD$HD$0Ht$ HxgHDrHD$f. H|$@趦t5 HHR It5 Hl$ IHD$ H\$@jHHt$@ @ H5 HjLjHHH{HkHSHtNLcH$ HT$@HD$@ H1HHH|$ HtuH HtpcHdHA]mHdHHR H 6 HD$nHD$HHH|$@HtHPHD$@ sIHHLp]HidHHR Hs 6 HD$inHD$LHHTnfAUIATUSHHHAL&oHf$tHPH=o tEX hHE1LLHH8 H|$H+Ht֩HH[]A\A]Ã@HHwhH|$Ht訩HnHf. AWAVAUIATIUSHH HHD$@ HD$H HD$P 2 HD$P HD$H ( 6hHHy @ @ HH3 H5K1 Lk HHHCHCH!2 HHCDtH\$8H\$`Ht$@HD$0HCt5 H|$ HL$0Ht$HHD$gI $HD$ HD$ HI$t*H8 H<gH\$ HtHڱ8 H"gH|$`HtgH|$8HtDH|$@HtfHĈ 1[]A\A]A^A_ÐH H9wgL< ILfJ 8L1HHHD$@HL$PHL$bHL$E1HL$Hf D O NL II9uq1%aHH|$@HtSfHlH|$`HHt9fH|$8HtjH D f. @ AWAVAUIATIUSHH HHD$@ HD$H HD$P 2 HD$P HD$H ( eHHy @ @ HHb1 H5 / Lk HHHCHCH/ HHCrH\$8H\$`Ht$@HD$0HȠ3t5 H|$ HL$0Ht$HHD$'I $HD$ HD$ HI$t*H贯8 HdH\$ HtH蚯8 HdH|$`HtdH|$8HtH|$@HtdHĈ 1[]A\A]A^A_ÐH H9wgL< ILdJ 8L1HHHD$@HL$PHL$`HL$E1HL$Hf D O NL II9uq1^HH|$@HtdH[jH|$`HHtcH|$8Ht*H D f. @ AWAVAUIATIUSHH HHD$@ HD$H HD$P 2 HD$P HD$H ( cHHy @ @ HH"/ H5, Lk HHHCHCH- HHCoH\$8H\$`Ht$@HD$0H舞cq5 H|$ HL$0Ht$HHD$I $HD$ HD$ HI$t*Ht8 HbH\$ HtHZ8 HbH|$`HtbH|$8HtģH|$@HtubHĈ 1[]A\A]A^A_ÐH H9wgL< IL~bJ 8L1HHHD$@HL$PHL$[^HL$E1HL$Hf D O NL II9uq1\HH|$@HtaHhH|$`HHtaH|$8HtH D f. @ AWAVAUIATIUSHH HHD$@ HD$H HD$P 2 HD$P HD$H ( vaHHy @ @ HH, H5* Lk HHHCHCHa+ HHCmH\$8H\$`Ht$@HD$0HHCp5 H|$ HL$0Ht$HHD$I $HD$ HD$ HI$t*H48 H|`H\$ HtH8 Hb`H|$`HtS`H|$8Ht脡H|$@Ht5`HĈ 1[]A\A]A^A_ÐH H9wgL< IL>`J 8L1HHHD$@HL$PHL$\HL$E1HL$Hf D O NL II9uq1eZHH|$@Ht_HeH|$`HHty_H|$8Ht誠H D f. @ AWAVIAUIATE1UHS1HhH ( a_HI @ @ LH* H5v( Mn HIIFIFHL) HIFokI)HD$0E1I1Lt$8MHD$@ HD$H HD$P N4(MHD$@HD$HLt$PtLHHcLt$Hun5 H|$ HL$0HT$@Ht$HD$GLe HD$ HD$ MHE t*LԨ8 L ^Hl$ HtH躨8 H^H|$@Ht]H|$8Ht$HtH]Hh1[]A\A]A^A_f D H I9 N, L] H H9wNH IHHT$]HT$1HHL$YE1O N IM9u1d WHHtH]HccH|$@HHt]H|$8Ht2WHH|$8HLf AWAVAUIATIUSHH HHD$@ HD$H HD$P 2 HD$P HD$H ( \HHy @ @ HH"( H5% Lk HHHCHCH& HHChH\$8H\$`Ht$@HD$0H舗3h5 H|$ HL$0Ht$HHD$I $HD$ HD$ HI$t*Ht8 H[H\$ HtHZ8 H[H|$`Ht[H|$8HtĜH|$@Htu[HĈ 1[]A\A]A^A_ÐH H9wgL< IL~[J 8L1HHHD$@HL$PHL$[WHL$E1HL$Hf D O NL II9uq1UHH|$@HtZHaH|$`HHtZH|$8HtH D f. @ AWAVIAUIATE1UHS1HhH ( ZHI @ @ LH
& H5# Mn HIIFIFH$ HIFfI)HD$0E1I1Lt$8MHD$@ HD$H HD$P N4(MHD$@HD$HLt$PtLHH^Lt$HEj5 H|$ HL$0HT$@Ht$HD$Le HD$ HD$ MHE t*L8 L\YHl$ HtH8 HBYH|$@Ht3YH|$8HtdHtHYHh1[]A\A]A^A_f D H I9 N, L Y H H9wNH IHHT$XHT$1HHL$TE1O N IM9u1d +SHHtH[XH^H|$@HHtAXH|$8HtrRHH|$8HLf AWAVAUIATIUSHH HHD$@ HD$H HD$P 2 HD$P HD$H ( WHHy @ @ HHb# H5 ! Lk HHHCHCH! HHCdH\$8H\$`Ht$@HD$0HȒ#e5 H|$ HL$0Ht$HHD$'I $HD$ HD$ HI$t*H贡8 HVH\$ HtH蚡8 HVH|$`HtVH|$8HtH|$@HtVHĈ 1[]A\A]A^A_ÐH H9wgL< ILVJ 8L1HHHD$@HL$PHL$RHL$E1HL$Hf D O NL II9uq1PHH|$@HtVH[\H|$`HHtUH|$8Ht*H D f. @ AWAVIAUIATE1UHS1HhH ( UHI @ @ LHM! H5 Mn HIIFIFH HIFaI)HD$0E1I1Lt$8MHD$@ HD$H HD$P N4(MHD$@HD$HLt$PtLHH ZLt$H5b5 H|$ HL$0HT$@Ht$HD$Le HD$ HD$ MHE t*LT8 LTHl$ HtH:8 HTH|$@HtsTH|$8Ht褕HtHWTHh1[]A\A]A^A_f D H I9 N, L]T H H9wNH IHHT$)THT$1HHL$PE1O N IM9u1d kNHHtHSHYH|$@HHtSH|$8Ht貔+NHH|$8HLf AWAVIAUIATE1UHS1HhH ( aSHI @ @ LH H5v Mn HIIFIFHL HIFo_I)HD$0E1I1Lt$8MHD$@ HD$H HD$P N4(MHD$@HD$HLt$PtLHHWLt$HEe5 H|$ HL$0HT$@Ht$HD$GLe HD$ HD$ MHE t*LԜ8 L RHl$ HtH躜8 HRH|$@HtQH|$8Ht$HtHQHh1[]A\A]A^A_f D H I9 N, LQ H H9wNH IHHT$QHT$1HHL$ME1O N IM9u1d KHHtHQHcWH|$@HHtQH|$8Ht2KHH|$8HLf AWAVIAUIATE1UHS1HhH ( PHI @ @ LHM H5 Mn HIIFIFH HIF\I)HD$0E1I1Lt$8MHD$@ HD$H HD$P N4(MHD$@HD$HLt$PtLHH ULt$HUc5 H|$ HL$0HT$@Ht$HD$Le HD$ HD$ MHE t*LT8 LOHl$ HtH:8 HOH|$@HtsOH|$8Ht褐HtHWOHh1[]A\A]A^A_f D H I9 N, L]O H H9wNH IHHT$)OHT$1HHL$KE1O N IM9u1d kIHHtHNHTH|$@HHtNH|$8Ht貏+IHH|$8HLf AWAVAUIATIUSHH HHD$@ HD$H HD$P 2 HD$P HD$H ( 6NHHy @ @ HH H5K Lk HHHCHCH! HHCDZH\$8H\$`Ht$@HD$0H^5 H|$ HL$0Ht$HHD$gI $HD$ HD$ HI$t*H8 H<MH\$ HtHڗ8 H"MH|$`HtMH|$8HtDH|$@HtLHĈ 1[]A\A]A^A_ÐH H9wgL< ILLJ 8L1HHHD$@HL$PHL$HHL$E1HL$Hf D O NL II9uq1%GHH|$@HtSLHRH|$`HHt9LH|$8HtjH D f. @ AWAVAUIATIUSHH HHD$@ HD$H HD$P 2 HD$P HD$H ( KHHy @ @ HHb H5 Lk HHHCHCH HHCXH\$8H\$`Ht$@HD$0HȆ]5 H|$ HL$0Ht$HHD$'I $HD$ HD$ HI$t*H贕8 HJH\$ HtH蚕8 HJH|$`HtJH|$8HtH|$@HtJHĈ 1[]A\A]A^A_ÐH H9wgL< ILJJ 8L1HHHD$@HL$PHL$FHL$E1HL$Hf D O NL II9uq1DHH|$@HtJH[PH|$`HHtIH|$8Ht*H D f. @ AWAVIAUIATE1UHS1HhH ( IHI @ @ LHM H5 Mn HIIFIFH HIFUI)HD$0E1I1Lt$8MHD$@ HD$H HD$P N4(MHD$@HD$HLt$PtLHH NLt$H5[5 H|$ HL$0HT$@Ht$HD$Le HD$ HD$ MHE t*LT8 LHHl$ HtH:8 HHH|$@HtsHH|$8Ht褉HtHWHHh1[]A\A]A^A_f D H I9 N, L]H H H9wNH IHHT$)HHT$1HHL$DE1O N IM9u1d kBHHtHGHMH|$@HHtGH|$8Ht貈+BHH|$8HLf AVAUATUSH HD$ HV ILHHHc H HL$ L+ H? H\$ HtHHD$舑8 HFHD$HĠ []A\A]A^ HL$ L[f HL$ L뙐HL$ L뉐HL$ Lvf D HL$ L ^f D HL$ LsFf D HL$ L.f D HL$ Lf D HL$ L+f D HL$ Lf D HL$ L{f D HL$ Lf D HL$ Lf D Hl$@H#TH5W HFHKH5V " HFH|$0HuSCHt$0 5 HD$HD$0Ht$ Hx BHJ H}HJH@H$ HL$@SHI H}XHJHRHL$@HIHT @HoI HH$ ?HD$ @ H|$@րN5 HHR IN5 Ld$ IHD$ H\$@DHHt$@ @ H LbLrHHH{LcHSHtnLkHD$@HD$@ HE 1"HHH|$ Ht PH Ht=H?LAGH>HHR H5 HD$HHD$HHH|$@HtHPHD$@ sIHHL
K]H>HHR H5 HD$HHD$kHH{Hf AUATA USHHHWH+HH LHt$,CLHKHHt$L)HH>H HtvH8H;HKH)HH Ll HtHHGH;IHtBH+LLkHkH[]A\A]f D HH9vIZ LE1H HIH9LF. HHGH;GHt$t%HtH0HGHHG1Hf1 @ Ht$H1HHt Ht/r;<HHR H5 H$FH$<HHR H5 H$FH$tf. AUATA USHHHWH+HH LHt$lALHKHHt$L)HH>H HtvH8H;HKH)HH Ll HtHHFH;IHt@H+LLkHkH[]A\A]f D HH9vIZ LE1H HIH9LF. HHG`H;GhHt$t%HtH0HG`HHG`1Hf1 @ Ht$HX1HHt Ht/9-;HHR H75 H$.EH$;HHR H
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m m m m 0 m @ m P m ` m p m m m m m m m m m m m m 0 m @ m P m ` m p m m m m m m m m m m m m 0 m @ m P m ` m p m m m m m m m m m m m m 0 m @ m P m ` m p m m m m m m m m m m m m 0 m @ m P m ` m p m m m m m m m m m m m m 0 m @ m P m ` m p m m m m m m m m m m m m m m m m m m m m m m 0 m @ m P m ` m p m m m m m m m m m m m m 0 m @ m P m ` m p m m m m m m m m m m m m 0 m @ m P m ` m p m m m m m m m m m m m m 0 m @ m P m ` m p m m `) m 0* m P, m - m . m . m . m / m / m / m 0/ m @/ m P/ m `/ m p/ m / m @1 m 2 m `4 m 6 m 06 m @ ` P P p @ ` @ p @ ` P 0 ` ` 0 ` 0 P p p 0 P p p 0 P p 0 P p 0 P p 0 P p 0 P p ! 0! P! p! ! ! ! ! " 0" P" p" " " " " # 0# P# p# # # # # $ 0$ P$ p$ $ $ $ $ % 0% P% p% % % % % & 0& P& p& & & & & ' > ? p? ? ? ? ? @ A A pB B B C C PD pD E 0E PE 0F PF pF F F H pH H 0I PI pI I I I pK K 0L PL pL L L L L N N O 0O PP P Q 0Q PQ pQ R R R R V V W 0W PX pX Y pY Y 0Z Z Z P[ [ \ p\ ^ 0_ _ _ P` c @c `c c c c c d d @d `d d d d d e e @e `e e e e e f f @f `f f f pn n Po po o o q r 0r Pr pr r r r t t t u 0u Pu pu u u u u v 0v y pz z { | | P} } } p~ ~ 0 P p 0 P p Ё 0 P 0 P p Ј Ќ 0 p Л О П @ ` ࣘ @ Э Я P 0 P г 0 P p p и @ 0 л @ ` ` 0 Ø 0Ø PØ pØ Ø Ø Ø Ø Ę 0Ę PĘ pĘ Ę Ę Ę Ę Ř 0Ř PŘ pŘ Ř Ř Ř Ř Ƙ 0Ƙ PƘ pƘ Ƙ Ƙ Ƙ Ƙ ǘ 0ǘ Pǘ pǘ ǘ ǘ ǘ ǘ Ș 0Ș PȘ pȘ Ș Ș Ș Ș ɘ 0ɘ Pɘ pɘ ɘ ɘ ɘ ɘ ʘ 0ʘ Pʘ pʘ ʘ ʘ ʘ ʘ ˘ 0˘ P˘ p˘ ˘ ˘ ˘ ˘ ̘ 0̘ P̘ p̘ ̘ ̘ @ `
+ P
+ @ P
0 P p 0 P p 0 P p ) ) * @* `* - 0- . . p0 P1 p1 1 2 4 p4 4 5 5 6 P7 p 6 < 2 2 2 n 2 @- 2 `7 2 D 2 5 5 5 ( 5 0 5 5 5 ` 5 @ 5 H 5 P 5 X 5 p 5 5 5 @ 5 5 5 5 5 5 f 5 g 5 ` 5 5 5 5 5 5 5 5 5 5 5 5 ` 5 p 5 ͘ 5 (͘ 5 0͘ 5 8͘ 5 Ϙ 5 Ϙ 5 Ϙ 5 Ϙ 5 5 @ 5 5 5 5 @ 5 5 5 5 5 5 5 5 5 5 5 P 5 X 5 ` 5 5 P 5 X 5 ` 5 h 5 p 5 x 5 5 ` 5 h 5 p 5
+ 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
5 ) 5 ) 5 ) 5 + 5 , 5 , 5 , 5 , 5 , 5 , 5 , 5 , 5 - 5 0 5 1 5 83 5 4 5 6 5 `T T T U `V \ ] `] ] ` a a b b q z @{ | @ @ ` ` ` ` ` ` ` @ ঘ ` / / q p 0 @ ` C C C x< C D C @q ਘ o B 5 p6 x6 6 (: : 8: H: P: `: h: " p: $ x: * : < : ? : C : V : Y : ] : ^ : ` : a : t : v : v ; ; ; ; (; 0; 8; @; H; P; X; `; h; D ; ; ; ; ; ; ; ; ; ; ; ; ; < < < < $ (< ' 0< ( 8< * @< - H< 3 P< 6 X< 7 `< < h< ? p< A < H D D < < < < < < <
+ < < <
< < = = = = = (= 0= 8= @= H= P= X= `= h= ! p= # x= % = & = ' = ( = ) = + = , = - = . = / = 0 = 1 = 2 = 3 = 4 = 5 = 6 > 7 > 8 > 9 > : > ; (> = 0> > 8> ? @> @ H> A P> B X> D `> E h> F p> G x> H > I > J > K > L > M > N > O > P > Q > R > S > T > U > W > X > Z ? [ ? \ ? ] ? _ ? b (? c 0? d 8? e @? f H? g P? h X? i `? j h? k p? l x? n ? o ? p ? q ? r ? s ? u ? w ? x ? y ? z ? { ? | ? } ? ~ ? ? @ @ @ @ @ (@ 0@ 8@ @@ J H@ P@ X@ `@ h@ p@ x@ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ A A A A A (A 0A 8A @A HA PA XA `A hA pA xA A A A A A A A A A A A A A A A A B B B B B (B 0B 8B @B HB PB XB `B hB pB xB B B B B B B B B B B B B B B B B C C C C C (C 0C 8C @C HC PC XC `C
+ hC pC xC
C C C C C C C C C C C C C ! C " C # C % D & D ) D + D , D . (D / 0D 0 8D 1 @D 4 HD 8 PD 9 XD : `D ; hD = pD > xD @ D D D E D F D G H= H 5
+K % K @ %
+K h %K h %J h %J h %J h %J h %J h %J h p%J h `%J h P%J h
+ @%J h 0%J h %J h
%J h %J h %J h %J h %zJ h %rJ h %jJ h %bJ h %ZJ h %RJ h p%JJ h `%BJ h P%:J h @%2J h 0%*J h %"J h %J h %J h %
+J h %J h! %I h" %I h# %I h$ %I h% %I h& %I h' p%I h( `%I h) P%I h* @%I h+ 0%I h, %I h- %I h. %I h/ %I h0 %I h1 %zI h2 %rI h3 %jI h4 %bI h5 %ZI h6 %RI h7 p%JI h8 `%BI h9 P%:I h: @%2I h; 0%*I h< %"I h= %I h> %I h? %
+I h@ %I hA %H hB %H hC %H hD %H hE %H hF %H hG p%H hH `%H hI P%H hJ @%H hK 0%H hL %H hM %H hN %H hO %H hP %H hQ %zH hR %rH hS %jH hT %bH hU %ZH hV %RH hW p%JH hX `%BH hY P%:H hZ @%2H h[ 0%*H h\ %"H h] %H h^ %H h_ %
+H h` %H ha %G hb %G hc %G hd %G he %G hf %G hg p%G hh `%G hi P%G hj @%G hk 0%G hl %G hm %G hn %G ho %G hp %G hq %zG hr %rG hs %jG ht %bG hu %ZG hv %RG hw p%JG hx `%BG hy P%:G hz @%2G h{ 0%*G h| %"G h} %G h~ %G h %
+G h %G h %F h %F h %F h %F h %F h %F h p%F h `%F h P%F h @%F h 0%F h %F h %F h %F h %F h %F h %zF h %rF h %jF h %bF h %ZF h %RF h p%JF h `%BF h P%:F h @%2F h 0%*F h %"F h %F h %F h %
+F h %F h %E h %E h %E h %E h %E h %E h p%E h `%E h P%E h @%E h 0%E h %E h %E h %E h %E h %E h %zE h %rE h %jE h %bE h %ZE h %RE h p%JE h `%BE h P%:E h @%2E h 0%*E h %"E h %E h %E h %
+E h %E h %D h %D h %D h %D h %D h %D h p%D h `%D h P%D h @%D h 0%D h %D h %D h %D h %D h %D h %zD h %rD h %jD h %bD h %ZD h %RD h p%JD h `%BD h P%:D h @%2D h 0%*D h %"D h %D h %D h %
+D h %D h %C h %C h %C h %C h %C h %C h p%C h `%C h P%C h @%C h 0%C h %C h %C h %C h %C h %C h %zC h %rC h %jC h %bC h %ZC h %RC h p%JC h `%BC h P%:C h @%2C h 0%*C h %"C h %C h %C h HSHtHw H[[ÐHSHtHwp H[[ÐSHHHtHGHt$HxUHmH[ÐHSHt%H HtOH{HtAH[8[HSHtx H[ [ÐAUATIUSHHt|H_HLl$Ht HCH+LHxHHID$@I|$8H 1ID$P ID$H I|$8I|$ I|$LH[]A\A]ÐSHHHtIHGHxH;=V7 t0H=8 HWt PJHЅ
+Ht$H,H[SHHHtIHGHxH;=6 t0H=x8 HWt PJHЅ
+Ht$HH[USHHH H HtH HtH{hHtH{PHtzH{8HtlHC0Hl$HHx7H{HtIHCHHxH1H[]SHHHtIHGHxH;=5 t0H=|7 HWt PJHЅ
+Ht$HH[ATUSHLgHH_@L9t6H;H-E H B H5wE H=hE H$ H9u1H[]A\SHHHtIHGHxH;=L5 t0H=6 HWt PJHЅ
+Ht$
+H"H[SHHHtIHGHxH;=4 t0H=n6 HWt PJHЅ
+Ht$HH[SHHHtIHGHxH;=4 t0H=6 HWt PJHЅ
+Ht$RHjH[SHHHtIHGHxH;=84 t0H=5 HWt PJHЅ
+Ht$HH[SHHHtIHGHxH;=3 t0H=Z5 HWt PJHЅ
+Ht$HH[SHHHtIHGHxH;=3 t0H=4 HWt PJHЅ
+Ht$>HVH[USHHH H Ht-H Ht H{hHtH{PHt H{8HtHC0H-2 HxH9t0H=N4 HWt PJHЅ
+Ht$H{HtHCHxH9t0H=4 HWt PJHЅ
+Ht$CH[H[]ATIUHSH HF@HXHB HHVD t Ht$HL$$Hd$HD$ 3 H []A\ATIUHSH HF@HXHB HHD t$Ht$HL$$Ld$Hd$HD$ H []A\AVIAUIATMUHSHF@LHxGB HHH B ttH
+t t1]IE@HxB LHHӘB tI$; uIF@HxB HT$0HH覘B tHD$0H ; []A\A]A^USHHH H HtH HtH{hHtH{PHtH{8HtHC0Hl$HHxUH{HtgHCHHx7HOH[]ATUHSHH HG HG ]L`HH HLcBLc[]A\H;HHtHASHHHtIHGHxH;=/ t0H=21 HWt PJHЅ
+Ht$rHH[AWIHAVAUEATIULSHH H|$ H|$@蒆 H|$PL腆 H|$`Hx H$ HHD$ V H5)] H.] L$ Ld$pEHDLL H$ Ht$ LH$ HHD$EB $ H$ H;=0 u2 H$ HH. H$ HH$
+Ht$H$ HHxBHD$pHt$Hx/Eu HD$O$HL$ Hx} HD$LL$ Hxb ƃ$ EIElj$ H|$@LLd$ : H|$PH- H|$`I IH?H9D$HDŽ$ HDŽ$ HDŽ$ v H=e] H|$ tLHD$H, HH$ HtHD$HHD$H$ H$ HH$ E J HD$ HD$(H$ HD$L9|$|#HT$H$ H$ H'o 1H$ H;$ t Ht HH$ H+$ I?HHEHrL9LFE1MtJ< IH$ H$ MH)HH ItAHtLLL$0YLL$0IH$ IHtLL$8LD$0LL$8LD$0KL$ L$ H$ HL9M1H$ H;$ t HAE t HH$
H|$L HII9M1H$ H;$ t HAE t HH$
H|$L HII9HD$(HD$HIIHL$MLLH7 H|$PH H|$`Hs H|$ IHDŽ$ HDŽ$ HDŽ$ y H=c] H|$ tJHD$IILH$ HtHD$(fHD$(H$ H$ LH$ Et0O4?E1M9}:HL$MLHH ILLMHL$MLHHk HD$H$ H|$HH.l H$ HtH$ H|$ H4D HH-V H|$`HtH|$PHD$` HtH|$@HD$P H HH$ HHx-HHD$pHHHx H$ HHt
+HH$ 9-V HH|$`Ht
HD$` HH|$PHtHD$P HH|$@HtHH H[]A\A]A^A_SHHHtIHGHxH;=r( t0H=) HWt PJHЅ
+Ht$0HHH[SHHHtIHGHxH;=( t0H=) HWt PJHЅ
+Ht$HH[SHHHtIHGHxH;=' t0H=8) HWt PJHЅ
+Ht$xHH[USHHH H HtgH HtVH{hHtHH{PHt:H{8Ht,HC0H-' HxH9t0H=( HWt PJHЅ
+Ht$H{HtHCHxH9t0H==( HWt PJHЅ
+Ht$}HH[]SHHHtIHGHxH;=b& t0H=' HWt PJHЅ
+Ht$ H8H[ATUHSHH |B Htx u
H)B Hu
{ ~<1 H B x(u H@ @ v{ b HW H
^ H5
^ 7 HHl$0I~ H0 IE11HHH H}PH5
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n^ H5Z
^ > HHl$0Im~ H/ IE11HHH H}PH5 ^ @H8AE LE
HH AE HLE HGH []A\AUIATIU1SHHI$ID$H)HH9 H<$B HB MMIU H$LH)HHI~2H9tdH;BuHXH;BuHLH H;BuIP<HLH)HHtHtHu!H9tII9 t II9 uLI9u HLup H7H[]A\A]AWAVAUMATIUHH5] SH8 I H$ H<$HT$(H$ LL$0DŽ$ H{I@DŽ$ L$ H{H5^ k H{(H5?b LH$ H{8H5 ] DŽ$ DŽ$ HHD$WH{@H5@^ 2k H$` H{XH5ٛb HHD$"H$ H{hH5] DŽ$ DŽ$ HHD$H{pH5^ j H$ H H5kb H$p H H5^ DŽ$ DŽ$ H H5e^ i H$ H H5b XH$ H|$@H 1H H- H{`$ H{0 H H;H L|$@ H{` H{0 H HD$`ML HHD$` HHD$h HD$p HD$ %C @t1A|$ = HHT$`HD$hH)HH9s&H< B H tA|$ _ 1) H: B H. B H,$HH HD$HD$`Hxl B H B HHH,$ HHD$`HxD B H B H<$H HD$8HE@ H<$1Hp# @uA|$ [' Ht$ LHT$I~H53] DŽ$ DŽ$$ sI~H5H^ Nh HT$I~(H5b IH$ I~8H5] DŽ$P DŽ$T HHD$I~@H5^ g H$p I~XH5b HHD$H$ I~hH5>^ DŽ$ DŽ$ I~pH5y^ g H$ I H5/b H$ HL "I~`HZ I~0Q LI J I~`H$ 3 I~0* LLt$` H|$MLHھ C @uA|$ HD$`H80 B H HD$`Hx B H m HD$`HxB H Q HD$`HxB Ht B H<$HH HHD$`HxB HO B H<$H# HHD$6 B H;D$02 A|$ 1H H|$`H HHH HH|$`HtL H H$ H
^ H5^ | Hv H( Lt$IE11LHLx I~PH5^ vUH$ H
u
^ H56^ HQv H' Lt$IE11LHLx I~PH5^ L9E H= H|$H8E HHH= H$ H
^ H5^ Hu H ' Lt$IE11LHLx I~PH5^ L8E H!= 7H|$Ho8E tH|$H]8E bZL$0 H
T ^ H5^ L0u H& Lt$ME11LHL~w I~PH5^ HE@H<$ HpN @ A|$ L$ H
^ H5^ Lt H% L$ ME11LLHv I~PH5 ^ oLg7E L; =H|$HM7E HL; ?A|$ .HD$8HL$(H@@H9 HD$H@@H9 L$` H
+^ H5^ Ls H3% Lt$ME11LHL(v I~PH5^ L16E L6; tH|$H6E 9L$ H
+^ H5A^ L\s H$ H$ ME11LHHu H}PH5^ 'dA|$ L$ H
+^ H5^ Lr HO$ H$ ME11LHHAu H}PH5^ H5E LQ: \IH5E LLLH5E <4LLwP L0 H8 @[]A\A]A^A_AWAVAUIHATIUSHH HT$7B x(uH@ H4J x H$0 L:B HHRV H7 H@ H$ H Hx1A HS(HHrѬV H H Hx1 HS(HHr覬V LLt$`) B HT] HLc L|$pLLH$` L.B HLL~y HL$pHHD$HyHL$`HHyH$ H5b LHHL$ HT$L$ LHLH$` HT$_H$ L$ L$ HHT$(HL H|$0 H$ HT$^IH5^ LLT$8H$ HT$]IH5^ LL\$@^LL$ A H^ HLLb H$ LILLLL$H. B HD$ LD$0MHL$8HT$@LHt$HH$ L$ HD$`C IH$ LHx~H$ LHxjH$ LHxVH$ LHxBH$ HtPH$ Ht>H$ HHx
+HeV HT$(H5Bd L$p 6H$ H;=G u2 +H$ H
HHQH$ H$ LH$ HxH;= t.H= HWt PJHЅLX$p H$ H5Bd LDŽ$< HDŽ$0
$p g IH$ Ht$^Hx_ HLLB H$ LHxHt$LhB HQV HH;V HD$6 L. IHD$pHHxIHD$`HLHxy IH$ LMHxZIH$ H$ Hx<IH$ H$ Hx IH$ H$ Hx IH$ Ht
+ IH$ Ht
+IH$ HLHx,IH$ LHxIHLPV HH:V HHH []A\A]A^A_SHHHtIHGHxH;=Z t0H= HWt PJHЅ
+Ht$H0H[SHHHtIHGHxH;= t0H=| HWt PJHЅ
+Ht$HH[SHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$`HxH[SHHHtIHGHxH;=F t0H= HWt PJHЅ
+Ht$H H[SHHHtIHGHxH;= t0H=h HWt PJHЅ
+Ht$HH[AVAUATIUSHH H H / I$ I$ I|$hHtkI|$PHt\I\$0L-@ Lt$HtYH{H+Ht5HCHxL9t.H= HWt PJHЅLHHID$(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^ÐAUATIUSHH# H H c I$ I$ N I|$hHtI|$PHtI\$0Ll$Ht]H{H+HtpHSHzH;=Q t.H= HOt JqrʅLHH&ID$(I|$ H\$H 1ID$8 ID$0 I|$ ID$HHxID$HHxI$HHxLH[]A\A]AVAUATIUSHH H H ~ I$ I$ ~ I|$hHtYI|$PHtJI\$0L-. Lt$HtYH{H+Ht#HCHxL9t.H= HWt PJHЅLHHID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H= HWt PJHЅ
+Ht$KID$HxL9t0H= HWt PJHЅ
+Ht$
I$HxL9t0H= HWt PJHЅ
+Ht$LH[]A\A]A^ÐSHHHtIHGHxH;= t0H=, HWt PJHЅ
+Ht$lHH[AUATIUSHH# H H { I$ I$ { I|$hHt1I|$PHt"I\$0Ll$Ht]H{H+HtHSHzH;= t.H=a
HOt JqrʅLHHID$(I|$ H\$H 1ID$8 ID$0 I|$ ID$HHxNID$HHx=I$HHx-LEH[]A\A]SHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$HH[SHHHtIHGHxH;=
+ t0H=0 HWt PJHЅ
+Ht$pHH[AVAUATIUSHH H H y I$ I$ y I|$hHt3I|$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$HxL9t0H=j
+ HWt PJHЅ
+Ht$LH[]A\A]A^ÐAVAUATIUSHH H H )x I$ I$ x I|$hHteI|$PHtVI\$0L-: Lt$HtYH{H+Ht/HCHxL9t.H= HWt PJHЅLHHID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H= HWt PJHЅ
+Ht$WID$HxL9t0H= HWt PJHЅ
+Ht$I$HxL9t0H= HWt PJHЅ
+Ht$LH[]A\A]A^ÐSHHHtIHGHxH;= t0H=8 HWt PJHЅ
+Ht$xHH[AUATIUSHH# H H v I$ I$ u I|$hHt=I|$PHt.I\$0Ll$Ht]H{H+HtHSHzH;= t.H=m HOt JqrʅLHHID$(I|$ H\$H 1ID$8 ID$0 I|$ ID$HHxZID$HHxII$HHx9LQH[]A\A]AVAUATIUSHH| H H t I$ I$ t I|$hHtI|$PHtI\$0L- Lt$HtYH{H+HtHCHxL9t.H=$ HWt PJHЅLfHH{ID$(I|$ H\$H 1ID$8 ID$0 I|$ BID$HxL9t.H= HWt PJHЅHID$HxL9t.H=h HWt PJHЅHI$HHx蚿LH[]A\A]A^ÐAVAUATIUSHH H H s I$ I$ s I|$hHtUI|$PHtFI\$0L-* Lt$HtYH{H+Ht HCHxL9t.H= HWt PJHЅLHHID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H= HWt PJHЅ
+Ht$GID$HxL9t0H= HWt PJHЅ
+Ht$ I$HxL9t0H= HWt PJHЅ
+Ht$̿LH[]A\A]A^ÐAVAUATIUSHH H H Kq I$ I$ 6q I|$hHtI|$PHtxI\$0L-\ Lt$HtYH{H+HtQHCHxL9t.H= HWt PJHЅLHH ID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H=9 HWt PJHЅ
+Ht$yID$HxL9t0H= HWt PJHЅ
+Ht$;I$HxL9t0H= HWt PJHЅ
+Ht$LH[]A\A]A^ÐAVAUATIUSHH H H }o I$ I$ ho I|$hHt蹾I|$PHt誾I\$0L- Lt$HtYH{H+Ht胾HCHxL9t.H= HWt PJHЅL(HH=ID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H=k HWt PJHЅ
+Ht$諼ID$HxL9t0H=- HWt PJHЅ
+Ht$mI$HxL9t0H= HWt PJHЅ
+Ht$0LHH[]A\A]A^ÐAVAUATIUSHH H H m I$ I$ m I|$hHtI|$PHtܼI\$0L- Lt$HtYH{H+Ht赼HCHxL9t.H= HWt PJHЅLZHHoID$(I|$ H 1ID$8 ID$0 I|$ ;ID$HxL9t0H= HWt PJHЅ
+Ht$ݺID$HxL9t0H=_ HWt PJHЅ
+Ht$蟺I$HxL9t0H=" HWt PJHЅ
+Ht$bLzH[]A\A]A^ÐAVAUATIUSHH H H k I$ I$ k I|$hHt I|$PHtI\$0L- Lt$HtYH{H+HtHCHxL9t.H=J HWt PJHЅL茹HH衺ID$(I|$ H 1ID$8 ID$0 I|$ mID$HxL9t0H= HWt PJHЅ
+Ht$ID$HxL9t0H= HWt PJHЅ
+Ht$ѸI$HxL9t0H=T HWt PJHЅ
+Ht$蔸L謹H[]A\A]A^ÐAVAUATIUSHH H H j I$ I$ i I|$hHtOI|$PHt@I\$0L-$ Lt$HtYH{H+HtHCHxL9t.H=| HWt PJHЅL辷HHӸID$(I|$ H 1ID$8 ID$0 I|$ 蟸ID$HxL9t0H= HWt PJHЅ
+Ht$AID$HxL9t0H= HWt PJHЅ
+Ht$I$HxL9t0H= HWt PJHЅ
+Ht$ƶLH[]A\A]A^ÐAVAUATIUSHH H H Eh I$ I$ 0h I|$hHt職I|$PHtrI\$0L-V Lt$HtYH{H+HtKHCHxL9t.H= HWt PJHЅLHHID$(I|$ H 1ID$8 ID$0 I|$ ѶID$HxL9t0H=3 HWt PJHЅ
+Ht$sID$HxL9t0H= HWt PJHЅ
+Ht$5I$HxL9t0H= HWt PJHЅ
+Ht$LH[]A\A]A^ÐAUATIUSHH# H H yf I$ I$ df I|$hHt赵I|$PHt覵I\$0Ll$Ht]H{H+Ht膵HSHzH;=g t.H= HOt JqrʅL'HH<ID$(I|$ H\$H 1ID$8 ID$0 I|$ ID$HHxұID$HHxI$HHx豱LɴH[]A\A]AVAUATIUSHH H H 3e I$ I$ e I|$hHtoI|$PHt`I\$0L-D Lt$HtYH{H+Ht9HCHxL9t.H= HWt PJHЅLHHID$(I|$ H 1ID$8 ID$0 I|$ 迳ID$HxL9t0H=! HWt PJHЅ
+Ht$aID$HxL9t0H= HWt PJHЅ
+Ht$#I$HxL9t0H= HWt PJHЅ
+Ht$LH[]A\A]A^ÐSHHHtIHGHxH;= t0H=B HWt PJHЅ
+Ht$肱H蚲H[AVAUATIUSHH H H c I$ I$ b I|$hHtEI|$PHt6I\$0L- Lt$HtYH{H+HtHCHxL9t.H=r HWt PJHЅL贰HHɱID$(I|$ H 1ID$8 ID$0 I|$ 蕱ID$HxL9t0H= HWt PJHЅ
+Ht$7ID$HxL9t0H= HWt PJHЅ
+Ht$I$HxL9t0H=| HWt PJHЅ
+Ht$輯LH[]A\A]A^ÐSHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$XHpH[AUATIUSHH# H H ` I$ I$ ` I|$hHt I|$PHtI\$0Ll$Ht]H{H+HtHSHzH;= t.H=M HOt JqrʅL菮HH褯ID$(I|$ H\$H 1ID$8 ID$0 I|$ kID$HHx:ID$HHx)I$HHxL1H[]A\A]SHHHtIHGHxH;= t0H=x HWt PJHЅ
+Ht$踭HЮH[AVAUATIUSHH H H ?_ I$ I$ *_ I|$hHt{I|$PHtlI\$0L-P Lt$HtYH{H+HtEHCHxL9t.H= HWt PJHЅLHHID$(I|$ H 1ID$8 ID$0 I|$ ˭ID$HxL9t0H=- HWt PJHЅ
+Ht$mID$HxL9t0H= HWt PJHЅ
+Ht$/I$HxL9t0H= HWt PJHЅ
+Ht$L
+H[]A\A]A^ÐAVAUATIUSHH H H q] I$ I$ \] I|$hHt譬I|$PHt螬I\$0L- Lt$HtYH{H+HtwHCHxL9t.H= HWt PJHЅL HH1ID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H=_ HWt PJHЅ
+Ht$蟪ID$HxL9t0H=! HWt PJHЅ
+Ht$aI$HxL9t0H= HWt PJHЅ
+Ht$$L<H[]A\A]A^ÐSHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$HتH[AVAUATIUSHHQ H H G[ I$ I$ 2[ I|$hHt胪I|$PHttI\$0L-X Lt$HtYH{H+HtMHCHxL9t.H= HWt PJHЅLHHID$(I|$ H\$H 1ID$8 ID$0 I|$ ΩID$HHx蝦ID$HxL9t.H= HWt PJHЅHaI$HHxQLiH[]A\A]A^SHHHtIHGHxH;=0 t0H= HWt PJHЅ
+Ht$HH[AVAUATIUSHH H H uY I$ I$ `Y I|$hHt豨I|$PHt袨I\$0L- Lt$HtYH{H+Ht{HCHxL9t.H= HWt PJHЅL HH5ID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H=c HWt PJHЅ
+Ht$裦ID$HxL9t0H=% HWt PJHЅ
+Ht$eI$HxL9t0H= HWt PJHЅ
+Ht$(L@H[]A\A]A^ÐSHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$ĥHܦH[AVAUATIUSHH H H KW I$ I$ 6W I|$hHt臦I|$PHtxI\$0L-\ Lt$HtYH{H+HtQHCHxL9t.H= HWt PJHЅLHH ID$(I|$ H 1ID$8 ID$0 I|$ ץID$HxL9t0H=9 HWt PJHЅ
+Ht$yID$HxL9t0H= HWt PJHЅ
+Ht$;I$HxL9t0H= HWt PJHЅ
+Ht$LH[]A\A]A^H5^ P<ZSHHHtIHGHxH;= t0H=D HWt PJHЅ
+Ht$脣H蜤H[SHHHtIHGHxH;=j t0H= HWt PJHЅ
+Ht$(H@H[AVAUATIUSHH H H T I$ I$ T I|$hHtI|$PHtܣI\$0L- Lt$HtYH{H+Ht赣HCHxL9t.H= HWt PJHЅLZHHoID$(I|$ H 1ID$8 ID$0 I|$ ;ID$HxL9t0H= HWt PJHЅ
+Ht$ݡID$HxL9t0H=_ HWt PJHЅ
+Ht$蟡I$HxL9t0H=" HWt PJHЅ
+Ht$bLzH[]A\A]A^ÐAVAUATIUSHH H H R I$ I$ R I|$hHt I|$PHtI\$0L- Lt$HtYH{H+HtHCHxL9t.H=J HWt PJHЅL茠HH衡ID$(I|$ H 1ID$8 ID$0 I|$ mID$HxL9t0H= HWt PJHЅ
+Ht$ID$HxL9t0H= HWt PJHЅ
+Ht$џI$HxL9t0H=T HWt PJHЅ
+Ht$蔟L謠H[]A\A]A^ÐSHHHtIHGHxH;=r t0H= HWt PJHЅ
+Ht$0HHH[AVAUATIUSHH H H P I$ I$ P I|$hHtI|$PHtI\$0L- Lt$HtYH{H+Ht轟HCHxL9t.H= HWt PJHЅLbHHwID$(I|$ H 1ID$8 ID$0 I|$ CID$HxL9t0H= HWt PJHЅ
+Ht$ID$HxL9t0H=g HWt PJHЅ
+Ht$觝I$HxL9t0H=* HWt PJHЅ
+Ht$jL肞H[]A\A]A^ÐAUATIUSHH# H H N I$ I$ N I|$hHt'I|$PHtI\$0Ll$Ht]H{H+HtHSHzH;=ޔ t.H=W HOt JqrʅL虜HH讝ID$(I|$ H\$H 1ID$8 ID$0 I|$ uID$HHxDID$HHx3I$HHx#L;H[]A\A]AVAUATIUSHH H H M I$ I$ M I|$hHtI|$PHtҜI\$0L-ݔ Lt$HtYH{H+Ht諜HCHxL9t.H=ߔ HWt PJHЅLPHHeID$(I|$ H 1ID$8 ID$0 I|$ 1ID$HxL9t0H=ޔ HWt PJHЅ
+Ht$ӚID$HxL9t0H=Uޔ HWt PJHЅ
+Ht$蕚I$HxL9t0H=ޔ HWt PJHЅ
+Ht$XLpH[]A\A]A^ÐAVAUATIUSHH H H K I$ I$ K I|$hHtI|$PHtI\$0L-۔ Lt$HtYH{H+HtݚHCHxL9t.H=@ݔ HWt PJHЅL肙HH藚ID$(I|$ H 1ID$8 ID$0 I|$ cID$HxL9t0H=ܔ HWt PJHЅ
+Ht$ID$HxL9t0H=ܔ HWt PJHЅ
+Ht$ǘI$HxL9t0H=Jܔ HWt PJHЅ
+Ht$芘L袙H[]A\A]A^ÐAVAUATIUSHH H H J I$ I$ I I|$hHtEI|$PHt6I\$0L-ڔ Lt$HtYH{H+HtHCHxL9t.H=r۔ HWt PJHЅL贗HHɘID$(I|$ H 1ID$8 ID$0 I|$ 蕘ID$HxL9t0H=ڔ HWt PJHЅ
+Ht$7ID$HxL9t0H=ڔ HWt PJHЅ
+Ht$I$HxL9t0H=|ڔ HWt PJHЅ
+Ht$輖LԗH[]A\A]A^ÐAVAUATIUSHH H H ;H I$ I$ &H I|$hHtwI|$PHthI\$0L-Lؔ Lt$HtYH{H+HtAHCHxL9t.H=ٔ HWt PJHЅLHHID$(I|$ H 1ID$8 ID$0 I|$ ǖID$HxL9t0H=)ٔ HWt PJHЅ
+Ht$iID$HxL9t0H=ؔ HWt PJHЅ
+Ht$+I$HxL9t0H=ؔ HWt PJHЅ
+Ht$LH[]A\A]A^ÐAVAUATIUSHH H H mF I$ I$ XF I|$hHt評I|$PHt蚕I\$0L-~֔ Lt$HtYH{H+HtsHCHxL9t.H=ה HWt PJHЅLHH-ID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H=[ה HWt PJHЅ
+Ht$蛓ID$HxL9t0H= ה HWt PJHЅ
+Ht$]I$HxL9t0H=֔ HWt PJHЅ
+Ht$ L8H[]A\A]A^ÐAUATIUSHH# H H D I$ I$ D I|$hHtݓI|$PHtΓI\$0Ll$Ht]H{H+Ht讓HSHzH;=Ԕ t.H=
֔ HOt JqrʅLOHHdID$(I|$ H\$H 1ID$8 ID$0 I|$ +ID$HHxID$HHxI$HHxُLH[]A\A]AVAUATIUSHH H H [C I$ I$ FC I|$hHt藒I|$PHt舒I\$0L-lӔ Lt$HtYH{H+HtaHCHxL9t.H=Ԕ HWt PJHЅLHHID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H=IԔ HWt PJHЅ
+Ht$艐ID$HxL9t0H= Ԕ HWt PJHЅ
+Ht$KI$HxL9t0H=Ӕ HWt PJHЅ
+Ht$L&H[]A\A]A^ÐAUATIUSHH# H H A I$ I$ zA I|$hHtːI|$PHt輐I\$0Ll$Ht]H{H+Ht蜐HSHzH;=}є t.H=Ҕ HOt JqrʅL=HHRID$(I|$ H\$H 1ID$8 ID$0 I|$ ID$HHxID$HHxI$HHxnjLߏH[]A\A]AUATIUSHH# H H K@ I$ I$ 6@ I|$hHt臏I|$PHtxI\$0Ll$Ht]H{H+HtXHSHzH;=9Д t.H=є HOt JqrʅLHHID$(I|$ H\$H 1ID$8 ID$0 I|$ ՎID$HHx褋ID$HHx蓋I$HHx胋L蛎H[]A\A]ATIUSH? t,HGHH_(HhH9vH;Hf[]I<$A\W[]A\SHHHtIHGHxH;=&ϔ t0H=Д HWt PJHЅ
+Ht$HH[AVAUATIUSHH H H k> I$ I$ V> I|$hHt觍I|$PHt蘍I\$0L-|Δ Lt$HtYH{H+HtqHCHxL9t.H=ϔ HWt PJHЅLHH+ID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H=Yϔ HWt PJHЅ
+Ht$虋ID$HxL9t0H=ϔ HWt PJHЅ
+Ht$[I$HxL9t0H=Δ HWt PJHЅ
+Ht$ L6H[]A\A]A^ÐSHHHtIHGHxH;=̔ t0H=zΔ HWt PJHЅ
+Ht$躊HҋH[AVAUATIUSHH H H A< I$ I$ ,< I|$hHt}I|$PHtnI\$0L-R̔ Lt$HtYH{H+HtGHCHxL9t.H=͔ HWt PJHЅLHHID$(I|$ H 1ID$8 ID$0 I|$ ͊ID$HxL9t0H=/͔ HWt PJHЅ
+Ht$oID$HxL9t0H=̔ HWt PJHЅ
+Ht$1I$HxL9t0H=̔ HWt PJHЅ
+Ht$L H[]A\A]A^P 蔅H] H5 HHHH P eH. H5 HHHH 豏AUATIUSHH# H H : I$ I$ : I|$hHtSI|$PHtDI\$0Ll$Ht]H{H+Ht$HSHzH;=ʔ t.H=˔ HOt JqrʅLŇHHڈID$(I|$ H\$H 1ID$8 ID$0 I|$ 衈ID$HHxpID$HHx_I$HHxOLgH[]A\A]AVAUATIUSHH H H 8 I$ I$ 8 I|$hHt
I|$PHtI\$0L-Ȕ Lt$HtYH{H+HtׇHCHxL9t.H=:ʔ HWt PJHЅL|HH葇ID$(I|$ H 1ID$8 ID$0 I|$ ]ID$HxL9t0H=ɔ HWt PJHЅ
+Ht$ID$HxL9t0H=ɔ HWt PJHЅ
+Ht$I$HxL9t0H=Dɔ HWt PJHЅ
+Ht$脅L蜆H[]A\A]A^ÐAVAUATIUSHH H H 7 I$ I$ 6 I|$hHt?I|$PHt0I\$0L-ǔ Lt$HtYH{H+Ht HCHxL9t.H=lȔ HWt PJHЅL讄HHÅID$(I|$ H 1ID$8 ID$0 I|$ 菅ID$HxL9t0H=ǔ HWt PJHЅ
+Ht$1ID$HxL9t0H=ǔ HWt PJHЅ
+Ht$I$HxL9t0H=vǔ HWt PJHЅ
+Ht$趃L΄H[]A\A]A^ÐSHHHtIHGHxH;=Ŕ t0H=ǔ HWt PJHЅ
+Ht$RHjH[AVAUATIUSHH H H 4 I$ I$ 4 I|$hHtI|$PHtI\$0L-Ĕ Lt$HtYH{H+Ht߃HCHxL9t.H=BƔ HWt PJHЅL脂HH虃ID$(I|$ H 1ID$8 ID$0 I|$ eID$HxL9t0H=Ŕ HWt PJHЅ
+Ht$ID$HxL9t0H=Ŕ HWt PJHЅ
+Ht$ɁI$HxL9t0H=LŔ HWt PJHЅ
+Ht$茁L褂H[]A\A]A^ÐAVAUATIUSHH H H 3 I$ I$ 2 I|$hHtGI|$PHt8I\$0L- Ô Lt$HtYH{H+HtHCHxL9t.H=tĔ HWt PJHЅL趀HHˁID$(I|$ H 1ID$8 ID$0 I|$ 藁ID$HxL9t0H=Ô HWt PJHЅ
+Ht$9ID$HxL9t0H=Ô HWt PJHЅ
+Ht$I$HxL9t0H=~Ô HWt PJHЅ
+Ht$LրH[]A\A]A^ÐAVAUATIUSHH H H =1 I$ I$ (1 I|$hHtyI|$PHtjI\$0L-N Lt$HtYH{H+HtCHCHxL9t.H= HWt PJHЅL~HHID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H=+ HWt PJHЅ
+Ht$k~ID$HxL9t0H= HWt PJHЅ
+Ht$-~I$HxL9t0H= HWt PJHЅ
+Ht$}LH[]A\A]A^ÐAVAUATIUSHH H H o/ I$ I$ Z/ I|$hHt~I|$PHt~I\$0L- Lt$HtYH{H+Htu~HCHxL9t.H= HWt PJHЅL}HH/~ID$(I|$ H 1ID$8 ID$0 I|$ }ID$HxL9t0H=] HWt PJHЅ
+Ht$|ID$HxL9t0H= HWt PJHЅ
+Ht$_|I$HxL9t0H=⿔ 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 PJHЅLL{HHa|ID$(I|$ H 1ID$8 ID$0 I|$ -|ID$HxL9t0H= HWt PJHЅ
+Ht$zID$HxL9t0H=Q HWt PJHЅ
+Ht$zI$HxL9t0H= HWt PJHЅ
+Ht$TzLl{H[]A\A]A^ÐAVAUATIUSHH H H + I$ I$ + I|$hHt{I|$PHt {I\$0L-仔 Lt$HtYH{H+HtzHCHxL9t.H=< HWt PJHЅL~yHHzID$(I|$ H 1ID$8 ID$0 I|$ _zID$HxL9t0H= HWt PJHЅ
+Ht$yID$HxL9t0H= HWt PJHЅ
+Ht$xI$HxL9t0H=F HWt PJHЅ
+Ht$xLyH[]A\A]A^ÐAVAUATIUSHH H H * I$ I$ ) I|$hHtAyI|$PHt2yI\$0L- Lt$HtYH{H+Ht yHCHxL9t.H=n HWt PJHЅLwHHxID$(I|$ H 1ID$8 ID$0 I|$ xID$HxL9t0H= HWt PJHЅ
+Ht$3wID$HxL9t0H= HWt PJHЅ
+Ht$vI$HxL9t0H=x HWt PJHЅ
+Ht$vLwH[]A\A]A^ÐAVAUATIUSHH H H 7( I$ I$ "( I|$hHtswI|$PHtdwI\$0L-H Lt$HtYH{H+Ht=wHCHxL9t.H= HWt PJHЅLuHHvID$(I|$ H 1ID$8 ID$0 I|$ vID$HxL9t0H=% HWt PJHЅ
+Ht$euID$HxL9t0H=縔 HWt PJHЅ
+Ht$'uI$HxL9t0H= HWt PJHЅ
+Ht$tLvH[]A\A]A^ÐAVAUATIUSHH H H i& I$ I$ T& I|$hHtuI|$PHtuI\$0L-z Lt$HtYH{H+HtouHCHxL9t.H=ҷ HWt PJHЅLtHH)uID$(I|$ H 1ID$8 ID$0 I|$ tID$HxL9t0H=W HWt PJHЅ
+Ht$sID$HxL9t0H= HWt PJHЅ
+Ht$YsI$HxL9t0H=ܶ HWt PJHЅ
+Ht$ sL4tH[]A\A]A^ÐAVAUATIUSHH H H $ I$ I$ $ I|$hHtsI|$PHtsI\$0L- Lt$HtYH{H+HtsHCHxL9t.H= HWt PJHЅLFrHH[sID$(I|$ H 1ID$8 ID$0 I|$ 'sID$HxL9t0H= HWt PJHЅ
+Ht$qID$HxL9t0H=K HWt PJHЅ
+Ht$qI$HxL9t0H= HWt PJHЅ
+Ht$NqLfrH[]A\A]A^ÐAVAUATIUSHH H H " I$ I$ " I|$hHt rI|$PHtqI\$0L- Lt$HtYH{H+HtqHCHxL9t.H=6 HWt PJHЅLxpHHqID$(I|$ H 1ID$8 ID$0 I|$ YqID$HxL9t0H= HWt PJHЅ
+Ht$oID$HxL9t0H=} HWt PJHЅ
+Ht$oI$HxL9t0H=@ HWt PJHЅ
+Ht$oLpH[]A\A]A^ÐSHHHtIHGHxH;=^ t0H=ܲ HWt PJHЅ
+Ht$ oH4pH[AUATIUSHH# H H I$ I$ I|$hHtoI|$PHtoI\$0Ll$Ht]H{H+HtoHSHzH;= t.H= HOt JqrʅLSnHHhoID$(I|$ H\$H 1ID$8 ID$0 I|$ /oID$HHxkID$HHxkI$HHxkLnH[]A\A]AVAUATIUSHH H H _ I$ I$ J I|$hHtnI|$PHtnI\$0L-p Lt$HtYH{H+HtenHCHxL9t.H=Ȱ HWt PJHЅL
+mHH nID$(I|$ H 1ID$8 ID$0 I|$ mID$HxL9t0H=M HWt PJHЅ
+Ht$lID$HxL9t0H= HWt PJHЅ
+Ht$OlI$HxL9t0H=ү HWt PJHЅ
+Ht$lL*mH[]A\A]A^ÐSHHHtIHGHxH;= t0H=n HWt PJHЅ
+Ht$kHlH[AVAUATIUSHHQ H H 5 I$ I$ I|$hHtqlI|$PHtblI\$0L-F Lt$HtYH{H+Ht;lHCHxL9t.H= HWt PJHЅLjHHkID$(I|$ H\$H 1ID$8 ID$0 I|$ kID$HHxhID$HxL9t.H=
HWt PJHЅHOjI$HHx?hLWkH[]A\A]A^AUATIUSHH# H H I$ I$ I|$hHtjI|$PHtjI\$0Ll$Ht]H{H+HtjHSHzH;= t.H=- HOt JqrʅLoiHHjID$(I|$ H\$H 1ID$8 ID$0 I|$ KjID$HHxgID$HHx gI$HHxfLjH[]A\A]AVAUATIUSHH H H { I$ I$ f I|$hHtiI|$PHtiI\$0L- Lt$HtYH{H+HtiHCHxL9t.H=䫔 HWt PJHЅL&hHH;iID$(I|$ H 1ID$8 ID$0 I|$ iID$HxL9t0H=i HWt PJHЅ
+Ht$gID$HxL9t0H=+ HWt PJHЅ
+Ht$kgI$HxL9t0H= HWt PJHЅ
+Ht$.gLFhH[]A\A]A^ÐAVAUATIUSHH H H I$ I$ I|$hHtgI|$PHtgI\$0L- Lt$HtYH{H+HtgHCHxL9t.H= HWt PJHЅLXfHHmgID$(I|$ H 1ID$8 ID$0 I|$ 9gID$HxL9t0H= HWt PJHЅ
+Ht$eID$HxL9t0H=] HWt PJHЅ
+Ht$eI$HxL9t0H= HWt PJHЅ
+Ht$`eLxfH[]A\A]A^ÐAVAUATIUSHH H H I$ I$ I|$hHtfI|$PHt fI\$0L- Lt$HtYH{H+HteHCHxL9t.H=H HWt PJHЅLdHHeID$(I|$ H 1ID$8 ID$0 I|$ keID$HxL9t0H=ͧ HWt PJHЅ
+Ht$
dID$HxL9t0H= HWt PJHЅ
+Ht$cI$HxL9t0H=R HWt PJHЅ
+Ht$cLdH[]A\A]A^ÐAVAUATIUSHH H H I$ I$ I|$hHtMdI|$PHt>dI\$0L-" Lt$HtYH{H+HtdHCHxL9t.H=z HWt PJHЅLbHHcID$(I|$ H 1ID$8 ID$0 I|$ cID$HxL9t0H= HWt PJHЅ
+Ht$?bID$HxL9t0H= HWt PJHЅ
+Ht$bI$HxL9t0H= HWt PJHЅ
+Ht$aLbH[]A\A]A^ÐAVAUATIUSHH H H C I$ I$ . I|$hHtbI|$PHtpbI\$0L-T Lt$HtYH{H+HtIbHCHxL9t.H= HWt PJHЅL`HHbID$(I|$ H 1ID$8 ID$0 I|$ aID$HxL9t0H=1 HWt PJHЅ
+Ht$q`ID$HxL9t0H= HWt PJHЅ
+Ht$3`I$HxL9t0H= HWt PJHЅ
+Ht$_LaH[]A\A]A^ÐSHHHtIHGHxH;=ԡ t0H=R HWt PJHЅ
+Ht$_H`H[AVAUATIUSHH H H I$ I$ I|$hHtU`I|$PHtF`I\$0L-* Lt$HtYH{H+Ht `HCHxL9t.H= HWt PJHЅL^HH_ID$(I|$ H 1ID$8 ID$0 I|$ _ID$HxL9t0H= HWt PJHЅ
+Ht$G^ID$HxL9t0H=ɡ HWt PJHЅ
+Ht$ ^I$HxL9t0H= HWt PJHЅ
+Ht$]L^H[]A\A]A^ÐAVAUATIUSHH H H K I$ I$ 6 I|$hHt^I|$PHtx^I\$0L-\ Lt$HtYH{H+HtQ^HCHxL9t.H= HWt PJHЅL\HH ^ID$(I|$ H 1ID$8 ID$0 I|$ ]ID$HxL9t0H=9 HWt PJHЅ
+Ht$y\ID$HxL9t0H= HWt PJHЅ
+Ht$;\I$HxL9t0H= HWt PJHЅ
+Ht$[L]H[]A\A]A^ÐAVAUATIUSHH H H }
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L H5L MH$ H[L$ L'5 L$ H
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- HH H{@C C C@ ?HHH HKHKdI H HC(H9 L, LL1HIH H5uJ Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $9) H5WJ Hz) H5#N H+) HH5jI HB ҕ) HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( Ht<HD$ HtH|$ HH0H[]A\A]A^IHCHHxHCHHxHHHxHLHD$ HtH|$ HHHHMIHt
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+ H LLHt$J :HUhHIJ:LH $HLM9ur @ M.I L$$HL,I MfL$I INH 6 D LIH D H LH Ht$J:HHLIJ9LLHH $`M9uHI HD$ IH HL$(INI H HI INH HD$XH$ H9D$X I INf D HLI D I IH $Ht$N:LLHIL9u%HI HD$hIH HL$pINI H HI INH H$ H$ H9$ tUI INHI H$` IH H$h INI H HI INH H$ H$ H9$ tUI INHI H$H IH H$P INI H HI INH H$ H$ H9$ tUI INhHI H$0 IH H$8 INI H HI INH H$ H$ H9$ tUI INHI H$ IH H$ INI H HI INH H$ H$ H9$ tUI INHI H$ IH H$ INI H HI INH H$ H$ H9$ tFIN`HH LHJ H$ HOH J H HGJHX []A\A]A^A_fAVH9AUATIUSHtDHNL.H?HCHL)H)HHH9wqLCLH)HH9w H H{H[]A\A]A^ D HIt H)H<HHtH L:HH<H{ D H H H9 L4 LHI H;Ht装K<4L#H{H{V HLH蹊HH;H{1f. H L萊LCH;IL$LH)H<HI4$H)HH D HLLJH;HVVf E1E15 D f. AWAVIAUIATUSH H\$ H$HL$H{Xb~H{Ń Ƅ$X HHDŽ$P Ƅ$Y 1HDŽ$` HDŽ$h HHDŽ$p HDŽ$x HD$xHJŃ HhLxH}Hl$ L?L%Ń H{@HD$0 HD$8 HD$@ HD$H HD$P ID$HD$X HD$ ID$@HD$xH-Ń HHD$(NjHŃ HsH{XD$h HHD$(Hă HHD$p"M L~LHH裄H$HH0HV萄HD$HH0HV|HsL@ID$Ht$ HD$ ID$@HD$xH@Ń HHD$(HD$pHxHZă H{@HHD$(HEHl$ H{XL| HÃ HHD$x|HĈ L[]A\A]A^A_ @ HD$ HHxw Y!HH9HшH{IHEHl$ LL| H-Ã H{XHHD$x{|H蓈Hf. @ AWAVIAUIATUSH H\$ H$HL$H{X{H Ƅ$X HHDŽ$P Ƅ$Y 1HDŽ$` HDŽ$h HHDŽ$p HDŽ$x HD$xH HhLxH}Hl$ L?L%:Ã H{@HD$0 HD$8 HD$@ HD$H HD$P ID$HD$X HD$ ID$@HD$xH} HHD$(H0Ã HsH{XD$h HHD$(H HHD$prM L!|LHHH$HH0HVHD$HH0HV́HsL~ID$Ht$ HD$ ID$@HD$xH HHD$(HD$pHx)}H H{@HHD$(`HEHl$ H{XL| H HHD$xIzHĈ L[]A\A]A^A_ @ HD$ HHxw 詅!HH艇H!H{IHEHl$ LL| H} H{XHHD$xyHHf. @ AWAVIAUIATUSH H\$ H$HL$H{XyH Ƅ$X HHDŽ$P Ƅ$Y 1HDŽ$` HDŽ$h HHDŽ$p HDŽ$x HD$xH꿃 HhLxH}Hl$ L?aL% H{@HD$0 HD$8 HD$@ HD$H HD$P ID$HD$X HD$ ID$@HD$xHͿ HHD$(gH HsH{XD$h HHD$(H HHD$pM LqyLHHCH$HH0HV0HD$HH0HV HsL{ID$Ht$ HD$ ID$@HD$xH࿃ HHD$(HD$pHxyzH H{@HHD$(谀HEHl$ H{XL| HG HHD$xwHĈ L[]A\A]A^A_ @ HD$ HHxw !HHلHqH{IeHEHl$ LL| Hͽ H{XHHD$xwH3Hf. @ AWAVIAUIATUSH H\$ H$HL$H{XRvHk Ƅ$X HHDŽ$P Ƅ$Y 1HDŽ$` HDŽ$h HHDŽ$p HDŽ$x HD$xH: HhLxH}Hl$ L?~L%ڽ H{@HD$0 HD$8 HD$@ HD$H HD$P ID$HD$X HD$ ID$@HD$xH HHD$(跃Hн HsH{XD$h HHD$(Hp HHD$p~M LvLHH|H$HH0HV|HD$HH0HVl|HsL0yID$Ht$ HD$ ID$@HD$xH0 HHD$(HD$pHxwHJ H{@HHD$( ~HEHl$ H{XL| H HHD$xtHĈ L[]A\A]A^A_ @ HD$ HHxw I!HH)HH{I赻HEHl$ LL| H H{XHHD$xktH胀Hf. @ AVAUATIUSHH H H *I$ I$ p*I|$hHtyI|$PHtyI\$0Lt$HtMH= L- u
D HH{H+HttyHCHxL9 H[yHuID$(I|$ 1H muI|$ ID$8 ID$0 !yID$LHxuID$LHxuI$LHxuLxH[]A\A]A^úPeLwXH{H+HtxHCHxL9uHxH6H̋PJ҉HLRwAVHAUATUHSH0HD$HHD$(HHD$ ) IkxHù 1H߾
+ HH H{@C C C@ ?HHH HKHKK H HC(H9 L, LwL1HIsH H5XM Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $N+ H5L6M H:+ H50Q H;+ HH5zK HB 2<+ HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( Ht<HD$ HtH|$ HH0H[]A\A]A^pIHCHHxrHCHHxrHHHxrHLuHD$ HtH|$ HH |HHMIHt
HHH} HtHE LH<$HtkH @ AVHAUATUHSH0HD$HHD$(HHD$ ) IkuHù 1H߾
+ HH H{@C C C@ ?HHH HKHKK H HC(H9 L, LtL1HIpH H5UM Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $K+ H5L3M H7+ H5-Q H8+ HH5zK HB 29+ HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( Ht<HD$ HtH|$ HH0H[]A\A]A^mIHCHHxoHCHHxoHHHxoHLrHD$ HtH|$ HH yHHMIHt
HHH} HtHE LH<$HtkH @ AVHAUATUHSH0HD$HHD$(HHD$ ) IkrHù 1H߾
+ HH H{@C C C@ ?HHH HKHKK H HC(H9 L, LqL1HImH H5RM Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $H+ H5L0M H4+ H5*Q H5+ HH5zK HB 26+ HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( Ht<HD$ HtH|$ HH0H[]A\A]A^jIHCHHxlHCHHxlHHHxlHLoHD$ HtH|$ HH vHHMIHt
HHH} HtHE LH<$HtkH @ AVH7AUATUHSH0HD$HHD$(HHD$ ) IkoHù 1H߾
+ HH H{@C C C@ ?HHH HKHKK H HC(H9 L, LnL1HIjH H5OM Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $E+ H5L-M H1+ H5'Q H2+ HH5zK HB 23+ HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( Ht<HD$ HtH|$ HH0H[]A\A]A^gIHCHHxiHCHHxiHHHxiHLlHD$ HtH|$ HH sHHMIHt
HHH} HtHE LH<$HtkH @ AVHAUATUHSH0HD$HHD$(HHD$ ) IklHù 1H߾
+ HH H{@C C C@ ?HHH HKHKK H HC(H9 L, LkL1HIgH H5LM Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $B+ H5L*M H.+ H5$Q H/+ HH5zK HB 20+ HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( Ht<HD$ HtH|$ HH0H[]A\A]A^dIHCHHxfHCHHxfHHHxfHLiHD$ HtH|$ HH pHHMIHt
HHH} HtHE LH<$HtkH @ AVHWAUATUHSH0HD$HHD$(HHD$ ) IkiHù 1H߾
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HHH} HtHE LH<$HtkH @ AVHwAUATUHSH0HD$HHD$(HHD$ ) IkfHù 1H߾
+ HH H{@C C C@ ?HHH HKHKK H HC(H9 L, LeL1HIaH H5FM Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $<+ H5L$M H(+ H5 Q H)+ HH5zK HB B +*+ HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( Ht5HD$ HtH|$ HH0H[]A\A]A^^IHCHHx`HCHHx`HHHx`HLcHD$ HtH|$ HHjHHMIHt
HHH} Ht}HE LH<$HtdHf. AVHAUATUHSH0HD$HHD$(HHD$ ) I[cHù 1H߾
+ HH H{@C C C@ ?HHH HKHKK H HC(H9 L, LbL1HI^H H5CM Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $9+ H5<!M H%+ H5Q H{&+ HH5jK HB B '+ HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( Ht%HD$ HtH|$ HH0H[]A\A]A^[IHCHHx]HCHHx]HHHx]HL`HD$ HtH|$ HH gHHMIHt
HHH} HtmHE LH<$HtTHf. AUATIUHSHH)HHHGHH)HH9 HHH)HH9v\HL,ItH H0eLEM)1IMtJ LLHeILH]H[]A\A] H HH]H[]A\A] D H H H9 L, Ht$L_HIHt$uRH} HtD_Le MLeLeH[]A\A]f. HHHQdHHH]bfLH5d E1E1Y AWAVAUATUSHHHw(H+w HHHHw" H H HopHHHH HHHWhHOHGHH)HH9H H9sHHGpH H HH)HH9 v HH L H LH)HH9, Hv HD H LCPHCXL)HH9 sIH HCXH L[8HK LShL @ HHFIH9HMIH9s t ITHTHH9s t HTITIH9s t ITITHIuI(L H/H LH)HH9 s HH L H LH)HH9_ s HH L H LH)HH9 s HH HHE1[]A\A]A^A_111 D H HbM H5SM $ "H H<M H5-M $ H HM H5M $ H[]A\A]A^A_ II)HOxH)HI9 I1HLL XHkpHs H)H L)HH9- H1LHLWH II)H L)HI9 IL1LMWL k D II),H L)HI9 IL1LMAWL D H{PH)4'LCPH HsII) H L)HI9 IL1LMVL HsHf D II)WH H)HI9 I1HLLVH Hs HFH HH)I9- I9HIICH^ LFZLshHKpHL)HIIHtLLH_LshII1LLMUMtLYHkhLLcpHkxHs?H HH)I9 I9HIICHK LYL H HL)HIIHtLLHm^L II1LLM?UMtLYH LL H HsH HH)I9 I9HIICHI LXH H IH)HIIHtHLL]H MI1LLMTHtHIXML L L HsL*H HH)I9 I9HIICH L*XL H IL)HHHH t HLLHL$\L HL$LI1LHHL$SHL$IMtLuWML L L HH HH)I9D I9HIICH L]WL H IL)HHHH t HLLHL$"\L HL$LI1LHHL$RHL$IMtLVML L L H HH)H9w H9HIHCH LVL H IL)HIIHtLLL^[L MH1HLL0RMtLUL MH L "H9uH IHIE1E1{H9H IHIE11H9]H HHIE1E1cH9H IHE1E1H9GH HHIE1E1MH9Hu,IHIE11H=lK QL, L, nL, L4
L$ L$ AWIIAVAUATUSH
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} LP HH HI9H H=} L-M} Lt$ fHHxL9 HI9uHH Ht&H0 HtH HtH HtH HtH HtѯH HtH Ht误H Ht螯Hg| HHHE 臯H[]A\A]A^LBH L8HHxL9uHI9uPJ҉HLfAVAUATUHSHH
+} LP HH HI9H H=\} L-} Lt$ fHHxL9 HI9uHH Ht覮H0 Ht蕮H Ht脮H HtsH HtbH HtQH Ht@H Ht/H Ht H| HHHE H[]A\A]A^LBH L踬HHxL9uHI9uPJ҉HL脬fAVAUATUHSHH } LP HH HI9H H=} L-M} Lt$ fHHxL9 HI9uHH Ht&H0 HtH HtH HtH HtH HtѬH HtH Ht诬H Ht螬Hg| HHHE 臬H[]A\A]A^LBH L8HHxL9uHI9uPJ҉HLfAVAUATUHSHH:} LP HH HI9H H=\} L-} Lt$ fHHxL9 HI9uHH Ht覫H0 Ht蕫H Ht脫H HtsH HtbH HtQH Ht@H Ht/H Ht H| HHHE H[]A\A]A^LBH L踩HHxL9uHI9uPJ҉HL脩fAV1IAUATE1UH, HSHHH HG HG u5N, HHLktLLH臯Lk[]A\A]A^f. H H9wL$ LQ躤H;HHtHRfAVAUATIUSHH H H eZI$ I$ PZI|$hHt衩I|$PHt蒩I\$0Lt$HtMH=} L-d} u
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:G H5G M HH G LHL^HLHD$@HHx轐HH5߅| Lי H5 G H H5G HՌ҃ HD$0HHxOH|$PH9|$Xt#AD$GAD$GAD$ Ht6H []A\A]A^A_HH|$PHtHjHHD$0HHHxӏHHD$0HHx轏HD$@H\$ HHx觏HD$PHHx薏HH$ HHtHD$pHt$ HxfHHD$@HHxPLHBHHHH\$ IHLL9HfAWAVAUATIUHSH Lt$PH4$HT$ H5AG L|$@L'HT$ H5/G LH} H$ Ll$pLHLHHHD$pe & H$ Hl$0LHIE(H$ Ht$HHHt$ HD$HyTHD$HgHD$pHHx6H5G H臊1҅= HD$0A$HHxHD$@HHxHD$PHHxH5XG LHHD$P HD$X HD$` H4$H|$ LH & Lt$ M IFHHx臍L蟐HD$0HHxnX $LI) L/ H
G H5+G M H#H\G LHLHL.HD$@HHxHH5 | L H5IG H1 H52G H҃ HD$0HHx菌H|$PH9|$Xt#AD$GAD$GAD$ HtvH []A\A]A^A_HH|$PHtRH誕HHD$0HHHxHHD$0HHxHD$@H\$ HHxHD$PHHxHNH$ HHtHD$pHt$ Hx見HHD$@HHx萋L舌BHHHH\$ IHLALyHfAWAVAUATIUHSH Lt$PH4$HT$ H5G L|$@LgHT$ H5oG LSH } H$ Ll$pLHLHHHD$p& H$ Hl$0LHIEhH$ Ht$HHHt$ HD$Hy蔊HD$H觍HD$pHHxvH5̹G Hdž1҅= HD$0A$HHxHHD$@HHx7HD$PHHx&H5G LHHD$P HD$X HD$` IH4$H|$ LH5& Lt$ M IFHHxljLߌHD$0HHx讉X dLIiL!/ H
G H5kG M HcHG LHLH6LnHD$@HHx=HH5_~| LW H5G Hq H5rG HU҃ HD$0HHxψH|$PH9|$Xt#AD$GAD$GAD$ Ht趋H []A\A]A^A_HH|$PHt蒋HHHD$0HHHxSHHD$0HHx=HD$@H\$ HHx'HD$PHHxH莑H$ HHtHD$pHt$ HxHHD$@HHxЇLȈBHHHH\$ IHLLHfAWAVAUATIUHSH Lt$PH4$HT$ H5G L|$@L觋HT$ H5G L蓋HL} H$ Ll$pLHLHHHD$p& H$ Hl$0LHIE訆H$ Ht$HHHt$ HD$HyԆHD$HHD$pHHx趆H5 G H1҅= HD$0A$HHx舆HD$@HHxwHD$PHHxfH5صG LHHD$P HD$X HD$` 艊H4$H|$ LHu& Lt$ M IFHHxL HD$0HHxX 褄LILa/ H
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.G ^D^A HD$O,LLI O$ YXHWAYAD HH9uMC~OHD$O,O 1M$1 ADYAXDADADHAHL9uIIIHHI9BH$ L4 5,F $ HD$pd WI>D(LoDzF HD$xIFHD$ HHHD$HH HD$HD$PH$ HHL$HHHHHD$HHL$`HH$ H$ HHHL$@HD$XH HD$hA(L1H f D (1
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ĬG H5=G M LHnG LLL(LLH$ HHxxH]H5m| L螁f D H5G Lt H5G Lt ~L(oH$ HL$ HxxH H5G LLH$7|H $H$ LL & H$ H H$ HL$ HxwX TvLIYL$ L / L$ H
ZG H5ӧG M LCHG LLLLLNH$ HHxwHH5<l| L4 @ H$ HHxvH H H+ H+ HHH9$ L$ HH H5G LLHD$zHL$H$ LL& H$ b L$0 H0 HT$ H5݊G LH$zH $H$ LLd& L$ MtL?H$0 HHx vH$ HtH$ HHxuM H HCx HCh HCp HH+ H HCxL 1E1H$? HtHHSxHH H HJH)IHHI9HSx},H H; uH4$LWHSxH HP H+H ƃ HH ƃ HKpHHsxH H H+ 1HH Hɉd HCh H8 H+0 HH L$P X OsLITL$ L/ H
]G H5֤G MB LFHG LLLLLQH$P HHx tHH5?i| L7} H H H+ H+ HHH9u H H H)HH C`~%W1.v .wrHH9uHĸ []A\A]A^A_ H8 H+0 HHtiHsxHCpHHZH H+ 1CP HH1QC` f. H$ HHxr HCp HHH HD$HH0 HtuH HtuH HtuH HtuH HtuH HtuH HtpuH Ht_uHH
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G H5G M' LHG LLLLYLH$@ HHx]pH6H5e| LwyHH|$nILM LMAH$@ HLHx
+pLqH|$,IIHHLpH|$IH$P HLHxoIHD$`HHxoHD$pHHxoH$ kIH$ VLIĐ;LsH$ HLHx<oBIIHH$ HooHHD$@LHx oH$ HggHHHD$0LHxnH$ HIIHHHD$ H$P HxnH$ HHHHf?ILLI@L$` X mLIL$ L. H
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oHHHHH[]HHnHufAWAVAUATIUSHHH HNe| HHG!% H{PLuHN| L$ Ll$ H5G HDŽ$ HDŽ$ LLHDŽ$ HHDoH$ H LLH'HD$ L$P LHxjH$ HtmL|$0H59G Hǃ Hǃ Hǃ LHǃ Hǃ LHǃ HDŽ$ HDŽ$ HDŽ$ nH HLLHD$0LHx jH$ HtmL|$@H5G LHDŽ$ HDŽ$ LHDŽ$ nH HLLWHD$@LHxiH$ HtlL|$PH5 G Hǃ Hǃ Hǃ LLHDŽ$ HDŽ$ HDŽ$ xmH HLLHD$PLHxiH$ HtlH$ H5#}G Hǃ0 Hǃ8 Hǃ@ LHǃH HǃP HHǃX HD$lHt$pLHH5|G H<$lL|$`HL$H$LLkH5|G Ld1҅J HD$`` HL$ Hx#hHD$pHHxhH$ HHxgH H5G LLH$1lH $H$ LL% H$ H ԳH$ HL$ HxgX NfLISL$ L. L$` H
G H5͗G M L=HG LLLLLHH$ HHxgHH56\| L.pf D H5G{G LIc H54{G L-c ~L踲oH$ HL$ HxfH H5 {G LLH$jH $H$ LL% H$ H jH$ HL$ Hx.fX dLIL$ L. L$ H
G H5cG M LӷHG LLLNLLH$ HHxeHH5Z| Ln @ H$ HHx|eH H H+ H+ HHH9$ L$ HH H5yG LLHD$yiHL$H$ LL!% H$ b L$0 H0 HT$ H5myG LH$+iH $H$ LL% L$ MtLϰH$0 HHxdH$ Ht詰H$ HHxudM H HCx HCh HCp HH+ H HCxL 1E1H$? HtHHSxHH H HJH)IHHI9HSx},H H; uH4$LXFHSxH HP H+H ƃ HH ƃ HKpHHsxH H H+ 1HH Hɉd HCh H8 H+0 HH L$P X aLIL$ L. H
G H5fG MB LִHG LLLQLLH$P HHxbHH5W| Lk H H H+ H+ HHH9u H H H)HH C`~%W1.v .wrHH9uHĸ []A\A]A^A_ H8 H+0 HHtiHsxHCpHHZH H+ 1CP HH1QC` f. H$ HHxa HCp HHH HD$HwH0 HtfdH HtUdH HtDdH Ht3dH Ht"dH HtdH Ht dH HtcHH
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JG H5#G MC L蓮HG LLLLfLH$` HHxj\HCH5Q| LeL$p X [LIL$@ L. H
G H5G MD HHG LHLtHLH$p HHx[HH5P| LdILMLMH$` HLHx[IIIH$ Ht聧H$ HHxM[IH$0 HHx1[IILMLM,H$ HLHxZL[HH HD$H}IIHIIGHHD$PLHxZIHLnL覾H$p LHxrZHH D f UHh SH]HHHHH[]HH:]HcfAWAVAUATIUSHHH HS| HHG% H{PLH | L$ Ll$ H5AG HDŽ$ HDŽ$ LLHDŽ$ HH]H$ H LLHHD$ L$P LHxNYH$ Ht\\L|$0H5ɈG Hǃ Hǃ Hǃ LHǃ Hǃ LHǃ HDŽ$ HDŽ$ HDŽ$ ]H HLLZHD$0LHxXH$ Ht[L|$@H5G LHDŽ$ HDŽ$ LHDŽ$ \H HLLHD$@LHx&XH$ Ht4[L|$PH5G Hǃ Hǃ Hǃ LLHDŽ$ HDŽ$ HDŽ$ \H HLLSHD$PLHxWH$ HtZH$ H5kG Hǃ0 Hǃ8 Hǃ@ LHǃH HǃP HHǃX HD$o[Ht$pLHH5TkG H<$T[L|$`HL$H$LLH5&kG L<S1҅J HD$`` HL$ HxVHD$pHHxVH$ HHxVH H57G LLH$ZH $H$ LL% H$ H dH$ HL$ Hx(VX TLIL$ L. L$` H
G H5]G M LͧHG LLLHL蠺LعH$ HHxUH}H5J| L^f D H5iG LQ H5iG LQ ~LHoH$ HL$ Hx$UH H5iG LLH$WYH $H$ LL@% H$ H H$ HL$ HxTX tSLIyL$ L). L$ H
:G H5G M LcHoG LLLL6LnH$ HHx:THH5\I| LT] @ H$ HHx TH H H+ H+ HHH9$ L$ HH H5;hG LLHD$ XHL$H$ LL% H$ b L$0 H0 HT$ H5gG LH$WH $H$ LL% L$ MtL_H$0 HHx+SH$ Ht9H$ HHxSM H HCx HCh HCp HH+ H HCxL 1E1H$? HtHHSxHH H HJH)IHHI9HSx},H H; uH4$L4HSxH HP H+H ƃ HH ƃ HKpHHsxH H H+ 1HH Hɉd HCh H8 H+0 HH L$P X oPLItL$ L$. H
=G H5G MB LfHG LLLL9LqH$P HHx=QHH5_F| LWZ H H H+ H+ HHH9u H H H)HH C`~%W1.v .wrHH9uHĸ []A\A]A^A_ H8 H+0 HHtiHsxHCpHHZH H+ 1CP HH1QC` f. H$ HHx P HCp HHH HD$HH0 HtRH HtRH HtRH HtRH HtRH HtRH HtRH HtRHH
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TyG H5tG M L]H uG LLLةL0LhH$ HHx4DH
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wG H5sG M LH4^G LLLnLƧLH$ HHxBHH57| LK @ H$ HHxBH H H+ H+ HHH9$ L$ HH H5VG LLHD$FHL$H$ LLA% H$ b L$0 H0 HT$ H5VG LH$KFH $H$ LL% L$ MtLH$0 HHxAH$ HtɍH$ HHxAM H HCx HCh HCp HH+ H HCxL 1E1H$? HtHHSxHH H HJH)IHHI9HSx},H H; uH4$Lx#HSxH HP H+H ƃ HH ƃ HKpHHsxH H H+ 1HH Hɉd HCh H8 H+0 HH L$P X >LIL$ L. H
tG H5pG MB LHqG LLLqLɤLH$P HHx?HH54| LH H H H+ H+ HHH9u H H H)HH C`~%W1.v .wrHH9uHĸ []A\A]A^A_ H8 H+0 HHtiHsxHCpHHZH H+ 1CP HH1QC` f. H$ HHx> HCp HHH HD$H藢H0 HtAH HtuAH HtdAH HtSAH HtBAH Ht1AH Ht AH HtAHH
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C H5*C Mຟ LH C LLLmLLH$p HHx9HH5w L3ILMLMZH$ HLHx~II @ ILM]LMH$ HLHx6II>ILMLMH$ HLHxgIIL$` X LI}L$ LFb+ L$p H
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+ HDŽ$ HDŽ$ HDŽ$ I|$ DŽ$ ?HDŽ$ D H H$ H9! H, H܃I1HHL$ L$
+ HDŽ$ HDŽ$ I} HDŽ$ DŽ$ ?HDŽ$( cD H H$ H9 H, HHI1HH(L$ HCPLsXI)IM H$0 1L$` H|$H$ H|$fHL9 HCPHHShLH LH$ H$h H$0 H$` Ht$LHuH$0 Ht$L$ HDŽ$ H$ HL9u L L+ 1L$ IMtY H <t<HCPH LLHH HH$ -H H@HL9uL L+ 1L$ IMtW D H <t<HCPH LLHH HH$ HP H@HL9uH$ L$ Lp Hu [Hm HtRH} uHELLH$ YHx H; HH@tHHm HHx HuH H HH)HH9 H9H H H H9 H)HH9 H; H\$@X {HHL$ L . L$ H
F H5wF M& LHF HLH:LLHD$@LHx|HrH5q{ H賅 LH$ LsH$ vH|$PHtgH []A\A]A^A_ D L^LKhHCPHT$XH|$PI4)L(L $ HLL0 H( MHpLh^H H+ HXLt$0X mzLHrH$ H". L$` H
SF H5F Iغ L\HɶF LLHL/HgHD$0LHx6{HH5Xp{ HPHL%H$ (~LH$ ~H|$PHt~H\ @ L|$ X yLIH\$pHA. H$0 H
rF H53F Iغ H{HF LHLHNHHD$ H$` HxPzH)H5ro{ LjIHLLHD$0H$0 HxzH{HIHLHH HD$ H$` HxyLzHHHLIrLH~LHD$@H$` HxyeH] ;w6wHkHNHÐHf AWAVAUATIUSHHH Hs{ L$ Ll$0HHG$ H
{ H5nF LLHDŽ$P HDŽ$X HDŽ$` HH}H$P H{PLLHHD$0L$ LHxxH$P Ht{L|$@H5F LHDŽ$P HDŽ$X LHDŽ$` |H{hHLL~HD$@LHx xH$P Ht+{L|$PH5F LHDŽ$P HDŽ$X LHDŽ$` |H HLL HD$PLHxwH$P HtzL|$`H51F LHDŽ$P HDŽ$X LHDŽ$` {H HLLHD$`LHx7wH$P HtEzL|$pH5˲F LHDŽ$P HDŽ$X LHDŽ$` :{H HLLHD$pLHxvH$P HtyL$ H5dF LHDŽ$P HDŽ$X LHDŽ$` zH HLLHHD$H$ LL$ Hx;vHSH5F Hǃ Hǃ Hǃ LLHDŽ$P HDŽ$X HDŽ$` ,zH HLLH$ LHxuH$P HtxL$ H5qF LHDŽ$P HDŽ$X LHDŽ$` yH HLLH$ LHx:uH$P HtHxL$ H5F LHDŽ$P HDŽ$X LHDŽ$` :yH( HLL%H$ LHxtH$P HtwL$ H5F LHDŽ$P HDŽ$X LHDŽ$` xH@ HLLH$ LHxHtH$P HtVwL$ H5'F LHDŽ$P HDŽ$X LHDŽ$` HxHX HLL3H$ LHxsH$P HtvL$ H5F LHDŽ$P HDŽ$X LHDŽ$` wHp HLLH$ LHxVsH$P HtdvL$ H5MF LHDŽ$P HDŽ$X LHDŽ$` VwH HLLH$ LHxrH$P HtuH L$ H5ۮF ǃ Hǃ LHǃ H LH HDŽ$P HDŽ$X HDŽ$` vH HLLH$ LHx/rH$P Ht=uL$ H5F LHDŽ$P HDŽ$X LHDŽ$` /vH HLLHHD$RH$ LL$ HxqHH5ɭF LLHDŽ$P HDŽ$X HDŽ$` uH HLLH$ LHx?qH$P HtMtH` Hǃ( Hǃ0 Hǃ8 HǃH
+ HǃP HǃX ǃ` ?Hǃh D H HH H9^ L< LsL1HH$o (kH $H5F H H$P Hǃp Hǃx Hǃ LH@ Hǃ HHD$xtH$@ LHH5ZF H<$ZtL$0 HL$H$LLH5)F L?l1҅ H$0 HHxoH$@ HHxoH$P HHxoHCPH9CX HH Hx H+@ H+p HHH9s H H+ HH9
HCpH H+ChH+ HHH9e H H+ HH9 H H+ HH9
+ H H+ HH9[ H H+ HH9 H H HH)HH9 H9 L$ X .mLI3L$ L- H
|F H5ݪF M L%H.F LLLLL0H$ HHxmHH5 c{ Lwf D H5/F L1j H5 F Lj Lf H H9 H H9 8 H0 H( HH)HHHH H: Hz Hz Hz y& fHz xIHx xjHx xkHx xtHHB HHuHH)HHtdHtTHuH> y fH9 H?HĈ []A\A]A^A_ D Hr(fHr0f. Hr8H> xHH> xHHHHrHrHrHH$P HtSoHp HtBoHX Ht1oH@ Ht oH( HtoH HtnH HtnH HtnH|$H HtnH HtnH HtnH{hHtnH{PHtnHLe{ HHHtiIH$P HLHx.kHp Ht=nH@ !H@ %nH( HtnH HtnH|$H HtmH H H HmmHjIH$ HLHx~jLvkCHH$P HHHD$0H$ Hx?jH$P HHLIL1wL$ X hLIL$ Lo- L$ H
F H5aF Mລ L詻HzF LLL$L|LH$ HHxiHYH5^{ LrL$ X hLI L$ L- L$ H
^F H5F M LHF LLLLLH$ HHxhHH5 ^{ LqHILMLMH$ HLHxhLiUIIHILMFLM{H$ HLHxDhIIHH$ LHx hH$P H&&HIH$ LHxgHLIHH$ LHxgH$P HHHH$ LHxgEHH$P HHH$ LHxZgH$P H""HHH$ LHx(gH$P HHHH$ LHxfH$P HIH$0 HHxfH$@ HHxfkH*I D HH$ LHxfH$P HHHH$ LHxRfH$P HooHIH$ LHx fHL5^IHHD$pLHxekI!HHD$`LHxeH$P H00HHHD$PLHxeH$P HHHHD$@LHxxeX L$ !dHT$/H5F LIiL$0 L- H
cF H5ġF Mຩ L HF LLLLLH$ HHxdHH5Z{ LmL$ X {cLIL$P L0- H
ɬF H5*F Mຯ HrHSF LHLHEL}H$ HHxIdH"H5kY{ LcmL$ X bLIL$ L- L$` H
'F H5F Mຣ LеH9F LLLKLLH$ HHxcHH5X{ LlIHLcLH$ LHxgcHHL$ X bLIL$ L- L$0 H
GF H5F Mຢ LH!F LLLkLLH$ HHxbHH5W{ LkL$ X _aLIdL$p L- L$ H
F H5F Mມ LNHGF LLLL!LYH$ HHx%bHH5GW{ L?kILMLMH$ HLHxaJIILMLMH$ HLHxaIIIL$ X 5`LI:L$P L- L$ H
{F H5ܝF Mຠ L$HF LLLLL/H$ HHx`HH5 V{ LjL$p X _LIL$0 LH- L$ H
٨F H5:F Mຟ L育HF LLLLULH$p HHxY`H2H5{U{ LsiILMLMJH$ HLHx`~II @ ILMLMH$ HLHx_6II>ILMLMH$ HLHx_IIL$` X !^LI&L$ L- L$p H
gF H5țF Mພ LH^F LLLLLH$` HHx^HH5 T{ LhHILMLMH$` HLHx^II HH$ LHxn^H$P HiiHILM%LMZH$p HLHx#^II @ f. UH SHMaHHHHH[]HH`HBgfHBAUIATI?IUIL9S| IC AELEMDHDIAIL9MHMJMHMJA MH};IMPK,IIHEMbHHHLKM9uL[M9ZHuHII?III9 HHHH9HYLYLA}5HPHH?HHHHHLJM9tnAHEu!HLBLZHZ[]A\A]f D H
LHLJLHLJLHHAII?IIH9}HL L;Zf. HD IIHHHLJMHLJMHLJA MH
@ AWHIH)AVAUIATIUSHHH?Ht$HD$~kILt$ I]HHHHl D HHu HMLHUE$ D$ H Ht$(HL$0HHT$8LLHuLl$H\$Hl$ IL9r} H I9vqHsIWH9tztHSIWH H{HKAHt$81HSIWCH|$(LKHL$0HHS$ LD$ I9wHH[]A\A]A^A_ IGH9Cyf. AVAUIATIUSHHH)H0H H3 Ho HL)IM$ HHLHHH?HHHILH IL$AH Et"L@II9uI|$H9xAH EuHB AH Et"LHHI9uHpI9t$AH EuL9vCMNLBHJIFA
INHBHJIFINHBAIF IH$ LLLLL)H Ht IIHl$LL$ LI It$IFLIVINL)A $LIvIt$I AF HT$ HHL$HIv0It$AN HD$HD$(Iv(1$ H?H0[]A\A]A^f. AWAVAUATUSHH HOPLoXHD$P HD$X HD$` Ƈ I)IM LOh11E1AILt$Pgf. LHHLH)HHDH)HHH H1HxH)H1H HM HxH)HM IM9tZHKPHJ, H)I4)H
I9tH9D$`P HLtLLLHPHT$XgI1f H@ HH H)HH~BLX L 1W
HH9t#I H H) A.vHƃ H9uH H+ L LH裸H L 1L$ I)IMuP f. H H; $ Ht
-HHL9H H L<H5C L1ɅtH5C L tH5|C L rH5jC Lc VH5YC LG :L]+ LLݹHL9JH@ LH H( 1L$ L H|$I)IMuU @ Ht HPH$ HPH$ HPH H0 LȷHL9 H@ L$ H LHp LHX $ HH$ HH$ HH$ H0 H;8 MH|$L谹LHp aH0 L( L9HI I? $ M)HLLHHH?HH)HI ? M$ $ LL莻L9M+ fLA IqMQIAMA2f D @@t9HJHH HJHHHJHH HHI9uL;P@@uI LBLRL9Hr
+HHL9H H L<H5F LR1ɅtH5F LR tH5F LR rH5zF LR VH5iF LgR :L + LLݹHL9JH@ LH H( 1L$ L H|$I)IMuU @ Ht HPH$ HPH$ HPH H0 LȷHL9 H@ L$ H LHp LHX $ HH$ HH$ HH$ H0 H;8 MH|$L谹LHp aH0 L( L9HI I? $ M)HLLHHH?HH)HI ? M$ $ LL莻L9M+ fLA IqMQIAMA2f D @@t9HJHH HJHHHJHH HHI9uL;P@@uI LBLRL9Hr
uL0 H( I)IM H$ I1ML@ H|$ D L9u'HL9tYH( HHHHPH@L9tH Ht$$ LIH$ IIL$ HL9uL$
- HDŽ$ HDŽ$ HDŽ$ I|$ DŽ$ ?HDŽ$ <B H H$ H9! H, HI1HHL$ L$
- HDŽ$ HDŽ$ I} HDŽ$ DŽ$ ?HDŽ$( <B H H$ H9 H, H8I1HH(L$ HCPLsXI)IM H$0 1L$` H|$H$ H|$fHL9 HCPHHShLH LH$ H$h H$0 H$` Ht$LHuH$0 Ht$L$ HDŽ$ H$ 西HL9u L L+ 1L$ IMtY H <t<HCPH LLHH HH$ MH H@HL9uL L+ 1L$ IMtW D H <t<HCPH LLHH HH$ ճHP H@HL9uH$ L$ Lp Hu [Hm HtRH} uHELLH$ yHx H; HH@tHHm HHx HuH H HH)HH9 H9H H H H9 H)HH9 H; H\$@X HHmL$ LR+ L$ H
C H5C M& LHC HLHLBLHD$@LHxHH5Kw H L訣H$ {L蓣H$ fH|$PHtWH []A\A]A^A_ D LHLKhHCPHT$XH|$PI4)L(L $ HLWL0 H( MHpLH H+ HXLt$0X mLHRlH$ HP+ L$` H
C H5$C Iغ LH٠C LLHgLHHD$0LHx6HH5w H0HLEH$ L0H$ H|$PHtH< @ L|$ X 艾LInkH\$pHO+ H$0 H
C H5CC Iغ HHC LHLHHHD$ H$` HxPHH5w LJIHL|HD$0H$0 HxHHIHLHH;HD$ H$` HxվL轿HHHLI>rLH.LHD$@H$` Hx耾eH] ;6HkHNHÐHf AWAVAUATIUSHHH Hw L$ Ll$0HHGd# H w H5~C LLHDŽ$P HDŽ$X HDŽ$` HHH$P H{PLLH覲HD$0L$ LHx荽H$P HtL|$@H5C LHDŽ$P HDŽ$X LHDŽ$` pH{hHLL.HD$@LHx H$P HtL|$PH5C LHDŽ$P HDŽ$X LHDŽ$` H HLL軱HD$PLHx誼H$P Ht訿L|$`H5AC LHDŽ$P HDŽ$X LHDŽ$` H HLL訲HD$`LHx7H$P Ht5L|$pH5ۜC LHDŽ$P HDŽ$X LHDŽ$` H HLL5HD$pLHxĻH$P Ht¾L$ H5tC LHDŽ$P HDŽ$X LHDŽ$` 褿H HLLHHD$H$ LL$ Hx;HH5 C Hǃ Hǃ Hǃ LLHDŽ$P HDŽ$X HDŽ$` H HLLǯH$ LHx賺H$P Ht豽L$ H5C LHDŽ$P HDŽ$X LHDŽ$` 蓾H HLLNH$ LHx:H$P Ht8L$ H5C LHDŽ$P HDŽ$X LHDŽ$` H( HLLծH$ LHxH$P Ht迼L$ H5C LHDŽ$P HDŽ$X LHDŽ$` 衽H@ HLL\H$ LHxHH$P HtFL$ H57C LHDŽ$P HDŽ$X LHDŽ$` (HX HLLH$ LHxϸH$P HtͻL$ H5̙C LHDŽ$P HDŽ$X LHDŽ$` 诼Hp HLLjH$ LHxVH$P HtTL$ H5]C LHDŽ$P HDŽ$X LHDŽ$` 6H HLLQH$ LHxݷH$P HtۺH L$ H5C ǃ Hǃ LHǃ H LH HDŽ$P HDŽ$X HDŽ$` 舻H HLL裭H$ LHx/H$P Ht-L$ H5qC LHDŽ$P HDŽ$X LHDŽ$` H HLLHHD$rH$ LL$ Hx覶HH5ٗC LLHDŽ$P HDŽ$X HDŽ$` 蘺H HLLSH$ LHx?H$P Ht=H` Hǃ( Hǃ0 Hǃ8 HǃH
- HǃP HǃX ǃ` ?Hǃh ,B H HH H9^ L< LL1HH$ڴ (kH $H5oC H H$P Hǃp Hǃx Hǃ LH@ Hǃ HHD$XH$@ LHH5LoC H<$:L$0 HL$H$LL讬H5oC L?1҅ H$0 HHx軴H$@ HHx觴H$P HHx蓴HCPH9CX HH Hx H+@ H+p HHH9s H H+ HH9
HCpH H+ChH+ HHH9e H H+ HH9 H H+ HH9
- H H+ HH9[ H H+ HH9 H H HH)HH9 H9 L$ X .LI_L$ LsC+ H
C H5C M LeH>C LLL0LL`H$ HHxHuH5~w Lf D H5!mC L1 H5mC L Lf H H9 H H9 8 H0 H( HH)HHHH H: Hz Hz Hz y& fHz xIHx xjHx xkHx xtHHB HHuHH)HHtdHtTHuH> y fH9 H?HĈ []A\A]A^A_ D Hr(fHr0f. Hr8H> xHH> xHHHHrHrHrHH$P HtCHp Ht2HX Ht!H@ HtH( HtH HtH HtݳH Ht̳H|$H Ht豳H Ht蠳H Ht菳H{hHt聳H{PHtsHw HHH譹IH$P HLHx.Hp Ht-H@ AH@ H( HtH HtH|$)H HtزH H H HmmHjIH$ HLHx~LfCHH$P HHHD$0H$ Hx?H$P HHLILawL$ X 躭LIZL$ L>+ L$ H
0C H5qC Mລ LHC LLLL,LH$ HHx耮HH52zw LzL$ X LIYL$ L]>+ L$ H
C H5ϏC M LGHC LLLLLBH$ HHxޭHWH5yw LضHILMBLMH$ HLHx萭LxUIIHILMLMH$ HLHxDIIHH$ LHx H$P H&&HIH$ LHxHL2IHH$ LHxH$P HHHH$ LHx菬EHH$P HHH$ LHxZH$P H""HHH$ LHx(H$P HHHH$ LHxH$P HIH$0 HHxɫH$@ HHx赫kH*I D HH$ LHx脫H$P HHHH$ LHxRH$P HooHIH$ LHx HLe^IHHD$pLHxkI!HHD$`LHx֪H$P H00HHHD$PLHx觪H$P HHHHD$@LHxxX L$ !HT$/H5C LIzL$0 LZ:+ H
C H5ԋC Mຩ LLHC LLLLLGH$ HHxH\H5uw LݲL$ X {LI`UL$P L9+ H
C H5:C Mຯ HHcC LHL}HLH$ HHxIHH5tw LCL$ X LITL$ L&9+ L$` H
WC H5C Mຣ LHIC LLLLSL H$ HHx觨H H5Ytw L衱IHLLH$ LHxgHHL$ X LISL$ LF8+ L$0 H
wC H5C Mຢ L0H1C LLLLsL+H$ HHxǧH@H5ysw LL$ X _LIDSL$p L7+ L$ H
ՓC H5C Mມ LHWC LLLYLLH$ HHx%HH5rw L ILMLMFH$ HLHxߦJIILMWLM H$ HLHx襦IIIL$ X 5LIRL$P Lz6+ L$ H
C H5C Mຠ LdHC LLL/LL_H$ HHxHtH5qw LL$p X 蓤LIxQL$0 L5+ L$ H
C H5JC Mຟ LH+C LLLLLH$p HHxYHH5 qw LSILMLMzH$ HLHx~II @ ILM}LM2H$ HLHxˤ6II>ILM9LMH$ HLHx臤IIL$` X !LIPL$ Lf4+ L$p H
C H5C Mພ LPHnC LLLLLKH$` HHxH`H5ow LHILMKLM H$` HLHx虣II HH$ LHxnH$P HiiHILMLMH$p HLHx#II @ f. UH SH=HHHHH[]HHڥH"fHBAUIATI?IUIL9S| IC AELEMDHDIAIL9MHMJMHMJA MH};IMPK,IIHEMbHHHLKM9uL[M9ZHuHII?III9 HHHH9HYLYLA}5HPHH?HHHHHLJM9tnAHEu!HLBLZHZ[]A\A]f D H
+ HDŽ$ HDŽ$ HDŽ$ I|$ DŽ$ ?HDŽ$ D H H$ H9! H, HUI1HHQL$ L$
+ HDŽ$ HDŽ$ I} HDŽ$ DŽ$ ?HDŽ$( D H H$ H9 H, HhUI1HHHQL$ HCPLsXI)IM H$0 1L$` H|$H$ H|$fHL9 HCPHHShLH LH$ H$h H$0 H$` Ht$LHuH$0 Ht$L$ HDŽ$ H$ 西HL9u L L+ 1L$ IMtY H <t<HCPH LLHH HH$ MH H@HL9uL L+ 1L$ IMtW D H <t<HCPH LLHH HH$ ճHP H@HL9uH$ L$ Lp Hu [Hm HtRH} uHELLH$ yHx H; HH@tHHm HHx HuH H HH)HH9 H9H H H H9 H)HH9 H; H\$@X MHHL$ L- L$ H
F H5F M& LߠHЋF HLHZL貳LHD$@LHxNHH5C{ HW L訣H$ QL蓣H$ QH|$PHtQH []A\A]A^A_ D L0LKhHCPHT$XH|$PI4)L(L $ HLWL0 H( MHpL0H H+ HXLt$0X LLHH$ HB- L$` H
F H54F Iغ L|HF LLHLOH臱HD$0LHxVMH/H5xB{ HpVHLEH$ HPL0H$ 3PH|$PHt$PH|V @ L|$ X KLI访H\$pHa- H$0 H
F H5SF Iغ H蛞HF LHLHnH覰HD$ H$` HxpLHIH5A{ LUIHLlHD$0H$0 Hx6LH.MHIHLHH+HD$ H$` HxKLLHHHLI记rLH螰L֯HD$@H$` HxKeH] [IVIHkHNHÐHf AWAVAUATIUSHHH H.E{ L$ Ll$0HHG$ Hm{ H5F LLHDŽ$P HDŽ$X HDŽ$` HH0OH$P H{PLLHHD$0L$ LHxJH$P HtML|$@H5F LHDŽ$P HDŽ$X LHDŽ$` NH{hHLL螳HD$@LHx=JH$P HtKML|$PH5F LHDŽ$P HDŽ$X LHDŽ$` @NH HLL+HD$PLHxIH$P HtLL|$`H5QF LHDŽ$P HDŽ$X LHDŽ$` MH HLLHD$`LHxWIH$P HteLL|$pH5F LHDŽ$P HDŽ$X LHDŽ$` ZMH HLL襳HD$pLHxHH$P HtKL$ H5F LHDŽ$P HDŽ$X LHDŽ$` LH HLLHHD$H$ LL$ Hx[HHsH5F Hǃ Hǃ Hǃ LLHDŽ$P HDŽ$X HDŽ$` LLH HLL7H$ LHxGH$P HtJL$ H5F LHDŽ$P HDŽ$X LHDŽ$` KH HLL辰H$ LHxZGH$P HthJL$ H5F LHDŽ$P HDŽ$X LHDŽ$` ZKH( HLLEH$ LHxFH$P HtIL$ H5F LHDŽ$P HDŽ$X LHDŽ$` JH@ HLL̯H$ LHxhFH$P HtvIL$ H5GF LHDŽ$P HDŽ$X LHDŽ$` hJHX HLLSH$ LHxEH$P HtHL$ H5܁F LHDŽ$P HDŽ$X LHDŽ$` IHp HLLڮH$ LHxvEH$P HtHL$ H5mF LHDŽ$P HDŽ$X LHDŽ$` vIH HLLH$ LHxDH$P Ht HH L$ H5F ǃ Hǃ LHǃ H LH HDŽ$P HDŽ$X HDŽ$` HH HLLH$ LHxODH$P Ht]GL$ H5XF LHDŽ$P HDŽ$X LHDŽ$` OHH HLLHHD$rH$ LL$ HxCHާH5F LLHDŽ$P HDŽ$X HDŽ$` GH HLLìH$ LHx_CH$P HtmFH` Hǃ( Hǃ0 Hǃ8 HǃH
+ HǃP HǃX ǃ` ?Hǃh 5D H HH H9^ L< LFL1HH$A (kH $H5VF H H$P Hǃp Hǃx Hǃ LH@ Hǃ HHD$FH$@ LHH5zVF H<$zFL$0 HL$H$LL H5IVF L_>1҅ H$0 HHxAH$@ HHxAH$P HHxAHCPH9CX HH Hx H+@ H+p HHH9s H H+ HH9
HCpH H+ChH+ HHH9e H H+ HH9 H H+ HH9
+ H H+ HH9[ H H+ HH9 H H HH)HH9 H9 L$ X N?LISL$ L- H
F H5|F M LEHNF LLLLLPH$ HHx @HH5>5{ L6If D H5OTF LQ< H5<TF L5< Lf H H9 H H9 8 H0 H( HH)HHHH H: Hz Hz Hz y& fHz xIHx xjHx xkHx xtHHB HHuHH)HHtdHtTHuH> y fH9 H?HĈ []A\A]A^A_ D Hr(fHr0f. Hr8H> xHH> xHHHHrHrHrHH$P HtsAHp HtbAHX HtQAH@ Ht@AH( Ht/AH Ht AH Ht
AH Ht@H|$H Ht@H Ht@H Ht@H{hHt@H{PHt@Hl7{ HHHF8;IH$P HLHxN=Hp Ht]@H@ AH@ E@H( Ht4@H Ht#@H|$H Ht@H H H HmmHjIH$ HLHx<L=CHH$P HHHD$0H$ Hx_<H$P HHLILQwL$ X :LI߮L$ L- L$ H
@F H5xF Mລ LɍHzF LLLDL蜠LԟH$ HHx;HyH50{ LDL$ X 8:LI=L$ L- L$ H
F H5wF M L'HyF LLL袠LL2H$ HHx:HH5 0{ LDHILM貟LMH$ HLHx:L;UIIHILMfLM蛞H$ HLHxd:IIHH$ LHx?:H$P H&&HIH$ LHx
:HL"IHH$ LHx9H$P HHHH$ LHx9EHH$P HHH$ LHxz9H$P H""HHH$ LHxH9H$P HHHH$ LHx9H$P HIH$0 HHx8H$@ HHx8kH*I D HH$ LHx8H$P HHHH$ LHxr8H$P HooHIH$ LHx@8HLU^IHHD$pLHx8kI!HHD$`LHx7H$P H00HHHD$PLHx7H$P HHHHD$@LHx7X L$ A6HT$/H5vF LI;L$0 L- H
~F H5sF Mຩ L,HvF LLL觜LL7H$ HHx7HܨH5%,{ L @L$ X 5LI蠩L$P LP- H
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+ A HHl$ Ht$H HHT$HmsH|$HtH<$HtHD$0Ht
HHH@H[]A\HHD$0HtH|$ HH/H|$HHt7H<$Ht)H @ f. @ ATH7N USHH@HD$ HrHD$8HvHD$0\ HIeH5 @ LH H5`@ H H5cC HQ HH5@> HB B
+ HHl$ Ht$H HHT$H rH|$HtNH<$Ht@HD$0Ht
HHH@H[]A\HHD$0HtH|$ HH9.H|$HHtH<$HtH @ f. @ ATHgL USHH@HD$ H"HD$8H&HD$0 HIcH5f@ LH/ H5@ HP H5C H HH5> HB HHl$ Ht$H HHT$HpH|$HtH<$HtHD$0Ht
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+ ! HHl$ Ht$H HHT$HMnH|$Ht~H<$HtpHD$0Ht
HHH@H[]A\HHD$0HtH|$ HHi*H|$HHtH<$Ht H @ f. @ ATHJ USHH@HD$ HRHD$8HVHD$0< HI_H5@ LH_ H5I@ H H5CC H1 HH5 > HB B
+ HHl$ Ht$H HHT$HlH|$Ht.H<$Ht HD$0Ht
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HHH@H[]A\HHD$0HtH|$ HHI%H|$HHtH<$HtH @ f. @ ATHG USHH@HD$ H2HD$8H6HD$0 HIZH5v? LH? H52@ H` H5#C H HH5 > HB B
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HHH@H[]A\HHD$0HtH|$ HH9H|$HHtH<$HtH @ f. @ ATH7 USHH@HD$ H"HD$8H&HD$0 w HICH5f? LH/ H5I@ HP H5C H HH5> HB B
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+ A HHl$ Ht$H HHT$HmJH|$HtH<$HtHD$0Ht
HHH@H[]A\HHD$0HtH|$ HHH|$HHt7H<$Ht)H @ f. @ ATH1 USHH@HD$ HrHD$8HvHD$0\o HI<H5? LH H5@ H H5cC HQ HH5@x> HB B
+ HHl$ Ht$H HHT$H IH|$HtNH<$Ht@HD$0Ht
HHH@H[]A\HHD$0HtH|$ HH9H|$HHtH<$HtH @ f. @ ATHg4 USHH@HD$ H"HD$8H&HD$0 o HI:H5f? LH/ H5S@ HP H5C H HH5v> HB HHl$ Ht$H HHT$HGH|$HtH<$HtHD$0Ht
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HHH@H[]A\HHD$0HtH|$ HH H|$HHtH<$HtH @ f. @ ATHG1 USHH@HD$ HHD$8HHD$0i HI5H5F? LH H5~ ? H0 H5C H HH5q> HB 舾 HHl$ Ht$H HHT$HBH|$HtH<$HtHD$0Ht
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+ q HHl$ Ht$H HHT$H=H|$HtH<$HtHD$0Ht
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P: H5a: Iغ) HIqH: LHLzHuHDyHD$0HHxIH\H5n LRHHD$@Ht$0HxIHSX Ld$/Ll$0[HH5: LLIMH\$@H! Hl$`H
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: H5: Iغ Hn1LHLawHrHvHD$ HHx0GHH5n L*PHLGHgPHHHIvHD$ Ht$HxFHf. D AWAVAUATUHiSH HFPMH$ H$ HT$XHL$`HDŽ$ H$ DŽ$ DŽ$ DŽ$ Ƅ$ DŽ$ HDŽ$P HDŽ$X HDŽ$` I@ I+@HH H$P LH0L$X H$P LH)HHHH H? H H h H c H1f D Hy ~[Hx Hx Hx HHA HHuLH)HH H HuH> I98 LD$pH|$hE1<LIHH H$X H$P A HD$pH$P HD$hH$ L$ H$ L$ H\$0HHD$(H$ LHD$ H$ HD$H$ HD$H$ HD$H$ H$H$ t]H
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8 H58 H$ LH$ H! Lt$PH
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#: H5: Iغ H|VH: LHLG_HZHw^H$ H$ Hx/HkH5m L8@ IHLqZHH&^H$ H$ Hx.L/iHHHkH,H$ HHt1H$h HrHX -HB L$@ H$ ME0H
8 H5ܬ8 H$ LH$ H'! Lt$PH
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8 H5 8 H$ LH$ HW! Lt$PH
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2: H: A H1肯! H$ H$ HH$p Ht H$ H []A\A]A^A_H$ HHH$ \H$ HH$ H$ 胤HH$ HC0HH$ H H$P HD$P 1HD$` H$ H$ H$ H$ HL$hH$ H$ $ t
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.: H: A 1聪! H$X H$ H$ HHt\H$ HH$ H H9 HH9 HH8HH0H(X L$@ H$ H5ܗ: LIK H$ H+! H$P H
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+HD$HH$ HH9 HD$H HcHτ\$D$ jf D H$ H+$ H|$XHDŽ$ HDŽ$ HDŽ$ HH4H$ H+$ 1HH~O H;$ t5H$ H4H$ H;$ txHHtHHH$ HH9uHt$XH$ V:H$ L薚H$ Ht}H$ H}f H|$X\ @ $ D$DHcHDŽ$ HD$x E1H$` H$ HD$pH|$h1蚤HHD$`HHp0ƘHt$pH{ȒH$ H H|$pH$ H|H
Y= H.S= A L1$ H$h H$ H$ HHtH8HH0H(H> oHH> aHRHOH*HBHOHOH$ HHtH$h Ht=H|$p#H$ Ht{H$` Ht{H7X L$0 ewH$ H5Q= LI|H$ H $ H$` H
|X= H5Q= Iغ HEHhQ= LHLHHPH$0 H$ HxxHH59mq L1@IHLHHH$0 H$ HxwLxHHÐHDHÐHH$ H~H$ HHtzοX L$P vME0H
c: H_: H5[: LH@ H$@ H$ Lt$XH
V= H5: Iغ LH: LLHhLHH$P LHxvHH5kq HHL3 H|$XIqHLH$P H$ HxmvHewIHHHH$ Ht_yH$ HHtHyH$ HHA7X L$@ tH$ H5Q= LH,zH$ H\$ L$ H
U= H5>O= Iغ LHQ= LLHLiHH$@ LHxmuHFH5jq H~H~HZLI HLQH$@ H$ HxuHv:IH Hf AWAVAUATUHiSH( HFPMH$ H$ HT$hHL$`HDŽ$ H$ DŽ$ DŽ$ DŽ$ Ƅ$ DŽ$ HDŽ$` HDŽ$h HDŽ$p { I@ I+@HH H$` LH0L$h H$` LH)HHHHF H? D H A H < H H1f D Hy ~[Hx Hx Hx HHA HHuLH)HH H HuH> I9 LD$xH$ E1?LVHH H$h H$` A HD$xH$` H$ H$ L$ H$ L$ H\$0HHD$(H$ LHD$ H$ HD$H$ HD$H$ HD$H$ H$H$ t]H
=Q= HK= A L1 $ H$ H$ HH$` Ht\uH$ H( []A\A]A^A_H$ HHH$ 1H|$`H$ HDŽ$ HDŽ$ HDŽ$ HD$pbH$ H$ H H$ HD$H 1HD$P HL$pH$` H$ H$ H$ H$ HL$XH$ H$ D $ t
HH9Y H$` HL$HHHcЉD$DH9 H$ H$ LHt$hHH$ HDŽ$` HDŽ$h G H$ H$` H$ H$h HH$ tH=3r HP H$ H$ H0HD$`HL$XE1HLHp0I H$ HtgsH$ HtH$ x= \$DLc$ $ H$ HL$PH$ H$ $ H4A9 ~FHcIIHIE1f D LLHxAIMIA9u\$D$ HcH$ H\$PH9D$xuH$ HxX tvH|$pLqL H$h HtHD$HH$ HH9 HD$H HcHOx\$D$ jf D H$ H+$ H|$XHDŽ$ HDŽ$ HDŽ$ HHr(H$ H+$ 1HH~O H;$ t5H$ H4H$ H;$ txHHtHHH$ HH9uHt$XH$ -H$ LH$ Ht4qH$ H qf H|$XvP @ $ D$DHcHDŽ$ HD$x E1H$` H$ HD$pH|$h1HHD$`HHp0FHt$pH{HH$ H H|$pH$ HYpH
K= HF= A L16# H$h H$ H$ HHtAH8HH0H(H> oHH> aHRHOH*HBHOHOH$ HHtoH$h HtH|$pH$ HtqoH$` Ht_oHuX L$0 jH$ H5 E= LI[pH$ H# H$` H
J= H5mE= Iغ HŽHD= LHL@HHH$0 H$ HxkHpH5`q Lt@IHLJHHH$0 H$ HxFkL>lHHÐHăHÐHH$ HR~H$ HHtnοX L$P iME0H
: H: H5: LHH$@ H0# Lt$XH
$I= H5]: Iغ LmH: LLHL@HxH$P LHxDjH H5f_q H^sHL H|$XIHL&H$P H$ HxiHjIHHHH$ HtlH$ HHtlH$ HHA}7X L$@ 6hH$ H5gE= LHmH$ H# L$ H
-H= H5B= Iغ LHWE= LLHLH!H$@ LHxhHH5^q HrH~HZLIHLH$@ H$ HxhHi:IH Hf AWAVAUATUHiSH8 HFPMH$ H$ LH$ H$ HDŽ$ H$0 DŽ$ DŽ$ DŽ$ Ƅ$ DŽ$ HDŽ$p HDŽ$x HDŽ$ I@ I+@HH H$p HL$x H$p LH)HHHH
+ H? H H i
+ H U
+ H+Hz ~[Hx ? Hx . Hx HHB HHuLH)HH H HuH9 I9 LD$xH$ E19ٷH H$x H$p A HL$xH$p H$ H$ H$ H$ L$ H\$0HT$(H$ HHL$HHHHT$ H$ HT$H$ HT$H$ HT$H$0 H$HUH$ t]H
C= Ht?= A H1# H$ H$ HH$p HthH$ H8 []A\A]A^A_H$ HHH$ "%H$ H$ HDŽ$ HDŽ$ HDŽ$ HD$p4H$ H$ HM H$ HD$P 1HD$` HL$pH$p H$ H$ H$ H$ HL$hH$0 H$ D $ t
HH9 H$p HL$PHHcЉD$\H9 H$ H$0 H$ H|$HHH$ HDŽ$p HDŽ$x H$ H$p H$ H$x HH$ tH=r HP H$ H$ H#H$ HL$hE1H|$HHHp0 H$ HtfH$ HtH$ x H$ HL$`Hc$ L$ L$ L,ȋD$\$ $ L$X9 ~wHE1HHD$8H HD$@f. H\$8LMHH~ @ LH-hIHHuALl$@Lt$8D;|$XuD$\$ HHD$`H$ H9D$xuH$ HxX H\$HH|$pH裾H H$x HtHD$PH$ HH9 HD$PHcHv @ LLegIIHuHH$ H+$ H|$hHDŽ$ HDŽ$ HDŽ$ HHpH$ H+$ 1HH~IH;$0 t5H$ H4H$ H;$ tqHHtHHH$ HH9uHt$hH$ !H$ H|$HDH$0 HtbdH$ HLdH|$hC뚋$ D$\HcxHDŽ$ HD$x E1H$p YH$ HD$pH$ 1RHH$ HHp0{Ht$pH{}yH$ H H|$pH$ HcHt$HH
== H9= A 1i# H$x H$ H$ HHttH9 :HH9 ,H H8HH0H(@X L$@ ^H$ H58= LIdH$ H4# H$p H
E== H59= Iغ HnH8= LHLHAHyH$@ H$ Hx@_HH5bTq LZhHH|$p]H$ Ht+bH$p HtbHqhX L$P ]H$ H5:= LHcH$ HE# H$ H
V<= H5'8= Iغ HIHD$HwH:= LLHLJHH$P LHxN^H'H5pSq HhgH$ HHyH|$HIHL"H$P H$ Hx]H^IH @ HIHLHHH$@ H$ Hx]L^HHfHHWsHHbHMH$0 HHtc`H$ HtQ`H|$H H$x HaHH$ HHtwHWHW HquH뛿X L$` z[HHD$HH
: H: H5: LL@0H$P H# L|$hH
9= H5>: Iغ LNHg: LLHL!HYH$` LHx%\HH5GQq H?eH$ HHt_H$ H4HH|$hIHLH$` H$0 Hx[H\kIH @ Hf AWAVAUI1ATUHSHHH , IH H IT$0IDJA A A AY H|$0LHLH+HD$0HD$0 HHtBHEL$ LHxZH]Hl$0HtHELHxZH]H H[]A\A]A^A_f D HLHLH;H$H$ HHtKuH<$Ht=u H|$LHLH;HD$HD$ HHuuH|$Hua @ H|$ LHLH;HD$ HD$ HH-tH|$ HkH$ X fXHIMd$0sH5'6= * HL|$@]LH袘 H52= H ]HuLZH;r Ll$pLd$PLHPH@H$ Hwr H$ HH$ H$ HxYHr H}@HH$ 6_Hr H}XHPH@H$ HRH Hr HH$ VL/# H
;= H52= Ml Lq1LLLLIL聼HD$@HHxPXH)H5rMq LjaHH;HtsH HaILM-HD$@HLHxWLXIIHbLH D HQq HHÐHQq HHÐHQq HHÐHQq HHÐHyQq HHÐHiQq HHÐHYQq HHÐHIQq HHÐH9Qq HHÐH)Qq HHÐHQq HHÐH Qq HHÐHPq HHÐHPq HH
Zf. HPq HHYf. HPq HHYf. HPq HHYf. HiPq HHYf. HIPq HHmYf. H)Pq HHMYf. H Pq HH-Yf. HOq HH
Yf. HOq HHXf. HOq HHXf. HOq HHXf. HiOq HHXf. AWAVAUATLgXUSHLH#RH<r ƃ8 HHǃ0 ƃ9 1Hǃ@ HǃH HHǃP HǃX HCXHr HhLhH}H+L/ZHr H{@HC HC HC LsHC( HC0 HPH@HC8 HCXHr HHHC_Hr CH LLHHCHWr HHCPYH[]A\A]A^A_HH!r LHHCXqQH]LI~HUH+LL,f. @ SHHHtHGHxH;=ԗr uHVH[H=Dr HWtHt$U͋PJHfAWAVAUIATUHSH HIq HT$HHHGHHD$REHuI}AERHE H+E1E1IE IE( IE0 HH7 LM} M}(I]0Lu LLeM9t Ht LHRIHM9uH̖r I](H\$PIE8 IE@ IEH HHIEP+L$ LELd$@HLD$RHt$@LHVVH55? HIVt$L[H5> HIyVHuLHViVHD$@HxH;= r L|$0IvLSH˖r LHH$ Hr H@H$ Hr HH$ H$ HxQHr I~@HH$ WHr I~XLxH@HHD$IGL$ H HCr HH$ NHt$0HHVUHD$0LHxQH5> H\UHD$HH0HVHUH5
@ H4UHEH9E t`H5: HUHEHm L`L)HH~4f D I4$HHVTH5z
@ HTIHuHl$ HsHQI}PHYHD$ LHx<PH%r H{HHD$PHr H@H$ 萔IGHL$H{XL|$PHLPHr HH$ AMH []A\A]A^A_ H H9wH H SIlML$ HIELHxOIEHt$PHxoOLYHXHHHHD$ LHxAOHZIEPHHHx&OI}8=H|$HHD$0LHxNH=r HWtFL|$0LPHHD$@Ht$0HxNLYfPJHHHLHLHL$ LI9tILIHx]NI9u3UHVI} HYQ @ AWAVAUIX ATUSH rQHHGq LH{HH Hq Ld$pHl$@H5A LHHHRH|$ HL$HLf HD$@HxH;=r u%H|$ uFHD$CPHĨ H[]A\A]A^A_H=r HWW Ht$PXO벿X L|$0LHT$H56= LIQLl$PL# H
L[= H5M7= M躪 LH; LLLpLȱL HD$0LHxLHH5Aq LUHH|$ HtHHFq HHHHOHVILMUL荰HD$0HLHxYLLQMIIHfHHD$@Ht$PHx(LH|wH냋PJHf D AWAVAUIX ATUSH BOHHEq LH{HH Hq Ld$pHl$@H5A LHHHOH|$ HL$HL6 HD$@Ll$PLHxpKH|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$0IHT$H54= LIuOL# H
k= H5G5= M躪 LH; LLLjL¯LHD$0LHxJHH5?q LSHH|$ HtHHDq HHHHMHSILMOL臮HD$0HLHxSJLKKIIĐHHHD$@Ht$PHx#JH{H @ AWAVAUIX ATUSH RMHHCq LH{HH Hq Ld$pHl$@H5A LHHHMH|$ HL$HLF HD$@HxH;=r u%H|$ uFHD$CPHĨ H[]A\A]A^A_H=r HWW Ht$P8K벿X L|$0GHT$H52= LI`MLl$PL# H
l= H5-3= M躪 L՚Hֈ; LLLPL設LHD$0LHxHHH5=q LQHH|$ HtHHmBq HHHHKHQILM5LmHD$0HLHx9HL1IIIHfHHD$@Ht$PHxHH|wH냋PJHf D AWAVAUIX ATUSH "KHHAq LH{HHu Hq Ld$pHl$@H5bA LHHHKH|$ HL$HL HD$@Ll$PLHxPGH|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$0EHT$H50= LIUKL# H
<= H5'1= M躪 LϘHІ; LLLJL被LڪHD$0LHxFHH5;q LOHH|$ HtHHg@q HHHHIHOILM/LgHD$0HLHx3FL+GIIĐHHHD$@Ht$PHxFH{H @ AWAVAUIX ATUSH 2IHH?q LH{HH H>q Ld$pHl$@H5rA LHHHIH|$ HL$HL& HD$@Ll$PLHx`EH|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$0CHT$H5.= LIeIL# H
6V= H57/= M躪 LߖH; LLLZL販LHD$0LHxDHH59q LMHH|$ HtHHw>q HHHHGHMILM?LwHD$0HLHxCDL;EIIĐHHHD$@Ht$PHxDH{H @ AWAVAUIX ATUSH BGHH=q LH{HH Hq Ld$pHl$@H5A LHHHGH|$ HL$HL6 HD$@Ll$PLHxpCH|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$0AHT$H5,= LIuGL# H
FW= H5G-= M躪 LH; LLLjL§LHD$0LHxBHH57q LKHH|$ HtHH<q HHHHEHKILMOL臦HD$0HLHxSBLKCIIĐHHHD$@Ht$PHx#BH{H @ AWAVAUIX ATUSH REHH;q LH{HH H q Ld$pHl$@H5A LHHHEH|$ HL$HLF HD$@Ll$PLHxAH|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$0 @HT$H5*= LIEL# H
VI= H5W+= M躪 LH ; LLLzLҥL
+HD$0LHx@HH55q LIHH|$ HtHH:q HHHHCH
JILM_L藤HD$0HLHxc@L[AIIĐHHHD$@Ht$PHx3@H{H @ AWAVAUIX ATUSH bCHH9q LH{HH赿 Hq Ld$pHl$@H5A LHHH DH|$ HL$HLV HD$@Ll$PLHx?H|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$0 >HT$H5)= LICL# H
Y= H5g)= M躪 LH; LLL芤LLHD$0LHx>H°H5 4q LHHH|$ HtHH8q HHHHAH HILMoL觢HD$0HLHxs>Lk?IIĐHHHD$@Ht$PHxC>H{H @ AWAVAUIX ATUSH rAHH7q LH{HHŽ Hq Ld$pHl$@H5A LHHHBH|$ HL$HLf HD$@Ll$PLHx=H|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$0,<HT$H5'= LIAL# H
@= H5w'= M躪 L H }; LLL蚢LL*HD$0LHx<HҮH52q LFHH|$ HtHH6q HHHH?H-FILML跠HD$0HLHx<L{=IIĐHHHD$@Ht$PHxS<H{H @ AWAVAUIX ATUSH ?HH6q LH{HHջ Hq Ld$pHl$@H5¬A LHHH+@H|$ HL$HLv HD$@Ll$PLHx;H|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$0<:HT$H5!%= LI?L# H
R= H5%= M躪 L/H0{; LLL誠LL:HD$0LHx ;HH5+0q L#DHH|$ HtHH4q HHHH=H=DILM菟LǞHD$0HLHx:L;IIĐHHHD$@Ht$PHxc:H{H @ AWAVAUIX ATUSH =HH4q LH{HH H q Ld$pHl$@H5ҪA LHHH;>H|$ HL$HL HD$@Ll$PLHx9H|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$0L8HT$H51#= LI=L# H
D= H5#= M躪 L?H@y; LLL躞LLJHD$0LHx9HH5;.q L3BHH|$ HtHH2q HHHH;HMBILM蟝LלHD$0HLHx8L9IIĐHHHD$@Ht$PHxs8H{H @ AWAVAUIX ATUSH ;HH(2q LH{HH Hq Ld$pHl$@H5A LHHHK<H|$ HL$HL HD$@Ll$PLHx7H|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$0\6HT$H5A!= LI;L
# H
T= H5!= M躪 LOHPw; LLLʜL"LZHD$0LHx)7HH5K,q LC@HH|$ Ht!HH0q HHHH:H]@ILM诛LHD$0HLHx6L7IIĐHHHD$@Ht$PHx6H{H @ AWAVAUIX ATUSH 9HH80q LH{HH Hq Ld$pHl$@H5A LHHH[:H|$ HL$HL HD$@Ll$PLHx5H|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$0l4HT$H5Q = LI9L # H
;= H5 = M躪 L_H`u; LLLښL2LjHD$0LHx95HH5[*q LS>HH|$ Ht1HH.q HHHH8Hm>ILM这LHD$0HLHx4L5IIĐHHHD$@Ht$PHx4H{H @ AWAVIAUIATIUSH H\$ H$LD$H{X/1HHxr Ƅ$X HHDŽ$P Ƅ$Y 1HDŽ$` HDŽ$h HHDŽ$p HDŽ$x HD$xHxr HhLxH}Hl$ L?9Hxr H{@HD$0 HD$8 HD$@ HD$H HD$P HHD$X HD$ Hmxr H@HD$xHwr HHD$(>Hxr HsH{XD$h HHD$(HHwr HHD$p8M L1LHHk7H$HH0,r M Lk1LHH=7HD$HH0q HsL3Hwr Ht$ HHD$ Hwr H@HD$xHwr HHD$(HD$pHx~2Hvr H{@HHD$(8HEHl$ H{XL| HLvr HHD$x/HĈ L[]A\A]A^A_f HD$ HHxw : @ HD$ HHxw :HH<HQ;H{IEvHEHl$ LL| Hur H{XHHD$x.H;Hf. @ AUATUSHH H9w0Ht$uHHĈ []A\A]ÿX Ll$ 0HT$LC0H
O: H5= LIHl$0H覽# H\$PH
)= H5Ӱ: I躩 HH= LHL^H趕HHD$ HHx0HH5%q L9HL1H:IHLfH螔HD$ Ht$ Hxk0HHÐf. @ AUATUSHH H9w0Ht$uHHĈ []A\A]ÿX Ll$ .HT$LC0H
: H5= LIsHl$0Hv# H\$PH
+(= H5: I躤 H賁H= LHL.H膔H输HD$ HHx/HfH5$q L8HLl0H8IHL6HnHD$ Ht$ Hx;/HHÐf. @ AWAVIAUIATUSH H\$ HT$H{X+Hrr Ƅ$X HHDŽ$P Ƅ$Y 1HDŽ$` HDŽ$h HHDŽ$p HDŽ$x HD$xHrr HhLxH}Hl$ L?54L%^sr H{@HD$0 HD$8 HD$@ HD$H HD$P ID$HD$X HD$ ID$@HD$xHrr HHD$(;9HTsr HsH{XD$h HHD$(Hqr HHD$p3M LE,LHH2HD$Hߋ07HsL.ID$Ht$ HD$ ID$@HD$xHrr HHD$(HD$pHxe-Hqr H{@HHD$(3HEHl$ H{XL| H3qr HHD$x*HĈ L[]A\A]A^A_HD$ HHxw 5=HH7Ha6H{IUqHEHl$ LL| Hpr H{XHHD$x *H#6Hf. @ AVAUATIUSHH H H %I$ I$ I|$hHta/I|$PHtR/I\$0Lt$HtMH=qr L-$pr u
D HH{H+Ht/HCHxL9 H.HuID$(I|$ 1H
+I|$ ID$8 ID$0 .ID$LHx+ID$LHx+I$LHxo+L.H[]A\A]A^úPeL@-XH{H+HtJ.HCHxL9uH5.H6H̋PJ҉HL,AVHAUATUHSH0HD$HHD$(HHD$ x I .Hù 1H߾
+ HHnr H{@C C C@ ?HHH HKHK貞: H HC(H9 L, L-L1HI{)H H5#j; Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $Y Hbr H5Ti; H H5[; Hd H5'? H HH5: HB HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( Ht&HD$ HtH|$ HH0H[]A\A]A^X&IHCHHxu(HCHHxe(HHHxV(HLk+HD$ HtH|$ HH1HHMIHt
HHH} HtnHE LH<$HtUHf. @ AVHWAUATUHSH0HD$HnHD$(HrHD$ X I*Hù 1H߾
+ HHkr H{@C C C@ ?HHH HKHK蒛: H HC(H9 L, L{*L1HI[&H H5g; Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $9 HB|r H54f; H H5X; HD H5? H HH5: HB B HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( HtHD$ HtH|$ HH0H[]A\A]A^1#IHCHHxN%HCHHx>%HHHx/%HLD(HD$ HtH|$ HH.HHMIHt
HHH} HtGHE LH<$Ht.H AVHAUATUHSH0HD$HNHD$(HRHD$ 8 I'Hù 1H߾
+ HH_hr H{@C C C@ ?HHH HKHKr: H HC(H9 L, L['L1HI;#H H5c; Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $ H"yr H5c; Hc H5U; H$ H5? H HH5Ğ: HB B u HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( HtHD$ HtH|$ HH0H[]A\A]A^ IHCHHx."HCHHx "HHHx"HL$%HD$ HtH|$ HHc+HHMIHt
HHH} Ht'HE LH<$HtH AVHWAUATUHSH0HD$H.HD$(H2HD$ I$Hù 1H߾
+ HH?er H{@C C C@ ?HHH HKHKR: 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 $ Hvr H5_; HC H5eR; H H5? H HH5: HB B U HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( HtHD$ HtH|$ HH0H[]A\A]A^ IHCHHx HCHHx HHHx HL"HD$ HtH|$ HHC(HHMIHt
HHH} HtHE LH<$HtH AVHgAUATUHSH0HD$HHD$(HHD$ I!Hù 1H߾
+ HH br H{@C C C@ ?HHH HKHK2: 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 $ Hrr H5\; H# H5EO; H H5? H HH5: HB B 5 HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( HtHD$ HtH|$ HH0H[]A\A]A^IHCHHxHCHHxHHHxHL HD$ HtH|$ HH#%HHMIHt
HHH} HtHE LH<$HtH AVHWAUATUHSH0HD$HHD$(HHD$ ؍ Ik Hù 1H߾
+ HH^r H{@C C C@ ?HHH HKHK: H HC(H9 L, L L1HIH H5Z; Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $ Hor H5Y; H H5%L; H H5? Hu HH5d: HB B HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( HtHD$ HtH|$ HH0H[]A\A]A^IHCHHxHCHHxHHHxHLHD$ HtH|$ HH"HHMIHt
HHH} HtHE LH<$HtH AVH'AUATUHSH0HD$HΡHD$(HҡHD$ 踕 IKHù 1H߾
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8 L' fI$ f D H<$L? H|$L$ Ll$0qHL$H$ Ht$ H$ H|$PH$ L$ L$ H$ H$ HL$H$ H$ HD$PHl$XHQH$ H$ rH$ I$ H_HuJ11 y o e [ Q GHH$ HHt_H|$`Ht_H\eH$ HHuHH$ HHt HHD$@H$ Hx[HmfHf D AWAVAUATIHUSHH( H4$1H$ # HI IHLHHIr H$ tGH
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+]HD$Hx H+ H<$ P IOH $HH<H?H9HFHHH HH
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[8 L fI$ f D H<$L$9 H|$L$ Ll$0HL$H$ Ht$ H$ H|$PH$ L$ L$ H$ H$ HL$H$ H$ HD$PHl$XHQH$ H$ H$ I$ HXHuJ11 s i _ U K AHH$ HHtXH|$`HttXH^H$ HHuHH$ HHt0HHD$@H$ HxUH_Hf D AWAVAUATIHUSHH( H4$1H$ ~ HI~ IHs~LHHIH$ tGH
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HHH@H[]A\HHD$0HtH|$ HHH|$HHt7H<$Ht)H @ f. @ ATHUSHH@HD$ HHD$8HHD$0l HI!EH53 LH H5B6 H H5s7 Ha HH5P2 HB B HHl$ Ht$H HHT$H-RH|$HtNH<$Ht@HD$0Ht
HHH@H[]A\HHD$0HtH|$ HHIH|$HHtH<$HtH @ f. @ ATHWUSHH@HD$ H2HD$8H6HD$0 HICH5v3 LH? H5A6 H` H5#7 H HH5 2 HB B HHl$ Ht$H HHT$HPH|$HtH<$HtHD$0Ht
HHH@H[]A\HHD$0HtH|$ HH H|$HHtH<$HtH @ f. @ ATHUSHH@HD$ HHD$8HHD$0x HIBH5&3 LH H5I@6 H H5ӿ7 H HH5~2 HB B a HHl$ Ht$H HHT$HOH|$HtH<$HtHD$0Ht
HHH@H[]A\HHD$0HtH|$ HH H|$HHtGH<$Ht9H @ f. @ ATHUSHH@HD$ HHD$8HHD$0|s HI1AH53 LH H5>6 H H57 Hq HH5`}2 HB B HHl$ Ht$H HHT$H=NH|$Ht^H<$HtPHD$0Ht
HHH@H[]A\HHD$0HtH|$ HHY
+H|$HHtH<$HtH @ f. @ ATHUSHHpHD$PHBHD$hHFHD$`,s HD$ "} HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@xHl$8w HI?H5(3 LH HT$0H525 H= H57=6 H H57 H HH5{2 HB
B
- D HHl$PHt$H HHT$HqH|$Ht,H<$Ht H|$0HtϝHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHأH|$HHtH<$HtH|$0HtYH @ H ATHGUSHHpHD$PHHD$hHHD$` HD$ HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@Hl$8^ HIbH5 "1 LH1U HT$0H5 s2 H}X H5_3 H^A H54 HB HH50 HB
+ O HHl$PHt$H HHT$H{LH|$HtH<$HtH|$0HtoHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHt&H<$HtH|$0HtH @ H ATHUSHHpHD$PHbHD$hHfHD$`Lq HD$ B{ HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@Hl$8{ HI=H5H3 LH HT$0H5 15 H] H5W;6 H H57 H HH5y2 HB
B
- B HHl$PHt$H HHT$HpH|$HtLH<$Ht>H|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHtH<$HtH|$0HtyH @ H ATHUSHHpHD$PH"HD$hH&HD$` HD$ HD$(HD$0 HD$8 HD$@ 8HhHt$ HHD$0Hl$@Hl$8~ HI`H5> 1 LHQS HT$0H5=q2 HV H5]3 H~? H54 H/@ HH50 HB
+ o HHl$PHt$H HHT$HJH|$HtH<$HtH|$0Ht HD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHtFH<$Ht8H|$0Ht H @ H ATHwUSHHpHD$PHHD$hHHD$`lo HD$ by HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@Hl$8{ HI;H5h3 LH1 HT$0H5=/5 H} H5w96 H> H57 H HH5w2 HB
B
- @ HHl$PHt$H HHT$H;nH|$HtlH<$Ht^H|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHtH<$HtH|$0Ht虙H @ H ATHUSHHpHD$PHBHD$hHFHD$` HD$ HD$(HD$0 HD$8 HD$@ XHhHt$ HHD$0Hl$@(Hl$8 HI_H5^ 1 LHqQ HT$0H5]o2 HT H5\3 H= H54 HO> HH50 HB
+ HHl$PHt$H HHT$HHH|$HtH<$HtH|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHtfH<$HtXH|$0Ht9H @ H ATHGUSHHpHD$PHHD$hHHD$`m HD$ w HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@Hl$8.o HI9H53 LHQ HT$0H5]-5 H H576 H^ H5!7 H HH5u2 HB
B
- > HHl$PHt$H HHT$H[lH|$HtH<$Ht~H|$0Ht/HD$`Ht
HHHpH[]A\HHD$`HtH|$P HH8H|$HHtH<$HtH|$0Ht蹗H @ H ATHgUSHHpHD$PHbHD$hHfHD$` HD$ HD$(HD$0 HD$8 HD$@ xHhHt$ HHD$0Hl$@HHl$8 HI3]H5~ 1 LHO HT$0H5}m2 HR H57Z3 H; H54 Ho< HH50 HB
+ HHl$PHt$H HHT$HFH|$HtH<$HtH|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHtH<$HtxH|$0HtYH @ H ATHgUSHHpHD$PHHD$hHƂHD$`k HD$ u HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@ Hl$8Ns HI8H53 LHq HT$0H5}+5 H H556 H~ H5A7 H/ HH5 t2 HB
B
- = HHl$PHt$H HHT$H{jH|$HtH<$HtH|$0HtOHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHXH|$HHt6H<$Ht(H|$0HtٕH @ H ATHUSHHpHD$PHHD$hHHD$`< HD$ 2 HD$(HD$0 HD$8 HD$@ 蘕HhHt$ HHD$0Hl$@hHl$8 HIS[H51 LHM HT$0H5k2 HP H5WX3 H9 H54 H: HH5#0 HB
+ HHl$PHt$H HHT$HDH|$Ht H<$HtH|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHtH<$HtH|$0HtyH @ H ATHUSHHpHD$PHHD$hHHD$`i HD$ s HD$(HD$0 HD$8 HD$@ 8HhHt$ HHD$0Hl$@Hl$8nm HI#6H53 LH HT$0H5)5 H H536 H螽 H5a7 HO HH5>r2 HB
B
- /; HHl$PHt$H HHT$HhH|$HtH<$HtH|$0HtoHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHxH|$HHtVH<$HtHH|$0HtH @ H ATHUSHHpHD$PHHD$hHHD$`\ HD$ R HD$(HD$0 HD$8 HD$@ 踓HhHt$ HHD$0Hl$@舘Hl$8 HIsYH51 LHK HT$0H5i2 H O H5wV3 H7 H5!4 H8 HH5C0 HB
+ HHl$PHt$H HHT$HCH|$Ht<H<$Ht.H|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HH(H|$HHtH<$HtH|$0HtH @ H ATHUSHHpHD$PHHD$hHHD$`g HD$ q HD$(HD$0 HD$8 HD$@ XHhHt$ HHD$0Hl$@8Hl$8g HIC4H53 LH HT$0H5'5 H H516 H辻 H57 Ho HH5^p2 HB
B
- O9 HHl$PHt$H HHT$HfH|$HtH<$HtH|$0Ht菒HD$`Ht
HHHpH[]A\HHD$`HtH|$P HH蘘H|$HHtvH<$HthH|$0HtH @ H ATH'USHHpHD$PH¥HD$hHƥHD$`| HD$ r HD$(HD$0 HD$8 HD$@ ؑHhHt$ HHD$0Hl$@訖Hl$8 HIWH51 LHI HT$0H5g2 H=M H5T3 H 6 H5A4 H6 HH5c 0 HB
+ HHl$PHt$H HHT$H;AH|$Ht\H<$HtNH|$0Ht/HD$`Ht
HHHpH[]A\HHD$`HtH|$P HHHH|$HHtH<$HtH|$0HtH @ H ATHUSHHpHD$PH"}HD$hH&}HD$` f HD$ p HD$(HD$0 HD$8 HD$@ xHhHt$ HHD$0Hl$@XHl$8o HIc2H53 LH HT$0H5%5 H H506 H H57 H菺 HH5~n2 HB
B
- o7 HHl$PHt$H HHT$HdH|$Ht H<$HtH|$0Ht诐HD$`Ht
HHHpH[]A\HHD$`HtH|$P HH踖H|$HHtH<$HtH|$0Ht9H @ H ATHUSHHpHD$PHHD$hHHD$` HD$ HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@ȔHl$8> HIUH51 LHH HT$0H5e2 H]K H5R3 H>4 H5a4 H4 HH5
-0 HB
+ / HHl$PHt$H HHT$H[?H|$Ht|H<$HtnH|$0HtOHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHhH|$HHtH<$HtH|$0HtH @ H ATHGUSHHpHD$PHB{HD$hHF{HD$`,d HD$ "n HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@xHl$8d HI0H5(3 LH HT$0H5#5 H= H57.6 H H57 H诸 HH5l2 HB
B
- 5 HHl$PHt$H HHT$HbH|$Ht,H<$Ht H|$0HtώHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHؔH|$HHtH<$HtH|$0HtYH @ H ATHGUSHH0HD$HHD$(HHD$ HI1TH5|1 LHF H5IQ3 H2 H54 H3 HH5 0 HB B !4 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHcHKIHt
HHH;Ht-H LH<$HtHf. @ ATHUSHH0HD$HHD$(HHD$ < HIRH51 LHE H5O3 HP1 H5s4 H2 HH50 HB B 2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtH LH<$HtHf. @ ATHUSHH0HD$HHD$(HHD$ HI1QH5|1 LHC H5IN3 H/ H54 H0 HH50 HB B !1 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHcHKIHt
HHH;Ht-H LH<$HtHf. @ ATHGUSHH0HD$HHD$(HHD$ < HIOH51 LHB H5L3 HP. H5s4 H/ HH50 HB B / HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtH LH<$HtHf. @ ATHUSHH0HD$HHD$(HHD$ HI1NH5|
1 LH@ H5IK3 H, H54 H- HH50 HB B !. HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHcHKIHt
HHH;Ht-H LH<$HtHf. @ ATHUSHH0HD$HHD$(HHD$ < HILH5 1 LH? H5I3 HP+ H5s4 H, HH50 HB B , HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtH LH<$HtHf. @ ATHGUSHH0HD$HHD$(HHD$ HI1KH5|
-1 LH= H5IH3 H) H54 H* HH5 0 HB B !+ HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHcHKIHt
HHH;Ht-H LH<$HtHf. @ ATHGUSHH0HD$HHD$(HHD$ < HIIH51 LH< H5F3 HP( H5s4 H) HH5/ HB B ) HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtH LH<$HtHf. @ ATHUSHH0HD$HHD$(HHD$ HI1HH5|1 LH: H5IE3 H& H54 H' HH5/ HB B !( HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHcHKIHt
HHH;Ht-H LH<$HtHf. @ ATHUSHH0HD$HHD$(HHD$ < HIFH51 LH9 H5C3 HP% H5s4 H& HH5/ HB B & HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtH LH<$HtHf. @ ATHGUSHH0HD$HHD$(HHD$ HI1EH5|1 LH7 H5IB3 H# H54 H$ HH5/ HB B !% HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHcHKIHt
HHH;Ht-H LH<$HtHf. @ ATHGUSHH0HD$HHD$(HHD$ < HICH51 LH6 H5@3 HP" H5s4 H# HH5/ HB B # HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtH LH<$HtHf. @ ATHUSHH0HD$HHD$(HHD$ HI1BH5|1 LH4 H5I?3 H H54 H! HH5/ HB B !" HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHcHKIHt
HHH;Ht-H LH<$HtHf. @ ATHwUSHH`HD$@HHD$XHHD$P< HD$2 HD$HD$ HD$( HD$0 zHhHt$ HHD$ Hl$0hHl$( HIS@H50 LH2 HT$ H5P2 H5 H5W=3 H H54 H HH5#/ HB 6 HH HD$PHC HHt HT$XHkHCHt$@HHS H<$HC( HtH|$ HtbyHD$PHtH|$@ HH`H[]A\HHD$PHtH|$@ HHiHKIHt
HHH;Ht3H LH<$HtH|$ HtxHHff. fATHUSHH`HD$@HrHD$XHvHD$P, HD$" HD$HD$ HD$( HD$0 xHhHt$ HHD$ Hl$0X}Hl$(κ HIC>H50 LH0 HT$ H5N2 H3 H5G;3 H H54 H HH5/ HB & HH HD$PHC HHt HT$XHkHCHt$@HHS H<$HC( HtH|$ HtRwHD$PHtH|$@ HH`H[]A\HHD$PHtH|$@ HHY}HKIHt
HHH;Ht#H LH<$Ht H|$ HtvHHff. fATHUSHH`HD$@HbHD$XHfHD$P HD$ HD$HD$ HD$( HD$0 xvHhHt$ HHD$ Hl$0H{Hl$(辵 HI3<H5~0 LH. HT$ H5}L2 H1 H5793 H H54 Ho HH5/ HB HH HD$PHC HHt HT$XHkHCHt$@HHS H<$HC( HtH|$ HtBuHD$PHtH|$@ HH`H[]A\HHD$PHtH|$@ HHI{HKIHt
HHH;HtH LH<$HtH|$ HttHHff. fATHUSHH`HD$@HRHD$XHVHD$P HD$ HD$HD$ HD$( HD$0 htHhHt$ HHD$ Hl$08yHl$(讷 HI#:H5n0 LH, HT$ H5mJ2 H/ H5'73 H H5ѧ4 H_ HH5/ HB HH HD$PHC HHt HT$XHkHCHt$@HHS H<$HC( HtH|$ Ht2sHD$PHtH|$@ HH`H[]A\HHD$PHtH|$@ HH9yHKIHt
HHH;HtH LH<$HtH|$ HtrHHff. fATHUSHH`HD$@HBHD$XHFHD$P HD$ HD$HD$ HD$( HD$0 XrHhHt$ HHD$ Hl$0(wHl$(螹 HI8H5^0 LHq* HT$ H5]H2 H- H553 H H54 HO HH5/ HB HH HD$PHC HHt HT$XHkHCHt$@HHS H<$HC( HtqH|$ Ht"qHD$PHtH|$@ HH`H[]A\HHD$PHtH|$@ HH)wHKIHt
HHH;HtH LH<$HtH|$ HtpHHff. fATHUSHH`HD$@H2HD$XH6HD$P HD$ HD$HD$ HD$( HD$0 HpHhHt$ HHD$ Hl$0uHl$(莴 HI6H5N0 LHa( HT$ H5MF2 H+ H533 H H54 H? HH5/ HB HH HD$PHC HHt HT$XHkHCHt$@HHS H<$HC( HtaH|$ HtoHD$PHtH|$@ HH`H[]A\HHD$PHtH|$@ HHuHKIHt
HHH;HtH LH<$HtH|$ Ht|nHHff. fATH7USHH`HD$@H"HD$XH&HD$Pܭ HD$ҹ HD$HD$ HD$( HD$0 8nHhHt$ HHD$ Hl$0sHl$(~ HI3H5>0 LHQ& HT$ H5=D2 H) H503 H~ H54 H/ HH5/ HB HH HD$PHC HHt HT$XHkHCHt$@HHS H<$HC( HtQH|$ HtmHD$PHtH|$@ HH`H[]A\HHD$PHtH|$@ HH sHKIHt
HHH;HtH LH<$HtH|$ HtllHHff. fATHUSHH`HD$@HHD$XHHD$P̫ HD$· HD$HD$ HD$( HD$0 (lHhHt$ HHD$ Hl$0pHl$(n HI1H5.0 LHA$ HT$ H5-B2 H' H5.3 Hn H54 H HH5/ HB HH HD$PHC HHt HT$XHkHCHt$@HHS H<$HC( HtAH|$ HtjHD$PHtH|$@ HH`H[]A\HHD$PHtH|$@ HHpHKIHt
HHH;HtH LH<$HtH|$ Ht\jHHff. fATHUSHH`HD$@H~HD$XH~HD$P輩 HD$貵 HD$HD$ HD$( HD$0 jHhHt$ HHD$ Hl$0nHl$(^ HI/H5 0 LH1" HT$ H5 @2 H}% H5,3 H^ H54 H HH5/ HB HH HD$PHC HHt HT$XHkHCHt$@HHS H<$HC( Ht1H|$ HthHD$PHtH|$@ HH`H[]A\HHD$PHtH|$@ HHnHKIHt
HHH;HtH LH<$HtH|$ HtLhHHff. fATHUSHH`HD$@H{HD$XH{HD$P謧 HD$袳 HD$HD$ HD$( HD$0 hHhHt$ HHD$ Hl$0lHl$(N HI-H50 LH! HT$ H5
>2 Hm# H5*3 HN H5q4 H HH5/ HB
HH HD$PHC HHt HT$XHkHCHt$@HHS H<$HC( Ht!H|$ HtfHD$PHtH|$@ HH`H[]A\HHD$PHtH|$@ HHlHKIHt
HHH;HtH LH<$HtH|$ Ht<fHHff. fATH7USHH`HD$@HyHD$XHyHD$P蜥 HD$蒱 HD$HD$ HD$( HD$0 eHhHt$ HHD$ Hl$0jHl$(> HI+H50 LH HT$ H5;2 H]! H5(3 H>
- H5a4 H
- HH5/ HB HH HD$PHC HHt HT$XHkHCHt$@HHS H<$HC( HtH|$ HtdHD$PHtH|$@ HH`H[]A\HHD$PHtH|$@ HHjHKIHt
HHH;HtH LH<$Ht{H|$ Ht,dHHff. fATHUSHH`HD$@HwHD$XHwHD$P茣 HD$肯 HD$HD$ HD$( HD$0 cHhHt$ HHD$ Hl$0hHl$(. HI)H50 LH HT$ H592 HM H5&3 H. H5Q4 H HH5s/ HB HH HD$PHC HHt HT$XHkHCHt$@HHS H<$HC( HtH|$ HtbHD$PHtH|$@ HH`H[]A\HHD$PHtH|$@ HHhHKIHt
HHH;HtH LH<$HtkH|$ Ht bHHff. fATHUSHH`HD$@HuHD$XHuHD$P| HD$r HD$HD$ HD$( HD$0 aHhHt$ HHD$ Hl$0fHl$( HI'H50 LH HT$ H572 H= H5$3 H H5A4 H HH5c/ HB v HH HD$PHC HHt HT$XHkHCHt$@HHS H<$HC( HtH|$ Ht`HD$PHtH|$@ HH`H[]A\HHD$PHtH|$@ HHfHKIHt
HHH;HtsH LH<$Ht[H|$ Ht `HHff. fATHǰUSHH`HD$@HsHD$XHsHD$Pl HD$b HD$HD$ HD$( HD$0 _HhHt$ HHD$ Hl$0dHl$( HI%H50 LH HT$ H552 H- H5"3 H H514 H HH5S/ HB
+ O HHl$PHt$H HHT$H{=H|$HtH<$HtH|$0HtoHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHt&H<$HtH|$0HtH @ H ATHUSHHpHD$PHbyHD$hHfyHD$`Lb HD$ Bl HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@Hl$8h HI.H5H3 LH HT$0H5 "5 H] H5W,6 H H57 H϶ HH5j2 HB
B
- _ HH HD$PHC HHt HT$XHkHCHt$@HHS H<$HC( HtڽH|$ Ht^HD$PHtH|$@ HH`H[]A\HHD$PHtH|$@ HHdHKIHt
HHH;Ht\H LH<$HtDH|$ Ht]HH ATH'USHH`HD$@HqHD$XHqHD$P\ HD$R HD$HD$ HD$( HD$0 ]HhHt$ HHD$ Hl$0bHl$( HIs#H50 LH HT$ H532 H H5w 3 H H5!4 H HH5C/ HB
+ o HHl$PHt$H HHT$H;H|$HtH<$HtH|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHtFH<$Ht8H|$0HtH @ H ATHGUSHHpHD$PHwHD$hHwHD$`l` HD$ bj HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@Hl$8i HI,H5h3 LH1 HT$0H5= 5 H} H5w*6 H> H57 H HH5h2 HB
B
- O HH HD$PHC HHt HT$XHkHCHt$@HHS H<$HC( HtʻH|$ Ht{\HD$PHtH|$@ HH`H[]A\HHD$PHtH|$@ HHbHKIHt
HHH;HtLH LH<$Ht4H|$ Ht[HH ATHUSHH`HD$@HoHD$XHoHD$PL HD$B HD$HD$ HD$( HD$0 [HhHt$ HHD$ Hl$0x`Hl$( HIc!H50 LH HT$ H512 H
H5g 3 H H54 H HH53/ HB
+ 菵 HHl$PHt$H HHT$H9H|$HtH<$HtH|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHtfH<$HtXH|$0Ht9H @ H ATH'USHHpHD$PHuHD$hHuHD$`^ HD$ h HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@Hl$8.a HI*H53 LHQ HT$0H5] 5 H H5(6 H^ H5!7 H HH5f2 HB
B
- ? HH HD$PHC HHt HT$XHkHCHt$@HHS H<$HC( Ht躹H|$ HtkZHD$PHtH|$@ HH`H[]A\HHD$PHtH|$@ HHr`HKIHt
HHH;Ht<H LH<$Ht$H|$ HtYHH ATHUSHH`HD$@HmHD$XHmHD$P< HD$2 HD$HD$ HD$( HD$0 YHhHt$ HHD$ Hl$0h^Hl$(ޟ HIS H50 LH HT$ H5/2 H H5W 3 H H54 H HH5#/ HB
+ 诳 HHl$PHt$H HHT$H7H|$HtH<$HtH|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHtH<$HtxH|$0HtYH @ H ATH7USHHpHD$PHsHD$hHsHD$`\ HD$ f HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@Hl$8N[ HI)H53 LHq HT$0H5} 5 H H5&6 H~ H5A7 H/ HH5 e2 HB
B
- / HH HD$PHC HHt HT$XHkHCHt$@HHS H<$HC( Ht誷H|$ Ht[XHD$PHtH|$@ HH`H[]A\HHD$PHtH|$@ HHb^HKIHt
HHH;Ht,H LH<$HtH|$ HtWHH UHSHHHu @ HH WHuHEH} 1H SHE HE H[]f. AWIAVIAUI LATU1SH LD$ LL$
- LH
- LHD$
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2 H5!2 M HyzHJ2 HHLDH~LtHD$`HxHSHH5 e L
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a H D$ $ E11H L HP H5( Hy Hl$pHH虵 H=MR H:H=PR *HHD$PHHxE*HD$`HHx4*H$ H- HHHH$ HtH|$p HH=OR *H>3HHHHD$PLd$@LHx)HD$`LHx)H$ HtH|$p HH=OR )H2HHLd$@HHHHHD$@Ll$0LHxA)HD$PLHx0)H$ HtH|$p HH= OR 8)H`2HHLl$0HHHHHD$@Ll$0LHx(HD$PLHx(H$ HtH|$p HH=NR (H1HHLl$0HHHH%HD$0HT$/HHT$HxJ(HT$HD$@HHT$Hx/(HT$HD$PHHx(H$ HtH|$p HH=&NR !(HI1H|HHT$/HHT$/HH$ HHtH|$p HHHHYHD$@Ll$0LHx'HD$PLHxr'H$ HtH|$p HH=MR z'H0HHLl$0HH$ HHtH|$p HHHHHD$PLd$@LHx&HD$`LHx&H$ HtH|$p HH=MR &H0HHLd$@HHHHFHD$PLd$@LHxp&HD$`LHx_&LHHLd$@f. ATUSHP =LR =KR s =KR tHP []A\f D H=KR /tH BHD$P Ht HD$@H%HD$XH%HD$PLd$0HT$ H5 L)Hl$ HT$ H5 H)H\$`= LHHԃH5 H HX HK L֭ H
U H D$ $ E11H L H
l H5 D$ $ E1ɾ H Lq H
H D$ $ E1ɾ H L= H
| H D$ $ E1ɾ H~ L H
H! D$ $ E11H} LF HߤP H5ʬ H Hl$@HH; H=&GR HܑH=IR @$HHD$ HHx#HD$0HHx#HD$PHHH HP []A\ H=qIR <-y HD$P &HD$@H Ld$0HT$ H5d HD$XHLHD$P{'Hl$ HT$ H5 Hb'H\$` LHHzH5 H苰 H H L` H
# Hk D$ $ E11H L/ H
* HD D$ $ E1ɾ H L
H
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H
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H D$ $ E1ɾ H L{ H
H D$ $ E1ɾ H LG H
H D$ $ E1ɾ H L H
H D$ $ E1ɾ HT Lߩ H
Hm! D$ $ E11HS L H
Hz D$ $ E1ɾ H L HP H5P H L HoP H5J H Hl$@HH軫 H=CR H\H=UFR H8HD$ HHxg HD$0HHxV HD$PH- HHf D H= FR )H5<HD$P Ht HD$@H HD$XH HD$PLd$0HT$ H5 L$Hl$ HT$ H5 H#H\$` LHH ~H5B H H脪 Hw H H5 A H& L H
H D$ $ E11H% L H
H " D$ $ E1ɾ H L| H
HJ5 D$ $ E1ɾ H LT,! H
H D$ $ E1ɾ H L H
H! D$ $ E11H L1 HP H5զ H& LG HP H5+! H Hl$@HH$ H=AR HŋH=CR ) HHD$ HHx HD$0HHx HD$PH HHwHHD$PHtH|$@ HH=\CR H&HHD$PHtH|$@ HH= CR w H&H\$ HHD$0HHHx# HHD$ H\$ HHx HHHHHD$ H\$ HHx HD$0HHHx HD$PHtH|$@ HH=tBR H%HHH\$ HHL$PHHtH|$@ HHHHD$ H\$ HHxD HD$0HHHx0 HHH\$ f. fAWAVAUATUSH =AR Y =AR W =nAR xU =QAR S =4AR Q =AR qO =@R 4M =@R GK =@R *I =@R F =@R D =i@R B =L@R @ =/@R > =@R < =?R : =?R r8 =?R e6 =?R 3 =?R 1 =d?R / =G?R , =*?R ) =
?R '& =>R # =>R =>R =>R =|>R =_>R =B>R =%>R G =>R r ==R - ==R ==R # ==R =w=R =Z=R ===R = =R uH==R #zY > Hv=<R H=<R P# H5HDŽ$ Ht&H$ HH$ HH$ L$ HT$PH5V L~ Ll$pHT$@H5 Le H$ ~ LLHzwH5 H苦 H H L| H
H D$ $ E11H LK H
m H D$ $ E11H Lh HP H5 H] H$ HHz H=;;R HH=d;R HHD$pHHx&H$ HHxH$ Ht
HHHĸ []A\A]A^A_ÐH=;R t! HDŽ$ H$ HL$ HT$0H5 D$P H$ HLLH$ Ll$pHT$/H5[ LL|$`HT$.H5K LjH$ LLHuH5 H萤 H H H5 M H LD$PH H5. HU L4 H
H D$ $ E11H L H
Hȿ D$ $ E1ɾ H` L H
H D$ $ E1ɾ H, L H
Hs D$ $ E1ɾ H Lg H
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HD$`HHx<HD$pHHx+H$ HHxH$ H HH @ H=98R H$ HT$PH5 HHD$@L$ H5 HLLl$pHT$0H5x LH$ LLHrH5 Hޡ HQ H H5h, I HC L H
8 H_ D$ $ E11H L H
" Hc D$ $ E1ɾ H LY H
H D$ $ E11H L H P H5 Hk H=_6R H<H=6R HHD$pHHxGH$ HHx3H$ HHx f. H=i6R HDŽ$ 6H$ HLl$pHT$/H5 H$ HLH$ "L$ HL语L|$`HT$.H5 LLt$PHT$-H5e LHD$@HT$,H5̈ HHqH$ LHHpH5ȭ H藟 H
- L H
H>o" D$ $ E11H Ld H
HŰ D$ $ E11H Lk H
r H D$ $ E1ɾ H L7 H
H{M D$ $ E1ɾ Hp LLLH H$ HH< H=4R H~H=f4R AHHD$@HHxHD$PHHxHD$`HHxL@HD$pHHxH$ H HHyf. H=3R N HDŽ$ H$ HL$ H5 H$ HLH$ HD$@HALl$pHT$0H5 L(L|$`HT$/H5
- LH$ ( LLH$nH5f H5 H訚 H! H5 M H L H
g H! D$ $ E11HY L H
He D$ $ E1ɾ H% L H
H D$ $ A H Ly H
ٷ H! D$ $ E11H L HOP H5: H H$ HH訙 H=n1R HI|H=1R H%HD$`HHxTHD$pHHxCH$ HHx/H$ H HH @ H=1R L|$`H$ H5 LHLt$PL$ H5 LLH$ HLHkH5= H H H H5) E1 H1 H H5 E1 H H H5 E1 H L H
? H'" D$ $ E11H Lc H
l HF D$ $ E1ɾ H L/ H
P H! D$ $ E1ɾ H L H
Hֿ D$ $ E1ɾ HX L H
x! H D$ $ E11HW LӴ HوP H5 H Lʹ HP H5f H H=.R HyH=M/R HH$ HHx H$ HHx h H=/R $D HDŽ$ H$ HLl$pHT$/H5 H$ H~LH$ W
L$ HL/L|$`HT$.H5 L&Lt$PHT$-H5 L
HD$@HT$,H5 HHH$ LHHiH5H H H芕 L H
Բ H! D$ $ E11HY L H
HL! D$ $ E1ɾ H% L H
H>! D$ $ E11H$ LLLH L HP H5v H H$ HHΔ H=,R HowH=(-R HKHD$@HHxz HD$PHHxi HD$`HHxX L8HD$pHHx? H$ H HH @ H=,R HDŽ$ > H$ HϬL|$`HT$0H5ζ HDŽ$ H$ H{LHD$p H$ Lt$PHT$/H5 L H$ LLHfH5 Hו HJ L H
h H9 D$ $ E11H L H
P HX D$ $ E11H L HʄP H5I H Lg HP H5* H LD$pH H5 H L$ H H59! H H$ HHn H=7*R HuH=*R sHHD$PHHxHD$`HHx H$ H HHf D H=*R tzL|$`H$ H5 LH
-Lt$PL$ H5 LL
-H$ HLHdH5 H HW L H
E H! D$ $ E11H& L H
% H5 D$ $ E11H% Lv HP H5V H L HՂP H57 H H H5h A H` HY H5X A H? H=Y(R H0sH= )R H H$ HHx8H$ HHx$ H=(R HDŽ$ >H$ H Lt$PL$ H5˲ H$ H\wLLH$ HD$@Ll$pH5 HLH$ c LLHbH5 H HV L H
H D$ $ E11H% L H
H D$ $ E1ɾ H L4i H
H0 D$ $ E11H L HP H5! H L/ HЀP H5 Hl LE HP H5h HJ H$ HHg H=2&R HqH=&R lHHD$pHHxH$ HHxH$ H HH} @ H=&R l X HDŽ$ H$ H? Lt$PL$ H5 H$ HtLLH$ HD$@Ll$pH5 HL}H$ LLH`H5ԝ H裏 H L H
H D$ $ E11H Lp H
H$ D$ $ E11H L HP H51 H H H52 A HA HZ H50 A H H H5? E1 H H H5 E1 H H H5 A H H$ HH H=#R HnH=$R H}HD$pHHx H$ HHx H$ H HH D H=I$R
- HDŽ$ H$ HLt$PL$ H5# H$ HqLLH$ 1HD$@Ll$pH5 HLH$ LLH*^H5l H; H變 H7 H5Ow E1 H` L H
" H\" D$ $ E11H_ L H
! H D$ $ E1ɾ H+ L H
վ H D$ $ E1ɾ H Lf H
H D$ $ E11H L H8|P H5' H L H|P H5 Hr H$ HH菉 H=\!R H0lH=9"R H HD$pHHx;H$ HHx'H$ H HH @ H=!R f HDŽ$ &H$ HLt$PL$ H5 H$ HnLLH$ HD$@Ll$pH5. HLH$ LLH[H5 Hˊ H> HǷ H5t E1 H L H
Ҹ HTZ" D$ $ E11H L^ H
ٸ H$ D$ $ E1ɾ H L- H
H= D$ $ E1ɾ H L H
i Hs D$ $ E11H L HyP H5 H$ L HyP H5 H H$ HH H= R HiH= R $H蜵HD$pHHxH$ HHxH$ H HH @ H= R $L|$`H$ H5h LHLt$PL$ H5LC! LLnH$ HLHYH{ H=
- R HhH= R @H踴H$ HHxH$ HHx8 H= R \L|$`H$ H5 LHLt$PL$ H5 LLH$ HLHXH#i H=C R HhH=M R xHH$ HHx H$ HHxc H= R ? HDŽ$ &H$ Hw Ll$pHT$/H5V H$ HjLH$ L$ HL蟔L|$`HT$.H5 LLt$PHT$-H5U L}HD$@HT$,H5 HHaH$ W LHHvWH5 H臆 H L H
H D$ $ E11H L~ H
# Hcp D$ $ E1ɾ H L H
! H{ D$ $ E11H LLLH H$ HH` H=1R HfH=J R eHݱHD$@HHx HD$PHHxHD$`HHxL2'HD$pHHxH$ H HHf D H=R < HDŽ$ H$ H/L|$`HT$.H5^ D$@ ?H$ HgLD$0 ?HDŽ$ H$ HD$p JLt$PHT$-H5- L1H$ LLHFUH5 HW Hʁ LU H
H D$ $ E11H L$ H
3 H D$ $ E1ɾ He L~ H
g H D$ $ E1ɾ H1 L~ H
Hpy D$ $ E11H0 L HsP H5}~ H LD$pH H56L H L$ H H5L H LD$0H H5ƛ H LD$@H H5 H H~, H5 E1 H
H`, H57 E1 H H$ HH, H=R HbH=&R 1H詮HD$PHHxHD$`HHxH$ H HH @ H=R 4 HDŽ$ H$ HuLl$pHT$0H5 HDŽ$ H$ H;dLH$ [L|$`HT$/H5 LBLt$PHT$.H5 L)H$ LLH>RH5 HO H~ H* H5 M H H* H5β E1 HV H* H5 E1 H8 H* H5 E1 H Hm* H5] E1 H L$ HG* H5J" H9 H,* H5ʲ E1 H H* H5 E1 H LX{ H
) H D$ $ E11H L'{ H
) H D$ $ E1ɾ Hh Lz H
) Hɐ D$ $ A H1 Lz H
M) Hpu D$ $ E11H0 L9 HoP H5}z H H$ HH| H=R H_H=R HhHD$PHHxHD$`HHxHD$pHHxuH$ H HHfH=R h HDŽ$ vH$ HqLl$pHT$0H5j HDŽ$ H$ H{`LH$ L|$`HT$/H5ʞ LLt$PHT$.H5 LH$ } LLHNH50 H} Hr{ H' H5 E1 H$ Hw' H5 E1 H HY' H5V E1 H H;' H50 E1 H H ' H51 M H L$ H& H5YG" H LDx H
& H D$ $ E11H Lx H
& H D$ $ E1ɾ HT L H
p& H D$ $ A H Lw H
9& H~ D$ $ A H Lqw H
& H%r D$ $ E11H L HlP H52w H胾 H$ HHy H=tR HA\H=R H HD$PHHxLHD$`HHx;HD$pHHx*H$ HE HH3 H=YR HDŽ$ &H$ H7nLl$pHT$0H5 HDŽ$ H$ H\LH$ L|$`HT$/H5z LLt$PHT$.H5O LH$ T LLHKH5 Hz H"x HE$ H5Y M H H'$ H5. E1 H H $ H5 E1 H L$ H# H5B H H# H5 E1 HW Lu H
# Hv D$ $ E11HV L H
H D$ $ E1ɾ H" Lt H
># Hao D$ $ E11H! L HjP H5nt H迻 H$ HHv H=R H}YH=R HYHD$PHHxHD$`HHxwHD$pHHxfH$ Ht HHb H=R >HEHDŽ$ Ht&H$ HH$ HH$ Ll$pHT$/H5} L`cL$ HL݅L|$`HT$.H5>s LLt$PHT$-H5 LHD$@HT$,H5 HHH$ F LHHHH5 Hw H8u Lr H
H' D$ $ E11H Lr H
'u! HFm D$ $ E11H LLLH腴 H$ HHt H= R HsWH= R HOHD$@HHx~HD$PHHxmHD$`HHx\LHD$pHHxCH$ HD HH2H=
R H%HDŽ$ Ht&H$ HH$ HH$ Lt$PL$ H5 LLHD$@Ll$pH5Z HLH$ 2 LLHF
- Hks Hss H H5 E1 H HǏ H5 E1 H Lp H
H˄ D$ $ E11H Lp H
Hs! D$ $ E11H L HeP H5Bp H蓷 H$ HHr H=
-R HQUH= R H-HD$pHHx\H$ HHxHH$ H< HH* D H= R L$ HT$0H5p L kH$ HHLl$pHT$/H5Yo LL|$`HT$.H5 LLt$PHT$-H5 LH$ & LLHD
- Heq Hmq Ln H
Hr! D$ $ E11H, LͿ H
H D$ $ E1ɾ H L H
Hά D$ $ E1ɾ Hļ Le H
Ht D$ $ A H荼 Ln H
1 Hp! D$ $ E11H LHLH Lܚ HcP H5 H H=!R HRH= R NHƞHD$PHHxHD$`HHxHD$pHHxHH$ HHxfH=I R D HDŽ$ H$ HlLt$PL$ H5c H$ HSLLH$ qHD$@Ll$pH5P HLUH$ LLHjB
- Hn Ho H H5 A H H H5j E1 H LAl H
HZ D$ $ E11H腺 Ll H
Hn! D$ $ E11H脾 L- HaP H5k H" H$ HH?n H=R HPH=R DH輜HD$pHHxH$ HHxH$ H HH @ H=YR DzHHDŽ$ Ht&H$ HuH$ HvH$ HD$@L$ H5J D$` ?HLgLl$pHT$0H5˯ LNH$ LLHc@
- Hl Hl H' H5N E1 H LD$`H7 H5a H@ L;j H
HT~ D$ $ E11H L
-j H
Hl! D$ $ E11H~ L' H_P H5i H H$ HH9l H=R HNH=R >H趚HD$pHHxH$ HHxH$ H HHf D H=aR <eHHDŽ$ Ht&H$ HmH$ HnH$ Lt$PL$ H5B LLgHD$@Ll$pH5w HLKH$ LLH`>
- Hj Hj H H5 E1 H蝿 H
- H5 E1 H L:h H
H} D$ $ E11H~ L h H
Hb D$ $ E11H} L& H]P H5g H H$ HH8j H=R HLH=R =H赘HD$pHHxH$ HHxH$ H HH~ D H=qR <XHHDŽ$ Ht&H$ HmH$ HnH$ L$ HT$0H5B D$` D$P ?LZLl$pHT$/H5lO LAH$ LLHV<
- Hh Hh LD$PH
- H5 HQ LD$`H H5 H1 L,f H
H{ D$ $ E11Hp Le H
ă H` D$ $ E11Ho L H\P H5e H
H$ HH*h H= R HJH=R /H觖HD$pHHxH$ HHxH$ Hu HHc H=qR ,;HHDŽ$ Ht&H$ H]H$ H^H$ Lt$PL$ H52 LLWHD$@Ll$pH5M HL;H$ LLHP: Hf He H5 E1 H蕻 HG H5 E1 Hw L2d H
0 HKx D$ $ E11Hv Ld H
Hf! D$ $ E11Hu L H'ZP H5c H H$ HH0f H=
Q HHH=Q 5H譔HD$pHHxH$ HHxH$ Hn HH\ D H=Q 46HHDŽ$ Ht&H$ HeH$ HfH$ L|$`HT$0H5= HDŽ$ HD$p LPLt$PHT$/H5 L7H$ { LLHL8 Hd LD$pH H5 H L$ H8 H5 H̼ L'b H
H@v D$ $ E11Hk La H
Hd! D$ $ E11Hj L H<XP H5a H H$ HH%d H=Q HFH=Q *H袒HD$PHHxHD$`HHxH$ HY HHG D H=Q ,!L|$`H$ H5p LHLt$PL$ H5 LLvH$ [ HLH6
- H c H&c Hύ H5` E1 Hȷ L` H
HG D$ $ E11HǮ LR` H
H" D$ $ E1ɾ H蓮 L ` H
% H~ D$ $ E1ɾ H_ L_ H
9 H!V D$ $ E1ɾ H+ L_ H
- H~ D$ $ E11H* Lӆ H VP H5w_ HȦ H=Q HDH=Q HuH$ HHxH$ HHx% H=Q HHDŽ$ Ht&H$ HEH$ HFH$ HD$@L$ H5 D$` ?HL7Ll$pHT$0H5m L H$ N LLH34
- H` H` LI^ H
| Hbr D$ $ E11H荬 L^ H
| H`! D$ $ E11H茰 L5 HTP H5] H* LD$`H{ H5s H誻 H$ HH'` H=Q HBH=Q ,H褎HD$pHHxH$ HHxH$ H> HH, @ H=Q , HDŽ$ H$ H?M Lt$PL$ H5K H$ H|BLLH$ YHD$@Ll$pH5z HL=H$ ) LLHR2
- H^ H^ Lh\ H
z H% D$ $ A 1H詪 L4\ H
z Hq D$ $ E11H訮 LQ HRP H5[ HF Hy
- H5! E1 H H[
- H5 E1 H H=
- H5z E1 H̲ H
- H5 z E1 H讲 H$ HH] H=Q H@H=Q HhHD$pHHxH$ HHxH$ H HHH=Q ¶L|$`H$ H58 LH]Lt$PL$ H5iy LL>H$
- HLHS0 H\ H H50y E1 H蘱 LSZ H
Hy D$ $ E11H藨 L"Z H
ه Hx D$ $ E1ɾ Hc LY H
Hx D$ $ E1ɾ H/ LY H
HO D$ $ E1ɾ H LY H
Hhx D$ $ E11H L HLPP H5GY H蘠 H=Q Hi>H=Q HEH$ HHxqH$ HHx]δ H=Q HUHDŽ$ Ht&H$ HH$ HH$ HD$@L$ H5} D$` ?HLLl$pHT$0H5= LH$ LLH. HZ L!X H
v H:l D$ $ E11He LW H
v H~Z! D$ $ E11Hd L
HNP H5W H LD$`Hyu H5m H肵 H$ HHY H=Q H<H= Q H|HD$pHHxH$ HHxH$ H HHݲ @ H=Q HDŽ$ H$ HB Lt$PL$ H5#| H$ H;LLH$ 1HD$@Ll$pH5t HLH$ LLH*, HX LHV H
t H D$ $ A 1H艤 LV H
lt Hxk D$ $ E11H舨 L1} HMP H5U H& HY H5z! E1 H H; H5g E1 Hʬ H H5s E1 H謬 H H5s E1 H莬 H$ HHW H=Q Hl:H=Q HHHD$pHHxwH$ HHxcH$ H HH霰H=Q {L$ HT$0H5 L@H$ HHgLl$pHT$/H5yT LL|$`HT$.H5y LLt$PHT$-H5 LH$ LLH) HV LT H
' HX! D$ $ E11HT L H
H# D$ $ E1ɾ H L H
* H D$ $ E1ɾ H L H
> Hn D$ $ A H赡 L@S H
Yq HU! D$ $ E11H败 LHLH3 L HUJP H5 HA H=VQ H8H=Q vHHD$PHHx HD$`HHx HD$pHHxHCH$ HHx)f. H=9Q dHHDŽ$ Ht&H$ HH$ HH$ HD$@L$ H5jw D$` ?HLLl$pHT$0H5: LnH$ LLH' HT LD$`Ho H5Ao H膯 LQ H
o Hf D$ $ E11Hş LPQ H
[o HL D$ $ E11Hģ Lmx HHP H5Q Hb H$ HHS H=eQ H 6H=Q HHD$pHHx+H$ HHxH$ HH HH6 @ H=iQ L|$`H$ H5u LHLt$PL$ H5ɇ LLH$ HLH% HvR H/{ H5 E1 H( HY{ H5e E1 H
- LO H
z Hc D$ $ E11H LO H
+{ H"R! D$ $ E11H Lv HFP H5UO H視 H=Q Hw4H= Q HSH$ HHxH$ HHxk雪f D H=Q tHeHDŽ$ Ht&H$ H%H$ H&H$ HD$@L$ H5s D$` ?HLLl$pHT$0H5{ LH$ y LLH$ HP Hx H5 E1 HX LD$`Hk H5d H LM H
x H b D$ $ E11H7 LM H
x HPP! D$ $ E11H6 Lt H8EP H5M HԔ H$ HHO H=Q H2H=KQ Hn~HD$pHHxH$ HHxH$ H HH鎨f D H=Q gHeHDŽ$ Ht&H$ H%H$ H&H$ Lt$PL$ H5q LL HD$@Ll$pH5/y HLH$ k LLH" HN Hi H5i E1 H] Hi H5i E1 H? LK H
i H^a D$ $ E11H> LK H
i H}F D$ $ E11H= Lr H_CP H5K Hے H$ HHM H=Q H0H=bQ Hu|HD$pHHxH$ HHxH$ H HH鈦 D H=Q bHmHDŽ$ Ht&H$ H-H$ H.H$ L$ HT$0H5p D$` D$P ?LLl$pHT$/H5,3 LH$ X LLH HL LD$PHg H5g H LD$`Hg H5g H LI H
g HX_ D$ $ E11H8 LI H
g HwD D$ $ E11H7 Lp HyAP H5I HՐ H$ HHK H=Q H.H=lQ HozHD$pHHxH$ HHxH$ H HHu HDŽ$ H$ H]Ll$pHT$0H5*n D$P'7H$ HS5LHDŽ$ H$ 'L|$`HT$/H5be LH$ C LLH# H5 H4M HJ HK L$ HØ H537 H觢 LD$PH H5f H H?M LG H
! H>] D$ $ E11H LG H
vh H] D$ $ E1ɾ H LuG H
Ph HK] D$ $ E1ɾ H趕 LAG H
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IL9uJ<9HtWIE HxH9 I(L9,$zH$ HtWH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?-WMuHE H}1H QSHE KD5 H}VI$HxH9 IPHPL9$$TH$ HtVH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?-VMuHE H}1H QRHE KD5 H}UI$HxH9 IPHPL9$$TH$ HtUH|$ H$ H , H$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$SHL$HT$HH$ LHxQH$ LHxQH$ LHxQHD$PHt$LHxlQHD$`LHx[QH$ HtH|$0 HHZHHH=mZH HW Ht$8RH=;ZH HW Ht$8RH= ZH HW mHt$8HL$RHL$TIHxH9u^IL9u|H=YH HW L8RPJH!PJHCpDFD@Ht$8HL$HT$QHL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H H$0 zRLt$ I yI~pI~XH$ I~ H$ LHx"ObH$ HHtHD$ H HjLH{H=%XH HWtNiHt$8PZH=WH HWt-&Ht$8~PPJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH=]WH HW sHt$8OdH=+WH HWu LOH=VH HW GHt$8~O8H=VH HW ,Ht$8LO H=VH HW Ht$8OH=eVH HW ,Ht$8N H=3VH HW Ht$8NH|$ HtTH=UH HWt0HHt$8vN9PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWH5] AVAUATUSH HD$pH|$(HHD$0HD$`HT$NH5 HHD$8OLl$MLt$PH5 LLOHD$PH KSH Ld$OHT$`H5 H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ hOLI
- H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X > H H$8 H9 H, HcMHH1ISI L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ LH$ Hۻ HDŽ$ H$ HݻH$ LH$ HhۻHDŽ$ H$ H H$ HD$ HP
- HD$PHxH9 HD$`HxH9= Ht$0H|$ Lڸ HF H5^ H L HF H5 4 H H5aZ H H5 H Hr H5 a H> H|$(H!Lt$ IP
- H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H H$0 JH$ H$ H9H$T LeHE IMILH)J!HxH9p JT9N49L9 H=PH f IHxH9
IL9uJ<9Ht9JIE HxH9 I(L9,$zH$ HtJH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?mIMuHE H}1H EHE KD5 H}7II$HxH9 IPHPL9$$TH$ HtIH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?mHMuHE H}1H DHE KD5 H}7HI$HxH9 IPHPL9$$TH$ HtHH|$ H$ H lH$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$EHL$HT$HH$ LHxCH$ LHxCH$ LHxCHD$PHt$LHxCHD$`LHxCH$ HtH|$0 HHLHHH=LH HW Ht$80EH={LH HW Ht$8DH=ILH HW mHt$8HL$DHL$TIHxH9u^IL9u|H=KH HW LxDPJH!PJHCpDFD@Ht$8HL$HT$0DHL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H H$0 DLt$ I I~pI~XH$ I~ H$ LHxbAbH$ HHtHD$ H Hj>H{H=eJH HWtNiHt$8BZH=7JH HWt-&Ht$8BPJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH=IH HW sHt$8 BdH=kIH HWu LAH=;IH HW GHt$8A8H= IH HW ,Ht$8A H=HH HW Ht$8ZAH=HH HW ,Ht$8(A H=sHH HW Ht$8@H|$ HTH=/HH HWt0HHt$8@9PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWH5v AVAUATUSH HD$pH|$(HHD$0HD$`HT$NH5G HHD$8#BLl$MLt$PH5) LLBHD$PH EH Ld$OHT$`H5t H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ ALI
- H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X ~ H H$8 H9 H, H?HH1I; L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ #?H$ Hͻ HDŽ$ H$ H$лH$ >H$ HͻHDŽ$ H$ H]лH$ HD$ HP
- HD$PHxH9 HD$`HxH9= Ht$0H|$ A L HF H5 H
- L HF H5`& H
- H5L H H5q H? H H5+ o H~ H|$(HaLt$ IP
- H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H H$0 =H$ H$ H9H$T LeHE IMILH)J!HxH9p JT9N49L9 H=BH f IHxH9
IL9uJ<9Hty<IE HxH9 I(L9,$zH$ HtH<H$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?;MuHE H}1H 7HE KD5 H}w;I$HxH9 IPHPL9$$TH$ HtB;H$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?:MuHE H}1H 6HE KD5 H}w:I$HxH9 IPHPL9$$TH$ HtB:H|$ H$ H H$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$=8HL$HT$HH$ LHx'6H$ LHx6H$ LHx5HD$PHt$LHx5HD$`LHx5H$ HtH|$0 HH?HHH=>H HW Ht$8p7H=>H HW Ht$8>7H=>H HW mHt$8HL$7HL$TIHxH9u^IL9u|H=3>H HW L6PJH!PJHCpDFD@Ht$8HL$HT$p6HL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H ݻH$0 6Lt$ I ԻI~pӻI~XӻH$ I~ FH$ LHx3bH$ HHtHD$ H Hj=1H{H=<H HWtNiHt$8,5ZH=w<H HWt-&Ht$84PJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH=;H HW sHt$8`4dH=;H HWu L04H={;H HW GHt$838H=I;H HW ,Ht$83 H=;H HW Ht$83H=:H HW ,Ht$8h3 H=:H HW Ht$863H|$ HTH=o:H HWt0HHt$829PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWH5B AVAUATUSH HD$pH|$(HHD$01HD$`HT$NH5 HHD$8c4Ll$MLt$PH5i LLG4HD$PH 7H Ld$OHT$`H5$g H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ 3LI
- H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X 辥 H H$8 H9 H, H1HH1I- L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ c1H$ H HDŽ$ H$ Hd»H$ '1H$ H迻HDŽ$ H$ H»H$ HD$ HP
- HD$PHxH9 HD$`HxH9= Ht$0H|$ 聻 LZ HCF H5 H/ L` HF H5 H
H5> HN H51d H H H5k S H边 H|$(HLt$ IP 0
- H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H [ջH$0 N/H$ H$ H9H$T LeHE IMILH)J!HxH9p JT9N49L9 H='5H f IHxH9
IL9uJ<9Ht.IE HxH9 I(L9,$zH$ Ht.H$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?-MuHE H}1H *HE KD5 H}-I$HxH9 IPHPL9$$TH$ Ht-H$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?,MuHE H}1H )HE KD5 H},I$HxH9 IPHPL9$$TH$ Ht,H|$ H$ H H$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$}*HL$HT$HH$ LHxg(H$ LHxS(H$ LHx?(HD$PHt$LHx,(HD$`LHx(H$ HtH|$0 HHW1HHH=-1H HW Ht$8)H=0H HW Ht$8~)H=0H HW mHt$8HL$G)HL$TIHxH9u^IL9u|H=s0H HW L(PJH!PJHCpDFD@Ht$8HL$HT$(HL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H GϻH$0 :)Lt$ I 9ǻI~pPŻI~XGŻH$ I~ H$ LHx%bH$ HHtHD$ H Hj}#H{H=.H HWtNiHt$8l'ZH=.H HWt-&Ht$8>'PJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH= .H HW sHt$8&dH=-H HWu Lp&H=-H HW GHt$8>&8H=-H HW ,Ht$8 & H=W-H HW Ht$8%H=%-H HW ,Ht$8% H=,H HW Ht$8v%H|$ H4ݻTH=,H HWt0HHt$86%9PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWH5 AVAUATUSH HD$pH|$(HHD$0HD$`HT$NH5ǽ HHD$8&Ll$MLt$PH5 LL&HD$PH *H Ld$OHT$`H5dY H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ (&LI
- H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X H H$8 H9 H, H#$HH1I L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ #H$ HT HDŽ$ H$ HH$ g#H$ H(HDŽ$ H$ HݴH$ HD$ HP T
- HD$PHxH9 HD$`HxH9= Ht$0H|$ L HäF H5 Ho L HF H5
- HM H51 H莫 H5qV H迬 H2 H5 H H|$(HLt$ IP p
- H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H ǻH$0 !H$ H$ H9H$T LeHE IMILH)J!HxH9p JT9N49L9 H=g'H f IHxH9
IL9uJ<9Ht IE HxH9 I(L9,$zH$ Ht H$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?- MuHE H}1H Q HE KD5 H} I$HxH9 IPHPL9$$TH$ Ht H$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?- MuHE H}1H QHE KD5 H} I$HxH9 IPHPL9$$TH$ Ht H|$ H$ H ,ԻH$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$ HL$HT$HH$ LHxH$ LHxH$ LHxHD$PHt$LHxlHD$`LHx[H$ HtH|$0 HH#HHH=m#H HW Ht$8H=;#H HW Ht$8H= #H HW mHt$8HL$HL$TIHxH9u^IL9u|H="H HW L8PJH!PJHCpDFD@Ht$8HL$HT$HL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H H$0 zLt$ I yI~p萷I~X臷H$ I~ лH$ LHx"bH$ HHtHD$ H HjH{H=%!H HWtNiHt$8ZH= H HWt-&Ht$8~PJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH=] H HW sHt$8dH=+ H HWu LH= H HW GHt$8~8H= H HW ,Ht$8L H= H HW Ht$8H=e H HW ,Ht$8 H=3 H HW Ht$8H|$ HtϻTH= H HWt0HHt$8v9PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWH5) AVAUATUSH HD$pH|$(HHD$0豞HD$`HT$NH5 HHD$8Ll$MLt$PH5 LLHD$PH K H Ld$OHT$`H5K H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ hLI
- H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X > H H$8 H9 H, HcHH1IS L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ H$ H HDŽ$ H$ H䦻H$ H$ HhHDŽ$ H$ H H$ HD$ HP 蔞
- HD$PHxH9 HD$`HxH9= Ht$0H|$ L HCF H5^ H L HF H5 H H5J# HΝ H5H H Hr H5 H> H|$(H!ٻLt$ IP 谟
- H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H ۹H$0 H$ H$ H9H$T LeHE IMILH)J!HxH9p JT9N49L9 H=H f IHxH9
IL9uJ<9Ht9IE HxH9 I(L9,$zH$ HtH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?mMuHE H}1H HE KD5 H}7I$HxH9 IPHPL9$$TH$ HtH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?mMuHE H}1H
HE KD5 H}7I$HxH9 IPHPL9$$TH$ HtH|$ H$ H lƻH$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$HL$HT$HH$ LHx H$ LHx H$ LHx HD$PHt$LHx HD$`LHx H$ HtH|$0 HHHHH=H HW Ht$80H={H HW Ht$8
H=IH HW mHt$8HL$
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-H=;H HW GHt$8
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- H=H HW Ht$8Z
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- H=sH HW Ht$8 H|$ HTH=/H HWt0HHt$8 9PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWH5i| AVAUATUSH HD$pH|$(HHD$0aHD$`HT$NH5G HHD$8# Ll$MLt$PH5) LL HD$PH H Ld$OHT$`H5= H$ HCH$ LHD$PHD$`H$ H$ HxIHD$
-LI
- H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X ~| H H$8 H9 H, HHH1I L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ #H$ HԖ HDŽ$ H$ H$H$ H$ HHDŽ$ H$ H]H$ HD$ HP Ԑ
- HD$PHxH9 HD$`HxH9= Ht$0H|$ A L| HcF H5 H L t H9F H5` H H5 H H5: H? H貎 H5+| H~ H|$(Ha˻Lt$ IP
- H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H H$0 H$ H$ H9H$T LeHE IMILH)J!HxH9p JT9N49L9 H= H f IHxH9
IL9uJ<9HtyIE HxH9 I(L9,$zH$ HtHH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?MuHE H}1H HE KD5 H}wI$HxH9 IPHPL9$$TH$ HtBH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?MuHE H}1H HE KD5 H}wI$HxH9 IPHPL9$$TH$ HtBH|$ H$ H 謸H$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$=HL$HT$HH$ LHx'H$ LHxH$ LHxHD$PHt$LHxHD$`LHxH$ HtH|$0 HHHHH=H HW Ht$8p H=H HW Ht$8> H=H HW mHt$8HL$ HL$TIHxH9u^IL9u|H=3H HW LPJH!PJHCpDFD@Ht$8HL$HT$pHL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H H$0 Lt$ I I~pI~XH$ I~ FH$ LHxbH$ HHtHD$ H Hj=H{H=H HWtNiHt$8,ZH=wH HWt-&Ht$8PJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH=H HW sHt$8`dH=H HWu L0H={H HW GHt$88H=IH HW ,Ht$8 H=H HW Ht$8H=H HW ,Ht$8h H=H HW Ht$86H|$ HTH=oH HWt0HHt$89PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWAVAUATUSHHH H1HD$@ P(HHt[@( uKHd
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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, HxHH1It L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ sxH$ H HDŽ$ H$ HH$ 7xH$ HhHDŽ$ H$ H H$ HD$ HP
+ HD$PHxH9 HD$`HxH9= Ht$0H|$ A Lr HS#H H5xX HA L0j H)#H H50 HA H5 H H51 H? H H5;r H~ H|$(HALt$ IP
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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, H3kHH1Ig L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ jH$ H HDŽ$ H$ H$H$ wjH$ HHDŽ$ H$ H]H$ HD$ HP
+ HD$PHxH9 HD$`HxH9= Ht$0H|$ Ld HH H5J H/4 Lp\ HH H5p H
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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, Hs]HH1ISY L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ \H$ H HDŽ$ H$ HdH$ \H$ HHDŽ$ H$ HH$ HD$ HP
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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, HOHH1IK L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ 3OH$ HT HDŽ$ H$ HH$ NH$ H(HDŽ$ H$ HH$ HD$ HP T
+ HD$PHxH9 HD$`HxH9= Ht$0H|$ L@ HcG H58/ H L@ H9G H5 H H5 H H5 H Hr H5H a H> H|$(HLt$ IP p
+ H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H kH$0 MH$ H$ H9H$T LeHE IMILH)J!HxH9p JT9N49L9 H=7I f IHxH9
IL9uJ<9HtLIE HxH9 I(L9,$zH$ HtXLH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?KMuHE H}1H GHE KD5 H}KI$HxH9 IPHPL9$$TH$ HtRKH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?JMuHE H}1H FHE KD5 H}JI$HxH9 IPHPL9$$TH$ HtRJH|$ H$ H H$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$MHHL$HT$HH$ LHx'FH$ LHxFH$ LHxEHD$PHt$LHxEHD$`LHxEH$ HtH|$0 HH7OHHH==I HW Ht$8GH= I HW Ht$8NGH=يI HW mHt$8HL$GHL$TIHxH9u^IL9u|H=I HW LFPJH!PJHCpDFD@Ht$8HL$HT$FHL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H WH$0
+GLt$ I II~p`I~XWH$ I~ H$ LHxCbH$ HHtHD$ H Hj=AH{H=I HWtNiHt$8<EZH=LjI HWt-&Ht$8EPJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH=-I HW sHt$8pDdH=I HWu L@DH=ˇI HW GHt$8D8H=I HW ,Ht$8C H=gI HW Ht$8CH=5I HW ,Ht$8xC H=I HW Ht$8FCH|$ HDTH=I HWt0HHt$8C9PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWH5; AVAUATUSH HD$pH|$(HHD$0HD$`HT$NH5 HHD$8DLl$MLt$PH5 LLgDHD$PH I Ld$OHT$`H5 H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ DLI
+ H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X H H$8 H9 H, HAHH1I= L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ sAH$ Hڶ HDŽ$ H$ HܶH$ 7AH$ HhڶHDŽ$ H$ H ݶH$ HD$ HP
+ HD$PHxH9 HD$`HxH9= Ht$0H|$ A L*3 HG H5x! H
+ L03 HiG H50} H
+ H5G H H5 H? H H5;; o H~ H|$(H
+Lt$ IP
+ H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H H$0 ^?H$ H$ H9H$T LeHE IMILH)J!HxH9p JT9N49L9 H=wI f IHxH9
IL9uJ<9Ht>IE HxH9 I(L9,$zH$ Ht>H$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?=MuHE H}1H :HE KD5 H}=I$HxH9 IPHPL9$$TH$ Ht=H$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?<MuHE H}1H 9HE KD5 H}<I$HxH9 IPHPL9$$TH$ Ht<H|$ H$ H <H$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$:HL$HT$HH$ LHxg8H$ LHxS8H$ LHx?8HD$PHt$LHx,8HD$`LHx8H$ HtH|$0 HHwAHHH=}}I HW Ht$89H=K}I HW Ht$89H=}I HW mHt$8HL$W9HL$TIHxH9u^IL9u|H=|I HW L9PJH!PJHCpDFD@Ht$8HL$HT$8HL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H H$0 J9Lt$ I I~pI~XH$ I~ H$ LHx5bH$ HHtHD$ H Hj}3H{H=5{I HWtNiHt$8|7ZH={I HWt-&Ht$8N7PJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH=mzI HW sHt$86dH=;zI HWu L6H= zI HW GHt$8N68H=yI HW ,Ht$8 6 H=yI HW Ht$85H=uyI HW ,Ht$85 H=CyI HW Ht$85H|$ HTH=xI HWt0HHt$8F59PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWH5\e AVAUATUSH HD$pH|$(HHD$01HD$`HT$NH5 HHD$86Ll$MLt$PH5 LL6HD$PH [vI Ld$OHT$`H54 H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ H6LI
+ H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X N H H$8 H9 H, H34HH1I0 L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ 3H$ H̶ HDŽ$ H$ H$϶H$ w3H$ H̶HDŽ$ H$ H]϶H$ HD$ HP Թ
+ HD$PHxH9 HD$`HxH9= Ht$0H|$ 聻 Lj% HG H5 H/ Lp% HG H5po H
H5 HN H5A H H H5{- S H边 H|$(HQLt$ IP
+ H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H H$0 1H$ H$ H9H$T LeHE IMILH)J!HxH9p JT9N49L9 H=sI f IHxH9
IL9uJ<9Ht 1IE HxH9 I(L9,$zH$ Ht0H$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?=0MuHE H}1H Q,HE KD5 H}0I$HxH9 IPHPL9$$TH$ Ht/H$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?=/MuHE H}1H Q+HE KD5 H}/I$HxH9 IPHPL9$$TH$ Ht.H|$ H$ H |H$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$,HL$HT$HH$ LHx*H$ LHx*H$ LHx*HD$PHt$LHxl*HD$`LHx[*H$ HtH|$0 HH3HHH=oI HW Ht$8 ,H=oI HW Ht$8+H=YoI HW mHt$8HL$+HL$TIHxH9u^IL9u|H=oI HW LH+PJH!PJHCpDFD@Ht$8HL$HT$ +HL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H ڶH$0 +Lt$ I ҶI~pжI~XжH$ I~ H$ LHx"(bH$ HHtHD$ H Hj%H{H=umI HWtNiHt$8)ZH=GmI HWt-&Ht$8)PJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH=lI HW sHt$8(dH={lI HWu L(H=KlI HW GHt$8(8H=lI HW ,Ht$8\( H=kI HW Ht$8*(H=kI HW ,Ht$8' H=kI HW Ht$8'H|$ HTH=?kI HWt0HHt$8'9PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWH5
+ AVAUATUSH HD$pH|$(HHD$0HD$`HT$NH57 HHD$8)Ll$MLt$PH5 LL(HD$PH hI Ld$OHT$`H5t H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ (LI
+ H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X 莗 H H$8 H9 H, Hs&HH1IS" L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ %H$ H HDŽ$ H$ HdH$ %H$ H辶HDŽ$ H$ HH$ HD$ HP
+ HD$PHxH9 HD$`HxH9= Ht$0H|$ L HsG H5 Ho L HIG H5a HM H5 H莫 H5 H迬 H2 H5 H H|$(HLt$ IP 0
+ H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H +ӶH$0 #H$ H$ H9H$T LeHE IMILH)J!HxH9p JT9N49L9 H=eI f IHxH9
IL9uJ<9HtI#IE HxH9 I(L9,$zH$ Ht#H$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?}"MuHE H}1H HE KD5 H}G"I$HxH9 IPHPL9$$TH$ Ht"H$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?}!MuHE H}1H HE KD5 H}G!I$HxH9 IPHPL9$$TH$ Ht!H|$ H$ H ߶H$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$
HL$HT$HH$ LHx H$ LHx H$ LHx HD$PHt$LHx HD$`LHx H$ HtH|$0 HH%HHH=aI HW Ht$8@ H=aI HW Ht$8 H=aI HW mHt$8HL$ HL$TIHxH9u^IL9u|H=CaI HW L PJH!PJHCpDFD@Ht$8HL$HT$@ HL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H ͶH$0 Lt$ I ŶI~p öI~XöH$ I~ VܶH$ LHxbbH$ HHtHD$ H HjH{H=_I HWtNiHt$8ZH=_I HWt-&Ht$8PJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH=^I HW sHt$80dH=^I HWu L H=^I HW GHt$88H=Y^I HW ,Ht$8 H='^I HW Ht$8jH=]I HW ,Ht$88 H=]I HW Ht$8H|$ H۶TH=]I HWt0HHt$89PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWH59 AVAUATUSH HD$pH|$(HHD$0豞HD$`HT$NH5w HHD$8CLl$MLt$PH5Y LL'HD$PH ZI 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 Ή H H$8 H9 H, HHH1I L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ 3H$ HT HDŽ$ H$ HH$ H$ H(HDŽ$ H$ HݳH$ HD$ HP T
+ HD$PHxH9 HD$`HxH9= Ht$0H|$ L HG H58 H L HG H5S H H5{ HΝ H5 H Hr H5 H> H|$(HLt$ IP p
+ H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H kŶH$0 H$ H$ H9H$T LeHE IMILH)J!HxH9p JT9N49L9 H=7XI f IHxH9
IL9uJ<9HtIE HxH9 I(L9,$zH$ HtXH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?MuHE H}1H HE KD5 H}I$HxH9 IPHPL9$$TH$ HtRH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?MuHE H}1H HE KD5 H}I$HxH9 IPHPL9$$TH$ HtRH|$ H$ H ѶH$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$MHL$HT$HH$ LHx'H$ LHxH$ LHxHD$PHt$LHxHD$`LHxH$ HtH|$0 HH7HHH==TI HW Ht$8H= TI HW Ht$8NH=SI HW mHt$8HL$HL$TIHxH9u^IL9u|H=SI HW LPJH!PJHCpDFD@Ht$8HL$HT$HL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H WH$0
+Lt$ I II~p`I~XWH$ I~ ζH$ LHx bH$ HHtHD$ H Hj=
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dH=PI HWu L@
H=PI HW GHt$8
8H=PI HW ,Ht$8 H=gPI HW Ht$8 H=5PI HW ,Ht$8x H=PI HW Ht$8F H|$ HDͶTH=OI HWt0HHt$8 9PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWH5y AVAUATUSH HD$pH|$(HHD$0aHD$`HT$NH5 HHD$8
Ll$MLt$PH5 LLg
HD$PH MI Ld$OHT$`H5 H$ HCH$ LHD$PHD$`H$ H$ HxIHD$
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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$8 H9 H, H
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+ HD$PHxH9 HD$`HxH9= Ht$0H|$ A L HG H5x H L0 HG H50F H H5-n H H5 H? H貎 H5; H~ H|$(HӶLt$ IP 谑
+ H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H 諷H$0 ^H$ H$ H9H$T LeHE IMILH)J!HxH9p JT9N49L9 H=wJI f IHxH9
IL9uJ<9HtIE HxH9 I(L9,$zH$ HtH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?MuHE H}1H HE KD5 H}I$HxH9 IPHPL9$$TH$ HtH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?MuHE H}1H HE KD5 H}I$HxH9 IPHPL9$$TH$ HtH|$ H$ H <ĶH$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$HL$HT$HH$ LHxgH$ LHxSH$ LHx?HD$PHt$LHx,HD$`LHxH$ HtH|$0 HHw
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+ Tl HH0V륐fAUIATUHS1HDg Et& D IE(HcH Htq A9uIEu HPN? H0VHH[]A\A] HH0Vfff. AWAVAUATIUSHHHtHtI$ H[]A\A]A^A_ D(LpE~C1H=? L|$tR I,HtHE HxH;=p? u HvթHA9I$Haթ덃PL+ԩI,HtHE HxH;=!? uH'թHA9믋PJ҉HLө @ AVAUATIUHSDo1E~) ID$ Hcӿ HtHS` D9uA\$ W EL$(E~<" Hpc At$8Hcc ED$,1At$(E~ ID$0HxHQ^ A|$@ * H'c At$PHc Et$@E~bE1 D } ID$HIcHcD HuHHv @ HHʀQHwHH))MHcHEAE9uEt$`E1E~$ID$hIcտ AHtHj E9uAt$p : Hcb A$ HSb Et$pE~cE1f D } ID$xIcHD HuHHv @ HHʀQHwHH))MHcHEAE9u A$ ~GR Ha A$ Ha A$ 1A$ ~ I$ HxH\ A$ Z Hda A$ HTa E$ E~iE1 @ } I$ IcHD HuHHv f HHʀQHwHH))MHcHEAE9u' E$ E1Et+ D I$ Icտ
AHtHl E9uڃ ID$ HI? H0V[H]A\A]A^G HH iHH HH AI$ H l I$ H i (I$ H f A$ H [ 8A$ H d I$ H f HH0V D HH=% ? H= ? H= ? H= ? b H=a ? A H=` ? H= ? H= ? H=
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+HɩH=W ? HOtHt$ȩыJqr @ SHWH[ɩfffff. wfff. H=? uH\@? Pw HN@? Zfff. fffff. HiA? HG`HGhHGpHGx ATUHSHLg`H :A? I9t"Mt I$HxH;= ? LȩLehI9t"Mt I$HxH;= ? LȩLepI9t"Mt I$HxH;= ? LȩHmxH9t HtHE HxH;=` ? uAHfȩH[]A\H=
+? HW QHt$ǩBH=
+? HW Ht$ƩH=t
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+? HWt.Ht$ƩPJHXPJH̋PJHkPJHfwfff. H=? uH=? Pu H=? Zfff. H;? H<? H f. H9?? HLJ HLJ HLJ HG`HGhHGpHGxÐf D ATUSHHLg`H->? I9t"Mt I$HxH;=? LƩLchI9t"Mt I$HxH;=f? LhƩLcpI9t"Mt I$HxH;=;? L=ƩLcxI9t MtI$HxH;=? u\LƩH; ? tH HtHPH[]A\H=b? HW 6Ht$ĩ'H=2? HW Ht${ĩxH=? HWtw4Ht$Oĩ%H=? HWt.Ht$#ĩPJHUPJH̋PJHhPJH @ wfff. H=? uH:? PUs H:? Zfff. fffff. H<? HGHG ATIUSHH_H-<? H9t HtHHxH;=b? u8HhĩI\$ H9t HtHHxH;=;? u6HAĩH[]A\H=? HWt<Ht$©H=? HWt$Ht$©륋PJH뾋PJH @ USHHH> HHHCu H> HC HHH[]HHx HuHHzH;=c? u
+HiéH=? HOtHt$$©ыJqr @ SHWH[ éfffff. wfff. HGSumH ;? H HXGtLt"HWHHJH\ ҍTIHӨt"HG H HPH\DIHÉH[H @ H=? uH7? Pp H7? Zfff. fffff. Hi:? HG0 @ SHH_0H; p? t HtHHxH;=? uH©H[H=k? HWtHt$͋PJH AUATUSHHH<> HHpH{(HCHtHt6HCHC( upHg> HC HHH[]A\A] D D'LoE~)1f. I| HtHPHA9H{( HCHC( tHHx HuH HxH;=? u
HeH=S? HWtHt$蠿PJHSHH[fffff. wfff. AUIATUSHHG H8? Ee H DHhEt41 IE(HcӃH|4ҍTIH HA9uAEt"IE0H HPHlDIHʼnLVHH[]A\A]Hofffff. H= ? uH4? P%n H4? Zfff. fffff. H7? H H f. ATIUSHH H-w7? H9t HtHHxH;= ? u;H%I$ H9t HtHHxH;=> u6HH[]A\H=b? HWt<Ht$诽H==? HWt$Ht$芽륋PJH뾋PJHfffff. wfff. H= ? uH2? Pl H2? Zfff. fffff. HG HG Ðfffff. fffff. USHHH> HHHCu H> HC HHH[]HHx HuHHzH;=> u
+H虽H= ? HOtHt$TыJqr @ SHWH[Nfffff. wfff. HGSuYHP5? H HXGt8tHWHHҍTIH\tHG HHDIH\H[H H=
+? uH0? P%k H0? Zfff. H0? H/? H f. H4? HLJ HLJ H H H Ðfff. ATUSHHH L%G4? L9t"Ht HE HxH;=> HH L9t"Ht HE HxH;=> H»H L9t HtHE HxH;=> uXH蘻H; > tH HtHPH[]A\H=> HWto_Ht$-PH=> HWtPHt$H=> HWt8<Ht$ܹ-PJH닋PJH몋PJHUSHHH0> HHH H QD E~H Ht
H? ~H Ht
H? st~H{xHt
H? H{XHshHKD~H{HHt
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H謶UH== HOtHt$dJqr @ SH'H[^fffff. wfff. AUATUHSHHG HP= H H}Lh \ DeU(MHI1Ht ҉й
- xуɍLIMd H}@E8IZ 1Ht ¹
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+H? C ~H{ HtH? t~HC| H> HC HHH[] D 軹7f D 諹Gf D 蛹Qf D 苹hf D {rf D kxHHx HrHHzH;=6> u
H<UH=> HOtHt$Jqr @ SH'H[fffff. wfff. AUATUHSHHG H0? H H}Lh[ DeU(MHI1Ht ҉й
+ xуɍLIMd H}@E8IZ 1Ht ¹
+ xɍLIMd u`UPHL~3H}h1 @ HTHHHJʃҍTIHH9H}p6[ 1Ht ¹
xɍLIH\ H1HHt ҉й
- xуɍLIH\ H H2[ 1Ht ¹
+ xуɍLIH\ H H[ 1Ht ¹
xɍLIH\ D EIIEt;1 H HcӃH|HTIHIA9uE? t%H HHJMd ҍTIIԨt%H HHJMd ҍTIIԨt%H HHJMd ҍTIIԨult!
x
ɍTIMd t!
- x
ҍDIMdDH7HL[]A\A]f H MdDIIċEtiH H=9= uHL= PUd H>= Zfff. H= H"= HP(HP0Ð HG( HG0 Ðfffff. H;== SHt#H(HtHPH{0Ht H[H@[fffff. USHHH@= HHC ~H{ HtH? t.HCu-H= HC HHH[]f HHx HuHHzH;== u
-HH=!= HOtHt$记ыJqrfffff. SH'H[鞱fffff. wfff. ATUSHGH H= H H{L`fW kLHŋCtuBuH[H]A\ @ H{0HlDIHf D H{(HlDIHŋCtHhfffff. H== uH= Pa H= Zfff. fffff. H= G( HGÐfff. wfff. HGS Hl= GH HXt"HGH HPH\DIHËG(t5u"HG H HPH\DIHÉwH[fHG HHDIH\QH[HgfH== uH<= P` H.= Zfff. USHH(tC( H[] Ho H;-U= tHtHE HxH;=߳= uH%C( H[]H=G= HWtHt$ԭŋPJH @ UHSHH_H; ܴ= t HtHHxH;=g= u H譮E(tH.H[]H=Ǵ= HWtHt$T뽋PJH @ USHHH= HHdHCu H= HC HHH[]HHx HuHHzH;== u
-HH='= HOtHt$贬ыJqr @ SHWH[鮭fffff. fffff. fffff. fffff. AUATUSHHHl= HHH{(HCHtHt6HCHC( upHף= HC HHH[]A\A] D D'LoE~)1f. I| HtHPHA9H{(άHCHC( tHHx HuH HxH;=R= u
H蘬eH=ò= HWtHt$PPJHSHH[Nfffff. wfff. AUIATUSHHGujHD= Ee H DHhEt81 IE(HcӃH|ҍTIH HA9uщLHH[]A\A]H뗐f. H=Y= uH= P\ H= Zfff. H= H= HPÐfff. HG G H;=ɯ= tHHtHH@ D fffff. USHHH= HHHCu H= HC HHH[]HHx HuHHzH;=c= u
-H詪H=װ= HOtHt$dыJqr @ SHWH[^fffff. wfff. USHHHGuxHX= H HhCt&u6t S
- x
ҍDIHlHHH[]H{HlDIHŋCuH뉐H=I= uH= PZ H= Zfff. H= H= HPÐfff. HG Ðf D H;== tHHtHH@ D fffff. USHHH`= HHHCu H= HC HHH[]HHx HuHHzH;=s= u
-H蹨H== HOtHt$tыJqr @ SHWH[nfffff. wfff. H== uH\= PuY HN= Zfff. HI= H= HPÐfff. HG G H;=)= tHHtHH@ D fffff. USHHH= HHHCu HE= HC HHH[]HHx HuHHzH;== u
-HYH== HOtHt$ыJqr @ SHWH[fffff. wfff. H=9= uH= PX H= Zfff. H)= Hr= HPÐfff. HG G H;=I= tHHtHH@ D fffff. USHHH= HHHCu H= HC HHH[]HHx HuHHzH;== u
-HH='= HOtHt$贤ыJqr @ SHWH[鮥fffff. wfff. USHHHGuxH= H HhCt&u6t S
- x
ҍDIHlHHH[]H{HlDIHŋCuH뉐H=y= uH= P%V H= Zfff. fffff. H= HGHG ATIUSHH_H-= H9t HtHHxH;=2= u8HxI\$ H9t HtHHxH;= = u6HQH[]A\H=x= HWt<Ht$H=S= HWt$Ht$ࢮ륋PJH뾋PJH @ USHHH = HHHCu He= HC HHH[]HHx HuHHzH;=3= u
-HyH== HOtHt$4ыJqr @ SHWH[.fffff. wfff. HGSumH0= H HXGtLt"HWHHJH\ ҍTIHӨt"HG H HPH\DIHÉH[H @ H== uH = PS H= Zfff. fffff. Hy= G( HGÐfff. wfff. USHHHG H4= CH Hht"HCH HPHlDIHŃ{(w7S(Hq
HcH @ H{ HlDIHʼnH^HH[] @ H{ D H{ ' D H{ D H{ H8fff. USHHHGuxHH= H HhCt&u6t S
- x
ҍDIHlHHH[]H{HlDIHŋCuH뉐USHHHG H= H HhCtPt"HSHHJHl
ҍTIHըt"HS HHJHl
ҍTIHըuH HH[]fH{(HlDIHHa @ AUATUSHHHGc H= Dk H EL`Et31 HC(HcՃH| ҍTIHIA9uDk8DIEt91f HC@HcՃH| ҍTIHIA9uDkPDIEt11HCXHcՃH|lҍTIHIA9uDkhDIEt11HCpHcՃH|,ҍTIHIA9uD DIEt91f D H HcՃH|ҍTIHIA9uD DIEt61 H HcՃH|IҍTIHIA9uD DIEt71 H HcՃH|qHTIHIA9uCtRt%H HHJMd ҍTIIԨt%H H HPMdDIIDHHL[]A\A]Hf. AUATUSHHHG& H`= H H{(kLh-C S({@HIՉIMd L~9LCH1f. ILHH HqɍLIHH9DkXEIEt61f D HC`HcՃH|\ҍTIHIA9uDkpDIEt11HCxHcՃH||ҍTIHIA9uD DEM$Dt91 D H HcՃH|HTIHIA9uS҉ t%H H HHMd DIIt%H H HHMd DIIt%H H HHMd DIIt%H H HHMd DIIĉШ @ D u^f D t2IT$LEt!
- x
ҍDIMdDHHL[]A\A] H HHҍTIMd H TMdDIIċC CH MdDIIċC@ H MdDIIċCQH AUIATUSHHG H= A} H Hp~8ME(1f IDHH H@ɍLIHH9A}8H~3ME@1 @ IDHH HpLIHH9EePDHEt71f D IEXHcӃH|ҍTIH HA9uAE t"IU`HHJHl
ҍTIHըt"IUhHHJHl
ҍTIHըt"IUpHHJHl
ҍTIHըt"IExH HPHlDIHʼnLHH[]A\A]Hlfff. USHHHGuHHX= CH Hht H{HlDIHʼnHHH[]H빐H=)= uH= PI H= Zfff. S(Hw%W(Hl
HcH H HtHPC( [ UHSHHHt C( Hk H[] UHSHHHt C( Hk H[] UHSHHOHt C( Hk H[] UHSHH Ht C( Hk H[] UHSHHHt C( Hk H[] UHSHH_H; |= t HtHHxH;== u HME(tHH[]H=g= HWtHt$뽋PJH @ USHHH= HHdHCu H= HC HHH[]HHx HuHHzH;=S= u
-H處H=Ǜ= HOtHt$TыJqr @ SHWH[Nfffff. fffff. HY= HG HGfff. SHH_H; = t HtHHxH;== uHᔮH[H= = HWtHt$蘓͋PJH USHHH = HHtHCu H%= HC HHH[]HHx HuHHzH;== u
-H9H=g= HOtHt$ыJqr @ SHWH[fffff. wfff. HGSucH= H HXGtBt"HWHHJH\ ҍTIHӨtHG HHDIH\H[Hfffff. H=)= uH,= PuD H = Zfff. fffff. H9= HLJ LJ HGxH ATIUSHH_xH-= H9t HtHHxH;=b= u;H訒I$ H9t HtHHxH;=8= u6H~H[]A\H== HWt<Ht$2H== HWt$Ht$
륋PJH뾋PJHwfff. AUATIUSHHG H= A|$ H Hp~7MD$(1 ITHHHJʃҍTIHH9A|$8H~2MD$@1fIDHH H@ɍLIHH9A|$PH~2MD$X1fIDHH H@ɍLIHH9El$hDHEt71 D ID$pHcӃH|HTIHHA9uAD$ t#IT$xHHJHl
ҍTIHըt&I$ HHJHl
ҍTIHըt I$ HHҍTIHlt#A$
- x
ҍDIHlLHH[]A\A]H*f. AUATUHSHHG H= Dm H EL`Et31 HE(HcӃH|ҍTIHIA9uDm8DIEt91f HE@HcӃH|ҍTIHIA9uDmPDEM$Dt11HEXHcӃH|,HTIHIA9uыE t"HU`HHJMd ҍTIIԨt"HUhHHJMd ҍTIIԨt"HUpHHJMd ҍTIIԨt"HUxHHJMd ҍTIIԨuZ tH HHҍTIMd@tH HHDIMdDHHL[]A\A] H $MdDIIċE t{H H== uH = P> H= Zfff. USHHH`u= HG G G HHH== uHH[]> fH{H" H= HHH|fff. SH
1- 1- 9 H = HZH=; H [ @ USHHHt:Hp uRHD H߾0 4 H1Ht HHH[] 0 ΌHHHH[]H52q= 0 H6 HH[H裒fUSHHHu= HG G G HHH=|= uHH[]B= fH{H H= HHH,fff. SHP
1- 1- H B= HZH=: H [e @ USHHHt:Hp uRH$ H߾( g3 H1Ht HHH[] ( ~HHHH[]H5Bp= ( 4 HH HSfUSHHHv= HG G G HHH= = uH H[]; fH{H HK= HHHܐfff. SH
1- 1- H = HZH=39 Hk [ @ USHHHt:Hp uRH H߾( 2 H1Ht HHH[] ( .HHHH[]H5Ro= ( 3 HH軉HfUSHHHw= HG G G HHH== uHH[]: fH{H2 H= HHH茏fff. SH
1- 1- I H ½= HZH=7 H [e @ USHHHt:Hp uRH H߾0 0 H1Ht HHH[] 0 ވHHHH[]H5bn= 0 X2 HHkH賎fUSHHH w= HG G G HHH=̘= uHH[]R9 fH{H H~= HHH<fff. SH`
1- 1- H = HZH=6 H [5 @ USHHHt:Hp uRH H߾( w/ H1Ht HHH[] ( 莇HHHH[]H5Rm= ( 1 HHHcfUSHHHPv= HG G G HHH=,= uHnH[]8 fH{H H[}= HHH쌮fff. USHHHt:Hp uRH H߾ g. H1Ht HEHH[] ~HH#HH[]H5bl= / HH HSfUSHHHu= HG G G HHH= = uHH[]6 fH{H HK|= HHH܋fff. USHHHt:Hp uRH H߾( W- H1Ht HEHH[] ( nHH#HH[]H5rk= ( . HHHCfUSHHHpu= HG G G HHH=<= uHH[]5 fH{Hr H;{= HHH̊fff. SH
1- 1- H = HZH=#3 H[ [u @ USHHHt:Hp uRHt H߾( , H1Ht HHH[] ( HHHH[]H5Bj= ( - HH諃HfUSHHHt= HG G G HHH=l= uHH[]4 fH{H" Hy= HHH|fff. SH
1- 1- 9 H = HZH=1 H [ @ USHHHt:Hp uRH4 H߾( * H1Ht HHH[] ( HHHH[]H5i= ( H, HH[H裈fUSHHHt= HG G G HHH=l= uHH[]B3 fH{H Hx= HHH,fff. UHP
1- 1- SH H ͵= HHH {0 H H-|= HHH H-= HHH 5H[]f. USHHHt:Hp uRH H߾0 ) H1Ht HHH[] 0 .HHHH[]H5g= 0 * HH軀HfUSHHHs= HG G G HHH=\= uH>H[]1 fH{H2 Hv= HHH茆fff. SH
1- 1- I H = HZH=. H [ @ USHHHt:Hp uRHd
H߾( ' H1Ht HHH[] ( HHHH[]H5bf= ( X) HHkH賅fAUATUSHHH|f= HH H H Ht H H{xHǃ HChHt H H{`HCx HCPHt H H{8HsHHC` pS,~H{0Ht
+ x
ҍDIMdDH7HL[]A\A]f H MdDIIċEtiH H=? uH(? P%d H(? Zfff. Hy(? H(? HP(HP0Ð HG( HG0 Ðfffff. H;=q> SHt#H(HtHPH{0Ht H[H@[fffff. USHHH> HHC ~H{ HtH? t.HCu-H> HC HHH[]f 諴HHx HuHHzH;=}> u
+H胴H=> HOtHt$>ыJqrfffff. SH'H[.fffff. wfff. ATUSHGH H&,? H H{L`6W kLHŋCtuBuH[H]A\ @ H{0HlDIHf D H{(HlDIHŋCtHhfffff. H= ? uH,&? Pa H &? Zfff. fffff. H9+? G( HGÐfff. wfff. HGS H*? GH HXt"HGH HPH\DIHËG(t5u"HG H HPH\DIHÉwH[fHG HHDIH\QH[HgfH=) ? uH$? P` H$? Zfff. USHH(tC( H[] Ho H;-%> tHtHE HxH;=> uH赱C( H[]H=> HWtHt$dŋPJH @ UHSHH_H; > t HtHHxH;=7> u H=E(tH.H[]H=> HWtHt$䯩뽋PJH @ USHHH> HHdHCu H> HC HHH[]HHx HuHHzH;=> u
+H艰H=> HOtHt$DыJqr @ SHWH[>fffff. fffff. fffff. fffff. AUATUSHHH<> HHH{(HCHtHt6HCHC( upH> HC HHH[]A\A] D D'LoE~)1f. I| HtHPHA9H{(^HCHC( tHHx HuH HxH;="> u
H(eH=> HWtHt$୩PJHSHH[ޮfffff. wfff. AUIATUSHHGujH&? Ee H DHhEt81 IE(HcӃH|ҍTIH HA9uщLHH[]A\A]H뗐f. H=> uH ? P\ H~ ? Zfff. Hi ? H2 ? HPÐfff. HG G H;=> tHHtHH@ D fffff. USHHH> HHHCu He> HC HHH[]HHx HuHHzH;=3> u
+H9H=> HOtHt$ыJqr @ SHWH[fffff. wfff. USHHHGuxH$? H HhCt&u6t S
+ x
ҍDIHlHHH[]H{HlDIHŋCuH뉐H=> uHl ? PZ H^ ? Zfff. H9 ? H" ? HPÐfff. HG Ðf D H;=> tHHtHH@ D fffff. USHHH0> HHHCu Hu> HC HHH[]HHx HuHHzH;=C> u
+HIH=> HOtHt$ыJqr @ SHWH[fffff. wfff. H= > uH ? PEY H ? Zfff. H? H ? HPÐfff. HG G H;=> tHHtHH@ D fffff. USHHHp> HHHCu H> HC HHH[]HHx HuHHzH;=> u
+H驩H=W> HOtHt$褨ыJqr @ SHWH[鞩fffff. wfff. H=> uH\? PW HN? Zfff. H? H? HPÐfff. HG G H;=> tHHtHH@ D fffff. USHHH> HHHCu H> HC HHH[]HHx HuHHzH;=> u
+H艨H=> HOtHt$DыJqr @ SHWH[>fffff. wfff. USHHHGuxH8 ? H HhCt&u6t S
+ x
ҍDIHlHHH[]H{HlDIHŋCuH뉐H=> uH<? PU H.? Zfff. fffff. Hy ? HGHG ATIUSHH_H-Z ? H9t HtHHxH;=> u8HI\$ H9t HtHHxH;=> u6HᦩH[]A\H=H> HWt<Ht$蕥H=#> HWt$Ht$p륋PJH뾋PJH @ USHHH> HHHCu H5> HC HHH[]HHx HuHHzH;=> u
+H H=w> HOtHt$ĤыJqr @ SHWH[龥fffff. wfff. HGSumH ? H HXGtLt"HWHHJH\ ҍTIHӨt"HG H HPH\DIHÉH[H @ H=> uH? PS H? Zfff. fffff. H ? G( HGÐfff. wfff. USHHHG H ? CH Hht"HCH HPHlDIHŃ{(w7S(HB HcH @ H{ HlDIHʼnH^HH[] @ H{ D H{ ' D H{ D H{ H8fff. USHHHGuxH? H HhCt&u6t S
+ x
ҍDIHlHHH[]H{HlDIHŋCuH뉐USHHHG HD? H HhCtPt"HSHHJHl
ҍTIHըt"HS HHJHl
ҍTIHըuH HH[]fH{(HlDIHHa @ AUATUSHHHGc H? Dk H EL`Et31 HC(HcՃH| ҍTIHIA9uDk8DIEt91f HC@HcՃH| ҍTIHIA9uDkPDIEt11HCXHcՃH|lҍTIHIA9uDkhDIEt11HCpHcՃH|,ҍTIHIA9uD DIEt91f D H HcՃH|ҍTIHIA9uD DIEt61 H HcՃH|IҍTIHIA9uD DIEt71 H HcՃH|qHTIHIA9uCtRt%H HHJMd ҍTIIԨt%H H HPMdDIIDHHL[]A\A]Hf. AUATUSHHHG& H? H H{(kLhB S({@HIՉIMd L~9LCH1f. ILHH HqɍLIHH9DkXEIEt61f D HC`HcՃH|\ҍTIHIA9uDkpDIEt11HCxHcՃH||ҍTIHIA9uD DEM$Dt91 D H HcՃH|HTIHIA9uS҉ t%H H HHMd DIIt%H H HHMd DIIt%H H HHMd DIIt%H H HHMd DIIĉШ @ D u^f D t2IT$LEt!
+ x
ҍDIMdDHHL[]A\A] H HHҍTIMd H TMdDIIċC CH MdDIIċC@ H MdDIIċCQH AUIATUSHHG H? A} H Hp~8ME(1f IDHH H@ɍLIHH9A}8H~3ME@1 @ IDHH HpLIHH9EePDHEt71f D IEXHcӃH|ҍTIH HA9uAE t"IU`HHJHl
ҍTIHըt"IUhHHJHl
ҍTIHըt"IUpHHJHl
ҍTIHըt"IExH HPHlDIHʼnLHH[]A\A]Hlfff. USHHHGuHH? CH Hht H{HlDIHʼnHHH[]H빐H=> uH ? PH H~ ? Zfff. S(Hw%W(H8 HcH H HtHPC( [ UHSHHHt C( Hk H[] UHSHHHt C( Hk H[] UHSHHOHt C( Hk H[] UHSHH Ht C( Hk H[] UHSHHHt C( Hk H[] UHSHH_H; L> t HtHHxH;=> u HݘE(tHH[]H=7> HWtHt$脗뽋PJH @ USHHH> HHdHCu HU> HC HHH[]HHx HuHHzH;=#> u
+H)H=> HOtHt$䖩ыJqr @ SHWH[ޗfffff. fffff. H? HG HGfff. SHH_H; > t HtHHxH;=k> uHqH[H=> HWtHt$(͋PJH USHHH> HHtHCu H> HC HHH[]HHx HuHHzH;=> u
+HɖH=7> HOtHt$脕ыJqr @ SHWH[~fffff. wfff. HGSucH? H HXGtBt"HWHHJH\ ҍTIHӨtHG HHDIH\H[Hfffff. H=> uH? PED H? Zfff. fffff. H
? HLJ LJ HGxH ATIUSHH_xH-
? H9t HtHHxH;=2> u;H8I$ H9t HtHHxH;=> u6HH[]A\H=u> HWt<Ht$H=P> HWt$Ht$蝓륋PJH뾋PJHwfff. AUATIUSHHG H ? A|$ H Hp~7MD$(1 ITHHHJʃҍTIHH9A|$8H~2MD$@1fIDHH H@ɍLIHH9A|$PH~2MD$X1fIDHH H@ɍLIHH9El$hDHEt71 D ID$pHcӃH|HTIHHA9uAD$ t#IT$xHHJHl
ҍTIHըt&I$ HHJHl
ҍTIHըt I$ HHҍTIHlt#A$
+ x
ҍDIHlLHH[]A\A]H*f. AUATUHSHHG H
+? Dm H EL`Et31 HE(HcӃH|ҍTIHIA9uDm8DIEt91f HE@HcӃH|ҍTIHIA9uDmPDEM$Dt11HEXHcӃH|,HTIHIA9uыE t"HU`HHJMd ҍTIIԨt"HUhHHJMd ҍTIIԨt"HUpHHJMd ҍTIIԨt"HUxHHJMd ҍTIIԨuZ tH HHҍTIMd@tH HHDIMdDHHL[]A\A] H $MdDIIċE t{H H=i> uH ? P> H ? Zfff. USHHH0> HG G G HHH=\> uHH[]b> fH{H" H> HHH fff. SH- 1- 1- H ? HZH=; H [ @ USHHHt:Hp uRHD H߾0 4 H1Ht HHH[] 0 ^HHHH[]H5> 0 6 HH뎩HCfUSHHH> HG G G HHH=> uHH[]= fH{H Hk> HHH̔fff. SH, 1- 1- H ? HZH=S: H [e @ USHHHt:Hp uRH$ H߾( 73 H1Ht HHH[] ( HHHH[]H5> ( 4 HH蛍HfUSHHHP> HG G G HHH=|> uH H[]; fH{H H> HHH|fff. SHP+ 1- 1- i H "? HZH=9 H; [ @ USHHHt:Hp uRH H߾( 1 H1Ht HHH[] ( 辌HHHH[]H5"> ( x3 HHKH裒fUSHHH> HG G G HHH= > uHH[]r: fH{H2 H˾> HHH,fff. SH * 1- 1- H R> HZH=7 H [e @ USHHHt:Hp uRH H߾0 0 H1Ht HHH[] 0 nHHHH[]H52> 0 (2 HHHSfUSHHHе> HG G G HHH=<> uHH[]"9 fH{H H{> HHHܐfff. SH( 1- 1- H > HZH=c6 H [5 @ USHHHt:Hp uRH H߾( G/ H1Ht HHH[] ( HHHH[]H5"> ( 0 HH諉HfUSHHH > HG G G HHH=> uHnH[]7 fH{H H+> HHH茏fff. USHHHt:Hp uRH H߾ 7. H1Ht HEHH[] HH#HH[]H52> / HH蛈HfUSHHH> HG G G HHH=> uHH[]6 fH{H H> HHH|fff. USHHHt:Hp uRH H߾( '- H1Ht HEHH[] ( HH#HH[]H5B> ( . HH苇H㍩fUSHHH@> HG G G HHH=> uHH[]5 fH{Hr H > HHHlfff. SH@% 1- 1- Y H > HZH=2 H+ [u @ USHHHt:Hp uRHt H߾( + H1Ht HHH[] ( 讆HHHH[]H5> ( h- HH;H蓌fUSHHH> HG G G HHH=> uHH[]b4 fH{H" H> HHH fff. SH# 1- 1- H "> HZH=1 H [ @ USHHHt:Hp uRH4 H߾( * H1Ht HHH[] ( ^HHHH[]H5> ( , HH넩HCfUSHHH> HG G G HHH=> uHH[]3 fH{H Hk> HHH̊fff. UH" 1- 1- SH H ]> HHH K0 H H- > HHHi H-2> HHHO 5H[]f. USHHHt:Hp uRH H߾0 ( H1Ht HHH[] 0 较HHHH[]H5b> 0 x* HHKH裉fUSHHH> HG G G HHH=> uH>H[]r1 fH{H2 H˵> HHH,fff. SH ! 1- 1- H > HZH=. H [ @ USHHHt:Hp uRHd
H߾( ' H1Ht HHH[] ( nHHHH[]H52> ( () HHHSfAUATUSHHHL> HH H H Ht H H{xHǃ HChHt H H{`HCx HCPHt H H{8HsHHC` pS,~H{0Ht
H? C ~H{ Ht
-H? H{s H<u= HHH[]A\A]f. D'LoE~$1fI| HtHPHA9H ;~f D D'LoE~!1fI| HtHPHA9H{x}f D'LoE~!1fI| HtHPHA9H{`}f }f D }f D SH'H[~}fffff. USHHHe= HG G G HG G HG$ HG( HHG0 G8 G< HG@ HGH GP GT HGX H== uH$H[]. H{HHsXH>H{0Hs@IH{Hs(<H{s H<s= HHH͂fff. USHHHt:Hp uRH H߾ G$ H1Ht HHH[] ^|HHHH[]H5`= % HH{H3fAUATUSHHH<d= HH0H{XHCHHtHtNH{0Hs@HCX 9H{Hs(,H{c
H,r= HHH[]A\A]f. D'LoE~!1fI| HtHPHA9H{X.{{ SH7H[{fffff. USHHH@e= HG G G HG G HG$ HG( HH=ދ= uHH[]+ H{Hs(HH{Y H"q= HHH賀 SH
1- 1- y H = H:H=) HK [e @ USHHHt:Hp uRH H߾8 ! H1Ht HHH[] 8 zHHHH[]H5^= 8 # HHyHfUSHHHb= HG G G HG G HG$ HG( HHG0 G8 G< HG@ HGH GP GT HGX H=R= uHtH[](* H{HHsXHv;H{0Hs@ H{Hs(;H{
- H\o= HHH~ffff. SH
1- 1- H 2= HH=C' H{ [鵳 @ USHHHt:Hp uRH H߾ ' H1Ht HHH[] >xHHcHH[]H5\= ! HHwH~fAUATUSHHH`= HH`H{XHCHHtHtVH{0Hs@HCX 蹐H{(HCHtHtwH{HC( 6 Hm= HHH[]A\A] D D'LoE~)1f. I| HtHPHA9H{Xvkf D'LoE~!1fI| HtHPHA9H{(vR SHH[vfffff. USHHHi= HG G G HG G HG$ HG( HHG0 G8 G< HG@ HGH GP GT HGX HG` Gh Gl HGp H== uHH[]
-' H{`HspHhH{HHsXKH{0Hs@>H{Hs(1H{h H1l= HHH{fSH
1- 1- H = HH=#$ H[ [ @ USHHHt:Hp uRH H߾ H1Ht HeHH[] uHHCHH[]H5[= HHtHzfAUATUSHHHg= HHH{pHC`HtHtVH{HHsXHCp H{0Hs@H{Hs(ߠH{ Hj= HHH[]A\A] D D'LoE~)1f. I| HtHPHA9H{psk SH'H[sfffff. USHHH^= HG G G HG G HG$ HG( HHG0 G8 G< HG@ HGH GP GT HGX HG` Gh Gl HGp HGx LJ LJ HLJ HLJ LJ LJ HLJ HLJ LJ LJ HLJ H=9= uH苵H[]# H H H'H H 85H{xH TH{`HspGH{HHsX:H{0Hs@]3H{Hs(谋H{ Hh= HHH1xUSHHHt:Hp uRH H߾ H1Ht HHH[] qHHHH[]H5V= H HH[qHwfAUATUSHHH,\= HH`H H H H Ht H H{xH Hǃ H{`HspH{HHsXH{@HC0HtHtzH{Hs(HC@ UH{| HEg= HHH[]A\A] D'LoE~$1fI| HtHPHA9H KpEf D D'LoE~!1fI| HtHPHA9H{@pO SHH[offfff. USHHH]= HG G G HG G HG$ HG( HH=N= uHH[] H{Hs(H2H{9 Hf= HHHu SH
1- 1- Y H = H:H= H+ [e @ USHHHt:Hp uRH H߾0 H1Ht HHH[] 0 nHHHH[]H5T= 0 h HH{nHtfUSHHH0[= HG G G G G HHG HH== uH8H[]L H{H H{ Hd= HHH/tffffff. SHP
1- 1- H = H:H= H [镺 @ USHHHt:Hp uRHt H߾8 g H1Ht HHH[] 8 ~mHHHH[]H5R= 8 HH mHSsfUSHHH T= HG G G G G HHG G( HG, HG0 HG8 G@ GD HGH HGP GX G\ HG` HGh Gp Gt HGx HLJ LJ LJ HLJ H=}= uHnH[]B fH H H踄H{hHsx-H{PHs` -H{8HsHaH{(x H{? H{ HOb= HHHqUH
1- 1- SH H = HeHH ; Hs H-= HHHY H-= HzHH? :UH[]
-f. USHHHt:Hp uRHT H߾ H1Ht HHH[] jHHHH[]H52O= h HH{jHpfUSHHHV= HG G G G G HHG G( HG, HG0 G@ GD HGH HGX G` Gd HGh Gp Gt HGx LJ LJ HLJ LJ LJ HLJ HLJ LJ LJ HLJ H=nz= uHpH[] H H HlH H t H{p H{XHsh螕H{@% H{(| H{ H{ H_= HHHo @ SH@
1- 1- H ҝ= H*H=s H [酮 @ USHHHt:Hp uRHT H߾ W H1Ht HHH[] nhHHHH[]H5M= HHgHCnfSHHHG HV HW VHF GWV FG W HV0F HG0HW0V(HF0G(W(V,F(G,W,HVHF,HGHHWHV@HFHG@W@F@VDGDWDHVhFDHGhHWhV`HFhG`W`VdF`GdWdHVxFdHGxHWxVpHFxGpWpVtFpGtWtH FtH H H H H H H H H H H H H H H H H H H H H H H H H H H V GWHFHF
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H[ tHHƃu%HH[rkH~) H{HƉH{Ht$
Ht$HǐfH9t &f D fffff. AWAVAUATIUSHHwHHHLt$Ih Ht$L{ Hl$ 1LH Mt$M|$ B D HH u
-@ ft@HH HH;C H蜅 ƍ@~vu H聈 HW= LHHD$ HH[]A\A]A^A_ @
-rAL$It$H;5i= t5H b D AL$It$ H;5i= uLIt$ LIt$1^1<HHՇ HHKW= HHHT$Lg HiH AVAUATIUSHHwH@HLt$I Ht$L͘ Hl$ 1LH[ Df HH u
-@ tCHHM H HSH9 H ƍ@~vu H؆ HQV= LHHD$p H@[]A\A]A^Ð@zH HSAL$H9sFx?HH ID$n @ H HSAL$H9s+x$HH ID$ @Hf} ua1[HS} tH HSf D 1 HU= HHHT$L HgHH HHH HSO @ AVAUATIUSHHwH@HLt$I Ht$L Hl$ 1LH苈 HHKMt$J D HH tCHHr V HHKH9_ H ƍ@~vu H HvT= LHHD$ H@[]A\A]A^f D @uLHHKH9 HHID$ AD$( Vf @%AL$It$H;5$f= H @A|$(t1LvH= I|$ AD$( ID$ H$It$ It$ H= H9uI|$ D H{ uX1LIt$m1H=S= HHHT$LY HeHHH葃 HHHKAWAVAUATIUSHHwHHHLt$I Ht$L諔 Hl$ 1LH9 Mt$M|$ B D HH u
-@ ft@HH# HH;C H ƍ@~vu H豂 H*R= LHHD$I HH[]A\A]A^A_ @
-rAL$It$H;5$d= t5H b D AL$It$ H;5c= uLDIt$ L5It$1^1<HH HH{Q= HHHT$L HcH AVAUATIUSHHwH@HLt$I Ht$L Hl$ 1LH苄 HHKMt$I D HH u
-@ ftCHHs HHKH9 H~ ƍ@~vu H HwP= LHHD$ H@[]A\A]A^ @
-rAL$It$H;5tb= tOH: b D HHKAL$H9sxHHID$ BHw uU1]LkIt$1,HHB HHO= HHHT$L H bHHHKfff. AWAVAUATIUSHHwHXLl$Lt$ L IuL* Hl$01LH踂 ID$Mt$0H$? HHt_u
-@ t@HH HH;C~ HJ| ƍ@~vu f@
-uAL$It$0H;5`= H ID$(H IcL$ 9 AL$$9 I|$IcT$ IL$(IǍrAt$ HDHH;Ss~?HTy y:1H~ HM= LHHD$ HX[]A\A]A^A_HHHo HD$H xHLtt$Ho QL|AT$ `LIt$0AL$$H<$qJ ID$(1HPM= HHHT$ Ll H_HHH} HȐfffff. AWAVAUATIUSHHwHXLl$Lt$ L IuL躎 Hl$01LHH ID$HD$2 @ HHu@
-tQt@HHB HH;CE Hy ƍ@~vu xf D ID$(H IcL$ 9 AL$$9 I|$WIcT$ IL$(IƍrAt$ HDHH;Ss~DH$w y?1HV| HK= LHHD$ HX[]A\A]A^A_ D HHHm IH xHL1tDHm D QLtAT$ YAL$$H|$q$ ID$(1H*K= HHHT$ LF H]HHH~{ HȐ AWAVAUATIUSHHwHHHLt$I Ht$L蛌 Hl$ 1LH)~ : HH t_u
-@ t?HH! tvH HSH9; Hw ƍ@~vu Af D @uH HSAL$H9 1 Hs H 1Hmz HI= LHHD$ HH[]A\A]A^A_ @ Mt$AL$MtzHH;Ss~UHt xHyk IH xHLctDHpk HH At$ HH뮉H HS1_IID$r1HI= HHHT$L0 Hx[1HHHay Hfff. AWAVAUATIUSHHwHHHLt$Ih Ht$L{ Hl$ 1LH | : HH t_u
-@ t?HH tvH HSH9; Hu ƍ@~vu Af D @uH HSAL$H9 1 Hpq H 1HMx HG= LHHD$ HH[]A\A]A^A_ @ Mt$AL$MtzHH;Ss~UHr xHYi IH xHLCtDHPi HH At$ HH뮉H HS1?IID$r1HF= HHHT$L HXY1HHHAw Hfff. AVAUATIUSHHwH@HLt$IJ Ht$L] Hl$ 1LHy <f HH t_u
-@ t?HH tnH HSH9 Hs ƍ@~vu 9f D @uH HSAL$H9 1vaHTo Hye1H5v HE= LHHD$ H@[]A\A]A^f D It$AL$Ht)H :HH At$ 4H HS18HID$1$HL\ HWH E= HHHT$1<HH}u H AWAVAUATIUSHHwH Ll$PLt$`L IuL藆 Hl$p1LH%x HD$@Lt$0L|$ H$ID$HD$ HH;C HHw$Hqi
HcH @ @B f D HH uf 1Ht HC= LHHD$` HĘ []A\A]A^A_f. @"uA|$( L6AD$( 1FHD$ID$ HH;Ss Hn g f@
-6A|$(q LҷAD$( 1HD$ID$ LH t$ H e HD$H H|$H ; f D @2AL$It$H;5U= R H @:A|$( L"AD$( 1HD$ID$ LH et$0HYd HD$H HH|$HK f D @*A|$(# L袶AD$( 1HD$ID$ HH;Ss k H+m Hc HD$H H|$H t$Hc 1Ho ƍ@~,Z jA|$( LAD$( 1THD$ID$ H4$H &t$@Hc HD$H H|$H<LID$ HD$ID$ HD$aID$ HD$ID$ HD$/ID$ HD$rHHHb HD$H H|$HM HHH|$It$Ha@= HHHT$`L} HRHHHp HAWAVAUATIUSHHwHHHLt$I Ht$Lہ Hl$ 1LHis Mt$ M|$C D HH t@HHR HH;C Hl ƍ@~vu Ho HY?= LHHD$x HH[]A\A]A^A_ @
-uAL$It$H;5TQ= tiH c D @EAL$It$ H;5$Q= uLjIt$ @It$(AL$Ht HN L1It$1HID$(11HHn HHf>= HHHT$L HPHfAVAUATIUSHHwH@HLt$I Ht$L Hl$ 1LH{q 7f HHu@
-tyt@HHz HH;C H#k ƍ@~vu Hn H== LHHD$ H@[]A\A]A^ÐIt$AL$Ht
H y1,HID$11HLL HOHHm HH<= HHHT$fffff. SHHHVHGHWHV HFHG HW VHF GWHFHFu
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H[ tHHƃu%HH[NH~y H{HƉH{Ht$] Ht$HǐfH9t Vf D fffff. SHHHVHGHWVHFGWHFHFu
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H[ tHHƃu%HH[NH~ H{HƉH{Ht$ Ht$HǐfH9t ff D fffff. SHHHVHGHWHV HFHG HW VHF GWHFHFu
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H[ tHHƃu%HH[RMH~ H{HƉH{Ht$ Ht$HǐfH9t Vf D fffff. SHHHVHGHWHV HFHG HW VHF GWHFHFu
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H[ tHHƃu%HH[LH~I H{HƉH{Ht$- Ht$HǐfH9t Vf D fffff. SHHHVHGHWV HFG W VF GWHFHFuuH[ tHHƃu%HH[KH~ H{HƉH{Ht$m Ht$HǐfH9t Vf D fffff. SHHHVHGHWHV HFHG HW HV(HF HG(HW(VHF(GWHFHFu
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H[ tHHƃu%HH[KH~ H{HƉH{Ht$ Ht$HǐfH9t Ff D fffff. SHHHVHGHWV HFG W VF GWHFHFuuH[ tHHƃu%HH[BJH~ H{HƉH{Ht$ Ht$HǐfH9t Vf D fffff. SHHHVHGHWV HFG W VF GWHFHFuuH[ tHHƃu%HH[IH~9 H{HƉH{Ht$ Ht$HǐfH9t Vf D fffff. SHHHVHGHWHV HFHG HW VHF GWHFHFu
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H[ tHHƃu%HH[HH~y H{HƉH{Ht$] Ht$HǐfH9t Vf D fffff. SHHHVHGHWHV HFHG HW V(HF G(W(VF(GWHFHFuuH[ tHHƃu%HH[GH~ H{HƉH{Ht$ Ht$HǐfH9t Ff D fffff. SHHHVHGHWHV HFHG HW V(HF G(W(VF(GWHFHFuuH[ tHHƃu%HH["GH~ H{HƉH{Ht$ Ht$HǐfH9t Ff D fffff. SHHHV(HG(HW(V HF(G W V$F G$W$VF$GWHFHFu
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H[ tHHƃu%HH[ZFH~ H{HƉH{Ht$ Ht$Hǐf. H9t Ff D fffff. SHHHV(HG(HW(V HF(G W V$F G$W$HV0F$HG0HW0VHF0GWHFHFu
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H[ tHHƃu%HH[zEH~1 H{HƉH{Ht$ Ht$Hǐf. H9t 6f D fffff. SHHHV HG HW VHF GWV FG W HV(F HG(HW(HV0HF(HG0HW0VHF0GWHFHFu
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H[ tHHƃu%HH[DH~A H{HƉH{Ht$% Ht$Hǐf. H9t &f D fffff. SHHHG(HV(HW(HF(V G W F V$G$W$F$HV@HG@HW@HF@V8G8W8F8V<G<W<F<HVXHGXHWXHFXVPGPWPFPVTGTWTHV`FTHG`HW`HVhHF`HGhHWhHVpHFhHGpHWpHVxHFpHGxHWxVHFxGWHFHF uH[ tHHƃu%HH["CH~ H{HƉH{Ht$ Ht$HǐfH9t f D fffff. SHHHG(HV(HW(HF(V G W F V$G$W$F$HV@HG@HW@HF@V8G8W8F8V<G<W<F<HVXHGXHWXHFXVPGPWPFPVTGTWTHV`FTHG`HW`HVhHF`HGhHWhHVpHFhHGpHWpHVxHFpHGxHWxH HFxH H H H H H H H H H VH GWHFHF
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H[ tHHƃu%HH[rAH~) H{HƉH{Ht$
Ht$HǐfH9t ff D fffff. AUATUHSHHHy&= HG HHFG GHFt%LgHIL LHg; HEHCHE HC H[]A\A]LHZ H#1= HHH@ @ SHHHG(HV(HW(HF(V G W F V$G$W$F$HV@HG@HW@HF@V8G8W8F8V<G<W<F<HVXHGXHWXHFXVPGPWPFPVTGTWTHVpFTHGpHWpVhHFpGhWhVlFhGlWlHVxFlHGxHWxH HFxH H H H H H H V GWHFHFu
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H[ tHHƃu%HH[2?H~ H{HƉH{Ht$ Ht$HǐfH9t vf D fffff. UHSHHHv@u4CttHSHUu EH[]f HS HU HGHH@t H9HHt$, Ht$Hf{f. SHHHG(HV(HW(HF(V G W F V$G$W$F$HV@HG@HW@HF@V8G8W8F8V<G<W<F<HVXHGXHWXHFXVPGPWPFPVTGTWTHVpFTHGpHWpVhHFpGhWhVlFhGlWlH FlH H H H H H H H H H H H H H H H H H VH GWHFHF
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H[ tHHƃu%HH[B<H~ H{HƉH{Ht$ Ht$HǐfH9t f D fffff. ATIUHSHHv@uy]t?AD$t)I|$AD$HuH9tH;=;= t77AD$u AD$H[]A\ HU IT$ D I|$гAD$HGHH@tH5gHHt$ Ht$H +f. SHHHG HV HW HF VGWFV G W F HV0HG0HW0HF0V(G(W(F(V,G,W,F,HVHHGHHWHHFHV@G@W@F@VDGDWDHV`FDHG`HW`VXHF`GXWXV\FXG\W\HVxF\HGxHWxVpHFxGpWpVtFpGtWtH FtH H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H V GWHFHF
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H[ tHHƃu%HH[8H~I H{HƉH{Ht$- Ht$HǐfH9t Ff D fffff. ATIUSHHHv@ k@t[@t"AL$I|$HsH9tH;=7= tH3t.AL$I|$ Hs H9tH;=7= t1H[]A\3 @ H[]A\ I|$̳ @ HI|$ []A\̳HGHH@tH2JHHt$! Ht$Hߐf D f. ATIUSHHHv@ k@t[@t"AL$I|$HsH9tH;=6= tH2t.AL$I|$ Hs H9tH;=6= t1H[]A\2 @ H[]A\ I|$˳ @ HI|$ []A\˳HGHH@tH1JHHt$! Ht$Hߐf D f. AUATLgUHSHHHu"= HG HHFG GHFu6EHj= HCtHuH{H9t ˳HE HC H[]A\A]HLI| LHQ0LHd H-&= HHH5fffff. UHSHHHv@ Ct#MH}HsH9tH;=85= 1C(tEu]}(H[ t^HH'i= H} E( HE HH[]:ʳf. }(H[ tHNE( H] H[] @ H} H;=4= tXHH[]0 D H}ɳ]HGHH@tH /HHt$- Ht$HH} Rf. f. AULoATUHSHHH= HG HHFG GHFuLEL%h= LctHuL9t H{ ɳELc tHu H{ L9t ɳH[]A\A]HLIf LH;.LHN H$= HHH3 AULoATUHSHHHe = HG HHFG GHFuLEL%7g= LctHuL9t H{MȳELc tHu H{ L9t0ȳH[]A\A]HLI LHk-LH~ HG#= HHH2 GuG H@u! HG HG f D H11HHP. @ UHSHHH9t HHH[]If H[]Ð AULoATUHSHHHE= HG HHFG GHF EL%e= LctHuL9t H{ƳC( E(t;u#HHm {H{ C( Lc HƳH[]A\A] HHm {Hk C( H[]A\A]HLI LH+RLHԼ H!= HHH.1fffff. SGHu H{HC C @u [ H11HHPD-fH11H[HP+-f. UHSHHH9t HHH[]yf H[]Ð USHHo@t @u8uH{C @uBH[] H{ 11HHP,fH11HHP|,t D H11HHPH[]V,f D UHSHHH9t ZHHH[]Yf H[]Ð USHHo@t @u8uH{C @uBH[] H{ 11HHP+fH11HHP+t D H11HHPH[]+f D UHSHHH9t ZHHH[]f H[]Ð SGHu HyH{C @u [ H11HHP+fH11H[HP*f. UHSHHH9t HHH[]f H[]Ð SGHu H.H{C @u [ H11HHPd*fH11H[HPK*f. USHHo@t@u8@uLu H{C @uTH[] D H{(W D H11HHP)@tH{ 11HHP)t H11HHPH[])fffff. SGHuH{C @u[ H D H11H[HPK)f. SGHu H{C C @u[ @ Ho D H11H[HP(fAUIATUSHo ~%HG(1L`fI<H9AE IEAE uH[]A\A] @ H11HH HPH[]A\A]w(f D SGHu H{C C @u[ @ HO D H11H[HP#(fSGHu H{C C @u[ @ H D H11H[HP'fAUIATUSHo ~%HG(1L`fI<H9AE AEu IEAE u4H[]A\A] D I}011HHP\'IEAE tH11HH HPH[]A\A]+'f. AUHwhATUHH`SHG G G <D Ep Dž Dž E~/H 1Lh D I| HA9Dž ]t uc0u HEE u)H[]A\A] @ HEHDž E tH11HH HPH[]A\A]& D H fH 11HHP%_H 11HHP%HH 11HHP%-fffff. USHHoG @t @u1u H{C @u+H[] @ H{0' D H(tH11HHPH[]%f D AUHw(ATUHH SH:Hu@H}8:DePE~*HEX1Lhf D I| H A9EP ]t u HEE u7H[]A\A] @ H}x11HHP\$HEE t D H11HH HPH[]A\A]'$ H}`11HHP $gH}h11HHP#SH}p11HHP#;T @ AUATUHSHDg E~$HG(1LhI| HA9E De8E~'HE@1Lh I| HBA9E8 DePE~'HEX1Lh I| HA9EP DehE~'HEp1Lh I| HA9Eh D E~/H 1Lh D I| HA9Dž D E~,H 1LhfI| HA9Dž D E~4H 1Lhf. I| HA9Dž ]t
-u^u!EE u4H[]A\A]f H EE t HH}[]A\A] D H ܰ t뵐 @ AUHwHATUHH@SHG G h7DeXE~)HE`1Lh D I| HRA9EX DepE~'HEx1Lh I| HA9Ep D E~/H 1Lh D I| HzA9Dž ]t; @uXHDž uEE u#H[]A\A]EHDž E tHH}[]A\A]酴 D H fH 11HHP9 OH 11HHP 8H 11HHP !H 11HHP
-H H @AAUHw(ATUHH SHV5Hu@H}8I5HuXH}P<5DehE~-HEp1Lhf I| H2A9Eh ]t uh uHEE u0H[]A\A]ÐHEHDž Dž E tH11HH HPH[]A\A] H 11HHP | @ H}x11HHP [ AUATUHSHDg E~$HG(1LhI| HA9E De8E~'HE@1Lh I| HA9E8 DePE~'HEX1Lh I| HBA9EP ] t) ue`fu EE u4H[]A\A]f D EHDž HDž E tHH}[]A\A]j f. H tfH}`11HHP WH}h11HHP CH}p11HHP /H}x11HHP wf D AWAVAUATIUSHHwHh L$ L$0 L] IwLqO H$@ 1LH@ H$ H H{HD$H$ HD$H$ HD$ H$ HD$H9 HH w(HY2
HcH @p6 f D HH荠 tqH H{@2uI|$X1HHù Ԑ@*0 @(fuHH;Cu D0A DH6 Hf
E1H< HV = LHH$0 r Hh D[]A\A]A^A_ D @.AL$Ht$@Hۨ AtD$@H H{A$ @ @ @
+H? H{s H > HHH[]A\A]f. D'LoE~$1fI| HtHPHA9H ˀf D D'LoE~!1fI| HtHPHA9H{x莀f D'LoE~!1fI| HtHPHA9H{`Nf ;f D +f D SH'H[fffff. USHHH> HG G G HG G HG$ HG( HHG0 G8 G< HG@ HGH GP GT HGX H=> uH$H[]- H{HHsXHgH{0Hs@IH{Hs(<H{s H > HHHmfff. USHHHt:Hp uRH H߾ $ H1Ht HHH[] ~HHHH[]H5> % HH{~HӄfAUATUSHHH > HH0H{XHCHHtHtNH{0Hs@HCX 9H{Hs(,H{c
H> HHH[]A\A]f. D'LoE~!1fI| HtHPHA9H{X}{ SH7H[}fffff. USHHH> HG G G HG G HG$ HG( HH=N> uHH[]+ H{Hs(HH{Y H> HHHS SH0 1- 1- I H "> H:H=( H [e @ USHHHt:Hp uRH H߾8 ! H1Ht HHH[] 8 |HHHH[]H5> 8 X# HH+|H胂fUSHHH`> HG G G HG G HG$ HG( HHG0 G8 G< HG@ HGH GP GT HGX H=> uHtH[]) H{HHsXHdH{0Hs@ H{Hs(,dH{
+ H,> HHH荁ffff. SH` 1- 1- y H > HH=' HK [鵳 @ USHHHt:Hp uRH H߾ H1Ht HHH[] zHHcHH[]H5> ! HH[zH賀fAUATUSHHH> HH`H{XHCHHtHtVH{0Hs@HCX 蹐H{(HCHtHtwH{HC( 6 HϬ> HHH[]A\A] D D'LoE~)1f. I| HtHPHA9H{Xykf D'LoE~!1fI| HtHPHA9H{(NyR SHH[.yfffff. USHHH`> HG G G HG G HG$ HG( HHG0 G8 G< HG@ HGH GP GT HGX HG` Gh Gl HGp H=> uHH[]& H{`HspHhH{HHsXKH{0Hs@>H{Hs(1H{h H> HHHb~fSH@ 1- 1- Y H 2> HH=# H+ [ @ USHHHt:Hp uRH H߾ H1Ht HeHH[] wHHCHH[]H5> h HH;wH}fAUATUSHHHl> HHH{pHC`HtHtVH{HHsXHCp H{0Hs@H{Hs(ߠH{ H> HHH[]A\A] D D'LoE~)1f. I| HtHPHA9H{pnvk SH'H[Nvfffff. USHHH`> HG G G HG G HG$ HG( HHG0 G8 G< HG@ HGH GP GT HGX HG` Gh Gl HGp HGx LJ LJ HLJ HLJ LJ LJ HLJ HLJ LJ LJ HLJ H=> uH苵H[]# H H H'H H \^H{xH TH{`HspGH{HHsX:H{0Hs@\H{Hs(谋H{ Hp> HHHzUSHHHt:Hp uRH H߾ H1Ht HHH[] ^tHHHH[]H5> HHsHCzfAUATUSHHH> HH`H H H H Ht H H{xH Hǃ H{`HspH{HHsXH{@HC0HtHtzH{Hs(HC@ UH{| H> HHH[]A\A] D'LoE~$1fI| HtHPHA9H rEf D D'LoE~!1fI| HtHPHA9H{@rO SHH[~rfffff. USHHH> HG G G HG G HG$ HG( HH=> uHH[] H{Hs(H[H{9 HҤ> HHH3x SH 1- 1- ) H "> H:H= H [e @ USHHHt:Hp uRH H߾0 H1Ht HHH[] 0 ~qHHHH[]H5b> 0 8 HH qHcwfUSHHH > HG G G G G HHG HH=v> uH8H[] H{H H{ Hn> HHHvffffff. SH 1- 1- H 2> H:H=S H [镺 @ USHHHt:Hp uRHt H߾8 7 H1Ht HHH[] 8 pHHHH[]H5> 8 HHoHufUSHHH> HG G G G G HHG G( HG, HG0 HG8 G@ GD HGH HGP GX G\ HG` HGh Gp Gt HGx HLJ LJ LJ HLJ H=<> uHnH[] fH H H踄H{hHsxVH{PHs`BVH{8HsHaH{(x H{? H{ H > HHHtUH` 1- 1- SHt H > HeHH HC H-L> HHH) H-2> HzHH :UH[]
+f. USHHHt:Hp uRHT H߾ H1Ht HHH[] ~mHHHH[]H5> 8 HH mHcsfUSHHH`> HG G G G G HHG G( HG, HG0 G@ GD HGH HGX G` Gd HGh Gp Gt HGx LJ LJ HLJ LJ LJ HLJ HLJ LJ LJ HLJ H=> uHpH[]T H H HlH H t H{p H{XHsh螕H{@% H{(| H{ H{ HS> HHHq @ SH 1- 1- H b> H*H=C H{ [酮 @ USHHHt:Hp uRHT H߾ ' H1Ht HHH[] jHHHH[]H5> HHjHpfSHHHG HV HW VHF GWV FG W HV0F HG0HW0V(HF0G(W(V,F(G,W,HVHF,HGHHWHV@HFHG@W@F@VDGDWDHVhFDHGhHWhV`HFhG`W`VdF`GdWdHVxFdHGxHWxVpHFxGpWpVtFpGtWtH FtH H H H H H H H H H H H H H H H H H H H H H H H H H H V GWHFHF
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H[ tHHƃu%HH[nH~) H{HƉH{Ht$
Ht$HǐfH9t &f D fffff. AWAVAUATIUSHHwHHHLt$Ih Ht$LK Hl$ 1LHً Mt$M|$ B D HH u
+@ ft@HH HH;C Hl ƍ@~vu HQ Hʖ> LHHD$ HH[]A\A]A^A_ @
+rAL$It$H;5Ĩ> t5H b D AL$It$ H;5> uLdHIt$ LUHIt$1^1<HH襇 HH> HHHT$Lg HOlH AVAUATIUSHHwH@HLt$I Ht$L蝘 Hl$ 1LH+ Df HH u
+@ tCHH H HSH9 HÃ ƍ@~vu H訆 H!> LHHD$p H@[]A\A]A^Ð@zH HSAL$H9sFx?HH ID$n @ H HSAL$H9s+x$HH ID$ @H6} ua1[H#} tH HSf D 1 Hb> HHHT$L HjHH軅 HHH HSO @ AVAUATIUSHHwH@HLt$I Ht$L͖ Hl$ 1LH[ HHKMt$J D HH tCHHB V HHKH9_ H ƍ@~vu Ḧ́ HF> LHHD$ H@[]A\A]A^f D @uLHHKH9 HHID$ AD$( Vf @%AL$It$H;5> H @A|$(t1LvH> I|$ AD$( ID$ HtDIt$ It$ Hq> H9uI|$ D Hz uX1L5DIt$m1H
> HHHT$LY HAhHHHa HHHKAWAVAUATIUSHHwHHHLt$I Ht$L{ Hl$ 1LH Mt$M|$ B D HH u
+@ ft@HH HH;C H ƍ@~vu H聂 H> LHHD$I HH[]A\A]A^A_ @
+rAL$It$H;5> t5H b D AL$It$ H;5> uLBIt$ LBIt$1^1<HHՁ HHK> HHHT$L HfH AVAUATIUSHHwH@HLt$I Ht$L͒ Hl$ 1LH[ HHKMt$I D HH u
+@ ftCHHC HHKH9 H} ƍ@~vu H HG> LHHD$ H@[]A\A]A^ @
+rAL$It$H;5D> tOH
+ b D HHKAL$H9sxHHID$ BH^w uU1]L@It$1,HH HH> HHHT$L HdHHHKfff. AWAVAUATIUSHHwHXLl$Lt$ L IuL Hl$01LH舂 ID$Mt$0H$? HHt_u
+@ t@HHq HH;C~ H| ƍ@~vu f@
+uAL$It$0H;5> Hj ID$(H IcL$ 9 AL$$9 I|$IcT$ IL$(IǍrAt$ HDHH;Ss~?H$y y:1HV~ Hό> LHHD$ HX[]A\A]A^A_HHHo HD$H xHLtt$Ho QL|AT$ `Le>It$0AL$$H<$q ID$(1H > HHHT$ Ll HTbHHHt} HȐfffff. AWAVAUATIUSHHwHXLl$Lt$ L IuL芎 Hl$01LH ID$HD$2 @ HHu@
+tQt@HH HH;CE Hy ƍ@~vu xf D ID$(H IcL$ 9 AL$$9 I|$WIcT$ IL$(IƍrAt$ HDHH;Ss~DHv y?1H&| H> LHHD$ HX[]A\A]A^A_ D HHHWm IH xHL1tDHRm D QLtAT$ YAL$$H|$q ID$(1H> HHHT$ LF H.`HHHN{ HȐ AWAVAUATIUSHHwHHHLt$I Ht$Lk Hl$ 1LH} : HH t_u
+@ t?HH tvH HSH9; Hw ƍ@~vu Af D @uH HSAL$H9 1 H`s H 1H=z H> LHHD$ HH[]A\A]A^A_ @ Mt$AL$MtzHH;Ss~UHt xHIk IH xHLctDH@k HH At$ HH뮉H HS1_IID$r1H> HHHT$L0 H^1HHH1y Hfff. AWAVAUATIUSHHwHHHLt$Ih Ht$LK Hl$ 1LH{ : HH t_u
+@ t?HH tvH HSH9; H{u ƍ@~vu Af D @uH HSAL$H9 1 H@q H 1H x H> LHHD$ HH[]A\A]A^A_ @ Mt$AL$MtzHH;Ss~UHr xH)i IH xHLCtDH i HH At$ HH뮉H HS1?IID$r1Hą> HHHT$L H[1HHHw Hfff. AVAUATIUSHHwH@HLt$IJ Ht$L- Hl$ 1LHy <f HH t_u
+@ t?HH tnH HSH9 H[s ƍ@~vu 9f D @uH HSAL$H9 1vaH$o Hye1Hv H~> LHHD$ H@[]A\A]A^f D It$AL$Ht)H :HH At$ 4H HS18HID$1$HL\ HDZH> HHHT$1<HHMu H AWAVAUATIUSHHwH Ll$PLt$`L IuLg Hl$p1LHw HD$@Lt$0L|$ H$ID$HD$ HH;C HHw$H
HcH @ @B f D HH uf 1HVt Hς> LHHD$` HĘ []A\A]A^A_f. @"uA|$( L6AD$( 1FHD$ID$ HH;Ss Hn g f@
+6A|$(q LҷAD$( 1HD$ID$ LH t$ Hd HD$H H|$H ; f D @2AL$It$H;5> R H @:A|$( L"AD$( 1HD$ID$ LH et$0H)d HD$H HH|$HK f D @*A|$(# L袶AD$( 1HD$ID$ HH;Ss k Hl Hc HD$H H|$H t$Hc 1Hn ƍ@~,Z jA|$( LAD$( 1THD$ID$ H4$H &t$@Hb HD$H H|$H<LID$ HD$ID$ HD$aID$ HD$ID$ HD$/ID$ HD$rHHHkb HD$H H|$HM HHH|$R1It$H1> HHHT$`L} HeUHHHp HAWAVAUATIUSHHwHHHLt$I Ht$L諁 Hl$ 1LH9s Mt$ M|$C D HH t@HH" HH;C Hl ƍ@~vu Ho H)~> LHHD$x HH[]A\A]A^A_ @
+uAL$It$H;5$> tiH c D @EAL$It$ H;5> uL/It$ @It$(AL$Ht HN L/It$1HID$(11HHn HH6}> HHHT$L HjSHfAVAUATIUSHHwH@HLt$I Ht$L Hl$ 1LHKq 7f HHu@
+tyt@HHJ HH;C Hj ƍ@~vu Hm HQ|> LHHD$ H@[]A\A]A^ÐIt$AL$Ht
H y1,HID$11HLL H4RHHYm HH{> HHHT$fffff. SHHHVHGHWHV HFHG HW VHF GWHFHFu
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H[ tHHƃu%HH[bQH~y H{HƉH{Ht$] Ht$HǐfH9t Vf D fffff. SHHHVHGHWVHFGWHFHFu
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H[ tHHƃu%HH[PH~ H{HƉH{Ht$ Ht$HǐfH9t ff D fffff. SHHHVHGHWHV HFHG HW VHF GWHFHFu
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H[ tHHƃu%HH[OH~ H{HƉH{Ht$ Ht$HǐfH9t Vf D fffff. SHHHVHGHWHV HFHG HW VHF GWHFHFu
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H[ tHHƃu%HH[2OH~I H{HƉH{Ht$- Ht$HǐfH9t Vf D fffff. SHHHVHGHWV HFG W VF GWHFHFuuH[ tHHƃu%HH[rNH~ H{HƉH{Ht$m Ht$HǐfH9t Vf D fffff. SHHHVHGHWHV HFHG HW HV(HF HG(HW(VHF(GWHFHFu
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H[ tHHƃu%HH[MH~ H{HƉH{Ht$ Ht$HǐfH9t Ff D fffff. SHHHVHGHWV HFG W VF GWHFHFuuH[ tHHƃu%HH[LH~ H{HƉH{Ht$ Ht$HǐfH9t Vf D fffff. SHHHVHGHWV HFG W VF GWHFHFuuH[ tHHƃu%HH["LH~9 H{HƉH{Ht$ Ht$HǐfH9t Vf D fffff. SHHHVHGHWHV HFHG HW VHF GWHFHFu
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H[ tHHƃu%HH[bKH~y H{HƉH{Ht$] Ht$HǐfH9t Vf D fffff. SHHHVHGHWHV HFHG HW V(HF G(W(VF(GWHFHFuuH[ tHHƃu%HH[JH~ H{HƉH{Ht$ Ht$HǐfH9t Ff D fffff. SHHHVHGHWHV HFHG HW V(HF G(W(VF(GWHFHFuuH[ tHHƃu%HH[IH~ H{HƉH{Ht$ Ht$HǐfH9t Ff D fffff. SHHHV(HG(HW(V HF(G W V$F G$W$VF$GWHFHFu
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H[ tHHƃu%HH[HH~ H{HƉH{Ht$ Ht$Hǐf. H9t Ff D fffff. SHHHV(HG(HW(V HF(G W V$F G$W$HV0F$HG0HW0VHF0GWHFHFu
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H[ tHHƃu%HH[HH~1 H{HƉH{Ht$ Ht$Hǐf. H9t 6f D fffff. SHHHV HG HW VHF GWV FG W HV(F HG(HW(HV0HF(HG0HW0VHF0GWHFHFu
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H[ tHHƃu%HH[*GH~A H{HƉH{Ht$% Ht$Hǐf. H9t &f D fffff. SHHHG(HV(HW(HF(V G W F V$G$W$F$HV@HG@HW@HF@V8G8W8F8V<G<W<F<HVXHGXHWXHFXVPGPWPFPVTGTWTHV`FTHG`HW`HVhHF`HGhHWhHVpHFhHGpHWpHVxHFpHGxHWxVHFxGWHFHF uH[ tHHƃu%HH[EH~ H{HƉH{Ht$ Ht$HǐfH9t f D fffff. SHHHG(HV(HW(HF(V G W F V$G$W$F$HV@HG@HW@HF@V8G8W8F8V<G<W<F<HVXHGXHWXHFXVPGPWPFPVTGTWTHV`FTHG`HW`HVhHF`HGhHWhHVpHFhHGpHWpHVxHFpHGxHWxH HFxH H H H H H H H H H VH GWHFHF
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H[ tHHƃu%HH[DH~) H{HƉH{Ht$
Ht$HǐfH9t ff D fffff. AUATUHSHHHIe> HG HHFG GHFt%LgHIL LH> HEHCHE HC H[]A\A]LHZ Ho> HHHTC @ SHHHG(HV(HW(HF(V G W F V$G$W$F$HV@HG@HW@HF@V8G8W8F8V<G<W<F<HVXHGXHWXHFXVPGPWPFPVTGTWTHVpFTHGpHWpVhHFpGhWhVlFhGlWlHVxFlHGxHWxH HFxH H H H H H H V GWHFHFu
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H[ tHHƃu%HH[AH~ H{HƉH{Ht$ Ht$HǐfH9t vf D fffff. UHSHHHv@u4CttHSHUu EH[]f HS HU HGHH@t H;HHt$, Ht$Hf{f. SHHHG(HV(HW(HF(V G W F V$G$W$F$HV@HG@HW@HF@V8G8W8F8V<G<W<F<HVXHGXHWXHFXVPGPWPFPVTGTWTHVpFTHGpHWpVhHFpGhWhVlFhGlWlH FlH H H H H H H H H H H H H H H H H H VH GWHFHF
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- HD$H HD$P HD$X D$` ?HD$h HD$ H HD$HH9 H, H0r1HHH nH\$@H\$0IuhHH\$%Ht$pLHD$p HD$x HDŽ$ HDŽ$ Ƅ$ Ƅ$ HDŽ$ DŽ$ H$ Ƅ$ HDŽ$ HDŽ$ Ƅ$ 'A L$ I I` I11LHPsIEHHD$(oX DD$EV D$ H|$H1LHHD$@HHHt2HHqf L9 H HtHq1HHH9t pHtH L`@ H|$ HLH HH H5T 1LqH|$H5> 1q D H|$ަH|$PHufHH oHuHD$HH|$@1H lH|$@HD$X HD$P o<f D E1 H9 H|$H1LHHD$@HHHt*HHq D L9t[H HtHq1HHH9t oHtH L`@ H|$ HLH HH H LLYHtSHt Hth@
ub1(f D H v p t6LL HHuE1H|$(stj H|$PHu @ HH enHuHD$HH|$@1H jH|$11HD$X HD$P L$ HHPpL$ E1H|$H1LHHD$@HHHt-HHq @ L9R H HtHq1HHH9t mHtH L`@ H|$ HLH*
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- HDŽ$ HDŽ$ HDŽ$ Hx DŽ$ ?HDŽ$ HD$? H H$ H9_ H, HdjHH1ITf@ L$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ iH$ H$ HHH,iHE H$ Hs(HU H$ H|$H H$ H$ HE H$ $ H H$ H$ H$ H$ D$`HD$`HHD$ K
+u A @ A { DHHELcIIILzu(Hs8H芷DLHHYA^ I$A HC(AD$C0 H}H5< KH$HHH$ HL$@H H$ L$ H9d L9t_MV L)HT$?1HHD$ H|$IHHq AE A@L;$t HD$A@ I AD HD$PH|$@HL$PHHD$脈HD$PHxH;<$t H|$@HM9 HWHԉHD$@D$`HxH;<$ Ml$ID$8 D A 1Vf D Hl$`D$` H}`{L59 L%9 1Ƅ$ HDŽ$ Ƅ$ HDŽ$ IFHDŽ$ HDŽ$ HDŽ$ H$ ID$HD$hHD$H@H|ID$HHD$ ۃL=9 H}HHD$x HDŽ$ HDŽ$ HDŽ$ HDŽ$ IGHDŽ$ HD$hIG@H$ H79 HD$HHD$p̈H9 HuH}`DŽ$ HD$HHD$pH}9 H$HH$ H}H5M_ . 裁H}H5!( 莁H} 耆H5 HlH}H5_ W|$` HT$ HD$ IOHL$hIO@H$ HL$HHL$pH$ HyH;<$` HD$HD$H}HH$HHHL$pقHD$H$H}`HD$hH@HThIFH$ yC@8 @ L9t[M L)1HHT$HT$?H踆H|$IHH AE A@L;$tHD$A@ I AD HD$PH|$@HL$PHHD$$ H}H58 H$HHH$ HL$@HQ H$ H9 H$ H9Ht$tNH|$ V H+D$HT$?1HHD$ ݅H|$ IHHd HD$ A@L;${ HD$PH|$@HL$PHHD$XHD$PHxH;<$ H|$@H!9 HWH訅HD$@D$`HxH;<$ ID$IT$H$ H9HD$tQH|$ b H+T$1HHT$ HT$?HH|$ IHH HD$ A@L;$ HD$PH|$@HL$PHHD$肃%HT$HLLD$ XLD$ HHT$HLLD$ 6LD$ HvHuXH|$@ HT$ Ht$HLD$(LD$(HHT$ Ht$HLD$(ۂLD$(H6HCH= izHD$PHD$HD$@Ht$Hx\yv H=i 4zHuXH|$@f~<HH=A zf. H=) yH=9 HOt>{Ht$PH$zH$dHD$PHt$>HxxIJqrHD$ A@ I AD jH}HIEHL$Ll$hHLhIFH}`H$ uHҁHHHRYH躁H}HH要H}H蚼HD$HL$ HD$hH@HLhH=9 HWt6Ht$PyHD$@Ht$?Hxw^PJHH==9 HWt$oHt$>y`PJH *HD$ A@ I AD H=ټ9 Hw Ht$PHD$H$yHD$H$^H=9 HWt^Ht$>xH=k9 HWtQHt$Pxf. PJH뜋qDFDALPJH멐fffff. AWAVAUATUSHH H$ 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 1HHH9~ AWAVAUMATUHSHHHHHrL$ HHD$0Ht$8A$ { SA @t Hu H9t$0t1HH[]A\A]A^A_ @ @ t2 AA : A^ V E1I AMD$ DD @ DHuH$ DKHT$0L$$EHHHL$HL$ AHL$ED7A<$ ?7|$ t { HE H;D$ @ M C HU HD$ H)IU IE lj H|$0Hu H|$8HuH9At:E1A D E1H$ D$ A A A E1D$ A HE HUHD$0HT$8f HE HU HUHL$ H)IM IUfAWAVE1AUIATUSH HT$L$tH D[]A\A]A^A_ D HD$@H|$`I
+ HD$H HD$P HD$X D$` ?HD$h HD$ H HD$HH9 H, Ht1HHHpH\$@H\$0IuhHH\$%Ht$pLHD$p HD$x HDŽ$ HDŽ$ Ƅ$ Ƅ$ HDŽ$ DŽ$ H$ Ƅ$ HDŽ$ HDŽ$ Ƅ$ 'A L$ I I` I11LHP}vIEHHD$(\rX DD$EV D$ H|$H1LHHD$@HHHt2HHqf L9 H HtHq1HHH9t tsHtH L`@ H|$ HLH HH H5, 1LtH|$H5, 1mt D H|$ަH|$PHufHH rHuHD$HH|$@1H nH|$@HD$X HD$P {r<f D E1 H9 H|$H1LHHD$@HHHt*HHq D L9t[H HtHq1HHH9t HrHtH L`@ H|$ HLH HH H LLYHtSHt Hth@
ub1(f D H v p t6LL HHuE1H|$(vtm H|$PHu @ HH %qHuHD$HH|$@1H 7mH|$11HD$X HD$P L$ HHPsL$ E1H|$H1LHHD$@HHHt-HHq @ L9R H HtHq1HHH9t pHtH L`@ H|$ HLH*
HH. H|$H1LHHD$@HHHt0HHq L9 H HtHq1HHH9t $pHtH L`@ H|$ HLH HH @ Htov@f/LL HHu Ӿ LlD$ D$9D$
IHH}H|$Ӿ lAD;|$tIEH\$HA HHx AH|$(HH|$H|$ O H|$@nH uhiHHfffff. ATAUHSH HHD[]A\H @ AWAVIAUATUS1Hh Ht$8tHh []A\A]A^A_HD$pHwhHHHD$@¡H$8 H$ LHDŽ$ HDŽ$ HHDŽ$ HDŽ$ Ƅ$ Ƅ$! Ƅ$" HDŽ$( DŽ$0 Ƅ$@ HDŽ$H HDŽ$P f H$( HU HK H$
+ HDŽ$ HDŽ$ HDŽ$ Hx DŽ$ ?HDŽ$ HD$? H H$ H9_ H, H$mHH1Ii@ L$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ lH$ H$ HHH,`lHE H$ Hs(HU H$ H|$H H$ H$ HE H$ $ H H$ H$ H$ H$ D$`HD$`HHD$ K
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:T A= ~H|$0HcFE1H 4 HD$PHD$(H$ HD$HH$ H9$ H8H|$H$ HWH9 HH$ HD$H9D$0M Ld$0 Mc,$Ht$LD HHHD$`l H L$ 1IH$ LIHM I:LHwH9t6f. HH| Hq1HII9g H9HHuH8 R A I2 BD(xMM,t-I
-Hq @ H9 H HtHq1HIH9t fHtH HT$`@ HPH|$HHH% HHAE IL9d$1HD$8Hx t.HD$H9 P HD$8۾ IDHP M$ tLfH|$0HtfH$ Ht8H$ H$ L`I9vH} HeI9wH$ eH$ HuHH/eHuH$ H$ 1H aH$ HDŽ$ HDŽ$ \e1H|$@ H?H9 H, HfeH<(1HHD$0H|$HKaI6 Hw A I BD(xAS D Hc@ H$ HT$(Ht$ H|$$ D$P H$ H$ HWH9 HH\$`tHH$ H\$`HL$ H$ 1HIH$ HL# D Hc@E H$ cH$ HPH$ H@H H$ H$ H$ H$ H$ HH+$ HH)H cHCH$ H\$`HtHH\$`H$ HPH$ H@H H$ H$ HL= uHD$0 HH:QH|$H1Ҿ H$ TH?H9 HcH 1HI^MHtLbH|$0HtbH$ Ht7H$ H$ L`I9vH;HdbI9wH$ RbHH|$ H$ 8bH|$@Hvh\HI]eHHWgH/]H$ aHDŽ$ HDŽ$ e\HHgoH$ H$ HH+$ HH)Hvb aHCH$ H$( HtHH$ HPH$ H@H H$ H$ H$ H$ 1Ҿ H$ HxHa[SH7HH[;f. H_uH]f UHSHHHu @ HH `HuHEH} 1H \HE HE H[]f. AWIAVAUIATI̹ UHH SHHWHwLwl H HH96 L< L>`1HLI.\Hu1HE LUHuU D IHIH2Ht?HHF1HHMMMuHEHHuMH> HI4HuH} w_1LLu HH]IIK>HHt8HI$H L HEHL[]A\A]A^A_Lu IK>HHuHUI$LeI$HtHB1HuLM$HE HUHf HYHYLu(rbH
-dHYL^UbHcHd D ATIUH SH^HH H LLPA$GLM1LIHE HHHt*HLC D M9t;HHtLC1LIH9tHLH[] A\f. ]H1[]A\ÐL% { AWIAVAUIATI̹ UHH SHHWHwLw H HH9. L< L]1HLIYHu1HE LUHuU D IHIH2Ht?HHF1HHMMMuHEHHuMH> HI4HuH} \L1H]HLu IIMl$K>HHt/HI$H L HEHL[]A\A]A^A_ Lu fHUI$LeI$HtHB1HuLM$HE HUH D H WHsWLu(_HaHZWL"\_HeaH]bf. AWAVAUATUHSHHHOHHw(HH)HL`HGM<,K<?H9vUL)H1HHEH,H+H9 HH)HH H HI` f D H9HHCLlH I9 J< T$ Hy[T$ LIL)1Hs(HHEH,HCHLHPH)HHt HH&`H;ZL3LkIHk(HU JD%HCHHSH HS H HC8H HC@H[]A\A]A^A_ HIH)HHtH J|% H)_0UHBSIL HH?HHHH9|
+Hq @ H9 H HtHq1HIH9t iHtH HT$`@ HPH|$HHH% HHAE IL9d$1HD$8Hx t.HD$H9 P HD$8۾ IDHP M$ tLhH|$0HthH$ Ht8H$ H$ L`I9vH} HhI9wH$ hH$ HuHH/ehHuH$ H$ 1H qdH$ HDŽ$ HDŽ$ h1H|$@ H?H9 H, H&hH<(1HHD$0H|$HcI6 Hw A I BD(xAS D Hc@ H$ HT$(Ht$ H|$$ D$P H$ H$ HWH9 HH\$`tHH$ H\$`HL$ H$ 1HIH$ HL# D Hc@E H$ fH$ HPH$ H@H H$ H$ H$ H$ H$ HH+$ HH)H fHCH$ H\$`HtHH\$`H$ HPH$ H@H H$ H$ HL= uHD$0 HH:QH|$H1Ҿ H$ TH?H9 HeH 1HIaMHtLeeH|$0HtVeH$ Ht7H$ H$ L`I9vH;H$eI9wH$ eHH|$ H$ dH|$@HFk_H_hHH'jH_H$ dHDŽ$ HDŽ$ Uh@_HHioH$ H$ HH+$ HH)Hvb dHCH$ H$( HtHH$ HPH$ H@H H$ H$ H$ H$ 1Ҿ H$ HxHaq^SH7HH[;f. HauH_f UHSHHHu @ HH ]cHuHEH} 1H q_HE HE H[]f. AWIAVAUIATI̹ UHH SHHWHwLwl H HH96 L< Lb1HLI^Hu1HE LUHuU D IHIH2Ht?HHF1HHMMMuHEHHuMH> HI4HuH} 7b1LLu HH]IIK>HHt8HI$H L HEHL[]A\A]A^A_Lu IK>HHuHUI$LeI$HtHB1HuLM$HE HUHf HC\H\Lu(BeHfH\Lja%eHfHg D ATIUH SHyaHH H LLPA$GLM1LIHE HHHt*HLC D M9t;HHtLC1LIH9tHLH[] A\f. `H1[]A\ÐL% { AWIAVAUIATI̹ UHH SHHWHwLw H HH9. L< Ln`1HLIN\Hu1HE LUHuU D IHIH2Ht?HHF1HHMMMuHEHHuMH> HI4HuH} _L1H]HLu IIMl$K>HHt/HI$H L HEHL[]A\A]A^A_ Lu fHUI$LeI$HtHB1HuLM$HE HUH D H YH#ZLu(bHRdH
+ZL^bH5dH-ef. AWAVAUATUHSHHHOHHw(HH)HL`HGM<,K<?H9vUL)H1HHEH,H+H9 HH)HH H HI{c f D H9HHCLlH I9 J< T$ H9^T$ LIL)1Hs(HHEH,HCHLHPH)HHt HHbH;]L3LkIHk(HU JD%HCHHSH HS H HC8H HC@H[]A\A]A^A_ HIH)HHtH J|% H)wbWHBSIL HH?HHHH9|
6 D IMZKIN EF;D } HL EH9F|uHII?LHH9t\HPII?IIH9~;FD90 DD9~ LIDI@HH?HHL9|N A [f D HD THAL f. AUHH)ATIUSHHCr HI HoHAL$II~9 9 9 E $A<$DNED$A $HIf. A9L} HDA9|HA9~ HDA9H9sD LSD DHDCA $LH$HL)HC MtJH49|%9UA $A$
-DNED$A $NE$A $ED$DN8HHHhIHHA LHLHuHA$ H1LL)HHHH[]A\A]f D AWDIAVAUEATUSH DD$$LF(H6H|$T$!L$#HI_ItIHHD$H H$HxA@
HD$ D$" E1I9 HD$@L$HD$8 HD$(HD$pHD$0 f|$! ELD$ Aw0LHH)XHHLD$B +HH|$HcHI H@ HF A
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ELc C u|$#
MwH $ H|$LƉLD$HHLD$=h8 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|$$ tdIwIGIGH9 .H|$HcHDxHHHa H H[MwHD$"VI7LLI9w nf. I_H %AxEMHcL A*Lp M OLf D IAQ;Ps)LPLcODIc*D9sA;t
ELcC uHD$ H Hc@f D @
t+IWPD$"HtH|$ I_HD$"JLI7LLAG@H|$0腊H|$({1H|$HΉH $HH $Ht$Ll$pHL蟉Iw8H|$ALIH H|$H衾H> LH$H$ H/H0HcH< LR MtqHt>f. HFt6;Bs)HzLcJ<DLcE9sC;t
DHc:AuD$"LGAG@1HD>
-HxtуuH$ H{H{H5ݪ PQH1AG@oHH|$0H|$(HOWHH1HHH$ HE{H{H5 PHm1AG@L萈1,HL~HVHH;1Hf D ATIUSH HtXRI$ Hu*f HH{H+Ht,RH4PHuI$ Ht PI|$pIDŽ$ HtPI|$`ID$p HtOI|$HID$` HtOI|$8ID$H HtO[]ID$8 A\fff. AWAVAUATUSH ҉L$0LD$@LL$8H$@ AHH D$XH$ ID$ HD$H$ Ht$HF HD$ H$ HD$PHD$pHD$H l$HcD$HHLbM Ht1V҉ L$0L=p I h HS HcH ;CZ L LH{LcD; sIB;
+DNED$A $NE$A $ED$DN8HHHhIHHA LHLHuHA$ H1LL)HHHH[]A\A]f D AWDIAVAUEATUSH DD$$LF(H6H|$T$!L$#HI_ItIHHD$H H$HxA@
HD$ D$" E1I9 HD$@L$HD$8 HD$(HD$pHD$0 f|$! ELD$ Aw0LHH)ZHHLD$B +HH|$HcHI H@ HF A
t3HCLsELDIGPHt Ht$~tm|$# D$"I9\ IL;$GEzkHtAw0L)LHN^HLD$} HXf. qxH9HcL qLX MdJ|f. HWC;Ps)L@LcODMcE9sC;t
ELc C u|$#
MwH $ H|$LƉLD$HHLD$=T9 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]3 TH1AG@oHH|$0H|$(H ZHH1HHH$ HE{H{H5"3 SHm1AG@L萈1,HL~HYHH;1Hf D ATIUSH Ht(UI$ Hu*f HH{H+HtTHRHuI$ HtRI|$pIDŽ$ HtRI|$`ID$p HtRI|$HID$` HtRI|$8ID$H HtzR[]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=2 I h HS HcH ;CZ L LH{LcD; sIB;
* D DDMcAIB
-DHc?HHD: LH{LK HcI} HPHLL4A6Ic<Lf D H|$ Ƅ$ GHR8 Ƅ$ 1HL$HDŽ$ Ƅ$ HDŽ$ HHDŽ$ HDŽ$ H$ HuR8 HDŽ$ LpIFL$ H|HMR8 LxL?PHR8 HL$HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHyHHDŽ$ H$ HR8 HDŽ$ H@H$ HR8 HH$ THR8 HL$H|$ DŽ$ HHqH$ H_Q8 HH$ .OHD$H5ڧ . HxMHD$H5o HxMHD$ HxRH56 HsMHD$H5
- HxYMHD$HxIRH5 H5M$ HQ8 HH$ HmQ8 H@H$ HQ8 HH$ H$ HxH;=[P8 HP8 HH$ HD$HxHNIFL$ H|$ L HP8 HH$ ED$ 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
} HxJHM8 HhH$ Hl$pH
L$ L$ L9 L9 M L)HT$o1HIOIHHH AEH;-M8 Ht$PH|$HH$ IfNH$ HxH;=L8 H|$pHM8 HWHOHD$pƄ$ HxH;=L8 HL8 LpLxf D M9 M& M)HT$o1LOIHHHu_AEH;-IL8 U Ht$PH|$HH$ IM. @ H []A\A]A^A_HLL`MHHLLJMH땿 L$\DT$4DL$(LD$XGH IcE LD$H< L9HHG?AU HEHLD$LcL$(DT$4L$\H-HXHD$H|$HHpXHsMH=p [DH=d ODL$ H}L[C@I̐H$ Ht$nHx5CHl$pE L} BD= Hl$pH=QL8 HWt%Ht$nD 듋PJHE L} BD= kH|$Hl&HLH|$HHCMHKHHD$HxSIFL$ L HJ8 H|$HH`H$ ?HKHH=xK8 HWt87Ht$PC(HD$pHt$oHxAPJHH=%K8 HWt%1Ht$PC"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(H8 Ƅ$ 1HDŽ$ Ƅ$ HDŽ$ HDŽ$ HHDŽ$ HDŽ$ H$ HG8 LpIFL$ I|HG8 H@HHD$EHH8 IHHDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHDŽ$ H$ H$H8 H@H$ HG8 HH$ `JHYH8 H|$IwDŽ$ HH$ HF8 HH$ DIH5n . LCIH5Ze 7CI] )HH58, HCIH5
- CIDGH5 HB$ Ll$ H&G8 HH$ HG8 H@H$ H`G8 HH$ H$ HxH;=F8 HlF8 IHHH$ ODIFL$ H|$L HE8 HH$ N;E E I$ Ht$HE I$ HF Hc6 H9uHcҋЃt @tAD$ HuI$ ~.HP1HL L LLLLHH9uM $AƄ$ E E HD$HHt$HP H
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- opaque( ml_type != nullptr non_tensor_base != nullptr onnxruntime_profile_ input name cannot be empty CPUExecutionProvider output name cannot be empty not enough space: expected , got != Tensor type mismatch. input array is too short space is not enough shape is invalid vector::reserve vector::_M_default_append vector::_M_range_check DmlExecutionProvider Encountered nullptr. Unsupported data type: Unsupported input type RegisterCustomOpsLibrary: Failed to load library RegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in library Specified domain and type names combination does not refer to a registered opaque type /onnxruntime_src/onnxruntime/core/session/onnxruntime_c_api.cc Opaque type is not a non_tensor type!!! /onnxruntime_src/include/onnxruntime/core/framework/ml_value.h onnxruntime::DataTypeImpl::GetType<T>() == type_ is not supported in this function /onnxruntime_src/include/onnxruntime/core/framework/tensor.h DataTypeImpl::GetType<T>() == dtype_ Input is not of one of the supported sequence types. Input is not of type sequence or map. Invalid index requested for map type. Invalid tensor element type in the input. Mem pattern should be disabled when using DML execution provider. Sequential execution should be enabled when using DML execution provider. Sequences must have tensors of the same data type. There was at least one tensor in the input that was different. Input is not of one of the supported map types. Number of values should be at least 1. For map type num_values MUST be 2 Key and value tensors have unequal number of elements. Value type is not supported yet. Key type is not supported yet. Each element of the sequence should be either tensor or map. At least one element in the sequence is of a type different from others. Either the key tensor or the value tensor has NumDimensions > 1 ` @` @``x76666667 747L7d7|777746 OrtStatus* OrtApis::GetOpaqueValue(const char*, const char*, const OrtValue*, void*, size_t) OrtStatus* OrtApis::CreateOpaqueValue(const char*, const char*, const void*, size_t, OrtValue**) 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 = double] T* onnxruntime::Tensor::MutableData() [with T = float] T* onnxruntime::Tensor::MutableData() [with T = bool] 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 = double] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = float] const T* onnxruntime::Tensor::Data() const [with T = bool] const T& OrtValue::Get() const [with T = std::map<long int, double>] 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>] const T& OrtValue::Get() const [with T = std::map<long int, std::basic_string<char> >] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, double>] 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> >] const T& OrtValue::Get() const [with T = std::vector<std::map<long int, float> >] const T& OrtValue::Get() const [with T = std::vector<std::map<std::basic_string<char>, float> >] const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] St11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EE PFvPvE St16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EE N11onnxruntime23NotImplementedExceptionE N11onnxruntime20OnnxRuntimeExceptionE N11onnxruntime10IAllocatorE N11onnxruntime16AllocatorWrapperE N11onnxruntime7logging5ISinkE N11onnxruntime7logging8CLogSinkE 8OrtValue 14LoggingWrapper St19_Sp_counted_deleterIPvPFvS0_ESaIiELN9__gnu_cxx12_Lock_policyE2EE St19_Sp_make_shared_tag St23_Sp_counted_ptr_inplaceIN11onnxruntime16AllocatorWrapperESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE execution_mode is not valid graph_optimization_level is not valid Result buffer is not large enough basic_string::_S_construct null not valid /onnxruntime_src/onnxruntime/core/session/custom_ops.cc %u Unsupported version ' ' in custom op ' is not implemented Description CreateCustomRegistry ComputeAsync *ZN11onnxruntime20CreateCustomRegistryERKSt6vectorIP17OrtCustomOpDomainSaIS2_EERSt10shared_ptrINS_14CustomRegistryEEEUlRKNS_12OpKernelInfoEE_ N4onnx15ISchemaRegistryE N11onnxruntime30IOnnxRuntimeOpSchemaCollectionE N11onnxruntime8OpKernelE N11onnxruntime14CustomOpKernelE N4onnx8OpSchema19num_inputs_allowed_MUliE_E N4onnx8OpSchema20num_outputs_allowed_MUliE_E St23_Sp_counted_ptr_inplaceIN11onnxruntime14KernelRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime27OnnxRuntimeOpSchemaRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime14CustomRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE 12OrtAllocator N3Ort9ExceptionE 16OrtAllocatorImpl 19OrtDefaultAllocator tensor(uint8) tensor(uint16) tensor(uint32) tensor(uint64) tensor(int8) tensor(int16) tensor(int32) tensor(int64) tensor(float16) tensor(float) tensor(double) tensor(string) tensor(bool) tensor(complex64) tensor(complex128) unknown Element type of input [TypeInferenceError] expected to have tensor type Input Output com.microsoft com.microsoft.nchwc com.microsoft.automl (domain: ) from file line , but it its domain is not known by the checker. , but it its version is not in the inclusive range [ ] (usually, this means you in onnx/defs/schema.h). Schema error: MemcpyFromHost input MemcpyToHost Exception caught: Trying to register schema with name , but it is already registered from file bumped the operator version but forgot to update the version range in DomainToVersionRange Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type. /onnxruntime_src/onnxruntime/core/session/environment.cc FvON4onnx8OpSchemaEE FvRN4onnx16InferenceContextEE N4onnx14InferenceErrorE N4onnx11SchemaErrorE PFvRN4onnx16InferenceContextEE PFvON4onnx8OpSchemaEE Cpu ) , expected: ( Model was not loaded Model was not loaded. Profiler is disabled. Session was not initialized Session not initialized. subgraph_session_state p_op_kernel %Y-%m-%d_%H-%M-%S .json Compute_ Create_State_ Release_State_ Error during EndProfiling(): intra_op_thread_pool inter_op_thread_pool CastFloat16Transformer Environment::IsInitialized() Invalid rank for input: Expected: for the following indices
- Output vector pointer is NULL p_fetches->size(): Invalid Output Name: Provider has already been registered. elements, but feeds has elements. Invalid Feed Input Name: Input with name: 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. CUDAExecutionProvider session_initialization Unexpected input data type. Actual: ( Attempt to use DefaultLogger but none has been registered. Received nullptr for graph transformer /onnxruntime_src/onnxruntime/core/session/inference_session.cc Could not write a profile because no model was loaded. Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast<int>(logging::Severity::kFATAL) Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast<int>(logging::Severity::kFATAL) CreateSubgraphSessionState should have created an entry earlier. /onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.h Allowed values are 1 and 2. Current level is set to graph_optimization_level < TransformerLevel::MaxTransformerLevel timeout_in_ms >0 is not supported Main Graph instance should have populated all subgraphs when being resolved. Unknown error during EndProfiling() Environment must be initialized before creating an InferenceSession. Received nullptr for custom registry Please fix either the inputs or the model. Got invalid dimensions for input: At least one output should be requested. Output vector incorrectly sized: output_names.size(): /onnxruntime_src/onnxruntime/core/framework/execution_providers.h allocator already registered. Received nullptr for exec provider Memory pattern must be disabled before registering DMLExecutionProvider Sequential execution must be enabled before registering DMLExecutionProvider Size mismatch: feed_names has is not expected to be of type tensor. Encountered unknown exception in Run() This session already contains a loaded model. Encountered unknown exception in Load() Could not find an implementation for the node All nodes have been placed on [ Session has already been initialized. Adding default CPU execution provider. Parallel execution is currently not supported for the registered CUDA Execution Provider. Serializing Optimized ONNX model with Graph Optimization level greater than 2 is not supported. Session successfully initialized. Exception during initialization: Encountered unknown exception in Initialize() Failed to load model because protobuf parsing failed. void onnxruntime::InferenceSession::AddPredefinedTransformers(onnxruntime::GraphTransformerManager&, onnxruntime::TransformerLevel, const std::vector<std::basic_string<char> >&) void onnxruntime::InferenceSession::InitLogger(onnxruntime::logging::LoggingManager*) const onnxruntime::logging::Logger& onnxruntime::InferenceSession::CreateLoggerForRun(const RunOptions&, std::unique_ptr<onnxruntime::logging::Logger>&) EndProfiling GetModelOutputs GetOverridableInitializers GetModelInputs onnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(const std::vector<std::basic_string<char> >&, const std::vector<std::basic_string<char> >&, const onnxruntime::OrtValueNameIdxMap&) FeedsFetchesInfo Run 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&, onnxruntime::logging::LoggingManager*) Max Used St14default_deleteIN11onnxruntime10DummyArenaEE *ZN11onnxruntime16InferenceSession4LoadEPKviEUlRSt10shared_ptrINS_5ModelEEE_ N11onnxruntime16IDeviceAllocatorE N6google8protobuf2io19ZeroCopyInputStreamE N5Eigen19ThreadPoolInterfaceE N5Eigen15ThreadPoolTemplINS_20StlThreadEnvironmentEEE N11onnxruntime16GraphTransformerE N11onnxruntime16InferenceSessionE N11onnxruntime15IArenaAllocatorE N11onnxruntime10DummyArenaE N11onnxruntime11controlflow18IControlFlowKernelE ZN11onnxruntime20CPUExecutionProviderC1ERKNS_24CPUExecutionProviderInfoEEUliE_ St19_Sp_counted_deleterIPN11onnxruntime10DummyArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE ZN11onnxruntime16InferenceSession4LoadIcEENS_6common6StatusERKSbIT_St11char_traitsIS4_ESaIS4_EEEUlRSt10shared_ptrINS_5ModelEEE_ St23_Sp_counted_ptr_inplaceISt13unordered_mapISsN11onnxruntime11FuncManager8FuncInfoESt4hashISsESt8equal_toISsESaISt4pairIKSsS3_EEESaISC_ELN9__gnu_cxx12_Lock_policyE2EE _ _ C/onnxruntime_src/onnxruntime/core/optimizer/graph_transformer.cc Apply /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_mgr.cc This transformer is already registered ApplyTransformers Level _RuleBasedTransformer NchwcTransformer EliminateIdentity EliminateSlice UnsqueezeElimination EliminateDropout FuseReluClip ShapeToInitializer ConvAddFusion ConvMulFusion ConvBNFusion Unsupported level false ConstantFolding RandomUniform RandomNormal RandomUniformLike RandomNormalLike Multinomial MatMulAddFusion GemmActivationFusion ConvActivationFusion GeluFusion 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 Cast 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 ai.onnx Add fused Matmul and Add gemm /onnxruntime_src/onnxruntime/core/optimizer/matmul_add_fusion.cc Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl N11onnxruntime15MatMulAddFusionE pads strides dilations kernel_shape auto_pad SAME_UPPER SAME_LOWER NOTSET VALID ReorderOutput size is different size_ == size data type is not supported reorder ReorderInput activation _nchwc FusedConv GlobalMaxPool GlobalAveragePool Concat /onnxruntime_src/onnxruntime/core/optimizer/initializer.h /onnxruntime_src/onnxruntime/core/optimizer/nchwc_transformer.cc |{||||{{||{{11111ĆD11·=Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl Initializer onnxruntime::Initializer::Initializer(const onnx::TensorProto&)
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+ opaque( ml_type != nullptr non_tensor_base != nullptr env_ptr == p_instance_ IsTensorSequence() elem_type_ != nullptr input name cannot be empty CPUExecutionProvider output name cannot be empty != T Tensor type mismatch. input array is too short space is not enough shape is invalid ++index < c.size() vector::reserve vector::_M_default_append DmlExecutionProvider onnxruntime_profile_ i < tensors_.size() vector::_M_range_check Encountered nullptr. value_type != nullptr in[idx]->IsTensor() Unsupported input type RegisterCustomOpsLibrary: Failed to load library RegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in library Specified domain and type names combination does not refer to a registered opaque type /onnxruntime_src/onnxruntime/core/session/onnxruntime_c_api.cc Opaque type is not a non_tensor type!!! Trying to get a Tensor, but got: /onnxruntime_src/include/onnxruntime/core/framework/ml_value.h Trying to get a TensorSeq, but got: is not supported in this function Tensor sequence must contain only primitive types /onnxruntime_src/onnxruntime/core/framework/TensorSeq.h /onnxruntime_src/include/onnxruntime/core/framework/tensor.h utils::IsPrimitiveDataType<T>(dtype_) Sequence is missing type entry for its element /onnxruntime_src/include/onnxruntime/core/framework/data_types_internal.h Map is missing type entry for its value Mem pattern should be disabled when using DML execution provider. Sequential execution should be enabled when using DML execution provider. onnxruntime::DataTypeImpl::GetType<T>() == type_ Input is not of one of the supported sequence types. Input is not of type sequence or map. Check for duplicate types in MLTypeCallDispatcherRet Unsupported tensor element type in the input: Invalid index requested for map type. Input is not of one of the supported map types. Number of values should be at least 1. For map type num_values MUST be 2 Key and value tensors have unequal number of elements. Tensor must always contain primitive types. Found: Value type is not supported yet: Key type is not supported yet. Each element of the sequence should be either tensor or map. At least one element in the sequence is of a type different from others. Expecting all elements to be tensors. Got: Sequences must have tensors of the same data type. There was at least one tensor in the input that was different. Either the key tensor or the value tensor has NumDimensions > 1 @P`p DL\l| static void OrtEnv::Release(OrtEnv*) OrtStatus* OrtApis::GetOpaqueValue(const char*, const char*, const OrtValue*, void*, size_t) OrtStatus* OrtApis::CreateOpaqueValue(const char*, const char*, const void*, size_t, OrtValue**) void onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType) T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = int] T* onnxruntime::Tensor::MutableData() [with T = short unsigned int] T* onnxruntime::Tensor::MutableData() [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::BFloat16] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] T* onnxruntime::Tensor::MutableData() [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] T* onnxruntime::Tensor::MutableData() [with T = bool] OrtStatus* OrtCreateValueImplSeqHelper(const OrtValue* const*, size_t, OrtValue**) OrtStatus* OrtCreateValueImplMapHelper(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; OrtStatus = OrtStatus; OrtValue = OrtValue] OrtStatus* OrtCreateValueImplMapHelper(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string<char>; OrtStatus = OrtStatus; OrtValue = OrtValue] const T& OrtValue::Get() const [with T = std::map<long int, double>] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = double; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] const T& OrtValue::Get() const [with T = std::map<long int, float>] const T& OrtValue::Get() const [with T = std::map<long int, long int>] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = long int; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] const T& OrtValue::Get() const [with T = std::map<long int, std::basic_string<char> >] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = std::basic_string<char>; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, double>] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = std::basic_string<char>; V = double; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, float>] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, long int>] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, std::basic_string<char> >] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = std::basic_string<char>; V = std::basic_string<char>; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] Ret onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper<Ret, UnsupportedPolicy>::Get() [with Ret = OrtStatus*; UnsupportedPolicy = c_api_internal::UnsupportedReturnFailStatus] const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = unsigned int] const T* onnxruntime::Tensor::Data() const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = short unsigned int] const T* onnxruntime::Tensor::Data() const [with T = short int] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = bool] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::BFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = double] const T* onnxruntime::Tensor::Data() const [with T = float] const onnxruntime::Tensor& onnxruntime::TensorSeq::Get(size_t) const const T& OrtValue::Get() const [with T = std::vector<std::map<long int, float> >] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::vector<_RealType> >::check(const Cont&, size_t) [with T = std::map<long int, float>; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] const T& OrtValue::Get() const [with T = std::vector<std::map<std::basic_string<char>, float> >] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = std::basic_string<char>; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::vector<_RealType> >::check(const Cont&, size_t) [with T = std::map<std::basic_string<char>, float>; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] St11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EE PFvPvE St16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EE N11onnxruntime23NotImplementedExceptionE N11onnxruntime20OnnxRuntimeExceptionE N11onnxruntime10IAllocatorE N11onnxruntime16AllocatorWrapperE N11onnxruntime7logging5ISinkE N11onnxruntime7logging8CLogSinkE 14LoggingWrapper St19_Sp_counted_deleterIPvPFvS0_ESaIiELN9__gnu_cxx12_Lock_policyE2EE St19_Sp_make_shared_tag St23_Sp_counted_ptr_inplaceIN11onnxruntime16AllocatorWrapperESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE execution_mode is not valid graph_optimization_level is not valid Result buffer is not large enough basic_string::_S_construct null not valid /onnxruntime_src/onnxruntime/core/session/custom_ops.cc %u Unsupported version ' ' in custom op ' is not implemented Description CreateCustomRegistry ComputeAsync *ZN11onnxruntime20CreateCustomRegistryERKSt6vectorIP17OrtCustomOpDomainSaIS2_EERSt10shared_ptrINS_14CustomRegistryEEEUlRKNS_12OpKernelInfoEE_ N4onnx15ISchemaRegistryE N11onnxruntime30IOnnxRuntimeOpSchemaCollectionE N11onnxruntime8OpKernelE N11onnxruntime14CustomOpKernelE N4onnx8OpSchema19num_inputs_allowed_MUliE_E N4onnx8OpSchema20num_outputs_allowed_MUliE_E St23_Sp_counted_ptr_inplaceIN11onnxruntime14KernelRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime27OnnxRuntimeOpSchemaRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime14CustomRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE 12OrtAllocator N3Ort9ExceptionE 16OrtAllocatorImpl 19OrtDefaultAllocator tensor(uint8) tensor(uint16) tensor(uint32) tensor(uint64) tensor(int8) tensor(int16) tensor(int32) tensor(int64) tensor(float16) tensor(float) tensor(double) tensor(string) tensor(bool) tensor(complex64) tensor(complex128) unknown Element type of input [TypeInferenceError] expected to have tensor type Input Output com.microsoft com.microsoft.nchwc com.microsoft.automl (domain: ) from file , but it its domain is not known by the checker. , but it its version is not in the inclusive range [ ] (usually, this means you in onnx/defs/schema.h). Schema error: MemcpyFromHost MemcpyToHost Exception caught: Trying to register schema with name , but it is already registered from file bumped the operator version but forgot to update the version range in DomainToVersionRange Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type. /onnxruntime_src/onnxruntime/core/session/environment.cc FvON4onnx8OpSchemaEE FvRN4onnx16InferenceContextEE N4onnx14InferenceErrorE N4onnx11SchemaErrorE PFvRN4onnx16InferenceContextEE PFvON4onnx8OpSchemaEE Cpu Model was not loaded Model was not loaded. Profiler is disabled. CUDAExecutionProvider subgraph_session_state p_op_kernel ) , expected: ( other_error [json.exception. %Y-%m-%d_%H-%M-%S .json Session was not initialized Session not initialized. Compute_ Create_State_ Release_State_ Error during EndProfiling(): Invalid rank for input: Expected: for the following indices
+ Output vector pointer is NULL p_fetches->size(): Invalid Output Name: Provider has already been registered. elements, but feeds has elements. Invalid Feed Input Name: Input with name: IsSparseTensor() Running with tag: model_run Exception during loading: Unknown exception in Load() model_loading_uri Load model from model_loading_proto model_loading_istream model_loading_array Node placements ]. Provider: [ MemcpyTransformer Initializing session. session_initialization Environment::IsInitialized() are '0' and '1'. model_proto intra_op_thread_pool inter_op_thread_pool CastFloat16Transformer ort_config session_options ORT_LOAD_CONFIG_FROM_MODEL Attempt to use DefaultLogger but none has been registered. Received nullptr for graph transformer /onnxruntime_src/onnxruntime/core/session/inference_session.cc Could not write a profile because no model was loaded. Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast<int>(logging::Severity::kFATAL) Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast<int>(logging::Severity::kFATAL) CreateSubgraphSessionState should have created an entry earlier. /onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.h Unexpected input data type. Actual: ( Exceeded max transformer level. Current level is set to graph_optimization_level <= TransformerLevel::MaxLevel timeout_in_ms >0 is not supported Main Graph instance should have populated all subgraphs when being resolved. Unknown error during EndProfiling() Received nullptr for custom registry Please fix either the inputs or the model. Got invalid dimensions for input: At least one output should be requested. Output vector incorrectly sized: output_names.size(): /onnxruntime_src/onnxruntime/core/framework/execution_providers.h allocator already registered. Received nullptr for exec provider Memory pattern must be disabled before registering DMLExecutionProvider Sequential execution must be enabled before registering DMLExecutionProvider Size mismatch: feed_names has is not expected to be of type tensor. is not expected to be of type sparse tensor. Trying to get a SparseTensor, but got: is not expected to be of type tensor sequence. Encountered unknown exception in Run() This session already contains a loaded model. Encountered unknown exception in Load() ModelProto corresponding to the model to be loaded has already been parsed. Invoke Load(). ModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction. model_loading_from_saved_proto Could not find an implementation for the node All nodes have been placed on [ Session has already been initialized. Adding default CPU execution provider. Parallel execution is currently not supported for the registered CUDA Execution Provider. Serializing Optimized ONNX model with Graph Optimization level greater than ORT_ENABLE_EXTENDED. The generated model may contain hardware and execution provider specific optimizations, and should only be used in the same environment the model was optimized for. Session successfully initialized. Exception during initialization: Encountered unknown exception in Initialize() Environment must be initialized before creating an InferenceSession. The only supported values for the environment variable The environment variable contained the value: Reading the provided model for the ORT config Model needs to be provided to check for ORT config within it Could not finalize session options while constructing the inference session. Error Message: Given model could not be parsed while creating inference session. Error message: Could not parse model successfully while constructing the inference session Failed to load model because protobuf parsing failed. void onnxruntime::InferenceSession::AddPredefinedTransformers(onnxruntime::GraphTransformerManager&, onnxruntime::TransformerLevel, const std::vector<std::basic_string<char> >&) void onnxruntime::InferenceSession::InitLogger(onnxruntime::logging::LoggingManager*) const onnxruntime::logging::Logger& onnxruntime::InferenceSession::CreateLoggerForRun(const RunOptions&, std::unique_ptr<onnxruntime::logging::Logger>&) EndProfiling GetModelOutputs GetOverridableInitializers GetModelInputs onnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(const std::vector<std::basic_string<char> >&, const std::vector<std::basic_string<char> >&, const onnxruntime::OrtValueNameIdxMap&) FeedsFetchesInfo Run const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq] const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor] ValidateInputs Initialize InitializeSubgraphSessions onnxruntime::common::Status onnxruntime::InferenceSession::InitializeSubgraphSessions(onnxruntime::Graph&, onnxruntime::SessionState&) CreateSubgraphSessionState onnxruntime::common::Status onnxruntime::InferenceSession::CreateSubgraphSessionState(onnxruntime::Graph&, onnxruntime::SessionState&) TransformGraph Load AddCustomOpDomains Add ~InferenceSession onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const void*, int, onnxruntime::logging::LoggingManager*) onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const string&, onnxruntime::logging::LoggingManager*) onnxruntime::common::Status onnxruntime::FinalizeSessionOptions(const onnxruntime::SessionOptions&, const onnx::ModelProto*, onnxruntime::SessionOptions&) FinalizeSessionOptions void onnxruntime::InferenceSession::ConstructorCommon(const onnxruntime::SessionOptions&, onnxruntime::logging::LoggingManager*) Max Used St14default_deleteIN11onnxruntime10DummyArenaEE *ZN11onnxruntime16InferenceSession4LoadEvEUlRSt10shared_ptrINS_5ModelEEE_ N11onnxruntime16IDeviceAllocatorE N6google8protobuf2io19ZeroCopyInputStreamE N5Eigen19ThreadPoolInterfaceE N5Eigen15ThreadPoolTemplINS_20StlThreadEnvironmentEEE N11onnxruntime16GraphTransformerE N11onnxruntime16InferenceSessionE N11onnxruntime15IArenaAllocatorE N11onnxruntime10DummyArenaE N11onnxruntime11controlflow18IControlFlowKernelE N8nlohmann6detail9exceptionE N8nlohmann6detail11other_errorE ZN11onnxruntime20CPUExecutionProviderC1ERKNS_24CPUExecutionProviderInfoEEUliE_ St19_Sp_counted_deleterIPN11onnxruntime10DummyArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceISt13unordered_mapISsN11onnxruntime11FuncManager8FuncInfoESt4hashISsESt8equal_toISsESaISt4pairIKSsS3_EEESaISC_ELN9__gnu_cxx12_Lock_policyE2EE _ _ C%lu at line , column parse_error parse error invalid_iterator type_error out_of_range object boolean discarded number cannot use at() with key ' ' not found map::at <U+%.4X> <uninitialized> true literal false literal null literal string literal number literal '[' '{' ']' '}' ':' ',' unknown token end of input '[', '{', or a literal <parse error> syntax error while parsing - ; last read: ' unexpected ; expected cannot get value type must be number, but is Sequential mode Parallel mode false true intra_op_num_threads inter_op_num_threads execution_mode Setting execution_mode to graph_optimization_level enable_profiling Setting enable_profiling to vector::_M_emplace_back_aux vector<bool>::_M_insert_aux iterator out of range cannot use erase() with invalid literal object key object separator number overflow parsing ' Parsing RunOptions from ModelProto is not supported yet 961c151d2e87f2686a955a9be24d316f1362bf21 3.7.1 The Model Proto hasn't been checked for the ORT config json. Did not find session options in the model file to be used while running the model cannot compare iterators of different containers intra_op_num_threads option in the model file must be an integer Unsupported value for intra_op_num_threads: Setting intra_op_num_threads to /onnxruntime_src/onnxruntime/core/session/inference_session_utils.cc inter_op_num_threads option in the model file must be an integer Unsupported value for inter_op_num_threads: Setting inter_op_num_threads to execution_mode option in the model file must be an integer Unsupported execution_mode value in ORT config: graph_optimization_level option in the model file must be an integer Setting graph_optimization_level to ORT_DISABLE_ALL Setting graph_optimization_level to ORT_ENABLE_BASIC Setting graph_optimization_level to ORT_ENABLE_EXTENDED Setting graph_optimization_level to ORT_ENABLE_ALL Unsupported graph_optimization_level value in ORT config: enable_profiling option in the model file must be an integer Unsupported value for enable_profiling option: Ignoring unsupported session option in ORT config: invalid string: ill-formed UTF-8 byte invalid string: missing closing quote invalid string: '\u' must be followed by 4 hex digits invalid string: surrogate U+DC00..U+DFFF must be followed by U+DC00..U+DFFF invalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFF invalid string: forbidden character after backslash invalid string: control character U+0000 (NUL) must be escaped to \u0000 invalid string: control character U+0001 (SOH) must be escaped to \u0001 invalid string: control character U+0002 (STX) must be escaped to \u0002 invalid string: control character U+0003 (ETX) must be escaped to \u0003 invalid string: control character U+0004 (EOT) must be escaped to \u0004 invalid string: control character U+0005 (ENQ) must be escaped to \u0005 invalid string: control character U+0006 (ACK) must be escaped to \u0006 invalid string: control character U+0007 (BEL) must be escaped to \u0007 invalid string: control character U+0008 (BS) must be escaped to \u0008 or \b invalid string: control character U+0009 (HT) must be escaped to \u0009 or \t invalid string: control character U+000A (LF) must be escaped to \u000A or \n invalid string: control character U+000B (VT) must be escaped to \u000B invalid string: control character U+000C (FF) must be escaped to \u000C or \f invalid string: control character U+000D (CR) must be escaped to \u000D or \r invalid string: control character U+000E (SO) must be escaped to \u000E invalid string: control character U+000F (SI) must be escaped to \u000F invalid string: control character U+0010 (DLE) must be escaped to \u0010 invalid string: control character U+0011 (DC1) must be escaped to \u0011 invalid string: control character U+0012 (DC2) must be escaped to \u0012 invalid string: control character U+0013 (DC3) must be escaped to \u0013 invalid string: control character U+0014 (DC4) must be escaped to \u0014 invalid string: control character U+0015 (NAK) must be escaped to \u0015 invalid string: control character U+0016 (SYN) must be escaped to \u0016 invalid string: control character U+0017 (ETB) must be escaped to \u0017 invalid string: control character U+0018 (CAN) must be escaped to \u0018 invalid string: control character U+0019 (EM) must be escaped to \u0019 invalid string: control character U+001A (SUB) must be escaped to \u001A invalid string: control character U+001B (ESC) must be escaped to \u001B invalid string: control character U+001C (FS) must be escaped to \u001C invalid string: control character U+001D (GS) must be escaped to \u001D invalid string: control character U+001E (RS) must be escaped to \u001E invalid string: control character U+001F (US) must be escaped to \u001F iterator does not fit current value invalid number; expected digit after '-' invalid number; expected digit after '.' invalid number; expected '+', '-', or digit after exponent invalid number; expected digit after exponent sign invalid BOM; must be 0xEF 0xBB 0xBF if given The Model Proto has already been checked for the ORT config json. Found session/run/environment configuration in the model file to be used while running the model ORT config json from the model: Json stored in the `ort_config` key cannot be parsed. Error message: ۦۦۦhۦ\ۦۦۦۦڦܦܦݦ4ݦܦܦܦܦTtDDD4$,d(8HHHXhxd,TT$|DtdTp X @ ( h P 8 x`H0 pXXHXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX X X X X X X X X X X X X X X X X X X X X X X X X X X X X X o ((*********************************D)*********:)*)***)*)*)*)*)*)*)*)*)*) )********************************)*T)********^)*******)*****)******)* )QVUU UT`T SRPQQQQO"hth igi4h4hThrvwdvvvvt tqrrrrp SetEnableProfiling SetGraphOptimizationLevel SetExecutionMode SetInterOpNumThreads SetIntraOpNumThreads ParseSessionOptionsFromModelProto ParseOrtConfigJsonInModelProto N8nlohmann6detail11parse_errorE N8nlohmann6detail16invalid_iteratorE N8nlohmann6detail10type_errorE N8nlohmann6detail12out_of_rangeE N8nlohmann6detail22input_adapter_protocolE N8nlohmann6detail20input_buffer_adapterE St23_Sp_counted_ptr_inplaceIN8nlohmann6detail20input_buffer_adapterESaIS2_ELN9__gnu_cxx12_Lock_policyE2EE /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer.cc Apply /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_mgr.cc This transformer is already registered ApplyTransformers Level _RuleBasedTransformer NchwcTransformer EliminateIdentity EliminateSlice UnsqueezeElimination EliminateDropout FuseReluClip ShapeToInitializer ConvAddFusion ConvMulFusion ConvBNFusion Unsupported level ConstantFolding RandomUniform RandomNormal RandomUniformLike RandomNormalLike Multinomial MatMulAddFusion ReshapeFusion GemmActivationFusion ConvActivationFusion GeluFusion AttentionFusion EmbedLayerNormFusion BiasGelu SkipLayerNormFusion GeluApproximation Unsupported level /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_utils.cc std::vector<std::unique_ptr<onnxruntime::GraphTransformer> > onnxruntime::optimizer_utils::GenerateTransformers(onnxruntime::TransformerLevel, gsl::span<const onnxruntime::FreeDimensionOverride>, const std::vector<std::basic_string<char> >&) std::vector<std::unique_ptr<onnxruntime::RewriteRule> > onnxruntime::optimizer_utils::GenerateRewriteRules(onnxruntime::TransformerLevel, const std::vector<std::basic_string<char> >&) N11onnxruntime11RewriteRuleE N11onnxruntime17EliminateIdentityE Max: node_index < nodes_.size() CastDef_%d Validating no unexpected access using an invalid node_index. Got: /onnxruntime_src/include/onnxruntime/core/graph/graph.h /onnxruntime_src/include/onnxruntime/core/optimizer/graph_transformer.h /onnxruntime_src/onnxruntime/core/optimizer/insert_cast_transformer.cc cast node to cast from float16 to float32 on cpu RemoveDuplicateCastTransformer ApplyImpl Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl N11onnxruntime21InsertCastTransformerE N11onnxruntime30RemoveDuplicateCastTransformerE size is different size_ == size data type is not supported ai.onnx ReduceMean Sub Div Sqrt Add Pow fused LayerNorm subgraphs epsilon /onnxruntime_src/onnxruntime/core/optimizer/initializer.h /onnxruntime_src/onnxruntime/core/optimizer/layer_norm_fusion.cc ;;;;;;499;;-::hhhhhhxhghhhYhKhInitializer onnxruntime::Initializer::Initializer(const onnx::TensorProto&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl N11onnxruntime15LayerNormFusionE fused Matmul and Add gemm /onnxruntime_src/onnxruntime/core/optimizer/matmul_add_fusion.cc Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl N11onnxruntime15MatMulAddFusionE pads strides dilations kernel_shape auto_pad SAME_UPPER SAME_LOWER NOTSET VALID ReorderOutput reorder ReorderInput activation _nchwc FusedConv GlobalMaxPool GlobalAveragePool Concat /onnxruntime_src/onnxruntime/core/optimizer/nchwc_transformer.cc tdTRecurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
- N11onnxruntime16NchwcTransformerE data type is not supported. min FuseReluClip_ _min_zero_constant Unexpected data type for Clip 'min' input of /onnxruntime_src/onnxruntime/core/optimizer/relu_clip_fusion.cc H HHHHXHH Q
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- onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ToProto onnxruntime::Initializer::Initializer(onnx::TensorProto_DataType, const string&, const std::vector<long int>&) virtual onnxruntime::common::Status onnxruntime::FuseReluClip::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&) const N11onnxruntime12FuseReluClipE /onnxruntime_src/onnxruntime/core/optimizer/rule_based_graph_transformer.cc Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl ApplyRulesOnNode N11onnxruntime25RuleBasedGraphTransformerE N11onnxruntime18ShapeToInitializerE starts ends =SDSDSDSD==SDSD==/>CCCC">>CC>=}>@@@@k>Y>@@G>5>>BBBB>>BB>>AAAAAAAAAAA+E8E8E8E8E EE8E8EED
+ N11onnxruntime16NchwcTransformerE data type is not supported. min FuseReluClip_ _min_zero_constant Unexpected data type for Clip 'min' input of /onnxruntime_src/onnxruntime/core/optimizer/relu_clip_fusion.cc X8u1uuuuuuP8XXXXXXX onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ToProto onnxruntime::Initializer::Initializer(onnx::TensorProto_DataType, const string&, const std::vector<long int>&) virtual onnxruntime::common::Status onnxruntime::FuseReluClip::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const N11onnxruntime12FuseReluClipE Gather Unsqueeze Reshape Fused reshape node: Cannot replace concat node with initializer: /onnxruntime_src/onnxruntime/core/optimizer/reshape_fusion.cc Total fused reshape node count: Fuse_Subgraph1 Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl N11onnxruntime13ReshapeFusionE /onnxruntime_src/onnxruntime/core/optimizer/rule_based_graph_transformer.cc Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl ApplyRulesOnNode N11onnxruntime25RuleBasedGraphTransformerE N11onnxruntime18ShapeToInitializerE SkipLayerNormalization /onnxruntime_src/onnxruntime/core/optimizer/skip_layer_norm_fusion.cc fused SkipLayerNorm subgraphs Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl N11onnxruntime19SkipLayerNormFusionE starts ends 1$
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- N11onnxruntime14EliminateSliceE TensorrtExecutionProvider NGRAPHExecutionProvider ' doesn't support memcpy Execution type ' Memcpy Copy from/to host memory MKLDNNExecutionProvider NupharExecutionProvider OpenVINOExecutionProvider /onnxruntime_src/onnxruntime/core/optimizer/transformer_memcpy.cc dup_replacements.find(&arg) == dup_replacements.end() onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&)::__lambda8 bool onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&) ForEachWithIndex void onnxruntime::TransformerMemcpyImpl::ProcessDefs(onnxruntime::Node&, const onnxruntime::KernelRegistryManager&, onnxruntime::InitializedTensorSet&) Recurse ApplyImpl *ZN11onnxruntime21TransformerMemcpyImpl11ProcessDefsERNS_4NodeERKNS_21KernelRegistryManagerERSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSA_EEEEUlRKNS_7NodeArgEmE_ *ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE_ *ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE0_ N11onnxruntime17MemcpyTransformerE Unsqueeze UnsqueezeElimination_ UnsqueezeElimination cannot remove node /onnxruntime_src/onnxruntime/core/optimizer/unsqueeze_elimination.cc Apply N11onnxruntime20UnsqueezeEliminationE Unsupported output type of . Can't constant fold node ' ort_value.IsTensor() /onnxruntime_src/onnxruntime/core/optimizer/constant_folding.cc Could not find OrtValue with name ' fetches.size() == node->OutputDefs().size() const T& OrtValue::Get() const [with T = onnxruntime::Tensor] Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const virtual onnxruntime::common::Status onnxruntime::ConstantFolding::ApplyImpl(onnxruntime::Graph&, bool&, int) const 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 zm] ?2???? ?? onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const onnxruntime::{anonymous}::GetClipConstantMinMax(const onnxruntime::Graph&, const onnxruntime::Node&, float&, float&)::__lambda15 Recurse ApplyImpl N11onnxruntime20ConvActivationFusionE conv_W_tensor_proto add_B_tensor_proto conv_B_tensor_proto ConvAddFusion_B_ ConvAddFusion_Add_B_
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-46606000066006|6D4C6D4D4D4D476+6D4D4"66262222662266J26J2J2J2J266J2J2669z999999999>:;9:IA::::=A1A::(A A3=@3=3=3=3=@@3=3=@@;@;;;;w@k@;;b@Y@(> @(>(>(>(>@@(>(>??9@9999@@99@@/onnxruntime_src/onnxruntime/core/optimizer/conv_bn_fusion.cc NMNNNN_MMNNLLUcTUUUUNTBTUUZTSLUTLULULULUTTLULUTS UT U U U UTT U UTTTTTTTT%T TTTTSTbTTTTTTTTTTLonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const virtual onnxruntime::common::Status onnxruntime::ConvBNFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&) const N11onnxruntime12ConvBNFusionE mul_B_tensor_proto ConvMulFusion_W_ ConvMulFusion_Mul_B_ /onnxruntime_src/onnxruntime/core/optimizer/conv_mul_fusion.cc `o3n`o`o`o`om{m`o`omlsrssssrrssqqpsMrssss7rrss'rqs0rssss0q qssAqo^s.s^s^s^s^sMs<s^s^s s>r<sp<s<s<s<s qp<s<sqmvuvvvvautvv"tsToProto onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const virtual onnxruntime::common::Status onnxruntime::ConvMulFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&) const N11onnxruntime13ConvMulFusionE
- N11onnxruntime16EliminateDropoutE FreeDimensionOverrideTransformer /onnxruntime_src/onnxruntime/core/optimizer/free_dim_override_transformer.cc with a fixed dimension denotation ' but the size of this dimension does not equal the specified override of Invalid free dimension override. The model has input ' ' ApplyImpl N11onnxruntime32FreeDimensionOverrideTransformerE Div Erf fused Gelu subgraphs Gelu /onnxruntime_src/onnxruntime/core/optimizer/gelu_fusion.cc ue⤮Ϥ 餮 onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl N11onnxruntime10GeluFusionE 4? ? ? <fused Gemm FusedGemm leaky_relu_ /onnxruntime_src/onnxruntime/core/optimizer/gemm_activation_fusion.cc onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl N11onnxruntime20GemmActivationFusionE _Map_base::at allocator_ptr_ != nullptr Tried to allocate without valid type information, ort_value index= Failed to get allocator for optimizer /onnxruntime_src/onnxruntime/core/optimizer/optimizer_execution_frame.cc ForEachWithIndex operator() onnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector<const onnxruntime::Node*>&, const InitializedTensorSet&) *ZN11onnxruntime23OptimizerExecutionFrame4InfoC1ERKSt6vectorIPKNS_4NodeESaIS5_EERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSE_EEEEUlRKNS_7NodeArgEmE_ N11onnxruntime23OptimizerExecutionFrameE /onnxruntime_src/onnxruntime/core/providers/cpu/cpu_execution_provider.cc virtual std::shared_ptr<onnxruntime::KernelRegistry> onnxruntime::CPUExecutionProvider::GetKernelRegistry() const GetKernelRegistry RegisterCPUKernels RegisterOnnxMLOperatorKernels RegisterOnnxOperatorKernels N11onnxruntime20CPUExecutionProviderE N11onnxruntime25IExecutionProviderFactoryE N11onnxruntime18CpuProviderFactoryE St23_Sp_counted_ptr_inplaceIN11onnxruntime18CpuProviderFactoryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE utils::HasDataType(t_proto) unpack_status.IsOK() T1 T2 ConstantOfShape Missing Input: vector::_M_range_insert 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 =E\EEDD<DC=C\CCBTB 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&) T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::common::Status onnxruntime::RandomNormalCompute(float, float, std::default_random_engine&, onnx::TensorProto::DataType, onnxruntime::Tensor&) onnxruntime::Multinomial::Multinomial(const onnxruntime::OpKernelInfo&) onnxruntime::RandomUniformLike::RandomUniformLike(const onnxruntime::OpKernelInfo&) onnxruntime::RandomNormalLike::RandomNormalLike(const onnxruntime::OpKernelInfo&) onnxruntime::RandomUniform::RandomUniform(const onnxruntime::OpKernelInfo&) onnxruntime::RandomNormal::RandomNormal(const onnxruntime::OpKernelInfo&) N11onnxruntime12RandomNormalE N11onnxruntime16RandomNormalLikeE N11onnxruntime13RandomUniformE N11onnxruntime17RandomUniformLikeE N11onnxruntime11MultinomialE 0 A C ? "~+@"~+@W6}+W6}++eG?+eG? ? ? ? ? @.? @.?ʫy>ʫy>K͉ ?K͉ ?~, ?~, ?_6.>_6.>9d?9d?t ?t ? @ @ delta in Range operator can not be zero! Range op: Unsupported tensor data type: start in Range operator should be scalar like tensor, yet got shape: limit in Range operator should be scalar like tensor, yet got shape: delta in Range operator should be scalar like tensor, yet got shape: T* onnxruntime::Tensor::MutableData() [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = double] T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime5RangeE min should be a scalar. max should be a scalar. /onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.h info.GetAttr<T>("max", &max_).IsOK() info.GetAttr<T>("min", &min_).IsOK() min->Shape().NumDimensions() == 0 max->Shape().NumDimensions() == 0 T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::Clip<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::Clip_6<T>::Clip_6(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime6Clip_6IfEE N11onnxruntime4ClipIfEE exclusive reverse CumSum starts.size()= dims.size()= dims.size() == starts.size() extents.size()= steps.size()= dims.size() == steps.size() ) but got: Axis should be in the range [ /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/utils.h dims.size() == extents.size() && dims.size() >= steps.size() dims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size() dims.size() == extents_.size() Axis tensor should be 0D or 1D void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double] T* onnxruntime::Tensor::MutableData() [with T = double] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = int] void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = int] T* onnxruntime::Tensor::MutableData() [with T = int] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int] onnxruntime::SliceSkips::SliceSkips(const onnxruntime::TensorShape&, gsl::span<const long int>, gsl::span<const long int>) T* onnxruntime::Tensor::MutableData() [with T = long int] N11onnxruntime6CumSumIfEE N11onnxruntime6CumSumIdEE N11onnxruntime6CumSumIiEE N11onnxruntime6CumSumIlEE DDD4$TDTdTdtTdDDtTdtdTdttDet X != nullptr /onnxruntime_src/onnxruntime/core/providers/cpu/math/det.cc Input tensor should have a rank of at least 2 Matrix dimensions are not equal. Square matrix is expected const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::Det<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] N11onnxruntime3DetIfEE j@fmod (fmod == 0) || (fmod == 1) Sub Abs Neg Floor Ceil Reciprocal Sqrt Pow Log Less Greater Equal BitShift Not And Or Xor Sin Cos Tan Asin Acos Atan Sinh Cosh Asinh Acosh Atanh PRelu Expand Mod axis == 1 || axis == largest direction LEFT RIGHT Invalid direction value of ' X_ptr != nullptr vector::_M_fill_insert Can broadcast 0 by 0 or 1. largest <= 1 failed to get first output! vs fmod_ Unsupported data type Must have 1 or more inputs inputCount >= 1 data_n.Shape() == shape input_count >= 1 data_1.Shape() == shape p_ml_value 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 X and Y input types do not match: fmod attribute must be true for float, float16 and double types All inputs must have the same shape Please fetch output tensor with specified shape. onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda100; Input1Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda101; General = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda102] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = double] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = double; TOutput = double; Input0Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda100; Input1Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda101; General = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda102] onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda126 onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda126 onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda126 onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long unsigned int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = int] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] const T* onnxruntime::Tensor::Data() const [with T = unsigned int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = short int] const T* onnxruntime::Tensor::Data() const [with T = short int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short unsigned int] T* onnxruntime::Tensor::MutableData() [with T = short unsigned int] const T* onnxruntime::Tensor::Data() const [with T = short unsigned int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short unsigned int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = signed char] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = double] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = double] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = float] virtual onnxruntime::common::Status onnxruntime::Mod::Compute(onnxruntime::OpKernelContext*) const T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] void onnxruntime::mod_internal::BroadCastMFloat16FMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) onnxruntime::Mod::Mod(const onnxruntime::OpKernelInfo&) onnxruntime::common::Status onnxruntime::Erf<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] T* onnxruntime::OpKernelContext::Output(int) [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda121; Input1Scalar = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda122; General = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda123] onnxruntime::common::Status onnxruntime::Mean_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = bool] const T* onnxruntime::Tensor::Data() const [with T = bool] onnxruntime::common::Status onnxruntime::Max_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Min_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda97; Input1Scalar = onnxruntime::Min_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda98; General = onnxruntime::Min_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda99] onnxruntime::common::Status onnxruntime::Min_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] void onnxruntime::BroadcastIterator::Append(int64_t, int64_t) void onnxruntime::BroadcastIterator::Init(int64_t, int64_t) onnxruntime::Broadcaster::Broadcaster(const std::vector<long int>&, const std::vector<long int>&) onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = float] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda94; Input1Scalar = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda95; General = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda96] onnxruntime::common::Status onnxruntime::Sum_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = long unsigned int] onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned int] onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned char] ( ( `( `(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfO ZNK11onnxruntime3PowIdE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIdLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEdE_ ZNK11onnxruntime3PowIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEfE_ ZNK11onnxruntime3PowIdE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIdLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEdE0_ ZNK11onnxruntime3PowIdE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIdLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEdE1_ ZNK11onnxruntime3PowIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEfE0_ ZNK11onnxruntime3PowIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEfE1_ N11onnxruntime3NotE N11onnxruntime3AndE N11onnxruntime2OrE N11onnxruntime3XorE N11onnxruntime3AddIfEE N11onnxruntime3AddIdEE N11onnxruntime3AddIiEE N11onnxruntime3AddIlEE N11onnxruntime3SubIfEE N11onnxruntime3SubIdEE N11onnxruntime3SubIiEE N11onnxruntime3SubIlEE N11onnxruntime3MulIfEE N11onnxruntime3MulIdEE N11onnxruntime3MulIiEE N11onnxruntime3MulIlEE N11onnxruntime3DivIfEE N11onnxruntime3DivIdEE N11onnxruntime3DivIiEE N11onnxruntime3DivIlEE N11onnxruntime3AbsIfEE N11onnxruntime3AbsIdEE N11onnxruntime3AbsIaEE N11onnxruntime3AbsIsEE N11onnxruntime3AbsIiEE N11onnxruntime3AbsIlEE N11onnxruntime3AbsIhEE N11onnxruntime3AbsItEE N11onnxruntime3AbsIjEE N11onnxruntime3AbsImEE N11onnxruntime3NegIfEE N11onnxruntime3NegIdEE N11onnxruntime3NegIaEE N11onnxruntime3NegIiEE N11onnxruntime5FloorIfEE N11onnxruntime4CeilIfEE N11onnxruntime10ReciprocalIfEE N11onnxruntime4SqrtIfEE N11onnxruntime4SqrtIdEE N11onnxruntime3PowIfEE N11onnxruntime3PowIdEE N11onnxruntime3ExpIfEE N11onnxruntime3ExpIdEE N11onnxruntime3LogIfEE N11onnxruntime5Sum_6IfEE N11onnxruntime5Sum_8IfEE N11onnxruntime5Min_6IfEE N11onnxruntime5Min_8IfEE N11onnxruntime5Max_6IfEE N11onnxruntime5Max_8IfEE N11onnxruntime5Max_8IdEE N11onnxruntime4LessIfEE N11onnxruntime4LessIdEE N11onnxruntime4LessIiEE N11onnxruntime4LessIlEE N11onnxruntime7GreaterIfEE N11onnxruntime7GreaterIiEE N11onnxruntime7GreaterIlEE N11onnxruntime5EqualIbEE N11onnxruntime5EqualIiEE N11onnxruntime5EqualIlEE N11onnxruntime5EqualIfEE N11onnxruntime6Mean_6IfEE N11onnxruntime6Mean_8IfEE N11onnxruntime8BitShiftIhEE N11onnxruntime8BitShiftIjEE N11onnxruntime8BitShiftImEE N11onnxruntime3ErfIfEE N11onnxruntime3SinIfEE N11onnxruntime3SinIdEE N11onnxruntime3CosIfEE N11onnxruntime3TanIfEE N11onnxruntime4AsinIfEE N11onnxruntime4AcosIfEE N11onnxruntime4AtanIfEE N11onnxruntime4SinhIfEE N11onnxruntime4CoshIfEE N11onnxruntime5AsinhIfEE N11onnxruntime5AcoshIfEE N11onnxruntime5AtanhIfEE N11onnxruntime5PReluIfEE N11onnxruntime8Expand_8IfEE N11onnxruntime8Expand_8IdEE N11onnxruntime8Expand_8IaEE N11onnxruntime8Expand_8IsEE N11onnxruntime8Expand_8IiEE N11onnxruntime8Expand_8IlEE N11onnxruntime8Expand_8IhEE N11onnxruntime8Expand_8ItEE N11onnxruntime8Expand_8IjEE N11onnxruntime8Expand_8ImEE N11onnxruntime8Expand_8IbEE N11onnxruntime8Expand_8INS_9MLFloat16EEE N11onnxruntime3ModE ,F F @ @@ ? ? ? ? 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"?"?"?"? @K @K @K @K ɿ ɿ ɿ ɿ .4 .4 .4 .4t.t.t.t. ,F ,F ,F ,F s7 s7 s7 s7nnnn*=*=*=*= pMpMpMpMӅ<Ӆ<Ӆ<Ӆ<**** F F F F -DT!< @ @right.NumDimensions() == 2 %ld N: K: GEMM: Dimension mismatch, W: M_ >= 0 && K_ > 0 && N_ >= 0 transA transB /onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm_helper.h left.NumDimensions() == 2 || left.NumDimensions() == 1 Gemm: Invalid bias shape for broadcast /onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm.h info.GetAttr<int64_t>("transA", &temp).IsOK() info.GetAttr<int64_t>("transB", &temp).IsOK() info.GetAttr<float>("alpha", &alpha_).IsOK() info.GetAttr<float>("beta", &beta_).IsOK() Not implemented fused activation: 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] N11onnxruntime4GemmIfEE A %~3a*7.A \3JCy7
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':Y;Y;Y;Y;Ӡ5Ӡ5Ӡ5Ӡ5֕8֕8֕8֕8;;;;Y;Y;Y;Y;Hardmax is not in valid range [- axis . N= , D= /onnxruntime_src/onnxruntime/core/providers/common.h axis >= -tensor_rank && axis <= tensor_rank - 1 Hardmax inputs N, D and N * D must be < T* onnxruntime::Tensor::MutableData() [with T = float] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime7HardmaxIfEE Right: MatMul dimension mismatch /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_helper.h Not satsified: left_num_dims >= 1 && right_num_dims >= 1
+ N11onnxruntime14EliminateSliceE /onnxruntime_src/onnxruntime/core/optimizer/transformer_memcpy.cc dup_replacements.find(&arg) == dup_replacements.end() TensorrtExecutionProvider NGRAPHExecutionProvider ' doesn't support memcpy Execution type ' Memcpy Copy from/to host memory DnnlExecutionProvider NupharExecutionProvider OpenVINOExecutionProvider ACLExecutionProvider onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&)::__lambda8 bool onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&) ForEachWithIndex void onnxruntime::TransformerMemcpyImpl::ProcessDefs(onnxruntime::Node&, const onnxruntime::KernelRegistryManager&, onnxruntime::InitializedTensorSet&) Recurse ApplyImpl *ZN11onnxruntime21TransformerMemcpyImpl11ProcessDefsERNS_4NodeERKNS_21KernelRegistryManagerERSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSA_EEEEUlRKNS_7NodeArgEmE_ *ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE_ *ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE0_ N11onnxruntime17MemcpyTransformerE UnsqueezeElimination_ UnsqueezeElimination cannot remove node /onnxruntime_src/onnxruntime/core/optimizer/unsqueeze_elimination.cc Apply N11onnxruntime20UnsqueezeEliminationE vector::_M_range_insert zk[Km}}qdWxh_
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+ 4 <Output edge count not expected for nodes in path v Failed in match Transpose attribute perm. Expected: 0, 2, 1, 3 Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3 v_reshape initializer value is not expected reshape initializer value is not expected Failed in match v_matmul and v_add input shape Output edge count not expected for mask nodes Softmax attribute axis is expected to be 3 mask_unsqueeze_1 axes not matched. Expect: 1 mask_unsqueeze_2 axes not matched. Expect: 2 mask_sub const input not matched mask_mul const input not matched q root should be layer normalization q_transpose perm attribute not matched q_matmul and q_add shape not matched k_transpose perm attribute not matched k_matmul and k_add shape not matched Failed to load Q, K and V weights, or data type is not float or float16. Failed to load Q, K and V bias tensors, or data type is not float or float16. Mask shape is unknown or not 2D, or data type unknown Mask data type is not int32 or int64 /onnxruntime_src/onnxruntime/core/optimizer/attention_fusion.cc shape of layer norm bias tensor not expected Attention subgraph expects 1 Add and 3 MatMul as children of LayerNormalization. Total fused Attention node count: qkv_weights qkv_bias Faild to find path v Failed to find path for mask Failed to find path for q q_reshape const not matched qk_div const not matched. perm Failed to find path for k k root is not layer norm k_reshape const not matched Mask_Int32 Cast mask from int64 to int32 MaskCast MaskIndex_Int32 Count number of words ReduceSum MaskIndex keepdims Failed to create mask index Fused Attention subgraphs Attention num_heads Fused an attention node. X
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+ProcessMask FuseSubGraph Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl N11onnxruntime15AttentionFusionE @ ?fused Add and Gelu /onnxruntime_src/onnxruntime/core/optimizer/bias_gelu_fusion.cc Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl N11onnxruntime8BiasGeluE Unsupported output type of . Can't constant fold node ' ort_value.IsTensor() /onnxruntime_src/onnxruntime/core/optimizer/constant_folding.cc Could not find OrtValue with name ' fetches.size() == node->OutputDefs().size() const T& OrtValue::Get() const [with T = onnxruntime::Tensor] Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const virtual onnxruntime::common::Status onnxruntime::ConstantFolding::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) const ApplyImpl N11onnxruntime15OpKernelContextE N11onnxruntime15ConstantFoldingE LeakyRelu fused Conv with activation fused activation_params /onnxruntime_src/onnxruntime/core/optimizer/conv_activation_fusion.cc Unexpected data type for Clip input of ΪΪΪΪΪΪΪΪΪΪqͪdͪ|ϪoϪ|Ϫ|Ϫ|Ϫ|Ϫ]ϪPϪ|Ϫ|Ϫ>Ϫ,ϪѪvѪѪѪѪѪiѪ\ѪѪѪOѪBѪܪܪܪܪܪܪ۪۪ܪܪ۪۪ onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const onnxruntime::{anonymous}::GetClipConstantMinMax(const onnxruntime::Graph&, const onnxruntime::Node&, float&, float&)::__lambda15 Recurse ApplyImpl N11onnxruntime20ConvActivationFusionE conv_W_tensor_proto add_B_tensor_proto conv_B_tensor_proto ConvAddFusion_B_ ConvAddFusion_Add_B_ @maXOX
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hhhhhThhTTTTTTTw>2& xl`WN(/onnxruntime_src/onnxruntime/core/optimizer/conv_bn_fusion.cc ('((((']'(('&^.b-^.^.^.^.M-A-^.^.Y-,.-.....-..---D,----;,2,--,,+------%- ----,-r+----+}+--i+;' onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const virtual onnxruntime::common::Status onnxruntime::ConvBNFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const N11onnxruntime12ConvBNFusionE mul_B_tensor_proto ConvMulFusion_W_ ConvMulFusion_Mul_B_ /onnxruntime_src/onnxruntime/core/optimizer/conv_mul_fusion.cc FEFFFF'EDFFkDDEKJEKEKEKEKIIEKEKH%H#KI#K#K#K#KIdI#K#KwIHKIKKKKHoHKKHGJ~JJJJJJJJJpJIJWGJJJJPH?HJJaHDMMMMMMML LMMrKJToProto onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const virtual onnxruntime::common::Status onnxruntime::ConvMulFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const N11onnxruntime13ConvMulFusionE
+ N11onnxruntime16EliminateDropoutE Input shape is unknown or not 2D, or data type unknown Input data type is not int32 or int64 /onnxruntime_src/onnxruntime/core/optimizer/embed_layer_norm_fusion.cc Failed to find path 1 of position shape. Output edge count not expected for nodes in path 1 of position shape. Second input of Gather in path 1 of position shape should be a constant with value 0. Failed to find path 2 of position shape. Output edge count not expected for nodes in path 2 of position shape. Gather node in path 2 is not linked to another subgraph. Second input of Gather in path 2 of position shape should be a constant with value 1. The parent of two shape nodes are expected to be input_ids. Cast Input from int64 to int32 Word embedding shape not expected. Input is expected to have dim value in all dimensions. Failed to get initializer tensor. Position embedding shape not matched. Position embedding data type shall be float or float16. Output edge count not expected for nodes in path1. Second input of Gather should be a constant with value 1. The parent of shape nodes are expected to be input_ids. Failed to match position subgraph. Output edge count not expected for nodes in path 1. The first input of Range should be a constant with value 0. The third input of Range should be a constant with value 1. Failed to match position embedding subgraph. Failed to get position embedding weights. Position embedding shape is not expected. Input_ids and segment id should have the same shape. Input_ids and mask should have the same shape. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). fused EmbedLayerNorm subgraphs _Cast position_embeddings Expand Squeeze NonZero ConstantOfShape Failed to match Shape node. Failed to find path 1. Input id is not valid. Segment id is not valid. Mask is not valid. EmbedLayerNormalization 唫{ onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const CheckInput MatchPositionEmbeddingSubgraph2 MatchPositionSubgraph MatchPositionEmbeddingSubgraph1 Recurse ApplyImpl N11onnxruntime20EmbedLayerNormFusionE FreeDimensionOverrideTransformer /onnxruntime_src/onnxruntime/core/optimizer/free_dim_override_transformer.cc with a fixed dimension denotation ' but the size of this dimension does not equal the specified override of Invalid free dimension override. The model has input ' ' ApplyImpl N11onnxruntime32FreeDimensionOverrideTransformerE Gelu approximation FastGelu /onnxruntime_src/onnxruntime/core/optimizer/gelu_approximation.cc Total Gelu Approximation (FastGelu) node count: Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl N11onnxruntime17GeluApproximationE Erf fused Gelu subgraphs /onnxruntime_src/onnxruntime/core/optimizer/gelu_fusion.cc onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl N11onnxruntime10GeluFusionE ? ?fused Gemm FusedGemm leaky_relu_ /onnxruntime_src/onnxruntime/core/optimizer/gemm_activation_fusion.cc onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl N11onnxruntime20GemmActivationFusionE _Map_base::at allocator_ptr_ != nullptr Tried to allocate without valid type information, ort_value index= Failed to get allocator for optimizer /onnxruntime_src/onnxruntime/core/optimizer/optimizer_execution_frame.cc void onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType) ForEachWithIndex operator() Info onnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector<const onnxruntime::Node*>&, const InitializedTensorSet&) *ZN11onnxruntime23OptimizerExecutionFrame4InfoC1ERKSt6vectorIPKNS_4NodeESaIS5_EERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSE_EEEEUlRKNS_7NodeArgEmE_ N11onnxruntime23OptimizerExecutionFrameE /onnxruntime_src/onnxruntime/core/providers/cpu/cpu_execution_provider.cc virtual std::shared_ptr<onnxruntime::KernelRegistry> onnxruntime::CPUExecutionProvider::GetKernelRegistry() const GetKernelRegistry RegisterCPUKernels RegisterOnnxMLOperatorKernels RegisterOnnxOperatorKernels N11onnxruntime20CPUExecutionProviderE N11onnxruntime25IExecutionProviderFactoryE N11onnxruntime18CpuProviderFactoryE St23_Sp_counted_ptr_inplaceIN11onnxruntime18CpuProviderFactoryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE utils::HasDataType(t_proto) unpack_status.IsOK() T1 T2 Missing Input: Must have a single dimension t_proto.dims_size() == 1 t_proto.dims()[0] == 1 Unsupported value attribute datatype with sizeof=: /onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape.h /onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape.cc TensorProto::DataType_IsValid(t_proto.data_type()) Value attribute unpacking failed: Unsupported value attribute datatype: /onnxruntime_src/include/onnxruntime/core/framework/op_kernel.h Must have a valid input shape. input_shape.NumDimensions() > 0 Must have a single dimension of 1 [ dc|c4cbb\b[ba|a<a` virtual onnxruntime::common::Status onnxruntime::ConstantOfShape::Compute(onnxruntime::OpKernelContext*) const Compute const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::ConstantOfShapeBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::Tensor**) const onnxruntime::ConstantOfShapeBase::ConstantOfShapeBase(const onnxruntime::OpKernelInfo&) void onnxruntime::ConstantOfShapeBase::SetValue(size_t, void*) void onnxruntime::ConstantOfShapeBase::SetValueFromTensorProto(const onnx::TensorProto&) N11onnxruntime19ConstantOfShapeBaseE FPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEE N11onnxruntime15ConstantOfShapeE PFPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEE seed dtype Invalid dtype of high sample_size FLOAT16 is not supported Invalid data type of input count mismatch batch_size is < 1 num_classes is < 1 num_samples is < 1 /onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.h info.GetAttr<float>("mean", &mean_).IsOK() info.GetAttr<float>("scale", &scale_).IsOK() info.GetAttr<int64_t>("dtype", &dtype).IsOK() ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINED info.GetAttrs<int64_t>("shape", shape).IsOK() info.GetAttr<float>("high", &high_).IsOK() info.GetAttr<float>("low", &low_).IsOK() info.GetAttr<int64_t>("sample_size", &num_samples_).IsOK() ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINED /onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.cc Could not infer data type from input tensor with data type Empty dimensions for input tensor T* onnxruntime::Tensor::MutableData() [with T = long int] MultinomialCompute T* onnxruntime::Tensor::MutableData() [with T = int] MultinomialCompute const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::RandomUniformCompute(float, float, std::default_random_engine&, onnx::TensorProto::DataType, onnxruntime::Tensor&) T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::common::Status onnxruntime::RandomNormalCompute(float, float, std::default_random_engine&, onnx::TensorProto::DataType, onnxruntime::Tensor&) onnxruntime::Multinomial::Multinomial(const onnxruntime::OpKernelInfo&) onnxruntime::RandomUniformLike::RandomUniformLike(const onnxruntime::OpKernelInfo&) onnxruntime::RandomNormalLike::RandomNormalLike(const onnxruntime::OpKernelInfo&) onnxruntime::RandomUniform::RandomUniform(const onnxruntime::OpKernelInfo&) onnxruntime::RandomNormal::RandomNormal(const onnxruntime::OpKernelInfo&) N11onnxruntime12RandomNormalE N11onnxruntime16RandomNormalLikeE N11onnxruntime13RandomUniformE N11onnxruntime17RandomUniformLikeE N11onnxruntime11MultinomialE 0 A C ? "~+@"~+@W6}+W6}++eG?+eG? ? ? ? ? @.? @.?ʫy>ʫy>K͉ ?K͉ ?~, ?~, ?_6.>_6.>9d?9d?t ?t ? @ @ Unsupported data type: delta in Range operator can not be zero! start in Range operator should be scalar like tensor, yet got shape: limit in Range operator should be scalar like tensor, yet got shape: delta in Range operator should be scalar like tensor, yet got shape: Ret onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy<Ret>::operator()(int32_t) const [with Ret = onnxruntime::common::Status; int32_t = int] T* onnxruntime::Tensor::MutableData() [with T = short int] const T* onnxruntime::Tensor::Data() const [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime5RangeE min should be a scalar. max should be a scalar. /onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.h info.GetAttr<T>("max", &max_).IsOK() info.GetAttr<T>("min", &min_).IsOK() min->Shape().NumDimensions() == 0 max->Shape().NumDimensions() == 0 T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::Clip<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::Clip_6<T>::Clip_6(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime6Clip_6IfEE N11onnxruntime4ClipIfEE exclusive reverse CumSum starts.size()= dims.size()= dims.size() == starts.size() extents.size()= steps.size()= dims.size() == steps.size() ) but got: Axis should be in the range [ /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/utils.h dims.size() == extents.size() && dims.size() >= steps.size() dims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size() dims.size() == extents_.size() Axis tensor should be 0D or 1D void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double] T* onnxruntime::Tensor::MutableData() [with T = double] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = int] void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = int] T* onnxruntime::Tensor::MutableData() [with T = int] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int] onnxruntime::SliceSkips::SliceSkips(const onnxruntime::TensorShape&, gsl::span<const long int>, gsl::span<const long int>) T* onnxruntime::Tensor::MutableData() [with T = long int] N11onnxruntime6CumSumIfEE N11onnxruntime6CumSumIdEE N11onnxruntime6CumSumIiEE N11onnxruntime6CumSumIlEE $$$d4$4D4DTdt4Dlt|$$T4DTD4DTTdtDet X != nullptr /onnxruntime_src/onnxruntime/core/providers/cpu/math/det.cc Input tensor should have a rank of at least 2 Matrix dimensions are not equal. Square matrix is expected const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::Det<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] N11onnxruntime3DetIfEE j@fmod (fmod == 0) || (fmod == 1) Abs Neg Floor Ceil Reciprocal Log Less Greater Equal BitShift Not And Or Xor Sin Cos Tan Asin Acos Atan Sinh Cosh Asinh Acosh Atanh PRelu Mod axis == 1 || axis == largest direction LEFT RIGHT Invalid direction value of ' X_ptr != nullptr vector::_M_fill_insert Can broadcast 0 by 0 or 1. largest <= 1 failed to get first output! Must have 1 or more inputs inputCount >= 1 data_n.Shape() == shape input_count >= 1 data_1.Shape() == shape p_ml_value helper.called_ < 2 helper.called_ == 1 vs fmod_ cur1 == end1 cur_out == end_out fmod must have value either 0 or 1 /onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.cc Attempting to broadcast an axis by a dimension other than 1. /onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.h '. Valid values are 'LEFT' or 'RIGHT'. All inputs must have the same shape Please fetch output tensor with specified shape. Shape must be 1 dimensional as it's tensor data is a shape tensor_shape.Shape().GetDims().size() == 1 Check for duplicate types in MLTypeCallDispatcher X and Y input types do not match: fmod attribute must be true for float, float16 and double types onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda100; Input1Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda101; General = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda102] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = double] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = double; TOutput = double; Input0Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda100; Input1Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda101; General = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda102] onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda126 onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda126 onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda126 onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long unsigned int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = int] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] const T* onnxruntime::Tensor::Data() const [with T = unsigned int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = short int] const T* onnxruntime::Tensor::Data() const [with T = short int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short unsigned int] T* onnxruntime::Tensor::MutableData() [with T = short unsigned int] const T* onnxruntime::Tensor::Data() const [with T = short unsigned int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short unsigned int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = signed char] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned char] void onnxruntime::utils::MLTypeCallDispatcher<Fn, Types>::Invoke(Args&& ...) const [with Args = {const bool&, const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*&}; Fn = onnxruntime::mod_internal::CallModImpl; Types = {unsigned char, signed char, short unsigned int, short int, unsigned int, int, long unsigned int, long int}] T* onnxruntime::Tensor::MutableData() [with T = double] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = double] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = float] virtual onnxruntime::common::Status onnxruntime::Mod::Compute(onnxruntime::OpKernelContext*) const T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] void onnxruntime::mod_internal::BroadCastMFloat16FMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) onnxruntime::Mod::Mod(const onnxruntime::OpKernelInfo&) onnxruntime::common::Status onnxruntime::Erf<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] T* onnxruntime::OpKernelContext::Output(int) [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda121; Input1Scalar = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda122; General = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda123] onnxruntime::common::Status onnxruntime::Mean_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = bool] const T* onnxruntime::Tensor::Data() const [with T = bool] onnxruntime::common::Status onnxruntime::Max_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Min_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda97; Input1Scalar = onnxruntime::Min_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda98; General = onnxruntime::Min_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda99] onnxruntime::common::Status onnxruntime::Min_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] void onnxruntime::BroadcastIterator::Append(int64_t, int64_t) void onnxruntime::BroadcastIterator::Init(int64_t, int64_t) onnxruntime::Broadcaster::Broadcaster(const std::vector<long int>&, const std::vector<long int>&) onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = float] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda94; Input1Scalar = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda95; General = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda96] onnxruntime::common::Status onnxruntime::Sum_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = long unsigned int] onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned int] onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned char] ( ( `( `(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfO ZNK11onnxruntime3PowIdE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIdLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEdE_ ZNK11onnxruntime3PowIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEfE_ ZNK11onnxruntime3PowIdE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIdLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEdE0_ ZNK11onnxruntime3PowIdE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIdLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEdE1_ ZNK11onnxruntime3PowIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEfE0_ ZNK11onnxruntime3PowIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEfE1_ N11onnxruntime3NotE N11onnxruntime3AndE N11onnxruntime2OrE N11onnxruntime3XorE N11onnxruntime3AddIfEE N11onnxruntime3AddIdEE N11onnxruntime3AddIiEE N11onnxruntime3AddIlEE N11onnxruntime3SubIfEE N11onnxruntime3SubIdEE N11onnxruntime3SubIiEE N11onnxruntime3SubIlEE N11onnxruntime3MulIfEE N11onnxruntime3MulIdEE N11onnxruntime3MulIiEE N11onnxruntime3MulIlEE N11onnxruntime3DivIfEE N11onnxruntime3DivIdEE N11onnxruntime3DivIiEE N11onnxruntime3DivIlEE N11onnxruntime3AbsIfEE N11onnxruntime3AbsIdEE N11onnxruntime3AbsIaEE N11onnxruntime3AbsIsEE N11onnxruntime3AbsIiEE N11onnxruntime3AbsIlEE N11onnxruntime3AbsIhEE N11onnxruntime3AbsItEE N11onnxruntime3AbsIjEE N11onnxruntime3AbsImEE N11onnxruntime3NegIfEE N11onnxruntime3NegIdEE N11onnxruntime3NegIaEE N11onnxruntime3NegIiEE N11onnxruntime5FloorIfEE N11onnxruntime4CeilIfEE N11onnxruntime10ReciprocalIfEE N11onnxruntime4SqrtIfEE N11onnxruntime4SqrtIdEE N11onnxruntime3PowIfEE N11onnxruntime3PowIdEE N11onnxruntime3ExpIfEE N11onnxruntime3ExpIdEE N11onnxruntime3LogIfEE N11onnxruntime5Sum_6IfEE N11onnxruntime5Sum_8IfEE N11onnxruntime5Min_6IfEE N11onnxruntime5Min_8IfEE N11onnxruntime5Max_6IfEE N11onnxruntime5Max_8IfEE N11onnxruntime5Max_8IdEE N11onnxruntime4LessIfEE N11onnxruntime4LessIdEE N11onnxruntime4LessIiEE N11onnxruntime4LessIlEE N11onnxruntime7GreaterIfEE N11onnxruntime7GreaterIiEE N11onnxruntime7GreaterIlEE N11onnxruntime5EqualIbEE N11onnxruntime5EqualIiEE N11onnxruntime5EqualIlEE N11onnxruntime5EqualIfEE N11onnxruntime6Mean_6IfEE N11onnxruntime6Mean_8IfEE N11onnxruntime8BitShiftIhEE N11onnxruntime8BitShiftIjEE N11onnxruntime8BitShiftImEE N11onnxruntime3ErfIfEE N11onnxruntime3SinIfEE N11onnxruntime3SinIdEE N11onnxruntime3CosIfEE N11onnxruntime3TanIfEE N11onnxruntime4AsinIfEE N11onnxruntime4AcosIfEE N11onnxruntime4AtanIfEE N11onnxruntime4SinhIfEE N11onnxruntime4CoshIfEE N11onnxruntime5AsinhIfEE N11onnxruntime5AcoshIfEE N11onnxruntime5AtanhIfEE N11onnxruntime5PReluIfEE N11onnxruntime8Expand_8IfEE N11onnxruntime8Expand_8IdEE N11onnxruntime8Expand_8IaEE N11onnxruntime8Expand_8IsEE N11onnxruntime8Expand_8IiEE N11onnxruntime8Expand_8IlEE N11onnxruntime8Expand_8IhEE N11onnxruntime8Expand_8ItEE N11onnxruntime8Expand_8IjEE N11onnxruntime8Expand_8ImEE N11onnxruntime8Expand_8IbEE N11onnxruntime8Expand_8INS_9MLFloat16EEE N11onnxruntime3ModE ,F F @ @@ ? ? ? ? B B B B ? ? ? ?5?5?5?5?!=!=!=!=뽸뽸뽸O]O]O]O]>>>>L>L>L>L>%=%=%=%=P*P*P*P*>>>>^^^^ 1? 1? 1? 1? BBBB¥¥¥;?;?;?;?giP9giP9giP9giP9C:C:C:C:<<<<*=*=*=*=*>*>*>*> B B B B ? ? ? ? "?"?"?"? @K @K @K @K ɿ ɿ ɿ ɿ .4 .4 .4 .4t.t.t.t. ,F ,F ,F ,F s7 s7 s7 s7nnnn*=*=*=*= pMpMpMpMӅ<Ӆ<Ӆ<Ӆ<**** F F F F -DT!< @ @right.NumDimensions() == 2 %ld N: K: GEMM: Dimension mismatch, W: M_ >= 0 && K_ > 0 && N_ >= 0 transA transB /onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm_helper.h left.NumDimensions() == 2 || left.NumDimensions() == 1 Gemm: Invalid bias shape for broadcast /onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm.h info.GetAttr<int64_t>("transA", &temp).IsOK() info.GetAttr<int64_t>("transB", &temp).IsOK() info.GetAttr<float>("alpha", &alpha_).IsOK() info.GetAttr<float>("beta", &beta_).IsOK() Not implemented fused activation: onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::Gemm<T>::Gemm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime4GemmIfEE A %~3a*7.A \3JCy7
':Y;Ӡ5֕8;Y; A A A A %%%%~3a*~3a*~3a*~3a*7777A \3A \3A \3A \3JCy7JCy7JCy7JCy7
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':Y;Y;Y;Y;Ӡ5Ӡ5Ӡ5Ӡ5֕8֕8֕8֕8;;;;Y;Y;Y;Y;Hardmax is not in valid range [- axis . N= , D= /onnxruntime_src/onnxruntime/core/providers/common.h axis >= -tensor_rank && axis <= tensor_rank - 1 Hardmax inputs N, D and N * D must be < T* onnxruntime::Tensor::MutableData() [with T = float] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime7HardmaxIfEE Right: MatMul dimension mismatch /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_helper.h Not satsified: left_num_dims >= 1 && right_num_dims >= 1
MatMul dimension mismatch, Left: Not satsified: K_ == right_shape[right_num_dims - 2]
left operand cannot broadcast on dim Not satsified: left_padded_dims_[idx_dim] == 1
right operand cannot broadcast on dim Not satsified: right_padded_dims_[idx_dim] == 1
Not satsified: num_dims_with_pad == num_output_dims
Not satsified: M_ == 1 && N_ == 1
Not satsified: num_dims_with_pad - 1 == num_output_dims
Not satsified: num_dims_with_pad - 2 == num_output_dims
Not satsified: K_ == right_shape[0]
- /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul.cc const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] Compute const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = double] Compute const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = int] Compute const T* onnxruntime::Tensor::Data() const [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] Compute const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] Compute const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] Compute Compute N11onnxruntime6MatMulIfEE N11onnxruntime6MatMulIdEE N11onnxruntime6MatMulIiEE N11onnxruntime6MatMulIjEE N11onnxruntime6MatMulIlEE N11onnxruntime6MatMulImEE T3 MatMulInteger a != nullptr && b != nullptr /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_integer.cc t->Shape().NumDimensions() <= 1 && t->Shape().Size() == 1 t->DataType() == DataTypeImpl::GetType<int8_t>() || t->DataType() == DataTypeImpl::GetType<uint8_t>() Currently only scalar zero_point is supported. TODO: add per channel zero point support. IsScalarOr1ElementVector(a_zero_point) IsScalarOr1ElementVector(b_zero_point) MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1 MatmulInteger : input2 zero point must be a scalar or 1D tensor of size 1 MatMulInteger: Unsupported input types with zero point const T* onnxruntime::Tensor::Data() const [with T = signed char] onnxruntime::MatMulInteger<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = signed char]::__lambda9 Compute onnxruntime::common::Status onnxruntime::MatMulInteger<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = signed char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = int] Compute onnxruntime::common::Status onnxruntime::MatMulInteger<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = unsigned char] N11onnxruntime13MatMulIntegerIhhEE N11onnxruntime13MatMulIntegerIhaEE QLinearMatMul /onnxruntime_src/onnxruntime/core/providers/cpu/math/quantize_linear_matmul.cc IsScalarOr1ElementVector(a_offset) IsScalarOr1ElementVector(b_offset) IsScalarOr1ElementVector(y_offset) IsScalarOr1ElementVector(a_scale) IsScalarOr1ElementVector(b_scale) IsScalarOr1ElementVector(y_scale) QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1 QLinearMatmul : weight zero point must be a scalar or 1D tensor of size 1 QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1 QLinearMatmul : input scale must be a scalar or 1D tensor of size 1 QLinearMatmul : weight scale must be a scalar or 1D tensor of size 1 QLinearMatmul : result scale must be a scalar or 1D tensor of size 1 const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] Compute onnxruntime::common::Status onnxruntime::QLinearMatMul<T1, T2, T3>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = unsigned char; T3 = unsigned char] N11onnxruntime13QLinearMatMulIhhhEE ORound T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime5RoundINS_9MLFloat16EEE N11onnxruntime5RoundIfEE N11onnxruntime5RoundIdEE Sign Unsupported input datatype 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 = unsigned int] const T* onnxruntime::Tensor::Data() const [with T = unsigned 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 = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with 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 = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::BFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::BFloat16] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] N11onnxruntime4SignE LogSoftmax int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) N11onnxruntime7SoftmaxIfLb0EEE N11onnxruntime7SoftmaxIfLb1EEE k_temp > 0 sorted I TopK k argument [ /onnxruntime_src/onnxruntime/core/providers/cpu/math/top_k.cc op_kernel_info.GetAttr<int64_t>("k", &k_temp).IsOK() op_kernel_info.GetAttr<int64_t>("axis", &axis_temp).IsOK() op_kernel_info.GetAttr<int64_t>("largest", &largest_temp).IsOK() op_kernel_info.GetAttr<int64_t>("sorted", &sorted_temp).IsOK() ] should not be greater than specified axis dim value [ output count mismatch, expected 2 outputs to be present for TopK operator input count mismatch, expected 1 input - the tensor to be processed input count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k value value of k must not be negative k tensor should be a 1D tensor of size 1 onnxruntime::TopK<OpSet, T>::TopK(const onnxruntime::OpKernelInfo&) [with int OpSet = 11; T = float] const T* onnxruntime::Tensor::Data() const [with T = long int] onnxruntime::TopK<OpSet, T>::TopK(const onnxruntime::OpKernelInfo&) [with int OpSet = 10; T = float] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::TopK<OpSet, T>::TopK(const onnxruntime::OpKernelInfo&) [with int OpSet = 9; T = float] N11onnxruntime4TopKILi9EfEE N11onnxruntime4TopKILi10EfEE N11onnxruntime4TopKILi11EfEE ArrayFeatureExtractor ai.onnx.ml ) >= Invalid argument: X input has empty dimensions. Invalid Y argument: num_indices = 0 Invalid Y argument: index is out of range: Y[ T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] N11onnxruntime2ml23ArrayFeatureExtractorOpIfEE N11onnxruntime2ml23ArrayFeatureExtractorOpIdEE N11onnxruntime2ml23ArrayFeatureExtractorOpIiEE N11onnxruntime2ml23ArrayFeatureExtractorOpIlEE N11onnxruntime2ml23ArrayFeatureExtractorOpISsEE Binarizer %lu Input data with index: is NaN T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime2ml11BinarizerOpIfEE cast_to TO_FLOAT TO_STRING TO_INT64 Invalid CAST_TO value of map_form DENSE Expected DENSE or SPARSE Invalid PACK_MAP value of max_map CastMap stof stoll %f Invalid input type of value: Unexpected CAST_TO value of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.h info.GetAttr<std::string>("cast_to", &attr).IsOK() Expected TO_FLOAT, TO_STRING or TO_INT64 /onnxruntime_src/onnxruntime/core/providers/cpu/ml/ml_common.h info.GetAttr<std::string>("map_form", &attr).IsOK() info.GetAttr<int64_t>("max_map", &max_map_).IsOK() max_map must be > 0 if map_form is SPARSE map_form_ != PACK_MAP::SPARSE || max_map_ > 0 /onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.cc cur_input == end_input || cur_input->first >= 0 Negative index values are not permitted. First entry in map has index value of Expected std::map<int64_t, float> or std::map<int64_t, std::string> onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = std::basic_string<char>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = std::basic_string<char>] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = long int] const T& OrtValue::Get() const [with T = std::map<long int, std::basic_string<char> >] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, std::basic_string<char> >] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = std::map<long int, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, float>] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = float] onnxruntime::ml::PACK_MAP onnxruntime::ml::MakePack(const string&) onnxruntime::ml::CAST_TO onnxruntime::ml::MakeCast(const string&) onnxruntime::ml::CastMap::CastMap(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml7CastMapE CategoryMapper cats_strings cats_int64s default_string default_int64 Input of string must have output of int64 Input of int64 must have output of string /onnxruntime_src/onnxruntime/core/providers/cpu/ml/category_mapper.h info.GetAttrs<std::string>("cats_strings", string_categories).IsOK() info.GetAttrs<int64_t>("cats_int64s", int_categories).IsOK() info.GetAttr<std::string>("default_string", &default_string_).IsOK() info.GetAttr<int64_t>("default_int64", &default_int_).IsOK() num_entries == int_categories.size() T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::CategoryMapper::CategoryMapper(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml14CategoryMapperE string_vocabulary int64_vocabulary DictVectorizer /onnxruntime_src/onnxruntime/core/providers/cpu/ml/dictvectorizer.h info.GetAttrs(std::is_same<AttrType, std::string>::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK() T* onnxruntime::Tensor::MutableData() [with T = long int] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, long int>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, long int>] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, float>] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, double>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, double>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = std::map<long int, std::basic_string<char> >] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, std::basic_string<char> >] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = std::map<long int, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, float>] T* onnxruntime::Tensor::MutableData() [with T = double] const T& OrtValue::Get() const [with T = std::map<long int, double>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, double>] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = double] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = float] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = std::basic_string<char>] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = double] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = float] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = long int] N11onnxruntime2ml16DictVectorizerOpISslEE N11onnxruntime2ml16DictVectorizerOpISsfEE N11onnxruntime2ml16DictVectorizerOpISsdEE N11onnxruntime2ml16DictVectorizerOpIlSsEE N11onnxruntime2ml16DictVectorizerOpIlfEE N11onnxruntime2ml16DictVectorizerOpIldEE inputdimensions FeatureVectorizer input_tensor_ptr != nullptr Invalid input type: Number of inputs ( /onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.h status.IsOK() && !input_dimensions_.empty() inputdimensions attribute must be provided /onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.cc input_count >= 0 && static_cast<size_t>(input_count) == input_dimensions_.size() ) does not match number of inputdimensions values ( const T* onnxruntime::Tensor::Data() const [with T = double] const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = float] virtual onnxruntime::common::Status onnxruntime::ml::FeatureVectorizer::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ml::FeatureVectorizer::FeatureVectorizer(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml17FeatureVectorizerE imputed_value_floats imputed_value_int64s replaced_value_float replaced_value_int64 Imputer Empty input dimensions. Invalid type Expected 'replaced_value_float' attribute since 'imputed_value_floats' is specified /onnxruntime_src/onnxruntime/core/providers/cpu/ml/imputer.cc Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specified Must provide imputed_values_float_ or imputed_values_int64_ but not both. imputed_values_float_.empty() ^ imputed_values_int64_.empty() Empty value of imputed values. T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::ml::ImputerOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ml::ImputerOp::ImputerOp(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml9ImputerOpE keys_floats values_strings _Unused keys_strings values_floats default_float keys_int64s values_int64s LabelEncoder attribtues in LabelEncoder and The values is and the number of ) must have the same length. num_keys == num_values classes_strings /onnxruntime_src/onnxruntime/core/providers/cpu/ml/label_encoder.h info.GetAttrs<TKey>(_key_field_name, keys).IsOK() info.GetAttrs<TValue>(_value_field_name, values).IsOK() However, the number of key is Input of tensor(string) must have output of tensor(int64) Input of tensor(int64) must have output of tensor(string) info.GetAttrs<std::string>("classes_strings", string_classes).IsOK() gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = std::basic_string<char>] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = std::basic_string<char>] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string<char>; TValue = long int] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = std::basic_string<char>] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = long int] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = float] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string<char>; TValue = float] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = std::basic_string<char>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = long int] onnxruntime::ml::LabelEncoder::LabelEncoder(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml12LabelEncoderE N11onnxruntime2ml14LabelEncoder_2IfSsEE N11onnxruntime2ml14LabelEncoder_2ISsfEE N11onnxruntime2ml14LabelEncoder_2IlfEE N11onnxruntime2ml14LabelEncoder_2IflEE N11onnxruntime2ml14LabelEncoder_2IlSsEE N11onnxruntime2ml14LabelEncoder_2ISslEE multi_class NONE post_transform LOGISTIC SOFTMAX SOFTMAX_ZERO intercepts classlabels_strings classlabels_ints coefficients !coefficients_.empty() LinearClassifier length overflow /onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearclassifier.cc Input shape needs to be at least a single dimension. const T* onnxruntime::Tensor::Data() const [with T = float] const T* onnxruntime::Tensor::Data() const [with T = double] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] void onnxruntime::ml::write_scores(std::vector<_RealType>&, onnxruntime::ml::POST_EVAL_TRANSFORM, int64_t, onnxruntime::Tensor*, int) [with T = float; int64_t = long int] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = int] onnxruntime::ml::LinearClassifier<T>::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = int] onnxruntime::ml::LinearClassifier<T>::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = long int] onnxruntime::ml::LinearClassifier<T>::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::LinearClassifier<T>::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml16LinearClassifierIfEE N11onnxruntime2ml16LinearClassifierIdEE N11onnxruntime2ml16LinearClassifierIlEE N11onnxruntime2ml16LinearClassifierIiEE 3ֳ@@LinearRegressor /onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearregressor.cc info.GetAttr<int64_t>("targets", &targets_).IsOK() info.GetAttrs<float>("coefficients", coefficients_).IsOK() T* onnxruntime::Tensor::MutableData() [with T = float] void onnxruntime::ml::write_scores(std::vector<_RealType>&, onnxruntime::ml::POST_EVAL_TRANSFORM, int64_t, onnxruntime::Tensor*, int) [with T = float; int64_t = long int] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::LinearRegressor<T>::LinearRegressor(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml15LinearRegressorIfEE Invalid normalize value of Invalid input type of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/normalizer.h info.GetAttr<std::string>("norm", &norm).IsOK() Unexpected NORMALIZE value of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/normalizer.cc const T* onnxruntime::Tensor::Data() const [with T = int] void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = long int] void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = double] void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::ml::Normalizer::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ml::NORMALIZE onnxruntime::ml::MakeNormalize(const string&) onnxruntime::ml::Normalizer::Normalizer(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml10NormalizerE OneHotEncoder zeros num_categories_ > 0 Unknown Category and zeros = 0. /onnxruntime_src/onnxruntime/core/providers/cpu/ml/onehotencoder.cc tmp_cats_int64s.empty() || tmp_cats_strings.empty() One and only one of the 'cats_*' attributes must be defined const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = float] const T* onnxruntime::Tensor::Data() const [with T = double] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] 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 /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 ( offset Empty scale in attributes !scale_.empty() Scale size: ( ) != ( ) or 1 const T* onnxruntime::Tensor::Data() const [with T = float] const T* onnxruntime::Tensor::Data() const [with T = double] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = int] onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = long int] onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml8ScalerOpIfEE N11onnxruntime2ml8ScalerOpIdEE N11onnxruntime2ml8ScalerOpIlEE N11onnxruntime2ml8ScalerOpIiEE SVMClassifier LINEAR kernel_type POLY RBF kernel_params vectors_per_class prob_a prob_b support_vectors rho coefficients_.size() > 0 No support vectors. /onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.h info.GetAttrs<float>("kernel_params", kernel_params).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.cc info.GetAttrs<float>("rho", rho_).IsOK() proba_.size() == probb_.size() info.GetAttrs<std::string>("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs<int64_t>("classlabels_ints", classlabels_ints_).IsOK() classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0 T* onnxruntime::Tensor::MutableData() [with T = float] void onnxruntime::ml::write_scores(std::vector<_RealType>&, onnxruntime::ml::POST_EVAL_TRANSFORM, int64_t, onnxruntime::Tensor*, int) [with T = float; int64_t = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = int] onnxruntime::ml::SVMClassifier<T>::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = int] onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = long int] onnxruntime::ml::SVMClassifier<T>::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = long int] onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::SVMClassifier<T>::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = float] onnxruntime::ml::SVMClassifier<T>::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml9SVMCommonIfEE N11onnxruntime2ml9SVMCommonIdEE N11onnxruntime2ml9SVMCommonIlEE N11onnxruntime2ml9SVMCommonIiEE N11onnxruntime2ml13SVMClassifierIfEE N11onnxruntime2ml13SVMClassifierIdEE N11onnxruntime2ml13SVMClassifierIlEE N11onnxruntime2ml13SVMClassifierIiEE
-ף;?n_supports one_class SVMRegressor /onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmregressor.cc T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = float] onnxruntime::ml::SVMRegressor<T>::SVMRegressor(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml12SVMRegressorIfEE BRANCH_EQ TreeEnsembleClassifier BRANCH_LEQ LEAF BRANCH_LT BRANCH_GTE BRANCH_GT it != parents.end() nodes_treeids nodes_nodeids nodes_featureids nodes_values nodes_hitrates nodes_modes nodes_truenodeids nodes_falsenodeids class_nodeids class_treeids class_ids class_weights base_values classlabels_int64s !nodes_treeids_.empty() Invalid mode of value: treeindex >= 0 X dims is empty. /onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_classifier.cc base_values_.empty() || base_values_.size() == static_cast<size_t>(class_count_) || base_values_.size() == weights_classes_.size() nodes_missing_value_tracks_true class_nodeids_.size() == class_ids_.size() class_nodeids_.size() == class_weights_.size() nodes_nodeids_.size() == nodes_featureids_.size() nodes_nodeids_.size() == nodes_modes_names_.size() nodes_nodeids_.size() == nodes_values_.size() nodes_nodeids_.size() == nodes_truenodeids_.size() nodes_nodeids_.size() == nodes_falsenodeids_.size() (nodes_nodeids_.size() == nodes_hitrates_.size()) || (nodes_hitrates_.empty()) Must provide classlabels_strings or classlabels_int64s but not both. classlabels_strings_.empty() ^ classlabels_int64s_.empty() std::all_of( std::begin(missing_tracks_true_), std::end(missing_tracks_true_), [](int64_t elem) { return elem >= 0; }) ּּPݼ8ݼ ݼ`ܼ8ܼhݼ@((Ponnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = float; int64_t = long int] const T* onnxruntime::Tensor::Data() const [with T = float] Compute onnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = double; int64_t = long int] const T* onnxruntime::Tensor::Data() const [with T = double] Compute onnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = long int; int64_t = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] Compute onnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = int; int64_t = long int] const T* onnxruntime::Tensor::Data() const [with T = int] Compute void onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = int] onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = int] void onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = long int] onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = long int] void onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = double] onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = double] void onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = float] onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml22TreeEnsembleClassifierIfEE N11onnxruntime2ml22TreeEnsembleClassifierIdEE N11onnxruntime2ml22TreeEnsembleClassifierIlEE N11onnxruntime2ml22TreeEnsembleClassifierIiEE TreeEnsembleRegressor target_nodeids target_treeids target_ids target_weights SUM aggregate_function AVERAGE MIN n_targets it != indices.end() /onnxruntime_src/onnxruntime/core/providers/cpu/ml/treeregressor.cc info.GetAttr<int64_t>("n_targets", &n_targets_).IsOK() target_nodeids_.size() == target_ids_.size() target_nodeids_.size() == target_weights_.size() nodes_id_size == nodes_featureids_.size() nodes_id_size == nodes_values_.size() nodes_id_size == nodes_modes_.size() nodes_id_size == nodes_truenodeids_.size() nodes_id_size == nodes_falsenodeids_.size() (nodes_id_size == nodes_hitrates_.size()) || (nodes_hitrates_.empty()) base_values_.empty() || base_values_.size() == static_cast<size_t>(n_targets_) treeindex evaluated to a negative value, which should not happen. 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::ml::TreeEnsembleRegressor<T>::TreeEnsembleRegressor(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml21TreeEnsembleRegressorIfEE ZipMap Input features_per_batch[ ] != number of classlabels[ /onnxruntime_src/onnxruntime/core/providers/cpu/ml/zipmap.cc Zipmap does not support empty dim count Zipmap only supports 1D or 2D input tensors T* OrtValue::GetMutable() [with T = std::vector<std::map<long int, float> >] T* OrtValue::GetMutable() [with T = std::vector<std::map<std::basic_string<char>, float> >] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::ZipMapOp::ZipMapOp(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml8ZipMapOpE MaxUnpool Unsupported pooling size. inferred output shape: No kernel shape is set. kernel_shape_[dim] > 0 Input dimension cannot be less than 3. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/Unpool.cc Not satsified: X_shape.NumDimensions() >= 3
+ /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul.cc const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] Compute const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = double] Compute const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = int] Compute const T* onnxruntime::Tensor::Data() const [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] Compute const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] Compute const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] Compute Compute N11onnxruntime6MatMulIfEE N11onnxruntime6MatMulIdEE N11onnxruntime6MatMulIiEE N11onnxruntime6MatMulIjEE N11onnxruntime6MatMulIlEE N11onnxruntime6MatMulImEE T3 MatMulInteger a != nullptr && b != nullptr /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_integer.cc t->Shape().NumDimensions() <= 1 && t->Shape().Size() == 1 t->IsDataType<int8_t>() || t->IsDataType<uint8_t>() Currently only scalar zero_point is supported. TODO: add per channel zero point support. IsScalarOr1ElementVector(a_zero_point) IsScalarOr1ElementVector(b_zero_point) MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1 MatmulInteger : input2 zero point must be a scalar or 1D tensor of size 1 MatMulInteger: Unsupported input types with zero point const T* onnxruntime::Tensor::Data() const [with T = signed char] onnxruntime::MatMulInteger<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = signed char]::__lambda9 Compute onnxruntime::common::Status onnxruntime::MatMulInteger<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = signed char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = int] Compute onnxruntime::common::Status onnxruntime::MatMulInteger<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = unsigned char] N11onnxruntime13MatMulIntegerIhhEE N11onnxruntime13MatMulIntegerIhaEE QLinearMatMul /onnxruntime_src/onnxruntime/core/providers/cpu/math/quantize_linear_matmul.cc IsScalarOr1ElementVector(a_offset) IsScalarOr1ElementVector(b_offset) IsScalarOr1ElementVector(y_offset) IsScalarOr1ElementVector(a_scale) IsScalarOr1ElementVector(b_scale) IsScalarOr1ElementVector(y_scale) QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1 QLinearMatmul : weight zero point must be a scalar or 1D tensor of size 1 QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1 QLinearMatmul : input scale must be a scalar or 1D tensor of size 1 QLinearMatmul : weight scale must be a scalar or 1D tensor of size 1 QLinearMatmul : result scale must be a scalar or 1D tensor of size 1 const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = float] Compute onnxruntime::common::Status onnxruntime::QLinearMatMul<T1, T2, T3>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = unsigned char; T3 = unsigned char] N11onnxruntime13QLinearMatMulIhhhEE ORound T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime5RoundINS_9MLFloat16EEE N11onnxruntime5RoundIfEE N11onnxruntime5RoundIdEE Sign T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] const T* onnxruntime::Tensor::Data() const [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = short unsigned int] const T* onnxruntime::Tensor::Data() const [with T = short unsigned int] T* onnxruntime::Tensor::MutableData() [with T = short int] const T* onnxruntime::Tensor::Data() const [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::utils::MLTypeCallDispatcher<Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor*&, onnxruntime::Tensor*&}; Fn = onnxruntime::sign_internal::CallSignImpl; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int}] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::BFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::BFloat16] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] N11onnxruntime4SignE LogSoftmax int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) N11onnxruntime7SoftmaxIfLb0EEE N11onnxruntime7SoftmaxIfLb1EEE k_temp > 0 sorted I TopK k argument [ /onnxruntime_src/onnxruntime/core/providers/cpu/math/top_k.cc op_kernel_info.GetAttr<int64_t>("k", &k_temp).IsOK() op_kernel_info.GetAttr<int64_t>("axis", &axis_temp).IsOK() op_kernel_info.GetAttr<int64_t>("largest", &largest_temp).IsOK() op_kernel_info.GetAttr<int64_t>("sorted", &sorted_temp).IsOK() ] should not be greater than specified axis dim value [ output count mismatch, expected 2 outputs to be present for TopK operator input count mismatch, expected 1 input - the tensor to be processed input count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k value value of k must not be negative k tensor should be a 1D tensor of size 1 onnxruntime::TopK<OpSet, T>::TopK(const onnxruntime::OpKernelInfo&) [with int OpSet = 11; T = float] const T* onnxruntime::Tensor::Data() const [with T = long int] onnxruntime::TopK<OpSet, T>::TopK(const onnxruntime::OpKernelInfo&) [with int OpSet = 10; T = float] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::TopK<OpSet, T>::TopK(const onnxruntime::OpKernelInfo&) [with int OpSet = 9; T = float] N11onnxruntime4TopKILi9EfEE N11onnxruntime4TopKILi10EfEE N11onnxruntime4TopKILi11EfEE ArrayFeatureExtractor ai.onnx.ml ) >= Invalid argument: X input has empty dimensions. Invalid Y argument: num_indices = 0 Invalid Y argument: index is out of range: Y[ T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] N11onnxruntime2ml23ArrayFeatureExtractorOpIfEE N11onnxruntime2ml23ArrayFeatureExtractorOpIdEE N11onnxruntime2ml23ArrayFeatureExtractorOpIiEE N11onnxruntime2ml23ArrayFeatureExtractorOpIlEE N11onnxruntime2ml23ArrayFeatureExtractorOpISsEE Binarizer Input data with index: is NaN T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime2ml11BinarizerOpIfEE /onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.h info.GetAttr<std::string>("cast_to", &attr).IsOK() Expected TO_FLOAT, TO_STRING or TO_INT64 /onnxruntime_src/onnxruntime/core/providers/cpu/ml/ml_common.h info.GetAttr<std::string>("map_form", &attr).IsOK() info.GetAttr<int64_t>("max_map", &max_map_).IsOK() max_map must be > 0 if map_form is SPARSE map_form_ != PACK_MAP::SPARSE || max_map_ > 0 /onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.cc cur_input == end_input || cur_input->first >= 0 Negative index values are not permitted. First entry in map has index value of Expected std::map<int64_t, float> or std::map<int64_t, std::string> CastMap cast_to TO_FLOAT TO_STRING TO_INT64 Invalid CAST_TO value of map_form DENSE Expected DENSE or SPARSE Invalid PACK_MAP value of max_map %f stof stoll Invalid input type of value: Unexpected CAST_TO value of onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = std::basic_string<char>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = std::basic_string<char>] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = long int] const T& OrtValue::Get() const [with T = std::map<long int, std::basic_string<char> >] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, std::basic_string<char> >] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = std::map<long int, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, float>] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = float] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] onnxruntime::ml::PACK_MAP onnxruntime::ml::MakePack(const string&) onnxruntime::ml::CAST_TO onnxruntime::ml::MakeCast(const string&) onnxruntime::ml::CastMap::CastMap(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml7CastMapE CategoryMapper cats_strings cats_int64s default_string default_int64 Input of string must have output of int64 Input of int64 must have output of string /onnxruntime_src/onnxruntime/core/providers/cpu/ml/category_mapper.h info.GetAttrs<std::string>("cats_strings", string_categories).IsOK() info.GetAttrs<int64_t>("cats_int64s", int_categories).IsOK() info.GetAttr<std::string>("default_string", &default_string_).IsOK() info.GetAttr<int64_t>("default_int64", &default_int_).IsOK() num_entries == int_categories.size() T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::CategoryMapper::CategoryMapper(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml14CategoryMapperE string_vocabulary int64_vocabulary DictVectorizer /onnxruntime_src/onnxruntime/core/providers/cpu/ml/dictvectorizer.h info.GetAttrs(std::is_same<AttrType, std::string>::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK() T* onnxruntime::Tensor::MutableData() [with T = long int] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, long int>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, long int>] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, float>] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, double>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, double>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = std::map<long int, std::basic_string<char> >] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, std::basic_string<char> >] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = std::map<long int, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, float>] T* onnxruntime::Tensor::MutableData() [with T = double] const T& OrtValue::Get() const [with T = std::map<long int, double>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, double>] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = double] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = float] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = std::basic_string<char>] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = double] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = float] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = long int] N11onnxruntime2ml16DictVectorizerOpISslEE N11onnxruntime2ml16DictVectorizerOpISsfEE N11onnxruntime2ml16DictVectorizerOpISsdEE N11onnxruntime2ml16DictVectorizerOpIlSsEE N11onnxruntime2ml16DictVectorizerOpIlfEE N11onnxruntime2ml16DictVectorizerOpIldEE inputdimensions FeatureVectorizer input_tensor_ptr != nullptr Invalid input type: Number of inputs ( /onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.h status.IsOK() && !input_dimensions_.empty() inputdimensions attribute must be provided /onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.cc input_count >= 0 && static_cast<size_t>(input_count) == input_dimensions_.size() ) does not match number of inputdimensions values ( const T* onnxruntime::Tensor::Data() const [with T = double] const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = float] virtual onnxruntime::common::Status onnxruntime::ml::FeatureVectorizer::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ml::FeatureVectorizer::FeatureVectorizer(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml17FeatureVectorizerE imputed_value_floats imputed_value_int64s replaced_value_float replaced_value_int64 Imputer Empty input dimensions. Invalid type Expected 'replaced_value_float' attribute since 'imputed_value_floats' is specified /onnxruntime_src/onnxruntime/core/providers/cpu/ml/imputer.cc Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specified Must provide imputed_values_float_ or imputed_values_int64_ but not both. imputed_values_float_.empty() ^ imputed_values_int64_.empty() Empty value of imputed values. T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::ml::ImputerOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ml::ImputerOp::ImputerOp(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml9ImputerOpE keys_floats values_strings _Unused keys_strings values_floats default_float keys_int64s values_int64s LabelEncoder attribtues in LabelEncoder and The values is and the number of ) must have the same length. num_keys == num_values classes_strings /onnxruntime_src/onnxruntime/core/providers/cpu/ml/label_encoder.h info.GetAttrs<TKey>(_key_field_name, keys).IsOK() info.GetAttrs<TValue>(_value_field_name, values).IsOK() However, the number of key is Input of tensor(string) must have output of tensor(int64) Input of tensor(int64) must have output of tensor(string) info.GetAttrs<std::string>("classes_strings", string_classes).IsOK() gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = std::basic_string<char>] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = std::basic_string<char>] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string<char>; TValue = long int] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = std::basic_string<char>] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = long int] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = float] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string<char>; TValue = float] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = std::basic_string<char>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = long int] onnxruntime::ml::LabelEncoder::LabelEncoder(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml12LabelEncoderE N11onnxruntime2ml14LabelEncoder_2IfSsEE N11onnxruntime2ml14LabelEncoder_2ISsfEE N11onnxruntime2ml14LabelEncoder_2IlfEE N11onnxruntime2ml14LabelEncoder_2IflEE N11onnxruntime2ml14LabelEncoder_2IlSsEE N11onnxruntime2ml14LabelEncoder_2ISslEE SOFTMAX_ZERO LinearClassifier multi_class NONE post_transform LOGISTIC SOFTMAX intercepts classlabels_strings classlabels_ints coefficients !coefficients_.empty() length overflow /onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearclassifier.cc Input shape needs to be at least a single dimension. const T* onnxruntime::Tensor::Data() const [with T = float] const T* onnxruntime::Tensor::Data() const [with T = double] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] void onnxruntime::ml::write_scores(std::vector<_RealType>&, onnxruntime::ml::POST_EVAL_TRANSFORM, int64_t, onnxruntime::Tensor*, int) [with T = float; int64_t = long int] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = int] onnxruntime::ml::LinearClassifier<T>::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = int] onnxruntime::ml::LinearClassifier<T>::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = long int] onnxruntime::ml::LinearClassifier<T>::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::LinearClassifier<T>::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml16LinearClassifierIfEE N11onnxruntime2ml16LinearClassifierIdEE N11onnxruntime2ml16LinearClassifierIlEE N11onnxruntime2ml16LinearClassifierIiEE @@3ֳLinearRegressor /onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearregressor.cc info.GetAttr<int64_t>("targets", &targets_).IsOK() info.GetAttrs<float>("coefficients", coefficients_).IsOK() T* onnxruntime::Tensor::MutableData() [with T = float] void onnxruntime::ml::write_scores(std::vector<_RealType>&, onnxruntime::ml::POST_EVAL_TRANSFORM, int64_t, onnxruntime::Tensor*, int) [with T = float; int64_t = long int] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::LinearRegressor<T>::LinearRegressor(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml15LinearRegressorIfEE Invalid normalize value of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/normalizer.h info.GetAttr<std::string>("norm", &norm).IsOK() Unexpected NORMALIZE value of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/normalizer.cc const T* onnxruntime::Tensor::Data() const [with T = int] void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = long int] void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = double] void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = float] void onnxruntime::utils::MLTypeCallDispatcher<Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::ml::Normalizer* const, onnxruntime::OpKernelContext*&}; Fn = onnxruntime::ml::Normalizer::CallNormalizerImpl; Types = {float, double, long int, int}] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::ml::Normalizer::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ml::NORMALIZE onnxruntime::ml::MakeNormalize(const string&) onnxruntime::ml::Normalizer::Normalizer(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml10NormalizerE Unknown Category and zeros = 0. /onnxruntime_src/onnxruntime/core/providers/cpu/ml/onehotencoder.cc tmp_cats_int64s.empty() || tmp_cats_strings.empty() One and only one of the 'cats_*' attributes must be defined OneHotEncoder zeros num_categories_ > 0 const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = float] const T* onnxruntime::Tensor::Data() const [with T = double] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] onnxruntime::ml::OneHotEncoderOp<T>::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = std::basic_string<char>] onnxruntime::ml::OneHotEncoderOp<T>::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::OneHotEncoderOp<T>::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = float] onnxruntime::ml::OneHotEncoderOp<T>::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = long int] N11onnxruntime2ml15OneHotEncoderOpIlEE N11onnxruntime2ml15OneHotEncoderOpIfEE N11onnxruntime2ml15OneHotEncoderOpIdEE N11onnxruntime2ml15OneHotEncoderOpISsEE offset Empty scale in attributes !scale_.empty() Scale size: ( ) != ( ) or 1 /onnxruntime_src/onnxruntime/core/providers/cpu/ml/scaler.cc scale_.size() == offset_.size() Invalid argument: input has empty dimensions. Either both scale and offset can be of feature size ( const T* onnxruntime::Tensor::Data() const [with T = float] const T* onnxruntime::Tensor::Data() const [with T = double] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = int] onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = long int] onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml8ScalerOpIfEE N11onnxruntime2ml8ScalerOpIdEE N11onnxruntime2ml8ScalerOpIlEE N11onnxruntime2ml8ScalerOpIiEE SVMClassifier LINEAR kernel_type POLY RBF kernel_params vectors_per_class prob_a prob_b support_vectors rho coefficients_.size() > 0 No support vectors. /onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.h info.GetAttrs<float>("kernel_params", kernel_params).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.cc info.GetAttrs<float>("rho", rho_).IsOK() proba_.size() == probb_.size() info.GetAttrs<std::string>("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs<int64_t>("classlabels_ints", classlabels_ints_).IsOK() classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0 T* onnxruntime::Tensor::MutableData() [with T = float] void onnxruntime::ml::write_scores(std::vector<_RealType>&, onnxruntime::ml::POST_EVAL_TRANSFORM, int64_t, onnxruntime::Tensor*, int) [with T = float; int64_t = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = int] onnxruntime::ml::SVMClassifier<T>::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = int] onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = long int] onnxruntime::ml::SVMClassifier<T>::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = long int] onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::SVMClassifier<T>::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = float] onnxruntime::ml::SVMClassifier<T>::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml9SVMCommonIfEE N11onnxruntime2ml9SVMCommonIdEE N11onnxruntime2ml9SVMCommonIlEE N11onnxruntime2ml9SVMCommonIiEE N11onnxruntime2ml13SVMClassifierIfEE N11onnxruntime2ml13SVMClassifierIdEE N11onnxruntime2ml13SVMClassifierIlEE N11onnxruntime2ml13SVMClassifierIiEE
+ף;?n_supports one_class SVMRegressor /onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmregressor.cc T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = float] onnxruntime::ml::SVMRegressor<T>::SVMRegressor(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml12SVMRegressorIfEE BRANCH_EQ TreeEnsembleClassifier BRANCH_LEQ LEAF BRANCH_LT BRANCH_GTE BRANCH_GT it != parents.end() nodes_treeids nodes_nodeids nodes_featureids nodes_values nodes_hitrates nodes_modes nodes_truenodeids nodes_falsenodeids class_nodeids class_treeids class_ids class_weights base_values classlabels_int64s !nodes_treeids_.empty() Invalid mode of value: treeindex >= 0 X dims is empty. /onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_classifier.cc base_values_.empty() || base_values_.size() == static_cast<size_t>(class_count_) || base_values_.size() == weights_classes_.size() nodes_missing_value_tracks_true class_nodeids_.size() == class_ids_.size() class_nodeids_.size() == class_weights_.size() nodes_nodeids_.size() == nodes_featureids_.size() nodes_nodeids_.size() == nodes_modes_names_.size() nodes_nodeids_.size() == nodes_values_.size() nodes_nodeids_.size() == nodes_truenodeids_.size() nodes_nodeids_.size() == nodes_falsenodeids_.size() (nodes_nodeids_.size() == nodes_hitrates_.size()) || (nodes_hitrates_.empty()) Must provide classlabels_strings or classlabels_int64s but not both. classlabels_strings_.empty() ^ classlabels_int64s_.empty() std::all_of( std::begin(missing_tracks_true_), std::end(missing_tracks_true_), [](int64_t elem) { return elem >= 0; }) @(P(X0 @ onnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = float; int64_t = long int] const T* onnxruntime::Tensor::Data() const [with T = float] Compute onnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = double; int64_t = long int] const T* onnxruntime::Tensor::Data() const [with T = double] Compute onnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = long int; int64_t = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] Compute onnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = int; int64_t = long int] const T* onnxruntime::Tensor::Data() const [with T = int] Compute void onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = int] onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = int] void onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = long int] onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = long int] void onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = double] onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = double] void onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = float] onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml22TreeEnsembleClassifierIfEE N11onnxruntime2ml22TreeEnsembleClassifierIdEE N11onnxruntime2ml22TreeEnsembleClassifierIlEE N11onnxruntime2ml22TreeEnsembleClassifierIiEE TreeEnsembleRegressor target_nodeids target_treeids target_ids target_weights SUM aggregate_function AVERAGE MIN n_targets it != indices.end() /onnxruntime_src/onnxruntime/core/providers/cpu/ml/treeregressor.cc info.GetAttr<int64_t>("n_targets", &n_targets_).IsOK() target_nodeids_.size() == target_ids_.size() target_nodeids_.size() == target_weights_.size() nodes_id_size == nodes_featureids_.size() nodes_id_size == nodes_values_.size() nodes_id_size == nodes_modes_.size() nodes_id_size == nodes_truenodeids_.size() nodes_id_size == nodes_falsenodeids_.size() (nodes_id_size == nodes_hitrates_.size()) || (nodes_hitrates_.empty()) base_values_.empty() || base_values_.size() == static_cast<size_t>(n_targets_) treeindex evaluated to a negative value, which should not happen. Compute Compute onnxruntime::ml::TreeEnsembleRegressor<T>::TreeEnsembleRegressor(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::TreeEnsembleRegressor<T>::TreeEnsembleRegressor(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml21TreeEnsembleRegressorIfEE N11onnxruntime2ml21TreeEnsembleRegressorIdEE ZipMap Input features_per_batch[ ] != number of classlabels[ /onnxruntime_src/onnxruntime/core/providers/cpu/ml/zipmap.cc Zipmap does not support empty dim count Zipmap only supports 1D or 2D input tensors T* OrtValue::GetMutable() [with T = std::vector<std::map<long int, float> >] T* OrtValue::GetMutable() [with T = std::vector<std::map<std::basic_string<char>, float> >] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::ZipMapOp::ZipMapOp(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml8ZipMapOpE MaxUnpool Unsupported pooling size. inferred output shape: No kernel shape is set. kernel_shape_[dim] > 0 Input dimension cannot be less than 3. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/Unpool.cc Not satsified: X_shape.NumDimensions() >= 3
Not satsified: I_shape == X_shape
Shape must be 1 dimensional as it's tensor data of a shape Not satsified: tensor_shape->Shape().GetDims().size() == 1
output_shape is smaller than minimum required. output_shape: Not satsified: given_shape.Size() >= shape.Size()
- Index tensor shape should be same as that of the input data tensor to unpool. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/unpool.h info.GetAttrs<int64_t>("kernel_shape", kernel_shape_).IsOK() pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim] strides_.size() == kernel_shape_.size() Pad should be smaller than kernel. T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] const T* onnxruntime::Tensor::Data() const [with T = float] Compute onnxruntime::MaxUnpool::MaxUnpool(const onnxruntime::OpKernelInfo&) N11onnxruntime9MaxUnpoolE st.IsOK() spatial Invalid input scale: dimension != Invalid input B: Invalid input mean: Invalid input var: /onnxruntime_src/onnxruntime/core/providers/cpu/nn/batch_norm.h Invalid input X: The rank of input X must be atleast 2. Got rank: Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input var: NumDimensions() != Invalid input var: 0th dimension != /onnxruntime_src/onnxruntime/core/providers/cpu/nn/batch_norm.cc T* onnxruntime::Tensor::MutableData() [with T = float] Compute onnxruntime::BatchNorm<T>::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime9BatchNormIfEE kernel_shape num_dims is not compatible with W num_dims. kernel_shape is not compatible with W shape. X num_dims does not match W num_dims. Input channels C is not equal to kernel channels * group. Output channels M is not divisible by group. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/autopad_type.h Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_attributes.h /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv.cc /onnxruntime_src/onnxruntime/core/util/math.h kernel_shape: X: C: kernel channels: group: M: Unknown AutoPadType String dilation == 1 pad type not supported. Invalid input shape: Out of bound access to array d_iter[d_i] < d_max onnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const string&) void onnxruntime::math::Im2colNd<T, Provider, 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*, Provider*, bool, T) [with T = float; Provider = onnxruntime::CPUMathUtil; 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, Provider, 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*, Provider*, bool, T) [with T = unsigned char; Provider = onnxruntime::CPUMathUtil; int64_t = long int] Compute virtual onnxruntime::common::Status onnxruntime::ConvInteger::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime11ConvIntegerE pad type not supported output_height >= H output_width >= W *out_size >= 0 ConvTranspose output_padding output_shape group count is <= 0 filter_number: num_input_channels: Output height cannot be smaller than input height. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose_attributes.h Output width cannot be smaller than input width. Input X must be 4-dimensional. filter number not equal to input channel number. Input channels is not divisible by group. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose.cc void onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape(int64_t, int64_t, int64_t, int64_t, int64_t, onnxruntime::AutoPadType, int64_t*, int64_t*, int64_t*) const void onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape(onnxruntime::TensorShape, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, std::vector<long int>*, std::vector<long int>*) const const T* onnxruntime::Tensor::Data() const [with T = long int] PrepareForCompute DoConvTranspose N11onnxruntime13ConvTransposeIfEE Flatten /onnxruntime_src/onnxruntime/core/providers/cpu/nn/flatten.h info.GetAttr<int64_t>("axis", &axis_).IsOK() The rank of input tensor must be >= axis gsl::narrow_cast<int64_t>(X_shape.NumDimensions()) >= axis onnxruntime::Flatten::Flatten(const onnxruntime::OpKernelInfo&) int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::Flatten::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7FlattenE Invalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: Mismatch between input data and scale: size of scale != input channel count Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count /onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.h op_kernel_info.GetAttr<float>("epsilon", &epsilon_).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.cc vs. InstanceNormalization const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::InstanceNorm<T>::InstanceNorm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime12InstanceNormIfEE p_ == 1 || p_ == 2 LpNormalization /onnxruntime_src/onnxruntime/core/providers/cpu/nn/lp_norm.h op_kernel_info.GetAttr<int64_t>("axis", &axis_).IsOK() op_kernel_info.GetAttr<int64_t>("p", &p_).IsOK() const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::LpNorm<T>::LpNorm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime6LpNormIfEE size_ > 0 size_ % 2 == 1 alpha_ > 0.0f beta_ > 0.0f bias LRN /onnxruntime_src/onnxruntime/core/providers/cpu/nn/lrn.h info.GetAttr<int64_t>("size", &size).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/nn/lrn.cc X->Shape().NumDimensions() == 4 onnxruntime::LRN<T>::LRN(const onnxruntime::OpKernelInfo&) [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::common::Status onnxruntime::LRN<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] N11onnxruntime3LRNIfEE GlobalLpPool ceil_mode count_include_pad storage_order kernel_shape[dim] > 0 input_dims.size() >= 2 Unsupported AutoPad Type. Unsupported pooling size : /onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool_base.h info.GetAttr<int64_t>("p", &p_).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool_attributes.h info.GetAttrs<int64_t>("kernel_shape", kernel_shape).IsOK() info.GetAttr<std::string>("auto_pad", &auto_padding).IsOK() info.GetAttr<int64_t>("count_include_pad", &temp).IsOK() pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim] strides.size() == kernel_shape.size() Dilations dimensions should match kernel shape dilations.size() == kernel_shape.size() Invalid input shape. Only N can be zero. Got: input_shape.Size() > 0 || input_shape[0] == 0 /onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool.cc Not satsified: input_dims >= 3
+ Index tensor shape should be same as that of the input data tensor to unpool. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/unpool.h info.GetAttrs<int64_t>("kernel_shape", kernel_shape_).IsOK() pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim] strides_.size() == kernel_shape_.size() Pad should be smaller than kernel. T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] const T* onnxruntime::Tensor::Data() const [with T = float] Compute onnxruntime::MaxUnpool::MaxUnpool(const onnxruntime::OpKernelInfo&) N11onnxruntime9MaxUnpoolE st.IsOK() spatial Invalid input scale: dimension != Invalid input B: Invalid input mean: Invalid input var: /onnxruntime_src/onnxruntime/core/providers/cpu/nn/batch_norm.h Invalid input X: The rank of input X must be atleast 2. Got rank: Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input var: NumDimensions() != Invalid input var: 0th dimension != /onnxruntime_src/onnxruntime/core/providers/cpu/nn/batch_norm.cc T* onnxruntime::Tensor::MutableData() [with T = float] Compute onnxruntime::BatchNorm<T>::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime9BatchNormIfEE kernel_shape num_dims is not compatible with W num_dims. kernel_shape is not compatible with W shape. X num_dims does not match W num_dims. Input channels C is not equal to kernel channels * group. Output channels M is not divisible by group. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/autopad_type.h Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_attributes.h /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv.cc /onnxruntime_src/onnxruntime/core/util/math.h kernel_shape: X: C: kernel channels: group: M: Unknown AutoPadType String dilation == 1 pad type not supported. Invalid input shape: Out of bound access to array d_iter[d_i] < d_max onnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const string&) void onnxruntime::math::Im2colNd<T, 2>::operator()(const T*, const int64_t*, const int64_t*, int64_t, int64_t, const int64_t*, const int64_t*, const int64_t*, const int64_t*, int64_t, T*, bool, T) [with T = float; int64_t = long int] onnxruntime::common::Status onnxruntime::ComputePadAndOutputShape(int64_t, int64_t, int64_t, int64_t, onnxruntime::AutoPadType, int64_t*, int64_t*, int64_t*) [with bool ForceSymmetricAutoPadding = false; int64_t = long int] InferOutputShape Compute N11onnxruntime4ConvIfEE /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_integer.cc IsScalarOr1ElementVector(X_Zero_Point) IsScalarOr1ElementVector(W_Zero_Point) Must be a scalar or 1D tensor or size 1. Non per-tensor quantization is not supported now. ConvInteger void onnxruntime::math::Im2colNd<T, 2>::operator()(const T*, const int64_t*, const int64_t*, int64_t, int64_t, const int64_t*, const int64_t*, const int64_t*, const int64_t*, int64_t, T*, bool, T) [with T = unsigned char; int64_t = long int] Compute virtual onnxruntime::common::Status onnxruntime::ConvInteger::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime11ConvIntegerE ConvTranspose *out_size >= 0 pad type not supported dim_size > 0 output_padding output_shape group count is <= 0 filter_number: num_input_channels: /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose_attributes.h Only 1D and 2D ConvTranspose is supported. filter number not equal to input channel number. Input channels is not divisible by group. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose.cc void onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape(int64_t, int64_t, int64_t, int64_t, int64_t, onnxruntime::AutoPadType, int64_t*, int64_t*, int64_t*) const void onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape(onnxruntime::TensorShape, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, int64_t, std::vector<long int>*, std::vector<long int>*) const const T* onnxruntime::Tensor::Data() const [with T = long int] PrepareForCompute DoConvTranspose N11onnxruntime13ConvTransposeIfEE Flatten /onnxruntime_src/onnxruntime/core/providers/cpu/nn/flatten.h info.GetAttr<int64_t>("axis", &axis_).IsOK() The rank of input tensor must be >= axis gsl::narrow_cast<int64_t>(X_shape.NumDimensions()) >= axis onnxruntime::Flatten::Flatten(const onnxruntime::OpKernelInfo&) int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::Flatten::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7FlattenE Invalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: Mismatch between input data and scale: size of scale != input channel count Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count /onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.h op_kernel_info.GetAttr<float>("epsilon", &epsilon_).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.cc vs. InstanceNormalization const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::InstanceNorm<T>::InstanceNorm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime12InstanceNormIfEE p_ == 1 || p_ == 2 LpNormalization /onnxruntime_src/onnxruntime/core/providers/cpu/nn/lp_norm.h op_kernel_info.GetAttr<int64_t>("axis", &axis_).IsOK() op_kernel_info.GetAttr<int64_t>("p", &p_).IsOK() const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::LpNorm<T>::LpNorm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime6LpNormIfEE size_ > 0 size_ % 2 == 1 alpha_ > 0.0f beta_ > 0.0f LRN /onnxruntime_src/onnxruntime/core/providers/cpu/nn/lrn.h info.GetAttr<int64_t>("size", &size).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/nn/lrn.cc X->Shape().NumDimensions() == 4 onnxruntime::LRN<T>::LRN(const onnxruntime::OpKernelInfo&) [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::common::Status onnxruntime::LRN<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] N11onnxruntime3LRNIfEE GlobalLpPool ceil_mode count_include_pad storage_order kernel_shape[dim] > 0 input_dims.size() >= 2 Unsupported AutoPad Type. Unsupported pooling size : /onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool_base.h info.GetAttr<int64_t>("p", &p_).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool_attributes.h info.GetAttrs<int64_t>("kernel_shape", kernel_shape).IsOK() info.GetAttr<std::string>("auto_pad", &auto_padding).IsOK() info.GetAttr<int64_t>("count_include_pad", &temp).IsOK() pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim] strides.size() == kernel_shape.size() Dilations dimensions should match kernel shape dilations.size() == kernel_shape.size() Invalid input shape. Only N can be zero. Got: input_shape.Size() > 0 || input_shape[0] == 0 /onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool.cc Not satsified: input_dims >= 3
kernel_shape num_dims is not compatible with X num_dims. Not satsified: pooling_dims == pool_attrs_.kernel_shape.size()
Not satsified: x_shape.NumDimensions() >= 3
- Compute void onnxruntime::PoolProcessContext::init(const onnxruntime::OpKernelInfo&) onnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const string&) onnxruntime::PoolAttributes::PoolAttributes(const onnxruntime::OpNodeProtoHelper<onnxruntime::ProtoHelperNodeContext>&, const string&, int) T* onnxruntime::Tensor::MutableData() [with T = long int] Compute void onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) const void onnxruntime::PoolAttributes::InferOutputSize(const std::vector<long int>&, std::vector<long int>*, std::vector<long int>*) const std::vector<long int> onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape&, int64_t, std::vector<long int>*) const Compute N11onnxruntime8PoolBaseE N11onnxruntime4PoolIfNS_7MaxPoolILi1EEEEE N11onnxruntime4PoolIfNS_11AveragePoolEEE N11onnxruntime4PoolIfNS_7MaxPoolILi8EEEEE N11onnxruntime4PoolIfNS_6LpPoolEEE /onnxruntime_src/onnxruntime/core/providers/cpu/nn/qlinearconv.cc IsScalarOr1ElementVector(input_offset) IsScalarOr1ElementVector(filter_offset) IsScalarOr1ElementVector(result_offset) IsScalarOr1ElementVector(input_scale) IsScalarOr1ElementVector(filter_scale) IsScalarOr1ElementVector(result_scale) QLinearConv : input zero point must be a scalar or 1D tensor of size 1 QLinearConv : filter zero point must be a scalar or 1D tensor of size 1 QLinearConv : result zero point must be a scalar or 1D tensor of size 1 QLinearConv : input scale must be a scalar or 1D tensor of size 1 QLinearConv : filter scale must be a scalar or 1D tensor of size 1 QLinearConv : result scale must be a scalar or 1D tensor of size 1 T4 QLinearConv const T* onnxruntime::Tensor::Data() const [with T = int] void onnxruntime::math::Im2colNd<T, Provider, 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*, Provider*, bool, T) [with T = unsigned char; Provider = onnxruntime::CPUMathUtil; 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 Input types for the Shrink operator are constrained to all numeric types only. Got bool type here. Input types for the Shrink operator are constrained to all numeric types only. Got std::string type here. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/shrink.cc /onnxruntime_src/onnxruntime/core/providers/cpu/nn/shrink.h op_kernel_info.GetAttr<float>("bias", &bias_temp).IsOK() op_kernel_info.GetAttr<float>("lambd", &lambd_temp).IsOK() Shrink Unknown tensor type of lambd 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 = unsigned int] const T* onnxruntime::Tensor::Data() const [with T = unsigned 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 = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with 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 = 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] virtual onnxruntime::common::Status onnxruntime::Shrink::Compute(onnxruntime::OpKernelContext*) const T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::BFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::BFloat16] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] onnxruntime::Shrink::Shrink(const onnxruntime::OpKernelInfo&) N11onnxruntime6ShrinkE WCHAR_T StringNormalizer is_case_sensitive case_change_action locale stopwords Empty stopwords not allowed !sw.empty() p.second wstr != wconv_error Conversion Error en_US.UTF-8 :Please, install necessary language-pack-XX and configure locales Failed to construct locale with name: /onnxruntime_src/onnxruntime/core/providers/cpu/nn/string_normalizer.cc attribute is_case_sensitive is not set attribute case_change_action is not set attribute case_change_action has invalid value Duplicate stopwords not allowed Stopword contains invalid utf8 chars Input contains invalid utf8 chars at: Single dimension value must be greater than 0 Input dimensions are either[C > 0] or [1][C > 0] allowed C o n v e r s i o n E r r o r const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::string_normalizer::Locale::Locale(const string&) onnxruntime::StringNormalizer::StringNormalizer(const onnxruntime::OpKernelInfo&) N11onnxruntime16StringNormalizerE TfIdfVectorizer mode is required TF TFIDF mode: min_gram_length min_gram_length is required impl_->min_gram_length_ > 0 max_gram_length >= max_skip_count max_skip_count is required impl_->max_skip_count_ >= 0 ngram_counts <= ngram_indexes Got weights of size: but ngram_indexes size: must be of equal size pool_strings !impl_->pool_strings_.empty() pool_int64s -grams (items % ngram_size == 0) -grams detected pool_int64s duplicate poll_strings duplicate Input shape must have either [C] or [B,C] dimensions with B > 0. Invalid type of the input argument /onnxruntime_src/onnxruntime/core/providers/cpu/nn/tfidfvectorizer.cc is unrecognized, acceptable values are TF,IDF,TFIDF impl_->weighting_criteria_ != kNone Required min_gram_length must be positive: min_gram_length >= max_gram_length required: impl_->max_gram_length_ >= impl_->min_gram_length_ max_skip_count must be non-negative: status.IsOK() && !impl_->ngram_counts_.empty() min_gram_length must be inbounds of ngram_counts: size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size() max_gram_length must be inbounds of ngram_counts: size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size() status.IsOK() && !impl_->ngram_indexes_.empty() Negative ngram_indexes values are not allowed std::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; }) impl_->weights_.size() == impl_->ngram_indexes_.size() pool_strings must not be empty if specified status.IsOK() && !pool_int64s.empty() n-gram counts out of bounds for end_idx >= start_idx && end_idx <= total_items Number of items must compose whole (before_insert + ngrams) == impl_->int64_set_.size() (before_insert + ngrams) == impl_->str_set_.size() Non-empty ngram_counts is required Non-empty ngram_indexes is required non-empty pool_int64s is required if pool_strings not provided const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::TfIdfVectorizer::TfIdfVectorizer(const onnxruntime::OpKernelInfo&) N11onnxruntime15TfIdfVectorizerE G G G Gcenter_point_box NonMaxSuppression boxes_tensor scores_tensor boxes must be a 3D tensor. scores must be a 3D tensor. Not satsified: ret.IsOK()
+ Compute void onnxruntime::PoolProcessContext::init(const onnxruntime::OpKernelInfo&) onnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const string&) onnxruntime::PoolAttributes::PoolAttributes(const onnxruntime::OpNodeProtoHelper<onnxruntime::ProtoHelperNodeContext>&, const string&, int) T* onnxruntime::Tensor::MutableData() [with T = long int] Compute void onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) const void onnxruntime::PoolAttributes::InferOutputSize(const std::vector<long int>&, std::vector<long int>*, std::vector<long int>*) const std::vector<long int> onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape&, int64_t, std::vector<long int>*) const Compute N11onnxruntime8PoolBaseE N11onnxruntime4PoolIfNS_7MaxPoolILi1EEEEE N11onnxruntime4PoolIfNS_11AveragePoolEEE N11onnxruntime4PoolIfNS_7MaxPoolILi8EEEEE N11onnxruntime4PoolIfNS_6LpPoolEEE /onnxruntime_src/onnxruntime/core/providers/cpu/nn/qlinearconv.cc IsScalarOr1ElementVector(input_offset) IsScalarOr1ElementVector(filter_offset) IsScalarOr1ElementVector(result_offset) IsScalarOr1ElementVector(input_scale) IsScalarOr1ElementVector(filter_scale) IsScalarOr1ElementVector(result_scale) QLinearConv : input zero point must be a scalar or 1D tensor of size 1 QLinearConv : filter zero point must be a scalar or 1D tensor of size 1 QLinearConv : result zero point must be a scalar or 1D tensor of size 1 QLinearConv : input scale must be a scalar or 1D tensor of size 1 QLinearConv : filter scale must be a scalar or 1D tensor of size 1 QLinearConv : result scale must be a scalar or 1D tensor of size 1 T4 QLinearConv const T* onnxruntime::Tensor::Data() const [with T = int] void onnxruntime::math::Im2colNd<T, 2>::operator()(const T*, const int64_t*, const int64_t*, int64_t, int64_t, const int64_t*, const int64_t*, const int64_t*, const int64_t*, int64_t, T*, bool, T) [with T = unsigned char; int64_t = long int] Compute virtual onnxruntime::common::Status onnxruntime::QLinearConv::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime11QLinearConvE pooled_shape pooled_shape.size() == 2 pooled_height_ > 0 pooled_width_ > 0 spatial_scale spatial_scale_ > 0 MaxRoiPool R->Shape()[1] == 5 roi_batch_id >= 0 roi_batch_id < batch_size /onnxruntime_src/onnxruntime/core/providers/cpu/nn/roi_pool.h info.GetAttrs<int64_t>("pooled_shape", pooled_shape).IsOK() info.GetAttr<float>("spatial_scale", &spatial_scale_).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/nn/roi_pool.cc T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::RoiPool<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::RoiPool<T>::RoiPool(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7RoiPoolIfEE lambd Shrink /onnxruntime_src/onnxruntime/core/providers/cpu/nn/shrink.h op_kernel_info.GetAttr<float>("bias", &bias_temp).IsOK() op_kernel_info.GetAttr<float>("lambd", &lambd_temp).IsOK() Ret onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper<Ret, UnsupportedPolicy>::Get() [with Ret = onnxruntime::common::Status; UnsupportedPolicy = onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy<onnxruntime::common::Status>] T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] const T* onnxruntime::Tensor::Data() const [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = short unsigned int] const T* onnxruntime::Tensor::Data() const [with T = short unsigned int] T* onnxruntime::Tensor::MutableData() [with T = short int] const T* onnxruntime::Tensor::Data() const [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::BFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::BFloat16] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] onnxruntime::Shrink::Shrink(const onnxruntime::OpKernelInfo&) N11onnxruntime6ShrinkE WCHAR_T StringNormalizer is_case_sensitive case_change_action locale stopwords Empty stopwords not allowed !sw.empty() p.second wstr != wconv_error Conversion Error en_US.UTF-8 :Please, install necessary language-pack-XX and configure locales Failed to construct locale with name: /onnxruntime_src/onnxruntime/core/providers/cpu/nn/string_normalizer.cc attribute is_case_sensitive is not set attribute case_change_action is not set attribute case_change_action has invalid value Duplicate stopwords not allowed Stopword contains invalid utf8 chars Input contains invalid utf8 chars at: Single dimension value must be greater than 0 Input dimensions are either[C > 0] or [1][C > 0] allowed C o n v e r s i o n E r r o r const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::string_normalizer::Locale::Locale(const string&) onnxruntime::StringNormalizer::StringNormalizer(const onnxruntime::OpKernelInfo&) N11onnxruntime16StringNormalizerE TfIdfVectorizer mode is required TF TFIDF mode: min_gram_length min_gram_length is required impl_->min_gram_length_ > 0 max_gram_length >= max_skip_count max_skip_count is required impl_->max_skip_count_ >= 0 ngram_counts <= ngram_indexes Got weights of size: but ngram_indexes size: must be of equal size pool_strings !impl_->pool_strings_.empty() pool_int64s -grams (items % ngram_size == 0) -grams detected pool_int64s duplicate poll_strings duplicate Input shape must have either [C] or [B,C] dimensions with B > 0. Invalid type of the input argument /onnxruntime_src/onnxruntime/core/providers/cpu/nn/tfidfvectorizer.cc is unrecognized, acceptable values are TF,IDF,TFIDF impl_->weighting_criteria_ != kNone Required min_gram_length must be positive: min_gram_length >= max_gram_length required: impl_->max_gram_length_ >= impl_->min_gram_length_ max_skip_count must be non-negative: status.IsOK() && !impl_->ngram_counts_.empty() min_gram_length must be inbounds of ngram_counts: size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size() max_gram_length must be inbounds of ngram_counts: size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size() status.IsOK() && !impl_->ngram_indexes_.empty() Negative ngram_indexes values are not allowed std::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; }) impl_->weights_.size() == impl_->ngram_indexes_.size() pool_strings must not be empty if specified status.IsOK() && !pool_int64s.empty() n-gram counts out of bounds for end_idx >= start_idx && end_idx <= total_items Number of items must compose whole (before_insert + ngrams) == impl_->int64_set_.size() (before_insert + ngrams) == impl_->str_set_.size() Non-empty ngram_counts is required Non-empty ngram_indexes is required non-empty pool_int64s is required if pool_strings not provided const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::TfIdfVectorizer::TfIdfVectorizer(const onnxruntime::OpKernelInfo&) N11onnxruntime15TfIdfVectorizerE G G G Gcenter_point_box NonMaxSuppression boxes_tensor scores_tensor boxes must be a 3D tensor. scores must be a 3D tensor. Not satsified: ret.IsOK()
output != nullptr iou_threshold must be in range [0, 1]. /onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.cc Not satsified: (iou_threshold >= 0 && iou_threshold <= 1.f)
center_point_box only support 0 or 1 /onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.h 0 == center_point_box_ || 1 == center_point_box_ Not satsified: boxes_shape.NumDimensions() == 3
Not satsified: scores_shape.NumDimensions() == 3
boxes and scores should have same num_batches. Not satsified: boxes_dims[0] == scores_dims[0]
boxes and scores should have same spatial_dimension. Not satsified: boxes_dims[1] == scores_dims[2]
The most inner dimension in boxes must have 4 data. Not satsified: boxes_dims[2] == 4
- virtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const Compute 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&) GetThresholdsFromInputs onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase(const onnxruntime::OpKernelInfo&) N11onnxruntime21NonMaxSuppressionBaseE N11onnxruntime17NonMaxSuppressionE Null input X ptr Null rois_ptr Null batch_indices_ptr RoiAlign avg output_height output_width sampling_ratio sampling_ratio_ >= 0 Invalid mode of value Number of dimensions for batch indices should be exactly 1 Number of dimensions for rois should be exactly Second dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't match /onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/roialign.h Sampling ratio should be >=0, but it was specified. It should be either avg or max const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::RoiAlignBase<T>::RoiAlignBase(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::RoiAlignBase<T>::RoiAlignBase(const onnxruntime::OpKernelInfo&) [with T = float] *ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIdEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUliE_ *ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIfEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUliE_ N11onnxruntime12RoiAlignBaseIfEE N11onnxruntime12RoiAlignBaseIdEE N11onnxruntime8RoiAlignIfEE N11onnxruntime8RoiAlignIdEE ReduceL1 ReduceL2 ReduceLogSum ReduceLogSumExp ReduceMax ReduceMean ReduceMin ReduceProd ReduceSum ReduceSumSquare ArgMax ArgMin keepdims in_dim != 0 /onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.h info.GetAttr("keepdims", &keepdims).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.cc Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape: bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = long int; int64_t = long int] bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = double; int64_t = long int] bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = float; int64_t = long int] bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = int; int64_t = long int] onnxruntime::ReduceKernelBase<allow_multi_axes>::ReduceKernelBase(const onnxruntime::OpKernelInfo&) [with bool allow_multi_axes = false] onnxruntime::ReduceKernelBase<allow_multi_axes>::ReduceKernelBase(const onnxruntime::OpKernelInfo&) [with bool allow_multi_axes = true] N11onnxruntime16ReduceKernelBaseILb0EEE N11onnxruntime16ReduceKernelBaseILb1EEE N11onnxruntime12ReduceKernelILb1EEE N11onnxruntime12ReduceKernelILb0EEE N11onnxruntime8ReduceL1IfEE N11onnxruntime8ReduceL1IiEE N11onnxruntime8ReduceL2IfEE N11onnxruntime8ReduceL2IiEE N11onnxruntime12ReduceLogSumIfEE N11onnxruntime12ReduceLogSumIiEE N11onnxruntime15ReduceLogSumExpIfEE N11onnxruntime15ReduceLogSumExpIiEE N11onnxruntime9ReduceMaxIfEE N11onnxruntime9ReduceMaxIiEE N11onnxruntime9ReduceMaxIlEE N11onnxruntime10ReduceMeanIfEE N11onnxruntime10ReduceMeanIiEE N11onnxruntime9ReduceMinIfEE N11onnxruntime9ReduceMinIiEE N11onnxruntime9ReduceMinIlEE N11onnxruntime10ReduceProdIfEE N11onnxruntime10ReduceProdIiEE N11onnxruntime9ReduceSumIfEE N11onnxruntime9ReduceSumIiEE N11onnxruntime9ReduceSumIlEE N11onnxruntime9ReduceSumIdEE N11onnxruntime15ReduceSumSquareIfEE N11onnxruntime15ReduceSumSquareIiEE N11onnxruntime15ReduceSumSquareIdEE N11onnxruntime6ArgMaxIfEE N11onnxruntime6ArgMaxIiEE N11onnxruntime6ArgMinIfEE N11onnxruntime6ArgMinIiEE GRU hidden_size activations activation_alpha activation_beta clip clip_ > 0.f forward bidirectional linear_before_reset cur + size <= end /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.h info.GetAttr("direction", &direction).IsOK() info.GetAttr("linear_before_reset", &int64_value).IsOK() info.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0 Invalid 'direction' argument of ' '. Must be one of 'forward', 'reverse', or 'bidirectional'. /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.h activation_func_names.size() == static_cast<size_t>(num_directions_) * 2 lda >= K && ldb >= K && ldc >= N A + (M * lda - (lda - K)) <= A_end B + (N * ldb - (ldb - K)) <= B_end C + (M * ldc - (ldc - N)) <= C_end offset + size <= size_t(span.size()) /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.cc GRU operator does not support double yet Invalid data type for GRU operator of T* onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span<T>::iterator, typename gsl::span<T>::iterator, size_t) [with T = float; typename gsl::span<T>::iterator = float*; size_t = long unsigned int] const T* onnxruntime::rnn::detail::SafeRawConstPointer(typename gsl::span<T>::const_iterator, typename gsl::span<T>::const_iterator, size_t) [with T = float; typename gsl::span<T>::const_iterator = const float*; size_t = long unsigned int] T* onnxruntime::rnn::detail::SafeRawPointer(gsl::span<T>, size_t, size_t) [with T = float; size_t = long unsigned int] void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = float*; TSpanBIter = const float*; TSpanCIter = float*] void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = const float*; TSpanBIter = const float*; TSpanCIter = float*] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] ComputeImpl virtual onnxruntime::common::Status onnxruntime::DeepCpuGruOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&) onnxruntime::DeepCpuGruOp::DeepCpuGruOp(const onnxruntime::OpKernelInfo&) N3gsl15narrowing_errorE N11onnxruntime12DeepCpuGruOpE ZN11onnxruntime10IAllocator13MakeUniquePtrIfEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPfE_ ZN11onnxruntime10IAllocator13MakeUniquePtrIiEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPiE_ }. Actual: Input P must have shape { std::future_error DEEPCPU_LSTM input_forget rows[ Processing batch Input initial_c must have shape { /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.cc /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.h activation_func_names.size() == static_cast<size_t>(num_directions_) * 3 LSTM operator does not support double yet Invalid data type for LSTM operator of ValidateInputs const T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span<T>, size_t, size_t) [with T = float; size_t = long unsigned int] const T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span<T>, size_t, size_t) [with T = const float; size_t = long unsigned int] ComputeImpl virtual onnxruntime::common::Status onnxruntime::DeepCpuLstmOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&) onnxruntime::DeepCpuLstmOp::DeepCpuLstmOp(const onnxruntime::OpKernelInfo&) NSt13__future_base7_ResultIvEE N11onnxruntime13DeepCpuLstmOpE NSt13__future_base11_State_base7_SetterIvNS0_19__exception_ptr_tagEEE NSt13__future_base11_State_base7_SetterIvvEE St23_Sp_counted_ptr_inplaceINSt13__future_base11_State_baseESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE ZN11onnxruntime3rnn6detail23ExecuteLambdaInParallelIZNS_6detail18UniDirectionalLstmIfE7ComputeERKN3gsl4spanIKfEERKNS7_IKiEEiSB_SB_RNS7_IfEESH_SH_EUliE_EEvRKSsT_iiRNS_11concurrency10ThreadPoolERKNS_7logging6LoggerEEUlvE_ St23_Sp_counted_ptr_inplaceISt7promiseIvESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE RNN Affine ThresholdedRelu ScaledTanh HardSigmoid Elu Softsign /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.cc /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.h info.GetAttr("direction", &direction_).IsOK() allowed_directions.find(direction_) != allowed_directions.end() info.GetAttrs("activations", activations_).IsOK() info.GetAttr("hidden_size", &hidden_size_).IsOK() activations_.size() == static_cast<size_t>(num_directions) allowed_activations.find(activations_[direction]) != allowed_activations.end() const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = float] Compute onnxruntime::RNN<T>::RNN(const onnxruntime::OpKernelInfo&) [with T = float] FffffE N11onnxruntime3RNNIfEE PFffffE Dump matrix: affine leakyrelu thresholdedrelu scaledtanh hardsigmoid softsign softplus Input 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] 7.7.7.7. A Anew_axis ConcatFromSequence Must have valid 'axis' attribute /onnxruntime_src/onnxruntime/core/providers/cpu//tensor/concat.h Got nullptr for sequence input. /onnxruntime_src/onnxruntime/core/providers/cpu/sequence/concat_from_sequence.cc const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::TensorSeq] virtual onnxruntime::common::Status onnxruntime::ConcatFromSequence::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo&, bool) N11onnxruntime10ConcatBaseE N11onnxruntime18ConcatFromSequenceE Tensor size ( ) != new size ( SequenceEmpty SequenceLength SequenceConstruct SequenceInsert SequenceErase SequenceAt SplitToSequence Y != nullptr Unsupported 'dtype' value: I != nullptr Invalid sequence index ( split_size_sum ( ) != split_dim_size ( S != nullptr Got nullptr for input tensor. ), input tensor data type ( num_inputs >= 1 Split should be > 0 split_scalar > 0 yet shape_.Size() == new_shape.Size() SequenceEmpty: Got nullptr for output sequence /onnxruntime_src/onnxruntime/core/providers/cpu/sequence/sequence_ops.cc SequenceLength: Got nullptr for output tensor Got nullptr input for index tensor ) specified for sequence of size ( SequenceAt: Got nullptr for output tensor Invalid data type for split tensor Data type of the input tensor MUST be same as that of the input sequence. Sequence data type ( SequenceInsert: Got nullptr for output sequence SequenceErase: Got nullptr for output sequence SequenceConstruct: Got nullptr for output sequence Violation of the requirment that all input tensors must have the same data type. Invalid value in 'split' input. All values must be > 0 std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value > 0; }) SplitToSequence operator does not support L| ,<LLL int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] ComputeImpl onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = std::basic_string<char>] T* onnxruntime::Tensor::MutableData() [with T = int] ComputeImpl onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = double] ComputeImpl onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = double] void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&) T* onnxruntime::Tensor::MutableData() [with T = float] void onnxruntime::GetSplitSizesInput(const onnxruntime::Tensor&, std::vector<long int>&) int64_t onnxruntime::GetScalarSplitInput(const onnxruntime::Tensor&) ComputeImpl onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = float] virtual onnxruntime::common::Status onnxruntime::SplitToSequence::Compute(onnxruntime::OpKernelContext*) const virtual onnxruntime::common::Status onnxruntime::SequenceConstruct::Compute(onnxruntime::OpKernelContext*) const virtual onnxruntime::common::Status onnxruntime::SequenceErase::Compute(onnxruntime::OpKernelContext*) const virtual onnxruntime::common::Status onnxruntime::SequenceInsert::Compute(onnxruntime::OpKernelContext*) const CreateCopyAndAppendCpuTensor T* OrtValue::GetMutable() [with T = onnxruntime::TensorSeq] virtual onnxruntime::common::Status onnxruntime::SequenceEmpty::Compute(onnxruntime::OpKernelContext*) const const T* onnxruntime::Tensor::Data() const [with T = long int] int64_t onnxruntime::GetSeqIdx(const onnxruntime::Tensor&) virtual onnxruntime::common::Status onnxruntime::SequenceAt::Compute(onnxruntime::OpKernelContext*) const T* onnxruntime::Tensor::MutableData() [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::TensorSeq] virtual onnxruntime::common::Status onnxruntime::SequenceLength::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime14SequenceLengthE N11onnxruntime10SequenceAtE N11onnxruntime13SequenceEmptyE N11onnxruntime14SequenceInsertE N11onnxruntime13SequenceEraseE N11onnxruntime17SequenceConstructE N11onnxruntime15SplitToSequenceE Attribute to is not set. Tensor type mismatch. type == dtype_ allocator != nullptr len > 0 buffer stoi stol stoul -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 ,d l T !, 4l#d l\ ]^aTXY<[ \Dd\d\gtgVW Txdytz{|Ԅ}|}uv<<\,\ ܓ| l,$ĩ4L d|Į<$T 4,DTT<dt$D, <l,< ,\ d $
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- L | l,--.1d)++,3478'(<= >>>>> ?AAA\@@L< void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = signed char] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = double] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = float] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = long unsigned int] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = unsigned int] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = short unsigned int] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = unsigned char] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = long int] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = int] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = short int] onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = std::basic_string<char>] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = signed char; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = double; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void* onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType) const void* onnxruntime::Tensor::DataRaw(onnxruntime::MLDataType) const void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = long unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = short unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = unsigned char; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = long int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = short int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = bool; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = onnxruntime::MLFloat16] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = double] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = double] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = double; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = double] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = float] onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = bool] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = bool] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = bool; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = bool] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = long int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = long int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = long int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = short int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = short int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = short int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = signed char] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = signed char; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = signed char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long unsigned int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = long unsigned int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = long unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = long unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = unsigned int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short unsigned int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = short unsigned int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = short unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = short unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned char] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = unsigned char; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = unsigned char] N11onnxruntime4CastIhEE N11onnxruntime4CastItEE N11onnxruntime4CastIjEE N11onnxruntime4CastImEE N11onnxruntime4CastIaEE N11onnxruntime4CastIsEE N11onnxruntime4CastIiEE N11onnxruntime4CastIlEE N11onnxruntime4CastIbEE N11onnxruntime4CastIfEE N11onnxruntime4CastIdEE N11onnxruntime4CastINS_9MLFloat16EEE N11onnxruntime4CastISsEE 8 O O O O A A A ACompress axis greater than input data dimension! /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/compress.cc axis < static_cast<int64_t>(rank) axes_right_stride >= 0 && static_cast<uint64_t>(axes_right_stride) < std::numeric_limits<size_t>::max() const T* onnxruntime::Tensor::Data() const [with T = bool] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) virtual onnxruntime::common::Status onnxruntime::Compress::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime8CompressE Cannot concatenate scalars /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.h /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.cc Not satsified: 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_11concurrency10ThreadPoolEEUliE_ *ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIfEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUliE_ N11onnxruntime12RoiAlignBaseIfEE N11onnxruntime12RoiAlignBaseIdEE N11onnxruntime8RoiAlignIfEE N11onnxruntime8RoiAlignIdEE ReduceL1 ReduceL2 ReduceLogSum ReduceLogSumExp ReduceMax ReduceMin ReduceProd ReduceSumSquare ArgMax ArgMin in_dim != 0 /onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.h info.GetAttr("keepdims", &keepdims).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.cc Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape: bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = long int; int64_t = long int] bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = double; int64_t = long int] bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = float; int64_t = long int] bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = int; int64_t = long int] onnxruntime::ReduceKernelBase<allow_multi_axes>::ReduceKernelBase(const onnxruntime::OpKernelInfo&) [with bool allow_multi_axes = false] onnxruntime::ReduceKernelBase<allow_multi_axes>::ReduceKernelBase(const onnxruntime::OpKernelInfo&) [with bool allow_multi_axes = true] N11onnxruntime16ReduceKernelBaseILb1EEE N11onnxruntime16ReduceKernelBaseILb0EEE N11onnxruntime12ReduceKernelILb1EEE N11onnxruntime12ReduceKernelILb0EEE N11onnxruntime8ReduceL1IfEE N11onnxruntime8ReduceL1IiEE N11onnxruntime8ReduceL2IfEE N11onnxruntime8ReduceL2IiEE N11onnxruntime12ReduceLogSumIfEE N11onnxruntime12ReduceLogSumIiEE N11onnxruntime15ReduceLogSumExpIfEE N11onnxruntime15ReduceLogSumExpIiEE N11onnxruntime9ReduceMaxIfEE N11onnxruntime9ReduceMaxIiEE N11onnxruntime9ReduceMaxIlEE N11onnxruntime10ReduceMeanIfEE N11onnxruntime10ReduceMeanIiEE N11onnxruntime9ReduceMinIfEE N11onnxruntime9ReduceMinIiEE N11onnxruntime9ReduceMinIlEE N11onnxruntime10ReduceProdIfEE N11onnxruntime10ReduceProdIiEE N11onnxruntime9ReduceSumIfEE N11onnxruntime9ReduceSumIiEE N11onnxruntime9ReduceSumIlEE N11onnxruntime9ReduceSumIdEE N11onnxruntime15ReduceSumSquareIfEE N11onnxruntime15ReduceSumSquareIiEE N11onnxruntime15ReduceSumSquareIdEE N11onnxruntime6ArgMaxIfEE N11onnxruntime6ArgMaxIiEE N11onnxruntime6ArgMinIfEE N11onnxruntime6ArgMinIiEE GRU hidden_size activations activation_alpha activation_beta clip clip_ > 0.f forward bidirectional linear_before_reset cur + size <= end /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.h info.GetAttr("direction", &direction).IsOK() info.GetAttr("linear_before_reset", &int64_value).IsOK() info.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0 Invalid 'direction' argument of ' '. Must be one of 'forward', 'reverse', or 'bidirectional'. /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.h activation_func_names.size() == static_cast<size_t>(num_directions_) * 2 lda >= K && ldb >= K && ldc >= N A + (M * lda - (lda - K)) <= A_end B + (N * ldb - (ldb - K)) <= B_end C + (M * ldc - (ldc - N)) <= C_end offset + size <= size_t(span.size()) /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.cc GRU operator does not support double yet Invalid data type for GRU operator of T* onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span<T>::iterator, typename gsl::span<T>::iterator, size_t) [with T = float; typename gsl::span<T>::iterator = float*; size_t = long unsigned int] const T* onnxruntime::rnn::detail::SafeRawConstPointer(typename gsl::span<T>::const_iterator, typename gsl::span<T>::const_iterator, size_t) [with T = float; typename gsl::span<T>::const_iterator = const float*; size_t = long unsigned int] T* onnxruntime::rnn::detail::SafeRawPointer(gsl::span<T>, size_t, size_t) [with T = float; size_t = long unsigned int] void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = float*; TSpanBIter = const float*; TSpanCIter = float*] void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = const float*; TSpanBIter = const float*; TSpanCIter = float*] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] ComputeImpl virtual onnxruntime::common::Status onnxruntime::DeepCpuGruOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&) onnxruntime::DeepCpuGruOp::DeepCpuGruOp(const onnxruntime::OpKernelInfo&) N3gsl15narrowing_errorE N11onnxruntime12DeepCpuGruOpE ZN11onnxruntime10IAllocator13MakeUniquePtrIfEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPfE_ ZN11onnxruntime10IAllocator13MakeUniquePtrIiEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPiE_ }. Actual: Input P must have shape { std::future_error DEEPCPU_LSTM input_forget rows[ Processing batch Input initial_c must have shape { /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.cc /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.h activation_func_names.size() == static_cast<size_t>(num_directions_) * 3 LSTM operator does not support double yet Invalid data type for LSTM operator of ValidateInputs const T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span<T>, size_t, size_t) [with T = float; size_t = long unsigned int] const T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span<T>, size_t, size_t) [with T = const float; size_t = long unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] ComputeImpl virtual onnxruntime::common::Status onnxruntime::DeepCpuLstmOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&) onnxruntime::DeepCpuLstmOp::DeepCpuLstmOp(const onnxruntime::OpKernelInfo&) NSt13__future_base7_ResultIvEE N11onnxruntime13DeepCpuLstmOpE NSt13__future_base11_State_base7_SetterIvNS0_19__exception_ptr_tagEEE NSt13__future_base11_State_base7_SetterIvvEE St23_Sp_counted_ptr_inplaceINSt13__future_base11_State_baseESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE ZN11onnxruntime3rnn6detail23ExecuteLambdaInParallelIZNS_6detail18UniDirectionalLstmIfE7ComputeERKN3gsl4spanIKfEERKNS7_IKiEEiSB_SB_RNS7_IfEESH_SH_EUliE_EEvRKSsT_iiRNS_11concurrency10ThreadPoolERKNS_7logging6LoggerEEUlvE_ St23_Sp_counted_ptr_inplaceISt7promiseIvESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE RNN Affine ThresholdedRelu ScaledTanh HardSigmoid Elu Softsign /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.cc /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.h info.GetAttr("direction", &direction_).IsOK() allowed_directions.find(direction_) != allowed_directions.end() info.GetAttrs("activations", activations_).IsOK() info.GetAttr("hidden_size", &hidden_size_).IsOK() activations_.size() == static_cast<size_t>(num_directions) allowed_activations.find(activations_[direction]) != allowed_activations.end() const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = float] Compute onnxruntime::RNN<T>::RNN(const onnxruntime::OpKernelInfo&) [with T = float] FffffE N11onnxruntime3RNNIfEE PFffffE Dump matrix: affine leakyrelu thresholdedrelu scaledtanh hardsigmoid softsign softplus * Input W must have shape { Input R must have shape { Input B must have shape { Expecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.cc Invalid activation function of Invalid LSTM merge activation function of Invalid GRU reset gate activation function: Invalid GRU hidden gate activation function: Input X must have 3 dimensions only. Actual: Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length= Input sequence_lens must have shape { Input initial_h must have shape { void (* onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByName(const string&))(float*, const float*, const float*, float*, int, float, float) void (* onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByName(const string&))(const float*, float*, float*, int, float, float) void (* onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByName(const string&))(const float*, float*, const float*, float*, int, float, float) void (* onnxruntime::rnn::detail::deepcpu::ActivationFuncByName(const string&))(float*, int, float, float) std::string onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCount(const string&, std::vector<float>::const_iterator&, const const_iterator&, std::vector<float>::const_iterator&, const const_iterator&, float&, float&) gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] 7.7.7.7. A Anew_axis ConcatFromSequence Must have valid 'axis' attribute /onnxruntime_src/onnxruntime/core/providers/cpu//tensor/concat.h Got nullptr for sequence input. /onnxruntime_src/onnxruntime/core/providers/cpu/sequence/concat_from_sequence.cc const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::TensorSeq] virtual onnxruntime::common::Status onnxruntime::ConcatFromSequence::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo&, bool) N11onnxruntime10ConcatBaseE N11onnxruntime18ConcatFromSequenceE Tensor size ( ) != new size ( Y != nullptr Unsupported 'dtype' value: SequenceEmpty SequenceLength SequenceConstruct SequenceErase SequenceInsert SequenceAt SplitToSequence I != nullptr Invalid sequence index ( split_size_sum ( ) != split_dim_size ( S != nullptr Got nullptr for input tensor. ), input tensor data type ( num_inputs >= 1 Split should be > 0 split_scalar > 0 yet shape_.Size() == new_shape.Size() SequenceEmpty: Got nullptr for output sequence /onnxruntime_src/onnxruntime/core/providers/cpu/sequence/sequence_ops.cc SequenceLength: Got nullptr for output tensor Got nullptr input for index tensor ) specified for sequence of size ( SequenceAt: Got nullptr for output tensor Invalid data type for split tensor Data type of the input tensor MUST be same as that of the input sequence. Sequence data type ( SequenceInsert: Got nullptr for output sequence SequenceErase: Got nullptr for output sequence SequenceConstruct: Got nullptr for output sequence Violation of the requirment that all input tensors must have the same data type. Invalid value in 'split' input. All values must be > 0 std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value > 0; }) SplitToSequence operator does not support 4L$LLLKKKKKKKKtKdK4L4L,K int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] ComputeImpl onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = std::basic_string<char>] T* onnxruntime::Tensor::MutableData() [with T = int] ComputeImpl onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = double] ComputeImpl onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = double] void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&) T* onnxruntime::Tensor::MutableData() [with T = float] void onnxruntime::GetSplitSizesInput(const onnxruntime::Tensor&, std::vector<long int>&) int64_t onnxruntime::GetScalarSplitInput(const onnxruntime::Tensor&) ComputeImpl onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = float] virtual onnxruntime::common::Status onnxruntime::SplitToSequence::Compute(onnxruntime::OpKernelContext*) const virtual onnxruntime::common::Status onnxruntime::SequenceConstruct::Compute(onnxruntime::OpKernelContext*) const virtual onnxruntime::common::Status onnxruntime::SequenceErase::Compute(onnxruntime::OpKernelContext*) const virtual onnxruntime::common::Status onnxruntime::SequenceInsert::Compute(onnxruntime::OpKernelContext*) const CreateCopyAndAppendCpuTensor void onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType) T* OrtValue::GetMutable() [with T = onnxruntime::TensorSeq] virtual onnxruntime::common::Status onnxruntime::SequenceEmpty::Compute(onnxruntime::OpKernelContext*) const const onnxruntime::Tensor& onnxruntime::TensorSeq::Get(size_t) const int64_t onnxruntime::GetSeqIdx(const onnxruntime::Tensor&) virtual onnxruntime::common::Status onnxruntime::SequenceAt::Compute(onnxruntime::OpKernelContext*) const T* onnxruntime::Tensor::MutableData() [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::TensorSeq] virtual onnxruntime::common::Status onnxruntime::SequenceLength::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime14SequenceLengthE N11onnxruntime10SequenceAtE N11onnxruntime13SequenceEmptyE N11onnxruntime14SequenceInsertE N11onnxruntime13SequenceEraseE N11onnxruntime17SequenceConstructE N11onnxruntime15SplitToSequenceE Attribute to is not set. Tensor type mismatch. type == dtype_ allocator != nullptr len > 0 buffer stol stoul stoi -INF stoull stod /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/cast_op.cc Casting from 'float16' to 'string' is not supported yet. Cast op must have 'to' argument of type DataType Unexpected 'to' argument value: Input is missing. The operator Cast expects one and only one input ,YYZZ[[[d\\]]l^ XXtyz|d~moqsu,$vvikT ԲLľܾL\\T<$$T$DL|l,\|tTL \ 4 t#$&l(<4,L 4* |=D>?A678:d;tC; <445T|ULVlXNOP|RLSZ|SSLMThhDikdeftghtmmmLnddduvTv4wxLyyy,zDzzD{t u| TD,<Td<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) const void* onnxruntime::Tensor::DataRaw(onnxruntime::MLDataType) const void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = long unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = short unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = unsigned char; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = long int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = short int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = bool; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = onnxruntime::MLFloat16] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = double] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = double] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = double; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = double] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = float] onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = bool] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = bool] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = bool; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = bool] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = long int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = long int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = long int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = short int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = short int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = short int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = signed char] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = signed char; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = signed char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long unsigned int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = long unsigned int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = long unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = long unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = unsigned int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short unsigned int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = short unsigned int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = short unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = short unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned char] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = unsigned char; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = unsigned char] N11onnxruntime4CastIhEE N11onnxruntime4CastItEE N11onnxruntime4CastIjEE N11onnxruntime4CastImEE N11onnxruntime4CastIaEE N11onnxruntime4CastIsEE N11onnxruntime4CastIiEE N11onnxruntime4CastIlEE N11onnxruntime4CastIbEE N11onnxruntime4CastIfEE N11onnxruntime4CastIdEE N11onnxruntime4CastINS_9MLFloat16EEE N11onnxruntime4CastISsEE 8 O O O O A A A ACompress /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/compress.cc axes_right_stride >= 0 && static_cast<uint64_t>(axes_right_stride) < std::numeric_limits<size_t>::max() const T* onnxruntime::Tensor::Data() const [with T = bool] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) virtual onnxruntime::common::Status onnxruntime::Compress::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime8CompressE Cannot concatenate scalars /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.cc Not satsified: input_count >= 1
Not satsified: inputs_0_rank > 0
Ranks of input data are different, cannot concatenate them. expected rank: inputs_n_rank == inputs_0_rank Non concat axis dimensions must match: Axis has mismatched dimensions of Not satsified: dim_value == inputs_0_dims[axis_index]
Not satsified: data_n.DataType() == p.output_tensor->DataType()
- 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 ԓ 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 Gather Missing/Invalid 'axis' attribute value /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather.h indices element out of data bounds, idx= must be within the inclusive range [ /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather.cc Type for Tind not supported yet in Gather. const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = int] Compute int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::GatherBase::GatherBase(const onnxruntime::OpKernelInfo&) N11onnxruntime10GatherBaseE N11onnxruntime6GatherE GatherElements ]. Actual value is , /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.h /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.cc GatherElements op: Data type for 'indices' tensor must be 'int32_t' and 'int64_t' GatherElements op: Value in indices must be within bounds [ GatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Cannot operate on scalar input GatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices' GatherElements op: Data type of input 'data' should match the data type of the output const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = int] 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>] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::GatherElements::GatherElements(const onnxruntime::OpKernelInfo&) N11onnxruntime14GatherElementsE GatherND invalid index found, index = GatherND op: Input count mismatch /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_nd.cc input_tensor != nullptr && indices_tensor != nullptr indices tensor must has rank larger than 0 last dimension of indices must not be larger than rank of input tensor const T* onnxruntime::Tensor::Data() const [with T = long int] onnxruntime::common::Status onnxruntime::GatherNDBase::PrepareForCompute(onnxruntime::OpKernelContext*, onnxruntime::GatherNDBase::Prepare&) const [with Tind = long int] const T* onnxruntime::Tensor::Data() const [with T = int] onnxruntime::common::Status onnxruntime::GatherNDBase::PrepareForCompute(onnxruntime::OpKernelContext*, onnxruntime::GatherNDBase::Prepare&) const [with Tind = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute N11onnxruntime12GatherNDBaseE N11onnxruntime8GatherNDE /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/identity_op.h onnxruntime::common::Status onnxruntime::IdentityOp<is_dropout>::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = true] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] 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 NonZero 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] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = float] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] N11onnxruntime7NonZeroIbEE N11onnxruntime7NonZeroIhEE N11onnxruntime7NonZeroIiEE N11onnxruntime7NonZeroIlEE N11onnxruntime7NonZeroIfEE OneHot Depth is negative. Invalid argument for values; either it's rank is more than 1 or it has more than 2 elements Invalid argument for depth; it's not a scalar. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/onehot.cc 'axis' attribute must have a value in the range [ Compute Compute Compute Compute Compute Compute Compute Compute Compute Compute N11onnxruntime8OneHotOpIlllEE N11onnxruntime8OneHotOpIfllEE N11onnxruntime8OneHotOpIlSslEE N11onnxruntime8OneHotOpIfSslEE N11onnxruntime8OneHotOpIlflEE N11onnxruntime8OneHotOpIifiEE N11onnxruntime8OneHotOpIiffEE N11onnxruntime8OneHotOpIfffEE N11onnxruntime8OneHotOpIlifEE N11onnxruntime8OneHotOpIlffEE >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.DataType() == DataTypeImpl::GetType<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() == DataTypeImpl::GetType<T>() && value_tensor->Shape().Size() == 1 Pads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank] Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensor void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float] PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = float] onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double] PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = double] onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = int] PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = int] onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int] PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = long int] onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] onnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo&) N11onnxruntime7PadBaseE N11onnxruntime3PadIfEE N11onnxruntime3PadIdEE N11onnxruntime3PadIiEE N11onnxruntime3PadIlEE y_zero_point x_scale x_zero_point DequantizeLinear /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/quantize_linear.cc IsScalarOr1ElementVector(&y_scale) IsScalarOr1ElementVector(&y_zero_point) y_scale.Shape().NumDimensions() == 1 && y_scale.Shape().Size() == broadcastDim x_zero_point must be 1D tensor with size y_zero_point.Shape().NumDimensions() == 1 && y_zero_point.Shape().Size() == broadcastDim x_scale must be a scalar or 1D tensor or size 1. x_zero_point must be a scalar or 1D tensor or size 1. x_scale must be 1D tensor with size x_scale.Shape().NumDimensions() == 1 && x_scale.Shape().Size() == broadcastDim x_zero_point.Shape().NumDimensions() == 1 && x_zero_point.Shape().Size() == broadcastDim IsScalarOr1ElementVector(&x_scale) IsScalarOr1ElementVector(&x_zero_point) onnxruntime::common::Status onnxruntime::DequantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::common::Status onnxruntime::DequantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] onnxruntime::common::Status onnxruntime::QuantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = signed char] onnxruntime::common::Status onnxruntime::QuantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char] N11onnxruntime16DequantizeLinearIhEE N11onnxruntime16DequantizeLinearIaEE N11onnxruntime14QuantizeLinearIhEE N11onnxruntime14QuantizeLinearIaEE BAttribute shape is not set. Reshape_1 Reshape requested_shape[i] >= -1 unknown_dim == -1 , requested shape: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape.h A dimension cannot be less than -1. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape_helper.h At most one dimension can be -1. The dimension with value zero exceeds the dimension size of the input tensor. i < input_shape.NumDimensions() The input tensor cannot be reshaped to the requested shape. Input shape: (input_shape.Size() % size) == 0 gsl::narrow_cast<int64_t>(input_shape.Size()) == size A shape tensor must be a vector tensor. shapeTensor->Shape().NumDimensions() == 1 onnxruntime::Reshape_1::Reshape_1(const onnxruntime::OpKernelInfo&) onnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape&, std::vector<long int>&) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] const T* onnxruntime::Tensor::Data() const [with T = long int] virtual onnxruntime::common::Status onnxruntime::Reshape::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7ReshapeE N11onnxruntime9Reshape_1E Scale value should be greater than or equal to 1. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsample.h Scale value should be greater than 0. 'Linear' mode only support 2-D inputs ('Bilinear') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1) info.GetAttr<std::string>("mode", &mode).IsOK() info.GetAttrs<float>("scales", scales_).IsOK() coordinate_transformation_mode exclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to scales size should be greater than 0. Resize operator Upsample operator scale >= 1 scale > 0 nearest cubic mode attribute is . It can only be (default) or scales extrapolation_value asymmetric pytorch_half_pixel tf_half_pixel_for_nn align_corners tf_crop_and_resize round_prefer_floor nearest_mode round_prefer_ceil cubic_coeff_a exclude_outside scales_size > 0 const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor*, std::vector<float>&) const void onnxruntime::UpsampleBase::ScalesValidation(const std::vector<float>&, onnxruntime::UpsampleMode) const onnxruntime::UpsampleMode onnxruntime::UpsampleBase::StringToUpsampleMode(const string&) onnxruntime::UpsampleBase::UpsampleBase(onnxruntime::OpKernelInfo) N11onnxruntime12UpsampleBaseE N11onnxruntime8UpsampleIfEE N11onnxruntime6ResizeIfEE N11onnxruntime8UpsampleIiEE N11onnxruntime6ResizeIiEE N11onnxruntime8UpsampleIhEE N11onnxruntime6ResizeIhEE ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateERKSsEUlffffffE_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateERKSsEUlffffffE0_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateERKSsEUlffffffE1_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateERKSsEUlffffffE2_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateERKSsEUlffffffE3_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateERKSsEUlffffffE4_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalERKSsiEUlfbE_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalERKSsiEUlfbE0_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalERKSsiEUlfbE1_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalERKSsiEUlfbE2_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalERKSsiEUlfbE3_ @ ?batch_axis Invalid batch_axis of . Must be 0 or 1 batch_axis < 2 Invalid time_axis of time_axis < 2 batch_axis != time_axis ReverseSequence . 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: 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 = 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 = bool] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = bool] 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 = unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned 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 = unsigned char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned char] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long 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 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 = double] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = double] 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= int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) virtual onnxruntime::common::Status onnxruntime::Scatter::Compute(onnxruntime::OpKernelContext*) const onnxruntime::Scatter::Scatter(const onnxruntime::OpKernelInfo&) N11onnxruntime7ScatterE ScatterND input_tensor != nullptr indice_tensor != nullptr update_tensor != nullptr , indices shape: updates shape: , data shape: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter_nd.cc input tensor and indices tensor must has rank larger than 0. updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. invalid indice found, indice = T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* 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::common::Status onnxruntime::ScatterNDBase::PrepareForCompute(onnxruntime::OpKernelContext*, onnxruntime::ScatterNDBase::Prepare&) const [with Tind = long int] Compute N11onnxruntime13ScatterNDBaseE N11onnxruntime9ScatterNDE T* onnxruntime::Tensor::MutableData() [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime5ShapeE Size T* onnxruntime::Tensor::MutableData() [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime4SizeE Starts must be a 1-D array Ends must be a 1-D array 'axes' has duplicates 'step' value cannot be 0 Cannot slice scalars /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.h has_starts && has_ends && attr_starts_.size() == attr_ends_.size() !has_axes || attr_axes_.size() == attr_starts_.size() Missing or invalid starts and ends attribute Invalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributes /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.cc nullptr != start_tensor && start_tensor->Shape().NumDimensions() == 1 nullptr != ends_tensor && ends_tensor->Shape().NumDimensions() == 1 start_tensor->Shape() == ends_tensor->Shape() nullptr == axes_tensor || start_tensor->Shape() == axes_tensor->Shape() nullptr == steps_tensor || start_tensor->Shape() == steps_tensor->Shape() Data type for starts and ends inputs' need to be int32_t or int64_t, but instead got Starts and ends shape mismatch Starts and axes shape mismatch Starts and steps shape mismatch 'axes' has an axis outside of the tensor dimension count Missing input tensor to be processed Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = float; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = double; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = bool; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>; bool dynamic = false] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char; bool dynamic = true] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = short unsigned int] const T* onnxruntime::Tensor::Data() const [with T = short unsigned int] T* onnxruntime::Tensor::MutableData() [with T = short unsigned int] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int; bool dynamic = true] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = unsigned int] const T* onnxruntime::Tensor::Data() const [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int; bool dynamic = true] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long unsigned int] const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int; bool dynamic = true] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = signed char] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char; bool dynamic = true] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = short int] const T* onnxruntime::Tensor::Data() const [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = short int] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short int; bool dynamic = true] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = int] T* onnxruntime::Tensor::MutableData() [with T = int] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = int; bool dynamic = true] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long int; bool dynamic = true] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = float; bool dynamic = true] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = double] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = double; bool dynamic = true] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16; bool dynamic = true] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = bool] const T* onnxruntime::Tensor::Data() const [with T = bool] T* onnxruntime::Tensor::MutableData() [with T = bool] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = bool; bool dynamic = true] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>; bool dynamic = true] void onnxruntime::SliceBase::FillVectorsFromInput(const onnxruntime::OpKernelContext*, std::vector<long int>&, std::vector<long int>&, std::vector<long int>&, std::vector<long int>&) const int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) onnxruntime::SliceBase::SliceBase(const onnxruntime::OpKernelInfo&, bool) N11onnxruntime9SliceBaseE N11onnxruntime5SliceIhLb0EEE N11onnxruntime5SliceItLb0EEE N11onnxruntime5SliceIjLb0EEE N11onnxruntime5SliceImLb0EEE N11onnxruntime5SliceIaLb0EEE N11onnxruntime5SliceIsLb0EEE N11onnxruntime5SliceIiLb0EEE N11onnxruntime5SliceIlLb0EEE N11onnxruntime5SliceIfLb0EEE N11onnxruntime5SliceIdLb0EEE N11onnxruntime5SliceINS_9MLFloat16ELb0EEE N11onnxruntime5SliceIbLb0EEE N11onnxruntime5SliceISsLb0EEE N11onnxruntime5SliceIhLb1EEE N11onnxruntime5SliceItLb1EEE N11onnxruntime5SliceIjLb1EEE N11onnxruntime5SliceImLb1EEE N11onnxruntime5SliceIaLb1EEE N11onnxruntime5SliceIsLb1EEE N11onnxruntime5SliceIiLb1EEE N11onnxruntime5SliceIlLb1EEE N11onnxruntime5SliceIfLb1EEE N11onnxruntime5SliceIdLb1EEE N11onnxruntime5SliceINS_9MLFloat16ELb1EEE N11onnxruntime5SliceIbLb1EEE N11onnxruntime5SliceISsLb1EEE blocksize CRD DCR SpaceToDepth DepthToSpace Attribute blocksize is not set. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.h info.GetAttr("blocksize", &blocksize_).IsOK() DepthToSpace op: only 'DCR' and 'CRD' modes are supported /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.cc input.Shape().NumDimensions() == 4 input_height % this->blocksize_ == 0 input_width % this->blocksize_ == 0 input_depth % (blocksize_ * blocksize_) == 0 onnxruntime::common::Status onnxruntime::DepthToSpace<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::SpaceToDepth<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::DepthToSpace<T>::DepthToSpace(const onnxruntime::OpKernelInfo&) [with T = float] onnxruntime::SpaceDepthBase::SpaceDepthBase(const onnxruntime::OpKernelInfo&) N11onnxruntime14SpaceDepthBaseE N11onnxruntime12SpaceToDepthIfEE N11onnxruntime12DepthToSpaceIfEE *> Axis= NumOutputs= Input shape= Split Invalid value in 'split' attribute. All values must be > 0 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.h std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value > 0; }) Input cannot be split evenly on selected axis. Input shape= Cannot split using values in 'split' attribute. Axis= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.cc Split operator does not support T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] ComputeImpl T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] ComputeImpl T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] ComputeImpl const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::Split::Compute(onnxruntime::OpKernelContext*) const int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) onnxruntime::SplitBase::SplitBase(const onnxruntime::OpKernelInfo&) N11onnxruntime9SplitBaseE N11onnxruntime5SplitE Squeeze . 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 perm is outside range. is repeated. [ perm: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.h v >= 0 && static_cast<uint64_t>(v) <= std::numeric_limits<size_t>::max() Attribute perm of Transpose has an invalid value. Value Mismatched data types between input and output Tensors. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.cc does not align with rank of input data: onnxruntime::TransposeBase::TransposeBase(const onnxruntime::OpKernelInfo&) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::Transpose::Compute(onnxruntime::OpKernelContext*) const T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] N11onnxruntime13TransposeBaseE N11onnxruntime9TransposeE Unique vector::_M_emplace_back_aux Unsupported tensor type of gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = signed char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char] N11onnxruntime6UniqueE 'axes' has a duplicate axis Missing/Invalid 'axes' attribute value /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.h info.GetAttrs("axes", axes_).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.cc 'axes' has an out of range axis Compute int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::UnsqueezeBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::UnsqueezeBase::Prepare&) const onnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo&) N11onnxruntime13UnsqueezeBaseE N11onnxruntime9UnsqueezeE Upsample Resize: unexpected mode Upsample: unexpected mode scales != nullptr sizes == nullptr Invalid roi input index. roi_input_idx_ > 0 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 Resize: size of roi array should be 2 * N where N is the rank of input tensor X. sizes != nullptr && sizes->Shape().Size() != 0 Either scales input or sizes input must be provided and the number of elements of scales or sizes should be the same as the rank of the input. BaseCompute onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&) 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 [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 [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = long int] 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 = int] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = int] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = long int] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = long int] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = float] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = std::basic_string<char>] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>] N11onnxruntime5WhereIiEE N11onnxruntime5WhereIlEE N11onnxruntime5WhereIfEE N11onnxruntime5WhereISsEE /onnxruntime_src/onnxruntime/contrib_ops/cpu_contrib_kernels.cc RegisterCpuContribKernels RegisterNchwcKernels /onnxruntime_src/onnxruntime/automl_ops/cpu_automl_kernels.cc RegisterCpuAutoMLKernels gamma Selu const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime7SigmoidIfEE N11onnxruntime4TanhIfEE N11onnxruntime3EluIfEE N11onnxruntime11HardSigmoidIfEE N11onnxruntime9LeakyReluIfEE N11onnxruntime4ReluIfEE N11onnxruntime4SeluIfEE N11onnxruntime18ParametricSoftplusIfEE N11onnxruntime8SoftsignIfEE N11onnxruntime15ThresholdedReluIfEE
-#<L>}-?_}?V If 'If' node has outputs. then_branch info == nullptr did not. else_branch ' attribute. Failed to create output tensor for If output outputs which doesn't match the subgraph's /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/if.cc num_subgraph_outputs == static_cast<size_t>(num_outputs) SetupSubgraphExecutionInfo should only be called once for each subgraph. Subgraph must have the shape set for all outputs but Failed to create output tensor for then_feeds_fetches_manager_ && else_feeds_fetches_manager_ Subgraph SessionState was not found for ' CreateFeedsFetchesManager must be called prior to execution of graph. info.GetAttr<ONNX_NAMESPACE::GraphProto>("then_branch", &proto).IsOK() info.GetAttr<ONNX_NAMESPACE::GraphProto>("else_branch", &proto).IsOK() Execute Initialize const T* onnxruntime::Tensor::Data() const [with T = bool] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute virtual onnxruntime::common::Status onnxruntime::If::Compute(onnxruntime::OpKernelContext*) const onnxruntime::If::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&) SetupSubgraphExecutionInfo virtual onnxruntime::common::Status onnxruntime::If::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) onnxruntime::If::If(const onnxruntime::OpKernelInfo&) *ZN11onnxruntime6IfImpl7ExecuteERKNS_19FeedsFetchesManagerEEUlRKNS_11TensorShapeERK13OrtMemoryInfoR8OrtValueRbE_ N11onnxruntime2IfE Loop inputs. Found: 'Loop' node has but has info_ == nullptr Expected: body feeds_fetches_manager_ 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 Inconsistent shape in loop output for output 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 T* onnxruntime::Tensor::MutableData() [with T = bool] T* onnxruntime::Tensor::MutableData() [with T = long int] Initialize const T* onnxruntime::Tensor::Data() const [with T = bool] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] 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&) N11onnxruntime4LoopE 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 /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_ 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] N11onnxruntime4ScanILi8EEE 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] 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_ ParametricSoftplus T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime7contrib10ScaledTanhIfEE N11onnxruntime7contrib4GeluIfEE 5?AttnLSTM DEEPCPU_ATTN_LSTM }, actural: ], while found! , am_attn_size}, Got: }, Got: }. Got: , aw_attn_size}. Got: /onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.h static_cast<int>(activation_func_names.size()) == num_directions_ * 3 Attention mechanism memory shape error! Expected: { Attention mechanism memory sequence lengths must have shape { Attention mechanism memory sequence lengths value must in (0, Attention memory layer weight shape error! Expected:{ Attention query layer weight shape error! Expected:{ Attention v weight shape error! Expected:{ Attention layer weight shape error! Expected: { /onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.cc ValidateInputs ComputeImpl virtual onnxruntime::common::Status onnxruntime::contrib::DeepCpuAttnLstmOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&) onnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOp(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib17DeepCpuAttnLstmOpE N11onnxruntime7contrib16AttentionWrapperIfEE row[ [seqno= sqeuclidean CDist /onnxruntime_src/onnxruntime/contrib_ops/cpu/cdist.h info.GetAttr<std::string>("metric", &metric).IsOK() The second input of CDist kernel has wrong shape: Input shape dimensions mismatch: The first input of CDist kernel has wrong shape: T* onnxruntime::Tensor::MutableData() [with T = float] T* onnxruntime::Tensor::MutableData() [with T = double] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::CDist<T>::CDist(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::contrib::CDist<T>::CDist(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib5CDistIfEE N11onnxruntime7contrib5CDistIdEE ConvTransposeWithDynamicPads N11onnxruntime7contrib28ConvTransposeWithDynamicPadsIfEE border input dimensions instead Input's height ( ) + bottomBorder ( Input's width ( ) + rightBorder ( ) + scale_[0] ( ) + scale_[1] ( Crop Attribute border needs to be specified with four border elements, got Input is expected to have four dimensions corresponding to [N,C,H,W], got ) needs to be greater than or equal to the topBorder ( ) needs to be greater than or equal to the leftBorder ( /onnxruntime_src/onnxruntime/contrib_ops/cpu/crop.h T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute N11onnxruntime7contrib8CropBaseE N11onnxruntime7contrib4CropIfEE bilinear CropAndResize Null crop_size_ptr /onnxruntime_src/onnxruntime/contrib_ops/cpu/crop_and_resize.h specified. It should be either bilinear or nearest Number of dimensions for crop size should be exactly 1 const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::CropAndResize<T>::CropAndResize(const onnxruntime::OpKernelInfo&) [with T = float] ZN11onnxruntime7contrib20CropAndResizeForwardIfEEvRKNS_11TensorShapeEPKT_fllS7_lPS5_RKSsPKiPNS_11concurrency10ThreadPoolEEUliE_ N11onnxruntime7contrib13CropAndResizeIfEE DynamicSlice Scale /onnxruntime_src/onnxruntime/contrib_ops/cpu/element_wise_ops.h info.GetAttr("scale", &scale_).IsOK() info.GetAttr("alpha", &alpha_).IsOK() info.GetAttr("beta", &beta_).IsOK() onnxruntime::contrib::Scale<T>::Scale(const onnxruntime::OpKernelInfo&) [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::Affine<T>::Affine(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib6AffineIfEE N11onnxruntime7contrib5ScaleIfEE ExpandDims Axis must be within range [ Axis is /onnxruntime_src/onnxruntime/contrib_ops/cpu/expand_dims.h axis_tensor->Shape().IsScalar() axis <= X_NumDims && axis >= -X_NumDims An axis tensor must be a scalar tensor. const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] const T* onnxruntime::Tensor::Data() const [with T = int] virtual onnxruntime::common::Status onnxruntime::contrib::ExpandDims::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7contrib10ExpandDimsE /onnxruntime_src/onnxruntime/contrib_ops/cpu/fused_conv.cc GetFusedActivationAttr(info, activation_).IsOK() onnxruntime::contrib::FusedConvFloat::FusedConvFloat(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib14FusedConvFloatE leaky_relu_alpha onnxruntime::Gemm<T>::Gemm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib9FusedGemmIfEE ImageScaler Bias size ( /onnxruntime_src/onnxruntime/contrib_ops/cpu/image_scaler.h info.GetAttrs<float>("bias", bias_).IsOK() ) does not match the number of channels ( T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::ImageScaler<T>::ImageScaler(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib11ImageScalerIfEE /onnxruntime_src/onnxruntime/contrib_ops/cpu/layer_norm.cc op_kernel_info.GetAttr("axis", &axis_).IsOK() const T* onnxruntime::Tensor::Data() const [with T = float] Compute 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 Type not supported. /onnxruntime_src/onnxruntime/contrib_ops/cpu/murmur_hash3.cc const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::contrib::MurmurHash3::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7contrib11MurmurHash3E invalid channel count channels_ > 0 X_shape.NumDimensions() == 4 channels_ <= Y_shape[1] Unsupported convolution size. /onnxruntime_src/onnxruntime/contrib_ops/cpu/nchwc_ops.h info.GetAttr<int64_t>("channels", &channels_).IsOK() pool_attrs_.kernel_shape.size() == 2 /onnxruntime_src/onnxruntime/contrib_ops/cpu/nchwc_ops.cc (X_shape[1] % MlasNchwcGetBlockSize()) == 0 (static_cast<size_t>(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0) Not satsified: Y->Shape() == sum_shape
- output and sum shape must match onnxruntime::common::Status onnxruntime::contrib::ReorderInput<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::common::Status onnxruntime::contrib::ReorderOutput<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] void onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) const void onnxruntime::PoolAttributes::InferOutputSize(const std::vector<long int>&, std::vector<long int>*, std::vector<long int>*) const std::vector<long int> onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape&, int64_t, std::vector<long int>*) const onnxruntime::common::Status onnxruntime::contrib::NchwcPoolBase::NchwcPool(onnxruntime::OpKernelContext*, MLAS_POOLING_KIND) const virtual onnxruntime::common::Status onnxruntime::contrib::NchwcConv::Compute(onnxruntime::OpKernelContext*) const Compute onnxruntime::contrib::NchwcPoolBase::NchwcPoolBase(const onnxruntime::OpKernelInfo&) onnxruntime::contrib::NchwcConv::NchwcConv(const onnxruntime::OpKernelInfo&) onnxruntime::contrib::ReorderOutput<T>::ReorderOutput(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib13NchwcPoolBaseE N11onnxruntime7contrib9NchwcConvE N11onnxruntime7contrib12NchwcMaxPoolE N11onnxruntime7contrib16NchwcAveragePoolE N11onnxruntime7contrib12ReorderInputIfEE N11onnxruntime7contrib13ReorderOutputIfEE y_scale SampleOp T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime7contrib8SampleOpIfEE Tokenizer mark attribute mark is not set pad_value mincharnum mincharnum_ > 0 separators tokenexp !tokenexp.empty() separators must not be empty !separators.empty() Can not digest separators: Can not digest tokenexp: /onnxruntime_src/onnxruntime/contrib_ops/cpu/tokenizer.cc attribute pad_value is not set attribute mincharnum is not set attribute mincharnum must have a positive value Either one of the separators OR tokenexp attributes required but none is set Expecting a non-empty tokenexp mincharnum is too big for char level tokenezation !char_tokenezation_ || mincharnum_ < 2 Input string contains invalid utf8 chars: Match contains invalid utf8 chars: tensor(string) expected as input Input dimensions are either [C] or [N][C] allowed const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::Tokenizer::Tokenizer(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib9TokenizerE Input tensor to Unique op should be 1D T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime7contrib6UniqueIfEE conv_window_size char_embedding_size embedding_size attribute: conv filter size: conv_window_size attribute: conv kernal size 1: Char embedding size: Conv kernal size 2 : WordConvEmbedding Conv filter size does not match embedding_size attribute. Conv kernal size 1 does not match conv_window_size attribute . Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Char embedding size does not match conv kernal size 2. /onnxruntime_src/onnxruntime/contrib_ops/cpu/word_conv_embedding.cc const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute N11onnxruntime7contrib17WordConvEmbeddingE hӥhӥhӥhDateTimeTransformer const T* onnxruntime::Tensor::Data() const [with T = long int] T* OrtValue::GetMutable() [with T = Microsoft::Featurizer::DateTimeFeaturizer::TimePoint] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime6automl19DateTimeTransformerE Real memory steps is not in (0, not support normalize yet. !normalize_ /onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/bahdanau_attention.cc mem_steps <= max_memory_steps_ && mem_steps > 0 void onnxruntime::contrib::BahdanauAttention<T>::PrepareMemory(const gsl::span<const T>&, const gsl::span<const int>&) [with T = float] onnxruntime::contrib::BahdanauAttention<T>::BahdanauAttention(onnxruntime::AllocatorPtr, const onnxruntime::logging::Logger&, int, int, int, int, int, bool, onnxruntime::concurrency::ThreadPool*) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] N11onnxruntime7contrib19IAttentionMechanismIfEE N11onnxruntime7contrib17BahdanauAttentionIfEE unimplemented activation: activation_params count mismatch Expecting an instance of ExternalTimePoint /onnxruntime_src/onnxruntime/automl_ops/automl_types.cc sizeof(DateTimeFeaturizerTimePointData) == data_size const T& OrtValue::Get() const [with T = Microsoft::Featurizer::DateTimeFeaturizer::TimePoint] static void onnxruntime::NonTensorTypeConverter<Microsoft::Featurizer::DateTimeFeaturizer::TimePoint>::ToContainer(const OrtValue&, size_t, void*) static void onnxruntime::NonTensorTypeConverter<Microsoft::Featurizer::DateTimeFeaturizer::TimePoint>::FromContainer(onnxruntime::MLDataType, const void*, size_t, OrtValue&) N11onnxruntime13NonTensorTypeIN9Microsoft10Featurizer18DateTimeFeaturizer9TimePointEEE N11onnxruntime10OpaqueTypeIN9Microsoft10Featurizer18DateTimeFeaturizer9TimePointEXadL_ZNS_15kMsAutoMLDomainEEEXadL_ZNS_14kTimepointNameEEEEE DateTimeFeaturizer_TimePoint com.microsoft.automl SSE, 12x4, depth 2 allocation failure gemmlowp error: %s
- N8gemmlowp10KernelBaseE N8gemmlowp24SSE4_64_Kernel12x4Depth2E N8gemmlowp17DefaultKernelImplILb0ELb0EEE N8gemmlowp4TaskE N8gemmlowp13DefaultKernelINS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEES3_EEEE N8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE1ELSD_1ELSD_1ENS_9VectorDupIKiLNS_11VectorShapeE0EEENSE_ISF_LSG_1EEESt5tupleIINS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEE N8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE1ELSD_1ELSD_1ENS_9VectorDupIKiLNS_11VectorShapeE0EEENSE_ISF_LSG_1EEESt5tupleIINS_23OutputStageBiasAdditionINS_9VectorMapISF_LSG_0EEEEENS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEE N8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE0ELSD_0ELSD_0ENS_9VectorDupIKiLNS_11VectorShapeE1EEENSE_ISF_LSG_0EEESt5tupleIINS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEE N8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE0ELSD_0ELSD_0ENS_9VectorDupIKiLNS_11VectorShapeE1EEENSE_ISF_LSG_0EEESt5tupleIINS_23OutputStageBiasAdditionINS_9VectorMapISF_LSG_1EEEEENS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEE @ @ /onnxruntime_src/onnxruntime/core/util/protobuf_parsing_utils.cc Can't BackUp() after failed Next(). CHECK failed: !is_closed_: close() failed: N6google8protobuf2io19CopyingOutputStreamE N6google8protobuf2io15FileInputStream22CopyingFileInputStreamE N6google8protobuf2io15FileInputStreamE N6google8protobuf2io16FileOutputStream23CopyingFileOutputStreamE N6google8protobuf2io16FileOutputStreamE OrtMemoryInfo: [ id: mem_type: Cuda CudaPinned Specified device is not supported. N11onnxruntime12CPUAllocatorE St14default_deleteIN11onnxruntime16IDeviceAllocatorEE St14default_deleteIN11onnxruntime8BFCArenaEE St19_Sp_counted_deleterIPN11onnxruntime8BFCArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE St19_Sp_counted_deleterIPN11onnxruntime16IDeviceAllocatorESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE /onnxruntime_src/onnxruntime/core/framework/bfc_arena.h /onnxruntime_src/onnxruntime/core/framework/bfc_arena.cc !c->in_use() && (c->bin_num != kInvalidBinNum) !c->in_use() && (c->bin_num == kInvalidBinNum) p_int < base_int + memory_size_ !c1->in_use() && !c2->in_use() bin->free_chunks.count(h) == 1 Creating bin of max chunk size BinForSize(bin_size) == BinFromIndex(b) BinForSize(bin_size + 255) == BinFromIndex(b) BinForSize(bin_size * 2 - 1) == BinFromIndex(b) BinForSize(bin_size * 2) != BinFromIndex(b) 0 == memory_size % kMinAllocationSize BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0 c->in_use() && (c->bin_num == kInvalidBinNum) b->free_chunks.size() == bin_info.total_chunks_in_bin - bin_info.total_chunks_in_use client-requested in use in bin. Summary of in-use Chunks by size: BFC Arena ran out of memory trying . Current allocation summary follows. reserved_chunks_.find(ptr) == reserved_chunks_.end() Could not find Region for h < chunks_.size() Size: | Requested Size: | in_use: p_int >= base_int c2->prev == h1 h != kInvalidChunkHandle c->bin_num == bin_num !chunk->in_use() Extended allocation by bytes. Total allocated bytes: Allocated memory at Could not find chunk in bin Chunk Free Bin ( ): Total Chunks: , Chunks in use: allocated for chunks. in use in bin. Bin for , Chunk State: , prev: , next: Chunks of size totalling Sum Total of in-use chunks: Limit: InUse: TotalAllocated: MaxInUse: NumAllocs: MaxAllocSize: Stats:
- tried to allocate 0 bytes to allocate DumpMemoryLog void onnxruntime::BFCArena::DumpMemoryLog(size_t) std::array<onnxruntime::BFCArena::BinDebugInfo, 21ul> onnxruntime::BFCArena::get_bin_debug_info() void onnxruntime::BFCArena::FreeAndMaybeCoalesce(onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::RemoveFreeChunkFromBin(onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::RemoveFreeChunkIterFromBin(onnxruntime::BFCArena::Bin::FreeChunkSet*, const iterator&) void onnxruntime::BFCArena::InsertFreeChunkIntoBin(onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::Merge(onnxruntime::BFCArena::ChunkHandle, onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::DeallocateRawInternal(void*) void onnxruntime::BFCArena::SplitChunk(onnxruntime::BFCArena::ChunkHandle, size_t) void* onnxruntime::BFCArena::FindChunkPtr(onnxruntime::BFCArena::BinNum, size_t, size_t) AllocateRawInternal virtual void* onnxruntime::BFCArena::Reserve(size_t) int onnxruntime::BFCArena::AllocationRegion::IndexFor(const void*) const RegionFor onnxruntime::BFCArena::AllocationRegion::AllocationRegion(void*, size_t) Extend onnxruntime::BFCArena::Chunk* onnxruntime::BFCArena::ChunkFromHandle(onnxruntime::BFCArena::ChunkHandle) onnxruntime::BFCArena::BFCArena(std::unique_ptr<onnxruntime::IDeviceAllocator>, size_t) BFCArena N11onnxruntime8BFCArenaE fff?/onnxruntime_src/onnxruntime/core/framework/data_transfer.cc src.SizeInBytes() == dst.SizeInBytes() virtual onnxruntime::common::Status onnxruntime::CPUDataTransfer::CopyTensor(const onnxruntime::Tensor&, onnxruntime::Tensor&, int) const N11onnxruntime13IDataTransferE N11onnxruntime15CPUDataTransferE Device: [ memory_type: device_id: Tensor size mismatch There's no data transfer registered for copying tensors from data_transfer registered is nullptr. (null) Not implemented tensor(MLFloat16) tensor(bfloat16) sparse tensor type value_proto != nullptr elem_proto != nullptr Map with key type: value type: is not registered /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::kMapType utils::HasKeyType(thisProto->map_type()) thisProto->value_case() == TypeProto::ValueCase::kSequenceType utils::HasElemType(thisProto->sequence_type()) thisProto->value_case() == TypeProto::ValueCase::kOpaqueType 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 utils::HasElemType(tensor_type) utils::HasElemType(sparse_tensor_type) is not currently registered or supported l$L h ( X98 9 9,9<9L9\9l999999998(PhOxOOOOOO(POOOPP(P(PPO static const onnxruntime::DataTypeImpl* onnxruntime::DataTypeImpl::TypeFromProto(const onnx::TypeProto&) 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 = long unsigned 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 = double] 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 = short int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = short unsigned int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = 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 = 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 = float] 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 = onnxruntime::Tensor] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = double] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = float] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = long int] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = std::basic_string<char>] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = double] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = float] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = long int] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = std::basic_string<char>] virtual void onnxruntime::NonTensorTypeBase::ToDataContainer(const OrtValue&, size_t, void*) const virtual void onnxruntime::NonTensorTypeBase::FromDataContainer(const void*, size_t, OrtValue&) const bool onnxruntime::NonTensorTypeBase::IsOpaqueCompatible(const onnx::TypeProto&) const bool onnxruntime::NonTensorTypeBase::IsSequenceCompatible(const onnx::TypeProto&) const bool onnxruntime::NonTensorTypeBase::IsMapCompatible(const onnx::TypeProto&) const virtual bool onnxruntime::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 N11onnxruntime12DataTypeImplE N11onnxruntime14TensorTypeBaseE N11onnxruntime20SparseTensorTypeBaseE N11onnxruntime17NonTensorTypeBaseE N11onnxruntime13NonTensorTypeINS_9TensorSeqEEE 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 N11onnxruntime12SequenceTypeINS_9TensorSeqEEE N11onnxruntime18SequenceTensorTypeIiEE N11onnxruntime18SequenceTensorTypeIfEE N11onnxruntime18SequenceTensorTypeIbEE N11onnxruntime18SequenceTensorTypeISsEE N11onnxruntime18SequenceTensorTypeIaEE N11onnxruntime18SequenceTensorTypeIhEE N11onnxruntime18SequenceTensorTypeItEE N11onnxruntime18SequenceTensorTypeIsEE N11onnxruntime18SequenceTensorTypeIlEE N11onnxruntime18SequenceTensorTypeIdEE N11onnxruntime18SequenceTensorTypeIjEE N11onnxruntime18SequenceTensorTypeImEE N11onnxruntime18SequenceTensorTypeINS_9MLFloat16EEE N11onnxruntime18SequenceTensorTypeINS_8BFloat16EEE N11onnxruntime13NonTensorTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEE N11onnxruntime12SequenceTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEE N11onnxruntime13NonTensorTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEE N11onnxruntime12SequenceTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEE N11onnxruntime11NonOnnxTypeIiEE N11onnxruntime11NonOnnxTypeIfEE N11onnxruntime11NonOnnxTypeIbEE N11onnxruntime11NonOnnxTypeISsEE N11onnxruntime11NonOnnxTypeIaEE N11onnxruntime11NonOnnxTypeIhEE N11onnxruntime11NonOnnxTypeItEE N11onnxruntime11NonOnnxTypeIsEE N11onnxruntime11NonOnnxTypeIlEE N11onnxruntime11NonOnnxTypeIdEE N11onnxruntime11NonOnnxTypeIjEE N11onnxruntime11NonOnnxTypeImEE N11onnxruntime11NonOnnxTypeINS_9MLFloat16EEE N11onnxruntime11NonOnnxTypeINS_8BFloat16EEE ZN11onnxruntime19data_types_internal16DataTypeRegistryC1EvEUlPKNS_12DataTypeImplEE_ ZN11onnxruntime19data_types_internal16DataTypeRegistryC1EvEUlPKNS_12DataTypeImplEE0_ Ss N11onnxruntime6TensorE 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= 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 feeds.size() == feed_mlvalue_idxs.size() fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size() node_index_info and ort_value_idx_map are out of sync and cannot be used node_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx() entries which doesn't match the number of fetches the frame was initialized with of Fetches vector passed to GetOutputs contains buffers_.find(mem_patterns_->locations[i]) == buffers_.end() 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. 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. OrtValue shape verification failed. Current shape: shape && tensor.Shape() == *shape size= TraceFree for ort_value_idx= ort_value.Fence() == nullptr For ort_value with index: but the actually size is: invalid index Invalid allocation kind: mlvalue.Fence() == nullptr Requested shape: TraceFree void onnxruntime::ExecutionFrame::TraceFree(int) TraceAllocate const onnxruntime::AllocPlanPerValue& onnxruntime::ExecutionFrame::GetAllocationPlan(int) ReleaseMLValueImpl onnxruntime::common::Status onnxruntime::AllocateSparseTensor(OrtValue&, const onnxruntime::DataTypeImpl&, onnxruntime::AllocatorPtr, const onnxruntime::TensorShape&, size_t, bool, const onnxruntime::SessionState&) AllocateAsPerAllocationPlan onnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlan(OrtValue&, int, const onnxruntime::TensorShape*, size_t) T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] AllocateMLValueTensorPreAllocateBuffer AllocateMLValueTensorSelfOwnBufferHelper onnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper(OrtValue&, int, onnxruntime::MLDataType, const OrtMemoryInfo&, const onnxruntime::TensorShape&, bool) onnxruntime::ExecutionFrame::ExecutionFrame(const std::vector<int>&, const std::vector<OrtValue>&, const std::vector<int>&, const std::vector<OrtValue>&, const std::unordered_map<long unsigned int, std::function<onnxruntime::common::Status(const onnxruntime::TensorShape&, const OrtMemoryInfo&, OrtValue&, bool&)> >&, const onnxruntime::SessionState&) int onnxruntime::NodeIndexInfo::GetMLValueIndex(int) const const OrtValue& onnxruntime::IExecutionFrame::GetMLValue(int) const const T& OrtValue::Get() const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(int, const onnxruntime::TensorShape*, OrtValue*&, size_t) onnxruntime::IExecutionFrame::IExecutionFrame(const std::vector<int>&, const std::vector<OrtValue>&, const std::unordered_map<int, OrtValue>&, const std::vector<int>&, const std::vector<OrtValue>&, const OrtValueNameIdxMap&, const onnxruntime::NodeIndexInfo&) N11onnxruntime15IExecutionFrameE N11onnxruntime14ExecutionFrameE duplicated allocator /onnxruntime_src/onnxruntime/core/framework/execution_provider.cc void onnxruntime::IExecutionProvider::InsertAllocator(onnxruntime::AllocatorPtr) N11onnxruntime18IExecutionProviderE Error mapping feeds: Error mapping output names: /onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.cc input_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknown void onnxruntime::FeedsFetchesManager::SetDeviceCopyChecks(onnxruntime::DeviceCopyCheck, onnxruntime::DeviceCopyCheck) onnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(const std::vector<std::basic_string<char> >&, const std::vector<std::basic_string<char> >&, const onnxruntime::OrtValueNameIdxMap&) FeedsFetchesInfo MapNamesToMLValueIdxs 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 Op: Incompatible types. kernel def can't be NULL Failed to add kernel for Version mismatch. node_version: kernel start version: kernel_end_version: kernel is not supported in Failed to find kernel for : Conflicting with a registered kernel with op versions. /onnxruntime_src/onnxruntime/core/framework/kernel_registry.cc len <= op_schema.inputs().size() Failed to create kernel for op: since it was ill-formed during registration Encountered following errors: ( TryFindKernel void onnxruntime::{anonymous}::TraverseFormalParametersWithTypeProto(const onnxruntime::Node&, ParamFilterFn, TraverseFn) [with ParamFilterFn = onnxruntime::{anonymous}::TypeBindingResolver::Resolve(const string&) const::__lambda9; TraverseFn = onnxruntime::{anonymous}::TypeBindingResolver::Resolve(const string&) const::__lambda10] void onnxruntime::{anonymous}::TraverseFormalParametersWithTypeProto(const onnxruntime::Node&, ParamFilterFn, TraverseFn) [with ParamFilterFn = onnxruntime::{anonymous}::TypeBindingResolver::TypeBindingResolver(const onnxruntime::Node&, bool)::__lambda7; TraverseFn = onnxruntime::{anonymous}::TypeBindingResolver::TypeBindingResolver(const onnxruntime::Node&, bool)::__lambda8] The node is not placed on any Execution Provider, therefore, can't find a suitable kernel for The node is not placed on any Execution Provider (node found duplicated provider in KernelRegistryManager nullptr != type_proto /onnxruntime_src/onnxruntime/core/framework/mldata_type_utils.cc const onnxruntime::DataTypeImpl* onnxruntime::utils::GetMLDataType(const onnxruntime::NodeArg&) /onnxruntime_src/onnxruntime/core/framework/node_index_info.cc onnxruntime::NodeIndexInfo::Init(const TValidNodes&, onnxruntime::NodeIndex, const OrtValueNameIdxMap&) [with TValidNodes = onnxruntime::ValidNodes<const std::vector<const onnxruntime::Node*> >; onnxruntime::NodeIndex = long unsigned int; onnxruntime::OrtValueNameIdxMap = onnxruntime::OrtValueNameIdxMap]::__lambda8 onnxruntime::NodeIndexInfo::Init(const TValidNodes&, onnxruntime::NodeIndex, const OrtValueNameIdxMap&) [with TValidNodes = onnxruntime::GraphNodes; onnxruntime::NodeIndex = long unsigned int; onnxruntime::OrtValueNameIdxMap = onnxruntime::OrtValueNameIdxMap]::__lambda8 ZN11onnxruntime13NodeIndexInfo4InitINS_10GraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ ZN11onnxruntime13NodeIndexInfo4InitINS_10GraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_ ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_ :tensor type Tensor types should have been handled already \\\,\ const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] TempSpace allocator not found Execution frame was null frame != nullptr OpKernel was null kernel != nullptr . Num args is Invalid arg_num of arg_num < arg_counts.size() /onnxruntime_src/onnxruntime/core/framework/op_kernel.cc node_offsets_index < node_offsets_size_ OrtValue* onnxruntime::OpKernelContext::GetOrCreateOutputMLValue(int) int onnxruntime::OpKernelContext::NumVariadicInputs(size_t) const OrtValue* onnxruntime::OpKernelContext::OutputMLValue(int, const onnxruntime::TensorShape&, size_t) T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] int onnxruntime::NodeIndexInfo::GetNodeOffset(onnxruntime::NodeIndex) const onnxruntime::OpKernelContext::OpKernelContext(onnxruntime::IExecutionFrame*, const onnxruntime::OpKernel*, onnxruntime::concurrency::ThreadPool*, const onnxruntime::logging::Logger&) cannot find allocator /onnxruntime_src/onnxruntime/core/framework/op_kernel_info.cc const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const OrtMemoryInfo& onnxruntime::OpKernelInfo::GetMemoryInfo(int, OrtMemType) const 'is defined. No attribute with name:' attr != nullptr No attribute with this name is defined. /onnxruntime_src/onnxruntime/core/framework/op_node_proto_helper.cc values.size() == static_cast<size_t>(attr->floats_size()) values.size() == static_cast<size_t>(attr->ints_size()) values.size() == static_cast<size_t>(attr->strings_size()) Attibute name and type don't match /onnxruntime_src/include/onnxruntime/core/framework/op_node_proto_helper.h values.size() == static_cast<size_t>(attr->tensors_size()) values.size() == static_cast<size_t>(attr->graphs_size()) ort_value.IsAllocated() . Shape: . Dimension 0 is Invalid dim0_offset of dim0_offset < dim0_size Can't slice a non-tensor OrtValue. Type was /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 OrtValue has not been allocated so can't be sliced. static onnxruntime::OrtValueTensorSlicer<T> onnxruntime::OrtValueTensorSlicer<T>::Create(T&, int64_t, int64_t) [with T = const OrtValue; int64_t = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] static onnxruntime::OrtValueTensorSlicer<T> onnxruntime::OrtValueTensorSlicer<T>::Create(T&, int64_t, int64_t) [with T = OrtValue; int64_t = long int] node_index_info_ with error: duplicated ort_value index: SaveMLValueNameIndexMapping is used by node ) and node Entry exists in node for attribute SetGraphAndCreateKernels must be called prior to GetExecutionInfo. /onnxruntime_src/onnxruntime/core/framework/session_state.cc Failed to find input name in the mapping: Failed to find output name in the mapping: Could not create kernel for node: as there's no execution provider allocated. Kernel creation failed for node: . Do you have duplicated calls to SessionState::AddInitializedTensor function? Done saving OrtValue mappings. Using an input in multiple nodes on different devices is not supported currently. Input: Only one node should produce an output. Existing entry for output_names_to_nodeinfo.empty() existing_entries.find(attribute_name) == existing_entries.cend() const onnxruntime::NodeIndexInfo& onnxruntime::SessionState::GetNodeIndexInfo() const void onnxruntime::SessionState::AddSubgraphSessionState(onnxruntime::NodeIndex, const string&, std::unique_ptr<onnxruntime::SessionState>) void onnxruntime::SessionState::AddOutputNameToNodeInfoMapping(const string&, const onnxruntime::SessionState::NodeInfo&) SetGraph /onnxruntime_src/onnxruntime/core/framework/session_state_initializer.cc OrtValue indexes should have been populated. ort_value_name_idx_map.MaxIdx() > 0 Invalid allocation info. Provider name = string tensor is not supported for copying between allocators Internal error. The preallocated buffer is too small. Requires Failed to copy tensor to execution provider: Done saving initialized tensors Execution plan was not found in SessionState. CreatePlan must be called first. Saving initialized tensors. , Got Deserialize tensor Subgraph Graph exec_plan_ptr input with name is not used by any node. ForEachWithIndex operator() operator() SaveInputOutputNamesToNodeMapping DeserializeTensorProto onnxruntime::common::Status onnxruntime::SaveInitializedTensors(const onnxruntime::Env&, const std::basic_string<char>&, const onnxruntime::Graph&, const onnxruntime::ExecutionProviders&, const OrtValueNameIdxMap&, onnxruntime::ITensorAllocator*, const T&, const onnxruntime::logging::Logger&, const onnxruntime::DataTransferManager&) [with T = onnxruntime::SessionStateInitializer::CreatePlan(const onnxruntime::Node*, const onnxruntime::ConstPointerContainer<std::vector<onnxruntime::NodeArg*> >*, ExecutionMode)::__lambda17; onnxruntime::OrtValueNameIdxMap = onnxruntime::OrtValueNameIdxMap] SaveInitializedTensors onnxruntime::common::Status onnxruntime::SessionStateInitializer::CreatePlan(const onnxruntime::Node*, const onnxruntime::ConstPointerContainer<std::vector<onnxruntime::NodeArg*> >*, ExecutionMode) CreatePlan *ZN11onnxruntimeL33SaveInputOutputNamesToNodeMappingERKNS_5GraphERKNS_21KernelRegistryManagerERNS_12SessionStateEPKNS_21ConstPointerContainerISt6vectorIPNS_7NodeArgESaISB_EEEEEUlRKSA_mE_ *ZN11onnxruntimeL33SaveInputOutputNamesToNodeMappingERKNS_5GraphERKNS_21KernelRegistryManagerERNS_12SessionStateEPKNS_21ConstPointerContainerISt6vectorIPNS_7NodeArgESaISB_EEEEEUlRKSA_mE0_ N11onnxruntime25ISequentialPlannerContextE N11onnxruntime24SequentialPlannerContextE tensor size overflow shape.Size() must >=0 p_type != nullptr /onnxruntime_src/onnxruntime/core/framework/tensor.cc void onnxruntime::Tensor::Init(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, onnxruntime::AllocatorPtr, int64_t) size_t onnxruntime::Tensor::SizeInBytes() const onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, std::shared_ptr<onnxruntime::IAllocator>, int64_t) onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, const OrtMemoryInfo&, int64_t) Internal error. Mem pattern for initializer ' is not found ' failed duplicated location /onnxruntime_src/onnxruntime/core/framework/tensor_allocator_with_mem_pattern.h Weight buffer for initializer ' Get preallocated buffer for initializer ' Failed to get allocator for location: Trace FinalizePlan N11onnxruntime16ITensorAllocatorE N11onnxruntime29TensorAllocatorWithMemPatternE Invalid dimension of dimension <= num_dims /onnxruntime_src/onnxruntime/core/framework/tensor_shape.cc dimstart <= dimend && dimend <= size() Invalid tensor shape slice argument. for SizeFromDimension. Tensor has onnxruntime::TensorShape onnxruntime::TensorShape::Slice(size_t, size_t) const int64_t onnxruntime::TensorShape::SizeFromDimension(size_t) const int64_t onnxruntime::TensorShape::SizeToDimension(size_t) const OrtValue is not a Tensor Argument is not a tensor /onnxruntime_src/onnxruntime/core/framework/tensor_type_and_shape.cc
-
const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] OrtStatus* OrtApis::GetTensorTypeAndShape(const OrtValue*, OrtTensorTypeAndShapeInfo**) UnpackTensor: the pre-allocated size does not match the raw data size, expected UnpackTensor: the pre-allocate size does not match the size in proto ) does not match the data size( corrupted protobuf data: tensor shape size( string tensor can not have raw data /onnxruntime_src/onnxruntime/core/framework/tensorprotoutils.cc 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: data overflow ) in proto Invalid TensorProto <f c7ec}dObe debdfcaa<f<f*a%{zzzLzLzzyzyzzLzyzzyz%{%{Lz`|6|{{{{6| | |{{ |6| |`|`|{
-
TensorProtoToMLValue NnapiExecutionProvider invalid allocator copy_info.size() == num_feeds /onnxruntime_src/onnxruntime/core/framework/utils.cc feed_locations.size() == copy_info.size() fetch_alloc_info.size() == copy_info.size() Only one thread was configured for parallel execution. Hence will use sequential execution. CopyOutputsAcrossDevices CopyInputsAcrossDevices onnxruntime::common::Status onnxruntime::utils::CopyInputsAcrossDevices(const std::vector<OrtValue>&, std::vector<OrtValue>&, const std::vector<onnxruntime::MLValueCopyInfo>&, const onnxruntime::DataTransferManager&) ExecuteGraphImpl ExecuteGraph T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] CopyMLValue const T& OrtValue::Get() const [with T = onnxruntime::Tensor] bool onnxruntime::utils::FinalizeCopyInfoForFetches(const onnxruntime::SessionState&, const std::vector<const OrtMemoryInfo*>&, std::vector<onnxruntime::MLValueCopyInfo>&) bool onnxruntime::utils::FinalizeCopyInfoForFeeds(const std::vector<OrtDevice>&, std::vector<onnxruntime::MLValueCopyInfo>&) CalculateStaticCopyInfoForFeed CalculateStaticCopyInfoForFeeds InitializeFeedFetchCopyInfo const OrtMemoryInfo& onnxruntime::utils::FindMemoryInfoForValue(const OrtValueNameIdxMap&, const onnxruntime::SequentialExecutionPlan&, const string&) FindMemoryInfoForValue const OrtMemoryInfo& onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::SessionState&, const string&) N11onnxruntime9IExecutorE /onnxruntime_src/onnxruntime/core/framework/allocation_planner.cc id >= 0 && static_cast<size_t>(id) < ort_value_info_.size() n >= 0 && static_cast<size_t>(n) < ort_value_info_.size() n >= 0 && static_cast<size_t>(n) < plan_.allocation_plan.size() kernel_create_info != nullptr && kernel_create_info->kernel_def != nullptr (ort_value_idx) output_name : <allocation plan>
+ /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.h const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) onnxruntime::common::Status onnxruntime::ConcatBase::PrepareForCompute(onnxruntime::OpKernelContext*, const std::vector<const onnxruntime::Tensor*>&, onnxruntime::Prepare&) const PrepareForCompute onnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo&, bool) N11onnxruntime6ConcatE DynamicQuantizeLinear x_ptr != nullptr /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/dynamicquantizelinear.cc T* onnxruntime::Tensor::MutableData() [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::DynamicQuantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] N11onnxruntime21DynamicQuantizeLinearIhEE CEyeLike T1 != nullptr EyeLike : Input tensor dimension is not 2 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/eye_like.cc T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = int] T* onnxruntime::Tensor::MutableData() [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::EyeLike::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7EyeLikeE Tind Missing/Invalid 'axis' attribute value /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather.h indices element out of data bounds, idx= must be within the inclusive range [ /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather.cc Type for Tind not supported yet in Gather. const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = int] Compute int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::GatherBase::GatherBase(const onnxruntime::OpKernelInfo&) N11onnxruntime10GatherBaseE N11onnxruntime6GatherE GatherElements ]. Actual value is , /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.h GatherElements op: Cannot operate on scalar input GatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices' GatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Data type for 'indices' tensor must be 'int32_t' and 'int64_t' /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.cc GatherElements op: Value in indices must be within bounds [ GatherElements op: Data type of input 'data' should match the data type of the output std::vector<long int> onnxruntime::parse_and_validate_indices_tensor(const onnxruntime::Tensor*, int64_t, const onnxruntime::TensorShape&) T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::GatherElements::GatherElements(const onnxruntime::OpKernelInfo&) N11onnxruntime14GatherElementsE GatherND invalid index found, index = GatherND op: Input count mismatch /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_nd.cc input_tensor != nullptr && indices_tensor != nullptr indices tensor must has rank larger than 0 last dimension of indices must not be larger than rank of input tensor const T* onnxruntime::Tensor::Data() const [with T = long int] onnxruntime::common::Status onnxruntime::GatherNDBase::PrepareForCompute(onnxruntime::OpKernelContext*, onnxruntime::GatherNDBase::Prepare&) const [with Tind = long int] const T* onnxruntime::Tensor::Data() const [with T = int] onnxruntime::common::Status onnxruntime::GatherNDBase::PrepareForCompute(onnxruntime::OpKernelContext*, onnxruntime::GatherNDBase::Prepare&) const [with Tind = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute N11onnxruntime12GatherNDBaseE N11onnxruntime8GatherNDE /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/identity_op.h onnxruntime::common::Status onnxruntime::IdentityOp<is_dropout>::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = true] void* onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::IdentityOp<is_dropout>::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = false] N11onnxruntime10IdentityOpILb1EEE N11onnxruntime10IdentityOpILb0EEE detect_positive detect_negative IsInf Failed to obtain detect_positive /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/isinf.cc Failed to obtain detect_negative Data type X must be float or double, but instead got const T* onnxruntime::Tensor::Data() const [with T = double] const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = bool] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::IsInf::Compute(onnxruntime::OpKernelContext*) const onnxruntime::IsInf::IsInf(const onnxruntime::OpKernelInfo&) N11onnxruntime5IsInfE IsNaN Null input ptr const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] T* onnxruntime::Tensor::MutableData() [with T = bool] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime5IsNaNIfEE N11onnxruntime5IsNaNINS_9MLFloat16EEE | | | | MeanVarianceNormalization Input is expected to have four dimensions corresponding to [N,C,H,W] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime27MeanVarianceNormalization_0IfEE N11onnxruntime27MeanVarianceNormalization_1IfEE X input is required! /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/nonzero_op.cc const T* onnxruntime::Tensor::Data() const [with T = bool] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = int] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = long int] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = float] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] N11onnxruntime7NonZeroIbEE N11onnxruntime7NonZeroIhEE N11onnxruntime7NonZeroIiEE N11onnxruntime7NonZeroIlEE N11onnxruntime7NonZeroIfEE OneHot Depth is negative. Invalid argument for values; either it's rank is more than 1 or it has more than 2 elements Invalid argument for depth; it's not a scalar. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/onehot.cc 'axis' attribute must have a value in the range [ Compute Compute Compute Compute Compute Compute Compute Compute Compute Compute Compute N11onnxruntime8OneHotOpIlllEE N11onnxruntime8OneHotOpIfllEE N11onnxruntime8OneHotOpIlSslEE N11onnxruntime8OneHotOpIfSslEE N11onnxruntime8OneHotOpIlflEE N11onnxruntime8OneHotOpIifiEE N11onnxruntime8OneHotOpIiffEE N11onnxruntime8OneHotOpIfffEE N11onnxruntime8OneHotOpIlifEE N11onnxruntime8OneHotOpIlffEE N11onnxruntime8OneHotOpIlfiEE >Unexpected mode of Pad reflect edge data_rank > 0 data_rank * 2 == pads.size() pads_size == 2 * data_rank Cannot use 'reflect' mode to pad dimension with a value of 0. Input shape: Invalid 'mode' attribute value /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/pad.h Invalid 'pads' attribute value Input tensor has no dimensions /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/pad.cc 'pads' has wrong number of values Pads tensor should be an INT64 tensor pads_tensor.IsDataType<int64_t>() pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1) Pads tensor size should be equal to twice the input dimension count value_tensor->IsDataType<T>() && value_tensor->Shape().Size() == 1 Pads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank] Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensor void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float] PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = float] onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double] PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = double] onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = int] PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = int] onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int] PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = long int] onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] onnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo&) N11onnxruntime7PadBaseE N11onnxruntime3PadIfEE N11onnxruntime3PadIdEE N11onnxruntime3PadIiEE N11onnxruntime3PadIlEE y_zero_point x_scale x_zero_point DequantizeLinear /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/quantize_linear.cc IsScalarOr1ElementVector(&y_scale) IsScalarOr1ElementVector(&y_zero_point) y_scale.Shape().NumDimensions() == 1 && y_scale.Shape().Size() == broadcastDim y_zero_point.Shape().NumDimensions() == 1 && y_zero_point.Shape().Size() == broadcastDim x_scale must be a scalar or 1D tensor or size 1. x_zero_point must be a scalar or 1D tensor or size 1. x_scale must be 1D tensor with size x_zero_point must be 1D tensor with size x_scale.Shape().NumDimensions() == 1 && x_scale.Shape().Size() == broadcastDim x_zero_point.Shape().NumDimensions() == 1 && x_zero_point.Shape().Size() == broadcastDim IsScalarOr1ElementVector(&x_scale) IsScalarOr1ElementVector(&x_zero_point) onnxruntime::common::Status onnxruntime::DequantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::common::Status onnxruntime::DequantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] onnxruntime::common::Status onnxruntime::QuantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = signed char] onnxruntime::common::Status onnxruntime::QuantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char] N11onnxruntime16DequantizeLinearIhEE N11onnxruntime16DequantizeLinearIaEE N11onnxruntime14QuantizeLinearIhEE N11onnxruntime14QuantizeLinearIaEE BAttribute shape is not set. requested_shape[i] >= -1 unknown_dim == -1 , requested shape: Reshape_1 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape.h A dimension cannot be less than -1. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape_helper.h At most one dimension can be -1. The dimension with value zero exceeds the dimension size of the input tensor. i < input_shape.NumDimensions() The input tensor cannot be reshaped to the requested shape. Input shape: (input_shape.Size() % size) == 0 gsl::narrow_cast<int64_t>(input_shape.Size()) == size A shape tensor must be a vector tensor. shapeTensor->Shape().NumDimensions() == 1 onnxruntime::Reshape_1::Reshape_1(const onnxruntime::OpKernelInfo&) onnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape&, std::vector<long int>&) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] const T* onnxruntime::Tensor::Data() const [with T = long int] virtual onnxruntime::common::Status onnxruntime::Reshape::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7ReshapeE N11onnxruntime9Reshape_1E Resize operator Upsample operator scale >= 1 scale > 0 nearest cubic mode attribute is . It can only be (default) or scales extrapolation_value asymmetric pytorch_half_pixel tf_half_pixel_for_nn align_corners tf_crop_and_resize coordinate_transform_mode:[ ] is not supportted! round_prefer_floor nearest_mode round_prefer_ceil nearst_mode:[ cubic_coeff_a exclude_outside scales_size > 0 Scale value should be greater than or equal to 1. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsample.h Scale value should be greater than 0. 'Linear' mode and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1) info.GetAttr<std::string>("mode", &mode).IsOK() info.GetAttrs<float>("scales", scales_).IsOK() coordinate_transformation_mode exclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to scales size should be greater than 0. const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor*, std::vector<float>&) const onnxruntime::ResizeNearestMode onnxruntime::UpsampleBase::StringToNearstMode(const string&) onnxruntime::ResizeCoordinateTransformationMode onnxruntime::UpsampleBase::StringToCoordinateTransformationMode(const string&) void onnxruntime::UpsampleBase::ScalesValidation(const std::vector<float>&, onnxruntime::UpsampleMode) const onnxruntime::UpsampleMode onnxruntime::UpsampleBase::StringToUpsampleMode(const string&) onnxruntime::UpsampleBase::UpsampleBase(onnxruntime::OpKernelInfo) N11onnxruntime12UpsampleBaseE N11onnxruntime8UpsampleIfEE N11onnxruntime6ResizeIfEE N11onnxruntime8UpsampleIiEE N11onnxruntime6ResizeIiEE N11onnxruntime8UpsampleIhEE N11onnxruntime6ResizeIhEE ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE0_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE1_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE2_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE3_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE4_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE0_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE1_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE2_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE3_ @ ?batch_axis Invalid batch_axis of . Must be 0 or 1 batch_axis < 2 Invalid time_axis of time_axis < 2 batch_axis != time_axis ReverseSequence Unknown tensor type of . batch_size= /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.h info.GetAttr<int64_t>("batch_axis", &batch_axis).IsOK() info.GetAttr<int64_t>("time_axis", &time_axis).IsOK() time_axis and batch_axis must have different values but both are /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.cc sequence_lens shape must be {batch_size}. Got: p:_8 31/G-7+)&$o" 9 p:p:'6 gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::BFloat16] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::BFloat16] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::MLFloat16] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::MLFloat16] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long unsigned int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = short unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short unsigned int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = short int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = signed char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = std::basic_string<char>] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = std::basic_string<char>] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = double] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = double] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = bool] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = bool] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::ReverseSequenceOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ReverseSequenceOp::ReverseSequenceOp(const onnxruntime::OpKernelInfo&) N11onnxruntime17ReverseSequenceOpE /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter.cc data type is different from updates type Indices and updates must have the same rank Indices vs updates dimensions differs at position= Indices must have the same rank as Input. Indices rank= Expecting indices to be either int32_t or int64_t ScatterElements Scatter . Input rank= Indices dim= at pos= is greater than input dim= _:\7B d?aBB< int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) virtual onnxruntime::common::Status onnxruntime::Scatter::Compute(onnxruntime::OpKernelContext*) const onnxruntime::Scatter::Scatter(const onnxruntime::OpKernelInfo&) N11onnxruntime7ScatterE ScatterND input_tensor != nullptr indice_tensor != nullptr update_tensor != nullptr , indices shape: updates shape: , data shape: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter_nd.cc input tensor and indices tensor must has rank larger than 0. updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. invalid indice found, indice = T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::ScatterNDBase::PrepareForCompute(onnxruntime::OpKernelContext*, onnxruntime::ScatterNDBase::Prepare&) const [with Tind = long int] Compute N11onnxruntime13ScatterNDBaseE N11onnxruntime9ScatterNDE T* onnxruntime::Tensor::MutableData() [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime5ShapeE Size T* onnxruntime::Tensor::MutableData() [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime4SizeE Starts must be a 1-D array Ends must be a 1-D array 'axes' has duplicates 'step' value cannot be 0 output == output_end Cannot slice scalars /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.h has_starts && has_ends && attr_starts_.size() == attr_ends_.size() !has_axes || attr_axes_.size() == attr_starts_.size() Missing or invalid starts and ends attribute Invalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributes /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.cc nullptr != start_tensor && start_tensor->Shape().NumDimensions() == 1 nullptr != ends_tensor && ends_tensor->Shape().NumDimensions() == 1 start_tensor->Shape() == ends_tensor->Shape() nullptr == axes_tensor || start_tensor->Shape() == axes_tensor->Shape() nullptr == steps_tensor || start_tensor->Shape() == steps_tensor->Shape() Data type for starts and ends inputs' need to be int32_t or int64_t, but instead got Starts and ends shape mismatch Starts and axes shape mismatch Starts and steps shape mismatch 'axes' has an axis outside of the tensor dimension count Missing input tensor to be processed Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = float; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = double; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = bool; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>; bool dynamic = false] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = unsigned char]::__lambda46 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = short unsigned int]::__lambda46 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = short unsigned int] T* onnxruntime::Tensor::MutableData() [with T = short unsigned int] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = unsigned int]::__lambda46 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = long unsigned int]::__lambda46 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = signed char]::__lambda46 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = signed char] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = short int]::__lambda46 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = short int] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short int; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = int]::__lambda46 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = int] T* onnxruntime::Tensor::MutableData() [with T = int] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = int; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = long int]::__lambda46 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long int; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = float]::__lambda46 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = float; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = double]::__lambda46 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double] T* onnxruntime::Tensor::MutableData() [with T = double] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = double; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = onnxruntime::MLFloat16]::__lambda46 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = onnxruntime::MLFloat16] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = bool]::__lambda46 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = bool] T* onnxruntime::Tensor::MutableData() [with T = bool] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = bool; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = std::basic_string<char>]::__lambda46 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = std::basic_string<char>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>; bool dynamic = true] void onnxruntime::SliceBase::FillVectorsFromInput(const onnxruntime::OpKernelContext*, std::vector<long int>&, std::vector<long int>&, std::vector<long int>&, std::vector<long int>&) const int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) onnxruntime::SliceBase::SliceBase(const onnxruntime::OpKernelInfo&, bool) N11onnxruntime9SliceBaseE N11onnxruntime5SliceIhLb0EEE N11onnxruntime5SliceItLb0EEE N11onnxruntime5SliceIjLb0EEE N11onnxruntime5SliceImLb0EEE N11onnxruntime5SliceIaLb0EEE N11onnxruntime5SliceIsLb0EEE N11onnxruntime5SliceIiLb0EEE N11onnxruntime5SliceIlLb0EEE N11onnxruntime5SliceIfLb0EEE N11onnxruntime5SliceIdLb0EEE N11onnxruntime5SliceINS_9MLFloat16ELb0EEE N11onnxruntime5SliceIbLb0EEE N11onnxruntime5SliceISsLb0EEE N11onnxruntime5SliceIhLb1EEE N11onnxruntime5SliceItLb1EEE N11onnxruntime5SliceIjLb1EEE N11onnxruntime5SliceImLb1EEE N11onnxruntime5SliceIaLb1EEE N11onnxruntime5SliceIsLb1EEE N11onnxruntime5SliceIiLb1EEE N11onnxruntime5SliceIlLb1EEE N11onnxruntime5SliceIfLb1EEE N11onnxruntime5SliceIdLb1EEE N11onnxruntime5SliceINS_9MLFloat16ELb1EEE N11onnxruntime5SliceIbLb1EEE N11onnxruntime5SliceISsLb1EEE blocksize CRD DCR DepthToSpace SpaceToDepth Attribute blocksize is not set. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.h info.GetAttr("blocksize", &blocksize_).IsOK() DepthToSpace op: only 'DCR' and 'CRD' modes are supported /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.cc input.Shape().NumDimensions() == 4 input_depth % (blocksize_ * blocksize_) == 0 input_height % this->blocksize_ == 0 input_width % this->blocksize_ == 0 onnxruntime::common::Status onnxruntime::DepthToSpace<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::SpaceToDepth<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::DepthToSpace<T>::DepthToSpace(const onnxruntime::OpKernelInfo&) [with T = float] onnxruntime::SpaceDepthBase::SpaceDepthBase(const onnxruntime::OpKernelInfo&) N11onnxruntime14SpaceDepthBaseE N11onnxruntime12SpaceToDepthIfEE N11onnxruntime12DepthToSpaceIfEE *> Axis= NumOutputs= Input shape= Split Invalid value in 'split' attribute. All values must be > 0 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.h std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value > 0; }) Input cannot be split evenly on selected axis. Input shape= Cannot split using values in 'split' attribute. Axis= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.cc Split operator does not support T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] ComputeImpl T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] ComputeImpl T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] ComputeImpl const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::Split::Compute(onnxruntime::OpKernelContext*) const int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) onnxruntime::SplitBase::SplitBase(const onnxruntime::OpKernelInfo&) N11onnxruntime9SplitBaseE N11onnxruntime5SplitE . shape= must be 1 instead of Dimension of input input_shape[i] == 1 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/squeeze.h int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) static std::vector<long int> onnxruntime::SqueezeBase::ComputeOutputShape(const onnxruntime::TensorShape&, const onnxruntime::TensorShape&) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime11SqueezeBaseE N11onnxruntime7SqueezeE Tile Input count of Tile OP mismatch, the first one is empty Input count of Tile OP mismatch, the second one is empty the tensor to be tiled using Tile OP must be atleast 1 dimensional 'repeat' input tensor must be 1 dimensional 'repeat' input tensor must have the same length as the 'input' tensor Tile doesn't have an implementation yet for the type: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/tile.cc const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::Tile::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime4TileE Mismatched data types between input and output Tensors. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.h v >= 0 && static_cast<uint64_t>(v) <= std::numeric_limits<size_t>::max() Attribute perm of Transpose has an invalid value. Value /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.cc does not align with rank of input data: is outside range. is repeated. [ perm: onnxruntime::TransposeBase::TransposeBase(const onnxruntime::OpKernelInfo&) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::Transpose::Compute(onnxruntime::OpKernelContext*) const T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] N11onnxruntime13TransposeBaseE N11onnxruntime9TransposeE Unique Unsupported tensor type of gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = signed char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char] N11onnxruntime6UniqueE 'axes' has a duplicate axis Missing/Invalid 'axes' attribute value /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.h info.GetAttrs("axes", axes_).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.cc 'axes' has an out of range axis Compute int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::UnsqueezeBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::UnsqueezeBase::Prepare&) const onnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo&) N11onnxruntime13UnsqueezeBaseE N11onnxruntime9UnsqueezeE Upsample Resize: unexpected mode Upsample: unexpected mode Invalid roi input index. roi_input_idx_ > 0 scales != nullptr sizes == nullptr Resize: input/output value is nullptr Upsample: input/output value is nullptr Resize: input/output value's dimension mismatch Upsample: input/output value's dimension mismatch Resize: input shape needs to be at least a single dimension Upsample: input shape needs to be at least a single dimension. Resize: input tensor's dimension does not match the scales. Upsample: input tensor's dimension does not match the scales. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsample.cc Rank of input and output tensor should be same. output_dims.size() == dims.size() Resize: size of roi array should be 2 * N where N is the rank of input tensor X. 'Linear' mode only support 2-D inputs ('Bilinear') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the Only one of scales or sizes must be provided as input. sizes != nullptr && sizes->Shape().Size() != 0 Resize: input tensor's rank does not match the output tensor's rank. X->Shape().GetDims().size() == output_dims.size() Either scales or sizes MUST be provided as input. BaseCompute onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = float] onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] BaseCompute onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = int] onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] BaseCompute onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = unsigned char] onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor*, std::vector<float>&) const void onnxruntime::UpsampleBase::ScalesValidation(const std::vector<float>&, onnxruntime::UpsampleMode) const @ A @Where condition && X && Y condition, X, and Y inputs are required! /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/where_op.cc T* onnxruntime::Tensor::MutableData() [with T = int] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = int] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = long int] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = long int] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = float] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = std::basic_string<char>] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>] N11onnxruntime5WhereIiEE N11onnxruntime5WhereIlEE N11onnxruntime5WhereIfEE N11onnxruntime5WhereISsEE /onnxruntime_src/onnxruntime/contrib_ops/cpu_contrib_kernels.cc RegisterCpuContribKernels RegisterNchwcKernels /onnxruntime_src/onnxruntime/automl_ops/cpu_automl_kernels.cc RegisterCpuAutoMLKernels gamma Selu const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime7SigmoidIfEE N11onnxruntime4TanhIfEE N11onnxruntime3EluIfEE N11onnxruntime11HardSigmoidIfEE N11onnxruntime9LeakyReluIfEE N11onnxruntime4ReluIfEE N11onnxruntime4SeluIfEE N11onnxruntime18ParametricSoftplusIfEE N11onnxruntime8SoftsignIfEE N11onnxruntime15ThresholdedReluIfEE
+#<L>}-?_}?V If 'If' node has outputs. then_branch info == nullptr did not. else_branch ' attribute. Failed to create output tensor for If output outputs which doesn't match the subgraph's /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/if.cc num_subgraph_outputs == static_cast<size_t>(num_outputs) SetupSubgraphExecutionInfo should only be called once for each subgraph. Subgraph must have the shape set for all outputs but Failed to create output tensor for then_feeds_fetches_manager_ && else_feeds_fetches_manager_ Subgraph SessionState was not found for ' CreateFeedsFetchesManager must be called prior to execution of graph. info.GetAttr<ONNX_NAMESPACE::GraphProto>("then_branch", &proto).IsOK() info.GetAttr<ONNX_NAMESPACE::GraphProto>("else_branch", &proto).IsOK() Execute Initialize const T* onnxruntime::Tensor::Data() const [with T = bool] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute virtual onnxruntime::common::Status onnxruntime::If::Compute(onnxruntime::OpKernelContext*) const onnxruntime::If::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&) SetupSubgraphExecutionInfo virtual onnxruntime::common::Status onnxruntime::If::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) onnxruntime::If::If(const onnxruntime::OpKernelInfo&) *ZN11onnxruntime6IfImpl7ExecuteERKNS_19FeedsFetchesManagerEEUlRKNS_11TensorShapeERK13OrtMemoryInfoR8OrtValueRbE_ N11onnxruntime2IfE Expected: Loop inputs. Found: 'Loop' node has but has info_ == nullptr body feeds_fetches_manager_ Inconsistent shape in loop output for output. Graph in 'body' attribute of Loop should have /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/loop.cc static_cast<size_t>(num_subgraph_inputs) == subgraph_inputs.size() outputs so the subgraph requires num_subgraph_outputs - 1 == num_outputs 'Loop' input 'M' should be a scalar tensor. Got shape of 'Loop' input 'cond' should be a scalar tensor. Got shape of Loop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}. Subgraph SessionState was not found for 'body' attribute. info.GetAttr<ONNX_NAMESPACE::GraphProto>("body", &proto).IsOK() Execute ConcatenateLoopOutput const T* onnxruntime::Tensor::Data() const [with T = bool] const T* onnxruntime::Tensor::Data() const [with T = long int] Compute virtual onnxruntime::common::Status onnxruntime::Loop::Compute(onnxruntime::OpKernelContext*) const onnxruntime::Loop::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&) SetupSubgraphExecutionInfo virtual onnxruntime::common::Status onnxruntime::Loop::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) onnxruntime::Loop::Loop(const onnxruntime::OpKernelInfo&) FN11onnxruntime6common6StatusERSt6vectorI8OrtValueSaIS3_EEPvmE N11onnxruntime4LoopE PFN11onnxruntime6common6StatusERSt6vectorI8OrtValueSaIS3_EEPvmE Scan<8> spec does not support transpose of output. This should never be called. /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_8.cc dimensions or more but input had shape of Scan inputs have inconsistent batch size. Previous value was Scan inputs have inconsistent sequence lengths. Previous value was Invalid entries in sequence_lens. Max sequence length was Output OrtValue has not been created for loop state variable output /onnxruntime_src/onnxruntime/core/framework/ort_value_tensor_slicer.h position_ >= 0 && position_ < sequence_length_ info.GetAttr<int64_t>("num_scan_inputs", &num_scan_inputs_).IsOK() feeds_fetches_manager_ && info_ Scan Invalid scan input: has batch size of has length of sequence_lens length of did not match batch size of p_mlvalue num_scan_inputs Subgraph in 'body' produces outputs but Scan expects T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] Execute typename std::enable_if<(! std::is_const<T&>::value), T&>::type onnxruntime::OrtValueTensorSlicer<T>::Iterator::operator*() [with T = const OrtValue; typename std::enable_if<(! std::is_const<T&>::value), T&>::type = const OrtValue&] CreateLoopStateVariables onnxruntime::common::Status onnxruntime::Scan8Impl::CreateLoopStateVariables(std::vector<std::vector<onnxruntime::scan::detail::LoopStateVariable> >&) AllocateOutputTensors gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long int] ValidateInput Initialize const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::common::Status onnxruntime::Scan<OpSet>::Compute(onnxruntime::OpKernelContext*) const [with int OpSet = 8] onnxruntime::common::Status onnxruntime::Scan<OpSet>::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) [with int OpSet = 8; std::string = std::basic_string<char>] onnxruntime::Scan<OpSet>::Scan(const onnxruntime::OpKernelInfo&) [with int OpSet = 8] FN11onnxruntime20OrtValueTensorSlicerIK8OrtValueEERS2_llE FN11onnxruntime20OrtValueTensorSlicerI8OrtValueEERS1_llE *ZN11onnxruntime4ScanILi8EEC1ERKNS_12OpKernelInfoEEUlRKSt6vectorImSaImEERKNS_6TensorERSA_E_ *ZN11onnxruntime4ScanILi8EEC1ERKNS_12OpKernelInfoEEUlPvmE0_ N11onnxruntime4ScanILi8EEE N11onnxruntime20OrtValueTensorSlicerIK8OrtValueE8IteratorE PFN11onnxruntime20OrtValueTensorSlicerIK8OrtValueEERS2_llE PFN11onnxruntime20OrtValueTensorSlicerI8OrtValueEERS1_llE final_output_mlvalue_ . Output tensor rank was but input ' ' dimension . Input tensor rank was output_mlvalue scan_input_directions scan_output_directions scan_input_axes but expected scan_output_axes /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_9.cc Attempt to retrieve final output before it was set. /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_utils.h Invalid value in scan_output_axes for output Outputs from Scan are not optional and should never be null. Invalid value in scan_input_axes for input Number of entries in 'scan_input_axes' was gsl::narrow_cast<int64_t>(input_axes_.size()) == num_scan_inputs_ Number of entries in 'scan_output_axes' was gsl::narrow_cast<int64_t>(output_axes_.size()) == num_scan_outputs const OrtValue& onnxruntime::scan::detail::OutputIterator::GetOutput() const TransposeOutput onnxruntime::common::Status onnxruntime::ScanImpl::TransposeOutput() Execute onnxruntime::common::Status onnxruntime::ScanImpl::CreateLoopStateVariables(std::vector<onnxruntime::scan::detail::LoopStateVariable>&) CreateLoopStateVariables AllocateOutputTensors T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] SetupInputs ValidateInput Initialize Compute onnxruntime::common::Status onnxruntime::Scan<OpSet>::Compute(onnxruntime::OpKernelContext*) const [with int OpSet = 9] onnxruntime::common::Status onnxruntime::Scan<OpSet>::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) [with int OpSet = 9; std::string = std::basic_string<char>] onnxruntime::Scan<OpSet>::Scan(const onnxruntime::OpKernelInfo&) [with int OpSet = 9] FN11onnxruntime6common6StatusERKSt6vectorImSaImEERKNS_6TensorERS7_E *ZN11onnxruntime4ScanILi9EEC1ERKNS_12OpKernelInfoEEUlPvmE_ N11onnxruntime4ScanILi9EEE PFN11onnxruntime6common6StatusERKSt6vectorImSaImEERKNS_6TensorERS7_E Number of entries in ' ' was Invalid values in ' is not compatible with !is_concrete_shape_ Misuse of LoopStateVariable. Attempt to move beyond end of sequence /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_utils.cc iteration_num_ < sequence_len_ Expected AllocateFinalOutput to have been called to before we increment the iterator num_entries < 0 || gsl::narrow_cast<int64_t>(directions.size()) == num_entries '. 0 == forward. 1 == reverse. cur_iteration_ < num_iterations_ Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator. Mismatch between expected shape and shape from first output The subgraph in 'body' requires inputs but Scan was only given num_variadic_inputs == num_subgraph_inputs Failed to create output tensor for output # If shape was concrete we shouldn't be using a custom allocator onnxruntime::scan::detail::OutputIterator& onnxruntime::scan::detail::OutputIterator::operator++() typename std::enable_if<(! std::is_const<T&>::value), T&>::type onnxruntime::OrtValueTensorSlicer<T>::Iterator::operator*() [with T = OrtValue; typename std::enable_if<(! std::is_const<T&>::value), T&>::type = OrtValue&] OrtValue& onnxruntime::scan::detail::OutputIterator::operator*() AllocateFinalOutput onnxruntime::common::Status onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput(const onnxruntime::TensorShape&) AllocateFinalBuffer const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Initialize void onnxruntime::scan::detail::LoopStateVariable::Next() const T& OrtValue::Get() const [with T = onnxruntime::Tensor] operator() typename std::enable_if<(! std::is_const<T&>::value), T&>::type onnxruntime::OrtValueTensorSlicer<T>::Iterator::operator*() [with T = const OrtValue; typename std::enable_if<(! std::is_const<T&>::value), T&>::type = const OrtValue&] IterateSequence CreateFeedsFetchesManager void onnxruntime::scan::detail::ReadDirections(const onnxruntime::OpKernelInfo&, const string&, std::vector<long int>&, int64_t) onnxruntime::scan::detail::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&, int, bool) *ZN11onnxruntime4scan6detail15IterateSequenceERNS_23OpKernelContextInternalERKNS_12SessionStateERSt6vectorINS1_17LoopStateVariableESaIS8_EERS7_INS_20OrtValueTensorSlicerIK8OrtValueE8IteratorESaISG_EEliiiRKS7_IPSE_SaISK_EERS7_ISt10unique_ptrINS1_14OutputIteratorESt14default_deleteISQ_EESaIST_EERKNS_19FeedsFetchesManagerEEUlRKNS_11TensorShapeERK13OrtMemoryInfoRSD_RbE_ N11onnxruntime20OrtValueTensorSlicerI8OrtValueE8IteratorE ParametricSoftplus T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] ZNK11onnxruntime7contrib4GeluIfE7ComputeEPNS_15OpKernelContextEEUliE_ N11onnxruntime7contrib10ScaledTanhIfEE N11onnxruntime7contrib4GeluIfEE 5?AttnLSTM DEEPCPU_ATTN_LSTM }, actural: ], while found! , am_attn_size}, Got: }, Got: }. Got: , aw_attn_size}. Got: /onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.h static_cast<int>(activation_func_names.size()) == num_directions_ * 3 Attention mechanism memory shape error! Expected: { Attention mechanism memory sequence lengths must have shape { Attention mechanism memory sequence lengths value must in (0, Attention memory layer weight shape error! Expected:{ Attention query layer weight shape error! Expected:{ Attention v weight shape error! Expected:{ Attention layer weight shape error! Expected: { /onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.cc ValidateInputs ComputeImpl virtual onnxruntime::common::Status onnxruntime::contrib::DeepCpuAttnLstmOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&) onnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOp(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib17DeepCpuAttnLstmOpE N11onnxruntime7contrib16AttentionWrapperIfEE row[ [seqno= /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention.cc info.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0 Input 0 is expected to have 3 dimensions, got Input 0 dimension 2 should be divisiable by value of the num_heads attribute. Input 1 is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0 Input 1 dimension 1 should be 3 times of dimension 0 Input 2 is expected to have 1 dimension, got Input 2 dimension 0 should have same length as dimension 1 of input 1 Input 3 is expected to have 1 dimension, got Inputs 3 and 0 shall have same length at dimension 0 T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] Compute const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::AttentionBase::AttentionBase(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib13AttentionBaseE ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUliE_ ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUliE0_ ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUliE1_ ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUliE2_ N11onnxruntime7contrib9AttentionIfEE @ @ @ @ Shape of Input 0 is expected to have at least 1 dimension, got Input 1 is expected to have 1 dimension, got dimension 0 of Input 1 should have same length as the last dimension of input 0 /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/bias_gelu_fusion.cc T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute ZNK11onnxruntime7contrib8BiasGeluIfE7ComputeEPNS_15OpKernelContextEEUliE_ N11onnxruntime7contrib8BiasGeluIfEE input index out of range /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/embed_layer_norm.cc T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute ZNK11onnxruntime7contrib14EmbedLayerNormIfE7ComputeEPNS_15OpKernelContextEEUliE_ N11onnxruntime7contrib14EmbedLayerNormIfEE .)input_ids is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got segment_embedding is expected to have 2 dimensions, got word_embedding and position_embedding shall have same dimension 1 word_embedding and segment_embedding shall have same dimension 1 beta is expected to have 1 dimensions, got beta is expected to have size of gamma is expected to have 1 dimensions, got gamma is expected to have size of Input 0 and 1 shall have same shape Input 0 and 7 (mask) shall have same shape const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] /onnxruntime_src/onnxruntime/contrib_ops/cpu/cdist.h info.GetAttr<std::string>("metric", &metric).IsOK() The second input of CDist kernel has wrong shape: Input shape dimensions mismatch: The first input of CDist kernel has wrong shape: CDist sqeuclidean T* onnxruntime::Tensor::MutableData() [with T = float] T* onnxruntime::Tensor::MutableData() [with T = double] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::CDist<T>::CDist(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::contrib::CDist<T>::CDist(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib5CDistIfEE N11onnxruntime7contrib5CDistIdEE ConvTransposeWithDynamicPads N11onnxruntime7contrib28ConvTransposeWithDynamicPadsIfEE border input dimensions instead Input's height ( ) + bottomBorder ( Input's width ( ) + rightBorder ( ) + scale_[0] ( ) + scale_[1] ( Crop Attribute border needs to be specified with four border elements, got Input is expected to have four dimensions corresponding to [N,C,H,W], got ) needs to be greater than or equal to the topBorder ( ) needs to be greater than or equal to the leftBorder ( /onnxruntime_src/onnxruntime/contrib_ops/cpu/crop.h T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute N11onnxruntime7contrib8CropBaseE N11onnxruntime7contrib4CropIfEE bilinear CropAndResize Null crop_size_ptr /onnxruntime_src/onnxruntime/contrib_ops/cpu/crop_and_resize.h specified. It should be either bilinear or nearest Number of dimensions for crop size should be exactly 1 const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::CropAndResize<T>::CropAndResize(const onnxruntime::OpKernelInfo&) [with T = float] ZN11onnxruntime7contrib20CropAndResizeForwardIfEEvRKNS_11TensorShapeEPKT_fllS7_lPS5_RKSsPKiPNS_11concurrency10ThreadPoolEEUliE_ N11onnxruntime7contrib13CropAndResizeIfEE DynamicSlice Scale /onnxruntime_src/onnxruntime/contrib_ops/cpu/element_wise_ops.h info.GetAttr("scale", &scale_).IsOK() info.GetAttr("alpha", &alpha_).IsOK() info.GetAttr("beta", &beta_).IsOK() onnxruntime::contrib::Scale<T>::Scale(const onnxruntime::OpKernelInfo&) [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::Affine<T>::Affine(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib6AffineIfEE N11onnxruntime7contrib5ScaleIfEE ExpandDims Axis must be within range [ Axis is /onnxruntime_src/onnxruntime/contrib_ops/cpu/expand_dims.h axis_tensor->Shape().IsScalar() axis <= X_NumDims && axis >= -X_NumDims An axis tensor must be a scalar tensor. const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] const T* onnxruntime::Tensor::Data() const [with T = int] virtual onnxruntime::common::Status onnxruntime::contrib::ExpandDims::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7contrib10ExpandDimsE /onnxruntime_src/onnxruntime/contrib_ops/cpu/fused_conv.cc GetFusedActivationAttr(info, activation_).IsOK() onnxruntime::contrib::FusedConvFloat::FusedConvFloat(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib14FusedConvFloatE leaky_relu_alpha onnxruntime::Gemm<T>::Gemm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib9FusedGemmIfEE ImageScaler Bias size ( /onnxruntime_src/onnxruntime/contrib_ops/cpu/image_scaler.h info.GetAttrs<float>("bias", bias_).IsOK() ) does not match the number of channels ( T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::ImageScaler<T>::ImageScaler(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib11ImageScalerIfEE /onnxruntime_src/onnxruntime/contrib_ops/cpu/layer_norm.cc op_kernel_info.GetAttr("axis", &axis_).IsOK() T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] Compute T* onnxruntime::Tensor::MutableData() [with T = double] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T* onnxruntime::Tensor::Data() const [with T = double] Compute onnxruntime::contrib::LayerNorm<T>::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::contrib::LayerNorm<T>::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = float] ZNK11onnxruntime7contrib9LayerNormIdE7ComputeEPNS_15OpKernelContextEEUliE_ ZNK11onnxruntime7contrib9LayerNormIfE7ComputeEPNS_15OpKernelContextEEUliE_ N11onnxruntime7contrib9LayerNormIfEE N11onnxruntime7contrib9LayerNormIdEE MatMulInteger16 A != nullptr && B != nullptr /onnxruntime_src/onnxruntime/contrib_ops/cpu/matmul_integer16.cc const T* onnxruntime::Tensor::Data() const [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = int] Compute onnxruntime::common::Status onnxruntime::contrib::MatMulInteger16<T1, T2, T3>::Compute(onnxruntime::OpKernelContext*) const [with T1 = short int; T2 = short int; T3 = int] N11onnxruntime7contrib15MatMulInteger16IssiEE MaxpoolWithMask /onnxruntime_src/onnxruntime/contrib_ops/cpu/maxpool_with_mask.h T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) const void onnxruntime::PoolAttributes::InferOutputSize(const std::vector<long int>&, std::vector<long int>*, std::vector<long int>*) const std::vector<long int> onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape&, int64_t, std::vector<long int>*) const const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute N11onnxruntime7contrib15MaxpoolWithMaskE across_channels normalize_variance /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/mean_variance_normalization.h info.GetAttr<int64_t>("across_channels", &across_channels_).IsOK() info.GetAttr<int64_t>("normalize_variance", &normalize_variance_).IsOK() onnxruntime::MeanVarianceNormalization_0<T>::MeanVarianceNormalization_0(const onnxruntime::OpKernelInfo&, bool) [with T = float] MurmurHash3 keys /onnxruntime_src/onnxruntime/contrib_ops/cpu/murmur_hash3.cc Invalid assumption of output element size sizeof(uint32_t) == output_element_bytes const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::contrib::MurmurHash3::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7contrib11MurmurHash3E invalid channel count channels_ > 0 X_shape.NumDimensions() == 4 channels_ <= Y_shape[1] Unsupported convolution size. /onnxruntime_src/onnxruntime/contrib_ops/cpu/nchwc_ops.h info.GetAttr<int64_t>("channels", &channels_).IsOK() pool_attrs_.kernel_shape.size() == 2 /onnxruntime_src/onnxruntime/contrib_ops/cpu/nchwc_ops.cc (X_shape[1] % MlasNchwcGetBlockSize()) == 0 (static_cast<size_t>(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0) Not satsified: Y->Shape() == sum_shape
+ output and sum shape must match onnxruntime::common::Status onnxruntime::contrib::ReorderInput<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::common::Status onnxruntime::contrib::ReorderOutput<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] void onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) const void onnxruntime::PoolAttributes::InferOutputSize(const std::vector<long int>&, std::vector<long int>*, std::vector<long int>*) const std::vector<long int> onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape&, int64_t, std::vector<long int>*) const onnxruntime::common::Status onnxruntime::contrib::NchwcPoolBase::NchwcPool(onnxruntime::OpKernelContext*, MLAS_POOLING_KIND) const virtual onnxruntime::common::Status onnxruntime::contrib::NchwcConv::Compute(onnxruntime::OpKernelContext*) const Compute onnxruntime::contrib::NchwcPoolBase::NchwcPoolBase(const onnxruntime::OpKernelInfo&) onnxruntime::contrib::NchwcConv::NchwcConv(const onnxruntime::OpKernelInfo&) onnxruntime::contrib::ReorderOutput<T>::ReorderOutput(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib13NchwcPoolBaseE N11onnxruntime7contrib9NchwcConvE N11onnxruntime7contrib12NchwcMaxPoolE N11onnxruntime7contrib16NchwcAveragePoolE N11onnxruntime7contrib12ReorderInputIfEE N11onnxruntime7contrib13ReorderOutputIfEE y_scale SampleOp T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime7contrib8SampleOpIfEE input is expected to have 3 dimensions, got gamma is expected to have 1 dimension, got Last dimension of gamma and input does not match beta is expected to have 1 dimension, got Last dimension of beta and input does not match bias is expected to have 1 dimension, got Last dimension of bias and input does not match skip is expected to have same shape as input const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] ZNK11onnxruntime7contrib13SkipLayerNormIdE7ComputeEPNS_15OpKernelContextEEUliE_ ZNK11onnxruntime7contrib13SkipLayerNormIfE7ComputeEPNS_15OpKernelContextEEUliE_ N11onnxruntime7contrib13SkipLayerNormIfEE N11onnxruntime7contrib13SkipLayerNormIdEE q=̼+Tokenizer mark attribute mark is not set pad_value mincharnum mincharnum_ > 0 separators tokenexp !tokenexp.empty() separators must not be empty !separators.empty() Can not digest separators: Can not digest tokenexp: /onnxruntime_src/onnxruntime/contrib_ops/cpu/tokenizer.cc attribute pad_value is not set attribute mincharnum is not set attribute mincharnum must have a positive value Either one of the separators OR tokenexp attributes required but none is set Expecting a non-empty tokenexp mincharnum is too big for char level tokenezation !char_tokenezation_ || mincharnum_ < 2 Input string contains invalid utf8 chars: Match contains invalid utf8 chars: tensor(string) expected as input Input dimensions are either [C] or [N][C] allowed const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::Tokenizer::Tokenizer(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib9TokenizerE Input tensor to Unique op should be 1D T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime7contrib6UniqueIfEE conv_window_size char_embedding_size embedding_size attribute: conv filter size: conv_window_size attribute: conv kernal size 1: Char embedding size: Conv kernal size 2 : WordConvEmbedding Conv filter size does not match embedding_size attribute. Conv kernal size 1 does not match conv_window_size attribute . Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Char embedding size does not match conv kernal size 2. /onnxruntime_src/onnxruntime/contrib_ops/cpu/word_conv_embedding.cc const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute N11onnxruntime7contrib17WordConvEmbeddingE hӥhӥhӥhDateTimeTransformer const T* onnxruntime::Tensor::Data() const [with T = long int] T* OrtValue::GetMutable() [with T = Microsoft::Featurizer::DateTimeFeaturizer::TimePoint] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime6automl19DateTimeTransformerE Real memory steps is not in (0, not support normalize yet. !normalize_ /onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/bahdanau_attention.cc mem_steps <= max_memory_steps_ && mem_steps > 0 void onnxruntime::contrib::BahdanauAttention<T>::PrepareMemory(const gsl::span<const T>&, const gsl::span<const int>&) [with T = float] onnxruntime::contrib::BahdanauAttention<T>::BahdanauAttention(onnxruntime::AllocatorPtr, const onnxruntime::logging::Logger&, int, int, int, int, int, bool, onnxruntime::concurrency::ThreadPool*) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] N11onnxruntime7contrib19IAttentionMechanismIfEE N11onnxruntime7contrib17BahdanauAttentionIfEE unimplemented activation: activation_params count mismatch Expecting an instance of ExternalTimePoint /onnxruntime_src/onnxruntime/automl_ops/automl_types.cc sizeof(DateTimeFeaturizerTimePointData) == data_size const T& OrtValue::Get() const [with T = Microsoft::Featurizer::DateTimeFeaturizer::TimePoint] static void onnxruntime::NonTensorTypeConverter<Microsoft::Featurizer::DateTimeFeaturizer::TimePoint>::ToContainer(const OrtValue&, size_t, void*) static void onnxruntime::NonTensorTypeConverter<Microsoft::Featurizer::DateTimeFeaturizer::TimePoint>::FromContainer(onnxruntime::MLDataType, const void*, size_t, OrtValue&) N11onnxruntime13NonTensorTypeIN9Microsoft10Featurizer18DateTimeFeaturizer9TimePointEEE N11onnxruntime10OpaqueTypeIN9Microsoft10Featurizer18DateTimeFeaturizer9TimePointEXadL_ZNS_15kMsAutoMLDomainEEEXadL_ZNS_14kTimepointNameEEEEE DateTimeFeaturizer_TimePoint com.microsoft.automl SSE, 12x4, depth 2 allocation failure gemmlowp error: %s
+ N8gemmlowp10KernelBaseE N8gemmlowp24SSE4_64_Kernel12x4Depth2E N8gemmlowp17DefaultKernelImplILb0ELb0EEE N8gemmlowp4TaskE N8gemmlowp13DefaultKernelINS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEES3_EEEE N8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE1ELSD_1ELSD_1ENS_9VectorDupIKiLNS_11VectorShapeE0EEENSE_ISF_LSG_1EEESt5tupleIINS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEE N8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE1ELSD_1ELSD_1ENS_9VectorDupIKiLNS_11VectorShapeE0EEENSE_ISF_LSG_1EEESt5tupleIINS_23OutputStageBiasAdditionINS_9VectorMapISF_LSG_0EEEEENS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEE N8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE0ELSD_0ELSD_0ENS_9VectorDupIKiLNS_11VectorShapeE1EEENSE_ISF_LSG_0EEESt5tupleIINS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEE N8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE0ELSD_0ELSD_0ENS_9VectorDupIKiLNS_11VectorShapeE1EEENSE_ISF_LSG_0EEESt5tupleIINS_23OutputStageBiasAdditionINS_9VectorMapISF_LSG_1EEEEENS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEE @ @ Gemv float found an unexpected CBLAS_TRANSPOSE input of /onnxruntime_src/onnxruntime/core/util/math_cpu.cc void onnxruntime::math::Im2colNd<T, 2>::operator()(const T*, const int64_t*, const int64_t*, int64_t, int64_t, const int64_t*, const int64_t*, const int64_t*, const int64_t*, int64_t, T*, bool, T) [with T = float; int64_t = long int] /onnxruntime_src/onnxruntime/core/util/protobuf_parsing_utils.cc Can't BackUp() after failed Next(). CHECK failed: !is_closed_: close() failed: N6google8protobuf2io19CopyingOutputStreamE N6google8protobuf2io15FileInputStream22CopyingFileInputStreamE N6google8protobuf2io15FileInputStreamE N6google8protobuf2io16FileOutputStream23CopyingFileOutputStreamE N6google8protobuf2io16FileOutputStreamE OrtMemoryInfo: [ id: mem_type: Cuda CudaPinned Specified device is not supported. N11onnxruntime12CPUAllocatorE St14default_deleteIN11onnxruntime16IDeviceAllocatorEE St14default_deleteIN11onnxruntime8BFCArenaEE St19_Sp_counted_deleterIPN11onnxruntime8BFCArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE St19_Sp_counted_deleterIPN11onnxruntime16IDeviceAllocatorESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE /onnxruntime_src/onnxruntime/core/framework/bfc_arena.h /onnxruntime_src/onnxruntime/core/framework/bfc_arena.cc !c->in_use() && (c->bin_num != kInvalidBinNum) !c->in_use() && (c->bin_num == kInvalidBinNum) p_int < base_int + memory_size_ !c1->in_use() && !c2->in_use() bin->free_chunks.count(h) == 1 Creating bin of max chunk size BinForSize(bin_size) == BinFromIndex(b) BinForSize(bin_size + 255) == BinFromIndex(b) BinForSize(bin_size * 2 - 1) == BinFromIndex(b) BinForSize(bin_size * 2) != BinFromIndex(b) 0 == memory_size % kMinAllocationSize BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0 c->in_use() && (c->bin_num == kInvalidBinNum) b->free_chunks.size() == bin_info.total_chunks_in_bin - bin_info.total_chunks_in_use client-requested in use in bin. Summary of in-use Chunks by size: BFC Arena ran out of memory trying . Current allocation summary follows. reserved_chunks_.find(ptr) == reserved_chunks_.end() Could not find Region for h < chunks_.size() Size: | Requested Size: | in_use: p_int >= base_int c2->prev == h1 h != kInvalidChunkHandle c->bin_num == bin_num !chunk->in_use() Extended allocation by bytes. Total allocated bytes: Allocated memory at Could not find chunk in bin Chunk Free Bin ( ): Total Chunks: , Chunks in use: allocated for chunks. in use in bin. Bin for , Chunk State: , prev: , next: Chunks of size totalling Sum Total of in-use chunks: Limit: InUse: TotalAllocated: MaxInUse: NumAllocs: MaxAllocSize: Stats:
+ tried to allocate 0 bytes to allocate DumpMemoryLog void onnxruntime::BFCArena::DumpMemoryLog(size_t) std::array<onnxruntime::BFCArena::BinDebugInfo, 21ul> onnxruntime::BFCArena::get_bin_debug_info() void onnxruntime::BFCArena::FreeAndMaybeCoalesce(onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::RemoveFreeChunkFromBin(onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::RemoveFreeChunkIterFromBin(onnxruntime::BFCArena::Bin::FreeChunkSet*, const iterator&) void onnxruntime::BFCArena::InsertFreeChunkIntoBin(onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::Merge(onnxruntime::BFCArena::ChunkHandle, onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::DeallocateRawInternal(void*) void onnxruntime::BFCArena::SplitChunk(onnxruntime::BFCArena::ChunkHandle, size_t) void* onnxruntime::BFCArena::FindChunkPtr(onnxruntime::BFCArena::BinNum, size_t, size_t) AllocateRawInternal virtual void* onnxruntime::BFCArena::Reserve(size_t) int onnxruntime::BFCArena::AllocationRegion::IndexFor(const void*) const RegionFor onnxruntime::BFCArena::AllocationRegion::AllocationRegion(void*, size_t) Extend onnxruntime::BFCArena::Chunk* onnxruntime::BFCArena::ChunkFromHandle(onnxruntime::BFCArena::ChunkHandle) onnxruntime::BFCArena::BFCArena(std::unique_ptr<onnxruntime::IDeviceAllocator>, size_t) BFCArena N11onnxruntime8BFCArenaE fff?/onnxruntime_src/onnxruntime/core/framework/data_transfer.cc src.SizeInBytes() == dst.SizeInBytes() virtual onnxruntime::common::Status onnxruntime::CPUDataTransfer::CopyTensor(const onnxruntime::Tensor&, onnxruntime::Tensor&, int) const N11onnxruntime13IDataTransferE N11onnxruntime15CPUDataTransferE Device: [ memory_type: device_id: Tensor size mismatch There's no data transfer registered for copying tensors from data_transfer registered is nullptr. (null) tensor(bfloat16) Not implemented sparse tensor type elem_proto != nullptr value_proto != nullptr MLDataType for: hXH8(ءx /onnxruntime_src/onnxruntime/core/framework/data_types.cc thisProto->value_case() == TypeProto::ValueCase::kTensorType utils::HasElemType(thisProto->tensor_type()) thisProto->value_case() == TypeProto::ValueCase::kSparseTensorType utils::HasElemType(thisProto->sparse_tensor_type()) thisProto->value_case() == TypeProto::ValueCase::kSequenceType utils::HasElemType(thisProto->sequence_type()) thisProto->value_case() == TypeProto::ValueCase::kMapType utils::HasKeyType(thisProto->map_type()) thisProto->value_case() == TypeProto::ValueCase::kOpaqueType Invalid DataTypeImpl TypeProto definition Only ONNX MLDataType can be registered We do not expect duplicate registration of types for: expected to be a registered ONNX type /onnxruntime_src/include/onnxruntime/core/framework/data_types.h is not currently registered or supported l$ܯp0$$4DTdt $$ onnxruntime::utils::ContainerChecker::ContainerChecker(onnxruntime::MLDataType) void onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataType(onnxruntime::MLDataType) static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = std::map<long int, float>] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = std::map<std::basic_string<char>, float>] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = onnxruntime::BFloat16] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = onnxruntime::MLFloat16] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = std::basic_string<char>] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = bool] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = long unsigned int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = long int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = unsigned int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = short unsigned int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = short int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = unsigned char] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = signed char] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = double] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = float] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = double] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = float] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = long int] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = std::basic_string<char>] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = double] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = float] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = long int] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = std::basic_string<char>] virtual void onnxruntime::NonTensorTypeBase::ToDataContainer(const OrtValue&, size_t, void*) const virtual void onnxruntime::NonTensorTypeBase::FromDataContainer(const void*, size_t, OrtValue&) const bool onnxruntime::NonTensorTypeBase::IsOpaqueCompatible(const onnx::TypeProto&) const bool onnxruntime::NonTensorTypeBase::IsSequenceCompatible(const onnx::TypeProto&) const bool onnxruntime::NonTensorTypeBase::IsMapCompatible(const onnx::TypeProto&) const virtual bool onnxruntime::SequenceTensorTypeBase::IsCompatible(const onnx::TypeProto&) const virtual bool onnxruntime::SparseTensorTypeBase::IsCompatible(const onnx::TypeProto&) const virtual bool onnxruntime::TensorTypeBase::IsCompatible(const onnx::TypeProto&) const bool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Sequence&, const onnx::TypeProto_Sequence&) bool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Map&, const onnx::TypeProto_Map&) GetElementType GetElementType GetElementType N11onnxruntime12DataTypeImplE N11onnxruntime14TensorTypeBaseE N11onnxruntime20SparseTensorTypeBaseE N11onnxruntime17NonTensorTypeBaseE N11onnxruntime22SequenceTensorTypeBaseE N11onnxruntime21PrimitiveDataTypeBaseE N11onnxruntime10TensorTypeIiEE N11onnxruntime10TensorTypeIfEE N11onnxruntime10TensorTypeIbEE N11onnxruntime10TensorTypeISsEE N11onnxruntime10TensorTypeIaEE N11onnxruntime10TensorTypeIhEE N11onnxruntime10TensorTypeItEE N11onnxruntime10TensorTypeIsEE N11onnxruntime10TensorTypeIlEE N11onnxruntime10TensorTypeIdEE N11onnxruntime10TensorTypeIjEE N11onnxruntime10TensorTypeImEE N11onnxruntime10TensorTypeINS_9MLFloat16EEE N11onnxruntime10TensorTypeINS_8BFloat16EEE N11onnxruntime16SparseTensorTypeIiEE N11onnxruntime16SparseTensorTypeIfEE N11onnxruntime16SparseTensorTypeIbEE N11onnxruntime16SparseTensorTypeIaEE N11onnxruntime16SparseTensorTypeIhEE N11onnxruntime16SparseTensorTypeItEE N11onnxruntime16SparseTensorTypeIsEE N11onnxruntime16SparseTensorTypeIlEE N11onnxruntime16SparseTensorTypeIdEE N11onnxruntime16SparseTensorTypeIjEE N11onnxruntime16SparseTensorTypeImEE N11onnxruntime16SparseTensorTypeINS_9MLFloat16EEE N11onnxruntime16SparseTensorTypeINS_8BFloat16EEE N11onnxruntime13NonTensorTypeISt3mapISsSsSt4lessISsESaISt4pairIKSsSsEEEEE N11onnxruntime7MapTypeISt3mapISsSsSt4lessISsESaISt4pairIKSsSsEEEEE N11onnxruntime13NonTensorTypeISt3mapISslSt4lessISsESaISt4pairIKSslEEEEE N11onnxruntime7MapTypeISt3mapISslSt4lessISsESaISt4pairIKSslEEEEE N11onnxruntime13NonTensorTypeISt3mapISsfSt4lessISsESaISt4pairIKSsfEEEEE N11onnxruntime7MapTypeISt3mapISsfSt4lessISsESaISt4pairIKSsfEEEEE N11onnxruntime13NonTensorTypeISt3mapISsdSt4lessISsESaISt4pairIKSsdEEEEE N11onnxruntime7MapTypeISt3mapISsdSt4lessISsESaISt4pairIKSsdEEEEE N11onnxruntime13NonTensorTypeISt3mapIlSsSt4lessIlESaISt4pairIKlSsEEEEE N11onnxruntime7MapTypeISt3mapIlSsSt4lessIlESaISt4pairIKlSsEEEEE N11onnxruntime13NonTensorTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEE N11onnxruntime7MapTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEE N11onnxruntime13NonTensorTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEE N11onnxruntime7MapTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEE N11onnxruntime13NonTensorTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEE N11onnxruntime7MapTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEE N11onnxruntime18SequenceTensorTypeIfEE N11onnxruntime18SequenceTensorTypeIdEE N11onnxruntime18SequenceTensorTypeIaEE N11onnxruntime18SequenceTensorTypeIhEE N11onnxruntime18SequenceTensorTypeIsEE N11onnxruntime18SequenceTensorTypeItEE N11onnxruntime18SequenceTensorTypeIiEE N11onnxruntime18SequenceTensorTypeIjEE N11onnxruntime18SequenceTensorTypeIlEE N11onnxruntime18SequenceTensorTypeImEE N11onnxruntime18SequenceTensorTypeIbEE N11onnxruntime18SequenceTensorTypeISsEE N11onnxruntime18SequenceTensorTypeINS_9MLFloat16EEE N11onnxruntime18SequenceTensorTypeINS_8BFloat16EEE N11onnxruntime13NonTensorTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEE N11onnxruntime12SequenceTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEE N11onnxruntime13NonTensorTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEE N11onnxruntime12SequenceTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEE N11onnxruntime17PrimitiveDataTypeIiEE N11onnxruntime17PrimitiveDataTypeIfEE N11onnxruntime17PrimitiveDataTypeIbEE N11onnxruntime17PrimitiveDataTypeISsEE N11onnxruntime17PrimitiveDataTypeIaEE N11onnxruntime17PrimitiveDataTypeIhEE N11onnxruntime17PrimitiveDataTypeItEE N11onnxruntime17PrimitiveDataTypeIsEE N11onnxruntime17PrimitiveDataTypeIlEE N11onnxruntime17PrimitiveDataTypeIdEE N11onnxruntime17PrimitiveDataTypeIjEE N11onnxruntime17PrimitiveDataTypeImEE N11onnxruntime17PrimitiveDataTypeINS_9MLFloat16EEE N11onnxruntime17PrimitiveDataTypeINS_8BFloat16EEE ZN11onnxruntime19data_types_internal16DataTypeRegistryC1EvEUlPKNS_12DataTypeImplEE_ ZN11onnxruntime19data_types_internal16DataTypeRegistryC1EvEUlPKNS_12DataTypeImplEE0_ Ss N11onnxruntime9MLFloat16E N11onnxruntime8BFloat16E St3mapISsfSt4lessISsESaISt4pairIKSsfEEE St3mapIlfSt4lessIlESaISt4pairIKlfEEE Caught exception while destructing CustomOpsLoader with message: /onnxruntime_src/onnxruntime/core/framework/ex_lib_loader.cc Caught exception while loading custom ops with message: Unloading DSO Failed to unload DSO: A dso with name has already been loaded. LoadExternalLib ~ExLibLoader N11onnxruntime11ExLibLoaderE Memory pattern planner is not enabled on this execution framework. /onnxruntime_src/onnxruntime/core/framework/execution_frame.h ort_value_index >= 0 && static_cast<size_t>(ort_value_index) < all_values_size_ /onnxruntime_src/onnxruntime/core/framework/node_index_info.h offset >= 0 && static_cast<size_t>(offset) < node_values_size_ /onnxruntime_src/onnxruntime/core/framework/execution_frame.cc ort_value_idx >= 0 && static_cast<size_t>(ort_value_idx) < alloc_plan.size() TraceAllocation for ort_value_idx= OrtValue shape verification failed. Current shape: shape && tensor.Shape() == *shape Shape mismatch attempting to re-use buffer. . Validate usage of dim_value (values should be > 0) and dim_param (all values with the same string should equate to the same size) in shapes in the model. entries which doesn't match the number of fetches the frame was initialized with of Fetches vector passed to GetOutputs contains feeds.size() == feed_mlvalue_idxs.size() fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size() node_index_info and ort_value_idx_map are out of sync and cannot be used node_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx() Trying to allocate memory for unused optional inputs/outputs Tensor shape cannot contain any negative value , block in memory pattern size is: , fall back to default allocation behavior , block not found in target location. fall back to default allocation behavior ort_value_index >= 0 && static_cast<size_t>(ort_value_index) < alloc_plan.size() We don't expect custom allocators for non-tensor types, so a shape is mandatory here. Allocation of tensor types requires a shape. buffers_.find(mem_patterns_->locations[i]) == buffers_.end() size= TraceFree for ort_value_idx= Requested shape: invalid index ort_value.Fence() == nullptr For ort_value with index: but the actually size is: Invalid allocation kind: mlvalue.Fence() == nullptr TraceFree void onnxruntime::ExecutionFrame::TraceFree(int) TraceAllocate const onnxruntime::AllocPlanPerValue& onnxruntime::ExecutionFrame::GetAllocationPlan(int) ReleaseMLValueImpl onnxruntime::common::Status onnxruntime::AllocateSparseTensor(OrtValue&, const onnxruntime::DataTypeImpl&, onnxruntime::AllocatorPtr, const onnxruntime::TensorShape&, size_t, bool, const onnxruntime::SessionState&) AllocateAsPerAllocationPlan onnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlan(OrtValue&, int, const onnxruntime::TensorShape*, size_t) T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] AllocateMLValueTensorPreAllocateBuffer AllocateMLValueTensorSelfOwnBufferHelper onnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper(OrtValue&, int, onnxruntime::MLDataType, const OrtMemoryInfo&, const onnxruntime::TensorShape&, bool) onnxruntime::ExecutionFrame::ExecutionFrame(const std::vector<int>&, const std::vector<OrtValue>&, const std::vector<int>&, const std::vector<OrtValue>&, const std::unordered_map<long unsigned int, std::function<onnxruntime::common::Status(const onnxruntime::TensorShape&, const OrtMemoryInfo&, OrtValue&, bool&)> >&, const onnxruntime::SessionState&) int onnxruntime::NodeIndexInfo::GetMLValueIndex(int) const const OrtValue& onnxruntime::IExecutionFrame::GetMLValue(int) const const T& OrtValue::Get() const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(int, const onnxruntime::TensorShape*, OrtValue*&, size_t) onnxruntime::IExecutionFrame::IExecutionFrame(const std::vector<int>&, const std::vector<OrtValue>&, const std::unordered_map<int, OrtValue>&, const std::vector<int>&, const std::vector<OrtValue>&, const OrtValueNameIdxMap&, const onnxruntime::NodeIndexInfo&) N11onnxruntime15IExecutionFrameE N11onnxruntime14ExecutionFrameE duplicated allocator /onnxruntime_src/onnxruntime/core/framework/execution_provider.cc void onnxruntime::IExecutionProvider::InsertAllocator(onnxruntime::AllocatorPtr) N11onnxruntime18IExecutionProviderE Error mapping feeds: Error mapping output names: /onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.cc input_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknown void onnxruntime::FeedsFetchesManager::SetDeviceCopyChecks(onnxruntime::DeviceCopyCheck, onnxruntime::DeviceCopyCheck) onnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(const std::vector<std::basic_string<char> >&, const std::vector<std::basic_string<char> >&, const onnxruntime::OrtValueNameIdxMap&) FeedsFetchesInfo MapNamesToMLValueIdxs /onnxruntime_src/onnxruntime/core/framework/func_kernel.h create_func(&context, &func_state_) == 0 /onnxruntime_src/onnxruntime/core/framework/graph_partitioner.cc Provider doesn't return correct number of compiled functions node_compute_funcs.size() == nodes_need_compile.size() No provider specified. 1 == capability->nodes.size() onnxruntime::FunctionKernel::FunctionKernel(const onnxruntime::OpKernelInfo&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const onnxruntime::Node* onnxruntime::PlaceNode(onnxruntime::Graph&, std::unique_ptr<onnxruntime::IndexedSubGraph>, const onnxruntime::KernelRegistryManager&, const string&, int&) onnxruntime::common::Status onnxruntime::GraphPartitioner::Partition(onnxruntime::Graph&, bool, onnxruntime::FuncManager&) const Partition N11onnxruntime14FunctionKernelE Op: Incompatible types. kernel def can't be NULL Failed to add kernel for Version mismatch. node_version: kernel start version: kernel_end_version: kernel is not supported in Failed to find kernel for : Conflicting with a registered kernel with op versions. /onnxruntime_src/onnxruntime/core/framework/kernel_registry.cc len <= op_schema.inputs().size() Failed to create kernel for op: since it was ill-formed during registration Encountered following errors: ( TryFindKernel void onnxruntime::{anonymous}::TraverseFormalParametersWithTypeProto(const onnxruntime::Node&, ParamFilterFn, TraverseFn) [with ParamFilterFn = onnxruntime::{anonymous}::TypeBindingResolver::Resolve(const string&) const::__lambda9; TraverseFn = onnxruntime::{anonymous}::TypeBindingResolver::Resolve(const string&) const::__lambda10] void onnxruntime::{anonymous}::TraverseFormalParametersWithTypeProto(const onnxruntime::Node&, ParamFilterFn, TraverseFn) [with ParamFilterFn = onnxruntime::{anonymous}::TypeBindingResolver::TypeBindingResolver(const onnxruntime::Node&, bool)::__lambda7; TraverseFn = onnxruntime::{anonymous}::TypeBindingResolver::TypeBindingResolver(const onnxruntime::Node&, bool)::__lambda8] The node is not placed on any Execution Provider, therefore, can't find a suitable kernel for The node is not placed on any Execution Provider (node found duplicated provider in KernelRegistryManager nullptr != type_proto /onnxruntime_src/onnxruntime/core/framework/mldata_type_utils.cc const onnxruntime::DataTypeImpl* onnxruntime::utils::GetMLDataType(const onnxruntime::NodeArg&) /onnxruntime_src/onnxruntime/core/framework/node_index_info.cc onnxruntime::NodeIndexInfo::Init(const TValidNodes&, onnxruntime::NodeIndex, const OrtValueNameIdxMap&) [with TValidNodes = onnxruntime::ValidNodes<const std::vector<const onnxruntime::Node*> >; onnxruntime::NodeIndex = long unsigned int; onnxruntime::OrtValueNameIdxMap = onnxruntime::OrtValueNameIdxMap]::__lambda8 onnxruntime::NodeIndexInfo::Init(const TValidNodes&, onnxruntime::NodeIndex, const OrtValueNameIdxMap&) [with TValidNodes = onnxruntime::GraphNodes; onnxruntime::NodeIndex = long unsigned int; onnxruntime::OrtValueNameIdxMap = onnxruntime::OrtValueNameIdxMap]::__lambda8 ZN11onnxruntime13NodeIndexInfo4InitINS_10GraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ ZN11onnxruntime13NodeIndexInfo4InitINS_10GraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_ ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_ :tensor type Tensor types should have been handled already |0||| |{||0|Ll L const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] TempSpace allocator not found Execution frame was null frame != nullptr OpKernel was null kernel != nullptr . Num args is Invalid arg_num of arg_num < arg_counts.size() /onnxruntime_src/onnxruntime/core/framework/op_kernel.cc node_offsets_index < node_offsets_size_ OrtValue* onnxruntime::OpKernelContext::GetOrCreateOutputMLValue(int) int onnxruntime::OpKernelContext::NumVariadicInputs(size_t) const OrtValue* onnxruntime::OpKernelContext::OutputMLValue(int, const onnxruntime::TensorShape&, size_t) T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] int onnxruntime::NodeIndexInfo::GetNodeOffset(onnxruntime::NodeIndex) const onnxruntime::OpKernelContext::OpKernelContext(onnxruntime::IExecutionFrame*, const onnxruntime::OpKernel*, onnxruntime::concurrency::ThreadPool*, const onnxruntime::logging::Logger&) cannot find allocator /onnxruntime_src/onnxruntime/core/framework/op_kernel_info.cc const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const OrtMemoryInfo& onnxruntime::OpKernelInfo::GetMemoryInfo(int, OrtMemType) const 'is defined. No attribute with name:' attr != nullptr No attribute with this name is defined. /onnxruntime_src/onnxruntime/core/framework/op_node_proto_helper.cc values.size() == static_cast<size_t>(attr->floats_size()) values.size() == static_cast<size_t>(attr->ints_size()) values.size() == static_cast<size_t>(attr->strings_size()) Attibute name and type don't match /onnxruntime_src/include/onnxruntime/core/framework/op_node_proto_helper.h values.size() == static_cast<size_t>(attr->tensors_size()) values.size() == static_cast<size_t>(attr->graphs_size()) ort_value.IsAllocated() . Shape: . Dimension 0 is Invalid dim0_offset of dim0_offset < dim0_size /onnxruntime_src/onnxruntime/core/framework/ort_value_tensor_slicer.cc Insufficient dimensions to slice on gsl::narrow_cast<int64_t>(tensor_shape.NumDimensions()) >= slice_dimension Can't slice a non-tensor OrtValue. Type was OrtValue has not been allocated so can't be sliced. static onnxruntime::OrtValueTensorSlicer<T> onnxruntime::OrtValueTensorSlicer<T>::Create(T&, int64_t, int64_t) [with T = const OrtValue; int64_t = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] static onnxruntime::OrtValueTensorSlicer<T> onnxruntime::OrtValueTensorSlicer<T>::Create(T&, int64_t, int64_t) [with T = OrtValue; int64_t = long int] node_index_info_ with error: duplicated ort_value index: SaveMLValueNameIndexMapping is used by node ) and node Entry exists in node for attribute SetGraphAndCreateKernels must be called prior to GetExecutionInfo. /onnxruntime_src/onnxruntime/core/framework/session_state.cc Failed to find input name in the mapping: Failed to find output name in the mapping: Could not create kernel for node: as there's no execution provider allocated. Kernel creation failed for node: . Do you have duplicated calls to SessionState::AddInitializedTensor function? Done saving OrtValue mappings. Using an input in multiple nodes on different devices is not supported currently. Input: Only one node should produce an output. Existing entry for output_names_to_nodeinfo.empty() existing_entries.find(attribute_name) == existing_entries.cend() const onnxruntime::NodeIndexInfo& onnxruntime::SessionState::GetNodeIndexInfo() const void onnxruntime::SessionState::AddSubgraphSessionState(onnxruntime::NodeIndex, const string&, std::unique_ptr<onnxruntime::SessionState>) void onnxruntime::SessionState::AddOutputNameToNodeInfoMapping(const string&, const onnxruntime::SessionState::NodeInfo&) SetGraph /onnxruntime_src/onnxruntime/core/framework/session_state_initializer.cc OrtValue indexes should have been populated. ort_value_name_idx_map.MaxIdx() > 0 Invalid allocation info. Provider name = string tensor is not supported for copying between allocators Internal error. The preallocated buffer is too small. Requires Failed to copy tensor to execution provider: Done saving initialized tensors Execution plan was not found in SessionState. CreatePlan must be called first. Saving initialized tensors. , Got Deserialize tensor Subgraph Graph exec_plan_ptr input with name is not used by any node. ForEachWithIndex operator() operator() SaveInputOutputNamesToNodeMapping DeserializeTensorProto onnxruntime::common::Status onnxruntime::SaveInitializedTensors(const onnxruntime::Env&, const std::basic_string<char>&, const onnxruntime::Graph&, const onnxruntime::ExecutionProviders&, const OrtValueNameIdxMap&, onnxruntime::ITensorAllocator*, const T&, const onnxruntime::logging::Logger&, const onnxruntime::DataTransferManager&) [with T = onnxruntime::SessionStateInitializer::CreatePlan(const onnxruntime::Node*, const onnxruntime::ConstPointerContainer<std::vector<onnxruntime::NodeArg*> >*, ExecutionMode)::__lambda17; onnxruntime::OrtValueNameIdxMap = onnxruntime::OrtValueNameIdxMap] SaveInitializedTensors onnxruntime::common::Status onnxruntime::SessionStateInitializer::CreatePlan(const onnxruntime::Node*, const onnxruntime::ConstPointerContainer<std::vector<onnxruntime::NodeArg*> >*, ExecutionMode) CreatePlan *ZN11onnxruntimeL33SaveInputOutputNamesToNodeMappingERKNS_5GraphERKNS_21KernelRegistryManagerERNS_12SessionStateEPKNS_21ConstPointerContainerISt6vectorIPNS_7NodeArgESaISB_EEEEEUlRKSA_mE_ *ZN11onnxruntimeL33SaveInputOutputNamesToNodeMappingERKNS_5GraphERKNS_21KernelRegistryManagerERNS_12SessionStateEPKNS_21ConstPointerContainerISt6vectorIPNS_7NodeArgESaISB_EEEEEUlRKSA_mE0_ N11onnxruntime25ISequentialPlannerContextE N11onnxruntime24SequentialPlannerContextE tensor size overflow shape.Size() must >=0 dtype_ != nullptr p_type != nullptr /onnxruntime_src/onnxruntime/core/framework/tensor.cc Tensor is expected to contain one of the primitive data types. Got: tensor failed memory size calculation void onnxruntime::Tensor::Init(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, onnxruntime::AllocatorPtr, int64_t) size_t onnxruntime::Tensor::SizeInBytes() const onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, std::shared_ptr<onnxruntime::IAllocator>, int64_t) onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, const OrtMemoryInfo&, int64_t) Internal error. Mem pattern for initializer ' is not found ' failed duplicated location /onnxruntime_src/onnxruntime/core/framework/tensor_allocator_with_mem_pattern.h Weight buffer for initializer ' Get preallocated buffer for initializer ' Failed to get allocator for location: Trace FinalizePlan N11onnxruntime16ITensorAllocatorE N11onnxruntime29TensorAllocatorWithMemPatternE Invalid dimension of dimension <= num_dims /onnxruntime_src/onnxruntime/core/framework/tensor_shape.cc dimstart <= dimend && dimend <= size() Invalid tensor shape slice argument. for SizeFromDimension. Tensor has onnxruntime::TensorShape onnxruntime::TensorShape::Slice(size_t, size_t) const int64_t onnxruntime::TensorShape::SizeFromDimension(size_t) const int64_t onnxruntime::TensorShape::SizeToDimension(size_t) const OrtValue is not a Tensor Argument is not a tensor /onnxruntime_src/onnxruntime/core/framework/tensor_type_and_shape.cc
+
const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] OrtStatus* OrtApis::GetTensorTypeAndShape(const OrtValue*, OrtTensorTypeAndShapeInfo**) Invalid TensorProto ) in proto data overflow UnpackTensor: the pre-allocate size does not match the size in proto source and destination buffer size mismatch /onnxruntime_src/onnxruntime/core/framework/endian_utils.h Not satsified: source_bytes.size_bytes() == destination.size_bytes()
+ UnpackTensor: the pre-allocated size does not match the raw data size, expected /onnxruntime_src/onnxruntime/core/framework/tensorprotoutils.cc ) does not match the data size( corrupted protobuf data: tensor shape size( string tensor can not have raw data tensor can't contain negative dims The buffer planner is not consistent with tensor buffer size, expected initialize preallocated buffer failed Initialized tensor with unexpected type: ^]4\\ZK]z[\Z[ [Y^YcZ^^X
+
GetFileContent TensorProtoToMLValue ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData NnapiExecutionProvider invalid allocator copy_info.size() == num_feeds /onnxruntime_src/onnxruntime/core/framework/utils.cc feed_locations.size() == copy_info.size() fetch_alloc_info.size() == copy_info.size() Only one thread was configured for parallel execution. Hence will use sequential execution. CopyOutputsAcrossDevices CopyInputsAcrossDevices onnxruntime::common::Status onnxruntime::utils::CopyInputsAcrossDevices(const std::vector<OrtValue>&, std::vector<OrtValue>&, const std::vector<onnxruntime::MLValueCopyInfo>&, const onnxruntime::DataTransferManager&) ExecuteGraphImpl ExecuteGraph T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] CopyMLValue const T& OrtValue::Get() const [with T = onnxruntime::Tensor] bool onnxruntime::utils::FinalizeCopyInfoForFetches(const onnxruntime::SessionState&, const std::vector<const OrtMemoryInfo*>&, std::vector<onnxruntime::MLValueCopyInfo>&) bool onnxruntime::utils::FinalizeCopyInfoForFeeds(const std::vector<OrtDevice>&, std::vector<onnxruntime::MLValueCopyInfo>&) CalculateStaticCopyInfoForFeed CalculateStaticCopyInfoForFeeds InitializeFeedFetchCopyInfo const OrtMemoryInfo& onnxruntime::utils::FindMemoryInfoForValue(const OrtValueNameIdxMap&, const onnxruntime::SequentialExecutionPlan&, const string&) FindMemoryInfoForValue const OrtMemoryInfo& onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::SessionState&, const string&) N11onnxruntime9IExecutorE /onnxruntime_src/onnxruntime/core/framework/allocation_planner.cc id >= 0 && static_cast<size_t>(id) < ort_value_info_.size() n >= 0 && static_cast<size_t>(n) < ort_value_info_.size() n >= 0 && static_cast<size_t>(n) < plan_.allocation_plan.size() kernel_create_info != nullptr && kernel_create_info->kernel_def != nullptr (ort_value_idx) output_name : <allocation plan>
No suitable kernel definition found for op Can not find the execution provider Allocate AllocateStatically PreExisting Reuse AllocateOutput Share nullptr != tensor_type_base reused != reused_for Can not find the node p_provider Allocation Plan:
Execution Plan:
- ) , use fence when async Index out-of-range! nullptr != node Free ml-values: INVALID INDEX vector<bool>::_M_fill_insert onnxruntime::OrtValueIndex& onnxruntime::PlannerImpl::Buffer(onnxruntime::OrtValueIndex) void onnxruntime::PlannerImpl::Reuse(onnxruntime::OrtValueIndex, onnxruntime::OrtValueIndex, onnxruntime::AllocKind) static size_t onnxruntime::PlannerImpl::GetElementSize(const string* const&) OrtMemoryInfo onnxruntime::PlannerImpl::GetLocationForNodeInput(size_t, const onnxruntime::Node&) GeneratePlanForWeights ComputeReusePlan onnxruntime::AllocPlanPerValue& onnxruntime::PlannerImpl::AllocPlan(onnxruntime::OrtValueIndex) int& onnxruntime::PlannerImpl::UseCount(onnxruntime::OrtValueIndex) void onnxruntime::PlannerImpl::ProcessDef(onnxruntime::OrtValueIndex, const onnxruntime::NodeArg*) onnxruntime::OrtValueIndex onnxruntime::PlannerImpl::Index(const OrtValueName&) ForEachWithIndex ComputeUseCounts CreatePlan N11onnxruntime17ExecutionPlanBaseE N11onnxruntime23SequentialExecutionPlanE ZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEmE_ ZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEbE0_ ZN11onnxruntime11PlannerImpl22GeneratePlanForWeightsEvEUlRKNS_7NodeArgEmE_ func info for node: already exist. Can't use func with null ptr /onnxruntime_src/onnxruntime/core/framework/fuse_nodes_funcs.cc GetFuncs *ZNK11onnxruntime11FuncManager8GetFuncsERKSsPSt8functionIFNS_6common6StatusEPvPK6OrtApiP16OrtKernelContextEEPS3_IFiPNS_14ComputeContextEPS6_EEPS3_IFvS6_EEEUlS6_S9_SB_E_ *ZNK11onnxruntime11FuncManager8GetFuncsERKSsPSt8functionIFNS_6common6StatusEPvPK6OrtApiP16OrtKernelContextEEPS3_IFiPNS_14ComputeContextEPS6_EEPS3_IFvS6_EEEUlSG_SH_E0_ *ZNK11onnxruntime11FuncManager8GetFuncsERKSsPSt8functionIFNS_6common6StatusEPvPK6OrtApiP16OrtKernelContextEEPS3_IFiPNS_14ComputeContextEPS6_EEPS3_IFvS6_EEEUlS6_E1_ '. Begin execution does not. entry != nullptr op_name _fence_before node. Name:' ' Status Message: _kernel_time _fence_after Multiple errors were found. ParallelExecutor::Execute Unknown exception was caught by catch-all handler. Exception running nodes starting at Exiting due to terminate flag being set to true. /onnxruntime_src/onnxruntime/core/framework/parallel_executor.cc Got nullptr from GetKernel for node: All implicit inputs should have OrtValue instances by now. /onnxruntime_src/onnxruntime/core/framework/op_kernel_context_internal.h Non-zero status code returned while running onnxruntime::OpKernelContextInternal::OpKernelContextInternal(const onnxruntime::SessionState&, onnxruntime::IExecutionFrame&, const onnxruntime::OpKernel&, const onnxruntime::logging::Logger&, const bool&) onnxruntime::common::Status onnxruntime::ParallelExecutor::RunNodeAsync(size_t, const onnxruntime::SessionState&, const onnxruntime::logging::Logger&) RunNodeAsync const T& OrtValue::Get() const [with T = onnxruntime::Tensor] Execute *ZN11onnxruntime16ParallelExecutor11EnqueueNodeEmRKNS_12SessionStateERKNS_7logging6LoggerEEUlvE_ N11onnxruntime16ParallelExecutorE N11onnxruntime23OpKernelContextInternalE SequentialExecutor::Execute /onnxruntime_src/onnxruntime/core/framework/sequential_executor.cc const T& OrtValue::Get() const [with T = onnxruntime::Tensor] ReleaseNodeMLValues onnxruntime::OpKernelContextInternal::OpKernelContextInternal(const onnxruntime::SessionState&, onnxruntime::IExecutionFrame&, const onnxruntime::OpKernel&, const onnxruntime::logging::Logger&, const bool&) Execute N11onnxruntime18SequentialExecutorE invalid ort_value_index: ', location: /onnxruntime_src/onnxruntime/core/framework/simple_tensor_allocator.cc Failed to get allocator for initializer ' GetPreallocatedBuffer N11onnxruntime21SimpleTensorAllocatorE model format error! Need a key for the external data info model format error! Need a value for the external data info model format error! Missing 'location' parsing checksum model format error! /onnxruntime_src/onnxruntime/core/graph/automl_ops/automl_defs.cc The input represents a number of seconds passed since the epoch, suitable to properly constructan instance of std::chrono::system_clock::time_point The output which is a Microsoft::DateTimeFeaturizer::TimePoint structure Constrain input type to int64 scalar tensor. Constrain output type to an AutoML specific Microsoft::Featurizers::TimePoint typecurrently not part of ONNX standard. When it becomes a part of the standard we will adjust thiskernel definition and move it to ONNX repo opaque(com.microsoft.automl,DateTimeFeaturizer_TimePoint) [ShapeInferenceError] Input's shape must be 4-D ) + bottom_border ( ) + right_border ( ) + scale[0] ( ) + scale[1] ( tensor rank too small Attention Number of attention heads num_heads mask_index EmbedLayerNormalization input_ids segment_ids mask 2D with shape (,hidden_size) word_embedding 2D with shape (, hidden_size) position_embedding segment_embedding SkipLayerNormalization skip Value of alpha Value of beta 1D input tensor 1D output tensor The scale to apply. Threshold value Input tensor Output tensor Sliced data tensor. GivenTensorFill The shape of filled tensor The filled tensor input_as_shape extra_shape Input data to be scaled Output data after scaling GRUUnit drop_states hidden_prev gates seq_lengths hidden Scaling value Strings to tokenize Tokenized strings N-dimensional matrix A N-dimensional matrix B ReduceSumInteger An input tensor. Reduced output tensor. reduced An input tensor to hash. 32-bit hash value. Tensor of rank r >= 1. Tensor of rank q >= 1. Specify weights of conv Specify bias of conv Constrain to tensor(int32). Constrain to tensor(float). Input tensor. Tensor after padding. idx 2D matrix with shape (M,N) 2D matrix with shape (K,N) rois batch_indices crop_size Scale tensor. Bias tensor. Output data tensor. inv_std_var The input data as Tensor. The output. gamma should have 1 dimension beta should have 1 dimension Attribute should specify a shape Input axis is invalid: Constrain input and output types to float tensors first input tensor has wrong dimension rois input tensor has wrong dimension batch_indices shape input tensor has wrong dimension crop_size shape input tensor has wrong dimension 'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border) 'Scale' must contain exactly 2 values - (height, width) ) needs to be greater than or equal to the top_border ( ) needs to be greater than or equal to the left_border ( First input does not have rank 2 Second input does not have rank 2 inputs are expected to have tensor type and output type should not be null. both data and indices tensor need to have rank larger than zero. last dimension of indices must not be larger and rank of data tensor 'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64 /onnxruntime_src/onnxruntime/core/graph/contrib_ops/contrib_defs.cc Constrain input and output types to float/quantized tensors 3D input tensor with shape (batch_size, sequence_length, hidden_size), hidden_size = num_heads * head_size 2D input tensor with shape (hidden_size, 3 * hidden_size) 1D input tensor with shape (3 * hidden_size) Attention mask index with shape (batch_size) 3D output tensor with shape (batch_size, sequence_length, hidden_size) Constrain input and output types to float tensors. Constrain mask index to integer types 2D words IDs with shape (batch_size, sequence_length) 2D segment IDs with shape (batch_size, sequence_length) 2D attention mask 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) 1D mask_index tensor with shape (batch_size) Constrain input and output integer tensors types Constrain input and output float tensors types. 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 Constrain input and output types to float or half 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 Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero. Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero. Input tensor C. The shape of C should be unidirectional broadcastable to (M, N). Output tensor of shape (M, N). Constrain input and output types to float/int tensors. Whether A should be transposed Whether B should be transposed Scalar multiplier for the product of input tensors A * B. Scalar multiplier for input tensor C. Specified axis to insert a dimension The axis along which same quantization parameters are applied. It's optional.If it's not specified, it means per-tensor quantization and input 'x_scale' and 'x_zero_point' must be scalars.If it's specified, it means per 'axis' quantization and input 'x_scale' and 'x_zero_point' must be 1-D tensors. N-D full precision Input tensor to be quantized. Scale for doing quantization to get 'y'. It could be a scalar or a 1-D tensor,which means a per-tensor or per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. Zero point for doing quantization to get 'y'. It could be a scalar or a 1-D tensor, which means a per-tensoror per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. N-D quantized output tensor. It has same shape as input 'x'. Constrain 'x', 'y_scale' to float tensors. Constrain 'y_zero_point' and 'y' to 8-bit integer tensors. N-D quantized Input tensor to be de-quantized. Scale for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. Zero point for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. N-D full precision output tensor. It has same shape as input 'x'. Constrain 'y', 'x_scale' to float tensors. Constrain 'x_zero_point' and 'x' to 8-bit integer tensors. Input/Output is a string tensor Boolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03). The string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers. An optional string. Token's regular expression in basic POSIX format (http://pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set. an optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string. Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignored Matrix multiply results from A * B Constrain input A data types as 16-bit integer tensor Constrain input B data types as 16-bit integer tensor Constrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16). Constrain input type to 8-bit integer tensor. Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8). A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Keep the reduced dimension or not, default 1 mean keep reduced dimension. Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode. Constrain output type to unsigned and signed 32-bit integer tensor. Seed for the hashing algorithm, unsigned 32-bit integer, default to 0. If value is 1, output type is uint32_t, else int32_t. Default value is 1. Constrain input and output types to any tensor type. Tensor of rank q-1+r-indices[-1]. Constrain indice type to int32 or int64 Integer representing the embedding vector size for each word.If not provide, use the fileter size of conv weight This operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernal shape. Integer representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector. Specify batchs of sequence words to embedding Specify embedding vector of char Three modes: `constant`(default) - pads with a given constant value, `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis, `edge` - pads with the edge values of array Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]. `pads` format (1D example) should be as follow [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. (Optional) A scalar or rank 1 tensor containing a single value to be filled if the mode chosen is `constant` (by default it is 0.0). Input can be of any tensor type. A 1-D input tensor that is to be processed. A 1-D tensor of the same type as 'x' containing all the unique values in 'x' sorted in the same order that they occur in the input 'x' A 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques' A 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x' The distance metric to use. If a string, the distance function can be "braycurtis", "canberra", "chebyshev", "cityblock", "correlation", "cosine", "dice", "euclidean", "hamming", "jaccard", "jensenshannon", "kulsinski", "mahalanobis", "matching", "minkowski", "rogerstanimoto", "russellrao", "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "wminkowski", "yule". A 2D Matrix that represents the distance between each pair of the two collections of inputs. Constrains input to only numeric types. The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'. Value used for extrapolation, when applicable. Default is 0.0f. Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[y1, x1, y2, x2], ...]. The RoIs' coordinates are normalized in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input. 1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch. 1-D tensor of 2 elements: [crop_height, crop_width]. All cropped image patches are resized to this size. Both crop_height and crop_width need to be positive. RoI pooled output, 4-D tensor of shape (num_rois, C, crop_height, crop_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1]. Constrain types to float tensors. Constrain types to int tensors. The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs). The epsilon value to use to avoid division by zero. Input data tensor from the previous layer. Saved mean used during training to speed up gradient computation Saved inverse standard variance used during training to speed up gradient computation. Constrain input and output types (except mean and inv_std_var) to float tensors. Constrain mean and inv_std_var to be float tensors. Inputs 0, 1 and 2 shall be 2 dimensions Inputs 0, 1 and 2 shall have same value in dimension 1 position_embedding should have 2 dimensions The last dimension of word_embedding and position_embedding does not match. segment_embedding should have 2 dimensions The last dimension of word_embedding and segment_embedding does not match. The last dimension of word_embedding and gamma does not match. The last dimension of word_embedding and beta does not match. Inputs 0, 1 and 2 shall have value in dimension 1 word_embedding should have 2 dimensions and dimension size is known. Negative values are not allowed in a shape specification FvRN4onnx8OpSchemaEE *ZN11onnxruntime7contrib26NchwcPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_ *ZN11onnxruntime7contrib32NchwcGlobalPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_ *ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE8_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE9_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE10_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE11_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE12_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE13_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE14_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE15_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE16_ PFvRN4onnx8OpSchemaEE start limit delta 1-D Tensor of the range. Unsupported type: Constrain input and output types. Tensor(scalar, or dims=[1]). First entry in the range. Tensor(scalar, or dims=[1]). Upper limit of sequence, exclusive. Tensor(scalar, or dims=[1]). Number that increments start. Defaults to 1. Can not get shape initializer data! Unsupported non-raw-data data type! *ZN11onnxruntime7contrib21RegisterRangeOpSchemaEON4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_ /onnxruntime_src/onnxruntime/core/graph/graph.cc This API is not supported when model is loaded from proto file right now. /onnxruntime_src/include/onnxruntime/core/common/const_pointer_container.h Graph attribute inferencing failed: Invalid source node arg slot specified when adding edge. Invalid destination node arg slot specified when adding edge. Argument type mismatch when adding edge. Invalid node indexes specified when adding edge. This is an invalid model. The sum of input arg count is not equal to size of input defs in node ( Invalid source node arg slot specified when removing edge. Invalid destination node arg slot specified when removing edge. Argument mismatch when removing edge. Invalid node indexes specified when removing edge. No Graph instance was found for attribute Error: Duplicate definition-site for ( '. It is not used by any node and should be removed from the model. '. It is no longer used by any node. 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 ctor should have created NodeArg for initializer. This is an invalid model. Graph output ( ) does not exist in the graph. must be either specified in graph inputs or graph initializers. 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. Only 'value' attribute is supported within a 'Constant' node in ORT nullptr != sub_graph && nullptr != sub_graph->GetMetaDef() This is an invalid model. Node ( ) does not have type information set by parent node. This is an invalid model. Type Error: Type ' ) does not match expected type ( This is an invalid model. Model input ( ) does not have type information. Type Error: Value of initializer Type Error: Shape of initializer 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() 'was added but does not exist. 8hddddindex < data_.size() Removing initializer ' Constant node_arg Can't remove node Target= Dimension= ' source: target: Output: Type mismatch. Current= Input= graph_proto cannot be null graph_proto != nullptr constant_attribute.has_t() NoOp ) input arg ( ' of input parameter ( ) of operator ( ) in node ( ) is invalid. Type Error: Type parameter ( ) bound to different types ( Node ( ) output arg ( ) type inference failed Type Error: Type ( ) of output arg ( ) of node ( Node: does not match its type. does not match. Fatal error: ) attribute ( inputs but subgraph has inputs and requires Subgraph input missing type. Outer scope node arg name ' void onnxruntime::Graph::SetOutputs(const std::vector<const onnxruntime::NodeArg*>&) void onnxruntime::Graph::SetInputs(const std::vector<const onnxruntime::NodeArg*>&) InlineFunction onnxruntime::Node& onnxruntime::Graph::FuseSubGraph(std::unique_ptr<onnxruntime::IndexedSubGraph>, const string&) gsl::not_null<onnxruntime::Node*> onnxruntime::Graph::AllocateNode() onnxruntime::common::Status onnxruntime::Graph::SetGraphInputsOutputs() CleanUnusedInitializers void onnxruntime::Graph::ToGraphProtoInternal(onnx::GraphProto&) const bool onnxruntime::Graph::RemoveNode(onnxruntime::NodeIndex) onnxruntime::common::Status onnxruntime::Graph::Resolve(bool) Resolve ForThisAndAllSubgraphs PerformTypeAndShapeInferencing InitInputsInitializersOutputs VerifyNodeAndOpMatch InferAndVerifySubgraphTypes const T* onnxruntime::ConstPointerContainer<Container>::at(size_t) const [with Container = std::vector<onnxruntime::NodeArg*>; onnxruntime::ConstPointerContainer<Container>::T = onnxruntime::NodeArg; size_t = long unsigned int] onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const BuildConnections void onnxruntime::Graph::RemoveEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int) void onnxruntime::Graph::AddEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int) SetOuterScopeNodeArgs Graph onnxruntime::Graph::Graph(onnx::GraphProto*, const std::unordered_map<std::basic_string<char>, int>&, onnxruntime::Version, onnxruntime::IOnnxRuntimeOpSchemaCollectionPtr, onnxruntime::Graph*, const onnxruntime::Node*, const std::unordered_map<std::basic_string<char>, const onnx::FunctionProto*>&) MergeShapeInfo UpdateTypeAndShape FN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_E *ZN11onnxruntime5Graph7ResolveEbEUlRS0_E0_ *ZN11onnxruntime5Graph7ResolveEbEUlRS0_E1_ *ZN11onnxruntime5Graph7ResolveEbEUlRS0_E2_ N4onnx15GraphInferencerE N4onnx16InferenceContextE N11onnxruntime5GraphE N11onnxruntime19GraphInferencerImplE N11onnxruntime20InferenceContextImplE PFN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_E /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. 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) ExplicitInputs: . Index: Invalid input index for node ImplicitInputs: Implicit input name cannot be safely updated to in one of the subgraphs. 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&) 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 CanUpdateImplicitInputNameInSubgraphs onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const *ZN11onnxruntime11GraphViewerC1ERKNS_5GraphEEUlPKNS_4NodeEE_ N11onnxruntime11NodeCompareE ModelProto was null. Protobuf parsing failed. <p_fd> is less than 0. Invalid istream object. Null model_proto ptr. <p_fd> less than 0. Load model failed. File doesn't exist system error number ModelProto does not have a graph. Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported. ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset model may run depending upon legacy support of some older opset version operators. /onnxruntime_src/onnxruntime/core/graph/model.cc No graph was found in the protobuf. Failed to load model with error: Protobuf serialization failed. Save Load SaveModel Load Model St15_Sp_counted_ptrIPN11onnxruntime5ModelELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime21SchemaRegistryManagerESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime5ModelESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE Domain already set in registry , but it its version is higher than the operator set version /onnxruntime_src/onnxruntime/core/graph/schema_registry.cc RegisterOpSet N11onnxruntime27OnnxRuntimeOpSchemaRegistryE N11onnxruntime21SchemaRegistryManagerE sequence_lens initial_h initial_c QW MW memory_seq_lens AW Y_h Y_c A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified. Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01. Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators. Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified. Couple the input and forget gates if 1, default 0. Number of neurons in the hidden layer. Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional. Constrain seq_lens to integral tensors. The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]` The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`. The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`. The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0. Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]` Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`. Optional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`. The weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0. The weight tensor of the query layer in the attention mechanism. Should be of shape `[num_directions, am_query_depth(hidden_size of lstm), am_attn_size]` The weight tensor of the memory layer in the attention mechanism. Should be of shape `[num_directions, memory_depth, am_attn_size]` The attention_v tensor in the attention mechanism. Should be of shape `[num_directions, am_attn_size]` The sequence of the memory (input) for attention mechanism. Should be of `[batch_size, max_memory_step, memory_depth]` The sequence length of the input memory for the attention mechanism. Should be of `[batch_size]` The weights of attention layer in the attention wrapper. If exists, should be of shape `[num_directions, memory_depth+hidden_size, aw_attn_size]. Please note that attention mechanism context depth is also memory_depth in the attention mechanism.` A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]` The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`. The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`. fused_function_subgraph input_arg->Type() != nullptr Resolve subgraph failed: A node with a function body within a subgraph within another function body is currently not supported in ORT /onnxruntime_src/onnxruntime/core/graph/function.cc void onnxruntime::update_subgraphs_within_function_body(onnx::GraphProto&, const onnxruntime::Graph&, const onnx::NodeProto&, const std::unordered_map<std::basic_string<char>, int>&, const std::unordered_map<std::basic_string<char>, int>&) onnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph&, const NodeIndex&, const onnx::FunctionProto&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const onnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph&, std::unique_ptr<onnxruntime::IndexedSubGraph>) *ZN11onnxruntime12FunctionImplC1ERKNS_5GraphERKmRKN4onnx13FunctionProtoEEUlRNS6_16InferenceContextEE_ N11onnxruntime8FunctionE N11onnxruntime12FunctionImplE Default logger already set. ISink must be provided. System onnxruntime default_logger_id must be provided if instance_type is InstanceType::Default Only one instance of LoggingManager created with InstanceType::Default can exist at any point in time. N@VIWEF is not a valid year is not a valid day is not a valid date N11onnxruntime7logging11OStreamSinkE Writing profiler data to file /onnxruntime_src/onnxruntime/core/common/profiler.cc Maximum number of events reached, could not record profile event. [
+ ) , use fence when async Index out-of-range! nullptr != node Free ml-values: INVALID INDEX vector<bool>::_M_fill_insert onnxruntime::OrtValueIndex& onnxruntime::PlannerImpl::Buffer(onnxruntime::OrtValueIndex) void onnxruntime::PlannerImpl::Reuse(onnxruntime::OrtValueIndex, onnxruntime::OrtValueIndex, onnxruntime::AllocKind) static size_t onnxruntime::PlannerImpl::GetElementSize(const string* const&) OrtMemoryInfo onnxruntime::PlannerImpl::GetLocationForNodeInput(size_t, const onnxruntime::Node&) GeneratePlanForWeights ComputeReusePlan onnxruntime::AllocPlanPerValue& onnxruntime::PlannerImpl::AllocPlan(onnxruntime::OrtValueIndex) int& onnxruntime::PlannerImpl::UseCount(onnxruntime::OrtValueIndex) void onnxruntime::PlannerImpl::ProcessDef(onnxruntime::OrtValueIndex, const onnxruntime::NodeArg*) onnxruntime::OrtValueIndex onnxruntime::PlannerImpl::Index(const OrtValueName&) ForEachWithIndex ComputeUseCounts CreatePlan N11onnxruntime17ExecutionPlanBaseE N11onnxruntime23SequentialExecutionPlanE ZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEmE_ ZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEbE0_ ZN11onnxruntime11PlannerImpl22GeneratePlanForWeightsEvEUlRKNS_7NodeArgEmE_ func info for node: already exist. Can't use func with null ptr /onnxruntime_src/onnxruntime/core/framework/fuse_nodes_funcs.cc GetFuncs *ZNK11onnxruntime11FuncManager8GetFuncsERKSsPSt8functionIFNS_6common6StatusEPvPK6OrtApiP16OrtKernelContextEEPS3_IFiPNS_14ComputeContextEPS6_EEPS3_IFvS6_EEEUlS6_S9_SB_E_ *ZNK11onnxruntime11FuncManager8GetFuncsERKSsPSt8functionIFNS_6common6StatusEPvPK6OrtApiP16OrtKernelContextEEPS3_IFiPNS_14ComputeContextEPS6_EEPS3_IFvS6_EEEUlSG_SH_E0_ *ZNK11onnxruntime11FuncManager8GetFuncsERKSsPSt8functionIFNS_6common6StatusEPvPK6OrtApiP16OrtKernelContextEEPS3_IFiPNS_14ComputeContextEPS6_EEPS3_IFvS6_EEEUlS6_E1_ Begin execution does not. entry != nullptr op_name _fence_before node. Name:' ' Status Message: _kernel_time _fence_after Multiple errors were found. ParallelExecutor::Execute Unknown exception was caught by catch-all handler. Exception running nodes starting at Exiting due to terminate flag being set to true. /onnxruntime_src/onnxruntime/core/framework/parallel_executor.cc Got nullptr from GetKernel for node: All implicit inputs should have OrtValue instances by now. /onnxruntime_src/onnxruntime/core/framework/op_kernel_context_internal.h Non-zero status code returned while running onnxruntime::OpKernelContextInternal::OpKernelContextInternal(const onnxruntime::SessionState&, onnxruntime::IExecutionFrame&, const onnxruntime::OpKernel&, const onnxruntime::logging::Logger&, const bool&) onnxruntime::common::Status onnxruntime::ParallelExecutor::RunNodeAsync(size_t, const onnxruntime::SessionState&, const onnxruntime::logging::Logger&) RunNodeAsync const T& OrtValue::Get() const [with T = onnxruntime::Tensor] Execute *ZN11onnxruntime16ParallelExecutor11EnqueueNodeEmRKNS_12SessionStateERKNS_7logging6LoggerEEUlvE_ N11onnxruntime16ParallelExecutorE N11onnxruntime23OpKernelContextInternalE SequentialExecutor::Execute /onnxruntime_src/onnxruntime/core/framework/sequential_executor.cc const T& OrtValue::Get() const [with T = onnxruntime::Tensor] ReleaseNodeMLValues onnxruntime::OpKernelContextInternal::OpKernelContextInternal(const onnxruntime::SessionState&, onnxruntime::IExecutionFrame&, const onnxruntime::OpKernel&, const onnxruntime::logging::Logger&, const bool&) Execute N11onnxruntime18SequentialExecutorE invalid ort_value_index: ', location: /onnxruntime_src/onnxruntime/core/framework/simple_tensor_allocator.cc Failed to get allocator for initializer ' GetPreallocatedBuffer N11onnxruntime21SimpleTensorAllocatorE model format error! Need a key for the external data info model format error! Need a value for the external data info model format error! Missing 'location' checksum model format error! /onnxruntime_src/onnxruntime/core/graph/automl_ops/automl_defs.cc The input represents a number of seconds passed since the epoch, suitable to properly constructan instance of std::chrono::system_clock::time_point The output which is a Microsoft::DateTimeFeaturizer::TimePoint structure Constrain input type to int64 scalar tensor. Constrain output type to an AutoML specific Microsoft::Featurizers::TimePoint typecurrently not part of ONNX standard. When it becomes a part of the standard we will adjust thiskernel definition and move it to ONNX repo opaque(com.microsoft.automl,DateTimeFeaturizer_TimePoint) [ShapeInferenceError] Input's shape must be 4-D ) + bottom_border ( ) + right_border ( ) + scale[0] ( ) + scale[1] ( tensor rank too small Number of attention heads mask_index input_ids segment_ids 2D with shape (,hidden_size) word_embedding 2D with shape (, hidden_size) position_embedding segment_embedding bias tensor skip Value of alpha Value of beta 1D input tensor 1D output tensor The scale to apply. Threshold value Input tensor Output tensor Sliced data tensor. GivenTensorFill The shape of filled tensor The filled tensor input_as_shape extra_shape Input data to be scaled Output data after scaling GRUUnit drop_states hidden_prev gates seq_lengths hidden Scaling value Strings to tokenize Tokenized strings N-dimensional matrix A N-dimensional matrix B ReduceSumInteger An input tensor. Reduced output tensor. reduced An input tensor to hash. 32-bit hash value. Tensor of rank r >= 1. Tensor of rank q >= 1. Specify weights of conv Specify bias of conv Constrain to tensor(int32). Constrain to tensor(float). Input tensor. Tensor after padding. idx 2D matrix with shape (M,N) 2D matrix with shape (K,N) rois batch_indices crop_size Scale tensor. Bias tensor. Output data tensor. inv_std_var The input data as Tensor. The output. The normal input data. Attribute should specify a shape Input axis is invalid: Constrain input and output types to float tensors first input tensor has wrong dimension rois input tensor has wrong dimension batch_indices shape input tensor has wrong dimension crop_size shape input tensor has wrong dimension 'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border) 'Scale' must contain exactly 2 values - (height, width) ) needs to be greater than or equal to the top_border ( ) needs to be greater than or equal to the left_border ( First input does not have rank 2 Second input does not have rank 2 inputs are expected to have tensor type and output type should not be null. both data and indices tensor need to have rank larger than zero. last dimension of indices must not be larger and rank of data tensor 'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64 /onnxruntime_src/onnxruntime/core/graph/contrib_ops/contrib_defs.cc Constrain input and output types to float/quantized tensors 3D input tensor with shape (batch_size, sequence_length, hidden_size), hidden_size = num_heads * head_size 2D input tensor with shape (hidden_size, 3 * hidden_size) 1D input tensor with shape (3 * hidden_size) Attention mask index with shape (batch_size) 3D output tensor with shape (batch_size, sequence_length, hidden_size) Constrain input and output types to float tensors. Constrain mask index to integer types 2D words IDs with shape (batch_size, sequence_length) 2D segment IDs with shape (batch_size, sequence_length) 1D gamma tensor for layer normalization with shape (hidden_size) 1D beta tensor for layer normalization with shape (hidden_size) 2D attention mask with shape (batch_size, sequence_length) 1D mask_index tensor with shape (batch_size) Constrain input and output integer tensors types Constrain input and output float tensors types. Constrain input and output types to float or half tensors. 3D input tensor with shape (batch_size, sequence_length, hidden_size) 3D skip tensor with shape (batch_size, sequence_length, hidden_size) 1D input tensor with shape (hidden_size) 1D skip tensor with shape (hidden_size 1D bias tensor with shape (hidden_size Bias applied to each channel, same size as C. Input tensor of shape [N,C,H,W] Result, has same shape and type as input A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder). A 1-D values of (height, width). Result, has same type as input, with H and W dimensions reduced. Constrain input and output types to all tensor types. Tensor of data to extract slices from. 1-D tensor of starting indices of corresponding axis in `axes` 1-D tensor of ending indices (exclusive) of corresponding axis in axes 1-D tensor of axes that `starts` and `ends` apply to. Constrain indices to integer types Bool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length. The previous GRU hidden state. Unactivated gate outputs from forget, update, and output gates, pre-activation. Array of sequence lengths. len(seq_lengths) should equal batch size N. The timestep for this operation. The new GRU hidden state calculated by this op. If 1, mean and variance are computed across channels. Default is 0. If 0, normalize the mean only. Default is 1. The scaled hyperbolic tangent values of the input tensor computed element-wise Constrain input0 and output types to float tensors Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero. Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero. Input tensor C. The shape of C should be unidirectional broadcastable to (M, N). Output tensor of shape (M, N). Constrain input and output types to float/int tensors. Whether A should be transposed Whether B should be transposed Scalar multiplier for the product of input tensors A * B. Scalar multiplier for input tensor C. Specified axis to insert a dimension The axis along which same quantization parameters are applied. It's optional.If it's not specified, it means per-tensor quantization and input 'x_scale' and 'x_zero_point' must be scalars.If it's specified, it means per 'axis' quantization and input 'x_scale' and 'x_zero_point' must be 1-D tensors. N-D full precision Input tensor to be quantized. Scale for doing quantization to get 'y'. It could be a scalar or a 1-D tensor,which means a per-tensor or per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. Zero point for doing quantization to get 'y'. It could be a scalar or a 1-D tensor, which means a per-tensoror per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. N-D quantized output tensor. It has same shape as input 'x'. Constrain 'x', 'y_scale' to float tensors. Constrain 'y_zero_point' and 'y' to 8-bit integer tensors. N-D quantized Input tensor to be de-quantized. Scale for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. Zero point for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. N-D full precision output tensor. It has same shape as input 'x'. Constrain 'y', 'x_scale' to float tensors. Constrain 'x_zero_point' and 'x' to 8-bit integer tensors. Input/Output is a string tensor Boolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03). The string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers. An optional string. Token's regular expression in basic POSIX format (http://pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set. an optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string. Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignored Matrix multiply results from A * B Constrain input A data types as 16-bit integer tensor Constrain input B data types as 16-bit integer tensor Constrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16). Constrain input type to 8-bit integer tensor. Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8). A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Keep the reduced dimension or not, default 1 mean keep reduced dimension. Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode. Constrain output type to unsigned and signed 32-bit integer tensor. Seed for the hashing algorithm, unsigned 32-bit integer, default to 0. If value is 1, output type is uint32_t, else int32_t. Default value is 1. Constrain input and output types to any tensor type. Tensor of rank q-1+r-indices[-1]. Constrain indice type to int32 or int64 Integer representing the embedding vector size for each word.If not provide, use the fileter size of conv weight This operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernal shape. Integer representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector. Specify batchs of sequence words to embedding Specify embedding vector of char Three modes: `constant`(default) - pads with a given constant value, `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis, `edge` - pads with the edge values of array Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]. `pads` format (1D example) should be as follow [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. (Optional) A scalar or rank 1 tensor containing a single value to be filled if the mode chosen is `constant` (by default it is 0.0). Input can be of any tensor type. A 1-D input tensor that is to be processed. A 1-D tensor of the same type as 'x' containing all the unique values in 'x' sorted in the same order that they occur in the input 'x' A 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques' A 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x' The distance metric to use. If a string, the distance function can be "braycurtis", "canberra", "chebyshev", "cityblock", "correlation", "cosine", "dice", "euclidean", "hamming", "jaccard", "jensenshannon", "kulsinski", "mahalanobis", "matching", "minkowski", "rogerstanimoto", "russellrao", "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "wminkowski", "yule". A 2D Matrix that represents the distance between each pair of the two collections of inputs. Constrains input to only numeric types. The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'. Value used for extrapolation, when applicable. Default is 0.0f. Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[y1, x1, y2, x2], ...]. The RoIs' coordinates are normalized in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input. 1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch. 1-D tensor of 2 elements: [crop_height, crop_width]. All cropped image patches are resized to this size. Both crop_height and crop_width need to be positive. RoI pooled output, 4-D tensor of shape (num_rois, C, crop_height, crop_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1]. Constrain types to float tensors. Constrain types to int tensors. The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs). The epsilon value to use to avoid division by zero. Input data tensor from the previous layer. Saved mean used during training to speed up gradient computation Saved inverse standard variance used during training to speed up gradient computation. Constrain input and output types (except mean and inv_std_var) to float tensors. Constrain mean and inv_std_var to be float tensors. The bias input data that is a 1D tensor. Inputs 0 shall be 2 dimensions word_embedding should have 2 dimensions and dimension size is known. Negative values are not allowed in a shape specification FvRN4onnx8OpSchemaEE *ZN11onnxruntime7contrib26NchwcPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_ *ZN11onnxruntime7contrib32NchwcGlobalPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_ *ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE8_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE9_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE10_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE11_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE12_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE13_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE14_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE15_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE16_ PFvRN4onnx8OpSchemaEE start limit delta 1-D Tensor of the range. Unsupported type: Constrain input and output types. Tensor(scalar, or dims=[1]). First entry in the range. Tensor(scalar, or dims=[1]). Upper limit of sequence, exclusive. Tensor(scalar, or dims=[1]). Number that increments start. Defaults to 1. Can not get shape initializer data! Unsupported non-raw-data data type! *ZN11onnxruntime7contrib21RegisterRangeOpSchemaEON4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_ index < data_.size() Not satsified: dims_eq()
+ Constant !found node_arg Removing initializer ' Can't remove node Target= Dimension= ' source: target: Output: Type mismatch. Current= Input= but different TensorProto. existing->second == &tensor graph_proto cannot be null graph_proto != nullptr constant_attribute.has_t() NoOp ) input arg ( ' of input parameter ( ) of operator ( ) in node ( ) is invalid. Type Error: Type parameter ( ) bound to different types ( Node ( ) output arg ( ) type inference failed Type Error: Type ( ) of output arg ( ) of node ( Node: ) Op ( does not match its type. does not match. Outer scope node arg name ' Fatal error: ) attribute ( inputs but subgraph has inputs and requires Subgraph input missing type. LLL,L(((`( /onnxruntime_src/onnxruntime/core/graph/graph.cc This API is not supported when model is loaded from proto file right now. /onnxruntime_src/include/onnxruntime/core/common/const_pointer_container.h Graph attribute inferencing failed: Invalid source node arg slot specified when adding edge. Invalid destination node arg slot specified when adding edge. Argument type mismatch when adding edge. Invalid node indexes specified when adding edge. This is an invalid model. The sum of input arg count is not equal to size of input defs in node ( Invalid source node arg slot specified when removing edge. Invalid destination node arg slot specified when removing edge. Argument mismatch when removing edge. Invalid node indexes specified when removing edge. No Graph instance was found for attribute Error: Duplicate definition-site for ( Failed to find existing initializer with name Not satsified: name_to_initializer_it != name_to_initial_tensor_.end()
+ Replacement tensor's data type does not match. Not satsified: old_initializer.data_type() == new_initializer.data_type()
+ graph_proto_ is not in sync with name_to_initial_tensor_ existing_entry != mutable_initializers.pointer_end() Replacement tensor's dimensions do not match. This is an invalid model. Error: two nodes with same node name ( This is an invalid model. Error: Duplicate definition of name ( This is an invalid model. Error: the graph is not acyclic. graph_proto_ is not in sync with name_to_initial_tensor_. Graph ctor should have created NodeArg for initializer. This is an invalid model. Graph output ( ) does not exist in the graph. must be either specified in graph inputs or graph initializers. '. It is not used by any node and should be removed from the model. '. It is no longer used by any node. as it still has output edges. node->GetOutputEdgesCount() == 0 This is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name= Graph may not conform to the ONNX spec and contain initializers that are not graph inputs. This is an invalid model. Failed to find NodeArg in all parent graphs. Name= nodes_.size() < static_cast<unsigned int>(std::numeric_limits<int>::max()) Mismatch between number of source and target dimensions. Source= Can't merge shape info. Both source and target dimension have values but they differ. Source= Error merging shape info for output. ' . Falling back to lenient merge. Tensor element type mismatch. SparseTensor element type mismatch. AddInitializedTensor already has tensor with name Only 'value' attribute is supported within a 'Constant' node in ORT nullptr != sub_graph && nullptr != sub_graph->GetMetaDef() This is an invalid model. Node ( ) does not have type information set by parent node. This is an invalid model. Type Error: Type ' ) does not match expected type ( This is an invalid model. Model input ( ) does not have type information. Type Error: Value of initializer Type Error: Shape of initializer 'was added but does not exist. is not a registered function/op ) is required but not specified. This is an invalid model. Error in Node: Size mismatch validating subgraph inputs. Got inputs. Either provide all subgraph inputs, or just the required inputs. Shouldn't be possible to have NodeArgs that haven't been handled already. outer_scope_node_args_consumed.empty() void onnxruntime::Graph::SetOutputs(const std::vector<const onnxruntime::NodeArg*>&) void onnxruntime::Graph::SetInputs(const std::vector<const onnxruntime::NodeArg*>&) InlineFunction onnxruntime::Node& onnxruntime::Graph::FuseSubGraph(std::unique_ptr<onnxruntime::IndexedSubGraph>, const string&) gsl::not_null<onnxruntime::Node*> onnxruntime::Graph::AllocateNode() onnxruntime::common::Status onnxruntime::Graph::SetGraphInputsOutputs() CleanUnusedInitializers void onnxruntime::Graph::ToGraphProtoInternal(onnx::GraphProto&) const bool onnxruntime::Graph::RemoveNode(onnxruntime::NodeIndex) void onnxruntime::Graph::RemoveInitializedTensor(const string&) void onnxruntime::Graph::AddInitializedTensor(const onnx::TensorProto&) onnxruntime::common::Status onnxruntime::Graph::Resolve(bool) Resolve ForThisAndAllSubgraphs PerformTypeAndShapeInferencing InitInputsInitializersOutputs VerifyNodeAndOpMatch InferAndVerifySubgraphTypes const T* onnxruntime::ConstPointerContainer<Container>::at(size_t) const [with Container = std::vector<onnxruntime::NodeArg*>; onnxruntime::ConstPointerContainer<Container>::T = onnxruntime::NodeArg; size_t = long unsigned int] onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const BuildConnections void onnxruntime::Graph::RemoveEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int) void onnxruntime::Graph::AddEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int) SetOuterScopeNodeArgs Graph onnxruntime::Graph::Graph(onnx::GraphProto*, const std::unordered_map<std::basic_string<char>, int>&, onnxruntime::Version, onnxruntime::IOnnxRuntimeOpSchemaCollectionPtr, onnxruntime::Graph*, const onnxruntime::Node*, const onnxruntime::logging::Logger&, const std::unordered_map<std::basic_string<char>, const onnx::FunctionProto*>&) MergeShapeInfo UpdateTypeAndShape FN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_E *ZN11onnxruntime5Graph7ResolveEbEUlRS0_E0_ *ZN11onnxruntime5Graph7ResolveEbEUlRS0_E1_ *ZN11onnxruntime5Graph7ResolveEbEUlRS0_E2_ N4onnx15GraphInferencerE N4onnx16InferenceContextE N11onnxruntime5GraphE N11onnxruntime19GraphInferencerImplE N11onnxruntime20InferenceContextImplE PFN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_E ExplicitInputs: . Index: Invalid input index for node ImplicitInputs: Implicit input name cannot be safely updated to in one of the subgraphs. -> /onnxruntime_src/onnxruntime/core/graph/graph_utils.cc index >= 0 && static_cast<size_t>(index) < inputs.size() Attempting to get an input that does not exist. index >= 0 && static_cast<size_t>(index) < outputs.size() Attempting to get an output that does not exist. Failed since multiple edges matched: Can only add a new input at the end of the current ones. num_explicit_inputs == static_cast<size_t>(target_input_idx) std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0 Node must only have one used output std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; }) Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here. Initializer with same name exists. Name: !graph.GetInitializedTensor(new_initializer.name(), existing) FindPath void onnxruntime::graph_utils::AddNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&) void onnxruntime::graph_utils::ReplaceNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&) onnxruntime::NodeArg& onnxruntime::graph_utils::AddInitializer(onnxruntime::Graph&, const onnx::TensorProto&) void onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph(onnxruntime::Node&, const string&, const string&) bool onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutput(onnxruntime::Graph&, onnxruntime::Node&) bool onnxruntime::graph_utils::RemoveNode(onnxruntime::Graph&, onnxruntime::Node&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const CanUpdateImplicitInputNameInSubgraphs const string& onnxruntime::graph_utils::GetNodeOutputName(const onnxruntime::Node&, int) const string& onnxruntime::graph_utils::GetNodeInputName(const onnxruntime::Node&, int) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const *ZN11onnxruntime11GraphViewerC1ERKNS_5GraphEEUlPKNS_4NodeEE_ N11onnxruntime11NodeCompareE Protobuf parsing failed. ModelProto was null. <p_fd> is less than 0. Invalid istream object. Null model_proto ptr. <p_fd> less than 0. failed. File doesn't exist Load model system error number ModelProto does not have a graph. Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported. ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset model may run depending upon legacy support of some older opset version operators. /onnxruntime_src/onnxruntime/core/graph/model.cc No graph was found in the protobuf. Failed to load model with error: Protobuf serialization failed. Save SaveModel Load Model St15_Sp_counted_ptrIPN11onnxruntime5ModelELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime21SchemaRegistryManagerESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE Domain already set in registry , but it its version is higher than the operator set version /onnxruntime_src/onnxruntime/core/graph/schema_registry.cc RegisterOpSet N11onnxruntime27OnnxRuntimeOpSchemaRegistryE N11onnxruntime21SchemaRegistryManagerE sequence_lens initial_h initial_c QW MW memory_seq_lens AW Y_h Y_c A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified. Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01. Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators. Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified. Couple the input and forget gates if 1, default 0. Number of neurons in the hidden layer. Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional. Constrain seq_lens to integral tensors. The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]` The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`. The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`. The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0. Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]` Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`. Optional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`. The weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0. The weight tensor of the query layer in the attention mechanism. Should be of shape `[num_directions, am_query_depth(hidden_size of lstm), am_attn_size]` The weight tensor of the memory layer in the attention mechanism. Should be of shape `[num_directions, memory_depth, am_attn_size]` The attention_v tensor in the attention mechanism. Should be of shape `[num_directions, am_attn_size]` The sequence of the memory (input) for attention mechanism. Should be of `[batch_size, max_memory_step, memory_depth]` The sequence length of the input memory for the attention mechanism. Should be of `[batch_size]` The weights of attention layer in the attention wrapper. If exists, should be of shape `[num_directions, memory_depth+hidden_size, aw_attn_size]. Please note that attention mechanism context depth is also memory_depth in the attention mechanism.` A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]` The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`. The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`. fused_function_subgraph input_arg->Type() != nullptr Resolve subgraph failed: A node with a function body within a subgraph within another function body is currently not supported in ORT /onnxruntime_src/onnxruntime/core/graph/function.cc void onnxruntime::update_subgraphs_within_function_body(onnx::GraphProto&, const onnxruntime::Graph&, const onnx::NodeProto&, const std::unordered_map<std::basic_string<char>, int>&, const std::unordered_map<std::basic_string<char>, int>&) onnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph&, const NodeIndex&, const onnx::FunctionProto&, const onnxruntime::logging::Logger&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const onnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph&, std::unique_ptr<onnxruntime::IndexedSubGraph>, const onnxruntime::logging::Logger&) *ZN11onnxruntime12FunctionImplC1ERKNS_5GraphERKmRKN4onnx13FunctionProtoERKNS_7logging6LoggerEEUlRNS6_16InferenceContextEE_ N11onnxruntime8FunctionE N11onnxruntime12FunctionImplE Default logger already set. ISink must be provided. System onnxruntime default_logger_id must be provided if instance_type is InstanceType::Default Only one instance of LoggingManager created with InstanceType::Default can exist at any point in time. N@VIWEF is not a valid year is not a valid day is not a valid date N11onnxruntime7logging11OStreamSinkE Writing profiler data to file /onnxruntime_src/onnxruntime/core/common/profiler.cc Maximum number of events reached, could not record profile event. [
]
{"cat" : " "pid" : "tid" : "dur" : "ts" : "ph" : "X", "name" :" "args" : { }
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+ enabled_ session_logger != nullptr custom_logger != nullptr Session Node EndProfiling EndTimeAndRecordEvent void onnxruntime::profiling::Profiler::Initialize(const onnxruntime::logging::Logger*) onnxruntime::TimePoint onnxruntime::profiling::Profiler::StartTime() const SUCCESS INVALID_ARGUMENT NO_SUCHFILE NO_MODEL ENGINE_ERROR RUNTIME_EXCEPTION INVALID_PROTOBUF MODEL_LOADED NOT_IMPLEMENTED INVALID_GRAPH EP_FAIL GENERAL ERROR OK SystemError [ONNXRuntimeError] E}oaS /onnxruntime_src/onnxruntime/core/common/status.cc code != static_cast<int>(common::OK) onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const char*) onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const string&) *ZN11onnxruntime11concurrency10ThreadPool11ParallelForEiSt8functionIFviEEEUliE_ *ZN11onnxruntime11concurrency10ThreadPool11ParallelForEiSt8functionIFviEEEUlvE0_ *ZN11onnxruntime11concurrency10ThreadPool16BatchParallelForEiSt8functionIFviEEiEUliE_ NSt6thread10_Impl_baseE NSt6thread5_ImplISt12_Bind_simpleIFSt8functionIFvvEEvEEEE ZN5Eigen15ThreadPoolTemplINS_20StlThreadEnvironmentEEC1EibS1_EUlvE_ St23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFSt8functionIFvvEEvEEEESaIS8_ELN9__gnu_cxx12_Lock_policyE2EE lib .so Failed to get symbol Got null library handle file " " failed: close open Failed to load library munmap failed. error code: - error code: fstat Not satsified: file_path
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==> Context: attr Attribute (name: since_version function ( Unable to open model file: \/ Unrecognized type value case (value_info name: setting data_type field (tensor name: ) is stored externally and should not have data field. Data of TensorProto ( tensor name: ) is stored externally but doesn't have a location. , but it doesn't exist or is not accessible. ) is 0-element but contains data! ) should contain one and only one value field. ) should not be stored in raw_data field Unrecognized data_type (tensor name: ) first dimension size does not equal NNZ. ) second dimension size does not match rank of tensor. ] not in lexicographic sorted order. ) dimensions are not positive. ) has zero input and zero output. is deprecated in domain_version of experimental ops. In the future, we may directly reject this operator. Please update your model as soon ' has been used as graph input names multiple times. Graph must be in single static assignment (SSA) form, however ' in initializer but not in graph input Nodes in a graph must be topologically sorted, however input '
is not output of any previous nodes. ' has been used as output names multiple times. ) should not contain more than one value field. type field and data field mismatch in attribute ) should refer to attribute in parent node. The model does not have an ir_version set properly. Your model ir_version is higher than the checker's. Your model has duplicate keys in metadata_props. model with IR version >= 3 must specify opset_import for ONNX model with IR version < 3 cannot have opset_import specified ' has been used multiple times. ) should not have duplicate outputs specified. ) should not have duplicate attributes specified. Nodes in a function must be topologically sorted, however input '
- is neither output of any previous nodes nor input of the function. Function must be in single static assignment (SSA) form, however ' . Please check if it is a valid file. . Please check if it is a valid protobuf file of model. Unable to parse model from file: \ed\e\eddc\eccPt0ssssssrrssqpp0sqs 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 \sparse_value not specified Value of attribute Invalid shape value: Constrain input types. Number of times to sample. Input tensor must have rank 2 loop_body_attribute cond_out current sub_result sub_result_casted delta_casted div_result ceil_result ceil_result_relu ceil_result_relu_int ceil_result_relu_bool variadic_output Scalar. Value to step by. Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node. One of the attributes 'value' or 'sparse_value' must be specified for a Constant node. Attribute expected to have tensor type Attribute expected to have a one-dim tensor should be of integer type and specify a type. does not specify a valid type. Input tensor must be 2-dimensional Input to 'Range' op should be scalars (Tensor with only one element and shape empty) All inputs to 'Range' op must be of the same type Shape input must be a one-dimensional tensor. The value for the elements of the output tensor. The value for the elements of the output tensor in sparse format. Output tensor containing the same value of the provided tensor. /onnxruntime_src/cmake/external/onnx/onnx/defs/generator/defs.cc (Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32 1D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar. Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32. Constrain output types to be numerics. (Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal. (Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'. 2D input tensor to copy shape, and optionally, type information from. Output tensor, same shape as input tensor T1. Constrain input types. Strings and complex are not supported. Constrain output types. Strings and complex are not supported. Lower boundary of the output values. Upper boundary of the output values. (Optional) Seed to the random generator, if not specified we will auto generate one. The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT. The shape of the output tensor. Output tensor of random values drawn from uniform distribution Constrain output types to float tensors. The mean of the normal distribution. The standard deviation of the normal distribution. The data type for the elements of the output tensor. Default is TensorProto::FLOAT. Output tensor of random values drawn from normal distribution (Optional) The data type for the elements of the output tensor, if not specified, we will usethe data type of the input tensor. Input tensor to copy shape and optionally type information from. (Optional) The data type for the elements of the output tensor, if not specified, we will use int32. Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch. Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch. Constrain input types to float tensors. Constrain output types to integral tensors. Output type must be int32 or int64 Scalar. First entry for the range of output values. Scalar. Exclusive upper limit for the range of output values. A 1-D tensor with same type as the inputs containing generated range of values. Constrain input types to common numeric type tensors. *ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25ConstantOfShape_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17EyeLike_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_23RandomUniform_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_22RandomNormal_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_27RandomUniformLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26RandomNormalLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_21Multinomial_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Range_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Attribute 'value' of Constant node must exist with 'Tensor' data. /onnxruntime_src/cmake/external/onnx/onnx/defs/generator/old.cc *ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_
+ is neither output of any previous nodes nor input of the function. Function must be in single static assignment (SSA) form, however ' . Please check if it is a valid file. . Please check if it is a valid protobuf file of model. Unable to parse model from file: |tD`````H``(ZZ(` 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 \PK\P\PN<M\PO,Ksparse_value not specified Value of attribute Invalid shape value: Constrain input types. Number of times to sample. Input tensor must have rank 2 loop_body_attribute cond_out current sub_result sub_result_casted delta_casted div_result ceil_result ceil_result_relu ceil_result_relu_int ceil_result_relu_bool variadic_output Scalar. Value to step by. Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node. One of the attributes 'value' or 'sparse_value' must be specified for a Constant node. Attribute expected to have tensor type Attribute expected to have a one-dim tensor should be of integer type and specify a type. does not specify a valid type. Input tensor must be 2-dimensional Input to 'Range' op should be scalars (Tensor with only one element and shape empty) All inputs to 'Range' op must be of the same type Shape input must be a one-dimensional tensor. The value for the elements of the output tensor. The value for the elements of the output tensor in sparse format. Output tensor containing the same value of the provided tensor. /onnxruntime_src/cmake/external/onnx/onnx/defs/generator/defs.cc (Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32 1D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar. Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32. Constrain output types to be numerics. (Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal. (Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'. 2D input tensor to copy shape, and optionally, type information from. Output tensor, same shape as input tensor T1. Constrain input types. Strings and complex are not supported. Constrain output types. Strings and complex are not supported. Lower boundary of the output values. Upper boundary of the output values. (Optional) Seed to the random generator, if not specified we will auto generate one. The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT. The shape of the output tensor. Output tensor of random values drawn from uniform distribution Constrain output types to float tensors. The mean of the normal distribution. The standard deviation of the normal distribution. The data type for the elements of the output tensor. Default is TensorProto::FLOAT. Output tensor of random values drawn from normal distribution (Optional) The data type for the elements of the output tensor, if not specified, we will usethe data type of the input tensor. Input tensor to copy shape and optionally type information from. (Optional) The data type for the elements of the output tensor, if not specified, we will use int32. Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch. Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch. Constrain input types to float tensors. Constrain output types to integral tensors. Output type must be int32 or int64 Scalar. First entry for the range of output values. Scalar. Exclusive upper limit for the range of output values. A 1-D tensor with same type as the inputs containing generated range of values. Constrain input types to common numeric type tensors. *ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25ConstantOfShape_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17EyeLike_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_23RandomUniform_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_22RandomNormal_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_27RandomUniformLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26RandomNormalLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_21Multinomial_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Range_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Attribute 'value' of Constant node must exist with 'Tensor' data. /onnxruntime_src/cmake/external/onnx/onnx/defs/generator/old.cc *ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_
Returns the tensor resulted from performing the `{name}` logical operation
elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
{broadcast_doc}
This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). First input operand for the logical operator. Second input operand for the logical operator. Constrains input to boolean tensor. Constrains output to boolean tensor. /onnxruntime_src/cmake/external/onnx/onnx/defs/logical/defs.cc Constrains input types to all numeric tensors.
@@ -5909,11 +6240,11 @@
- ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0
- ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0
- Ht = (1 - zt) (.) ht + zt (.) Ht-1
- A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified. When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate. The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`. The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`. The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0 First input tensor must have rank 3 *ZN4onnx15RNNDocGeneratorEPKcEUlRNS_8OpSchemaEE_ foward output_sequence Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. The sequence output for the hidden is optional if 0. Default 0. A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0. /onnxruntime_src/cmake/external/onnx/onnx/defs/rnn/old.cc *ZN4onnx18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_ Attributes: (no type) (no doc) (unnamed) Outputs: Defined at (no documentation yet) Operator ' ) has input size not in range [min= , max= not in allowed input sizes. ) has output size not in allowed output sizes. Attribute ' has unknown expected type ' is missing. Required attribute ' ) than declared ( ) has more inputs ( ) in op definition. )'s input ) has more outputs ( )'s output ' appeared multiple times. for operator Unrecognized attribute: ONNX Schema (inputs_.size() - 1) == i (outputs_.size() - 1) == i !(it.GetName().empty()) (no explicit description available) ' has been deprecated since version Mismatched attribute type in ' ' is expected to have field 't' ' is expected to have field 'sparse_tensor' ' is expected to have field 'g' ' is expected to have field 'floats' ' is expected to have field 'ints' ' is expected to have field 'strings' ' is expected to have field 'tensors' ' is expected to have field 'graphs' is marked single but has an empty string in the graph : failed validating the check: Duplicate type constraint name Attribute specification type mismatch. ?p7p7p7X7p>=<p;:0:h8p7 *ZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_ *ZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_ N4onnx16OpSchemaRegistryE seq(tensor(uint8)) seq(tensor(uint16)) seq(tensor(uint32)) seq(tensor(uint64)) seq(tensor(int8)) seq(tensor(int16)) seq(tensor(int32)) seq(tensor(int64)) seq(tensor(float16)) seq(tensor(float)) seq(tensor(double)) seq(tensor(string)) seq(tensor(bool)) seq(tensor(complex64)) seq(tensor(complex128)) ], Value= sum of split values= Empty sequence. Tensors. Constrain to any tensor type. Input sequence. input_sequence position The tensor to split Concatenated tensor concat_result Tensor= Attribute dtype should be of integer type and specify a type. Required attribute axis is missing new_axis must be either 0 or 1 Invalid value of attribute 'axis'. Accepted range=[ Invalid value of attribute 'axis'. Rank= Only supports `int32_t` or `int64_t` inputs for split Input 'split' can not be empty. Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size= Constrain output types to any tensor type. (Optional) The data type of the tensors in the output sequence. The default type is 'float'. /onnxruntime_src/cmake/external/onnx/onnx/defs/sequence/defs.cc Constrain input types to any tensor type. Sequence enclosing the input tensors. Input tensor to be inserted into the input sequence. Position in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape). Output sequence that contains the inserted tensor at given position. Constrain position to integral tensor. It must be a scalar(tensor of empty shape). Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape). Output tensor at the specified position in the input sequence. Output sequence that has the tensor at the specified position removed. Length of input sequence. It must be a scalar(tensor of empty shape). Constrain output to integral tensor. It must be a scalar(tensor of empty shape). Constrain output types to all tensor types. Constrain input types to all tensor types. Length of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be positive. One or more outputs forming a sequence of tensors after splitting Constrain split size to integral tensor. Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1]. Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored. Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. Insert and concatenate on a new axis or not, default 0 means do not insert new axis. Sequence of tensors for concatenation Input Sequence and Tensor are expected to have the same elem type. Sequence= SequenceConstruct is expected to have at least 1 input. Element type of inputs are expected to be the same. *ZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ axis must be in [-r, r-1] 'input' must have rank >= 2 Invalid attribute perm { }, input shape = { Invalid position of 0 Invalid dimension value: Input tensor to be cast. Specified shape for output. Reshaped data. reshaped Shape of the input tensor length of each output steps Transposed output. transposed updates Tensor of rank q + (r - 1). squeezed Original tensor expanded Input tensor of any shape. repeats N-D tensor N-D tensor after resizing roi sizes Tensor to copy input into. condition depth Constrain to boolean tensors. output (always 2D tensor) Tensor of rank r >= 2. inverse_indices constant_value Input axes has invalid data 'step' cannot be 0 Blocksize must be positive 'Repeats' input must be 1D tensor of type int64 'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions. axis must be in [-rank, rank-1]. All inputs to Concat must have same rank data tensor must have rank >= 1 Indices tensor must have rank >= 1 OneHot node must have three inputs. Input 'depth' must be a scalar or rank 1 tensor. Input 'depth' must have exactly one element. Input 'values' must be rank 1 tensor. Input 'values' must have exactly two elements. 'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64 Pads has incorrect number of values 'sequence_lens' must have rank of 1 Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0. Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensor Target shape may not have multiple -1 dimensions Invalid Target shape product of 0 Dimension could not be inferred: incompatible shapes values in 'axes' are beyond the bounds of the computed output shape 'axes' attribute must not contain any duplicates Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/steps The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto Output tensor with the same shape as input with type specified by the 'to' argument Constrain input types. Casting from complex is not supported. Constrain output types. Casting to complex is not supported. /onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/defs.cc Input tensor can be of arbitrary type. Constrain output to int64 tensor. Total number of elements of the input tensor Constrain output to int64 tensor, which should be a scalar though. Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs).. List of tensors for concatenation One or more outputs forming list of tensors after splitting Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input). 1-D tensor of ending indices (exclusive) of corresponding axis in `axes` 1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). 1-D tensor of slice step of corresponding axis in `axes`. Default to 1. A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given. Input and output types can be of any tensor type. Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds. Tensor of rank r >=1 (same rank and shape as indices) Tensor of rank r >= 1 (same rank as input). Tensor of rank q + r - indices_shape[-1] - 1. Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds. Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds. Tensor of the same shape as indices. List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Tensors with at least max(dims) dimensions. Reshaped tensor with same data as input. List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded). Blocks of [blocksize, blocksize] are moved. Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width. Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize]. DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order. Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize]. 1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions. Output tensor of the same dimension and type as tensor input. output_dim[i] = input_dim[i] * repeats[i] Constrain repeat's type to int64 tensors. Constrain input 'X' and output 'Y' to all tensor types. Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc) The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'. Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor). The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic". If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.
+ A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified. When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate. The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`. The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`. The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0 First input tensor must have rank 3 *ZN4onnx15RNNDocGeneratorEPKcEUlRNS_8OpSchemaEE_ foward output_sequence Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. The sequence output for the hidden is optional if 0. Default 0. A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0. /onnxruntime_src/cmake/external/onnx/onnx/defs/rnn/old.cc *ZN4onnx18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_ Attributes: (no type) (no doc) (unnamed) Outputs: Defined at (no documentation yet) Operator ' ) has input size not in range [min= , max= not in allowed input sizes. ) has output size not in allowed output sizes. Attribute ' has unknown expected type ' is missing. Required attribute ' ) than declared ( ) has more inputs ( ) in op definition. )'s input ) has more outputs ( )'s output ' appeared multiple times. for operator Unrecognized attribute: ONNX Schema (inputs_.size() - 1) == i (outputs_.size() - 1) == i !(it.GetName().empty()) (no explicit description available) ' has been deprecated since version Mismatched attribute type in ' ' is expected to have field 't' ' is expected to have field 'sparse_tensor' ' is expected to have field 'g' ' is expected to have field 'floats' ' is expected to have field 'ints' ' is expected to have field 'strings' ' is expected to have field 'tensors' ' is expected to have field 'graphs' is marked single but has an empty string in the graph : failed validating the check: Duplicate type constraint name Attribute specification type mismatch. ```H` ` X` *ZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_ *ZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_ N4onnx16OpSchemaRegistryE seq(tensor(uint8)) seq(tensor(uint16)) seq(tensor(uint32)) seq(tensor(uint64)) seq(tensor(int8)) seq(tensor(int16)) seq(tensor(int32)) seq(tensor(int64)) seq(tensor(float16)) seq(tensor(float)) seq(tensor(double)) seq(tensor(string)) seq(tensor(bool)) seq(tensor(complex64)) seq(tensor(complex128)) ], Value= sum of split values= Empty sequence. Tensors. Constrain to any tensor type. Input sequence. input_sequence position The tensor to split Concatenated tensor concat_result Tensor= Attribute dtype should be of integer type and specify a type. Required attribute axis is missing new_axis must be either 0 or 1 Invalid value of attribute 'axis'. Accepted range=[ Invalid value of attribute 'axis'. Rank= Only supports `int32_t` or `int64_t` inputs for split Input 'split' can not be empty. Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size= Constrain output types to any tensor type. (Optional) The data type of the tensors in the output sequence. The default type is 'float'. /onnxruntime_src/cmake/external/onnx/onnx/defs/sequence/defs.cc Constrain input types to any tensor type. Sequence enclosing the input tensors. Input tensor to be inserted into the input sequence. Position in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape). Output sequence that contains the inserted tensor at given position. Constrain position to integral tensor. It must be a scalar(tensor of empty shape). Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape). Output tensor at the specified position in the input sequence. Output sequence that has the tensor at the specified position removed. Length of input sequence. It must be a scalar(tensor of empty shape). Constrain output to integral tensor. It must be a scalar(tensor of empty shape). Constrain output types to all tensor types. Constrain input types to all tensor types. Length of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be positive. One or more outputs forming a sequence of tensors after splitting Constrain split size to integral tensor. Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1]. Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored. Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. Insert and concatenate on a new axis or not, default 0 means do not insert new axis. Sequence of tensors for concatenation Input Sequence and Tensor are expected to have the same elem type. Sequence= SequenceConstruct is expected to have at least 1 input. Element type of inputs are expected to be the same. *ZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ axis must be in [-r, r-1] 'input' must have rank >= 2 Invalid attribute perm { }, input shape = { Invalid position of 0 Invalid dimension value: Input tensor to be cast. Specified shape for output. Reshaped data. reshaped Shape of the input tensor length of each output steps Transposed output. transposed updates Tensor of rank q + (r - 1). squeezed Original tensor expanded Input tensor of any shape. repeats N-D tensor N-D tensor after resizing roi sizes Tensor to copy input into. condition depth Constrain to boolean tensors. output (always 2D tensor) Tensor of rank r >= 2. inverse_indices constant_value Input axes has invalid data 'step' cannot be 0 Blocksize must be positive 'Repeats' input must be 1D tensor of type int64 'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions. axis must be in [-rank, rank-1]. All inputs to Concat must have same rank data tensor must have rank >= 1 Indices tensor must have rank >= 1 OneHot node must have three inputs. Input 'depth' must be a scalar or rank 1 tensor. Input 'depth' must have exactly one element. Input 'values' must be rank 1 tensor. Input 'values' must have exactly two elements. 'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64 Pads has incorrect number of values 'sequence_lens' must have rank of 1 Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0. Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensor Target shape may not have multiple -1 dimensions Invalid Target shape product of 0 Dimension could not be inferred: incompatible shapes values in 'axes' are beyond the bounds of the computed output shape 'axes' attribute must not contain any duplicates Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/steps The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto Output tensor with the same shape as input with type specified by the 'to' argument Constrain input types. Casting from complex is not supported. Constrain output types. Casting to complex is not supported. /onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/defs.cc Input tensor can be of arbitrary type. Constrain output to int64 tensor. Total number of elements of the input tensor Constrain output to int64 tensor, which should be a scalar though. Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs).. List of tensors for concatenation One or more outputs forming list of tensors after splitting Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input). 1-D tensor of ending indices (exclusive) of corresponding axis in `axes` 1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). 1-D tensor of slice step of corresponding axis in `axes`. Default to 1. A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given. Input and output types can be of any tensor type. Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds. Tensor of rank r >=1 (same rank and shape as indices) Tensor of rank r >= 1 (same rank as input). Tensor of rank q + r - indices_shape[-1] - 1. Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds. Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds. Tensor of the same shape as indices. List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Tensors with at least max(dims) dimensions. Reshaped tensor with same data as input. List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded). Blocks of [blocksize, blocksize] are moved. Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width. Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize]. DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order. Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize]. 1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions. Output tensor of the same dimension and type as tensor input. output_dim[i] = input_dim[i] * repeats[i] Constrain repeat's type to int64 tensors. Constrain input 'X' and output 'Y' to all tensor types. Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc) The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'. Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor). The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic". If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.
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/>
@@ -5931,25 +6262,26 @@
if coordinate_transformation_mode is "tf_half_pixel_for_nn", <br/>
x_original = (x_resized + 0.5) / scale, <br/>
if coordinate_transformation_mode is "tf_crop_and_resize", <br/>
-x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1). Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest". When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f. 1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize" The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified. If 'size' is needed, the user can use an empty string as the name of 'scales' in this operator's input list. The size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified. Constrain roi type to float or double. (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded. Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1. Constrains to boolean tensors. (Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices). Input tensor containing indices. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use. Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [-depth, depth-1]. In case 'depth' is of non-integer type, it will be casted to int64 before use. Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor. Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used. Constrain output types to boolean tensors. (Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false. (Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false. When True (nonzero), yield X, otherwise yield Y values selected at indices where condition is True values selected at indices where condition is False Tensor of shape equal to the broadcasted shape of condition, X, and Y. Constrain to all tensor types. (Optional) Specify which axis is time axis. Must be one of 0 (default), or 1. (Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0. Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`. Tensor with same shape of input. (Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default). (Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). A N-D input tensor that is to be processed. A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X' A 1-D INT64 tensor containing indices of 'Y' elements' first occurance in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor. A 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'. A 1-D INT64 tensor containing the count of each element of 'Y' in input 'X' Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds. Constrains input and output to only numeric types. Supported modes: `constant`(default), `reflect`, `edge` Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`. (Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0). Slice op must have either three, four or five inputs. Incorrect or missing input value for starts and ends Input axes has incorrect length Input steps has incorrect length Input tensor must be 4-dimensional *ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Ranks inferred ( Which axis to concat on outputs... Which axis to split on paddings New shape tiles Axis along which to repeat. width_scale height_scale 4-D tensor, [N,C,H,W] Which axis to split on. 'axis' must be in [-rank(indices)-1, rank(indices)] rank must be greater than axis ) is not equal to the existing rank value ( Number of elements of attribute 'scales' must be same as rank of input 'X' Attribute 'scales' must have floats type. Attribute 'scales' is required. Attribute value for pads is required Attribute pads has incorrect length Incorrect or missing attribute value for starts and ends Attribute axes has incorrect length Invalid value of attribute 'axis' Constrain input types. Casting from strings and complex are not supported. Constrain output types. Casting to strings and complex are not supported. /onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/old.cc Which axis to concat on. Default value is 1. Optional list of output lengths (see also arg 'split') List of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. Three modes: constant(default), reflect, edge One float, indicates the value to be filled, default is 0 Number of repeated copies to make of the input tensor. Output tensor of same shape and type as input. Constrain tiles and axis's type to int64 tensors. The scale along width dimension. It takes value greater than or equal to 1. The scale along height dimension. It takes value greater than or equal to 1. Two interpolation modes: nearest(default), bilinear 4-D tensor after resizing, [N,C,H,W] Constrain output types to bool, int32, int64, float16, float, double tensors. The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1]. Starting indices of corresponding axis in `axes` Ending indices (exclusive) of corresponding axis in axes` Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] Tensor of int32/int64 indices, of r >= 1 (same rank as input). Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. It is an error if any of the index values are out of bounds. List of non-negative integers, indicate the dimensions to squeeze. List of non-negative integers, indicate the dimensions to be inserted (Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Input tensor containing indices. The values must be non-negative integers. Any entries in the 'indices' input tensor with values outside the range [0, depth) will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use. Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [0, depth). In case 'depth' is of non-integer type, it will be casted to int64 before use. (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length alone the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded. List of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. One float, indicates the value to be filled. *ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Split_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_13Pad_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ ) is not equal to the existing dim value ( Number of elements of input 'sizes' must be same as rank of input 'X' Input 'sizes' must have int64 element type. Number of elements of input 'scales' must be same as rank of input 'X' Input 'scales' must have float element type. Dimension value inferred ( Data to be selected Data to be binarized Binarized output data Input data A dictionary. Data to be processed. Imputed output data Value(s) to change to A value that needs replacing. Value(s) to change to. Output data. A list of ints. A list of floats. A string. An integer. A float. Data to be classified. A collection of intercepts. Data to be regressed. Weights of the model(s). Encoded output data One of 'MAX,' 'L1,' 'L2' Data to be encoded. Data to be scaled. Scaled output data. Chosen support vectors Support vector coefficients. Input of shape [N,F] N, Top class for each point Tree id for each node. Feature id for each node. N classes The node id of each weight The weight for each target The total number of targets. The input values The output map seq(map(string, float)) seq(map(int64, float)) map(string, int64) map(int64, string) map(int64, float) map(int64, double) map(string, float) map(string, double) Label encoder has only one input. Label encoder has only one output. Only one of keys_*'s can be set in label encoder. Input type is not string tensor but key_strings is set Input type is not int64 tensor but keys_int64s is set Input type is not float tensor but keys_floats is set Only one of values_*'s can be set in label encoder. The indices, based on 0 as the first index of any dimension. Selected output data as an array The input must be a tensor of a numeric type or string. The output will be of the same tensor type. /onnxruntime_src/cmake/external/onnx/onnx/defs/traditionalml/defs.cc The input must be a tensor of a numeric type. The output will be of the same tensor type. Values greater than this are mapped to 1, others to 0. The input map that is to be cast to a tensor A tensor representing the same data as the input map, ordered by their keys The input must be an integer map to either string or float. The output is a 1-D tensor of string, float, or integer. A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'. Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).<br>One of 'DENSE', 'SPARSE'. If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor. Output data. If strings are input, the output values are integers, and vice versa. The input must be a tensor of strings or integers, either [N,C] or [C]. The output is a tensor of strings or integers. Its shape will be the same as the input shape. The strings of the map. This sequence must be the same length as the 'cats_int64s' sequence The integers of the map. This sequence must be the same length as the 'cats_strings' sequence. A string to use when an input integer value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined. An integer to use when an input string value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined. A 1-D tensor holding values from the input dictionary. The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same. The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary. A string vocabulary array.<br>One and only one of the vocabularies must be defined. An integer vocabulary array.<br>One and only one of the vocabularies must be defined. The input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape. Input data. It can be either tensor or scalar. The input type is a tensor of any shape. Output type is determined by the specified 'values_*' attribute. A list of strings. One and only one of 'keys_*'s should be set. A list of strings. One and only one of 'value_*'s should be set. Classification outputs (one class per example). Classification scores ([N,E] - one score for each class and example The input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C] The output will be a tensor of strings or integers. A collection of weights of the model(s). Indicates whether to do OvR or multinomial (0=OvR is the default). Class labels when using string labels. One and only one 'classlabels' attribute must be defined. Class labels when using integer labels. One and only one 'classlabels' attribute must be defined. Indicates the transform to apply to the scores vector.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' Regression outputs (one per target, per example). The input must be a tensor of a numeric type. Indicates the transform to apply to the regression output vector.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' Weights of the intercepts, if used. The total number of regression targets, 1 if not defined. Data to be encoded, a tensor of shape [N,C] or [C] Encoded output data, having one more dimension than X. List of categories, ints.<br>One and only one of the 'cats_*' attributes must be defined. List of categories, strings.<br>One and only one of the 'cats_*' attributes must be defined. If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail. First, offset by this.<br>Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count. Second, multiply by this.<br>Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.<br>Must be same length as 'offset' Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores. The input must be a tensor of a numeric type, either [C] or [N,C]. The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Its size will match the bactch size of the input. The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'. List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel. First set of probability coefficients. Second set of probability coefficients. This array must be same size as prob_a.<br>If these are provided then output Z are probability estimates, otherwise they are raw scores. Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' Class labels if using string labels.<br>One and only one of the 'classlabels_*' attributes must be defined. Class labels if using integer labels.<br>One and only one of the 'classlabels_*' attributes must be defined. Regression outputs (one score per target per example). The input type must be a tensor of a numeric type, either [C] or [N,C]. Flag indicating whether the regression is a one-class SVM or not. The number of support vectors. Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.' The class score for each class, for each point, a tensor of shape [N,E]. The input type must be a tensor of a numeric type. The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Node id for each node. Ids may restart at zero for each tree, but it not required to. Thresholds to do the splitting on for each node. Popularity of each node, used for performance and may be omitted. The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.<br>One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF' Child node if expression is true. Child node if expression is false. For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.<br>This attribute may be left undefined, and the defalt value is false (0) for all nodes. The id of the tree that this node is in. node id that this weight is for. The index of the class list that each weight is for. The weight for the class in class_id. Indicates the transform to apply to the score. <br> One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.' Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0) Node id for each node. Node ids must restart at zero for each tree and increase sequentially. Child node if expression is true Child node if expression is false For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.<br>This attribute may be left undefined and the defalt value is false (0) for all nodes. The id of the tree that each node is in. The index of the target that each weight is for Defines how to aggregate leaf values within a target. <br>One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.' The output will be a sequence of string or integer maps to float. The keys when using string keys.<br>One and only one of the 'classlabels_*' attributes must be defined. The keys when using int keys.<br>One and only one of the 'classlabels_*' attributes must be defined. An ordered collection of tensors, all with the same element type. The output array, elements ordered as the inputs. The size of each input in the input list Input's shape should be 1D or 2D *ZN4onnx11GetOpSchemaINS_19CastMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26CategoryMapper_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26DictVectorizer_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28LinearClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25SVMClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_34TreeEnsembleClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18ZipMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Input data. A list of labels. The input type must be a tensor of integers or strings, of any shape. The output type will be a tensor of strings or integers, and will have the same shape as the input. /onnxruntime_src/cmake/external/onnx/onnx/defs/traditionalml/old.cc *ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ is out of bounds ) vs ( inferred= type case mismatch. existing= type mismatch. existing= does not contain a graph. no name (op_type: , name: inputs but Graph has were provided Graph input # Inferred elem type differs from existing elem type: ( Inferred shape and existing shape differ in rank: ( Inferred shape and existing shape differ in dimension type case unsupported. existing= GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. is tensor type, but provided type is N4onnx15shape_inference19GraphInferencerImplE N4onnx15shape_inference20InferenceContextImplE Func_ Input for function node Output for function node onnx.AttributeProto onnx.ValueInfoProto onnx.NodeProto onnx.ModelProto onnx.StringStringEntryProto onnx.TensorAnnotation onnx.GraphProto onnx.TensorProto.Segment onnx.TensorProto onnx.SparseTensorProto onnx.TensorShapeProto onnx.TypeProto.Tensor onnx.TypeProto.Sequence onnx.TypeProto.Map onnx.TypeProto.SparseTensor onnx.TypeProto.Opaque onnx.TypeProto onnx.OperatorSetIdProto onnx.FunctionProto onnx.TensorShapeProto.Dimension /build/Release/onnx/onnx-ml.pb.cc EEEEEFEEEOOOOOOOOOl (px|T \ |d|||||| ld DT|l$L,T4|8P888888h8888888888888888888888888888888888888888888888888888888888888888888888888888888888888(<EhhEhEhhiEhPhh
+x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1). Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest". When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f. 1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize" The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified. If 'size' is needed, the user can use an empty string as the name of 'scales' in this operator's input list. The size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified. Constrain roi type to float or double. (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded. Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1. Constrains to boolean tensors. (Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices). Input tensor containing indices. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use. Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [-depth, depth-1]. In case 'depth' is of non-integer type, it will be casted to int64 before use. Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor. Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used. Constrain output types to boolean tensors. (Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false. (Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false. When True (nonzero), yield X, otherwise yield Y values selected at indices where condition is True values selected at indices where condition is False Tensor of shape equal to the broadcasted shape of condition, X, and Y. Constrain to all tensor types. (Optional) Specify which axis is time axis. Must be one of 0 (default), or 1. (Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0. Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`. Tensor with same shape of input. (Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default). (Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). A N-D input tensor that is to be processed. A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X' A 1-D INT64 tensor containing indices of 'Y' elements' first occurance in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor. A 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'. A 1-D INT64 tensor containing the count of each element of 'Y' in input 'X' Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds. Constrains input and output to only numeric types. Supported modes: `constant`(default), `reflect`, `edge` Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`. (Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0). Slice op must have either three, four or five inputs. Incorrect or missing input value for starts and ends Input axes has incorrect length Input steps has incorrect length Input tensor must be 4-dimensional *ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Ranks inferred ( Which axis to concat on outputs... Which axis to split on paddings New shape tiles Axis along which to repeat. width_scale height_scale 4-D tensor, [N,C,H,W] Which axis to split on. 'axis' must be in [-rank(indices)-1, rank(indices)] rank must be greater than axis ) is not equal to the existing rank value ( Number of elements of attribute 'scales' must be same as rank of input 'X' Attribute 'scales' must have floats type. Attribute 'scales' is required. Attribute value for pads is required Attribute pads has incorrect length Incorrect or missing attribute value for starts and ends Attribute axes has incorrect length Invalid value of attribute 'axis' Constrain input types. Casting from strings and complex are not supported. Constrain output types. Casting to strings and complex are not supported. /onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/old.cc Which axis to concat on. Default value is 1. Optional list of output lengths (see also arg 'split') List of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. Three modes: constant(default), reflect, edge One float, indicates the value to be filled, default is 0 Number of repeated copies to make of the input tensor. Output tensor of same shape and type as input. Constrain tiles and axis's type to int64 tensors. The scale along width dimension. It takes value greater than or equal to 1. The scale along height dimension. It takes value greater than or equal to 1. Two interpolation modes: nearest(default), bilinear 4-D tensor after resizing, [N,C,H,W] Constrain output types to bool, int32, int64, float16, float, double tensors. The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1]. Starting indices of corresponding axis in `axes` Ending indices (exclusive) of corresponding axis in axes` Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] Tensor of int32/int64 indices, of r >= 1 (same rank as input). Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. It is an error if any of the index values are out of bounds. List of non-negative integers, indicate the dimensions to squeeze. List of non-negative integers, indicate the dimensions to be inserted (Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Input tensor containing indices. The values must be non-negative integers. Any entries in the 'indices' input tensor with values outside the range [0, depth) will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use. Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [0, depth). In case 'depth' is of non-integer type, it will be casted to int64 before use. (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length alone the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded. List of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. One float, indicates the value to be filled. *ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Split_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_13Pad_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ ) is not equal to the existing dim value ( Number of elements of input 'sizes' must be same as rank of input 'X' Input 'sizes' must have int64 element type. Number of elements of input 'scales' must be same as rank of input 'X' Input 'scales' must have float element type. Dimension value inferred ( Data to be selected Data to be binarized Binarized output data Input data A dictionary. Data to be processed. Imputed output data Value(s) to change to A value that needs replacing. Value(s) to change to. Output data. A list of ints. A list of floats. A string. An integer. A float. Data to be classified. A collection of intercepts. Data to be regressed. Weights of the model(s). Encoded output data One of 'MAX,' 'L1,' 'L2' Data to be encoded. Data to be scaled. Scaled output data. Chosen support vectors Support vector coefficients. Input of shape [N,F] N, Top class for each point Tree id for each node. Feature id for each node. N classes The node id of each weight The weight for each target The total number of targets. The input values The output map seq(map(string, float)) seq(map(int64, float)) map(string, int64) map(int64, string) map(int64, float) map(int64, double) map(string, float) map(string, double) Label encoder has only one input. Label encoder has only one output. Only one of keys_*'s can be set in label encoder. Input type is not string tensor but key_strings is set Input type is not int64 tensor but keys_int64s is set Input type is not float tensor but keys_floats is set Only one of values_*'s can be set in label encoder. The indices, based on 0 as the first index of any dimension. Selected output data as an array The input must be a tensor of a numeric type or string. The output will be of the same tensor type. /onnxruntime_src/cmake/external/onnx/onnx/defs/traditionalml/defs.cc The input must be a tensor of a numeric type. The output will be of the same tensor type. Values greater than this are mapped to 1, others to 0. The input map that is to be cast to a tensor A tensor representing the same data as the input map, ordered by their keys The input must be an integer map to either string or float. The output is a 1-D tensor of string, float, or integer. A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'. Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).<br>One of 'DENSE', 'SPARSE'. If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor. Output data. If strings are input, the output values are integers, and vice versa. The input must be a tensor of strings or integers, either [N,C] or [C]. The output is a tensor of strings or integers. Its shape will be the same as the input shape. The strings of the map. This sequence must be the same length as the 'cats_int64s' sequence The integers of the map. This sequence must be the same length as the 'cats_strings' sequence. A string to use when an input integer value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined. An integer to use when an input string value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined. A 1-D tensor holding values from the input dictionary. The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same. The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary. A string vocabulary array.<br>One and only one of the vocabularies must be defined. An integer vocabulary array.<br>One and only one of the vocabularies must be defined. The input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape. Input data. It can be either tensor or scalar. The input type is a tensor of any shape. Output type is determined by the specified 'values_*' attribute. A list of strings. One and only one of 'keys_*'s should be set. A list of strings. One and only one of 'value_*'s should be set. Classification outputs (one class per example). Classification scores ([N,E] - one score for each class and example The input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C] The output will be a tensor of strings or integers. A collection of weights of the model(s). Indicates whether to do OvR or multinomial (0=OvR is the default). Class labels when using string labels. One and only one 'classlabels' attribute must be defined. Class labels when using integer labels. One and only one 'classlabels' attribute must be defined. Indicates the transform to apply to the scores vector.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' Regression outputs (one per target, per example). The input must be a tensor of a numeric type. Indicates the transform to apply to the regression output vector.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' Weights of the intercepts, if used. The total number of regression targets, 1 if not defined. Data to be encoded, a tensor of shape [N,C] or [C] Encoded output data, having one more dimension than X. List of categories, ints.<br>One and only one of the 'cats_*' attributes must be defined. List of categories, strings.<br>One and only one of the 'cats_*' attributes must be defined. If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail. First, offset by this.<br>Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count. Second, multiply by this.<br>Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.<br>Must be same length as 'offset' Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores. The input must be a tensor of a numeric type, either [C] or [N,C]. The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Its size will match the bactch size of the input. The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'. List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel. First set of probability coefficients. Second set of probability coefficients. This array must be same size as prob_a.<br>If these are provided then output Z are probability estimates, otherwise they are raw scores. Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' Class labels if using string labels.<br>One and only one of the 'classlabels_*' attributes must be defined. Class labels if using integer labels.<br>One and only one of the 'classlabels_*' attributes must be defined. Regression outputs (one score per target per example). The input type must be a tensor of a numeric type, either [C] or [N,C]. Flag indicating whether the regression is a one-class SVM or not. The number of support vectors. Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.' The class score for each class, for each point, a tensor of shape [N,E]. The input type must be a tensor of a numeric type. The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Node id for each node. Ids may restart at zero for each tree, but it not required to. Thresholds to do the splitting on for each node. Popularity of each node, used for performance and may be omitted. The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.<br>One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF' Child node if expression is true. Child node if expression is false. For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.<br>This attribute may be left undefined, and the defalt value is false (0) for all nodes. The id of the tree that this node is in. node id that this weight is for. The index of the class list that each weight is for. The weight for the class in class_id. Indicates the transform to apply to the score. <br> One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.' Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0) Node id for each node. Node ids must restart at zero for each tree and increase sequentially. Child node if expression is true Child node if expression is false For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.<br>This attribute may be left undefined and the defalt value is false (0) for all nodes. The id of the tree that each node is in. The index of the target that each weight is for Defines how to aggregate leaf values within a target. <br>One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.' The output will be a sequence of string or integer maps to float. The keys when using string keys.<br>One and only one of the 'classlabels_*' attributes must be defined. The keys when using int keys.<br>One and only one of the 'classlabels_*' attributes must be defined. An ordered collection of tensors, all with the same element type. The output array, elements ordered as the inputs. The size of each input in the input list Input's shape should be 1D or 2D *ZN4onnx11GetOpSchemaINS_19CastMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26CategoryMapper_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26DictVectorizer_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28LinearClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25SVMClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_34TreeEnsembleClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18ZipMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Input data. A list of labels. The input type must be a tensor of integers or strings, of any shape. The output type will be a tensor of strings or integers, and will have the same shape as the input. /onnxruntime_src/cmake/external/onnx/onnx/defs/traditionalml/old.cc *ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ is out of bounds ) vs ( inferred= type case mismatch. existing= type mismatch. existing= does not contain a graph. no name (op_type: , name: inputs but Graph has were provided Graph input # Inferred elem type differs from existing elem type: ( Inferred shape and existing shape differ in rank: ( Inferred shape and existing shape differ in dimension type case unsupported. existing= GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. is tensor type, but provided type is N4onnx15shape_inference19GraphInferencerImplE N4onnx15shape_inference20InferenceContextImplE Func_ Input for function node Output for function node onnx.AttributeProto onnx.ValueInfoProto onnx.NodeProto onnx.ModelProto onnx.StringStringEntryProto onnx.TensorAnnotation onnx.GraphProto onnx.TensorProto.Segment onnx.TensorProto onnx.SparseTensorProto onnx.TensorShapeProto onnx.TypeProto.Tensor onnx.TypeProto.Sequence onnx.TypeProto.Map onnx.TypeProto.SparseTensor onnx.TypeProto.Opaque onnx.TypeProto onnx.OperatorSetIdProto onnx.FunctionProto onnx.TensorShapeProto.Dimension /build/Release/onnx/onnx-ml.pb.cc llllL_P__PP_PP\ \\LLxEF@FGFEEIXI I(HHKIJhE,Z]L]^] ]\\l\<\[\[,Z[,Z,Z,Z,Z,Z,ZZ|ZTZ Zcdlcccc<cc|fdh,h|f|f i|f|f|f|fhgglgflf<l<mmmlmll\l,lo8r rrprqqpHpoooooqooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooď|ďď4ď\@
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- N4onnx14AttributeProtoE N4onnx14ValueInfoProtoE N4onnx9NodeProtoE N4onnx10ModelProtoE N4onnx22StringStringEntryProtoE N4onnx16TensorAnnotationE N4onnx10GraphProtoE N4onnx19TensorProto_SegmentE N4onnx11TensorProtoE N4onnx17SparseTensorProtoE N4onnx26TensorShapeProto_DimensionE N4onnx16TensorShapeProtoE N4onnx16TypeProto_TensorE N4onnx18TypeProto_SequenceE N4onnx13TypeProto_MapE N4onnx22TypeProto_SparseTensorE N4onnx16TypeProto_OpaqueE N4onnx9TypeProtoE N4onnx18OperatorSetIdProtoE N4onnx13FunctionProtoE N6google8protobuf8internal29InternalMetadataWithArenaBaseISsNS1_29InternalMetadataWithArenaLiteEE9ContainerE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/arena.cc CHECK failed: (min_bytes) <= (std::numeric_limits<size_t>::max() - kBlockHeaderSize): /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/generated_message_util.cc CHECK failed: (scc->visit_status.load(std::memory_order_relaxed)) == (SCCInfoBase::kRunning): Not implemented field number with type ZjOMz`]j'ޱMOX~7
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- ϔU֓Xn БRِ*ܼ D$̾<t|$\TT$T<Dddl\< 4|d4lTT4l\LD$LDt$T4d$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 google/protobuf/io/coded_stream.h. CHECK failed: (buffer_size) >= (0): /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream.cc This ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug. N6google8protobuf2io20ZeroCopyOutputStreamE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl_lite.cc CHECK failed: (last_returned_size_) > (0): BackUp() can only be called after a successful Next(). CHECK failed: (count) <= (last_returned_size_): CHECK failed: (count) >= (0): CHECK failed: target_ != NULL: CHECK failed: (buffer_used_) == (buffer_size_): BackUp() can only be called after Next(). CHECK failed: (count) <= (buffer_used_): Can't back up over more bytes than were returned by the last call to Next(). CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: Parameter to BackUp() can't be negative. CHECK failed: (count) <= (target_->size()): Cannot allocate buffer larger than kint32max for CHECK failed: (backup_bytes_) == (0): StringOutputStream. N6google8protobuf2io16ArrayInputStreamE N6google8protobuf2io17ArrayOutputStreamE N6google8protobuf2io18StringOutputStreamE N6google8protobuf2io18CopyingInputStreamE N6google8protobuf2io25CopyingInputStreamAdaptorE N6google8protobuf2io26CopyingOutputStreamAdaptorE (cannot determine missing fields for lite message) /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/message_lite.cc CHECK failed: !coded_out.HadError(): " because it is missing required fields: CHECK failed: (byte_size_before_serialization) == (byte_size_after_serialization): was modified concurrently during serialization. CHECK failed: (bytes_produced_by_serialization) == (byte_size_before_serialization): Byte size calculation and serialization were inconsistent. This may indicate a bug in protocol buffers or it may be caused by concurrent modification of This shouldn't be called if all the sizes are equal. Exceeded maximum protobuf size of 2GB: Can't parse message of type " N6google8protobuf11MessageLiteE [libprotobuf %s %s:%d] %s
- %d.%d.%d %c %g %p %lld %llu ".) INFO WARNING FATAL /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/stubs/common.cc This program requires version of the Protocol Buffer runtime library, but the installed version is . Please update your library. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in " This program was compiled against version of the Protocol Buffer runtime library, which is not compatible with the installed version ( ). Contact the program author for an update. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in " N6google8protobuf7ClosureE N6google8protobuf8internal16FunctionClosure0E N6google8protobuf14FatalExceptionE , lo= /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/stubs/int128.cc Division or mod by zero: dividend.hi= CANCELLED UNKNOWN DEADLINE_EXCEEDED NOT_FOUND ALREADY_EXISTS PERMISSION_DENIED UNAUTHENTICATED RESOURCE_EXHAUSTED FAILED_PRECONDITION ABORTED OUT_OF_RANGE UNIMPLEMENTED INTERNAL UNAVAILABLE DATA_LOSS HIHII J J@J`JJJJJ K@II`IJ!KJMKcKyKKKKKKKL)LJ7K K OHOhONNORR`SQpQS
+ N4onnx14AttributeProtoE N4onnx14ValueInfoProtoE N4onnx9NodeProtoE N4onnx10ModelProtoE N4onnx22StringStringEntryProtoE N4onnx16TensorAnnotationE N4onnx10GraphProtoE N4onnx19TensorProto_SegmentE N4onnx11TensorProtoE N4onnx17SparseTensorProtoE N4onnx26TensorShapeProto_DimensionE N4onnx16TensorShapeProtoE N4onnx16TypeProto_TensorE N4onnx18TypeProto_SequenceE N4onnx13TypeProto_MapE N4onnx22TypeProto_SparseTensorE N4onnx16TypeProto_OpaqueE N4onnx9TypeProtoE N4onnx18OperatorSetIdProtoE N4onnx13FunctionProtoE N6google8protobuf8internal29InternalMetadataWithArenaBaseISsNS1_29InternalMetadataWithArenaLiteEE9ContainerE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/arena.cc CHECK failed: (min_bytes) <= (std::numeric_limits<size_t>::max() - kBlockHeaderSize): /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/generated_message_util.cc CHECK failed: (scc->visit_status.load(std::memory_order_relaxed)) == (SCCInfoBase::kRunning): Not implemented field number with type .32b21w1&10}0 0w/.p.--7-,b,+33+6+*z**)^))(x('-'&j&%%p%%$338$#r#"R"!f!! ( 4 " 8 J332:z(8*33F
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+ P Y338\4444556L6666$7d7748889T9D]D]9 :<:l::4;;;<4<<==L>t>>?D?t?D]D]?PtQRRST$UUdVZ<YY|WdX$\\ NND]D]O@t@@tABBCtCCdDDTEEdFGdGGHD]D]TIIJ4JdJJJJ$KKKTLLLLMDMtMMD]D]M$4 /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/coded_stream.cc A protocol message was rejected because it was too big (more than bytes). To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h. CHECK failed: (buffer_size) >= (0): /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream.cc This ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug. N6google8protobuf2io20ZeroCopyOutputStreamE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl_lite.cc CHECK failed: (last_returned_size_) > (0): BackUp() can only be called after a successful Next(). CHECK failed: (count) <= (last_returned_size_): CHECK failed: (count) >= (0): CHECK failed: target_ != NULL: CHECK failed: (buffer_used_) == (buffer_size_): BackUp() can only be called after Next(). CHECK failed: (count) <= (buffer_used_): Can't back up over more bytes than were returned by the last call to Next(). CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: Parameter to BackUp() can't be negative. CHECK failed: (count) <= (target_->size()): Cannot allocate buffer larger than kint32max for CHECK failed: (backup_bytes_) == (0): StringOutputStream. N6google8protobuf2io16ArrayInputStreamE N6google8protobuf2io17ArrayOutputStreamE N6google8protobuf2io18StringOutputStreamE N6google8protobuf2io18CopyingInputStreamE N6google8protobuf2io25CopyingInputStreamAdaptorE N6google8protobuf2io26CopyingOutputStreamAdaptorE (cannot determine missing fields for lite message) /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/message_lite.cc CHECK failed: !coded_out.HadError(): " because it is missing required fields: CHECK failed: (byte_size_before_serialization) == (byte_size_after_serialization): was modified concurrently during serialization. CHECK failed: (bytes_produced_by_serialization) == (byte_size_before_serialization): Byte size calculation and serialization were inconsistent. This may indicate a bug in protocol buffers or it may be caused by concurrent modification of This shouldn't be called if all the sizes are equal. Exceeded maximum protobuf size of 2GB: Can't parse message of type " N6google8protobuf11MessageLiteE [libprotobuf %s %s:%d] %s
+ %d.%d.%d %c %g %p %lld %llu ".) INFO WARNING FATAL /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/stubs/common.cc This program requires version of the Protocol Buffer runtime library, but the installed version is . Please update your library. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in " This program was compiled against version of the Protocol Buffer runtime library, which is not compatible with the installed version ( ). Contact the program author for an update. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in " N6google8protobuf7ClosureE N6google8protobuf8internal16FunctionClosure0E N6google8protobuf14FatalExceptionE , lo= /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/stubs/int128.cc Division or mod by zero: dividend.hi= CANCELLED UNKNOWN DEADLINE_EXCEEDED NOT_FOUND ALREADY_EXISTS PERMISSION_DENIED UNAUTHENTICATED RESOURCE_EXHAUSTED FAILED_PRECONDITION ABORTED OUT_OF_RANGE UNIMPLEMENTED INTERNAL UNAVAILABLE DATA_LOSS @` @` \\%;Qg}u p`X
- /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/wire_format_lite.cc CHECK failed: (value.size()) <= (kint32max): buffer. Use the 'bytes' type if you intend to send raw parsing serializing '%s' String field contains invalid UTF-8 data when a protocol bytes. N6google8protobuf8internal12FieldSkipperE N6google8protobuf8internal29CodedOutputStreamFieldSkipperE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/extension_set.cc Non-primitive types can't be packed. Don't lookup extension types if they aren't present (1). Don't lookup extension types if they aren't present (2). CHECK failed: extension != NULL: Index out-of-bounds (field is empty). Multiple extension registrations for type " CHECK failed: type == WireFormatLite::TYPE_MESSAGE || type == WireFormatLite::TYPE_GROUP: CHECK failed: (type) == (WireFormatLite::TYPE_ENUM): CHECK failed: (type) != (WireFormatLite::TYPE_ENUM): CHECK failed: (type) != (WireFormatLite::TYPE_MESSAGE): CHECK failed: (type) != (WireFormatLite::TYPE_GROUP): can't reach here. Can't get here. Extension not found. ", field number 2r@r@r@r@r@r@r@r@rrrt wv|zzzxLwww ydyyy<z\xxxvstttttu(u@uXupuuu vuuuvtls ~}O}|Y||{{&s&s&s&s{zy=~9~,lCMMMM܁<,<,'̂4̂0,<E(܇ (х.pʆQ(94$$؉ rllllllll|Whhh88] Ddth(X0x@xGoffff[BV 8Ph$Td N6google8protobuf8internal20RepeatedPtrFieldBaseE N6google8protobuf8internal15ExtensionFinderE N6google8protobuf8internal24GeneratedExtensionFinderE N6google8protobuf8internal12ExtensionSet8KeyValueE N6google8protobuf13RepeatedFieldIiEE N6google8protobuf13RepeatedFieldIlEE N6google8protobuf13RepeatedFieldIjEE N6google8protobuf13RepeatedFieldImEE N6google8protobuf13RepeatedFieldIfEE N6google8protobuf13RepeatedFieldIdEE N6google8protobuf13RepeatedFieldIbEE N6google8protobuf16RepeatedPtrFieldISsEE N6google8protobuf16RepeatedPtrFieldINS0_11MessageLiteEEE St3mapIiN6google8protobuf8internal12ExtensionSet9ExtensionESt4lessIiESaISt4pairIKiS4_EEE N6google8protobuf8internal19ImplicitWeakMessageE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/stubs/stringprintf.cc CHECK failed: (v.size()) <= (kStringPrintfVectorMaxArgs): StringPrintfVector currently only supports up to Feel free to add support for more if you need it. arguments. Error reverse compiling ' Unknown encoding Error parsing ' ': Error compiling ' \x00 Invalid RE2: startpos: endpos: text size: DFA out of memory: size bytemap range list count SearchDFA inconsistency SearchOnePass inconsistency SearchBitState inconsistency SearchNFA inconsistency requested group in regexp invalid rewrite pattern: /onnxruntime_src/cmake/external/re2/re2/re2.cc pattern too large - reverse compile failed pattern too large - compile failed RE2: invalid startpos, endpos pair. [ Rewrite schema error: '\' not allowed at end. Rewrite schema error: '\' must be followed by a digit or '\'. Rewrite schema requests %d matches, but the regexp only has %d parenthesized subexpressions.
-
Regexp not destroyed. Stack not empty. Bad reference count Walk NULL no error unexpected error invalid escape sequence invalid character class invalid character class range missing ] missing ) trailing \ invalid repetition size bad repetition operator invalid perl operator invalid UTF-8 invalid named capture group /onnxruntime_src/cmake/external/re2/re2/regexp.cc Unexpected op in Regexp::Equal: NumCapturesWalker::ShortVisit called NamedCapturesWalker::ShortVisit called CaptureNamesWalker::ShortVisit called /onnxruntime_src/cmake/external/re2/re2/walker-inl.h no argument for repetition operator ̝̝̞<,,̝̝̝̝̝̝̝|ܝD,,Ļ\t N3re26Regexp6WalkerIiEE N3re217NumCapturesWalkerE N3re219NamedCapturesWalkerE N3re218CaptureNamesWalkerE \%03o basic_string::erase GrowStack() failed: njob_ = job_.size() = Unexpected opcode: arg Bad arg in kInstAltMatch: Bad arg in kInstCapture: /onnxruntime_src/cmake/external/re2/re2/bitstate.cc mjjggff$fCompiler::Copy called! should never happen No ranges in char class Missing case in Compiler: hello, world /onnxruntime_src/cmake/external/re2/re2/compile.cc $D$D ԨĨԧ|d N3re26Regexp6WalkerINS_4FragEEE N3re28CompilerE /onnxruntime_src/cmake/external/re2/re2/dfa.cc StateSaver failed to restore state. Unexpected special state in RunStateOnByte RunStateOnByteUnlocked failed after ResetCache RunStateOnByteUnlocked failed after Reset Failed to analyze start state. %s%d (%p) flag=%#x || unhandled opcode: DeadState in RunStateOnByte NULL state in RunStateOnByte context does not contain text (unhandled in AddToThreadq Unhandled in step (?,?) (%d,?) (%d,%d) Bad args: nsubmatch= in ComputeFirstByte in Prog::Fanout() /onnxruntime_src/cmake/external/re2/re2/nfa.cc Unexpected opcode in short circuit: HV [0Y(ZY`YY
Y0 `ЅЅЅEؐ@X /onnxruntime_src/cmake/external/re2/re2/onepass.cc Cannot use SearchOnePass for unanchored matches. q,ҭTT T 0 0 0 0 Bad hex digit Concat of Any RE2: unexpected op: unknown round: /onnxruntime_src/cmake/external/re2/re2/parse.cc RepetitionWalker::ShortVisit called AddFoldedRange recurses too much. Bad call to ParseState::ParsePerlFlags ppppppp^L:(.\2l3l3l31110l3l3\3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3L3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l332l32l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3l3|2l296;6;6;6;6;6;6;6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:6:.;6:6:6:6:&;6:6:6:6:6:6:6: ;6:6:6:;6:;6::6:h: 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 parsing serializing '%s' String field contains invalid UTF-8 data when a protocol bytes. N6google8protobuf8internal12FieldSkipperE N6google8protobuf8internal29CodedOutputStreamFieldSkipperE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/extension_set.cc Non-primitive types can't be packed. Don't lookup extension types if they aren't present (1). Don't lookup extension types if they aren't present (2). CHECK failed: extension != NULL: Index out-of-bounds (field is empty). Multiple extension registrations for type " CHECK failed: type == WireFormatLite::TYPE_MESSAGE || type == WireFormatLite::TYPE_GROUP: CHECK failed: (type) == (WireFormatLite::TYPE_ENUM): CHECK failed: (type) != (WireFormatLite::TYPE_ENUM): CHECK failed: (type) != (WireFormatLite::TYPE_MESSAGE): CHECK failed: (type) != (WireFormatLite::TYPE_GROUP): can't reach here. Can't get here. Extension not found. ", field number X<| L,t \L80H`x(@Xp GW`
#?
hrh&hy2\,, LpL |\QcJ X((,p@ --t..../$/-- .(>BDChDBB=HCxBA(A@@@?h?>>,>EE;o:;%FkE6;====:W=DrD<]<MNNNNN OO0O@ONtMlPLL4OtR|STUVWQ N6google8protobuf8internal20RepeatedPtrFieldBaseE N6google8protobuf8internal15ExtensionFinderE N6google8protobuf8internal24GeneratedExtensionFinderE N6google8protobuf8internal12ExtensionSet8KeyValueE N6google8protobuf13RepeatedFieldIiEE N6google8protobuf13RepeatedFieldIlEE N6google8protobuf13RepeatedFieldIjEE N6google8protobuf13RepeatedFieldImEE N6google8protobuf13RepeatedFieldIfEE N6google8protobuf13RepeatedFieldIdEE N6google8protobuf13RepeatedFieldIbEE N6google8protobuf16RepeatedPtrFieldISsEE N6google8protobuf16RepeatedPtrFieldINS0_11MessageLiteEEE St3mapIiN6google8protobuf8internal12ExtensionSet9ExtensionESt4lessIiESaISt4pairIKiS4_EEE N6google8protobuf8internal19ImplicitWeakMessageE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/stubs/stringprintf.cc CHECK failed: (v.size()) <= (kStringPrintfVectorMaxArgs): StringPrintfVector currently only supports up to Feel free to add support for more if you need it. arguments. Error reverse compiling ' Unknown encoding Error parsing ' ': Error compiling ' \x00 Invalid RE2: startpos: endpos: text size: DFA out of memory: size bytemap range list count SearchDFA inconsistency SearchOnePass inconsistency SearchBitState inconsistency SearchNFA inconsistency requested group in regexp invalid rewrite pattern: /onnxruntime_src/cmake/external/re2/re2/re2.cc pattern too large - reverse compile failed pattern too large - compile failed RE2: invalid startpos, endpos pair. [ Rewrite schema error: '\' not allowed at end. Rewrite schema error: '\' must be followed by a digit or '\'. Rewrite schema requests %d matches, but the regexp only has %d parenthesized subexpressions.
+
Regexp not destroyed. Stack not empty. Bad reference count Walk NULL no error unexpected error invalid escape sequence invalid character class invalid character class range missing ] missing ) trailing \ invalid repetition size bad repetition operator invalid perl operator invalid UTF-8 invalid named capture group /onnxruntime_src/cmake/external/re2/re2/regexp.cc Unexpected op in Regexp::Equal: NumCapturesWalker::ShortVisit called NamedCapturesWalker::ShortVisit called CaptureNamesWalker::ShortVisit called /onnxruntime_src/cmake/external/re2/re2/walker-inl.h no argument for repetition operator lLLL ,LLLLLLL\<<4224333333d3D32222222,321 N3re26Regexp6WalkerIiEE N3re217NumCapturesWalkerE N3re219NamedCapturesWalkerE N3re218CaptureNamesWalkerE \%03o basic_string::erase GrowStack() failed: njob_ = job_.size() = Unexpected opcode: arg Bad arg in kInstAltMatch: Bad arg in kInstCapture: /onnxruntime_src/cmake/external/re2/re2/bitstate.cc \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 `XWxW(XWxW(Xunhandled 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 ~Z[[[[[[[y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\[y\y\y\y\[y\y\y\y\y\y\y\[y\y\y\g\y\U\y\[y\Zܩ\, |ܪ̪D4)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
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: SSTHThTTSTxxwwwxwxyxwe{ {|||{|{ 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 țț` hȧ((n(hh((((((h PP8 N3re26Regexp6WalkerIPS0_EE N3re214CoalesceWalkerE N3re214SimplifyWalkerE []^-\ \r \t \n \f \x%02x \x{%x} (){}[]*+?|.^$\ [^\x00-\x{10ffff}] (?:) Bad final char: ? + {%d,} {%d} {%d,%d} \C ^ $ (?-m:^) (?-m:$) \z \b \B (?HaveMatch:%d) (?: kRegexpCapture cap() == 0 ?P< [truncated] /onnxruntime_src/cmake/external/re2/re2/tostring.cc L$.TT,|dD$ldd< 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: 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 8666666777 8@86666666666 N3re26Regexp6WalkerIPS0_EE N3re214CoalesceWalkerE N3re214SimplifyWalkerE []^-\ \r \t \n \f \x%02x \x{%x} (){}[]*+?|.^$\ [^\x00-\x{10ffff}] (?:) Bad final char: ? + {%d,} {%d} {%d,%d} \C ^ $ (?-m:^) (?-m:$) \z \b \B (?HaveMatch:%d) (?: kRegexpCapture cap() == 0 ?P< [truncated] /onnxruntime_src/cmake/external/re2/re2/tostring.cc Tbeeetbbbggtgg hg<hdfffff<f femmm n n n n n 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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@@ -6333,7734 +6665,8124 @@
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-8C0A(B BBBB T \ d2 BBB E(D0A8JH
-8C0A(B BBBB T f2 G BBB E(D0A8JH
-8C0A(B BBBB T hw BBE E(D0D8Fa
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-8C0A(B BBBB T lw BBE E(D0D8Fa
-8C0A(B BBBG T n2 [ BBB E(D0A8JH
-8C0A(B BBBB T l pw BBE E(D0D8Fa
-8C0A(B BBBG T sw BBE E(D0D8Fa
-8C0A(B BBBG T 0uw BBE E(D0D8Fa
-8C0A(B BBBG T t Xw2 u BBB E(D0A8JH
-8C0A(B BBBB T @y2 7 BBB E(D0A8JH
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+8D0A(B BBBK \ $ u BEB D(A0Dc
+0A(A BBBC U
+0A(A BBBA < @" u BBA A(GU
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8A0A(B BBBE D
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+8A0A(B BBBA \ t BBB A(A0G|
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-(A ABBA < ! 2 BBA A(JY
-(A ABBA < ! 2 j BBA A(JY
-(A ABBA < " 2 M BBA A(JY
-(A ABBA < D" 2 0 BBA A(JY
-(A ABBA < " 2 BBA A(JY
-(A ABBA < " 2 BBA A(JY
-(A ABBA < # 2 BBA A(JY
-(A ABBA < D# 2 BBA A(JY
-(A ABBA < # 2 BBA A(JY
-(A ABBA < # 2 BBA A(JY
-(A ABBA < $ 2 e BBA A(JY
-(A ABBA < D$ 2 H BBA A(JY
-(A ABBA < $ 2 + BBA A(JY
-(A ABBA < $ 2 BBA A(JY
-(A ABBA < % 2 BBA A(JY
-(A ABBA < D% 2 BBA A(JY
-(A ABBA L % BBE D(C0G
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-(A ABBG < ) H BBG A(G@
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+8A0A(B BBBB T P =r BBE E(A0A8G
+8D0A(B BBBI , ADD0e
+AAA L H] BBB H(A0D8DP
+8A0A(B BBBG T X q BBE B(A0D8G
+8D0A(B BBBG T _ Oq BBB E(A0A8GT
+8A0A(B BBBF T < [q BBB E(A0A8G"
+8A0A(B BBBH T cq BBE E(A0A8G
+8D0A(B BBBE < ' &q BBA A(JU
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p BBA D(GpS
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+(A ABBD < " o BBA A(GU
+(A ABBA < < " o BBA A(GU
+(A ABBA < | " o BBA A(GU
+(A ABBA < " no BBA A(GU
+(A ABBA < " Mo BBA A(GU
+(A ABBA < < " ,o BBA A(GU
+(A ABBA < | " o BBA A(GU
+(A ABBA < " n BBA A(GU
+(A ABBA < " n BBA A(GU
+(A ABBA < < x" n BBA A(GU
+(A ABBA < | h " n BBA A(GU
+(A ABBA < X " fn BBA A(GU
+(A ABBA < H " En BBA A(GU
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+8A0A(B BBBG L # , BBB B(A0A8Gp8A0A(B BBB $$ 3 DR
+E A d D$ (. BLB B(A0A8D@q
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-8D0A(B BBBD D \1 BBB A(A0G@_
-0A(A BBBA L 1 BBB B(A0D8Dm8A0A(B BBB L 1 BBB B(A0D8Dm8A0A(B BBB L D2 BBB B(A0D8Dm8A0A(B BBB L 2 P
- BBB B(A0A8Gp8A0A(B BBB L 2 BBB B(A0A8Gp8A0A(B BBB L <1 P
BBB B(A0A8Jq
-8A0A(B BBBA 3 0pV Ko
-A L 3 - BEB B(A0A8DP
-8A0A(B BBBA L 1 0R / BBI B(H0A8DP
-8A0A(B BBBC < L2 03y ' BJA A(DP
-(A ABBA 4 p4" DN
-N A 4 2 4 BID D0]
- AABA 4 5% , 2 5, AGG XCA , ,3 50 ADG _CA \3 5$ _ D_ t5 05 5 (5 5 5 5 5 , 3 5,
- AGG XCA 6 5 6 5 46 5r L6 h5 d6 `5 L |6 X5 BHB E(A0A8D`
-8A0A(B BBBE 4 4 6 BAD G@W
- CABE 7 m. K^ 7 h7, D0g 47 7 T5 x7- s Hd l7 7 A\ 7 7 7 7 7 x7 7 p7 7 h7 8 `7 8 X7 48 P7 T6 X74 w Hg l8 x7 8 7 8 7 8 7 8 7 8 7 8 7 9 7 ,9 7 HS L L9 7 AAG ]
+8A0A(B BBBF D # / [l BBJ A(D0
+(A BBEA L $ X0 3l BLE B(A0D8D@
+8A0A(B BBBG L l$ 2( l BBB E(D0D8JV
+8A0A(B BBBC < $ XZ" Sn BBA A(GU
+(A ABBA < $ H[" 2n BBA A(GU
+(A ABBA < <% 8\" n BBA A(GU
+(A ABBA < |% (]" m BBA A(GU
+(A ABBA < % ^" m BBA A(GU
+(A ABBA < % _" m BBA A(GU
+(A ABBA < <& _" m BBA A(GU
+(A ABBA < |& `" lm BBA A(GU
+(A ABBA < & a" Km BBA A(GU
+(A ABBA < & b" *m BBA A(GU
+(A ABBA < <' c" m BBA A(GU
+(A ABBA < |' d" l BBA A(GU
+(A ABBA < ' e" l BBA A(GU
+(A ABBA < ' f l BBD D(D
+(D ABBK L <( x3 l BBH E(D0D8DPj
+8D0A(B BBBD L d) y9 BBB B(A0D8D 8A0A(B BBB L ) { BBB B(A0D8Dm8A0A(B BBB L * 8~ BBB B(A0D8Dm8A0A(B BBB L T* x BBB B(A0D8Dm8A0A(B BBB L * BBB B(A0A8Gp8A0A(B BBB L * 8 BBB B(A0A8Gp8A0A(B BBB L l* * {j BBB B(A0A8Jq
+8A0A(B BBBA + ho KD
+E L + 诛 BEB B(A0A8DP
+8A0A(B BBBA L ,+ 8R l BBI B(H0A8DP
+8A0A(B BBBC < |+ Hy l BJA A(DP
+(A ABBA , " DN
+N A 4 + k BID D0]
+ AABA , % , ,, 8, k AGG XCA , \, 80 {k ADG _CA , 8$ Ok D_ - H - @ - 8 - 0 , - (, j AGG XCA . ( ,. D. r \. t. x L . p BHB E(A0A8D`
+8A0A(B BBBE 4 . i BAD G@W
+ CABE / X . K^ ,/ , D0g D/ . - ci Hd |/ A\ / / / / / 0 x ,0 p D0 h / p4 gh Hg |0 0 0 0 0 0 1 $1 ȹ <1 й HS L \1 й AAG ]
CAF [
ACA R
-CAJ JCA , 9 7b D\
+CAJ JCA , 1 b D\
H P
H Q
-G I , 9 (8b D\
+G I , 1 @b D\
H P
H Q
-G I 9 h8- : 8- ,: 8- < D: 8 BAA a
+G I 2 - $2 - <2 - < T2 Ⱥ BAA a
ABH i
-ABL < 8 9 C BED A(Dpz
-(A ABBH < 8 P: BED A(Dpz
-(A ABBH ; ;' Ae $; 0;' Ae L9 @; AN < l9 0;P o BEJ D(DPn(C ABB L 9 @; 3 BEE E(A0A8D
-8A0A(B BBBG , ; <0 AI
FK
- C D ,: =2 BBB D(A0Dn
-0D(A BBBA L l< ? BBB B(A0A8D
-8A0A(B BBBA L < hA BBB B(A0A8D
-8A0A(B BBBA T ; C\
- BBE E(D0A8G
-8D0A(B BBBF $ l; H AFD@ L = HW BBB E(A0A8DP
-8A0A(B BBBA L = I BBB B(A0A8Gp8A0A(B BBB L 4< K 7 BEB E(I0H8DP
-8D0A(B BBBD 4 |> L@ BAD D0
- AABA D > MB BBE A(A0D@m
-0A(A BBBA $ > NV ADD JAA< ,= N^ g BBA D(G0
-(A ABBA 4 l= P O BFD D0I
- DABA D = P ? BBB D(A0G@:
-0A(A BBBA L = @R] 7 BBB B(D0A8D`
-8A0A(B BBBA 4@ PU
L@ c. K^ D d@ 0U" BBB A(A0G@m
-0A(A BBBA T > V| s BBL B(A0A8D
-8A0A(B BBBA L A @] BBB B(A0A8Gp8A0A(B BBB L TA _ BBE B(A0A8D`
-8A0A(B BBBA D ? ` BBJ A(D0G@q
-0D(A BBBA T ? b
- BEB B(A0A8G
-8A0A(B BBBA L L@ Pm BLB B(D0A8DP
-8A0A(B BBBE T @ ow' BBB B(D0A8G
-8A0A(B BBBB B ( C C 4C LC (+ Hb lC 82 He C X C P $ C Hg AG k
-AA $ C o AG s
-AA 4 B ؖ BAA G@I
- AABA DD P \D H tD @ , B H" AAG0i
-AAD D _T KH D 0 D (F E ` E XF 4E - AG cA $ TE pg AG k
-AA $ |E g AG k
-AA $ E o AG s
-AA $ E Ho AG s
-AA L C > BBB B(A0A8GZ
-8D0A(B BBBH LD \F x N D tF BBE A(A0D@l
-0A(A BBBA < D + BBD A(G0T
-(A ABBA , E Ȟt 3 AID {
-DAA 4 4E BFD D0
- DABA , dG ADD Z
-AAA 4 G P BDA w
+ABL < 1 3f BED A(Dpz
+(A ABBH < 1 h e BED A(Dpz
+(A ABBH 3 8' Ae 43 H' Ae |2 X {e AN < 2 HP _e BEJ D(DPn(C ABB L 2 X #e BEE E(A0A8D
+8A0A(B BBBG D ,3 2 d BBB D(A0Dn
+0D(A BBBA L L4 BBB B(A0A8D
+8A0A(B BBBA L 4 BBB B(A0A8D
+8A0A(B BBBA T 4 0ě\ *d BBE E(D0A8G
+8D0A(B BBBF $ l4 8ɛ d AFD@ L l5 ɛW BBB E(A0A8DP
+8A0A(B BBBA L 5 ʛ BBB B(A0A8Gp8A0A(B BBB L 45 0̛ Wc BEB E(I0H8DP
+8D0A(B BBBD 4 \6 ͛@ BAD D0
+ AABA D 6 ΛB BBE A(A0D@m
+0A(A BBBA $ 6 ϛV ADD JAA< ,6 Л^ b BBA D(G0
+(A ABBA 4 l6 (ћ ob BFD D0I
+ DABA D 6 ћ _b BBB D(A0G@:
+0A(A BBBA L 6 Xӛ] Wb BBB B(D0A8D`
+8A0A(B BBBA 8 h֛
,8 p. K^ D D8 H֛" BBB A(A0G@m
+0A(A BBBA T 7 0כ| a BBL B(A0A8D
+8A0A(B BBBA D 8 Xޛ ?a BBJ A(D0G@q
+0D(A BBBA T T8
+ _a BEB B(A0A8G
+8A0A(B BBBA L 8 ( a BLB B(D0A8DP
+8A0A(B BBBE T 8 a BBB B(A0A8G
+8A0A(B BBBA L ,: P BBE B(A0A8D`
+8A0A(B BBBA T 9 k a BBB B(D0A8G
+8A0A(B BBBE : : ; ; 4; + Hb T; 2 He t; ; $ ; g AG k
+AA $ ; @o AG s
+AA 4 ; ` BAA G@I
+ AABA ,< D< \< , ; " +` AAG0i
+AAD < (T KH < < F < = F = - AG cA $ <= g AG k
+AA $ d= hg AG k
+AA $ = o AG s
+AA $ = o AG s
+AA L = @> ^ BBB B(A0A8GZ
+8D0A(B BBBH T= 0 ^ D> ( N D \> 0 BBE A(A0D@l
+0A(A BBBA < = ?^ BBD A(G0T
+(A ABBA , > x t G^ AID {
+DAA 4 <> 3^ BFD D0
+ DABA , L? ! ADD Z
+AAA 4 |? " BDA w
AEA {
-AFI T E ؠ] BEB B(A0A8G
-8D0A(B BBBD T ,F ॡ BEB B(A0A8G
-8D0A(B BBBA T F H] BEB B(A0A8G
-8D0A(B BBBD L F P% n BCA G`w
+AFI T > "] ] BEB B(A0A8G
+8D0A(B BBBD T 4? ' ] BEB B(A0A8G
+8D0A(B BBBA T ? ,] ] BEB B(A0A8G
+8D0A(B BBBD L ? 2% ] BCA G`w
AABH C
- AABI $I [. K^ <I TI 4 tG (5 - ADG
+ AABI A . K^ $A 5 <A 5 4 |@ 55 A] ADG
-AAA , G 0á ADGv
-AAA , G áQ ADGj
-AAA , H áQ ADGj
-AAA , <H ġQ ADGj
-AAA 4 lH @ġu ADGO
-AAA 4 H ȡ ADG
-AAA , H ̡ ADG
-AAA 4 I ̡5 ADG4
-AAA 4 DI С ADG&
-AAA 4 |I pӡ ADG
-AAA 4 I ܡf ADG
-AAA , I ߡQ ADGj
-AAA T J @ߡ o BIB B(A0A8DI
-8A0A(B BBBA < lL x BEE A(D@M
-(A ABBA L J w BEB E(I0H8D@
-8D0A(B BBBD L K X: O BJE B(A0A8D@
-8A0A(B BBBA L LM H BBE B(A0A8D`
-8A0A(B BBBA L K BEB E(I0H8D@
-8D0A(B BBBD L K BJE B(A0A8D`8A0A(B BBB D DL (c h BBB A(C0K@
-0A(A BBBA L L P O BBH E(D0D8D`
-8D0A(B BBBH l L 2 ' BBB E(D0I8D6
+AAA , @ D a ADGv
+AAA , @ `EQ a ADGj
+AAA , A EQ ` ADGj
+AAA , DA EQ ` ADGj
+AAA 4 tA Eu ` ADGO
+AAA 4 A 8J a ADG
+AAA , A M b ADG
+AAA 4 B N5 b ADG4
+AAA 4 LB R c ADG&
+AAA 4 B U d ADG
+AAA 4 B ^f g ADG
+AAA , B `Q g ADGj
+AAA T $C ` g BIB B(A0A8DI
+8A0A(B BBBA < TD (i BEE A(D@M
+(A ABBA L C i g BEB E(I0H8D@
+8D0A(B BBBD L D k: cg BJE B(A0A8D@
+8A0A(B BBBA L 4E k BBE B(A0A8D`
+8A0A(B BBBA L D l f BEB E(I0H8D@
+8D0A(B BBBD L D Hn f BJE B(A0A8D`8A0A(B BBB D LE nc |f BBB A(C0K@
+0A(A BBBA L E r cf BBH E(D0D8D`
+8D0A(B BBBH l E pw2 ;f BBB E(D0I8D6
8D0A(B BBBA }
-8D0A(B BBBA T LM BEB B(A0A8J
-8A0A(B BBBA T M : ; BBB B(D0A8G
-
-8D0A(B BBBH , O E BIA rAE $P <P TP lP P P
P P P 9 Hp Q Q 4Q LQ dQ ( Af Q Q Q Q @ Hs Q 8 R @ R H 4R P, Aj TR `- L lR x AAG ]
-CAF k
+8D0A(B BBBA T TF @} f BEB B(A0A8J
+8A0A(B BBBA T F : Of BBB B(D0A8G
+
+8D0A(B BBBE , G E BIA rAE H $H <H TH lH H
H H H 9 Hp H I I 4I LI ( Af lI I I I Ȓ@ Hs I 蒜 I J J 3 Aq <J - TJ 8- AG cA tJ H- AG cA L J X AAG ]
+CAF [
ACA R
-CAJ JCA , R b D\
+CAJ JCA , J b D\
H P
H Q
-G I L R AAG ]
-CAF [
-ACA R
-CAJ JCA < DQ ( g BBA A(D@(A ABB < |S BEB A(A0v
-(A BBBA 4 Q h BAD D@Z
- AABA S - , R AID o
-DAA , DR AAD@s
-AAA lT D |
-H l T R L BBB E(A0A8D+
-8A0A(B BBBA T AL T S BBB E(A0D8D
-8D0A(B BBBG L dS 8 r BBB B(E0A8J@
-8A0A(B BBBA $ U h ^ AG b
-AA $ U ^ AG b
-AA < T p 0 BED A(G@|
-(A ABBA L DT BFB E(A0A8GP
-8A0A(B BBBH $ T X RFD@ $ V ^ AG b
-AA D V BBE A(A0D@p
-0A(A BBBA $ $W _ AAG LDAD TU ; BEE A(C0G@n
-0A(A BBBC L U !+ BBI A(D0MZ
-0D(A BBBD L U #3 BBI A(D0MZ
-0D(A BBBD L <V p%3 BBI A(D0MZ
-0D(A BBBD < V `' BBA D(Jz
-(D ABBJ \ V *9 BBB A(A0J
-0A(A BBBC {
-0A(A BBBE D ,W , BBB A(A0J
-0A(A BBBH , tW H/ ADG
-DAA T W /R BBE B(A0A8J2
-8A0A(B BBBE < W 4% BEA D(G
-(D ABBA L <X 5 c BEE E(A0C8J
-8A0A(B BBBC Z @8E < X x8_ G BED D(G
-(D ABBH $ X 9 AFD@ < Y :_ BED D(G
-(D ABBH $ LY ; AFD@ < tY ;Z BDA z
+G I L <J ȓr c BBB B(E0A8J@
+8A0A(B BBBA dK
0 De
+E A < J ؔ 0c BBA A(D@(A ABB < K x BEB A(A0v
+(A BBBA 4 ,K b BAD D@Z
+ AABA , dK b AID o
+DAA lL - , K 8 Xb AAD@s
+AAA L D |
+H l T K H b BBB E(A0A8D+
+8A0A(B BBBA ,M j DN
+E A LM AL < L p a BED A(G@|
+(A ABBA $ L Qa RFD@ 4 L ( :a BFA K0
+ AABA N - AG cA ,N 2 Al L tM ` BFB E(A0A8GP
+8A0A(B BBBH $ N `_ AAG LDAD M ` BEE A(C0G@n
+0A(A BBBC L 4N `+ g` BBI A(D0MZ
+0D(A BBBD L N @
+ R` BBB H(D0M2
+0D(A BBBD L N
+ =` BBB H(D0M2
+0D(A BBBD < $O (` BBA D(Jz
+(D ABBJ \ dO `9 C` BBB A(A0J
+0A(A BBBE s
+0A(A BBBE D O @ ` BBB A(A0J
+0A(A BBBH T P _ BEB E(A0A8G
+8A0A(B BBBI < dP % ` BEA D(G
+(D ABBA L P _ BEE E(A0C8J
+8A0A(B BBBB Q `E < Q _ _ BED D(G
+(D ABBH $ LQ _ AFD@ < tQ _ _ BED D(G
+(D ABBH $ Q @ W_ AFD@ < Q Z ;_ BDA z
ABL A
-ABA < Y ; w BBI A(D0
-(A ABBA 4 [ X<Z BDA y
+ABA < R ȹ ^ BBI A(D0
+(A ABBA < \R x ^ BBD D(J@F
+(D ABBA \ R ȺG ^ BBB D(D0Du
+0D(A BBBF K
+0D(A BBBA T R + y^ BEL B(A0D8G
+8D0A(B BBBD L TS c^ BBB B(D0A8D`
+8D0A(B BBBI 4 |T PZ BDA y
ABM A
-ABA 4 $\ <Z BDA y
+ABA 4 T xZ BDA y
ABM A
-ABA d \\ < BEB B(A0A8D@{
+ABA d T BEB B(A0A8D@{
8A0A(B BBBJ D
-8A0A(B BBBA < Z @=g r BED D(G
-(D ABBD d ] p> BLB B(A0A8D@q
-8A0A(B BBBE
-8A0A(B BBBA L t[ @
BEE A(A0G
-0A(A BBBA ] A < ] A BEB D(A0M
-(A BBBF 4 ^ @CZ BDA y
+8A0A(B BBBA L |T 8Ü
] BEE A(A0G
+0A(A BBBA U Ĝ < U Ĝ BEB D(A0M
+(A BBBF 4 U pƜZ BDA y
ABM A
-ABA T T\ hCV ! BBB E(A0D8D
-8A0A(B BBBA D \ pD: BBB A(D0IU
-0A(A BBBG , \ hF AID0y
-DAA 4 _ F BEA A(D@q(A ABB$ T_ Gf AAK0VAA< ] hG [ BBD A(G@,
-(A ABBA 4 _ H BFD A(D@(A ABBT ] I BEB A(A0[
+ABA T \U ƜP \ BBB B(A0A8Gw
+8A0A(B BBBA , U ɜ \ AID0y
+DAA 4 V ɜ BEA A(D@q(A ABB$ V Hʜf AAK0VAA< DV ʜ ;\ BBD A(G@,
+(A ABBA 4 \W ̜ BFD A(D@(A ABBT V ̜ [ BEB A(A0[
(A BBBK A
-(A BBBA L L` IM BBB B(A0D8Dp18A0A(B BBB ` KI AG A L ^ K [ BLB A(A0Jn
-0A(A BBBF a hOQ AG GAD ,a O BBE A(A0D@l
-0A(A BBBA ta @PA AK sA a pPI AK {A d a P BEE F(A0A8D@c
+(A BBBA d W ̜; BLB B(A0A8D`q
+8A0A(B BBBE S
+8A0A(B BBBK L TX ΜM BBB B(A0D8Dp18A0A(B BBB X МI AG A L W М Z BLB A(A0Jv
+0A(A BBBF Y xԜQ AG GA4 4Y Ԝu BEA A(D@a(A ABB lY ՜A AK sA Y 0՜I AK {A d Y `՜ BEE F(A0A8D@c
8F0A(B BBBF
-8D0A(B BBGA D b 8R BBE A(A0D@l
-0A(A BBBA 4 db R BEA A(D0y(A ABB$ b (SV ADD JAAL b `S) BEB B(A0A8D`
-8A0A(B BBBA 4 c @T BEA A(D@o(A ABB< Lc T BEE A(D0(A BBB D a U BBB A(D0D@
-0A(A BBBA c W" DN
-N A D c W BBB B(A0A8D`8A0A(B BBBL Db hX ' BBB E(D0C8KP9
-8A0A(B BBBH T b ` o BBE B(A0D8G
-8A0A(B BBBA , b de c AID l
-DAA D c d O BBB D(A0&
-(A BBBA < \e f BEB A(A0{
-(A BBRA D e xf BBE A(A0D@
-0A(A BBBA , e 0g> BDA sAB L f @g BBB B(A0A8Gp8A0A(B BBB 4 df h BBH A(D@(A ABB4 f hi BEA A(D@o(A ABBT d i BBB B(A0A8J
-8A0A(B BBBK ,g n AL T Te n c BBB J(A0A8N
-8A0A(B BBBF L g BBB B(A0D8D`O
-8A0A(B BBBF T e BBD D(D@%
+8D0A(B BBGA 4 Z ֜u BEA A(D@a(A ABB, LZ @לv BDA YAT $ |Z לV ADD JAAL Z ל) BEB B(A0A8D`
+8A0A(B BBBA 4 Z BEA A(D@o(A ABB< ,[ ٜ BEE A(D0(A BBB D Z ٜ sX BBB A(D0D@
+0A(A BBBA [ ۜ" DN
+N A D [ ܜ BBB B(A0A8D`8A0A(B BBBT D[ ܜ
W BBB E(A0A8GX
+8A0A(B BBBA , [ e gX AID l
+DAA D [ SX BBB D(A0&
+(A BBBA D \ ?X BJB A(A0Gp
+0A(A BBBA < 4] BEB A(A0{
+(A BBRA 4 t] < BBD A(D@(A ABB< \ 0E W BBA I(D0
+(A DBBA , ] @> BDA sAB L ^ P BBB B(A0A8Gp8A0A(B BBB 4 l^ BBH A(D@(A ABB4 ^ x BEA A(D@o(A ABBT ^ V BBB B(A0A8J
+8A0A(B BBBK 4_ ( AL L T_ ( BBB B(A0D8D`O
+8A0A(B BBBF T ^ U BBD D(D@%
(D ABBB ]
-(D ABBJ 4 Tf ȂJ BGA G`k
- DABE , h v BAA i
-ABA L h 01 BBB B(A0A8D`
-8A0A(B BBBA L i BLB B(G0D8DP
-8A0A(B BBBD < Ti BBG A(G@
-(A ABBH L g ` g BEE E(D0A8G
-8A0A(B BBBF \ i BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBT Lh 0 BBE B(D0A8G
-8D0A(B BBBK \ j h BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBL i W BEB E(I0H8DP
-8D0A(B BBBD T Ti x / BGB D(A0z
+(D ABBJ 4 $_ PJ U BGA G`k
+ DABE , 4` hv BAA i
+ABA 4 d` ~ BEA A(D@j(A ABBL ` BLB B(A0A8D`
+8A0A(B BBBJ < ` BBG A(G@
+(A ABBH L T` p T BEE E(D0A8G
+8A0A(B BBBF \ |a BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBBT a @K T BBE B(D0A8G
+8D0A(B BBBH \ 4b 8 BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBBL a S BEB E(I0H8DP
+8D0A(B BBBD T b WS BGB D(A0z
(A BBDE H
-(A BBGA T i BFB E(A0D8Jg
-8A0A(B BBBA L j H BBB B(A0D8Gp
-8D0A(B BBBB \ Ll ؗ BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBL j ( l BBL B(A0D8G
-8A0A(B BBBE T k J s BIB B(D0G8J
-
-8D0A(B BBBI < \k BLD D(G0
-(A ABBE 4 k = BFD D0
- DABF < k BED I(K@`
-(D ABBH T l h` BBB B(A0D8G
-8A0A(B BBBG < ll p K BLD D(G0
-(A ABBE < l 0* / BED I(KPc
-(D ABBE T l BBB B(A0D8G
-8A0A(B BBBF <o x AL T dm h BIB E(A0G8J m
-8D0A(B BBBB T m BHE E(A0A8G
-8D0A(B BBBG p H AL D 4n 8 BEB K(D0Fp
-0D(A BBBA D tp BBE A(A0D@e
-0A(A BBBA L n Ƚ k BEB E(I0H8D@
-8D0A(B BBBD L o H* C BJE B(A0A8D@
-8A0A(B BBBA L do ( BEE B(A0A8Og
-8D0A(B BBBE L o ˢ BBB I(A0G8J(
-8D0A(B BBBD $ p ݢ~ AG0qD T ,p pݢ BBB B(A0C8J
-8A0A(B BBBD |r 2T KH< p BEA D(GY
-(D ABBK L r BBB B(A0A8D
-8A0A(B BBBA $s @2. K^ <s 䀞\ AG RAT dq h3 BHD D(D`
-(D ABBC Q
-(D ABBF s 1. K^ l q 8 BHB B(F0A8D]
-8A0A(B BBBH
-8D0A(B BBBG L Dr ' BEB E(I0H8DP
-8D0A(B BBBD L r ( BJE B(A0A8D@
-8A0A(B BBBA L r BEB E(I0H8D@
-8D0A(B BBBD L ,u ] BBB H(A0D8DP
-8A0A(B BBBG T s _ BBE B(D0D8D
-8D0A(B BBBE u /. K^ u Y , v D AKD i
-DAA 4v @Y } N $ Lv g AG k
-AA , tv 0 AC
FK
- E 4 t M R BRD DP
- DABA 4 v Z BDA y
+(A BBGA T db #S BFB E(A0D8Jb
+8A0A(B BBBA b
+ &S BBB A(D0G`
+0D(A BBBF ]
+0D(A BBBH m
+0D(A BBBH ]
+0D(A BBBA \ d 0 BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBBL c \R BBL B(D0A8G
+8A0A(B BBBA T c R BEB B(D0D8J
+
+8D0A(B BBBI < Ld ! ?S BLD D(G0
+(A ABBE 4 d "= #S BFD D0
+ DABF < d # S BED I(K@`
+(D ABBH T e @$` R BBB B(A0D8G
+8A0A(B BBBG < \e H' R BLD D(G0
+(A ABBE < e ( R BED I(KPc
+(D ABBE T e ( R BBB B(A0D8G
+8A0A(B BBBF g - AL T Tf - R BBB E(A0D8J ~
+8D0A(B BBBK T f x4 R BBD A(Gp
+(D ABBK F
+(D ABBA g 5( AK ZA L $g 5 7R BEB E(I0H8D@
+8D0A(B BBBD L tg 07* R BJE B(A0A8D@
+8A0A(B BBBA L g 8 Q BEB B(I0D8Ge
+8D0A(B BBBG L h pC oR BBB B(A0D8J(
+8D0A(B BBBA 4 <i Uh BEA A(G@Q(A ABB\ ti U BBD A(D@V
+(A ABBG Y
+(A ABBA a
+(A ABBI T h XV /S BBB B(A0A8G]
+8A0A(B BBBH T Ti l ?T BBB B(A0A8N
+8A0A(B BBBA T i xV T BBB B(A0A8N
+8A0A(B BBBD $ j ~ T AG0qD T ,j X T BBB B(A0C8J
+8A0A(B BBBD , \k Й AHD0
+AAA k ` k X% HX k h- H` k x% HX l - H` $l <l Tl ll
l x l l l l l % HX m - H` <m % HX \m Љ- H` |m % HX m - H` m % HX m - H` m - , n 80 AF
FK
+ D $ Dn 8^ AG b
+AA L m p zQ BVB D(A0D3
+0D(A BBBG T m 0 TQ BGB B(A0D8G
+8D0A(B BBBH L <n ` ]Q BLB E(A0A8Gp
+8D0A(B BBBA L n ` 0Q BLB E(A0A8Gp
+8D0A(B BBBA L n ` Q BLB E(A0A8Gp
+8D0A(B BBBA , p ADD0e
+AAA , 4p ( AAG0M
+AAH 4 o vP BAA G0c
+ AABF T o 0 BP BBE E(A0A8G
+8D0A(B BBBA L p hE P BBB B(A0A8G
+8E0A(B BBBF , lp hY O ADG i
+AAA T p O AAG@r
+AAC G
+AAG y
+AAE ]
+AAA L p = O BBB B(D0A8DP
+8D0A(B BBBA T Dq Н4 aO BBE E(K0A8G
+8D0A(B BBBG < q 5 tO NAG JH
+CAA < q 5 pO KAG MH
+CAA D r lO AAD@e
+AAC H
+AAF h
+AAE <s L |r 0O BBB B(A0A8Gi
+8A0A(B BBBD L r 8 O BBB B(D0A8GA{
+8A0A(B BBBG < s ̝7 BBA A(G0\
+(A ABBA d 4t ͝ BEB E(A0D8Gp
+8A0A(B BBBD f
+8A0A(B BBBA t @ѝp \ Sd t ѝT BIB I(D0H8G
+8A0A(B BBBJ Q
+8A0A(B BBGH d u ӝ BBE F(A0H8GP
+8C0A(B BBBD
+8F0A(B BBBE L t H՝ ^O BFB B(E0A8GP{
+8A0A(B BBBB < u ܝ BBG A(G@
+(A ABBG v xݝ $ Tu pݝb N AY
+F A L |u ݝ% N BBB B(A0A8Dp
+8D0A(B BBBF < v BBA A(G@d
+(A ABBI v h HN BFB A(E0G@
+0F(A BBBH y
+0A(A BBBG
+0F(A BBBD
+0F(A BBBD d tw BBB A(A0
+(A BBBA
+(A BBBE N
+(A BBBE , w C YM AID \
+DAA L 4w @ ;M BBB B(A0D8DP
+8A0A(B BBBF 4 \x AAG0^
+AAG H
+AAF < x BBG A(G@
+(A ABBG L x H= BBB H(A0D8DP
+8A0A(B BBBE L $y 8= BBB H(A0D8DP
+8A0A(B BBBE , ty ( AG ]
+AJ D
+AK L y @ BBB H(A0D8D@
+8A0A(B BBBJ L y = BBB H(A0D8DP
+8A0A(B BBBE L ly #K BBE B(A0D8D@
+8A0A(B BBBF L z x= BBB H(A0D8DP
+8A0A(B BBBE L z hM BBB H(A0D8DP
+8A0A(B BBBE 4{ h5 D tz CJ BAC GPF
+ EABE
+ AABD D z J BAA GP9
+ ECBB X
+ CABB < { 0 I AAGPF
+EAK
+AAD D D{ I BAA GPA
+ ECBJ X
+ CABB D { {I BAA GP9
+ ECBB X
+ CABB D { CI BAC GPF
+ EABE
+ AABI D | I BAA GP9
+ ECBB X
+ CABB d <} E BBB B(A0A8DP
+8A0A(B BBBE \
+8F0A(B BBBA l | = {H BBB E(I0D8D#
+8D0A(B BBBD
+8D0A(B BBBA L ~ ' BFB E(A0A8Dp
+8F0A(B BBBH T } G BBB B(A0D8J
+8C0A(B BBBH , } H AID0H
+DAA D ~ l H BBE A(D0K@
+0D(A BBBA \~ ! 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}
+FAC
+FAA T L $
G BBB B(A0A8O
+8F0A(B BBBE 4 | @' BAA G`{
+ FABA T ( G BBB B(A0D8J
+8A0A(B BBBD T 4 = WH BBE E(A0A8J
+8A0A(B BBBK T E gH BBB B(A0D8J
F
+8D0A(B BBBF $ AHD0AA< M BDA \
+ABJ i
+ABL L $ hN BBB B(A0A8D
+8A0A(B BBBA t . K^ ֘\ AG RAT ԁ R; G BKD D(D`
+(D ABBH Q
+(D ABBF X. K^ l D T qG BBB K(A0A8Dg
+8A0A(B BBBH
+8D0A(B BBBE L V G BEB E(I0H8DP
+8D0A(B BBBD L W F BJE B(A0A8D@
+8A0A(B BBBA L T pX F BEB E(I0H8D@
+8D0A(B BBBD L | Z] BBB H(A0D8DP
+8A0A(B BBBG T 0[ OF BBE B(D0D8D
+8D0A(B BBBE $ h. K^ < ] Y , T ]0 AC
FK
+ E , ^D AKD i
+DAA ^Y } N $ ̅ _g AG k
+AA 4 H_ BE BRA GP
+ DABA 4 , `Z BDA y
ABM A
-ABA L w BBB B(A0A8GPl
-8A0A(B BBBI L dw ` BBB B(A0A8GPl
-8A0A(B BBBI L w BBB B(A0A8GPl
-8A0A(B BBBI L x BBB B(A0A8GPl
-8A0A(B BBBI L Tx P BBB B(A0A8GPl
-8A0A(B BBBI L x BBB B(A0A8GPl
-8A0A(B BBBI L x BBB B(A0A8GPl
-8A0A(B BBBI L Dy @ BBB B(A0A8GPl
-8A0A(B BBBI L w { BJB B(A0D8G`$
-8A0A(B BBBA T w 0 w BBB B(A0A8N
-8A0A(B BBBA L <z ] BBB H(A0D8DP
-8A0A(B BBBG z = T x BEB B(A0A8G
-8A0A(B BBBA T y x 3 BBE B(D0A8G:
-8D0A(B BBBB L \y BHI E(A0A8G`
-8A0A(B BBBE T y BBB B(A0A8J
-8A0A(B BBBA T z BBE B(A0A8G
-8A0A(B BBBA T| ). K^ $ l| 8#0 AGJ XDA | @# | 8#Y } N $ | #g AG k
-AA 4 z # BHA G@h
- DABA $} ). K^ <} @w\ AG RAD \} 8$7 BBE A(A0D@
-0A(A BBBA D } 0%7 BBE A(A0D@
-0A(A BBBA D } (&C BBE A(A0D@
-0A(A BBBA D 4~ 0'C BBE A(A0D@
-0A(A BBBA , |~ 8(U AGG m
-AAB l | h( BBB B(A0A8D1
+ABA L d (` BBB B(A0A8GPl
+8A0A(B BBBI L xa BBB B(A0A8GPl
+8A0A(B BBBI L b BBB B(A0A8GPl
+8A0A(B BBBI L T d BBB B(A0A8GPl
+8A0A(B BBBI L he BBB B(A0A8GPl
+8A0A(B BBBI L f BBB B(A0A8GPl
+8A0A(B BBBI L D g BBB B(A0A8GPl
+8A0A(B BBBI L Hi BBB B(A0A8GPl
+8A0A(B BBBI L j BBB B(A0A8GPl
+8A0A(B BBBI L 4 k BBB B(A0A8GPl
+8A0A(B BBBI L 8m BBB B(A0A8GPl
+8A0A(B BBBI L ԉ n BBB B(A0A8GPl
+8A0A(B BBBI L $ o BBB B(A0A8GPl
+8A0A(B BBBI L t q BBB B(A0A8GPl
+8A0A(B BBBI L Ċ hr BBB B(A0A8GPl
+8A0A(B BBBI L < s ;@ BBB B(M0A8GP7
+8A0A(B BBBA < 8u' #@ BED I(D0
+(A ABBA D ̊ (v ? BBB I(A0D
+0A(A EBBA L v] BBB H(A0D8DP
+8A0A(B BBBG < x= T | (x4 S? BBB E(A0A8G
+8A0A(B BBBA T ԋ ? BBB B(D0A8G1
+8A0A(B BBBA L , g? BHI E(A0A8G`
+8A0A(B BBBE T | m ;? BBB J(A0D8Dd
+8D0A(B BBBA $ = ADG nAA T (k > BEB B(A0A8G
+8A0A(B BBBA , . K^ $ D (0 AGJ XDA l 0 (Y } N $ pg AG k
+AA 4 > BHA G@h
+ DABA . K^ ʘ\ AG RAD 4 (7 BBE A(A0D@
+0A(A BBBA D | 7 BBE A(A0D@
+0A(A BBBA D ď C BBE A(A0D@
+0A(A BBBA D C BBE A(A0D@
+0A(A BBBA , T (U AGG m
+AAB l X O= BBB B(A0A8D1
8A0A(B BBBA Z
-8A0A(B BBBA T $} + BEE E(D0A8G
-8D0A(B BBBE L t 1 BBB B(A0A8Gp8A0A(B BBB $ 2V ADD JAAL 2 BBB B(A0A8Gp8A0A(B BBB < < 84 BBG A(G@
-(A ABBF l | 4y BBF D(A0G@I
+8A0A(B BBBA T Ƞ < BEE E(D0A8G
+8D0A(B BBBE L L BBB B(A0A8Gp8A0A(B BBB $ pV ADD JAAL đ BBB B(A0A8Gp8A0A(B BBB < ( BBG A(G@
+(A ABBF l T ةy BBF D(A0G@I
0A(A BBBI w
0A(A BBBA D
-0F(A BBBG l 6y BBF D(A0G@I
+0F(A BBBG l Ē 諞y BBF D(A0G@I
0A(A BBBI w
0A(A BBBA D
-0F(A BBBG L d 9 BEB E(I0H8D@
-8D0A(B BBBA D : c BKI D(A0G@L
-0A(A BBBA T <
C BBB B(D0D8J
-8A0A(B BBBF L $. K^ L l PI+ w BEB E(I0H8DP
-8D0A(B BBBD d 0K O BBB B(I0A8G`
+0F(A BBBG L \ : BEB E(I0H8D@
+8D0A(B BBBA D x : BKI D(A0G@L
+0A(A BBBA T
: BBB B(D0D8J
+8A0A(B BBBF $ . K^ L d + : BEB E(I0H8DP
+8D0A(B BBBD d : BBB B(I0A8G`
8F0A(B BBBF g
-8F0A(B BBBA T $ XL BEB B(A0D8G
-8D0A(B BBBA L | SJ BEB B(A0A8G{
-8A0A(B BBBA ă q\ AG RAD g7 BBE A(A0D@
-0A(A BBBA D , xhC BBE A(A0D@
-0A(A BBBA < t i BBG A(G@
-(A ABBH < j BBG A(G@
-(A ABBG j5 x!T KH4 $ jJ BDA m
+8F0A(B BBBA T ( h: BEB B(A0D8G
+8D0A(B BBBA L t pȞM q: BEB B(A0A8G
+8A0A(B BBBA $ fĘ AAG0AA ĕ Ę\ AG RAD (ܞ7 BBE A(A0D@
+0A(A BBBA D , ݞC BBE A(A0D@
+0A(A BBBA t (ޞ+ \N < @ޞB 9 BBA A(Jd
+(A ABBA | ̖ Pߞ BBE B(G0L8DPu
+8A0A(B BBBG S
+8E0A(B BBBG W
+8E0A(B BBBK , L ØV BAA D0I AAB< | BBG A(G@
+(A ABBG < 0 BBG A(G@x
+(A ABBG < BBG A(G@
+(A ABBD < < P BBG A(G@
+(A ABBD T 8 BBB E(A0A8J
+8A0A(B BBBH Ԙ % T ) 8 BEE B(A0G8G Y
+8A0A(B BBBA < l : BIA A(D`9
+(A ABBA \ AG RAD 7 BBE A(A0D@
+0A(A BBBA D C BBE A(A0D@
+0A(A BBBA < 4 BBG A(G@
+(A ABBH < t X BBG A(G@
+(A ABBG 5 ̚ T KH4 ( J BDA m
ABI A
-ABA T d k BBE B(A0G8GK
-8A0A(B BBBA D BBE A(A0D@
-0A(A BBBA 8o\ AG RA, H AAG0
-AAA D L 7 BBE A(A0D@
-0A(A BBBA $ Q AG L
-DH oA, Ą H Y AAG@G
-AAF - AU
-J L L BEB B(A0K8D
-8A0A(B BBBD T d BBE B(A0A8Gv
-8A0A(B BBBD 4 3 BAD
-ABE AAB 菣* A[
-L A L BBB B(A0D8G@^
-8A0A(B BBBD < \ Ac
+ABA T D @ 8 BEE B(A0G8GM
+8A0A(B BBBA D t 3 BBE A(A0D@
+0A(A BBBA \ AG RA, ܛ 4 AAG0
+AAA D p57 BBE A(A0D@
+0A(A BBBA $ T h6Q AG L
+DH oA, 6 h8 AAG@G
+AAF P7- AU
+J L L `7 *8 BEB B(A0K8D
+8A0A(B BBBD T D < 8 BBE B(A0A8Gv
+8A0A(B BBBD 4 t 8B3 BAD
+ABE AAB @D* A[
+L A L ̝ PD BBB B(A0D8G@^
+8A0A(B BBBD < E Ac
D ]
K E
K H
H H
H E
-K C < BDA k
+K C < \ pE BDA k
ABK @
-ABA L ܈ h BOB A(A0]
+ABA L E BOB A(A0]
(A BBBG Y
-(A BBBH < , ؑ4 BBB A(A0d
-(A BBBE L l ؒK BBB B(A0A8G@d
-8A0A(B BBBA ؓT < ԉ BBG A(G@
-(A ABBG < BBG A(G@x
-(A ABBG < T @ BBG A(G@
-(A ABBD < BBG A(G@
-(A ABBD T ܈
BBB B(D0A8J
-8A0A(B BBBE , 4 @ F BDA b
-ABA L d ( ' BEB E(I0H8D@
-8D0A(B BBBA d ȥj BBB E(D0A8GP
+(A BBBH < 0F4 BBB A(A0d
+(A BBBE L , 0GK BBB B(A0A8G@d
+8A0A(B BBBA | 0HT , xH@ 5 BDA b
+ABA L H 5 BEB E(I0H8D@
+8D0A(B BBBA d < (Jj 5 BBB E(D0A8GP
8A0A(B BBBK D
-8A0A(B BBBA L Ц BEB E(I0H8D@
-8D0A(B BBBA < d P BDI ~
+8A0A(B BBBA L 0K S5 BEB E(I0H8D@
+8D0A(B BBBA < ̠ L BDI ~
AGK K
-ABB < Ш BBG A(G@
-(A ABBG L BBB B(A0D8GP>
-8A0A(B BBBD , 4 Ъ BAD b
-ABD d T KH | 8" DR
-J A , Ht AAG0v
-AAA d ԋ BBB E(D0A8Dp
+ABB < 0M BBG A(G@
+(A ABBG L L M BBB B(A0D8GP>
+8A0A(B BBBD , 0O BAD b
+ABD ̡ T KH O" DR
+J A , Ot AAG0v
+AAA d \ O 3 BBB E(D0A8Dp
8A0A(B BBBE k
-8A0A(B BBBA T < 8 BDB B(D0D8G
-8A0A(B BBBA T BBE E(D0A8G
-8A0A(B BBBA l 0 BBB L(A0D8DE
-8A0A(B BBBA
-8A0A(B BBBA \ Q BDB B(D0D8DpL
+8A0A(B BBBA T ġ Q8 z3 BDB B(D0D8G
+8A0A(B BBBA T V n3 BBE E(D0A8G
+8A0A(B BBBA l t Y L3 BBB L(A0D8DM
+8A0A(B BBBA
+8A0A(B BBBA [ 2 BDB B(D0D8DpL
8A0A(B BBBD P
8A0A(B BBBF
-8A0A(B BBBA L 蹣_ BBB B(A0A8JU
-8A0A(B BBBE L 4 BBB E(A0A8J
-8A0A(B BBBB L Z
BBB B(A0D8Jd
-8A0A(B BBBK T Ԏ ͣ
/ BBB B(A0K8G<
-8A0A(B BBBG T , `ۣ
- BBE E(A0D8G
-8A0A(B BBBA | Y } N $ `g AG k
-AA 4 ď o BHA G@h
- DABA L P0 S BBB B(D0A8D
-8A0A(B BBBE , L 0> 2 BDI X
-ABA , t @ AAG0
-AAA L BBB A(A0
+8A0A(B BBBA L l ^_ 2 BBB B(A0A8JU
+8A0A(B BBBE L ` 2 BBB E(A0A8J
+8A0A(B BBBB L dZ
n2 BBB B(A0D8Jd
+8A0A(B BBBK T \ q 2 BBB B(A0A8Q<
+8A0A(B BBBG T @}'
+ K3 BEE E(A0D8G
+8A0A(B BBBA Y } N $ `g AG k
+AA 4 L 3 BHA G@h
+ DABA L P@ 2 BBB B(D0A8D
+8A0A(B BBBG , ԥ @> 2 BDI X
+ABA , ܦ P AAG0
+AAA L BBB A(A0
(A BBBK Y
-(A EBBM L BBB A(A0
+(A EBBM L \ Ќ BBB A(A0
(A BBBG Y
-(A EBBE L D p BBB A(A0
+(A EBBE L BBB A(A0
(A BBBK Y
-(A EBBM L 0 BBB A(A0
+(A EBBM L @ BBB A(A0
(A BBBG Y
-(A EBBE L BJB A(A0W
-(A BBDH ^(A BBBT 4 00 BAA G0@
+(A EBBE L t J1 BJB A(A0W
+(A BBDH ^(A BBBT @0 BAA G0@
AABA
AABG e
- AABG T g BHB B(D0D8JW
-8A0A(B BBBG `T KHT W BBE B(A0A8G
-8A0A(B BBBJ T <`\ AG RAD t # BBB D(A0D@
-0A(A BBBA D & BBB D(A0D@
-0A(A BBBA d ( BBB B(D0D8G`
-8D0A(B BBBJ O
-8A0A(B BBBA T t ()I * BBE E(G0A8G^
-8A0A(B BBBK < Ė . BBG A(G@
-(A ABBF L . BEB E(I0H8DP
-8D0A(B BBBD T \ 03 BBE E(A0A8D
-8A0A(B BBBE . K^ ė p3Y } N $ ܗ 3g AG k
-AA \ 4 ; BBB K(D0D
-0C(A BBBD x
-0A(A BBBA 4 l 5 BHA G@h
- DABA 8T KHT 06\ BEE B(A0A8G?
-8D0A(B BBBA $ 890 AGJ XDA $ 4 @9D XYOC 4 \ h9c AAG \
-CAG dFA 9Y } N $ 9g AG k
-AA 4 ܗ 0: BHA G@h
- DABA L : BBB A(A0
+ AABG T 0 BHB B(D0D8JW
+8A0A(B BBBG L T KHT h 0 BBE B(A0A8G
+8A0A(B BBBJ P\ AG RAD ܩ ß7 BBE A(A0D@
+0A(A BBBA D $ ğC BBE A(A0D@
+0A(A BBBA , l ş AAG0s
+AAA D ĩ Ɵ 0 BGB D(M0B
+(A BBBK L xƟX 0 BIN B(A0C8Gpg
+8A0A(B BBBI L 4 ȟn BBB B(A0A8Dp
+8A0A(B BBBA L ɟ BBB A(A0
(A BBGG A
-(A BBDA L d 8;| k BBB E(A0A8G
-8C0A(B BBBE
T KHL Ě PO AAG ]
-CAF k
+(A BBDA ԫ T KHT ɟ6 / BBB B(A0A8J
+
+8C0A(B BBBH T l ܟv o0 BEE E(A0G8G
+
+8A0A(B BBBA ̯\ AG RAD BBB D(A0D@
+0A(A BBBA D X BBB D(A0D@
+0A(A BBBA d t ?0 BBB E(D0D8D`
+8D0A(B BBBD O
+8A0A(B BBBA T ܬ xN / BBE E(G0A8Gb
+8A0A(B BBBG < p BBG A(G@
+(A ABBF L t / BEB E(I0H8DP
+8D0A(B BBBD T ĭ 3 / BBE E(A0A8D
+8A0A(B BBBE ؒ. K^ Y } N $ $ g AG k
+AA l t P . BBB B(D0K8G
+8C0A(B BBBE |
+8A0A(B BBBA 4 . BHA G@h
+ DABA T KHT 4 p \ o. BEE B(A0A8G?
+8D0A(B BBBA d `\ AG RAD X7 BBE A(A0D@
+0A(A BBBA D ̰ PC BBE A(A0D@
+0A(A BBBA < X
BEA A(G@[
+(A ABBG < T (a BEA A(G@[
+(A ABBG T X?! - BHE B(A0A8Ga
+8A0A(B BBBA 4 p /. BHA D@
+ AABA $ $ (0 AGJ XDA $ L (D XYOC 4 t 8(c AAG \
+CAG dFA p(Y } N $ IJ (g AG k
+AA 4 ) W- BHA G@h
+ DABA L L )| ;- BBB E(A0A8J
+8C0A(B BBBB t T KHL = AAG e
+CAF s
ACA R
-CAJ JCA L O AAG ]
+CAJ JCA L ܳ > AAG ]
CAF c
CAI R
-CAJ JCA L d O AAG ]
+CAJ JCA L , P> AAG ]
CAF c
CAI R
-CAJ JCA 8Z\ AG RA4 ԛ O BDD G@g
- AABD D HP BBE A(A0D@
+CAJ JCA | \ AG RA4 p> BDD G@g
+ AABD D Դ > BBE A(A0D@
+0A(A BBBA D @ BBE A(A0D@
-0A(A BBBA D T R BBE A(A0D@
-0A(A BBBA , S GA A(D T Ԛ XU| BBE E(D0A8G
-8D0A(B BBBE L $ Z BBB B(A0A8Gp8A0A(B BBB L t \ BBB B(A0A8Gp8A0A(B BBB L ĝ ] BBB B(A0A8Gp8A0A(B BBB L _; BBB B(A0A8Gp 8A0A(B BBB $ d ` AG
-AA L a BFE E(A0A8DP
-8D0A(B BBBJ d ܞ hbr BFB E(D0A8DP
+0A(A BBBA T hB| + BBE E(D0A8G
+8D0A(B BBBE L G BBB B(A0A8Gp8A0A(B BBB L I BBB B(A0A8Gp8A0A(B BBB L \ J BBB B(A0A8Gp8A0A(B BBB L L; BBB B(A0A8Gp 8A0A(B BBB $ N AG
+AA L $ N BFE E(A0A8DP
+8D0A(B BBBJ d t xOr BFB E(D0A8DP
8F0A(B BBBG
-8D0A(B BBGA d D c2 BFE E(A0A8DP
+8D0A(B BBGA d ܷ P2 BFE E(A0A8DP
8F0A(B BBBD
-8D0A(B BBGD Xe d ğ @f5 BBE B(D0I8DP
+8D0A(B BBGD D hR d \ PS5 BBE B(D0I8DP
8D0A(B BBBC q
-8D0A(B BBBB T 4 i BBB B(A0A8G
-8A0A(B BBBB L Pn BBB B(D0A8D@d
-8D0A(B BBBF L ܞ o BEB E(I0H8DP
-8D0A(B BBBD d $ qI BBE E(A0A8D@
+8D0A(B BBBB T (V ( BBB B(A0A8G
+8A0A(B BBBB L `[ BBB B(D0A8D@d
+8D0A(B BBBF L \ ' BEB E(I0H8DP
+8D0A(B BBBD d ^I BBE E(A0A8D@
8F0A(B BBBC S
-8F0A(B BBBF L hr BBB E(A0D8D@x
-8A0A(B BBBJ T (s 3 BBH B(A0D8Dc
-8D0A(B BBBI L 4 u BBB E(A0D8D@x
-8A0A(B BBBJ L pvG BBH E(A0D8Jy
-8A0A(B BBBA , Ԣ p~ ADD ]
-AAA D ~ BBJ A(D0
-(A BBEA L H. K^ d " DR
-J A T S BIE E(G0A8J
-8A0A(B BBBI T XD [ BBB B(A0A8J
-8C0A(B BBBF T < P* BBB B(A0I8Gl
-8D0A(B BBBA T ( [ BBE E(D0A8J
-8A0A(B BBBA ! A[ Y } N $ Hg AG k
-AA 4 L BHA G@h
- DABA | H. K^ T = BEE B(A0A8G
-8A0A(B BBBG ,P\ AG RAD < 觤 BBE A(A0D@
-0A(A BBBA D BBE A(A0D@
-0A(A BBBA L Ԥ 8 BBB B(A0D8DP+
-8A0A(B BBBJ L $ h BJB A(A0W
-(A BBDH ^(A BBB l T KHT % o BHB B(A0A8G
-8A0A(B BBBG ܧ N\ AG RAD פ7 BBE A(A0D@
-0A(A BBBA D D ؤC BBE A(A0D@
-0A(A BBBA T ٤7 ; BEE B(A0G8G
-8A0A(B BBBA p T KH Y } N $ g AG k
-AA $ < M AAG0AA4 l BHA G@h
- DABA L f BBB B(A0D8DP
-8D0A(B BBBG T K BEB B(A0A8JU
-8A0A(B BBBJ L L 0" B BBB E(K0A8D
-8A0A(B BBBA T KHL X'u BBB E(A0D8J
-8D0A(B BBBF 2Y } N $ 2g AG k
-AA , < 3 AAG0
-AAA 4 t 3 / BHA G@h
- DABA L 4& BBB B(A0D8DP
-8D0A(B BBBG L p; BBB B(A0D8DP
-8D0A(B BBBG L D B~
+8F0A(B BBBF L $ x_ BBB E(A0D8D@x
+8A0A(B BBBJ T 8` ' BBH E(A0A8D{
+8D0A(B BBBA L ̺ b BBB E(A0D8D@x
+8A0A(B BBBJ , c ADD ]
+AAA D t c g& BBJ A(D0
+(A BBEA , d ?& GA A(D L 8fG 0& BBE H(A0A8J~
+8A0A(B BBBA h. K^ , q" DR
+J A T t 0qV Y& BLB E(G0A8J&
+8A0A(B BBBI T ̻ 8x a& BBB B(A0A8J
+8C0A(B BBBF T $ * & BBB B(A0I8Gl
+8D0A(B BBBA T | r a& BBE E(D0A8J
+8A0A(B BBBA ! A[ ̽ Y } N $ 8g AG k
+AA 4 4 % BHA G@h
+ DABA D h. K^ T % BEE E(A0A8Gu
+8A0A(B BBBG $ Ȟ AAG0AA ܾ & ș$ A[
+D C $ ؙd AW
+H y
+G C T d _% BED G(GP
+(C ABBE I
+(C CBBE D h $% BDG GP3
+ CABC I
+ CCBG | ܿ BBB B(D0A8DP
+8A0A(B BBBB U
+8G0A(B BBBK
+8A0A(B BBBI L @] {$ BBB B(A0D8Jpi
+8D0A(B BBBC 0V Ko
+A l 0 BBB A(A0G`]
+0A(A BBBD c
+0C(A BBBK D0F(A BBB , < <Z BDD D@G AAB, l f_ BDD D@L AAB< BEE D(D0(A BBB $ AAG0AA4 , g " BAD D
+ABA < \ AG RAD \ p BBE A(A0D@
+0A(A BBBA D H7 BBE A(A0D@
+0A(A BBBA , @ " AAG@G
+AAF % AQ
+F L < . Aa
+F E < \ Ac
+D ]
+K E
+K H
+H H
+H E
+K C D p r! BGB D(M0B
+(A BBBK D ة 9! BGB D(M0B
+(A BBBK , @m A~
+A l d L = BIB B(A0A8D`
+8F0A(B BBBI
+8D0A(B BBBA L h BBB B(A0A8D
+8A0A(B BBBA L 8 BBB B(A0D8D@
+8A0A(B BBBJ < T BBG A(G@
+(A ABBG \ KFF E(D0D8G@8A0A(B BBBG@ < h BBG A(G@
+(A ABBG 4 % L ~T KHL 䚘 BHB E(D0D8J8D0A(B BBB T TM BBB B(A0A8G`
+
+8A0A(B BBBE T 4 k# BBE B(A0D8M
+8A0A(B BBBA d ʠ\ AG RAD 7 BBE A(A0D@
+0A(A BBBA D C BBE A(A0D@
+0A(A BBBA T <
+ # BEE B(A0G8G%
+8A0A(B BBBA l 0}T KH 8 0 ֟\ AG RAD BBE A(A0D@
+0A(A BBBA D BBE A(A0D@
+0A(A BBBA D h p l" BBB A(D0D@%
+0A(A BBBD L % (" BJB A(A0W
+(A BBDH ^(A BBBD % BBE A(A0D@l
+0A(A BBBA D {T KHT `&W" ! BBB B(G0A8G
+8A0A(B BBBI 2\ AG RAD HH7 BBE A(A0D@
+0A(A BBBA D @IC BBE A(A0D@
+0A(A BBBA T HJO7 #" BEE B(A0G8G
+8A0A(B BBBA zT KH (Y } N $ pg AG k
+AA $ . AAG0AA4 d # BHA G@h
+ DABA L t 8f BBB B(A0D8DP
+8D0A(B BBBG T X 3# BEB B(A0A8JU
+8A0A(B BBBJ L D В *# BBB E(K0A8D
+8A0A(B BBBA l PyT KHL u " BBB E(A0D8J
+8D0A(B BBBD (Y } N $ pg AG k
+AA , AAG0
+AAA 4 l # BHA G@h
+ DABA L | 0& BBB B(A0D8DP
+8D0A(B BBBG L BBB B(A0D8DP
+8D0A(B BBBG L p~
BEE B(A0A8G`
-8A0A(B BBBB L M* # BBB E(K0A8D
-8C0A(B BBBF L R BEB A(D0l
+8A0A(B BBBB L * " BBB E(K0A8D
+8C0A(B BBBF L á BEB A(D0l
(A BBBG
-(A BBBA 4 T KHT T S BBB B(A0D8J]
-8A0A(B BBBB `pY } N $ pg AG k
-AA , p AAG0
-AAA 4 q c BHA G@h
- DABA L T hr G BBB E(K0A8D
-8A0A(B BBBA T KHT w% BEB B(A0D8G
-8A0A(B BBBH $ h0 AGJ XDA 4 pY } N $ L g AG k
-AA \ | BLB D(A0D
-0C(A BBBD `
-0D(A BBBA 4 ܭ 0 G BHA G@h
- DABA . K^ D $ O BBI A(A0D@
-0A(A BBBA D l ȗa BBB H(A0D@
-0A(A BBBA l BBB B(A0D8Gp
+(A BBBA wT KHT L 8ġU ! BBB B(A0D8J]
+8A0A(B BBBB | @ޡY } N $ ޡg AG k
+AA , ޡ AAG0
+AAA 4 ߡ G" BHA G@h
+ DABA L L H +" BBB E(K0A8D
+8A0A(B BBBA t vT KHL p ! BEB B(A0D8G
+8A0A(B BBBF $ 0 AGJ XDA (Y } N $ pg AG k
+AA d l z" BEL B(D0A8D
+8C0A(B BBBE c
+8D0A(B BBBA 4 +" BHA G@h
+ DABA t. K^ 4 $ g ! BAD D
+ABA 4 \ g ! BAD D
+ABA l Җ\ AG RAD BBE A(A0D@
+0A(A BBBA D 7 BBE A(A0D@
+0A(A BBBA 4 D ~W BAD JA AAB T hE lX , M ADG0_
+DAA < BBG A(G@
+(A ABBG `% 4 BED C(D@(A ABBL T
+ BBB E(D0K8G
+8D0A(B BBB | rT KHL
v BBB H(D0A8G8A0A(B BBB T p9 BEE B(A0G8GrA
+8A0A(B BBBA D < @O BBI A(A0D@
+0A(A BBBA D Aa BBB H(A0D@
+0A(A BBBA l B 3# BBB B(A0D8Gp
8F0A(B BBBA
-8D0A(B BBBA T , 0 3 BBB B(A0A8Lq
-8A0A(B BBBG | . K^ T О
BBB B(A0A8Ga
-8A0A(B BBBA F\ AG RAD 7 BBE A(A0D@
-0A(A BBBA D T C BBE A(A0D@
-0A(A BBBA | BBE B(G0L8DPu
-8A0A(B BBBG S
-8E0A(B BBBG W
-8E0A(B BBBK D $ BAG G@]
- CABD
- CABH T l @[ BBE B(A0G8G
-8A0A(B BBBA < ı x BIA A(D`9
-(A ABBA \D\ AG RAD ͥ7 BBE A(A0D@
-0A(A BBBA D d ΥC BBE A(A0D@
-0A(A BBBA T ϥ| BEE B(A0G8G^
-8A0A(B BBBA 0T KH AZ < T HS L t AAG ]
+8D0A(B BBBA T d E " BBB B(A0A8Lq
+8A0A(B BBBG p. K^ T H " BBB B(A0A8GQ
+8A0A(B BBBA B\ AG RAD $ T7 BBE A(A0D@
+0A(A BBBA D l UC BBE A(A0D@
+0A(A BBBA T Vn ! BBE B(A0D8Mr
+8A0A(B BBBG 4 4 o " BHA D@
+ AABA D ^\ AG RAD d i7 BBE A(A0D@
+0A(A BBBA D jC BBE A(A0D@
+0A(A BBBA T kP ! BEE B(A0G8G
+8A0A(B BBBA < t p # BIA A(D`9
+(A ABBA r\ AG RAD 7 BBE A(A0D@
+0A(A BBBA D C BBE A(A0D@
+0A(A BBBA T d ;" BEE B(A0G8Gn
+8A0A(B BBBA pT KH AZ HS L AAG ]
CAF c
CAI R
-CAJ JCA $ ĵ @^ AG b
-AA 4 x BAA G0y
- AABH $ $ DX
+CAJ JCA $ T ȡ^ AG b
+AA 4 | BAA G0y
+ AABH $ h DX
D n
-B D T Xc BLD D(D
-(A ABBA T BHE B(D0A8I
-8D0A(B BBBA L BEB E(I0H8D@
-8D0A(B BBBA L D 8 BEB E(I0H8D@
-8D0A(B BBBA , BCD D0 AABL ĵ x ; BEB E(I0H8D@
-8D0A(B BBBA L BEB E(I0H8D@
-8D0A(B BBBA L d BEB E(I0H8D@
-8D0A(B BBBA T 8 BIB B(A0A8G
-8A0A(B BBBA L BEB E(I0H8DP
-8D0A(B BBBD d \ , W BGE E(A0A8D@
+B T ! BKI B(A0A8G
+8A0A(B BBBD T \ 8 ! BHE B(D0A8L{
+8D0A(B BBBF L p ! BEB E(I0H8D@
+8D0A(B BBBA L ! BEB E(I0H8D@
+8D0A(B BBBA , , BCD D0 AABL {! BEB E(I0H8D@
+8D0A(B BBBA L S! BEB E(I0H8D@
+8D0A(B BBBA L $ @ +! BEB E(I0H8D@
+8D0A(B BBBA T t ย ! BBB B(H0A8Gx
+8A0A(B BBBF L Ϣ " BEB E(I0H8DP
+8D0A(B BBBD d Т, ! BGE E(A0A8D@
8F0A(B BBBJ g
-8F0A(B BBBA T ķ BBB B(A0A8G/
-8A0A(B BBBF T KH , D $ Ab d P>\ AG RA, AAG0W
-AAF 4 n BDA D0
- AABA 4 8 : BDA D0
- AABA L , BBE B(A0A8DPn
-8A0A(B BBBA T | ! BBB I(A0A8G
-8A0A(B BBBA T Թ $ z BBB I(A0A8G
-8A0A(B BBBA 4 , P(c : BAA G`
- DABB D d * ? BBA D(GD
-(D ABBC P. K^ H, Լ @, 8, 0, (, 4 0, L 8, d @, | H,- 4 `, BGI _
-ABD NAB , Ի ,E BDC rAH T , BBB J(A0A8G
-8D0A(B BBBG L T 2/ BEB B(A0A8D`u
-8A0A(B BBBH , `4 ~ AAG0}
-CAF Ծ P. K^ 5 5F 85 4 05 L 9\ AG RAL t 5X BBB B(A0A8Ji
-8A0A(B BBBI T Ľ (> BBE B(D0A8G
-8D0A(B BBBG T B, BBE B(D0A8G
-8D0A(B BBBK T t G BBB B(D0D8G
-8A0A(B BBBH D 8 BBB D(A0D@0A(A BBB T a BIB E(A0A8G5
-8D0A(B BBBA L l Pf { BDE E(G0A8G
-8D0A(B BBBG L l# BBB I(A0A8JY
-8D0A(B BBBG PT KHL $ hu BBE B(D0A8G
-8A0A(B BBBA , t xg AID @
-DAA x, Hc y3 Hf (y, Hc 8y3 Hf Xy, Hc < hy3 Hf \ y< Hs | y< Hs yC Hv yC Hv $ (z^ AG b
-AA T `z D BBE B(D0A8G
-8D0A(B BBBC T d ~L * BBB B(A0D8R
-8A0A(B BBBH , > AID W
-DAA T L BBB B(A0D8R
-8A0A(B BBBH , D > 2 AID W
-DAA T t g BBB B(D0A8R|
-8A0A(B BBBK , > X AID W
-DAA T 0g 8 BBB B(D0A8R|
-8A0A(B BBBK , T H> ~ AID W
-DAA T X ^ BBB B(A0I8G
-8D0A(B BBBF T ~ BBE B(D0A8G
-8D0A(B BBBB T 4 8 d BBE B(D0A8G
-8D0A(B BBBC D г BBB D(A0D@
-0A(A BBBA D (| BIA D(D
-(D ABBA T ` BIB E(A0A8D
-8D0A(B BBBA L t BIB A(D0D
-0D(A BBBA T X BIB E(A0A8D
-8D0A(B BBBA T Ŧ ~ BIB E(A0A8D
-8D0A(B BBBA < t ɦ W BBD D(J@F
-(D ABBA L 8ʦ| # BBB B(D0F8Gc
-8A0A(B BBBJ < h̦ + AAG L
+8F0A(B BBBA T Ѣ ! BBB B(A0A8G/
+8A0A(B BBBF lT KH آ آ$ Ab V\ AG RA, $ آ AAG0W
+AAF 4 | ٢ ! BDA D0
+ AABA 4 @ڢ BDA D0
+ AABA L ڢ BBE B(A0A8DPn
+8A0A(B BBBA T < ݢ W BBB I(A0A8G
+8A0A(B BBBA T BBB I(A0A8G
+8A0A(B BBBA 4 Xc BAA G`
+ DABB D $ BBA D(GD
+(D ABBC D i. K^ \ P t H @ 8 0 8 @ H P- 4 4 h BGI _
+ABD NAB , E BDC rAH T v BBB J(A0A8G
+8D0A(B BBBG L X/ BEB B(A0A8D`u
+8A0A(B BBBH , l 8 AAG0}
+CAF t g. K^ F ʘ\ AG RAL 4 X W BBB B(A0A8Ji
+8A0A(B BBBI T w BBE B(D0A8G
+8D0A(B BBBG T , ] BBE B(D0A8G
+8D0A(B BBBK T 4 C BBB B(D0D8G
+8A0A(B BBBH D d BBB D(A0D@0A(A BBB T ; BIB E(A0A8G5
+8D0A(B BBBA L , (" BDE E(G0A8G
+8D0A(B BBBH L | (# k BBB I(A0A8JY
+8D0A(B BBBG dT KHL `1 W BBE B(D0A8G
+8A0A(B BBBA , 4 4g ` AID @
+DAA < 4, Hc \ 53 Hf | 5, Hc 053 Hf P5, Hc `53 Hf 5< Hs 5< Hs < 5C Hv \ 5C Hv $ | 6^ AG b
+AA T X6 BBE B(D0A8G
+8D0A(B BBBH T $ ;L BBB B(A0D8R
+8A0A(B BBBH , | F> R AID W
+DAA T (FL 2 BBB B(A0D8R
+8A0A(B BBBH , Q> AID W
+DAA T 4 0Qg BBB B(D0A8R|
+8A0A(B BBBK , HY> AID W
+DAA T XYg BBB B(D0A8R|
+8A0A(B BBBK , pa> AID W
+DAA T D a BBB B(A0I8G
+8D0A(B BBBF T f BBE B(D0A8G
+8D0A(B BBBG T k BBE B(D0A8G
+8D0A(B BBBH D $ Xp BBB D(A0D@
+0A(A BBBA D q| BIA D(D
+(D ABBA T t BIB E(A0A8D
+8D0A(B BBBA T 4 y X BIB E(A0A8D
+8D0A(B BBBA L X~ 1 BIB A(D0D
+0D(A BBBA T BIB E(A0A8D
+8D0A(B BBBA L 4 p BBB B(G0D8G
+8A0A(B BBBA < AAG L
AAI p
-AAB L D ̦q BBB A(A0G
-0D(A BBBF < Ԧx BBA N(M0{
-(D ABBA L HԦ BBB G(D0G8G
-8D0A(B BBBD D $ צ BBA A(G
-(D ABBE < l `ܦx BBA N(M0{
-(D ABBA L ܦ BBB G(D0G8G
-8D0A(B BBBD < g BBA A(DV(A ABB T < @B g BBE B(A0A8L
-8A0A(B BBBA (. K^ , [ _ AID t
-DAA , P[ ? AID t
-DAA T < BBE B(D0A8G
-8D0A(B BBBG D - BBB D(A0D@0A(A BBB T BIB E(A0A8G
-8D0A(B BBBA T 4 BIB E(A0A8G
-8D0A(B BBBA T q BBE B(D0A8G
-8D0A(B BBBB L U# W BDB B(D0A8M
-8D0A(B BBBH , ߞ. K^ D * \ * t * * , *[ AID t
-DAA T + BBB B(A0I8G
-8D0A(B BBBD D , , BBB D(A0D@0A(A BBB T | .\ BIE B(A0A8DI
-8D0A(B BBBA T 3b ] BIE B(A0A8DQ
-8D0A(B BBBA T , 9 + BBB E(F0G8G
-8D0A(B BBBK T @ g BDB E(A0G8G
-8D0A(B BBBC @ݞ. K^ P P P 4 P L O d P | P P *,\ AG RAT O BBE E(D0D8G2
-8A0A(B BBBA L $ `Rq BBA D(G@n
+AAB L q BBB A(A0G
+0D(A BBBF < Px BBA N(M0{
+(D ABBA T T W BBB G(D0G8G
+8D0A(B BBBH D o BBA A(G
+(D ABBE < `x i BBA N(M0{
+(D ABBA T 4 ; BBB G(D0G8G
+8D0A(B BBBH < d g BBA A(DV(A ABB T 8 BBE B(A0A8L
+8A0A(B BBBA Z. K^ x , p , l x[ AID t
+DAA , [ AID t
+DAA < <D BBD A(D@0(A ABB T & BIB E(A0A8G
+8D0A(B BBBA T d p& e BIB E(A0A8G
+8D0A(B BBBA 4 H ? BGB A(C0G T 0 % BBE B(D0A8G
+8D0A(B BBBB L L U BBB B(F0G8G
+8D0A(B BBBK T ţ\& g BDB B(G0D8G
>
+8D0A(B BBBI @X. K^ , , l [ AID t
+DAA T ( BBB B(A0I8G
+8D0A(B BBBD D X BBB D(A0D@0A(A BBB T < \ w BIE B(A0A8DI
+8D0A(B BBBA T b E BIE B(A0A8DQ
+8D0A(B BBBA T 8 BDE B(A0G8G
+8D0A(B BBBI T D # BBB G(D0G8G
+8D0A(B BBBB t U. K^ @ 8 0 ( ( 0 4 8 L \ AG RAT S BBE E(D0D8G2
+8A0A(B BBBA , BGD l
+ABD L q BBA D(G@n
(A ABBD
-(A ABBH T | S BBB E(A0A8KP
-8A0A(B BBBF , 8U> AID W
-DAA T HU BBB E(A0A8KP
-8A0A(B BBBF , \ V> AID W
-DAA T W a BBB E(A0A8KP
-8A0A(B BBBF , X> , AID W
-DAA T X BBB E(A0A8KP
-8A0A(B BBBF , l `Z> AID W
-DAA D pZ BBB D(A0D@
-0A(A BBBA D [ o BIB A(D0D`+
-0D(A BBBA D , ^ M BIB A(D0D`+
-0D(A BBBA D t 8a + BIB A(D0D`+
-0D(A BBBA D c BIB A(D0D`+
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-8A0A(B BBBA T d r y BEE B(D0D8J_
-8A0A(B BBBA T v u BEJ E(D0D8G(
-8D0A(B BBBF T x 0 BEJ E(D0D8G(
-8D0A(B BBBF T l y BEJ E(D0D8G(
-8D0A(B BBBF T { BEB E(D0D8J
-8A0A(B BBBK T N BEB E(A0D8G
-8A0A(B BBBF T t BBB E(A0D8G
-8A0A(B BBBA T P \ BEB B(A0A8Jm
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-8A0A(B BBBB T t e BEB E(D0D8J
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-8A0A(B BBBF T $ _ BBB E(A0D8G
-8A0A(B BBBA T | BEB B(A0A8Je
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-8A0A(B BBBK T | F c BEB E(A0D8G
-8A0A(B BBBF T x BBB E(A0D8G
-8A0A(B BBBA T , 0 BEB B(A0A8Je
-8A0A(B BBBJ L Ƨ BEB E(A0A8G
-8A0A(B BBBB T Ч BEB E(D0D8J
-8A0A(B BBBK T , ֧F BEB E(A0D8G
-8A0A(B BBBF T xا BBB E(A0D8G
-8A0A(B BBBA T 0ܧ BEB B(A0A8Je
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-DAA D y BBB B(A0A8[
-0A(B BBBA D < BEB B(A0A8
-0A(B BBBA 02 I X AN
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+DAA T ` BBB E(A0A8KP
+8A0A(B BBBF , \ > o AID W
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+0D(A BBBA D ( BIB A(D0D`+
+0D(A BBBA D d ! BIB A(D0D`+
+0D(A BBBA D $ BIB A(D0D`+
+0D(A BBBA T P' BEJ E(D0D8G:
+8D0A(B BBBD $ (* A[
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+L A d )* A[
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+L A H)* A[
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+L A T L h) : BBB B(A0A8Jk
+8A0A(B BBBA T - 7 BEE B(A0D8JR
+8A0A(B BBBA T / BBB B(A0A8Jk
+8A0A(B BBBA T T p3 BBB B(A0A8Jk
+8A0A(B BBBA T (7 BEJ E(D0D8G(
+8D0A(B BBBF T 8 BEJ E(D0D8G(
+8D0A(B BBBF T \ 8: BEJ E(D0D8G(
+8D0A(B BBBF T ; A BBB B(A0A8J
+8A0A(B BBBK T (BN BEB E(A0D8G
+8A0A(B BBBF T d D ; BBB E(A0D8G
+8A0A(B BBBA T G BEB B(A0A8Jm
+8A0A(B BBBB L @N BEB E(A0A8G
+8A0A(B BBBB T d W BBB B(A0A8J
+8A0A(B BBBK T (^F 9 BEB E(A0D8G
+8A0A(B BBBF T ` BBB E(A0D8G
+8A0A(B BBBA T l c BEB B(A0A8Je
+8A0A(B BBBJ L 0j r BEB E(A0A8G
+8A0A(B BBBB T s BBB B(A0A8J
+8A0A(B BBBK T l zF BEB E(A0D8G
+8A0A(B BBBF T | BBB E(A0D8G
+8A0A(B BBBA T i BEB B(A0A8Je
+8A0A(B BBBJ L t + BEB E(A0A8G
+8A0A(B BBBB T l BBB B(A0A8J
+8A0A(B BBBK T F BEB E(A0D8G
+8A0A(B BBBF T t f BBB E(A0D8G
+8A0A(B BBBA T " BEB B(A0A8Je
+8A0A(B BBBJ L $ BEB E(A0A8G
+8A0A(B BBBB L FT KH d x | p , x[ AID t
+DAA D y BBB B(A0A8[
+0A(B BBBA D ତ BEB B(A0A8
+0A(B BBBA T X2 I l AN
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- DABA 4 `7 BHA G`
- DABA 4 h7 u BHA G`
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- DABA 4 l 7 BHA G`
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- DABA 4 7 ښ BHA G`
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- DABA 4 L 7 BHA G`
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- DABA 4 7 ^ BHA G`
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- DABA 4 غ7 Ù BHA G`
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- DABA 4 D 輨7 BHA G`
- DABA 4 | 7 f BHA G`
- DABA 4 7 G BHA G`
- DABA 4 7 ( BHA G`
- DABA 4 $ 7 BHA G`
- DABA 4 \ ¨7 BHA G`
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- DABA 4 Ĩ7 BHA G`
- DABA 4 (Ũ7 BHA G`
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- DABA < Ҩ\ BIA A(Gp
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- DABA 4 ! `7 BHA G`
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- DABA 4 ,$ 7 BHA G`
- DABA 4 d$ 7 s BHA G`
- DABA 4 $ 7 T BHA G`
- DABA 4 $ 7 5 BHA G`
- DABA 4 % 7 BHA G`
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- DABA 4 |% 7 ؐ BHA G`
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- DABA 4 % 7 BHA G`
- DABA 4 $& ) { AHJ
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+ DABA 4 9 f7 BHA G`
+ DABA 4 9 (g7 BHA G`
+ DABA 4 9 0h7 j BHA G`
+ DABA 4 ,: 8i7 K BHA G`
+ DABA 4 d: @j7 , BHA G`
+ DABA 4 : Hk7
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+ DABA 4 : Pl7 BHA G`
+ DABA 4 ; Xm7 BHA G`
+ DABA 4 D; `n7 BHA G`
+ DABA 4 |; ho7 BHA G`
+ DABA 4 ; pp7 r BHA G`
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+ DABA 4 $< r7 4 BHA G`
+ DABA 4 \< s7 BHA G`
+ DABA 4 < t7 BHA G`
+ DABA 4 < u7 BHA G`
+ DABA 4 = v7 BHA G`
+ DABA 4 <= w7 BHA G`
+ DABA 4 t= x7 z BHA G`
+ DABA 4 = y7 [ BHA G`
+ DABA 4 = z7 < BHA G`
+ DABA 4 > {7 BHA G`
+ DABA 4 T> |7 BHA G`
+ DABA 4 > }7 BHA G`
+ DABA 4 > ~7 BHA G`
+ DABA 4 > 7 BHA G`
+ DABA 4 4? 7 BHA G`
+ DABA 4 l? 7 c BHA G`
+ DABA 4 ? 7 D BHA G`
+ DABA 4 ? 7 % BHA G`
+ DABA 4 @ 7 BHA G`
+ DABA 4 L@ 7 BHA G`
+ DABA 4 @ 7 BHA G`
+ DABA 4 @ (7 BHA G`
+ DABA 4 @ 07 BHA G`
+ DABA 4 ,A 8B k BHA G`
+ DABA 4 dA P7 L BHA G`
+ DABA 4 A XB - BHA G`
+ DABA 4 A p7 BHA G`
+ DABA 4 B xB BHA G`
+ DABA 4 DB 7 BHA G`
+ DABA 4 |B 7 BHA G`
+ DABA < B \ BIA A(Gp
+(D ABBA < B \ k BIA A(Gp
+(D ABBA < 4C U D BIA A(Gp
+(D ABBA < tC U BIA A(Gp
+(D ABBA < C \ BIA A(Gp
+(D ABBA < C @U BIA A(Gp
+(D ABBA < 4D `U BIA A(Gp
+(D ABBA < tD \ BIA A(Gp
+(D ABBA < D \ Z BIA A(Gp
+(D ABBA < D \ 3 BIA A(Gp
+(D ABBA < 4E U BIA A(Gp
+(D ABBA < tE U BIA A(Gp
+(D ABBA < E U BIA A(Gp
+(D ABBA < E @U BIA A(Gp
+(D ABBA 4 4F `B p BHA G`
+ DABA 4 lF x7 Q BHA G`
+ DABA 4 F 7 2 BHA G`
+ DABA 4 F 7 BHA G`
+ DABA 4 G 7 BHA G`
+ DABA 4 LG 7 BHA G`
+ DABA < G U BIA A(Gp
+(D ABBA < G U BIA A(Gp
+(D ABBA < H U h BIA A(Gp
+(D ABBA < DH U A BIA A(Gp
+(D ABBA 4 H 7 BHA G`
+ DABA 4 H (7 BHA G`
+ DABA 4 H 07 BHA G`
+ DABA 4 ,I 87 BHA G`
+ DABA 4 dI @7 BHA G`
+ DABA 4 I H7 BHA G`
+ DABA 4 I P7 ` BHA G`
+ DABA 4 J X7 A BHA G`
+ DABA 4 DJ `7 " BHA G`
+ DABA 4 |J h7 BHA G`
+ DABA 4 J p7 BHA G`
+ DABA 4 J x7 BHA G`
+ DABA 4 $K B BHA G`
+ DABA 4 \K 7 BHA G`
+ DABA 4 K 7 h BHA G`
+ DABA 4 K 7 I BHA G`
+ DABA 4 L 7 * BHA G`
+ DABA 4 <L 7 BHA G`
+ DABA 4 tL 7 BHA G`
+ DABA 4 L ȿ7 BHA G`
+ DABA 4 L 7 BHA G`
+ DABA 4 M 7 BHA G`
+ DABA 4 TM ¥7 p BHA G`
+ DABA 4 M å7 Q BHA G`
+ DABA 4 M ĥ7 2 BHA G`
+ DABA 4 M ť) AHJ
+DAA < O ǥ BBA A(G@a
+(A ABBD < tN ȥ BBA A(JE
+(A ABBE D N ʥY BBB A(A0Gj
+0A(A BBBG d O 8̥B BBB B(A0A8G@x
8A0A(B BBBE
-8A0A(B BBBF T ' @ BBE E(D0A8G
-8D0A(B BBBI L ' M G BBB B(A0D8N
-8A0A(B BBBH , 4( > K AID W
-DAA L d( M + BBB B(A0D8N
-8A0A(B BBBH , ( > / AID W
-DAA L ( M BBB B(A0D8N
-8A0A(B BBBH , 4) > AID W
-DAA L d) k BGB B(A0D8Gc
-8A0A(B BBBJ L ) ( b BDB B(A0D8Gc
-8A0A(B BBBE D * H S BEB A(A0Gh
-0A(A BBBA < L* ` S BBD A(JX
-(D ABBA < , BEG A(D0M
-(D ABBG L , #J BBB B(A0A8GP
-8A0A(B BBBK < - $ BHB A(A0n(A BBL L T- p$z BBB B(A0A8GPJ
-8A0A(B BBBK < - & BHB A(A0~(A BBL < - ' BHB A(A0~(A BBL < $. `' BHB A(A0~(A BBL L d. ' BBB B(A0A8GP
-8A0A(B BBBK < . )j BEG A(D0A
-(D ABBC < . * BEG A(D0J
-(D ABBJ < 4/ `- BEG A(D0J
-(D ABBJ < t/ 0 BHB A(A0n(A BBL < / P0 BHB A(A0n(A BBL < / 0 BHB A(A0n(A BBL < 40 0 BHB A(A0n(A BBL < t0 @1 BHB A(A0n(A BBL < 0 1 BHB A(A0n(A BBL L 0 1 BBB B(A0A8G
-8A0A(B BBBD L D1 `7 BBB B(A0A8G
-8A0A(B BBBD < / <R BBD A(Jr
-(A ABBA L / =. BBE G(A0D
-0D(A BBBF D ,0 A BBE G(A0D
-0D(A BBBA D t0 8D% BBE G(A0D
-0D(A BBBD L 0 G BBE G(A0D6
-0D(A BBBJ D 1 PJ z BBE G(A0D
-0D(A BBBA L T1 Lb u BBE G(A0D
-0D(A BBBH L 1 P{ h BBE G(A0D
-0D(A BBBH < 1 U2 [ BBA A(JY
-(A ABBA | ,4 V BEB B(D0A8Dp
+8A0A(B BBBF T dO ͥ BBE E(D0A8G
+8D0A(B BBBI L O ϥM BBB B(A0D8N
+8A0A(B BBBH , P ҥ> AID W
+DAA L <P ҥM BBB B(A0D8N
+8A0A(B BBBH , P ե> AID W
+DAA L P եM BBB B(A0D8N
+8A0A(B BBBH , Q إ> AID W
+DAA L <Q إt BGB B(A0D8G
+8A0A(B BBBA L Q ۥs BDB B(A0D8G
+8A0A(B BBBA D Q HݥS BEB A(A0Gh
+0A(A BBBA < $R `ޥS m BBD A(JX
+(D ABBA < <S ߥ BEG A(D0M
+(D ABBG L |S J BBB B(A0A8GP
+8A0A(B BBBK < S BHB A(A0n(A BBL L T pz BBB B(A0A8GPJ
+8A0A(B BBBK < \T BHB A(A0~(A BBL < T BHB A(A0~(A BBL < T ` BHB A(A0~(A BBL L U BBB B(A0A8GP
+8A0A(B BBBK < lU j BEG A(D0A
+(D ABBC < U BEG A(D0J
+(D ABBJ < U ` BEG A(D0J
+(D ABBJ < ,V BHB A(A0n(A BBL < lV P BHB A(A0n(A BBL < V BHB A(A0n(A BBL < V BHB A(A0n(A BBL < ,W @ BHB A(A0n(A BBL < lW BHB A(A0n(A BBL L W BBB B(A0A8G
+8A0A(B BBBD L W ` BBB B(A0A8G
+8A0A(B BBBD < tW G B BBA D(Go
+(A ABBA L W & BBE A(G0D
+0D(A BBBE D X BBE A(G0D
+0D(A BBBA D LX BBE G(A0D
+0D(A BBBF L X b BBE G(A0D4
+0D(A BBBD D X BBE A(G0D
+0D(A BBBA L ,Y H K BBE A(G0D
+0D(A BBBG L |Y H] BBE G(A0D
+0D(A BBBB | Z X BEB B(D0A8Dp
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA < 2 XR BBD A(Jt
-(A ABBA | 4 Y BEB B(D0A8Dp
+8A0A(B BBBA < LZ H c BBA D(Gp
+(A ABBA | d[ BEB B(D0A8Dp
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA L l5 h\& BEE B(A0A8D
-8A0A(B BBBA L 5 H_& BEE B(A0A8D
-8A0A(B BBBA L 6 (bz BBE E(A0A8DP
-8A0A(B BBBA | \6 XdY BEE B(A0A8D
+8A0A(B BBBA d [ BBB B(D0A8DP
+8A0A(B BBBF
+8A0A(B BBBH T t[ _ BEB E(A0A8J'
+8A0A(B BBBE , [ (y AEG V
+AAA T [ (] BBB B(A0A8J
+8A0A(B BBBI ,] 0 AO D t\ 0 " BBB A(E0J{
+0A(A BBBA T \ 83 # BBB B(A0A8J
+8A0A(B BBBH ] : AO T 4] : BBB B(A0A8J
+8A0A(B BBBG d^ HF AO T ] HF a BBB B(A0A8J
+8A0A(B BBBE ^ Q AO T $^ Ql BBB B(A0H8G
+8A0A(B BBBA L |^ WG BBB H(A0G
+0A(A BBBA < ^ Y BEA A(G@}
+(D ABBJ < _ Y BEG A(D0
+(D ABBC < $` Zm BEA G(D0
+(D ABBB < d` ]m BEA G(D0
+(D ABBB L ` 8_Z BBB B(A0A8GP2
+8A0A(B BBBC < ` Ha BHB A(A0n(A BBL L \` aG ~ BBB H(A0G
+0A(A BBBA D ` c p BBB A(E0J{
+0A(A BBBA L a e& BEE B(A0A8D
+8A0A(B BBBA L Da h ! BBB H(A0G<
+0A(A BBBA L lb n& BEE B(A0A8D
+8A0A(B BBBA L a p BBB H(A0G<
+0A(A BBBA L c vz BBE E(A0A8DP
+8A0A(B BBBA L b @x BBB H(A0G<
+0A(A BBBA | c p}Y BEE B(A0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA | 6 8g BBE E(A0A8DP
+8A0A(B BBBA L Tc P BBB H(A0G=
+0A(A BBBA | |d BBE E(A0A8DP
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB | \7 hY BEE B(A0A8D
+8A0A(B BBBA 8A0A(B BBB L $d BBB H(A0G=
+0A(A BBBA | Le PY BEE B(A0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA | 7 k BBE E(A0A8DP
+8A0A(B BBBA L d 0 i BBB H(A0G=
+0A(A BBBA | f ` BBE E(A0A8DP
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB | \8 mY BEE B(A0A8D
+8A0A(B BBBA 8A0A(B BBB L e 1 BBB H(A0G=
+0A(A BBBA | f Y BEE B(A0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA | 8 p BBE E(A0A8DP
+8A0A(B BBBA L f BBB H(A0G=
+0A(A BBBA | g У BBE E(A0A8DP
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB < d7 hrR 2 BBD A(Jt
-(A ABBA | 9 s BEE B(A0A8Dp
+8A0A(B BBBA 8A0A(B BBB L dg p BBB H(A0G=
+0A(A BBBA < g H BBA D(Gp
+(A ABBA | h BEE B(A0A8Dp
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA | : (v BBE E(A0A8DP
+8A0A(B BBBA L th P m BBB H(A0G=
+0A(A BBBA | i BBE E(A0A8DP
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB : 8x BEE B(A0A8D_
+8A0A(B BBBA 8A0A(B BBB L Di 5 BBB H(A0G=
+0A(A BBBA lj BEE B(A0A8D_
8A0A(B BBBC
8A0A(B BBBA
-8A0A(B BBBA | $; }. BBE E(A0A8DP
+8A0A(B BBBA L j X BBB H(A0G=
+0A(A BBBA | Dk Ŧ. BBE E(A0A8DP
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB | ; R BEE B(A0A8D
+8A0A(B BBBA 8A0A(B BBB L j 8Ǧ BBB H(A0G=
+0A(A BBBA | l h̦R BEE B(A0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA | $< ` BBE E(A0A8DP
+8A0A(B BBBA | l HϦ BBE E(A0A8DP
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB d < BBB B(D0A8DP
-8A0A(B BBBF
-8A0A(B BBBH T ; BEB E(A0A8J'
-8A0A(B BBBE , l; y AEG V
-AAA T ; Г] ʀ BBB B(A0A8J
-8A0A(B BBBI = ؛ AO D < ؛ i BBB E(A0Jy
-0A(A BBBA T \< @ o BBB B(A0A8J
-8A0A(B BBBH > 襩 AO T < 襩 BBB B(A0A8J
-8A0A(B BBBG $? P AO T L= P BBB B(A0A8J
-8A0A(B BBBE ? AO D = L BBB E(A0Jy
-0A(A BBBA T > p| R BBB B(A0H8G
-8A0A(B BBBA L d> ĩ [ BBB H(A0G?
-0A(A BBBA L > ɩ BBB H(A0G?
-0A(A BBBA L ? Xϩ BBB H(A0G@
-0A(A BBBA L T? ԩ B BBB H(A0G@
-0A(A BBBA L ? ک BBB H(A0G@
-0A(A BBBA L ? xߩ ܀ BBB H(A0G@
-0A(A BBBA L D@ ) BBB H(A0G?
-0A(A BBBA L @ 8 v BBB H(A0G@
-0A(A BBBA L @ Á BBB H(A0G@
-0A(A BBBA L 4A BBB H(A0G@
-0A(A BBBA L A X ] BBB H(A0G@
-0A(A BBBA L A BBB H(A0G@
-0A(A BBBA L $B BBB H(A0G@
-0A(A BBBA L tB x
-G D BBB H(A0G
-0A(A BBBA < B x 6 BEA A(G@}
-(D ABBJ < D X
BEG A(D0
-(D ABBC < <E 8m BEA G(D0
-(D ABBB < |E hm BEA G(D0
-(D ABBB L E Z BBB B(A0A8GP2
-8A0A(B BBBC < F BHB A(A0n(A BBL L TD G BBB H(A0G
-0A(A BBBA T D BBB B(A0J8G1
-8A0A(B BBBA D D `C BHD A(G
-(D ABBK < DE h 2 j BBA A(JY
-(A ABBA < |G h q BEG A(D0
-(D ABBF < G "* BEA G(D0
-(D ABBA < F #2 ̀ BBA A(JY
-(A ABBA < DF $2 BBA A(JY
-(A ABBA T F % BBB B(D0G8GY
-8D0A(B BBBH T F P, BBB B(A0D8G8
-8A0A(B BBBJ < 4G 3 l BHD A(G
-(D ABBA T tG h5 Q BBB E(G0A8G
-8D0A(B BBBG T G 0; : BBB B(D0G8G
-8D0A(B BBBJ T $H B # BBB B(A0D8G
-8A0A(B BBBA T |H F * BBB B(A0D8G
-8A0A(B BBBA T H K BBB B(A0D8G
-8A0A(B BBBI T ,I P
- BBB B(A0D8G}
-8A0A(B BBBE T I HX BBB B(A0D8G@
-8A0A(B BBBB T I ^ BBB B(A0D8G}
-8A0A(B BBBE T 4J e BBB B(A0D8G@
-8A0A(B BBBB T J l BBB B(A0D8G
-8A0A(B BBBA L J qi BBB H(A0G
-0A(A BBBA L 4K ti BBB H(A0G
-0A(A BBBA T K wU BEB B(G0A8JC
-8D0A(B BBBC $ M ) KA|A T L BBB B(D0A8Gm
-8A0A(B BBBA T \L # BBB B(A0A8G[
-8A0A(B BBBA $ N x- ADK WDA T L BBB B(D0A8Gm
-8A0A(B BBBA 4 ,O ( BBA A(^
- ABBG T lM BBB B(D0A8G.
-8A0A(B BBBD T M x BBB B(A0A8G
-8A0A(B BBBA $ P - ADK WDA T DN r BBB B(A0A8G
-8A0A(B BBBA < N z BBE D(Mi
-(D ABBD T N BBB B(D0A8G
-8A0A(B BBBA L 4O HŪ BBE B(A0A8J
-8A0A(B BBBI T O ʪ z BEE H(A0D8GP
-8D0A(B BBBA Q `Ϫ DX L O `Ϫ" BBE B(A0A8J
-8A0A(B BBBD T LP @ժ o BEE H(A0D8GP
-8D0A(B BBBA R ٪ DX L P ٪ v BBE B(A0A8J
-8A0A(B BBBJ T Q d BEE H(A0D8GO
-8D0A(B BBBA dS DX | S BBE E(A0A8D`t
+8A0A(B BBBA 8A0A(B BBB $ m Ѧ) KA|A T dl Ӧ BBB B(D0A8Gm
+8A0A(B BBBA T l 8צ# BBB B(A0A8G[
+8A0A(B BBBA $ m - ADK WDA T <m BBB B(D0A8Gm
+8A0A(B BBBA 4 ln BBA A(^
+ ABBG T m X ] BBB B(D0A8G.
+8A0A(B BBBD T $n a BBB B(A0A8G
+8A0A(B BBBA $ To H- ADK WDA T n P A BBB B(A0A8G
+8A0A(B BBBA < n I BBE D(Mi
+(D ABBD T <o h ] BBB B(D0A8G
+8A0A(B BBBA T o a BBB B(A0J8G1
+8A0A(B BBBA D o X) C BBG D(D
+(D ABBF < q @ q BEG A(D0
+(D ABBF < Lq * BEA G(D0
+(D ABBA T p p! BBB B(D0G8GY
+8D0A(B BBBH T q (( BBB B(A0D8G8
+8A0A(B BBBJ < dq / y BBG D(D
+(D ABBA T q 01 ^ BBB E(G0A8G
+8D0A(B BBBG T q 6 G BBB B(D0G8G
+8D0A(B BBBJ T Tr P>k 0 BBB B(A0D8G
+8A0A(B BBBA T r hB 7 BBB B(A0D8G
+8A0A(B BBBA T s F ' BBB B(A0D8G
+8A0A(B BBBI T \s L BBB B(A0D8G}
+8A0A(B BBBE T s S BBB B(A0D8G@
+8A0A(B BBBB T t (Z BBB B(A0D8G}
+8A0A(B BBBE T dt a BBB B(A0D8G@
+8A0A(B BBBB T t g BBB B(A0D8G
+8A0A(B BBBA L u @mT BBB H(A0G
+0A(A BBBA L du PpT BBB H(A0G
+0A(A BBBA L u `s%
+ BBE E(A0A8Jc
+8A0A(B BBBI L v @y4
+ BEB A(G0GJ
+0D(A BBBC L Tv 0} % BBE B(A0A8J
+8A0A(B BBBI T v Ђ BEE B(G0D8GJ
+8D0A(B BBBA w DX L w " BBE B(A0A8J
+8A0A(B BBBD T lw BEE B(G0D8GJ
+8D0A(B BBBA x @ DX L w @ BBE B(A0A8J
+8A0A(B BBBJ T 4x BEE B(G0D8GH
+8D0A(B BBBA dy 螧 DX | y 螧 BBE E(A0A8D`t
8A0A(B BBBF {
8A0A(B BBBC
-8A0A(B BBBA L R t ~ BBH A(D0G!
-0D(A BBBA TT ' AJ0WD | tT 0 BBE E(A0A8D@
+8A0A(B BBBA T ,y Hi r BBB B(G0D8G
+8D0A(B BBBA \z `' AJ0WD | |z p BBE E(A0A8D@
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB L R t 7~ BBH A(D0G!
-0D(A BBBA DU ' AJ0WD | dU BBE E(A0A8DP
+8A0A(B BBBA 8A0A(B BBB T $z Ыi BBB B(G0D8G
+8D0A(B BBBA T{ 譧' AJ0WD | t{ BBE E(A0A8DP
8A0A(B BBBI
8A0A(B BBBE
-8A0A(B BBBK L S @t } BBH A(D0G!
-0D(A BBBA 4V p ' AJ0WD | TV o BBE E(A0A8D@,
+8A0A(B BBBK L { h BBB G(D0GF
+0D(A BBBA D| ' AJ0WD | d| ȹo BBE E(A0A8D@,
8A0A(B BBBF K
8A0A(B BBBK s
-8A0A(B BBBC L T p | BBH A(D0GM
-0D(A BBBA $W ' AJ0WD | DW BBE E(A0A8D@
+8A0A(B BBBC L | } BBB G(D0GF
+0D(A BBBA 4} ŧ' AJ0WD | T} ŧ BBE E(A0A8D@
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB L U P
t W| BBH A(D0G!
-0D(A BBBA X ' AJ0WD | 4X BBE E(A0A8DP
+8A0A(B BBBA 8A0A(B BBB T | xƧi BBB B(G0D8G
+8D0A(B BBBA ,~ ȧ' AJ0WD | L~ ȧ BBE E(A0A8DP
8A0A(B BBBJ
8A0A(B BBBE
-8A0A(B BBBK L V t { BBH A(D0G!
-0D(A BBBA Y P' AJ0WD | $Y ` BBE E(A0A8DP
+8A0A(B BBBK L } 0Ч D BBB G(D0GF
+0D(A BBBA ԧ' AJ0WD | < ԧ BBE E(A0A8DP
8A0A(B BBBH A
8A0A(B BBBE
-8A0A(B BBBI L W !t z BBH A(D0G!
-0D(A BBBA Y #' AJ0WD | Z # BBE E(A0A8D@
+8A0A(B BBBI T ~ ܧi BBB B(G0D8G
+8D0A(B BBBA ާ' AJ0WD | 4 ާ BBE E(A0A8D@
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB L X @%t ;z BBH A(D0G!
-0D(A BBBA Z p'' AJ0WD | [ ' BBE E(A0A8DP
+8A0A(B BBBA 8A0A(B BBB T Xi BBB B(G0D8G
+8D0A(B BBBA p' AJ0WD | , BBE E(A0A8DP
8A0A(B BBBI
8A0A(B BBBF
-8A0A(B BBBK L Y /t y BBH A(D0G!
-0D(A BBBA [ @1' AJ0WD | [ P1\
+8A0A(B BBBK L Ԁ O BBB G(D0GF
+0D(A BBBA `' AJ0WD | p\
BBE E(A0A8D`
8A0A(B BBBG w
8A0A(B BBBG
-8A0A(B BBBH L |Z 0;t x BBH A(D0G!
-0D(A BBBA \ `=' AJ0WD | \ p= BBE E(A0A8DP
+8A0A(B BBBH T ā Pi BBB B(G0D8G
+8D0A(B BBBA h' AJ0WD | x BBE E(A0A8DP
8A0A(B BBBG
8A0A(B BBBF
-8A0A(B BBBI L l[ Et x BBH A(D0G!
-0D(A BBBA ] G' AJ0WD D [ GS w BEB A(A0Gh
-0A(A BBBA L ^ H BEE B(A0A8D`6
-8A0A(B BBBD L l^ Pl BEE B(A0A8Dp
-8A0A(B BBBC T \ Y -w BBB B(A0A8G#
-8A0A(B BBBA $ _ p^; ADK@eDA | <_ ^ BBE E(A0A8D
+8A0A(B BBBI T i BBB B(G0D8G
+8D0A(B BBBA ' AJ0WD D 4 S BEB A(A0Gh
+0A(A BBBA L T BEE B(A0A8D`6
+8A0A(B BBBD L
l BEE B(A0A8Dp
+8A0A(B BBBC T BBB B(A0A8G#
+8A0A(B BBBA $ L `; ADK@eDA | t x BBE E(A0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA L ] ` v BBE G(D0GX
-0D(A BBBA 4 ^ 8b mv BAD M
- DABH | D` e BBE E(A0A8D
+8A0A(B BBBA L BBE G(D0GX
+0D(A BBBA 4 l ( \ BAD M
+ DABH | | " BBE E(A0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA L ^ g u BBE G(D0GX
-0D(A BBBA 4 _ h u BAD M
- DABH | La k BBE E(A0A8D@
+8A0A(B BBBA L $ $ BBE G(D0GX
+0D(A BBBA 4 t % BAD M
+ DABH | ( BBE E(A0A8D@
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB L _ mt Au BBH A(D0G"
-0D(A BBBA b Ho' AJ0WD | <b Xo BBE E(A0A8D@
+8A0A(B BBBA 8A0A(B BBB T , *r 0 BBB B(G0D8G!
+8D0A(B BBBA \ 0,' AJ0WD | | @, BBE E(A0A8D@
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB L ` pt t BBH A(D0G"
-0D(A BBBA c r' AJ0WD L ,c r BEE B(A0A8D`
-8A0A(B BBBE D a XzU t BBD G(G
-(D ABBA c p{' AJ0WD L c {- BEE B(A0A8Dp
-8A0A(B BBBH D <b `U os BBD G(G
-(D ABBA |d x' AJ0WD | d BBB B(A0D8GP
+8A0A(B BBBA 8A0A(B BBB L $ - t BBB G(D0GH
+0D(A BBBA L 1' AJ0WD L l 2 BEE B(A0A8D`
+8A0A(B BBBE D `9U ' BBD G(G
+(D ABBA x:' AJ0WD L $ :- BEE B(A0A8Dp
+8A0A(B BBBH D hCU BBD G(G
+(D ABBA D' AJ0WD | ܊ D BBB B(A0D8GP
8A0A(B BBBB ;
8A0A(B BBBK
-8A0A(B BBBJ L $c t r BBH A(D0G"
-0D(A BBBA le H' AJ0WD L e X BEE B(A0A8D`
-8A0A(B BBBE L c t !r BBH A(D0G"
-0D(A BBBA ,f ؖ' AJ0WD L Lf 薫 BEE B(A0A8D`
-8A0A(B BBBE D d HU q BBD G(G
-(D ABBA f `' AJ0WD L g p- BEE B(A0A8Dp~
-8A0A(B BBBD D \e PU q BBD G(G
-(D ABBA g h' AJ0WD | g xw BBB B(A0D8GP
+8A0A(B BBBJ T Hr BBB B(G0D8G!
+8D0A(B BBBA HJ' AJ0WD L ԋ XJ BEE B(A0A8D`
+8A0A(B BBBE L L S 7 BBB G(D0GH
+0D(A BBBA t W' AJ0WD L X BEE B(A0A8D`
+8A0A(B BBBE D h_U BBD G(G
+(D ABBA , `' AJ0WD L L `- BEE B(A0A8Dp~
+8A0A(B BBBD D Č piU P BBD G(G
+(D ABBA j' AJ0WD | jw BBB B(A0D8GP
8A0A(B BBBF
8A0A(B BBBH
-8A0A(B BBBJ L Df xt 9p BBH A(D0G"
-0D(A BBBA h ' AJ0WD L h q BEE B(A0A8D`d
-8A0A(B BBBF L g 趫 o BBH A(D0G:
-0D(A BBBA Li 8' AJ0WD L li H BEE B(A0A8D`
-8A0A(B BBBE D g U Oo BBD G(G
-(D ABBA j «' AJ0WD L $j «- BEE B(A0A8Dp
-8A0A(B BBBH D |h ˫U n BBD G(G
-(D ABBA j ̫' AJ0WD | j ̫w BBB B(A0D8GP
+8A0A(B BBBJ T mr BBB B(G0D8G!
+8D0A(B BBBA o' AJ0WD L oq BEE B(A0A8D`d
+8A0A(B BBBF L t x BBB G(D0GH
+0D(A BBBA P|' AJ0WD L `| BEE B(A0A8D`
+8A0A(B BBBE D 4 U BBD G(G
+(D ABBA T ' AJ0WD L t 脨- BEE B(A0A8Dp
+8A0A(B BBBH D ȍU BBD G(G
+(D ABBA ' AJ0WD | , w BBB B(A0D8GP
8A0A(B BBBJ
8A0A(B BBBK
-8A0A(B BBBJ L di ϫt m BBH A(D0G"
-0D(A BBBA k ҫ' AJ0WD L k ҫa BEE B(A0A8D`\
-8A0A(B BBBF L $j 8ګ gm BBH A(D0G:
-0D(A BBBA ll ݫ' AJ0WD L l ݫ BEE B(A0A8D`
-8A0A(B BBBE D j U m BBD G(G
-(D ABBA $m ' AJ0WD L Dm BBB B(D0A8J
-8A0A(B BBBF D k U gl BBD G(G
-(D ABBA m ' AJ0WD m T l ` l BBB B(A0A8G#
-8A0A(B BBBA $ ln ; ADK@eDA n @A. K^ $ n m AG q
-AA $ n @u AG y
-AA n ֑\ AG RAT $m x ck BBB B(D0A8J
-8A0A(B BBBJ T |m k BBB B(D0A8N1
-8A0A(B BBBA , m 8 > l AID W
-DAA , n H > k AID W
-DAA , 4n X > k AID W
-DAA < \p ҐD BBD A(D@0(A ABB T n ( {k BIB E(A0A8D6
-8D0A(B BBBA T n ` Ik BIB E(A0A8DF
-8D0A(B BBBA T To k BIB E(A0A8DF
-8D0A(B BBBA D q X BBB B(A0A8D0A(B BBB D q 8 BBB B(A0A8$0A(B BBB T <p , Uj BBE B(D0A8G
-8D0A(B BBBK D p 3 ;j BDA DPN
+8A0A(B BBBJ T Ԑ r I BBB B(G0D8G!
+8D0A(B BBBA ' AJ0WD L $ (a BEE B(A0A8D`\
+8A0A(B BBBF L H BBB G(D0GH
+0D(A BBBA Ē ' AJ0WD L BEE B(A0A8D`
+8A0A(B BBBE D \ U p BBD G(G
+(D ABBA | ' AJ0WD L 0 BBB B(D0A8J
+8A0A(B BBBF D U ֿ BBD G(G
+(D ABBA 4 Ȱ' AJ0WD T ذ T p t BBB B(A0A8G#
+8A0A(B BBBA $ Ĕ ; ADK@eDA . K^ $ m AG q
+AA $ , Pu AG y
+AA T \ AG RAT Ҿ BBB B(D0A8J
+8A0A(B BBBJ T Ũ - BBB B(D0A8N1
+8A0A(B BBBA , L HΨ> AID W
+DAA , | XΨ> j AID W
+DAA , hΨ> J AID W
+DAA D BBB D(A0D@0A(A BBB T $ 0Ψ BIB E(A0A8D6
+8D0A(B BBBA T | hҨ BIB E(A0A8DF
+8D0A(B BBBA T Ԗ ֨ ~ BIB E(A0A8DF
+8D0A(B BBBA D ڨX BBB B(A0A8D0A(B BBB D L ܨ8 BBB B(A0A8$0A(B BBB T ݨ, BBE B(D0A8G
+8D0A(B BBBK D 3 BDA DPN
AABC
- AABB $ r $) SAzA r & AO
-H T $q x) i BBB G(G0A8J&
-8D0A(B BBBF ts <. K^ s 7 s 7 T q 7r Gj BBA I(Dp
+ AABB $ 4 ) SAzA \ AO
+H T
' BBB G(G0A8JE
+8D0A(B BBBG ԙ X. K^ T D r BBA I(Dp
(D ABBH L
-(D ABBA T r 8r j BBA I(Dp
+(D ABBA T r G BBA I(Dp
(D ABBH L
-(D ABBA T tr :1 i BEE E(D0A8GY
-8D0A(B BBBE D t BBB D(A0D@0A(A BBB T s ?R mi BIE B(A0A8DA
-8D0A(B BBBA T ls DL ;i BIE B(A0A8D9
-8D0A(B BBBA T s I i BBB H(A0A8L4
-8D0A(B BBBC v :. K^ ,v XW Dv PW \v HW tv @W v 8W v 0W v (W v 0W v 8W w @W w HW 4w PW , Tu XW[ h AID t
-DAA , u W[ _h AID t
-DAA , u W[ ?h AID t
-DAA , u W[ h AID t
-DAA , v X[ g AID t
-DAA , Dv HX[ g AID t
-DAA , tv xX[ g AID t
-DAA , v X[ g AID t
-DAA L v Xr g BBB B(E0A8J@
-8A0A(B BBBA $ y Z^ AG b
-AA L Dy @Z>
BEB B(A0A8G
8A0A(B BBB < y 0g BEB D(A0M
-(A BBBF T w h f BBE E(A0A8G
-8D0A(B BBBE T 4x k Of BBE E(A0A8G
-8D0A(B BBBE T x `m f BBE E(A0A8G
-8D0A(B BBBE T x o e BBE E(A0A8G
-8D0A(B BBBE D 4{ r BBB D(A0D@
-0A(A BBBA D y hs Pe BIB A(D0D`+
-0D(A BBBA D y v .e BIB A(D0D`+
-0D(A BBBA D z x e BIB A(D0D`+
-0D(A BBBA D \z { d BIB A(D0D`+
-0D(A BBBA D z H~ d BIB A(D0D`+
-0D(A BBBA D z d BIB A(D0D`+
-0D(A BBBA D 4{ d BIB A(D0D`2
-0D(A BBBA D |{ bd BIB A(D0D`2
-0D(A BBBA \ } Hm BBD D(J@
+(D ABBA T 1 BEE E(D0A8GY
+8D0A(B BBBE D $ BBB D(A0D@0A(A BBB T L BIE B(A0A8D9
+8D0A(B BBBA T R w BIE B(A0A8DA
+8D0A(B BBBA T D G BBB H(A0A8L
+8D0A(B BBBA t 貘. K^ p h ` Ԝ X P H @ 4 H L P d X | ` h , Ԝ p[ AID t
+DAA , [ AID t
+DAA , 4 [ { AID t
+DAA , d [ [ AID t
+DAA , 0[ ; AID t
+DAA , ĝ `[ AID t
+DAA , [ AID t
+DAA , $ [ ۺ AID t
+DAA L T r BBB B(E0A8J@
+8A0A(B BBBA $ | ^ AG b
+AA L X >
BEB B(A0A8G
8A0A(B BBB < H) BEB D(A0M
+(A BBBF T \ * ȹ BBE E(A0A8G
+8D0A(B BBBE T - BBE E(A0A8G
+8D0A(B BBBE T x/ N BBE E(A0A8G
+8D0A(B BBBE T d 1 BBE E(A0A8G
+8D0A(B BBBE D (4 BBB D(A0D@
+0A(A BBBA D 5 BIB A(D0D`+
+0D(A BBBA D L 88 j BIB A(D0D`+
+0D(A BBBA D : H BIB A(D0D`+
+0D(A BBBA D ܡ = & BIB A(D0D`+
+0D(A BBBA D $ `@ BIB A(D0D`+
+0D(A BBBA D l C BIB A(D0D`+
+0D(A BBBA D E BIB A(D0D`2
+0D(A BBBA D H BIB A(D0D`2
+0D(A BBBA \ `Km BBD D(J@
(A ABBD T
(A ABBF _
-(A ABBK d ~ X?
+(A ABBK d | pL?
BBB B(A0A8GP
8G0A(B BBBP ^
-8A0A(B BBBH T | 0 xc BHB B(A0A8GH
-8A0A(B BBBG ~ A
-J AT } 8 d BDE B(A0A8J
-8A0A(B BBBD T \} d BDE B(A0A8J
-8A0A(B BBBD T } 軬 d BDE B(A0A8J
-8A0A(B BBBD T ~ @¬ d BDE B(A0A8J
-8A0A(B BBBD T d~ Ȭ !e BDE B(A0A8J
-8A0A(B BBBD T ~ ά Ce BDE B(A0A8J
-8A0A(B BBBD X/. K^ $ 0լ < (լ T լ l (լ 4 0լ d BAI a
-ABA 4 լ d BAI a
-ABA L\ AG RAD լ BBB D(A0D@
-0A(A BBBA T d 8 ?d BIB B(A0D8Df
-8D0A(B BBBA T ۬B
d BIB B(A0D8Dg
-8D0A(B BBBA \ ެ c BBB A(A0Dz
-0C(A BBBH W
-0C(A BBBA T t (W c BBB B(A0F8J
-8A0A(B BBBG T ́ 0 7d BBB B(A0F8J
-8A0A(B BBBG x,. K^ 4 p L h , l p[ ;d AID t
-DAA < D BBD A(D@0(A ABB T ܂ ` c BIB B(A0D8Df
-8D0A(B BBBA T 4 c BDB B(A0A8J
-8A0A(B BBBD @+. K^ ̅ , 4 [ Pd AID t
-DAA , d H[ 0d AID t
-DAA , x[ d AID t
-DAA D $ BBB D(A0D@0A(A BBB T `L c BIE B(A0A8D9
-8D0A(B BBBA T d XL vc BIE B(A0A8D9
-8D0A(B BBBA T PL Dc BIE B(A0A8D9
-8D0A(B BBBA L H c BDH B(A0D8G
-8D0A(B BBBA L d " Wc BDH B(A0D8G
-8D0A(B BBBA L ' c BDH B(A0D8G
-8D0A(B BBBA 'V Ko
-A 8, 4 0, , T 8,[ c AID t
-DAA D | 2~ BBB D(A0D@0A(A BBB T ̇ ,; 'c BIB E(A0A8G(
-8D0A(B BBBA D $ 2 c BBE A(H0D
-0A(A BBBA D l 4 b BBB D(H0D
-0A(A BBBA T 87# b BDB H(D0A8GS
-8D0A(B BBBD P&. K^ hZ 4 `Z L XZ d `Z D | hZ BEE D(A0D`n
-0A(A BBBE ċ [j D X[ d BHB A(D0J^
-0A(A BBBA T , \ c BBD A(Kp
+8A0A(B BBBH T HV2 BHB B(A0A8GH
+8A0A(B BBBG < 0q A
+J AT q| BDB E(A0A8J
+8A0A(B BBBH T ܤ w| BDB E(A0A8J
+8A0A(B BBBH T 4 ~| & BDB E(A0A8J
+8A0A(B BBBH T 8| H BDB E(A0A8J
+8A0A(B BBBH T `| j BDB E(A0A8J
+8A0A(B BBBH T < | BDB E(A0A8J
+8A0A(B BBBH l . K^ ̧ 4 6 BAI a
+ABA 4 D BAI a
+ABA T \ AG RA\ H з BBB A(A0Dj
+0C(A BBBH V
+0C(A BBBH < Ԩ nD BBD A(D@0(A ABB T < p BIB B(A0D8Df
+8D0A(B BBBA T > BIB B(A0D8Df
+8D0A(B BBBA T X
+ BGB B(A0A8J
+8A0A(B BBBD T D ୩ BHB B(A0A8J
+8D0A(B BBBK t H. K^ , [ AID t
+DAA D BBB D(A0D@0A(A BBB T \ x % BIB B(A0D8Df
+8D0A(B BBBA T ຩ BDB E(A0A8G
+8A0A(B BBBI . K^ @ϩ 8ϩ , 0ϩ D (ϩ \ 0ϩ t 8ϩ , @ϩ[ AID t
+DAA , pϩ[ AID t
+DAA , ϩ[ w AID t
+DAA D BBB D(A0D@0A(A BBB T ϩL BIE B(A0A8D9
+8D0A(B BBBA T ԩL ݶ BIE B(A0A8D9
+8D0A(B BBBA T < x٩L BIE B(A0A8D9
+8D0A(B BBBA T pީ { BDH B(A0D8Gl
+8D0A(B BBBA T BDH B(A0D8Gd
+8D0A(B BBBA T D ۶ BDH B(A0D8Gd
+8D0A(B BBBA t T KH 0 ( , 0[ ö AID t
+DAA D
+ߗ BBB D(A0D@0A(A BBB T \ ; [ BIB E(A0A8G(
+8D0A(B BBBA D 6 BBE A(H0D
+0A(A BBBA < hb 2 BBA D(K
+(A ABBA T < 4 BBE B(D0A8G
+8D0A(B BBBH L ` BBE E(A0D8Gx
+8A0A(B BBBD T , BDB B(D0A8M/
+8D0A(B BBBH h. K^ , D x \ p t x D BEE D(A0D`n
+0A(A BBBE Բ j D p BHB A(D0J^
+0A(A BBBA T \ BBD A(Kp
(A ABBE L
-(A ABBA , X]> c AID W
-DAA , h]> c AID W
-DAA T x] xc BBD A(Kp
+(A ABBA , p > AID W
+DAA , > AID W
+DAA T x BBD A(Kp
(A ABBE L
-(A ABBA , < ^> 7c AID W
-DAA , l ^> c AID W
-DAA < 0^ BBD A(D@(A ABB 4 ܋ _k b BHA GP
- DABA 4 H`k b BHA GP
- DABA 4 L ar yb BHA GP
- DABA 4 br Zb BHA GP
- DABA $ d AG y
-AF xe AM T eh BIA
+(A ABBA , l (!> 7 AID W
+DAA , 8!> AID W
+DAA < H! BBD A(D@(A ABB 4 ("k BHA GP
+ DABA 4 D `#k BHA GP
+ DABA 4 | $r y BHA GP
+ DABA 4 %r Z BHA GP
+ DABA $ ĵ (' AG y
+AF ( AM T )h BIA
ABD @
ABE
ABF U
-ABP L T jS BBE B(A0A8G
-8A0A(B BBBD L l BEB B(A0A8D)
-8A0A(B BBBD < m BBG A(G@
-(A ABBF 4 pn5 < L n BEA A(D`H
-(A ABBE L HoS BBE B(A0A8G
-8A0A(B BBBD $ ܐ Xq AGpZ
-AE @r{ d r BBB B(A0A8D_
+ABP L d -S BBE B(A0A8G
+8A0A(B BBBD L (/ BEB B(A0A8D)
+8A0A(B BBBD < 0 BBG A(G@
+(A ABBF D 15 < \ 1 BEA A(D`H
+(A ABBE L `2S BBE B(A0A8G
+8A0A(B BBBD $ p4 AGpZ
+AE X5{ d , 5 BBB B(A0A8D_
8A0A(B BBBE
-8A0A(B BBBA L 0to BEB B(A0D8J
-8A0A(B BBBA L ԑ Py' BBB B(A0D8Jm
-8A0A(B BBBB d $ 0 BBB B(A0A8D_
+8A0A(B BBBA L H7o BEB B(A0D8J
+8A0A(B BBBA L h<' BBB B(A0D8Jm
+8A0A(B BBBB d 4 HB BBB B(A0A8D_
8A0A(B BBBE
-8A0A(B BBBA L o BEB B(A0D8J
-8A0A(B BBBA L ܒ ' BBB B(A0D8Jm
-8A0A(B BBBB d , BBB B(A0A8D_
+8A0A(B BBBA L Co BEB B(A0D8J
+8A0A(B BBBA L H' BBB B(A0D8Jm
+8A0A(B BBBB d < N BBB B(A0A8D_
8A0A(B BBBE
-8A0A(B BBBA L @o BEB B(A0D8J
-8A0A(B BBBA L `' BBB B(A0D8Jm
-8A0A(B BBBB 4 @ L { d L | BHB B(A0D8G
-8A0A(B BBBA , ̔ (j AAG@O
-FAA l hX BBE D(A0DP
+8A0A(B BBBA L XPo BEB B(A0D8J
+8A0A(B BBBA L xU' BBB B(A0D8Jm
+8A0A(B BBBB D X[ \ [{ t 8\ L \ BHB B(A0D8G
+8A0A(B BBBA , ܻ @_j AAG@O
+FAA l _X BBE D(A0DP
0D(A BBBJ ^
0A(E BBEC L
-0A(A FBBD L l X BEE F(A0A8Fpv8A0A(B BBB L BEB B(A0A8J8A0A(B BBB l XD BBB A(D0G@
+0A(A FBBD L | pa BEE F(A0A8Fpv8A0A(B BBB L ̼ b BEB B(A0A8J8A0A(B BBB l peD BBB A(D0G@
0D(A BBBK `
0A(E BBEA L
-0A(A FBBD L | 8 BEB B(A0A8Jn8A0A(B BBB 4 ̖ x BBA A(q
- ABBD T Y BBB E(A0D8Gb
-8A0A(B BBBA T d X* Y BBB B(A0A8J
-8A0A(B BBBJ 4 0 BBA A(q
- ABBD T س (Y BIB I(A0D8G9
-8A0A(B BBBA T L 0z X BBB B(A0A8J0
-8A0A(B BBBB < X BFB D(A0
-(A BBBE L ܘ K BBB B(D0A8J
-8A0A(B BBBA T 4 * X BBB B(A0A8J
-8A0A(B BBBB 4 ŭ BBA A(q
- ABBD T ė ȭ W BIB I(A0D8G9
-8A0A(B BBBA T ̭ PW BBB B(A0A8JP
-8A0A(B BBBB L l 8ѭ BBG E(A0A8M7
-8D0A(B BBBE L xҭ BBG E(A0A8M>
-8D0A(B BBBF x. K^ $ ӭ < ӭ T ӭ l ӭ ӭ ӭ T ӭn U BBB B(A0D8N+
-8A0A(B BBBH , ح> U AID W
-DAA L D حY U BBB E(A0A8N
-8A0A(B BBBK , ڭ> U AID W
-DAA T Ě ڭ U BBB B(D0A8N
-8A0A(B BBBF , 0> U AID W
-DAA < D 8lD BBD A(D@0(A ABB T aU BIB E(A0A8DV
-8D0A(B BBBA T X /U BIB E(A0A8D^
-8D0A(B BBBA T < T BIB E(A0A8D^
-8D0A(B BBBA d BLB B(A0A8D@c
+0A(A FBBD L Pf BEB B(A0A8Jn8A0A(B BBB 4 ܽ h BBA A(q
+ ABBD T < 8k BBB E(A0D8Gb
+8A0A(B BBBA T pp* BBB B(A0A8J
+8A0A(B BBBJ 4 ľ Ht BBA A(q
+ ABBD T $ v ( BIB I(A0D8G9
+8A0A(B BBBA T | H{z BBB B(A0A8J0
+8A0A(B BBBB < p BFB D(A0
+(A BBBE L 0K BBB B(D0A8J
+8A0A(B BBBA T d 0* BBB B(A0A8J
+8A0A(B BBBB 4 BBA A(q
+ ABBD T BIB I(A0D8G9
+8A0A(B BBBA T L P BBB B(A0A8JP
+8A0A(B BBBB L | P BBG E(A0A8M7
+8D0A(B BBBE L BBG E(A0A8M>
+8D0A(B BBBF . K^ 4 L d | T n Ũ BBB B(A0D8N+
+8A0A(B BBBH , D Л> AID W
+DAA L t Y ʨ BBB E(A0A8N
+8A0A(B BBBK , > AID W
+DAA T BBB B(D0A8N
+8A0A(B BBBF , L H> AID W
+DAA D T p̗ BBB D(A0D@0A(A BBB T Y BIB E(A0A8D^
+8D0A(B BBBA T h ' BIB E(A0A8D^
+8D0A(B BBBA T t BIB E(A0A8DV
+8D0A(B BBBA d BLB B(A0A8D@c
8A0A(B BBBK |
-8A0A(B BBBJ $ t EA
-AG D BHB E(A0D8D`8A0A(B BBB$ d | EA
-AD D BHB E(A0D8D`8A0A(B BBB< ԟ BBG A(G@
-(A ABBF `5 , D ` \ H9 L t p BKE B(A0A8DPa8A0A(B BBB Ġ I L ܠ BKE B(A0A8DPq8A0A(B BBB T 4 8% +R BBB B(A0A8J
-8A0A(B BBBA L N R BBB G(D0A8ML
-8D0A(B BBBH L ܟ "r R BBB G(D0A8M
-8D0A(B BBBB L , )r S BBB G(D0A8M
-8D0A(B BBBB t @. K^ 0 0 0 Ԣ 0 0 0 0 4 0 L 0 d 0 , 0[ R AID t
-DAA , 1[ sR AID t
-DAA , 81[ SR AID t
-DAA , h1[ 3R AID t
-DAA , D 1[ R AID t
-DAA D l Tf BBB D(A0D@0A(A BBB T 1 Q BIB E(A0A8D6
-8D0A(B BBBA T 5 yQ BIB E(A0A8D6
-8D0A(B BBBA T l 9 GQ BIB E(A0A8D6
-8D0A(B BBBA T ģ (> Q BIB E(A0A8D6
-8D0A(B BBBA T `B P BIB E(A0A8D6
-8D0A(B BBBA T t F P BEE E(D0A8G*
-8D0A(B BBBD T ̤ 0L P BDB B(A0D8G&
-8A0A(B BBBA T $ Q P BDE H(A0A8Gl
-8D0A(B BBBA T | [ #Q BDE H(A0A8Gd
-8D0A(B BBBA T ԥ d Q BDE H(A0A8Gd
-8D0A(B BBBA T , n #R BDE H(A0A8Gl
-8D0A(B BBBA | h . K^ Pw Hw D ̦ Pwb [R BBB A(D0Kp
-0A(A BBBJ , xx> *R AID W
-DAA D < Rc BBB D(A0D@0A(A BBB T @xL Q BIE B(A0A8D9
-8D0A(B BBBA L 8}r Q BBE B(G0F8G
-8D0A(B BBBA , . K^ D P \ H , t P0 AF
FK
- D T P| wQ BBE E(D0A8G
-8D0A(B BBBE T x| ]Q BBE E(D0A8G
-8D0A(B BBBE T \ E
- CQ BBB E(A0A8N
-8A0A(B BBBA , > Q AID W
-DAA D ܫ b BBB D(A0D@0A(A BBB T , ` aQ BIB E(A0A8GJ
-8D0A(B BBBA T 螮 GQ BBE B(D0A8G
-8D0A(B BBBG T ܪ /Q BBB B(A0D8L
-8A0A(B BBBG T 4 8 sQ BGB B(A0D8J
-8A0A(B BBBA T а[ Q BBB B(A0F8J
-8A0A(B BBBJ T ظ" 'R BBB H(F0A8J
-8D0A(B BBBD T < 9 oR BBE G(A0G8J
-8D0A(B BBBD T Į R BBB B(A0F8J
-8A0A(B BBBF T 0̮y #S BBG B(D0G8G
-8D0A(B BBBG < . K^ D T @Ԯ BBE A(A0D@
-0A(A BBBA D ֮ BBE A(A0D@
-0A(A BBBA D F` BBB D(A0D@0A(A BBB T 4 Hٮu SR BIB E(A0A8G=
-8D0A(B BBBA T p߮U ;R BBB E(G0F8G
-8D0A(B BBBD < x R BLD D(G0
-(A ABBE < $ 8* R BED I(KPc
-(D ABBE < d ( sR BLD D(G0
-(A ABBE < * WR BED I(KP_
-(D ABBI T - 7R BBM B(A0D8G
-8A0A(B BBBD , < > R AID W
-DAA d . K^ | 0 Hg 7 Hj 0 Hg ܲ 7 Hj 0 Hg 7 Hj D < 8 BBE A(A0D@g
-0A(A BBBA D BBE A(A0D@g
-0A(A BBBA D ̳ H BBE A(A0D@g
-0A(A BBBA D BBE A(A0D@o
-0A(A BBBA D \ h BBE A(A0D@o
-0A(A BBBA D BBE A(A0D@o
-0A(A BBBA L KP BBJ B(A0A8G
-8D0A(B BBBD L D 6P BBJ B(A0A8G
-8D0A(B BBBD L X !P BBJ B(A0A8G
-8D0A(B BBBD L P BBB B(A0I8G
-8D0A(B BBBD L 4 O BBB B(A0I8G
-8D0A(B BBBD L X O BBB B(A0I8G
-8D0A(B BBBD D ̶ ,[ BBB D(A0D@0A(A BBB T ` O BIB E(A0A8DV
-8D0A(B BBBA T t SO BIB E(A0A8DV
-8D0A(B BBBA T ̵ !O BIB E(A0A8DV
-8D0A(B BBBA T $ h N BIB E(A0A8DV
-8D0A(B BBBA T | ! N BIB E(A0A8DV
-8D0A(B BBBA T Զ & N BIB E(A0A8DV
-8D0A(B BBBA T , p* [N BBB B(D0F8G2
-8A0A(B BBBK T / N BBB B(D0F8G1
-8A0A(B BBBD T ܷ 05 N BBG B(A0A8J
-8A0A(B BBBA T 4 : N BBB B(D0A8L6
-8A0A(B BBBG T ? O BBB E(F0A8My
-8D0A(B BBBA T D +O BBB E(F0A8Mx
-8D0A(B BBBA 4 p. K^ L I0 Hg l I7 Hj T 8I N BBB J(A0A8G?
-8D0A(B BBBK T L N BEE E(D0A8G
-8D0A(B BBBH T D (R N BBE B(D0A8G
-8D0A(B BBBG D 2W BBB D(A0D@0A(A BBB T V bN BIB E(A0A8G
-8D0A(B BBBA T < \c ?N BBB B(A0A8J+
-8A0A(B BBBG H. K^ x> Hu Ľ @xE Hx W\ AG RAT Px
- O BBB B(D0A8N
-8A0A(B BBBD , d > fO AID W
-DAA D dW BBB D(A0D@0A(A BBB T ܼ N BIB E(A0A8D
-8D0A(B BBBA T 4 (V N BBB E(D0A8G4
-8D0A(B BBBH T 0 O BBB E(D0A8G8
-8D0A(B BBBD L ?O BBB G(D0A8Me
-8D0A(B BBBG , . K^ $ D p ADD0AA$ l ADD0AA$ ADD0AA$ ADD0AAD 1 BBE A(A0D@
-0A(A BBBA D , > BBB D(A0D@
-0A(A BBBA D t F BBB D(A0D@
-0A(A BBBA D N BBB D(A0D@
-0A(A BBBA $ ^ AG b
-AA L 4 ȝ2 cM BGB B(A0K8U
-8A0A(B BBBA < +M BQA A(N`
-(A ABBA < X L BQA A(N`
-(A ABBA < L BQA A(N`
-(A ABBA L D 2 L BGB B(A0K8U
-8A0A(B BBBA < iL BQA A(N`
-(A ABBA D ( BBB D(A0D@
-0A(A BBBA < K BHA J
- DABA < \ ९ K BHA J
- DABA < @ K BHA J
- DABA < K BHA J
- DABA < K BHA J
- DABA D \ ` K BIA D(G
-(D ABBA < 8 K BHA J
- DABA D BBE A(A0D@l
-0A(A BBBA $ $ Pi AAG0]AA$ L q AAG0eAAD t BBE A(A0D@l
-0A(A BBBA $ | AAG0pAA ෯i AG _A 0q AG gA$ $ AAG0xAAT T J BEJ E(D0D8G
-8D0A(B BBBG T R I BBB I(A0A8JN
-8A0A(B BBBA , ï> J AID W
-DAA \ , ï BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBL (į I BBB E(G0F8G
-8D0A(B BBBD < ȯ I BGB D(A0~
-(A BBBK T $ HɯM
- gI BBB M(A0D8Gg
-8A0A(B BBBA , | @ӯ> I AID W
-DAA T Pӯ I BBB E(A0H8K
-8A0A(B BBBA , ٯ> I AID W
-DAA T 4 ٯ I BBB E(I0A8K}
-8D0A(B BBBF T Pܯ gI BBB B(A0H8JH
-8A0A(B BBBA , > I AID W
-DAA L ( oI BEB E(I0H8DP
-8D0A(B BBBD L d GI BEB E(I0H8D@
-8D0A(B BBBA T h( I BBB I(A0A8J
-8A0A(B BBBA , @> FI AID W
-DAA T < P 'I BBB E(I0A8K}
-8D0A(B BBBF T F H BBB B(A0D8N
-8A0A(B BBBA , > &I AID W
-DAA \ BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBT | H BBB M(A0A8J
-8A0A(B BBBA , h> H AID W
-DAA < xR H BBA D(Jn
-(D ABBA < D R H BBA D(Jn
-(D ABBA l H BBB E(A0F8DQ
+8A0A(B BBBJ $ t EA
+AG D 4 BHB E(A0D8D`8A0A(B BBB$ | | EA
+AD D BHB E(A0D8D`8A0A(B BBB< BBG A(G@
+(A ABBF , p5 D \ p t X9 L BKE B(A0A8DPa8A0A(B BBB I L BKE B(A0A8DPq8A0A(B BBB T l H& # BBB B(A0A8J
+8A0A(B BBBA T R BBB G(D0A8M
+8D0A(B BBBC L BBB G(D0A8M
+8D0A(B BBBJ L l BBB G(D0A8M
+8D0A(B BBBJ H. K^ $ < T l , [ o AID t
+DAA , P[ O AID t
+DAA , $ [ / AID t
+DAA , T [ AID t
+DAA , [ AID t
+DAA D Ǘ BBB D(A0D@0A(A BBB T BIB E(A0A8D6
+8D0A(B BBBA T T U BIB E(A0A8D6
+8D0A(B BBBA T 8 # BIB E(A0A8D6
+8D0A(B BBBA T p BIB E(A0A8D6
+8D0A(B BBBA T \ BIB E(A0A8D6
+8D0A(B BBBA T
+ BEE E(D0A8G*
+8D0A(B BBBD T x s BDB B(A0D8G&
+8A0A(B BBBA T d U BDE H(A0A8G$
+8D0A(B BBBA T U BDE H(A0A8G$
+8D0A(B BBBA T 'U W BDE H(A0A8G$
+8D0A(B BBBA T l 0U ä BDE H(A0A8G$
+8D0A(B BBBA p. K^ 9 9 D 9b BBB A(D0Kp
+0A(A BBBJ , T ;> AID W
+DAA D \ ė BBB D(A0D@0A(A BBB T :L N BIE B(A0A8D9
+8D0A(B BBBA L $ ?l BBE B(F0G8G
+8D0A(B BBBI L . K^ d F | F , F0 AF
FK
+ D T G| BBE E(D0A8G
+8D0A(B BBBE T D M| BBE E(D0A8G
+8D0A(B BBBE D t × BBB D(A0D@0A(A BBB T Q BIB E(A0A8GJ
+8D0A(B BBBA T < hW e BBE B(D0A8G
+8D0A(B BBBG T 0\E
+ K BBB E(A0A8N
+8A0A(B BBBA , (f> ѣ AID W
+DAA T 8f. BBB B(A0F8J
+8A0A(B BBBB T t n # BBB B(A0F8J
+8A0A(B BBBF T u BBB H(F0A8J
+8D0A(B BBBD T $ 0{2 ۤ BBE G(A0G8J
+8D0A(B BBBD T | BBB B(A0D8L
+8A0A(B BBBG T c BGB B(A0D8J
+8A0A(B BBBA T , ( BBB G(A0D8M
+8D0A(B BBBB \ {. K^ D t BBE A(A0D@
+0A(A BBBA D 0 BBE A(A0D@
+0A(A BBBA D BBB D(A0D@0A(A BBB T t u ؤ BIB E(A0A8G=
+8D0A(B BBBA T t BBE B(A0G8L-
+8D0A(B BBBG < $ ੫ ? BLD D(G0
+(A ABBE < d * # BED I(KPc
+(D ABBE < BLD D(G0
+(A ABBE < P* BED I(KP_
+(D ABBI T $ @- Ǥ BBM B(A0D8G
+8A0A(B BBBD , | > AID W
+DAA x. K^ 0 Hg 7 Hj @0 Hg P7 Hj p0 Hg < 7 Hj D \ BBE A(A0D@g
+0A(A BBBA D ( BBE A(A0D@g
+0A(A BBBA D BBE A(A0D@g
+0A(A BBBA D 4 8« BBE A(A0D@o
+0A(A BBBA D | « BBE A(A0D@o
+0A(A BBBA D hë BBE A(A0D@o
+0A(A BBBA L 4 ī ۢ BBJ B(A0A8G
+8D0A(B BBBD L `ƫ Ƣ BBJ B(A0A8G
+8D0A(B BBBD L ȫ BBJ B(A0A8G
+8D0A(B BBBD L $ ˫ BBB B(A0I8G
+8D0A(B BBBD L t pͫ BBB B(A0I8G
+8D0A(B BBBD L ϫ r BBB B(A0I8G
+8D0A(B BBBD < D BBD A(D@0(A ABB T T ѫ BIB E(A0A8DV
+8D0A(B BBBA T (֫ BIB E(A0A8DV
+8D0A(B BBBA T ګ BIB E(A0A8DV
+8D0A(B BBBA T \ ޫ BIB E(A0A8DV
+8D0A(B BBBA T 0 U BIB E(A0A8DV
+8D0A(B BBBA T # BIB E(A0A8DV
+8D0A(B BBBA T d BBB E(F0A8My
+8D0A(B BBBA T x BBB E(F0A8Mx
+8D0A(B BBBA T ; BBG B(A0A8J
+8A0A(B BBBA T l H k BBB B(D0F8G2
+8A0A(B BBBK T BBB B(D0F8G1
+8A0A(B BBBD T BBB B(D0A8L6
+8A0A(B BBBG L o. K^ d H
+0 Hg X
+7 Hj T x
+ BBB J(A0A8G?
+8D0A(B BBBK T $
b BEE E(D0A8G
+8D0A(B BBBH T | h H BBE B(D0A8G
+8D0A(B BBBG D b BBB D(A0D@0A(A BBB T BIB E(A0A8G
+8D0A(B BBBA T t ` 5 à BBB B(F0A8G
+8A0A(B BBBE Xm. K^ 09> Hu P9E Hx \ AG RAT D `9
+ BBB B(D0A8N
+8A0A(B BBBD , D> AID W
+DAA D BBB D(A0D@0A(A BBB T C BIB E(A0A8D
+8D0A(B BBBA T l 8Iu g BBE E(A0A8GV
+8D0A(B BBBF T `OU BBE B(D0A8GH
+8D0A(B BBBD T hU ߡ BBB G(D0A8M
+8D0A(B BBBE L j. K^ $ d X ADD0AA$ `Y ADD0AA$ Y ADD0AA$ pZ ADD0AAD Z1 BBE A(A0D@
+0A(A BBBA D L [> BBB D(A0D@
+0A(A BBBA D \F BBB D(A0D@
+0A(A BBBA D ]N BBB D(A0D@
+0A(A BBBA $ $ ^^ AG b
+AA L t 0_2 BGB B(A0K8U
+8A0A(B BBBA < ` BQA A(N`
+(A ABBA < ` BQA A(N`
+(A ABBA < D `a _ BQA A(N`
+(A ABBA L b2 1 BGB B(A0K8U
+8A0A(B BBBA < b BQA A(N`
+(A ABBA D c BBB D(A0D@
+0A(A BBBA < \ d BHA J
+ DABA D Hg t BIA D(G
+(D ABBA D k i BIA D(G
+(D ABBA D , n ^ BIA D(G
+(D ABBA < t r S BHA J
+ DABA < 0u D BHA J
+ DABA < w 5 BHA J
+ DABA D z BBE A(A0D@l
+0A(A BBBA $ T zi AAG0]AA$ | zq AAG0eAAD H{ BBE A(A0D@l
+0A(A BBBA $ {| AAG0pAA 8|i AG _A 4 |q AG gA$ T | AAG0xAAT P} BEJ E(D0D8G
+8D0A(B BBBG T R q BBB I(A0A8JN
+8A0A(B BBBA , T > AID W
+DAA \ \ 0 BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBBL BBB E(A0G8L
+8D0A(B BBBA < 4 P BGB D(A0~
+(A BBBK T t M
+ BBB M(A0D8Gg
+8A0A(B BBBA , 藬> G AID W
+DAA T ' BBB E(A0H8K
+8A0A(B BBBA , T p> M AID W
+DAA T / BBB E(I0A8K}
+8D0A(B BBBF T BBB B(A0H8JH
+8A0A(B BBBA , 4 > . AID W
+DAA L d Щ BEB E(I0H8DP
+8D0A(B BBBD L BEB E(I0H8D@
+8D0A(B BBBA T ( BBB I(A0A8J
+8A0A(B BBBA , \ 賬> AID W
+DAA T Ǜ BBB E(I0A8K}
+8D0A(B BBBF T 0F BBB B(A0D8N
+8A0A(B BBBA , < (> ƛ AID W
+DAA \ D 8 BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBBT G BBB M(A0A8J
+8A0A(B BBBA , $ Ƭ> AID W
+DAA < T ƬH o BBA A(Gp
+(D ABBA < 0ǬH S BBA A(Gp
+(D ABBA l @Ȭ 7 BBB E(A0F8DQ
8A0A(B BBBG j
-8A0A(B BBBA < XR ?H BBA D(Jn
-(D ABBA < 4 xR #H BBA D(Jn
-(D ABBA L t e H BEB B(G0A8Ip
-8D0A(B BBBC < R G BBA D(Jn
-(D ABBA l @ G BEB B(A0A8I
+8A0A(B BBBA < D ɬH ߚ BBA A(Gp
+(D ABBA < ʬH Ú BBA A(Gp
+(D ABBA L ̬e BEB B(G0A8Ip
+8D0A(B BBBC < ͬH i BBA A(Gp
+(D ABBA l T 0ά@ M BEB B(A0A8I
8A0A(B BBBA @
-8A0A(B BBBA T t ^ SG BBB E(A0F8G
-8A0A(B BBBA <
R G BBA D(Jn
-(D ABBA L g F BEB B(G0A8Ip
-8D0A(B BBBE T \ F BBH E(A0F8DG
-8D0A(B BBBH . K^ E\ AG RAD h BBE A(A0D@
-0A(A BBBA D D 0 BBE A(A0D@
-0A(A BBBA D BBE A(A0D@
-0A(A BBBA D BBE A(A0D@
-0A(A BBBA D BBE A(A0D@
-0A(A BBBA D d ` BBE A(A0D@
-0A(A BBBA D 8 BBE A(A0D@
-0A(A BBBA D BBE A(A0D@
-0A(A BBBA T D
C BEB B(H0A8GP
-8A0A(B BBBK , 0%> SD AID W
-DAA T @%
3D BEB B(H0A8GP
-8A0A(B BBBK , $ 2> D AID W
-DAA T T 2
D BEB B(H0A8GP
-8A0A(B BBBK , ?> D AID W
-DAA T ?
D BEB B(H0A8GP
-8A0A(B BBBK , 4 8M> 7E AID W
-DAA D \ HM BBB D(A0D@
-0A(A BBBA T N D BIB E(A0A8DN
-8D0A(B BBBA D R D BIB A(D0D`A
-0D(A BBBA T L U {D BIB E(A0A8DN
-8D0A(B BBBA T Z ID BIB E(A0A8DN
-8D0A(B BBBA P^% h^% D , ^ C BBB D(A0Dp-
-0A(A BBBD < t a2 C BBA A(JY
-(A ABBA T bp C BBB E(D0A8G
-8A0A(B BBBF T jp C BBB E(D0A8G
-8A0A(B BBBF T d 8rc lC BBE E(D0A8G
-8A0A(B BBBB T P}1 dC BBB G(A0D8G
-8A0A(B BBBH T 81 [C BBB G(A0D8G
-8A0A(B BBBH T l A RC BBB G(A0D8G
-8A0A(B BBBH T 1 IC BBB G(A0D8G
-8A0A(B BBBH $ P؝ @C AK
-AA < > Hu \ E Hx | <\ AG RAT
- B BBB B(D0A8N
-8A0A(B BBBA , `> C AID W
-DAA D $ R< BBB D(A0D@0A(A BBB T t (L B BIE B(A0A8D9
-8D0A(B BBBA T wB BBB G(A0D8G
-
-8A0A(B BBBA ם. K^ 4 L T l B BBB B(D0D8Gd
-8A0A(B BBBK T ư, B BBE B(D0A8G
-8D0A(B BBBK L ʰ B BBB J(A0A8G
-8D0A(B BBBH T l ΰ B BBE B(D0A8G
-8D0A(B BBBG D ; BBB D(A0D@0A(A BBB T 0Ӱ& LB BIB E(A0A8G
-8D0A(B BBBA T d ذ &B BBE B(D0A8G
-8D0A(B BBBH T ܰ
- B BBB E(A0I8G
-8A0A(B BBBB T (
- 7B BBB E(A0I8G
-8A0A(B BBBB T l _B BBB E(A0I8G
-8A0A(B BBBB T h
- B BBB E(A0I8G
-8A0A(B BBBB L x B BBB G(D0A8M
-8D0A(B BBBA d ӝ. K^ | :\ AG RAD
-} BBB D(A0D@
-0A(A BBBA D } BBB D(A0D@
-0A(A BBBA D , (
} BBB D(A0D@
-0A(A BBBA D t `} BBB D(A0D@
-0A(A BBBA D BBB D(A0D@
-0A(A BBBA D BBB D(A0D@
-0A(A BBBA D L ( BBB D(A0D@
-0A(A BBBA D p BBB D(A0D@
-0A(A BBBA D 8 BBB D(A0D@0A(A BBB T , p /@ BIB E(A0A8D6
-8D0A(B BBBA T ? BIB E(A0A8D6
-8D0A(B BBBA T ? BIB E(A0A8D6
-8D0A(B BBBA T 4 ! ? BIB E(A0A8D6
-8D0A(B BBBA D P% BBE A(A0D@l
-0A(A BBBA T % ? BBB B(A0D8L7
-8A0A(B BBBF T , * 2? BBB H(A0D8L
-8D0A(B BBBH T . E? BBB H(A0D8L
-8D0A(B BBBH T @3 X? BBB H(A0D8L
-8D0A(B BBBH $ , 7V ADD JAAL \ 7 C? BEB E(I0H8D@
-8D0A(B BBBA L P9 ? BJE B(A0A8D@
-8A0A(B BBBA T :\ > BBB B(H0A8J
-8A0A(B BBBG , T (E> S? AID W
-DAA T 8E\ 3? BBB B(H0A8J
-8A0A(B BBBG , @P> ? AID W
-DAA T PP s? BBB B(H0A8J
-8A0A(B BBBG , d [> ? AID W
-DAA T \\ ? BBB B(H0A8J
-8A0A(B BBBG , g> @ AID W
-DAA ʝ. K^ , g> Hu L (g> Hu l Hg> Hu hg> Hu gE Hx gE Hx gE Hx hE Hx , 3\ AG RA L (h_ AF
-I D l ,3 BBB D(A0D@0A(A BBB T h S> BIB E(A0A8DF
-8D0A(B BBBA T hl !> BIB E(A0A8DF
-8D0A(B BBBA T l p = BIB E(A0A8DF
-8D0A(B BBBA T t = BIB E(A0A8DF
-8D0A(B BBBA T @y
- = BEB B(A0A8N
-8A0A(B BBBH , t > = AID W
-DAA T
- = BEB B(A0A8N
-8A0A(B BBBH , > = AID W
-DAA T ,
- = BEB B(A0A8N
-8A0A(B BBBH , > > AID W
-DAA T Ȗ
- = BEB B(A0A8N
-8A0A(B BBBH , > ?> AID W
-DAA T < > BBG B(A0G8J
-8D0A(B BBBK T s> BBG B(A0G8J
-8D0A(B BBBK T
- > BBG B(A0G8J
-8D0A(B BBBE T D 輱 ? BBG B(A0G8J
-8D0A(B BBBK 0ĝ. K^ XƱd D L
-H G Ʊ KN $ Ʊ^ AG b
-AA D Ʊv BBB A(D0D@
-0A(A BBBA D T ȱv BBB A(D0D@
-0A(A BBBA D Xɱv BBB A(D0D@
-0A(A BBBA D ʱv BBB A(D0D@
-0A(A BBBA , ˱ H L x̱ H l (ͱ H ͱ H D α W= BGB D(M0B
-(A BBBK D α BBB D(A0D@
-0A(A BBBA L D Hб < BIB A(D0D
-0D(A BBBA L ӱ < BIB A(D0D
-0D(A BBBA L ֱ < BIB A(D0D
-0D(A BBBA L 4 8ڱ y< BIB A(D0D
-0D(A BBBA 4 ݱZ Z< BDA G`
- DABJ 4 ޱZ 6< BDA G`
- DABJ D ߱ < BEB A(A0G`l
-0D(A BBBA T < ; BBB B(D0D8G
-8A0A(B BBBA T ; BBB B(D0D8G
-8A0A(B BBBA T ; BBB B(D0D8G
-8A0A(B BBBA T D ; BBB B(D0D8G
-8A0A(B BBBA T P ; BBB B(H0A8JZ
-8A0A(B BBBA T ' ; BBB B(D0A8NS
-8A0A(B BBBH , L > < AID W
-DAA T | ' q< BBB B(D0A8NS
-8A0A(B BBBH , > = AID W
-DAA T ' b= BBB B(D0A8NS
-8A0A(B BBBH , \ '> s> AID W
-DAA T '' S> BBB B(D0A8NS
-8A0A(B BBBH , h8> d? AID W
-DAA L x8; BBB B(A0D8DPd
-8A0A(B BBBA L \ h; BBA D(G0o
+8A0A(B BBBA T Ь^ BBB E(A0F8G
+8A0A(B BBBA < ҬH BBA A(Gp
+(D ABBA L \ Ӭg BEB B(G0A8Ip
+8D0A(B BBBE T 8Ԭ W BBH E(A0F8DG
+8D0A(B BBBH Y. K^ լ լ KN $ ⥗\ AG RAD D լ BBE A(A0D@
+0A(A BBBA D ֬ BBE A(A0D@
+0A(A BBBA D H BBE A(A0D@
+0A(A BBBA D ج BBE A(A0D@
+0A(A BBBA D d ج BBE A(A0D@
+0A(A BBBA D ٬ BBE A(A0D@
+0A(A BBBA D ڬ BBE A(A0D@
+0A(A BBBA D < `۬ BBE A(A0D@
+0A(A BBBA < ޣD BBD A(D@0(A ABB T ۬ / BIB E(A0A8DN
+8D0A(B BBBA T D @ BIB E(A0A8DN
+8D0A(B BBBA T ˕ BIB E(A0A8DN
+8D0A(B BBBA T BIB E(A0A8DN
+8D0A(B BBBA $ % T d 0
O BEB B(H0A8GP
+8A0A(B BBBK , h> AID W
+DAA T x
BEB B(H0A8GP
+8A0A(B BBBK , D > AID W
+DAA T t
BEB B(H0A8GP
+8A0A(B BBBK , > S AID W
+DAA T
3 BEB B(H0A8GP
+8A0A(B BBBK , T @"> AID W
+DAA < P"" BBA A(GU
+(A ABBA @#% L X# F BEE D(A0D
+0A(A BBBH L , , / BEE D(A0D
+0A(A BBBH L | 4 BEE D(A0D
+0A(A BBBH T = BBB G(A0D8G
+8A0A(B BBBH T $ PEg BBB G(A0D8G&
+8A0A(B BBBG T | hN BBB G(A0D8G
+8A0A(B BBBH T 0V BBB G(A0D8G
+8A0A(B BBBH $ , P AK
+AA , ]> Hu L ]E Hx l :\ AG RAT ^
+ p BBB B(D0A8N
+8A0A(B BBBA , Xh> AID W
+DAA D BBB D(A0D@0A(A BBB T hL N BIE B(A0A8D9
+8D0A(B BBBA T mb BBB B(F0D8G
+8A0A(B BBBA PO. K^ $ } < } T | } 7 BBB B(D0D8G_
+8A0A(B BBBH T , K BBE B(D0A8G
+8D0A(B BBBK L , 1 BBB J(A0A8G
+8D0A(B BBBH D T | BBB D(A0D@0A(A BBB T @& BIB E(A0A8G
+8D0A(B BBBA T ̔ BBE B(D0A8G
+8D0A(B BBBH T t
+ BBB B(F0A8G
+8A0A(B BBBB T
+ ۔ BBB B(F0A8G
+8A0A(B BBBJ T $
+ BBB B(F0A8G
+8A0A(B BBBB T | (
+ 3 BBB B(F0A8G
+8A0A(B BBBB T ` [ BBB G(D0A8M
+8D0A(B BBBB K. K^ \ AG RAD < `ŭ} BBB D(A0D@
+0A(A BBBA D ƭ} BBB D(A0D@
+0A(A BBBA D ǭ} BBB D(A0D@
+0A(A BBBA D ɭ} BBB D(A0D@
+0A(A BBBA D \ @ʭ BBB D(A0D@
+0A(A BBBA D ˭ BBB D(A0D@
+0A(A BBBA D ̭ BBB D(A0D@
+0A(A BBBA D 4
+ έ BBB D(A0D@
+0A(A BBBA D |
+ ~v BBB D(A0D@^0A(A BBB T ϭ ˒ BIB E(A0A8D6
+8D0A(B BBBA T D
+ Pӭ BIB E(A0A8D6
+8D0A(B BBBA T
+ g BIB E(A0A8D6
+8D0A(B BBBA T
+ ۭ 5 BIB E(A0A8D6
+8D0A(B BBBA D $ ߭ BBE A(A0D@l
+0A(A BBBA $ l V ADD JAAL BEB E(I0H8D@
+8D0A(B BBBA L H k BJE B(A0A8D@
+8A0A(B BBBA T \ C BBB B(A0D8L7
+8A0A(B BBBF T \ S BBB B(H0A8J
+8A0A(B BBBG ,
> AID W
+DAA T <
\ BBB B(H0A8J
+8A0A(B BBBG ,
> AID W
+DAA T
ӑ BBB B(H0A8J
+8A0A(B BBBG , x > 3 AID W
+DAA T L \ BBB B(H0A8J
+8A0A(B BBBG , > s AID W
+DAA T S BBB H(A0D8L
+8D0A(B BBBH T , ` BBB H(A0D8L
+8D0A(B BBBH T p m BBB H(A0D8L
+8D0A(B BBBH C. K^ !> Hu !> Hu !> Hu , "> Hu L 0"E Hx l `"E Hx "E Hx "E Hx \ AG RA "_ AF
+I T 4 #
+ " BEB B(A0A8N
+8A0A(B BBBH , ,> g AID W
+DAA T ,
+ G BEB B(A0A8N
+8A0A(B BBBH , 6> AID W
+DAA T D 6
+ l BEB B(A0A8N
+8A0A(B BBBH , @> AID W
+DAA T @
+ BEB B(A0A8N
+8A0A(B BBBH , $ `J> ֑ AID W
+DAA D , BBB D(A0D@0A(A BBB T (J n BIB E(A0A8DF
+8D0A(B BBBA T pN < BIB E(A0A8DF
+8D0A(B BBBA T L R
+ BIB E(A0A8DF
+8D0A(B BBBA T W ؐ BIB E(A0A8DF
+8D0A(B BBBA T H[ BBG E(A0G8GY
+8D0A(B BBBK T T c BBG E(A0G8GY
+8D0A(B BBBK T Hm G BBG E(A0G8GY
+8D0A(B BBBK T v
+ BBG E(A0G8Go
+8D0A(B BBBE 4 <. K^ L d D L
+H G l @ KN $ H^ AG b
+AA D v BBB A(D0D@
+0A(A BBBA D v BBB A(D0D@
+0A(A BBBA D < v BBB A(D0D@
+0A(A BBBA D (v BBB A(D0D@
+0A(A BBBA D `~ BBB A(D0D@
+0A(A BBBA D ~ BBB A(D0D@
+0A(A BBBA D \ Ј~ BBB A(D0D@
+0A(A BBBA D ~ BBB A(D0D@
+0A(A BBBA D @ / BGB D(M0B
+(A BBBK D 4 BBB D(A0D@
+0A(A BBBA L BIB A(D0D
+0D(A BBBA L P BIB A(D0D
+0D(A BBBA L D p BIB A(D0D
+0D(A BBBA L Q BIB A(D0D
+0D(A BBBA 4 @Z 2 BDA G`
+ DABJ 4 hZ BDA G`
+ DABJ D T BEB A(A0G`l
+0D(A BBBA T h BBB B(D0D8G
+8A0A(B BBBA T Р BBB B(D0D8G
+8A0A(B BBBA T L 8 BBB B(D0D8G
+8A0A(B BBBA T BBB B(D0D8G
+8A0A(B BBBA T BBB B(H0A8JZ
+8A0A(B BBBA T T ' X BBB B(D0A8NS
+8A0A(B BBBH , h®> i AID W
+DAA T x®' I BBB B(D0A8NS
+8A0A(B BBBH , 4 PӮ> Z AID W
+DAA T d `Ӯ' : BBB B(D0A8NS
+8A0A(B BBBH , 8> K AID W
+DAA T H' + BBB B(D0A8NS
+8A0A(B BBBH , D > < AID W
+DAA L L 0; BBB B(A0D8DPd
+8A0A(B BBBA L BBA D(G0o
(A ABBK K
-(A ABBG L <K BBB B(A0A8G@d
-8A0A(B BBBA T = T> BBE E(A0D8G
-8A0A(B BBBK L \ H L> BEE E(D0D8J_
-8A0A(B BBBD L `J !> BEE E(D0D8Jh
-8C0A(B BBBI T 0LE = BBB B(D0A8Lp
-8A0A(B BBBE T T ([I = BBB B(D0A8Lp
-8A0A(B BBBE T j5 = BBB B(D0A8Lp
-8A0A(B BBBE T yE = BBB B(D0A8Lp
-8A0A(B BBBE T . K^ l 臲R HI (Y HL #\ AG RAT H s= BBB B(D0A8N
-8A0A(B BBBI , , > ;> AID W
-DAA D T n# BBB D(A0D@0A(A BBB T hL = BIE B(A0A8D9
-8D0A(B BBBA T ` = BBB B(D0A8L
-8A0A(B BBBB L ض. K^ d KN | $ HN $\ AG RAD ଲ BBB D(A0D@
-0A(A BBBA L 8 g= BIB A(D0D
-0D(A BBBA L T H= BIB A(D0D
-0D(A BBBA L ش )= BIB A(D0D
-0D(A BBBA L (
-= BIB A(D0D
-0D(A BBBA L D x < BBB E(A0A8G
-8D0A(B BBBB L ؼ BBB B(A0A8Gp8A0A(B BBB $ XV ADD JAA Hw $ Hw D P Hw d ² Hw IJ Hz Ų Hz Ʋ Hz `Ȳ Hz L ɲ BOB A(A0]
+(A ABBG L K BBB B(A0A8G@d
+8A0A(B BBBA d d , BBA A(GO
+(A ABBF M
+(A ABBE P
+(F ABBA d BBA A(GX
+(C ABBC w
+(C ABBA v
+(C ABBA L 4! BEE D(A0D
+0A(A BBBH T ! 05 BBB B(D0A8Lp
+8A0A(B BBBE T ! BBB B(D0A8Lp
+8A0A(B BBBE T 4" p%I BBB B(D0A8Lp
+8A0A(B BBBE T " h4E BBB B(D0A8Lp
+8A0A(B BBBE # `0. K^ # HCR HI # CY HL $ \ AG RAT \# C BBB B(D0A8N
+8A0A(B BBBI , # X> ֏ AID W
+DAA D $ : BBB D(A0D@0A(A BBB T ,$ WL n BIE B(A0A8D9
+8D0A(B BBBA T $ \ ? BBB B(D0A8L
+8A0A(B BBBA % .. K^ % 0h KN % ߄ HN % ބ\ AG RAD & h BBB D(A0D@
+0A(A BBBA L % Xi BIB A(D0D
+0D(A BBBA L % l BIB A(D0D
+0D(A BBBA L ,& o Ŏ BIB A(D0D
+0D(A BBBA L |& Hs BIB A(D0D
+0D(A BBBA L & v BBB E(A0A8G
+8D0A(B BBBB L ' w BBB B(A0A8Gp8A0A(B BBB $ D( xyV ADD JAA l( y Hw ( { Hw ( p| Hw ( } Hw ( 0 Hz ) Hz ,) Hz L) Hz L l) BOB A(A0]
(A BBBG Y
-(A BBBH d T @ʲ0 BBE B(D0E8D@P
+(A BBBH d ) `0 BBE B(D0E8D@P
8F0A(B BBBA
-8D0A(B BBGA ˲T < P˲ BBG A(G@
-(A ABBH < ˲ BBE A(D0
-(A BBBA , T ̲8 WEAG , Ͳ8 WEAG , β8 WEAG , в8 WEAG Ѳ L 4 Ҳ +9 BEB E(I0H8D@
-8D0A(B BBBA 4 | (Բ AIG
+8D0A(B BBGA $* (T < <* p BBG A(G@
+(A ABBH < |* BBE A(D0
+(A BBBA , * 8 WEAG , * 8 WEAG , + 8 WEAG , L+ 8 WEAG |+ 0 L * ȍ NJ BEB E(I0H8D@
+8D0A(B BBBA 4 + H AIG
FAI I
-FAH Բ Xֲ ײ 4 ٲ FEH G(
+FAH , 4, x L, 4 d, FEH G(
- ABBG L 4 ۲U BKB E(D0A8D
-8A0A(B BBBH D ܲ BBE D(A0M`0A(A BBB T ݲ'
7 BBB B(A0A8J
+ ABBG L , 0U BKB E(D0A8D
+8A0A(B BBBH D , @ BBE D(A0M`0A(A BBB T \, '
O BBB B(A0A8J
-8A0A(B BBBF T , 5 7 BBB B(D0A8N }
-8A0A(B BBBF , > 9 AID W
-DAA 4 FEH G(
+8A0A(B BBBF T , ९5 BBB B(D0A8N }
+8A0A(B BBBF , - > u AID W
+DAA 4 . FEH G(
- ABBG L @U BKB E(D0A8D
-8A0A(B BBBH D 4 P BBE D(A0M`0A(A BBB T
-'
8 BBB B(A0A8J
+ ABBG L L. `¯U BKB E(D0A8D
+8A0A(B BBBH D . pï BBE D(A0M`0A(A BBB T . 8ů'
BBB B(A0A8J
-8A0A(B BBBF T 5 9 BBB B(D0A8N }
-8A0A(B BBBF , 4 1> ; AID W
-DAA 4 \ 1 FEH G(
+8A0A(B BBBF T d. ү5 BBB B(D0A8N }
+8A0A(B BBBF , . > AID W
+DAA 4 / FEH G(
- ABBG L p3U BKB E(D0A8D
-8A0A(B BBBH D 4 BBE D(A0M`0A(A BBB T 4 H6'
: BBB B(A0A8J
+ ABBG L / U BKB E(D0A8D
+8A0A(B BBBH D L0 BBE D(A0M`0A(A BBB T / h'
BBB B(A0A8J
-8A0A(B BBBF T C5 V: BBB B(D0A8N }
-8A0A(B BBBF , ^> E< AID W
-DAA 4
- ^ FEH G(
+8A0A(B BBBF T 0 @5 BBB B(D0A8N }
+8A0A(B BBBF , l0 (> AID W
+DAA 4 t1 8 FEH G(
- ABBG L D
- _U BKB E(D0A8D
-8A0A(B BBBH D
- ` BBE D(A0M`0A(A BBB T xb'
U; BBB B(A0A8J
+ ABBG L 1 U BKB E(D0A8D
+8A0A(B BBBH D 1 BBE D(A0M`0A(A BBB T l1 '
BBB B(A0A8J
-8A0A(B BBBF T < Po5 ; BBB B(D0A8N }
-8A0A(B BBBF , 8> {= AID W
-DAA d HA BBB E(D0E8D@P
+8A0A(B BBBF T 1 p*5 ( BBB B(D0A8N }
+8A0A(B BBBF , 2 XE> AID W
+DAA d $3 hEA BBB E(D0E8D@P
8F0A(B BBBA
-8D0A(B BBGA , $ 0 ADD ]
-AAA T T T t
- 苳 < BBB B(A0D8GE
-8A0A(B BBBE T
- < BBB B(A0D8GE
-8A0A(B BBBE T $ X i< BBB B(A0D8GE
-8A0A(B BBBE T | H< BBB B(A0D8GE
-8A0A(B BBBE
ؠ BBF D(A0G@I
+8D0A(B BBGA , 3 PF ADD ]
+AAA 3 FT T 2 G G BBB B(A0D8GE
+8A0A(B BBBE T T3 @L & BBB B(A0D8GE
+8A0A(B BBBE T 3 xQ BBB B(A0D8GE
+8A0A(B BBBE T 4 V BBB B(A0D8GE
+8A0A(B BBBE 45 [ BBF D(A0G@I
0A(A BBBI }
0D(A BBBH D
0F(A BBBG c
-0A(A BBBE D T BBE E(D0D@
-0E(A BBBA T W; BBB E(D0F8G
-8A0A(B BBBK T 0 ; BBB E(D0F8G
-8A0A(B BBBK T T
; BBB E(D0F8G
-8A0A(B BBBK T
ų ; BBB E(D0F8G
-8A0A(B BBBK X. K^ \ AG RA, 4 `г ADD AA , d ѳ ADD AA D BBB D(A0D@0A(A BBB T ӳ ; BIB E(A0A8DF
-8D0A(B BBBA , 4 P׳8 WEAG d `س 4 | ٳ AIG
+0A(A BBBE D 5 ] BBE E(D0D@
+0E(A BBBA T ,5 ] BBB E(D0F8G
+8A0A(B BBBC T 5 @i BBB E(D0F8G
+8A0A(B BBBC T 5 t 7 BBB E(D0F8G
+8A0A(B BBBC T 46 Y BBB E(D0F8G
+8A0A(B BBBC d7 . K^ $ |7 `^ AG b
+AA D 7 BBB D(A0D@
+0A(A BBBA D 7 BIB A(D0D`7
+0D(A BBBA D \7 ь BIB A(D0D`7
+0D(A BBBA |8 D H; 8 D H; 8 K H> 8 K H> 8 @T 9 ,9 o 4 D9 x BDD D0
+ AABH 4 |9 AIG
FAI I
-FAH 4 ڳ FEH G(
+FAH 9 9 Po 4 9 BDD D0
+ AABH 4 : P FEH G(
- ABBG L ܳU BKB E(D0A8D
-8A0A(B BBBH D < (ݳ BBE D(A0M`0A(A BBB T G) 9 BEB B(A0A8N*
-8A0A(B BBBA , > < AID W
-DAA $ V ADD JAAL < 0 ; BEB E(I0H8D@
-8D0A(B BBBA , BFA D0 AABL @
- BBB E(A0A8G`h
-8D0A(B BBBA T /; BBE B(A0A8L
-8A0A(B BBBA \ (. K^ t \ AG RAD BBE A(A0D@u
-0A(A BBBA D x BBE A(A0D@}
-0A(A BBBA T , W : BBI B(A0A8J
-8A0A(B BBBA , !> : AID W
-DAA D BBB D(A0D@0A(A BBB T [: BIB E(A0A8D
-8D0A(B BBBA \ L H& KLE B(A0A8DPi8A0A(B BBBIP \ x( KLE B(A0A8DPi8A0A(B BBBIP d *E BBB B(A0A8DP
+ ABBG L T: ؞U BKB E(D0A8D
+8A0A(B BBBH D : 蟰 BBE D(A0M`0A(A BBB T : & ? BBE B(A0A8NB
+8A0A(B BBBA , l: Ȱ> ƌ AID W
+DAA 4 t; Ȱ FEH G(
+
+ ABBG L ; ɰU BKB E(D0A8D
+8A0A(B BBBH D ; ʰ BBE D(A0M`0A(A BBB T l; x̰& BBE B(A0H8G4
+8A0A(B BBBA , ; > Y AID W
+DAA $ < V ADD JAA < T L 4< @ BEB E(I0H8D@
+8D0A(B BBBA , \= BFA D0 AAB4 = P AIG
+FAA I
+FAA d = BBB B(A0A8Ge
+8A0A(B BBBH
+8A0A(B BBBA T T= P BBE B(A0A8Lq
+8A0A(B BBBA L > BBB E(A0A8GX
+8D0A(B BBBA T = BBE B(A0A8Lq
+8A0A(B BBBA ,? . K^ D? k\ AG RAD d? ( BBE A(A0D@u
+0A(A BBBA D ? BBE A(A0D@}
+0A(A BBBA T ? X W BBI B(A0A8J
+8A0A(B BBBA , t? `> AID W
+DAA D |@ k BBB D(A0D@0A(A BBB T ? ( S BIB E(A0A8D
+8D0A(B BBBA \ A KLE B(A0A8DPi8A0A(B BBBIP \ |A KLE B(A0A8DPi8A0A(B BBBIP d A E BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA l | +B 9 BBB J(D0D8D4
+8F0A(B BBBA l lA B BBB J(D0D8D4
8D0A(B BBBC i
-8A0A(B BBBE L p/P 8 BBB B(A0A8J
-8A0A(B BBBA 4 . K^ L XBO HF l BV HI T B< ?9 BBE B(D0A8G
-8D0A(B BBBB T G| %9 BBE E(D0A8G
-8D0A(B BBBF T D L< 9 BBE B(D0A8G
-8D0A(B BBBB D
BBB D(A0D@0A(A BBB T xQ 8 BIB E(A0A8D^
-8D0A(B BBBA T < U w8 BIB E(A0A8DV
-8D0A(B BBBA T (Zh G8 BGH E(A0A8G
-8D0A(B BBBK T @t 9 BBF B(D0A8Nk
-8A0A(B BBBA , D }> 9 AID W
-DAA , t }> 9 AID W
-DAA H. K^ } } L }r v9 BBB B(E0A8J@
-8A0A(B BBBA 4 \ AG RA4 T ~l ADG b
-AAH l
-AAA T ~ 8 BBB B(A0I8G
-8D0A(B BBBG T 8 BBB B(A0I8G
-8D0A(B BBBG T D h 8 BEE E(D0A8G
-8D0A(B BBBF T 8 BBE B(D0A8G
-8D0A(B BBBH T X 8 BBE B(D0A8G
-8D0A(B BBBG T L q8 BEE E(D0A8G
-8D0A(B BBBF T H W8 BBB B(D0A8G
-8D0A(B BBBD D
- BBB D(A0D@0A(A BBB T D 8 BIB B(A0D8D
-8D0A(B BBBA T P 8 BIB B(A0D8D
-8D0A(B BBBA L 趴 ]8 BBE G(A0A8G
-8A0A(B BBBA < ؕ. K^ T 0ȴ DV $ l 8ȴ^ AG b
-AA $ pȴ AG u
-AA $ ɴ AG }
-AA T ɴ 7 BBB E(D0D8J
-8D0A(B BBBD T D Ҵ 7 BBB B(A0D8J
-8D0A(B BBBH | ! ٴ BBB B(D0A8DP
+8A0A(B BBBE L A BBB B(A0A8J
+8A0A(B BBBA C . K^ C 3O HF <C @3V HI T B 3< / BBE B(D0A8G
+8D0A(B BBBB T B h8< BBE B(D0A8G
+8D0A(B BBBB D D Ti BBB D(A0D@0A(A BBB T |C = BIB E(A0A8DV
+8D0A(B BBBA T C `A BIB E(A0A8D^
+8D0A(B BBBA T ,D E{ O BGE B(A0A8G
+8A0A(B BBBI T D _ BBF B(D0A8Nk
+8A0A(B BBBA , D (i> AID W
+DAA , E 8i> AID W
+DAA F . K^ ,F 0i DF (i L E 0ir z BBB B(E0A8J@
+8A0A(B BBBA F h\ AG RAT E @j BBB B(A0I8G
+8D0A(B BBBG T LF m
BBB B(A0I8G
+8D0A(B BBBG T F p BEE E(D0A8G
+8D0A(B BBBF T F u BBE B(D0A8G
+8D0A(B BBBH T TG z lj BBE B(D0A8G
+8D0A(B BBBG T G BEE E(D0A8G
+8D0A(B BBBF T H E BBE B(A0A8G
+8D0A(B BBBC D 4I Vf BBB D(A0D@0A(A BBB T H ` BIB B(A0D8D
+8D0A(B BBBA T H щ BIB B(A0D8D
+8D0A(B BBBA L TI BBB B(D0A8G
+8A0A(B BBBA |J
+. K^ J 贱 DV $ J ^ AG b
+AA $ J ( AG u
+AA $ J AG }
+AA T LJ X BBB E(D0D8J
+8D0A(B BBBD T J 4
# BBB B(D0A8J
+8D0A(B BBBH | K α BBB B(D0A8DP
8A0A(B BBBB U
8G0A(B BBBK
-8A0A(B BBBI " 0ܴ D ," 8ܴ BBB D(A0D@
-0A(A BBBA D | ݴ 6 BIB A(D0D`+
-0D(A BBBA T H 6 BIB E(A0A8DF
-8D0A(B BBBA T ! 6 BBB L(A0A8J
-8A0A(B BBBG 4 t! 8x 6 BAI H
-ABA 4 ! x 6 BAI H
-ABA T ! d6 BBB B(A0A8G
-8A0A(B BBBK L <" @. Z6 BBB E(A0A8J
-8A0A(B BBBG $ . K^ $ $ ^ AG b
-AA $ $ @ AG u
-AA $ $ AG }
-AA < % :D BBD A(D@0(A ABB T \# 0 (6 BIB B(A0D8Df
-8D0A(B BBBA 4 #
q 5 BAI A
-ABA L #
5 BBE B(A0A8J
-8A0A(B BBBG 4& @. K^ L& x# DV d& #Z $ $ #n T6 AFD@ L $ $r 46 BBB B(E0A8J@
-8A0A(B BBBA $ & @%^ AG b
-AA $ ' x% AG
-AA $ D' 0& AG
-AA $ l' & AG M
-AA T % P' Y5 BBB B(A0A8G
-8A0A(B BBBA D ' . BBB D(A0D@
-0A(A BBBA T <& @0 35 BIB E(A0A8D6
-8D0A(B BBBA D & x4 5 BIB A(D0D`2
-0D(A BBBA T & @7 4 BBB B(A0I8G
-8D0A(B BBBF T 4' ; 4 BBB B(A0I8G
-8D0A(B BBBF \ ) ?m BBD D(J@
+8A0A(B BBBI TL Xѱ < lL dD BBD A(D@0(A ABB T K ѱ j BIB E(A0A8DF
+8D0A(B BBBA T ,L hձ 8 BIB E(A0A8D6
+8D0A(B BBBA T L ٱ| BBB L(A0A8J
+8A0A(B BBBG 4 L x % BAI H
+ABA 4 M x BAI H
+ABA T LM X ݇ BBB B(A0A8G
+8A0A(B BBBK L M . Ӈ BBB E(A0A8J
+8A0A(B BBBG N . K^ $ N ^ AG b
+AA $ O AG u
+AA $ 4O h AG }
+AA D \O @c BBB D(A0D@0A(A BBB T N BIB B(A0D8Df
+8D0A(B BBBA 4 $O q g BAI A
+ABA L \O hU C BBE B(A0A8J
+
+8A0A(B BBBA P . K^ L O `%r BBB B(E0A8J@
+8A0A(B BBBA $ P &^ AG b
+AA $ Q & AG
+AA $ <Q ' AG
+AA , P 8( I AAG@A
+AAA $ Q ( AG M
+AA T P ( BBE E(A0A8G
+8D0A(B BBBE D R h+ BBB D(A0D@
+0A(A BBBA T Q , p BIB E(A0A8D6
+8D0A(B BBBA D Q 0 > BIB A(D0D`2
+0D(A BBBA T $R 3W BHE E(D0A8GG
+8A0A(B BBBA T |R ; BBB B(A0I8G
+8D0A(B BBBF T R @ BBB B(A0I8G
+8D0A(B BBBF \ T XDm BBD D(J@
(A ABBD T
(A ABBF _
-(A ABBK T ' @ ]4 BGE B(D0G8I
-8D0A(B BBBK <* PB Az
-E A T d( HP 4 BBB B(D0D8J
-8A0A(B BBBA * X AN * Ћ. K^ * xX + pX L $) xXq N4 BBB B(A0I8G
-8D0A(B BBBD L t) Zq 84 BBB B(A0I8G
-8D0A(B BBBD L ) \q "4 BBB B(A0I8G
-8D0A(B BBBD D , BBB D(A0D@0A(A BBB T \* ^b 3 BIE B(A0A8DQ
-8D0A(B BBBA T * c\ 3 BIE B(A0A8DI
-8D0A(B BBBA T + hb `3 BIE B(A0A8DQ
-8D0A(B BBBA T d+ m /3 BBH B(D0F8GN
-8D0A(B BBBF - . K^ - t - t T , t /3 BBE E(A0A8G
-8D0A(B BBBE L \, q 4 BBB B(A0I8G
-8D0A(B BBBD D . BBB D(A0D@0A(A BBB T , L 3 BIE B(A0A8D9
-8D0A(B BBBA T L- B 3 BBB B(A0A8J
-8A0A(B BBBA / h. K^ / p / h , / p MPAC , 0 JXAD T L. j 3 BBB B(A0D8R
-8A0A(B BBBD , . > 3 AID W
-DAA D 0 b BBB D(A0D@0A(A BBB T / L 3 BIE B(A0A8D9
-8D0A(B BBBA L t/ E _3 BBH G(D0A8G
-8D0A(B BBBE 1 x. K^ 1 1 T 0 x 3 BBB B(D0A8N9
-8A0A(B BBBB , d0 õ> Y4 AID W
-DAA D 2 p BBB D(A0D@0A(A BBB T 0 õL 3 BIE B(A0A8D9
-8D0A(B BBBA T 41 ȵ2 3 BBB B(A0A8J
-8A0A(B BBBA 3 . K^ L 3 X BBB B(A0A8Dpn
-8A0A(B BBBA L 3 ص BBB B(A0D8G
-8A0A(B BBBG L <4 h۵~ BBB E(A0A8G
-8A0A(B BBBG L 4 ߵ BBE B(A0A8D`t8A0A(B BBB L 4 BEB B(A0A8Dd
-8A0A(B BBBA L ,5 BBE B(A0A8Di8A0A(B BBB $ |5 X^ AG b
-AA $ 5 AG r
-AA $ 5 AG r
-AA $ 5 AG r
-AA $ 6 ( AG r
-AA $ D6 AG z
-AA $ l6 H AG z
-AA $ 6 AG z
-AA $ 6 x AG z
-AA D 4 0 BEB A(A0Nj
-0A(A BBBA L 45 0 BBB L(D0C8M
-8A0A(B BBBJ T 5 1 BBE E(A0A8G
-8D0A(B BBBE $ 7 0 AG L
-AA \ 6 $ b1 BBB A(I0D@
+(A ABBK T S hEP BBE E(D0A8M
+8D0A(B BBBG T `VZ AK
+L AT T V2
BBB E(A0D8I
+8A0A(B BBBA 4U d AN TU x . K^ lU `d U Xd L T `dq BBB B(A0I8G
+8D0A(B BBBD L U fq BBB B(A0I8G
+8D0A(B BBBD L dU hq { BBB B(A0I8G
+8D0A(B BBBD T U j g BBH B(D0F8Gd
+8D0A(B BBBH D V ] BBB D(A0D@0A(A BBB T TV q k BIB E(A0A8DF
+8D0A(B BBBA T V Xu 9 BIB E(A0A8DO
+8D0A(B BBBA T W y BIB E(A0A8DN
+8D0A(B BBBA 4X . K^ LX ~ dX } T W ~ BBE E(A0A8G
+8D0A(B BBBE L W q j BBB B(A0I8G
+8D0A(B BBBD D $Y ] BBB D(A0D@0A(A BBB T X L BIE B(A0A8D9
+8D0A(B BBBA T X x ۄ BBB B(A0A8J"
+8A0A(B BBBH Z . K^ 4Z LZ , dZ MPAC , Z JXAD T Y xj BBB B(A0D8R
+8A0A(B BBBD , DZ > @ AID W
+DAA D L[ \ BBB D(A0D@0A(A BBB T Z XL BIE B(A0A8D9
+8D0A(B BBBA L [ P BBE G(A0G8G
+8D0A(B BBBG <\ . K^ T\ l\ T [ x BBB B(D0A8N9
+8A0A(B BBBB , \ Ͳ> AID W
+DAA D ] \ BBB D(A0D@0A(A BBB T |\ ̲L 1 BIE B(A0A8D9
+8D0A(B BBBA T \ Ѳ BBB B(A0A8J
+8A0A(B BBBA ^ . K^ L ^ BBB B(A0A8Dpn
+8A0A(B BBBA L l^ p BBB B(A0D8G
+8A0A(B BBBG L ^ 0~ BBB E(A0A8G
+8A0A(B BBBG L _ ` BBE B(A0A8D`t8A0A(B BBB L \_ BEB B(A0A8Dd
+8A0A(B BBBA L _ BBE B(A0A8Di8A0A(B BBB $ _ ^ AG b
+AA $ $` X AG r
+AA $ L` AG r
+AA $ t` h AG r
+AA $ ` AG r
+AA $ ` x AG z
+AA $ ` AG z
+AA $ a AG z
+AA $ <a @ AG z
+AA D ` BEB A(A0Nj
+0A(A BBBA L ` P BBB L(D0C8M
+8A0A(B BBBJ T $a BBE E(A0A8G
+8D0A(B BBBE $ Tb AG L
+AA \ a ` $ BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ d6 h$ #1 BBB A(I0D@
+0D(A BBBA \ b 0
+$ S BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ 6 8$ 0 BBB A(I0D@
+0D(A BBBA \ db $ BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ $7 $ 0 BBB A(I0D@
+0D(A BBBA \ b $ Ձ BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ 7 $ f0 BBB A(I0D@
+0D(A BBBA \ $c $ BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ 7 $ '0 BBB A(I0D@
+0D(A BBBA \ c p
$ W BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ D8 x$ / BBB A(I0D@
+0D(A BBBA \ c @$ BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ 8 H$ / BBB A(I0D@
+0D(A BBBA \ Dd $ ـ BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ 9 $ j/ BBB A(I0D@
+0D(A BBBA \ d $ BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ d9 $ +/ BBB A(I0D@
+0D(A BBBA \ e $ [ BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ 9 $ . BBB A(I0D@
+0D(A BBBA \ de $ BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ $: $ . BBB A(I0D@
+0D(A BBBA \ e P$ BBB A(I0D@
0D(A BBBG W
-0D(A BBBA D |< X
- BBB D(A0D@
-0A(A BBBA 4 : k &. BHA GP
- DABA 4 ; k . BHA GP
- DABA 4 <; k - BHA GP
- DABA 4 t; Xk - BHA GP
- DABA 4 ; k - BHA GP
- DABA 4 ; r - BHA GP
- DABA 4 < r l- BHA GP
- DABA 4 T< Xr M- BHA GP
- DABA 4 < r .- BHA GP
- DABA < < - BIA A(G`
-(D ABBA < = 8 , BIA A(G`
-(D ABBA < D= , BIA A(G`
-(D ABBA T |? U BAA G0
+0D(A BBBA D f BBB D(A0D@
+0A(A BBBA 4 lf xk V BHA GP
+ DABA 4 f k 7 BHA GP
+ DABA 4 f k BHA GP
+ DABA 4 g k ~ BHA GP
+ DABA 4 Lg Xk ~ BHA GP
+ DABA 4 g r ~ BHA GP
+ DABA 4 g r ~ BHA GP
+ DABA 4 g r }~ BHA GP
+ DABA 4 ,h h r ^~ BHA GP
+ DABA < dh ?~ BIA A(G`
+(D ABBA < h ! ~ BIA A(G`
+(D ABBA < h `" } BIA A(G`
+(D ABBA T i #U BAA G0
AABA
AABK f
- AABF T = B, BEE B(G0D8G
-8D0A(B BBBD T 4> )E h, BBE G(A0G8G.
-8D0A(B BBBF @ 1 AR @ 1 AY T > 1y S, BEB B(D0A8J{
-8D0A(B BBBF T $? < , BEB H(A0F8G(
-8D0A(B BBBD tA v. K^ $ A E^ AG b
-AA $ A @E AG u
-AA $ A E AG }
-AA < B D BBD A(D@0(A ABB T L@ 0F + BIB B(A0D8D
-8D0A(B BBBA 4 @ Jq + BAI A
-ABA T @ K: + BBB B(A0A8J
-o
-8A0A(B BBBC ,C t. K^ DC f \C f T |A f , BBB B(A0D8N
-8A0A(B BBBC , A (o> - AID W
-DAA D C BBB D(A0D@0A(A BBB T LB nL , BIE B(A0A8D9
-8D0A(B BBBA T B s" , BBB B(A0A8J
-8A0A(B BBBA D s. K^ E H $E @ $ <E H^ AG b
-AA 4 lC - BHA G@@
- DABA 4 C `- BHA G@@
- DABA T C p 9- BBE B(D0A8G
-8D0A(B BBBG D ,F v BBB D(A0D@^0A(A BBB T |D , BIB E(A0A8DF
-8D0A(B BBBA D D 8 , BEI A(D0G
-0D(A BBBA D E , BBE H(D0G
-0D(A BBBA D dE H
- , BBE H(D0G
-0D(A BBBK D E * , BBE H(D0G
-0D(A BBBK D E , BBE D(H0G
-0D(A BBBA D <F , BBE D(H0G
-0D(A BBBA D F , BBE H(D0G
-0D(A BBBA D F @ , BBE H(D0G
-0D(A BBBA D G }, BBE D(H0G
-0D(A BBBA D \G x, BBE D(H0G
-0D(A BBBA D G s, BBE D(H0G
-0D(A BBBA L G P| n, BBE D(H0G#
-0D(A BBBF T <H c, BBB G(D0A8M
-8D0A(B BBBB T H (϶y , BBB B(A0I8G>
-8A0A(B BBBG J @m. K^ $ J 8Ӷ^ AG b
-AA D $K pӶ BBB D(A0D@
-0A(A BBBA D lK ֶ BBB D(A0D@%
-0A(A BBBA D I ض + BBB D(A0Gn
-0D(A BBBE D J (ڶ + BBB D(A0Gn
-0D(A BBBE T LJ ۶ g+ BEE E(D0A8G2
-8D0A(B BBBD < J h; O+ BBA A(JO
-(A ABBA d L h BLB B(A0A8D@c
+ AABF T |i ' r} BEE B(G0D8G
+8D0A(B BBBD T i 2E } BBE G(A0G8G.
+8D0A(B BBBF k : AR $k : AY T lj :B } BBB E(D0A8J{
+8D0A(B BBBF T j E } BEB H(A0F8GR
+8D0A(B BBBB k . K^ $ l M^ AG b
+AA $ 4l N AG u
+AA $ \l N AG }
+AA < l OD BBD A(D@0(A ABB T k N } BIB B(A0D8D
+8D0A(B BBBA 4 Dl Sq | BAI A
+ABA T |l Sz | BBB B(A0A8J
+8A0A(B BBBH m @. K^ m p m p T m p } BBB B(A0D8N
+8A0A(B BBBC , tm 0y> U~ AID W
+DAA D |n jN BBB D(A0D@0A(A BBB T m xL } BIE B(A0A8D9
+8D0A(B BBBA T Dn }" } BBB B(A0A8J
+8A0A(B BBBA to . K^ o P o H T n Py ~ BBB B(A0I8G>
+8A0A(B BBBG < p ND BBD A(D@0(A ABB T |o 8 ~ BIB E(A0A8DF
+8D0A(B BBBA D o d m~ BEI A(D0D
+0D(A BBBA < p r h~ BBH D(J
+(D ABBA D \p 謳 j~ BBE H(D0D
+0D(A BBBF D p e~ BBE H(D0D
+0D(A BBBF D p X `~ BBE D(H0D
+0D(A BBBA D 4q [~ BBE D(H0D
+0D(A BBBA D |q V~ BBE D(H0D
+0D(A BBBA D q P Q~ BBE D(H0D
+0D(A BBBA D r L~ BBE D(H0D
+0D(A BBBA D Tr G~ BBE D(H0D
+0D(A BBBA D r H³ B~ BBE D(H0D
+0D(A BBBA L r ijV =~ BBE D(H0D
+0D(A BBBI T 4s ȳ 3~ BBB E(G0A8L
+
+8D0A(B BBBJ dt . K^ $ |t PԳ^ AG b
+AA D t Գ BBB D(A0D@
+0A(A BBBA D t ׳ BBB D(A0D@%
+0A(A BBBA D \t ٳ } BBB D(A0Gn
+0D(A BBBE D t @۳ } BBB D(A0Gn
+0D(A BBBE T t ܳ w} BEE E(D0A8G2
+8D0A(B BBBD d v BLB B(A0A8D@c
8A0A(B BBBK s
-8A0A(B BBBC < DM D BBD A(D@0(A ABB T K * BIB E(A0A8D6
-8D0A(B BBBA d K e* BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d LL ` * BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB < N BBG A(G@
-(A ABBF L N BHB E(A0A8D`
-8A0A(B BBBE d DM ) BJE B(A0A8D@
+8A0A(B BBBC < v tID BBD A(D@0(A ABB T u | BIB E(A0A8D6
+8D0A(B BBBA d Dv | BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB d v x | BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB < w BBG A(G@
+(A ABBF L ,x BHB E(A0A8D`
+8A0A(B BBBE d w +{ BJE B(A0A8D@
8F0A(B BBBA b
-8F0A(B BBBA L M ( BEB E(I0H8DP
-8D0A(B BBBD T M ( BBE B(A0A8M =
-8A0A(B BBBA , TN 8> ) AID W
-DAA T N H ) BBE B(D0A8GR
-8A0A(B BBBE T N
- ) BBE B(D0A8GM
-8A0A(B BBBB T 4O B * BBB B(D0A8Jc
-8A0A(B BBBD L O ]
?* BBB B(A0A8G
-8A0A(B BBBH $ Q f AHD0AA$ Q +^ AG b
-AA < $R +~ BEA A(D0j(A ABB dR - AL T P -C * BBE E(D0A8G
-8D0A(B BBBH T P 2 ) BBE B(D0A8G
-8D0A(B BBBF T <Q 6 ) BBE B(D0A8G
-8D0A(B BBBF T Q H;3 ) BBE E(D0A8G:
-8D0A(B BBBG T Q 0>C |) BBE E(D0A8G
-8D0A(B BBBH T DR (CC b) BBE E(D0A8G
-8D0A(B BBBH D T H BBB D(A0D@
-0A(A BBBA L R xI" ) BIB B(A0D8DpH
-8D0A(B BBBA T 4S XL ( BIB E(A0A8DV
-8D0A(B BBBA L S P" ( BIB B(A0D8DpH
-8D0A(B BBBA L U S BGB A(A0J
-(A BBDH x(A BBB L $V S BBB B(A0A8GPd
-8A0A(B BBBA T |T @WG ' BBB B(D0A8J$
-8A0A(B BBBK T T 8_ ' BBB B(A0A8G1
-8A0A(B BBBA T ,U f ' BBB B(A0A8G1
-8A0A(B BBBA d |W (n: BEB B(D0A8D`
-8C0A(B BBBH D8D0A(B BBB T U o & BBB B(A0A8G1
-8A0A(B BBBA L DV v & BBG B(D0A8J
-8D0A(B BBBF D V (|* & BGE D(D0
-(A BBBA L V } & BEB E(I0H8D@
-8D0A(B BBBD L ,W ~@ & BGB E(D0A8GP
-8D0A(B BBBA L |W s& BEB E(I0H8D@
-8D0A(B BBBD T W 77 K& BEB B(A0A8J
-8A0A(B BBBA , $X > _) AID W
-DAA , TX > ?) AID W
-DAA , X (> ) AID W
-DAA Z x^. K^ Z Z 4 Z ޜ> BDA f
-AFE AAB , 4Y 跷 ( AAG q
-AAA L \[ BBB B(A0D8G@r
-8A0A(B BBBH [ (ޜ\ AG RA< [ BHH R
+8F0A(B BBBA L x z BEB E(I0H8DP
+8D0A(B BBBD < \x ; z BBA A(JO
+(A ABBA T x z BBE B(A0A8M =
+8A0A(B BBBA , x P> { AID W
+DAA T $y ` { BBE B(D0A8GR
+8A0A(B BBBE T |y { BBE B(D0A8GS
+8A0A(B BBBD T y p | BBE B(D0A8GS
+8A0A(B BBBD L ,z 8 I
3| BBB B(A0A8G
+8A0A(B BBBA $ T{ (ܗ AHD0AA$ |{ +^ AG b
+AA < { H+~ BEA A(D0j(A ABB { , AL T ,{ ,C { BBE E(D0A8G
+8D0A(B BBBH T { 1 { BBE B(D0A8G
+8D0A(B BBBF T { 6 { BBE B(D0A8G
+8D0A(B BBBF T 4| :3 { BBE E(D0A8G:
+8D0A(B BBBG T | =C p{ BBE E(D0A8G
+8D0A(B BBBH T | BC V{ BBE E(D0A8G
+8D0A(B BBBH T <} GC <{ BBE E(D0A8G
+8D0A(B BBBH D l~ L BBB D(A0D@
+0A(A BBBA L } M" z BIB B(A0D8DpH
+8D0A(B BBBA T ,~ P z BIB E(A0A8DV
+8D0A(B BBBA L ~ U" ~z BIB B(A0D8DpH
+8D0A(B BBBA L W BGB A(A0J
+(A BBDH x(A BBB L PX BBB B(A0A8GPd
+8A0A(B BBBA T t [ y BBB E(A0D8G"
+8A0A(B BBBE T hc y BBB B(A0A8G1
+8A0A(B BBBA T $ j cy BBB B(A0A8G1
+8A0A(B BBBA d T Xr: BEB B(D0A8D`
+8C0A(B BBBH D8D0A(B BBB T 0s x BBB B(A0A8G1
+8A0A(B BBBA L < z x BBB G(D0A8J
+8D0A(B BBBE D * x BGE D(D0
+(A BBBA L ԁ x BEB E(I0H8D@
+8D0A(B BBBD L $ @ {x BGB E(D0A8GP
+8D0A(B BBBA L t Ox BEB E(I0H8D@
+8D0A(B BBBD T Ă 4 'x BEB B(A0A8J
+8A0A(B BBBA , h> /{ AID W
+DAA , L x> { AID W
+DAA , | > z AID W
+DAA ӗ. K^ x 4 ̄ p<> BDA f
+AFE AAB , , H Oz AAG q
+AAA L 4 蹴 BBB B(A0D8G@r
+8A0A(B BBBH ;\ AG RA< h BHH R
ABI >
-ABG L \ , BFL H(A0A8DxK
-8A0A(B BBBI T dZ w' BBB B(D0A8Gm
-8D0A(B BBBB L Z PǷ t' BBB B(I0A8G
-8D0A(B BBBE L [ ɷ ^' BBB B(I0A8G
-8D0A(B BBBE T \[ ˷< H' BBE B(D0A8G
-8D0A(B BBBB T [ з< .' BBE B(D0A8G
-8D0A(B BBBB T \ շ< ' BBE B(D0A8G
-8D0A(B BBBB < \^ ۜD BBD A(D@0(A ABB T \ hڷr & BIB J(A0A8D!
-8D0A(B BBBA T \ c & BIB J(A0A8D
-8D0A(B BBBA T T] v W& BBE E(A0A8O
-8D0A(B BBBA < _ 1 BBG A(GP
-(A ABBF T ] ' BIE B(A0G8G.
-8A0A(B BBBA <` Y. K^ T` l` ` ` d ` BHB B(A0A8G
+ABG L , BFL H(A0A8DxK
+8A0A(B BBBI T \ 迴 Gy BBB B(D0A8Gm
+8D0A(B BBBB L ȴ Dy BBB B(I0A8G
+8D0A(B BBBE L ˴ .y BBB B(I0A8G
+8D0A(B BBBE T T Pʹ< y BBE B(D0A8G
+8D0A(B BBBB T 8Ҵ< x BBE B(D0A8G
+8D0A(B BBBB T ״< x BBE B(D0A8G
+8D0A(B BBBB D 4 9 BBB D(A0D@0A(A BBB T ۴c x BIB J(A0A8D
+8D0A(B BBBA T ߴr Px BIB J(A0A8D!
+8D0A(B BBBA T T % x BBE E(I0A8G
+8D0A(B BBBI < 1 BBG A(GP
+(A ABBF T oy BIE B(A0G8G.
+8A0A(B BBBA `Η. K^ 4 ( L d | d ( BHB B(A0A8G
8D0A(B BBBM
-8A0A(B BBBA d a W BHB B(A0A8GV
-8D0A(B BBBF
-8A0A(B BBBA L a # ADD e
+8A0A(B BBBA d x W BHB B(A0A8GN
+8D0A(B BBBN
+8A0A(B BBBA | p$ L h$ ADD e
CAF {
ACI V
-CAF JCA L a $ ADD e
+CAF JCA L % ADD e
CAF {
ACI V
-CAF JCA < ,` $
& BED A(GPa
-(A ABBF db ٜ\ AG RAD ` `% % BAA GP
+CAF JCA < \ %\ w BBA A(D0K(A ABB < %\ aw BBA A(D0K(A ABB 6\ AG RAD % w BAA GP
DABK
- DABG D b ٜv BBB D(A0D@^0A(A BBB T a ' `% BIB E(A0A8DV
-8D0A(B BBBA T ta (, .% BIB E(A0A8DV
-8D0A(B BBBA T a 0| $ BBE E(D0A8G
-8D0A(B BBBE T $b 5 $ BBE B(D0A8G
-8D0A(B BBBG T |b p: $ BBB L(A0D8G
-8A0A(B BBBB 4 b Cq $ BAI A
-ABA T c D $ BBB L(A0D8G
-8A0A(B BBBB 4 dc hMq $ BAI A
-ABA T c M $ BGB B(G0A8Jk
-8D0A(B BBBI T c 8_ % BGB B(G0A8Jk
-8D0A(B BBBI Df @S. K^ \f p0 Hg |f p7 Hj f p0 Hg f p7 Hj f q0 Hg f q7 Hj g 8q0 Hg <g Hq7 Hj \g hq0 Hg |g xq7 Hj g q0 Hg g q7 Hj g q0 Hg g q7 Hj h q0 Hg <h r7 Hj \h (r0 Hg |h 8r7 Hj h Xr0 Hg h hr7 Hj h r0 Hg h r7 Hj i r0 Hg <i r7 Hj \i r0 Hg |i r7 Hj i s0 Hg i (s7 Hj i Hs0 Hg i Xs7 Hj j xs0 Hg <j s7 Hj \j s0 Hg |j s7 Hj j s0 Hg j s7 Hj j t0 Hg j t7 Hj k 8t0 Hg <k Ht7 Hj \k ht0 Hg |k xt7 Hj k t0 Hg k t7 Hj k t0 Hg k t7 Hj l t0 Hg <l u7 Hj \l (u0 Hg |l 8u7 Hj l Xu0 Hg l hu7 Hj l u0 Hg l u7 Hj m u0 Hg <m u7 Hj \m u0 Hg |m u7 Hj m v0 Hg m (v7 Hj m Hv0 Hg m Xv7 Hj < n xv BBA A(G@k
-(A ABBB zPR x+ L $ x BBB B(A0D8D@[
-8A0A(B BBBJ L t y BBB B(A0D8DP
-8A0A(B BBBB L X~ BBB B(D0A8D@{
-8A0A(B BBBJ L BBB B(A0A8G@0
-8A0A(B BBBE L d BBB B(A0A8GP
-8A0A(B BBBB D p ( BBB D(A0D@
-0A(A BBBA 4 \n B BHA G`
- DABA 4 n B BHA G`
- DABA 4 n 7 BHA G`
- DABA 4 o 7 BHA G`
- DABA 4 <o B BHA G`
- DABA 4 to ،B p BHA G`
- DABA 4 o 7 Q BHA G`
- DABA 4 o 7 2 BHA G`
- DABA 4 p B BHA G`
- DABA 4 Tp B BHA G`
- DABA 4 p 07 BHA G`
- DABA 4 p 87 BHA G`
- DABA 4 p @B BHA G`
- DABA 4 4q XB x BHA G`
- DABA 4 lq p7 Y BHA G`
- DABA 4 q x7 : BHA G`
- DABA 4 q B BHA G`
- DABA 4 r B BHA G`
- DABA 4 Lr 7 BHA G`
- DABA 4 r 7 BHA G`
- DABA 4 r B BHA G`
- DABA 4 r ؝7 BHA G`
- DABA 4 ,s B a BHA G`
- DABA 4 ds B B BHA G`
- DABA 4 s 7 # BHA G`
- DABA 4 s 7 BHA G`
- DABA 4 t B BHA G`
- DABA 4 Dt 8B BHA G`
- DABA 4 |t P7 BHA G`
- DABA 4 t X7 BHA G`
- DABA 4 t `B i BHA G`
- DABA 4 $u x7 J BHA G`
- DABA 4 \u B + BHA G`
- DABA 4 u B BHA G`
- DABA 4 u 7 BHA G`
- DABA 4 v 7 BHA G`
- DABA 4 <v B BHA G`
- DABA 4 tv خB BHA G`
- DABA 4 v 7 q BHA G`
- DABA 4 v 7 R BHA G`
- DABA 4 w B 3 BHA G`
- DABA 4 Tw 7 BHA G`
- DABA 4 w B BHA G`
- DABA 4 w 87 BHA G`
- DABA 4 w @B BHA G`
- DABA 4 4x XB BHA G`
- DABA 4 lx p7 y BHA G`
- DABA 4 x x7 Z BHA G`
- DABA 4 x B ; BHA G`
- DABA 4 y 7 BHA G`
- DABA 4 Ly B BHA G`
- DABA 4 y B BHA G`
- DABA 4 y о7 BHA G`
- DABA 4 y ؿ7 BHA G`
- DABA 4 ,z B BHA G`
- DABA 4 dz B b BHA G`
- DABA 4 z ø7 C BHA G`
- DABA 4 z ĸ7 $ BHA G`
- DABA T { Ÿ3 BBB B(K0A8Jl
-8A0A(B BBBD 4 d{ ɸq BAI A
-ABA 4 { Pɸq BAI A
-ABA 4 { ɸq BAI A
-ABA 4 | ɸq BAI A
-ABA 4 D| (ʸq g BAI A
-ABA 4 || pʸq C BAI A
-ABA 4 | ʸq BAI A
-ABA 4 | ˸q BAI A
-ABA 4 $} H˸q BAI A
-ABA 4 \} ˸q BAI A
-ABA 4 } ˸q BAI A
-ABA 4 } ̸q k BAI A
-ABA 4 ~ h̸q G BAI A
-ABA 4 <~ ̸q # BAI A
-ABA 4 t~ ̸q BAI A
-ABA 4 ~ @q BAI A
-ABA 4 ~ q BAI A
-ABA 4 q BAI A
-ABA 4 T θq o BAI A
-ABA 4 `θq K BAI A
-ABA 4 θq ' BAI A
-ABA 4 θq BAI A
-ABA 4 4 8ϸq BAI A
-ABA 4 l ϸq BAI A
-ABA 4 ϸq BAI A
-ABA 4 ܀ иq s BAI A
-ABA 4 Xиq O BAI A
-ABA 4 L иq + BAI A
-ABA 4 иq BAI A
-ABA 4 0Ѹq BAI A
-ABA 4 xѸq BAI A
-ABA 4 , Ѹq BAI A
-ABA 4 d Ҹq w BAI A
-ABA 4 PҸq S BAI A
-ABA 4 Ԃ Ҹq / BAI A
-ABA 4 Ҹq BAI A
-ABA 4 D (Ӹq BAI A
-ABA 4 | pӸq BAI A
-ABA 4 Ӹq BAI A
-ABA 4 Ըq { BAI A
-ABA 4 $ HԸq W BAI A
-ABA 4 \ Ըq 3 BAI A
-ABA 4 Ըq BAI A
-ABA 4 ̄ ոq BAI A
-ABA 4 hոq BAI A
-ABA 4 < ոq BAI A
-ABA 4 t ոq BAI A
-ABA 4 @ָq [ BAI A
-ABA 4 ָq 7 BAI A
-ABA 4 ָq BAI A
-ABA T T
- BBB B(A0I8G
-8D0A(B BBBF T ٸ
- BBB B(A0I8G
-8D0A(B BBBF T ܸ
- BBB B(A0I8G
-8D0A(B BBBF T \ @߸
- BBB B(A0I8G
-8D0A(B BBBF T
- BBB B(A0I8G
-8D0A(B BBBF T
- BBB B(A0I8G
-8D0A(B BBBF T d h
- BBB B(A0I8G
-8D0A(B BBBF T
- x BBB B(A0I8G
-8D0A(B BBBF D g BBF K(H0G@Q
-0A(A BBBC D \ @ - BGB D(D0J
-(A BBBD < /
BEI D(G
-(D ABBG < ܋ BED A(D0Y
-(A ABBA d BBB B(A0D8DPJ
+ DABG D 6 BBB D(A0D@0A(A BBB T 8( v BIB E(A0A8DV
+8D0A(B BBBA T , nv BIB E(A0A8DV
+8D0A(B BBBA T < 0| <v BBE E(D0A8G
+8D0A(B BBBE T 6 "v BBE B(D0A8G
+8D0A(B BBBG T : v BBB L(A0D8G
+8A0A(B BBBB 4 D 0Dq 0v BAI A
+ABA T | xD v BBB L(A0D8G
+8A0A(B BBBB 4 Ԏ Mq 4v BAI A
+ABA T NW v BGB B(A0A8J
+8A0A(B BBBB T d aW 'w BGB B(A0A8J
+8A0A(B BBBB ȗ. K^ t0 Hg ̐ t7 Hj @t0 Hg Pt7 Hj , pt0 Hg L t7 Hj l t0 Hg t7 Hj t0 Hg ̑ t7 Hj u0 Hg u7 Hj , 0u0 Hg L @u7 Hj l `u0 Hg pu7 Hj u0 Hg ̒ u7 Hj u0 Hg u7 Hj , u0 Hg L v7 Hj l v0 Hg 0v7 Hj Pv0 Hg ̓ `v7 Hj v0 Hg v7 Hj , v0 Hg L v7 Hj l v0 Hg v7 Hj w0 Hg ̔ w7 Hj @w0 Hg Pw7 Hj , pw0 Hg L w7 Hj l w0 Hg w7 Hj w0 Hg ̕ w7 Hj x0 Hg x7 Hj , 0x0 Hg L @x7 Hj l `x0 Hg px7 Hj x0 Hg ̖ x7 Hj x0 Hg x7 Hj , x0 Hg L y7 Hj l y0 Hg 0y7 Hj Py0 Hg ̗ `y7 Hj y0 Hg y7 Hj , y0 Hg L y7 Hj < l y BBA A(G@k
+(A ABBB zPR xV, L $ { BBB B(D0A8D@{
+8A0A(B BBBJ L t @} BBB B(A0D8D@[
+8A0A(B BBBJ L ~ BBB B(A0D8DP
+8A0A(B BBBB L BBB B(A0A8G@0
+8A0A(B BBBE L d BBB B(A0A8GP
+8A0A(B BBBB D \ BBB D(A0D@
+0A(A BBBA 4 ̙ 芵B +n BHA G`
+ DABA 4 B n BHA G`
+ DABA 4 < 7 m BHA G`
+ DABA 4 t 7 m BHA G`
+ DABA 4 (B m BHA G`
+ DABA 4 @B m BHA G`
+ DABA 4 X7 qm BHA G`
+ DABA 4 T `7 Rm BHA G`
+ DABA 4 hB 3m BHA G`
+ DABA 4 ě B m BHA G`
+ DABA 4 7 l BHA G`
+ DABA 4 4 7 l BHA G`
+ DABA 4 l B l BHA G`
+ DABA 4 B l BHA G`
+ DABA 4 ܜ ؙ7 yl BHA G`
+ DABA 4 7 Zl BHA G`
+ DABA 4 L 蛵B ;l BHA G`
+ DABA 4 B l BHA G`
+ DABA 4 7 k BHA G`
+ DABA 4 7 k BHA G`
+ DABA 4 , (B k BHA G`
+ DABA 4 d @7 k BHA G`
+ DABA 4 HB k BHA G`
+ DABA 4 Ԟ `B bk BHA G`
+ DABA 4 x7 Ck BHA G`
+ DABA 4 D 7 $k BHA G`
+ DABA 4 | B k BHA G`
+ DABA 4 B j BHA G`
+ DABA 4 7 j BHA G`
+ DABA 4 $ 7 j BHA G`
+ DABA 4 \ ȪB j BHA G`
+ DABA 4 7 jj BHA G`
+ DABA 4 ̠ 謵B Kj BHA G`
+ DABA 4 B ,j BHA G`
+ DABA 4 < 7
j BHA G`
+ DABA 4 t 7 i BHA G`
+ DABA 4 (B i BHA G`
+ DABA 4 @B i BHA G`
+ DABA 4 X7 i BHA G`
+ DABA 4 T `7 ri BHA G`
+ DABA 4 hB Si BHA G`
+ DABA 4 Ģ 7 4i BHA G`
+ DABA 4 B i BHA G`
+ DABA 4 4 7 h BHA G`
+ DABA 4 l B h BHA G`
+ DABA 4 B h BHA G`
+ DABA 4 ܣ ػ7 h BHA G`
+ DABA 4 ༵7 zh BHA G`
+ DABA 4 L 轵B [h BHA G`
+ DABA 4 7 <h BHA G`
+ DABA 4 B h BHA G`
+ DABA 4 B g BHA G`
+ DABA 4 , 8µ7 g BHA G`
+ DABA 4 d @õ7 g BHA G`
+ DABA 4 HĵB g BHA G`
+ DABA 4 ԥ `ŵB g BHA G`
+ DABA 4 xƵ7 cg BHA G`
+ DABA 4 D ǵ7 Dg BHA G`
+ DABA T | ȵ3 %g BBB B(K0A8Jl
+8A0A(B BBBD 4 Ԧ p̵q g BAI A
+ABA 4 ̵q f BAI A
+ABA 4 D ͵q f BAI A
+ABA 4 | H͵q f BAI A
+ABA 4 ͵q f BAI A
+ABA 4 ͵q cf BAI A
+ABA 4 $ εq ?f BAI A
+ABA 4 \ hεq f BAI A
+ABA 4 εq e BAI A
+ABA 4 ̨ εq e BAI A
+ABA 4 @ϵq e BAI A
+ABA 4 < ϵq e BAI A
+ABA 4 t ϵq ge BAI A
+ABA 4 еq Ce BAI A
+ABA 4 `еq e BAI A
+ABA 4 еq d BAI A
+ABA 4 T еq d BAI A
+ABA 4 8ѵq d BAI A
+ABA 4 Ī ѵq d BAI A
+ABA 4 ѵq kd BAI A
+ABA 4 4 ҵq Gd BAI A
+ABA 4 l Xҵq #d BAI A
+ABA 4 ҵq c BAI A
+ABA 4 ܫ ҵq c BAI A
+ABA 4 0ӵq c BAI A
+ABA 4 L xӵq c BAI A
+ABA 4 ӵq oc BAI A
+ABA 4 Եq Kc BAI A
+ABA 4 PԵq 'c BAI A
+ABA 4 , Եq c BAI A
+ABA 4 d Եq b BAI A
+ABA 4 (յq b BAI A
+ABA 4 ԭ pյq b BAI A
+ABA 4 յq sb BAI A
+ABA 4 D ֵq Ob BAI A
+ABA 4 | Hֵq +b BAI A
+ABA 4 ֵq b BAI A
+ABA 4 ֵq a BAI A
+ABA 4 $ q a BAI A
+ABA 4 \ hq a BAI A
+ABA 4 q wa BAI A
+ABA 4 ̯ q Sa BAI A
+ABA 4 @صq /a BAI A
+ABA 4 < صq a BAI A
+ABA 4 t صq ` BAI A
+ABA 4 ٵq ` BAI A
+ABA 4 `ٵq ` BAI A
+ABA 4 ٵq {` BAI A
+ABA 4 T ٵq W` BAI A
+ABA 4 8ڵq 3` BAI A
+ABA T ı ڵ
+ ` BBB B(A0I8G
+8D0A(B BBBF T 8ݵ
+ _ BBB B(A0I8G
+8D0A(B BBBF T t ߵ
+ _ BBB B(A0I8G
+8D0A(B BBBF T ̲
+ _ BBB B(A0I8G
+8D0A(B BBBF T $ `
+ _ BBB B(A0I8G
+8D0A(B BBBF T |
+ _ BBB B(A0I8G
+8D0A(B BBBF T Գ
+ _ BBB B(A0I8G
+8D0A(B BBBF T ,
+ _ BBB B(A0I8G
+8D0A(B BBBF D @ _ BBF K(H0G@Q
+0A(A BBBC D ̴ M_ BGB D(D0J
+(A BBBD < / _ BEI D(G
+(D ABBG < , BED A(D0Y
+(A ABBA d l p BBB B(A0D8DPJ
8A0A(B BBBK
-8A0A(B BBBD d @ BBB B(A0D8DPZ
+8A0A(B BBBD d Զ BBB B(A0D8DPZ
8A0A(B BBBK
-8A0A(B BBBH d BBB B(D0A8DP
+8A0A(B BBBH d < BBB B(D0A8DP
8A0A(B BBBE
-8A0A(B BBBG T 0 E
-K < t BKD A(G0(A ABB < BDD
+8A0A(B BBBG E
+K < ķ x BKD A(G0(A ABB < BDD
ABA L
-ABA T BBB B(D0A8G
-8C0A(B BBBB L T { BIE B(A0G8G
-8D0A(B BBBE < BID D(D
-(D ABBA < ( BID D(D
-(D ABBA T $ x \ BBB B(D0A8G
-8C0A(B BBBB 4 | */ BHA J
- DABA 4 +/ BHA J
- DABA T , \ BIB B(A0A8Gw
-8A0A(B BBBA L D h-% BIE B(A0G8G
-8D0A(B BBBE T H0
- BIB B(A0A8J
-8D0A(B BBBA L 2
- BIE B(A0G8G
-8D0A(B BBBE T < 48 D
- BIB B(A0A8J
-8D0A(B BBBA T 6 BIB B(A0A8G
-8A0A(B BBBA T @;5 BBB B(A0D8G
-8C0A(B BBBG $ D (L( AQp
-DA $ l 0M( AQp
-DA L 8N BIH A(A0Dc
-0D(A BBBA L O BIE B(A0G8G
-8D0A(B BBBE T 4 XR G BIB B(A0A8J
-8D0A(B BBBA L T BIE B(A0G8G
-8D0A(B BBBE T ܑ W BIB B(A0A8JE
-8D0A(B BBBA T 4 Y q BIB B(A0A8D
-8A0A(B BBBG T \ ' BBB B(D0A8G
-8C0A(B BBBB L n { BIE B(A0G8G
-8D0A(B BBBE T 4 p 9 BIB B(A0A8D
-8A0A(B BBBG L s BBB B(A0A8DF
-8A0A(B BBBB $ ܓ {( AT
-DA D | BBB B(A0A8
-0A(B BBBF $ L P( = AT
-DA D l X BBB B(A0A8
-0A(B BBBA T ЂL BIB B(D0D8G
-8D0A(B BBBE D ȉs BBB B(A0A8I
-0A(B BBBA T \ I BIB B(D0D8G
-8D0A(B BBBA D BBB B(A0A8
-0A(B BBBA T 0l BIB B(D0D8G
-8D0A(B BBBE D L Hs BBB B(A0A8I
-0A(B BBBA T % BIB B(D0D8G
-8D0A(B BBBA D 81 BBB B(A0A8
-0A(B BBBF $ < 0( AT
-DA \ 8 J D | ȡ BBB B(A0A8Q
-0A(B BBBL $ ̗ ( AT
-DA . K^ 4 L HS l ڢ/ A L ह BBB B(A0A8GP8A0A(B BBB L Ԛ BEB B(A0A8DP8A0A(B BBB D $ 0 BBE A(A0D@g
-0A(A BBBA D l BBE A(A0D@o
-0A(A BBBA $ P^ AG b
-AA D ܛ BBE A(A0D@P
-0A(A BBBA $ , A
+ABA T l x \ BBB B(D0A8G
+8C0A(B BBBB L ķ { "] BIE B(A0G8G
+8D0A(B BBBE < @ \ BID D(D
+(D ABBA < T \ BID D(D
+(D ABBA T |\ BBB B(D0A8G
+8C0A(B BBBB 4 x-/ \ BHA J
+ DABA 4 $ p./ \ BHA J
+ DABA T \ h/ |\ BIB B(A0A8Gw
+8A0A(B BBBA L 0% 2\ BIE B(A0G8G
+8D0A(B BBBE T 3 [ BIB B(A0A8J
+8D0A(B BBBA L \ H6 [ BIE B(A0G8G
+8D0A(B BBBE T x78 d[ BIB B(A0A8J
+8D0A(B BBBA T `: [ BIB B(A0A8G
+8A0A(B BBBA T \ > Z BBB B(D0A8G
+8C0A(B BBBB $ @O( $[ AQp
+DA $ ܻ HP(
+[ AQp
+DA L PQ Z BIH A(A0Dc
+0D(A BBBA L T R Z BIE B(A0G8G
+8D0A(B BBBE T pU lZ BIB B(A0A8J
+8D0A(B BBBA L W "Z BIE B(A0G8G
+8D0A(B BBBE T L Z Y BIB B(A0A8JE
+8D0A(B BBBA T ] Y BIB B(A0A8D
+8A0A(B BBBG T _ LY BBB B(D0A8G
+8C0A(B BBBB L T pp Y BIE B(A0G8G
+8D0A(B BBBE T @s ^Y BIB B(A0A8D
+8A0A(B BBBG L Կ u BBB B(A0A8DF
+8A0A(B BBBB $ L (~( X AT
+DA D L 0 BBB B(A0A8
+0A(B BBBF $ ( bX AT
+DA D BBB B(A0A8
+0A(B BBBA T , (L X BIB B(D0D8G
+8D0A(B BBBE D \ s BBB B(A0A8I
+0A(B BBBA T X nW BIB B(D0D8G
+8D0A(B BBBA D BBB B(A0A8
+0A(B BBBA T l l V BIB B(D0D8G
+8D0A(B BBBE D s BBB B(A0A8I
+0A(B BBBA T JV BIB B(D0D8G
+8D0A(B BBBA D < BBB B(A0A8
+0A(B BBBF $ h( U AT
+DA D p1 BBB B(A0A8
+0A(B BBBF $ h( VU AT
+DA . K^ 4 X L P d H | P HS 1/ A L 8 BBB B(A0A8GP8A0A(B BBB L 訶 BEB B(A0A8DP8A0A(B BBB D T BBE A(A0D@g
+0A(A BBBA D BBE A(A0D@o
+0A(A BBBA $ ^ AG b
+AA D BBE A(A0D@P
+0A(A BBBA $ | X S A
G A
-A L L AAG {
+A L | ௶ AAG {
CAH z
CAB R
CAJ J
-CAA L AAG {
+CAA L ` AAG {
CAH z
CAB R
CAJ J
-CAA T BBB E(D0A8G
-8D0A(B BBBA D D BBB D(A0D@
-0A(A BBBA L p BIB A(D0D
-0D(A BBBA L h) S BBB B(A0D8D`
-8A0A(B BBBA L 4 H) # BBB B(A0D8D`
-8A0A(B BBBA T ( BEB B(H0A8G 6
+CAA T D శ @R BBB E(D0A8G
+8D0A(B BBBA D t BBB D(A0D@
+0A(A BBBA L p Q BIB A(D0D
+0D(A BBBA L 4 ) Q BBB B(A0D8D`
+8A0A(B BBBA L ) gQ BBB B(A0D8D`
+8A0A(B BBBA T 7Q BEB B(H0A8G 6
-8A0A(B BBBE , ܜ Ϲ> AID W
-DAA $ ϹG As
+8A0A(B BBBE , , Ѷ> CR AID W
+DAA $ \ ѶG #R As
L A
-A T 4 Ϲ BEB E(A0D8G&
-8D0A(B BBBC $ PӹG x As
+A T Ҷ Q BEB E(A0D8G&
+8D0A(B BBBC $ նG Q As
L A
-A < xӹR T BBA D(Jn
-(D ABBA T Թ 8 BFA A(GP
+A T ն Q BFA A(GP
(A ABBE S
-(A ABBA l L ڹ * BBB B(A0A8G
+(A ABBA l \ H۶ Q BBB B(A0A8G
8A0A(B BBBD
-8A0A(B BBBF T p BBE E(A0D8G
-8D0A(B BBBE L BBB B(A0A8J8A0A(B BBB < d B 6 BBA A(G
-(A ABBA , i PGA A(D , ԟ V ]GA A(D , x C PGA A(D < 4 HB 0 BBA A(G
-(A ABBA T t X^ c BEB B(A0D8J
-8A0A(B BBBA T ̠ ` BBH B(F0A8J
-8D0A(B BBBD T $ K BBH B(A0F8J=
-8D0A(B BBBG t p. K^ ԣ 4 L (* ` , d @M AAD z
-AAA ` h HS ̤ / A L P AAG ]
+8A0A(B BBBF T )Q BBE E(A0D8G
+8D0A(B BBBE L 0 BBB B(A0A8J8A0A(B BBB < t B P BBA A(G
+(A ABBA , P PGA A(D , P ]GA A(D , P PGA A(D < D B P BBA A(G
+(A ABBA T ^ P BEB B(A0D8J
+8A0A(B BBBA T {P BGH B(A0A8J
+8D0A(B BBBH T 4 `F P BBB H(F0D8G
+8D0A(B BBBA d . K^ | @ H @ 8 0 8 @ $ H < P* ` , T hM AAD z
+AAA HS @/ A L x AAG ]
CAF c
CAI R
-CAJ JCA L 4 AAG ]
+CAJ JCA L $ AAG ]
CAF [
ACA R
-CAJ JCA @ Dz 4
BDA D0e
- AABA ԥ `- x- $ ^ AG b
-AA D , BBL A(A0D@p
-0A(A BBBA D t ` BBL A(A0D@x
-0A(A BBBA , 0 AC
FK
- E , A ADL
-AAA < $ ( BBP J(G
-(D ABBH \ " L t AAG
+CAJ JCA t @ Dz 4 N BDA D0e
+ AABA - - $ ^ AG b
+AA D BBL A(A0D@p
+0A(A BBBA D d BBL A(A0D@x
+0A(A BBBA , 0 0 AC
FK
+ E , 0 A LM ADL
+AAA < 4 P AM BBP J(G
+(D ABBH L 0"" L d H" AAG
CAD V
CAF R
CAJ J
-CAA T ̥ . c BBB B(A0A8D
-8A0A(B BBBA D ! BBB D(A0D@
-0A(A BBBA L l " BIB A(D0D
-0D(A BBBA T @& BBB B(D0A8N
-]
-8A0A(B BBBF , 5> | AID W
-DAA $ D 6g \ AR
+CAA T ". L BBB B(A0A8D
+8A0A(B BBBA D $ BBB D(A0D@
+0A(A BBBA L | & [L BIB A(D0D
+0D(A BBBA T h) <L BBB B(D0A8N
+d
+8A0A(B BBBG , $ 9> L AID W
+DAA $ T 09g L AR
M A
-A T l P7_ 8 BBB E(D0A8G
-8D0A(B BBBA T ħ X9I BBB E(D0A8G
-8D0A(B BBBA D P; BEA G@
+A T | x:_ L BBB E(D0A8G
+8D0A(B BBBA T <I mL BBB E(D0A8G
+8D0A(B BBBA < , x>H *L BBA A(Gp
+(D ABBA D l ? L BEA G@
AABH
- AABA l d B BBB B(A0A8G@=
+ AABA l @F "L BBB B(A0A8G@=
8A0A(B BBBH u
-8A0A(B BBBA , Ԩ hP f ZGA A(D , HQ S ZGA A(D T 4 (R @ BBB B(A0A8G;
-8A0A(B BBBJ T ] # BBB B(A0A8G
-8A0A(B BBBA L ܫ ef BEB A(A0|
+8A0A(B BBBA , $ T K ZGA A(D , T U K ZGA A(D T `V K BBB B(A0A8G;
+8A0A(B BBBJ T (b K BBB B(A0A8G
+8A0A(B BBBA L if BEB A(A0|
(A BBBJ A
-(A BBBA d , ec BLB B(A0A8DPc
+(A BBBA d \ jc BLB B(A0A8DPc
8A0A(B BBBK
-8A0A(B BBBG L f BBB B(A0D8D`4
-8A0A(B BBBA T h5 BDB B(A0A8I
-8A0A(B BBBK T D o BEB B(A0A8J
-8A0A(B BBBA T } BHB B(F0A8J
-8D0A(B BBBG T BBB H(F0A8J
-8D0A(B BBBE D . K^ \ Ȕ t Д Ȕ Ȕ Ԯ Ȕ F ) G] 4 J W c L H D k
-Q z l H G
-A 8 H0W
-I $ ؖ^ AG b
-AA D ܭ q BBE A(A0D@
-0A(A BBBA $ $ 藺g - AG k
-AA , L 0Y ADG i
-AAA < t 0D BBD A(D@0(A ABB T BIB E(A0A8DV
-8D0A(B BBBA D x| BBB A(D0D@
-0A(A BBBA D T BBB A(D0D@
-0A(A BBBA T $ BEB B(A0A8G`
-8A0A(B BBBA T С BBB B(A0A8G!
-8A0A(B BBBD L L BBF B(A0A8D@
-8D0A(B BBBB D f BBE A(A0D@
-0A(A BBBA L # BEB E(I0H8D@
-8D0A(B BBBD L < `w BHI E(A0A8Gp
-8A0A(B BBBF L BJE B(A0A8D@
-8A0A(B BBBA L ܱ `V BBB B(A0D8J
-8D0A(B BBBE T , p BBB B(A0D8J
-8A0A(B BBBA d | ǺE BBB B(A0A8DP
+8A0A(B BBBG L k BBB B(A0D8D`4
+8A0A(B BBBA T < Xl5 [J BDB B(A0A8I
+8A0A(B BBBK T @s sJ BEB B(A0A8J
+8A0A(B BBBA T ȁ 7J BHB B(F0A8J
+8D0A(B BBBG T D ` zJ BBB G(G0D8Gw
+8D0A(B BBBA t Ȁ. K^ 4 F L @) G] d XJ W c | D k
+Q z H G
+A H0W
+I $ ^ AG b
+AA D , X H BBE A(A0D@
+0A(A BBBA $ t 0g H AG k
+AA , xY `H ADG i
+AAA < D BBD A(D@0(A ABB T h G BIB E(A0A8DV
+8D0A(B BBBA D < | BBB A(D0D@
+0A(A BBBA D BBB A(D0D@
+0A(A BBBA T @e :G BEB B(A0A8GL
+8A0A(B BBBA T L X G BBB B(A0A8G
+8A0A(B BBBG L | BBF B(A0A8D@
+8D0A(B BBBB D F BBE A(A0D@
+0A(A BBBA L < 詷 cF BEB E(I0H8D@
+8D0A(B BBBD L hw ;F BHI E(A0A8Gp
+8A0A(B BBBF L +F BJE B(A0A8D@
+8A0A(B BBBA L , hV F BBB B(A0D8J
+8D0A(B BBBE T | x E BBB B(A0D8J
+8A0A(B BBBA d ʷE BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA L Ⱥ BJB B(D0A8GPx
-8A0A(B BBBB T < pʺ o BBB B(H0A8G
-8A0A(B BBBA , ܺ> [ AID W
-DAA . K^ Ե ܺ xr J e 4 p L 7 d 0Z | x PB Ķ p ܶ XJ D BBE A(A0D@u
-0A(A BBBA D < (
- BBE A(A0D@u
-0A(A BBBA
-J
-J L 0 BBB B(A0A8D`
-8A0A(B BBBA , AAD@W
-AAA , < AAD@W
-AAA , l ` AAD@W
-AAA , AAD@W
-AAA , ̶
c AAD@W
-AAA ,
? AAD@W
-AAA , ,
AAD@W
-AAA , \ @ AAD@W
-AAA $ RMIp L BBB B(A0A8D`
-8A0A(B BBBA , [ AAD@W
-AAA , 4 7 AAD@W
-AAA , d H AAD@W
-AAA , AAD@W
-AAA , ĸ AAD@W
-AAA , h AAD@W
-AAA , $ AAD@W
-AAA , T ( _ AAD@W
-AAA L | BBB B(A0A8Dp
-8A0A(B BBBA , Թ H AAD@W
-AAA , AAD@W
-AAA , 4 AAD@W
-AAA , d h AAD@W
-AAA , [ AAD@W
-AAA , ĺ ( 7 AAD@W
-AAA , AAD@W
-AAA , $ AAD@W
-AAA < L H BBA A(D@i
-(A ABBA , AAD@W
-AAA , Ļ g AAD@W
-AAA , H C AAD@W
-AAA , $ AAD@W
-AAA , T AAD@W
-AAA , h AAD@W
-AAA , AAD@W
-AAA , ( AAD@W
-AAA T k BBB B(D0D8G
-8D0A(B BBBJ T l > BEE E(D0A8G
-8D0A(B BBBD T Ľ (%, $ BBE B(D0A8G
-8D0A(B BBBK T *J
- BIA A(GT
+8F0A(B BBBA T < ˷ E BBB B(A0A8N
+8A0A(B BBBA , P߷> F AID W
+DAA y. K^ H߷ r J e , 7 D Z \ t B 0 J L 8 r {E BBB B(E0A8J@
+8A0A(B BBBA D $ h
BBE A(A0D@u
+0A(A BBBA D l BBE A(A0D@u
+0A(A BBBA J J L BBB B(A0A8D`
+8A0A(B BBBA , \ x 0D AAD@W
+AAA , D AAD@W
+AAA , 8 C AAD@W
+AAA , C AAD@W
+AAA , C AAD@W
+AAA , L X |C AAD@W
+AAA , | XC AAD@W
+AAA , 4C AAD@W
+AAA $ x RMIp L BBB B(A0A8D`
+8A0A(B BBBA , T ` B AAD@W
+AAA , tB AAD@W
+AAA , PB AAD@W
+AAA , ,B AAD@W
+AAA , B AAD@W
+AAA , D @ A AAD@W
+AAA , t A AAD@W
+AAA , A AAD@W
+AAA L ` BBB B(A0A8Dp
+8A0A(B BBBA , $ (A AAD@W
+AAA , T A AAD@W
+AAA , @ AAD@W
+AAA , @ @ AAD@W
+AAA , @ AAD@W
+AAA , t@ AAD@W
+AAA , D ` P@ AAD@W
+AAA , t ,@ AAD@W
+AAA < | BBA A(D@i
+(A ABBA , ` ? AAD@W
+AAA , ? AAD@W
+AAA , D ? AAD@W
+AAA , t \? AAD@W
+AAA , 8? AAD@W
+AAA , @ ? AAD@W
+AAA , > AAD@W
+AAA , 4 > AAD@W
+AAA T d ` > BBB B(D0D8G
+8D0A(B BBBJ T " {> BEE E(D0A8G
+8D0A(B BBBD T ), a> BBE B(D0A8G
+8D0A(B BBBK T l -J G> BIA A(GT
(A ABBB ^
-(A ABBD T t +, BBE B(D0A8G
-8D0A(B BBBK T ̾ 0J BIA A(GT
+(A ABBD T /, > BBE B(D0A8G
+8D0A(B BBBK T 4J = BIA A(GT
(A ABBB ^
-(A ABBD T $ 2, BBE B(D0A8G
-8D0A(B BBBK T | 7J f BIA A(GT
+(A ABBD T t 6, = BBE B(D0A8G
+8D0A(B BBBK T x;J = BIA A(GT
(A ABBB ^
-(A ABBD T Կ 9, . BBE B(D0A8G
-8D0A(B BBBK T , p>J BIA A(GT
+(A ABBD T $ p=, k= BBE B(D0A8G
+8D0A(B BBBK T | HBJ Q= BIA A(GT
(A ABBB ^
-(A ABBD T h@ BEB B(A0D8G7
-8D0A(B BBBE L W _ BBB B(D0A8G`8
-8A0A(B BBBJ T , Y ? BBB B(D0A8G
-8A0A(B BBBA L [ BEB E(I0H8DP
-8D0A(B BBBD L ] BHI E(A0A8Gps
-8A0A(B BBBB L $ X` BEB E(I0H8DP
-8D0A(B BBBD L t b BHI E(A0A8Gpr
-8A0A(B BBBC T BIB B(A0A8GS
-8A0A(B BBBA pd , hd T L pd * BBB J(A0A8G
-8D0A(B BBBD D Ly BBB D(A0D@0A(A BBB T gL BIE B(A0A8D9
-8D0A(B BBBA < < xl BBG A(G@
-(A ABBG T 8m _ BBB H(A0A8G
-8D0A(B BBBI T KH s s s 4 s L s d s | s s s s s s s $ s , D s[ 3 AID t
-DAA , t s[ AID t
-DAA , t[ AID t
-DAA , Ht[ AID t
-DAA , xt[ AID t
-DAA , t2 I L L tr { BBB B(E0A8J@
-8A0A(B BBBA $ v^ AG b
-AA 8v d @v BBB B(A0D8Op
+(A ABBD T @D~ = BBB B(D0D8GL
+8D0A(B BBBH L , hT w= BBB B(D0A8G`8
+8A0A(B BBBJ T | V W= BBB B(D0A8G
+8A0A(B BBBA L 0Y #= BEB E(I0H8DP
+8D0A(B BBBD L $ Z < BHI E(A0A8Gps
+8A0A(B BBBB L t ] < BEB E(I0H8DP
+8D0A(B BBBD L `_ < BHI E(A0A8Gpr
+8A0A(B BBBC T j < BIB B(A0A8GS
+8A0A(B BBBA D a \ a T a B< BBB J(A0A8G
+8D0A(B BBBD D ז BBB D(A0D@0A(A BBB T < dL ; BIE B(A0A8D9
+8D0A(B BBBA < l i BBG A(G@
+(A ABBG T j w; BEB B(G0A8G
+8D0A(B BBBJ lT KH q 4 p L p d p | p p p p p p p $ p < p T p , q[ C: AID t
+DAA , 0q[ #: AID t
+DAA , `q[ : AID t
+DAA , $ q[ 9 AID t
+DAA , T q[ 9 AID t
+DAA \ q2 I 4 t r BAA s
+AEK AAB L prr S9 BBB B(E0A8J@
+8A0A(B BBBA $ s^ AG b
+AA $ s d d s 8 BBB B(A0D8Op
8A0A(B BBBH U
-8A0A(B BBBA , D v> AID W
-DAA T t w BBB B(D0A8K#
-8A0A(B BBBK , px> [ AID W
-DAA D x ; BBB D(E0Ml
-0A(A BBBH T D 8} H BEE E(D0D8G(
-8D0A(B BBBK d ~= BLB B(A0A8DPq
+8A0A(B BBBA , t> 8 AID W
+DAA T t g8 BBB B(D0A8K#
+8A0A(B BBBK , T v> 38 AID W
+DAA D v 8 BBB A(A0JR
+0A(A BBBA D v 7 BBB D(E0Ml
+0A(A BBBH , { 7 qGA A(D L D |t 7 BBB D(D0J
+0D(A BBBG T Ѐ~ 7 BBA A(Ik
+(C ABBF
+(A ABBA T 7 BEE E(D0D8G(
+8D0A(B BBBK d = BLB B(A0A8DPq
8A0A(B BBBE
-8A0A(B BBBE < g BED D(G
-(D ABBD D < Ȁ BBB D(A0D@
-0A(A BBBA D % BIB A(D0D`+
-0D(A BBBA L BIB B(A0D8DpE
-8D0A(B BBBA L $ BIB B(A0D8DpE
-8D0A(B BBBA L t x BIB A(D0D
-0D(A BBBA L ȍ BIB A(D0D
-0D(A BBBA D $ q BIA A(G
-(D ABBA < \ N BHA J
- DABA T k ? BBA A(JE
-(A ABBE G
-(C ABBA L d BBB D(D0J"
-0D(A BBBK L D ؚ| BBB B(D0F8Gc
-8A0A(B BBBJ D 6 BDB A(G0G
-0A(D BBBA \ # @ BBB A(A0J
-0A(A BBBF R
-0A(A BBBA L < С& BDE G(A0Ga
-0D(A BBBJ T " * BBA A(Jy
-(A ABBA R
-(A ABBA T a
BEE B(D0A8G
-8D0A(B BBBI L 4 B BLH B(D0A8DP
-8A0A(B BBBB 9 Af
-I D L BEB B(G0A8Jh
-8D0A(B BBBF T `$ e BDB H(D0A8G
-8D0A(B BBBH T T 8r BDB B(A0D8GF
-8A0A(B BBBJ T `" BBB B(D0A8Go
-8A0A(B BBBA < 8 u BBH A(Jx
-(A ABBK T D » U BBB B(A0E8G
-8A0A(B BBBA T pϻ BBB B(A0E8G
-8A0A(B BBBA T ۻ BBB B(A0E8G
-8A0A(B BBBA T L `| ~ BBB B(A0E8G
-8A0A(B BBBA L BBG B(D0A8M
-8D0A(B BBBE V Ko
-A $ < T l , D \ t 4 L d L r + BBB B(E0A8J@
-8A0A(B BBBA $ ^ AG b
-AA T P BBE E(A0A8G4
-8A0A(B BBBK L T * BBB J(A0A8G
-8D0A(B BBBA L * BBB J(A0A8G
-8D0A(B BBBA L x* BBB J(A0A8G
-8D0A(B BBBA L D X* BBB J(A0A8G
-8D0A(B BBBA L 8* { BBB J(A0A8G
-8D0A(B BBBA L e BBB J(A0A8G
-8D0A(B BBBA L 4 * O BBB J(A0A8G
-8D0A(B BBBA L
-* 9 BBB J(A0A8G
-8D0A(B BBBA L x * # BBB J(A0A8G
-8D0A(B BBBA L $ X
BBB J(A0A8G
-8D0A(B BBBA L t BBB J(A0A8G
-8D0A(B BBBA L * BBB J(A0A8G
-8D0A(B BBBA L x* BBB J(A0A8G
-8D0A(B BBBA T d X BBE E(D0A8G/
-8D0A(B BBBJ < " x BBA A(JU
-(A ABBA < " W BBA A(JU
-(A ABBA T < 6 BBE E(A0A8G
-8D0A(B BBBA D BBB D(A0D@
-0A(A BBBA D p BIB A(D0D`A
-0D(A BBBA D $ H BIB A(D0D`H
-0D(A BBBA D l # m BIB A(D0D`H
-0D(A BBBA D % K BIB A(D0D`H
-0D(A BBBA D ( ) BIB A(D0D`H
-0D(A BBBA D D + BIB A(D0D`H
-0D(A BBBA D . BIB A(D0D`H
-0D(A BBBA D X1 BIB A(D0D`H
-0D(A BBBA D 04 BIB A(D0D`H
-0D(A BBBA D d 7 BIB A(D0D`H
-0D(A BBBA D 9 ] BIB A(D0D`H
-0D(A BBBA D < ; BIB A(D0D`H
-0D(A BBBA D < ? BIB A(D0D`H
-0D(A BBBA < hB BED D(G
-(A ABBA < D BEG A(G
-(A ABBA T EU BBB E(A0A8G
-8A0A(B BBBD T \ J BBB E(A0A8G'
-8A0A(B BBBK L HP BBB B(D0A8M
-8A0A(B BBBA T R5 BBB E(A0A8G
-8A0A(B BBBD L \ pVt BBB B(A0G8J
-8A0A(B BBBA T Xe BBB E(A0A8G
-8A0A(B BBBF T ] BBB E(A0A8G
-8A0A(B BBBA T \ p` BBB E(A0A8G
-8A0A(B BBBE L ey BBB B(A0G8J
-8A0A(B BBBA T g5 BBB E(A0A8G
-8A0A(B BBBD L \ kt BBB B(A0G8J
-8A0A(B BBBA T n BBB E(A0A8G
-8A0A(B BBBC T 8sE BBB E(A0A8G
-8A0A(B BBBA T \ 0v BBB E(A0A8G-
-8A0A(B BBBE L y BBB B(A0D8M
-8A0A(B BBBA T { BBB E(A0A8G
-8A0A(B BBBE T \ 0 BBG B(D0G8G
-8D0A(B BBBD T ȍ
- BBB B(A0D8M
-8A0A(B BBBA T d BBB G(D0G8G
-8D0A(B BBBD T d BBB B(D0A8M
-8A0A(B BBBA T P BBB B(A0D8M
-8A0A(B BBBA T BBB B(A0D8M
-8A0A(B BBBA T l
- BBB B(A0D8M
-8A0A(B BBBA T h BBB B(D0A8M
-8A0A(B BBBA T Ƽ BBB B(A0D8M
-8A0A(B BBBA T t ʼ BBB B(A0D8M
-8A0A(B BBBA T ϼ BBB B(A0D8M
-8A0A(B BBBA L $ (Լ BBB B(A0D8M
-8A0A(B BBBH T t hټX BBB G(D0G8G
-8D0A(B BBBD L p BBB B(A0D8M
-8A0A(B BBBC T 5 BBB E(A0A8G
-8A0A(B BBBH T t BBB G(D0G8G0
-8D0A(B BBBD T @ BBB E(A0A8G1
-8A0A(B BBBA T $ m BBB G(D0G8G
-8D0A(B BBBD T | "5 BBB E(A0A8G
-8A0A(B BBBA T % BBB G(D0G8G
-8D0A(B BBBD T , p3e BBB E(A0A8G
-8A0A(B BBBH T 8t BBB G(D0G8G
-8D0A(B BBBD T U5 BBB E(A0A8G
-8A0A(B BBBH T 4 Yb BBB G(D0G8G
-8D0A(B BBBD T n BBB E(A0A8G
-8A0A(B BBBG T r= BBB G(D0G8G
-8D0A(B BBBD T < BBB E(A0A8G
-8A0A(B BBBA T
BBB G(D0G8G
-8D0A(B BBBD T 0 BBB E(A0A8G
-8A0A(B BBBA T D 蘽 BBB G(D0G8G`
-8D0A(B BBBD T 0 BBB E(A0A8G)
-8A0A(B BBBI T
BBB G(D0G8GH
-8D0A(B BBBD T L 0 BBG E(G0A8G
-8D0A(B BBBD , ̜< AK
-AA Ž Ž L Žm BBB B(A0I8Dp
-8D0A(B BBBF L T ƽm T BBB B(A0I8Dp
-8D0A(B BBBF D M BBB D(A0D@0A(A BBB T ǽr BIE B(A0A8Da
-8D0A(B BBBA T D ̽l BIE B(A0A8DY
-8D0A(B BBBA T ѽF w BBE B(A0A8G
-8A0A(B BBBA x˜. K^ 4 PM\ AG RAL T BLB B(A0A8Dq
-8A0A(B BBBE L BBB B(I0A8G
-8D0A(B BBBF L p BBB B(I0A8G
-8D0A(B BBBF T L w BEE E(D0A8G
-8D0A(B BBBE L BOB A(A0k
+8A0A(B BBBE < Xg 7 BED D(G
+(D ABBD D BBB D(A0D@
+0A(A BBBA D 4 6 BIB A(D0D`+
+0D(A BBBA L | {6 BIB B(A0D8DpE
+8D0A(B BBBA L h Q6 BIB B(A0D8DpE
+8D0A(B BBBA < 8 '6 BHA Gi
+ DABA < \ 6 BHA Gi
+ DABA D ؒ$ 5 BIA A(G
+(D ABBA < 5 BHA J
+ DABA L $ ` 5 BBB B(G0D8G
+8A0A(B BBBA D t 4 5 BDB A(G0G
+0A(D BBBA \ " 5 BBB A(A0G
+0A(A BBBI O
+0A(A BBBA T hf 5 BDB H(A0D8G
+8D0A(B BBBG < t 0 5 BBA A(Jc
+(A ABBG T pa 5 BEE B(D0A8G
+8D0A(B BBBI L B BLH B(D0A8DP
+8A0A(B BBBB 4 9 Af
+I D L | 5 BHB E(A0A8Gn
+8D0A(B BBBK T ( 4 BDB E(A0A8G
+8A0A(B BBBJ T $ ම4 @5 BDB B(D0A8G
+8A0A(B BBBH T | Ȼ _5 BBB B(A0D8G
+8A0A(B BBBA < U5 BBH A(Jx
+(A ABBK T w 55 BBB B(A0E8G
+8A0A(B BBBA T l ̸w 5 BBB B(A0E8G
+8A0A(B BBBA T ٸw 5 BBB B(A0E8G
+8A0A(B BBBA T H [6 BBB B(A0E8G
+8A0A(B BBBA L t 6 BBB G(A0D8M
+8D0A(B BBBG P^V Ko
+A 4 L d | $ < T l L T r 4 BBB B(E0A8J@
+8A0A(B BBBA $ | P^ AG b
+AA T L 3 BBB E(A0D8G
+8A0A(B BBBH L $ * 3 BBB J(A0A8G
+8D0A(B BBBA L t ` 3 BBB J(A0A8G
+8D0A(B BBBA L * 3 BBB J(A0A8G
+8D0A(B BBBA L q3 BBB J(A0A8G
+8D0A(B BBBA L d [3 BBB J(A0A8G
+8D0A(B BBBA L E3 BBB J(A0A8G
+8D0A(B BBBA L /3 BBB J(A0A8G
+8D0A(B BBBA L T ` 3 BBB J(A0A8G
+8D0A(B BBBA L 3 BBB J(A0A8G
+8D0A(B BBBA L
+ 2 BBB J(A0A8G
+8D0A(B BBBA L D @ 2 BBB J(A0A8G
+8D0A(B BBBA L
2 BBB J(A0A8G
+8D0A(B BBBA L 2 BBB J(A0A8G
+8D0A(B BBBA T 4 2 BBE E(D0A8G/
+8D0A(B BBBJ < " X2 BBA A(JU
+(A ABBA < " 72 BBA A(JU
+(A ABBA T 2 BBE E(A0A8G
+8D0A(B BBBA D < BBB D(A0D@
+0A(A BBBA D 8 1 BIB A(D0D`A
+0D(A BBBA D o1 BIB A(D0D`H
+0D(A BBBA D < M1 BIB A(D0D`H
+0D(A BBBA D ! +1 BIB A(D0D`H
+0D(A BBBA D $ 1 BIB A(D0D`H
+0D(A BBBA D p' 0 BIB A(D0D`H
+0D(A BBBA D \ H* 0 BIB A(D0D`H
+0D(A BBBA D - 0 BIB A(D0D`H
+0D(A BBBA D / 0 BIB A(D0D`H
+0D(A BBBA D 4
+ 2 _0 BIB A(D0D`H
+0D(A BBBA D |
+ 5 =0 BIB A(D0D`H
+0D(A BBBA D
+ 8 0 BIB A(D0D`H
+0D(A BBBA D X; / BIB A(D0D`H
+0D(A BBBA < T 0> / BBA A(G
+(A ABBA D ? / BBE G(A0D
+0A(A BBBA T XAU / BBB E(A0A8G
+8A0A(B BBBD T 4 `F / BBB E(A0A8G
+8A0A(B BBBK T M5 / BBB E(A0A8G
+8A0A(B BBBD L Q / BBB B(A0G8J
+8A0A(B BBBA T 4
TU / BBB E(A0A8G
+8A0A(B BBBF T
Y / BBB E(A0A8G
+8A0A(B BBBA T
[ / BBB E(A0A8G
+8A0A(B BBBE L < a / BBB B(A0G8J
+8A0A(B BBBA T c5 / BBB E(A0A8G
+8A0A(B BBBD L g / BBB B(A0G8J
+8A0A(B BBBA T 4 j / BBB E(A0A8G-
+8A0A(B BBBE T mE / BBB E(A0A8G
+8A0A(B BBBA T pu / BBB E(A0A8G
+8A0A(B BBBC L < ut 0 BBB B(A0D8M
+8A0A(B BBBA T w 0 BBB E(A0A8G
+8A0A(B BBBE T } 0 BBG B(D0G8G
+8D0A(B BBBD T < 0 BBB B(A0D8M
+8A0A(B BBBA T 0d 0 BBB G(D0G8G
+8D0A(B BBBD T H 0 BBB B(D0A8M
+8A0A(B BBBA T D 0 BBB B(A0D8M
+8A0A(B BBBA T ( 0 BBB B(A0D8M
+8A0A(B BBBA T 0 BBB B(A0D8M
+8A0A(B BBBA T L 0 BBB B(D0A8M
+8A0A(B BBBA T 0 BBB B(A0D8M
+8A0A(B BBBA T Ĺ 0 BBB B(A0D8M
+8A0A(B BBBA T T Pɹ 0 BBB B(A0D8M
+8A0A(B BBBA L e 0 BBB B(A0D8M
+8A0A(B BBBH T ҹX 0 BBB G(D0G8G
+8D0A(B BBBD L T U 0 BBB B(A0D8M
+8A0A(B BBBC T 5 0 BBB E(A0A8G
+8A0A(B BBBH T 0 BBB G(D0G8G0
+8D0A(B BBBD T T 0 BBB E(A0A8G1
+8A0A(B BBBA T m 0 BBB G(D0G8G
+8D0A(B BBBD T 5 0 BBB E(A0A8G
+8A0A(B BBBA T \ 0 BBB G(D0G8G
+8D0A(B BBBD T ,e 0 BBB E(A0A8G
+8A0A(B BBBH T 1t 0 BBB G(D0G8G
+8D0A(B BBBD T d N5 0 BBB E(A0A8G
+8A0A(B BBBH T Rb 0 BBB G(D0G8G
+8D0A(B BBBD T g 0 BBB E(A0A8G
+8A0A(B BBBG T l Hk= 0 BBB G(D0G8G
+8D0A(B BBBD T 0 0 BBB E(A0A8G
+8A0A(B BBBA T 聺
1 BBB G(D0G8G
+8D0A(B BBBD T t p 1 BBB E(A0A8G
+8A0A(B BBBA T (
1 BBB G(D0G8G`
+8D0A(B BBBD T $ p 1 BBB E(A0A8G)
+8A0A(B BBBI T | 螺 1 BBB G(D0G8GH
+8D0A(B BBBD T p 1 BBG E(G0A8G
+8D0A(B BBBD , , HA< 2 AK
+AA 4 L L m 1 BBB B(A0I8Dp
+8D0A(B BBBF L úm 1 BBB B(A0I8Dp
+8D0A(B BBBF D : BBB D(A0D@0A(A BBB T t úl #1 BIE B(A0A8DY
+8D0A(B BBBA T Ⱥr 0 BIE B(A0A8Da
+8D0A(B BBBA T $ κ 0 BBB B(A0F8G
+8A0A(B BBBA T 8@. K^ l @ 8 p\ AG RAL BLB B(A0A8Dq
+8A0A(B BBBE T 4 0 BEE E(D0A8G
+8D0A(B BBBE L d BOB A(A0k
(A BBBI b
-(A BBBG T x<
BBE B(D0A8G
-8D0A(B BBBB D D K BBB D(A0D@0A(A BBB T BIB E(A0A8D6
-8D0A(B BBBA T P y BIB E(A0A8DF
-8D0A(B BBBA L < BBB E(D0D8G@L
-8D0A(B BBBE < BLH D(A0
-(A BBBF T BBB B(A0A8G 0
-8A0A(B BBBE T , R BEI E(A0A8G|
-8D0A(B BBBI | ǜ. K^ , [ o AID t
-DAA D J BBB D(A0D@0A(A BBB T D L BIE B(A0A8D9
-8D0A(B BBBA T #5 BJB B(A0D8G
-8D0A(B BBBA Ŝ. K^ / / T < / 7 BBB B(I0A8D'
-8D0A(B BBBF D J BBB D(A0D@0A(A BBB T 1 BIB E(A0A8G
-8D0A(B BBBA L 4 88 BBB B(A0D8Jo
-8D0A(B BBBE L < BBB B(A0D8Jo
-8D0A(B BBBE L ? g BBB B(A0D8Jo
-8D0A(B BBBE L $ C H BBB B(A0D8Jo
-8D0A(B BBBE L t hF ) BBB B(A0D8Jo
-8D0A(B BBBE L (I
BBB G(D0G8G
-8D0A(B BBBF V Ko
-A , hV D `V L d hV K BBB B(D0A8DP
-8A0A(B BBBG L 8W BBB B(D0A8DP
-8A0A(B BBBH L X BBB B(I0A8G
-8D0A(B BBBF L T HZ BBB B(I0A8G
-8D0A(B BBBF D H BBB D(A0D@0A(A BBB T @\ ? BIE B(A0A8D
-8D0A(B BBBA T D a BIE B(A0A8D
-8D0A(B BBBA T @gU BBH E(A0A8L
-8D0A(B BBBE T Hp+ o BEE E(A0A8J1
-8D0A(B BBBE T L x+ ~ BEE E(A0A8J/
-8D0A(B BBBG L I BFB B(A0A8JS
-8D0A(B BBBH h. K^ 4 L T 肾 BBB B(A0I8G
-8D0A(B BBBD T ( BBE B(D0A8G
-8D0A(B BBBG T BEE E(D0A8G*
-8D0A(B BBBD D L F BBB D(A0D@0A(A BBB T @ j BIE B(A0A8D
-8D0A(B BBBA T C BBE E(D0A8J
-8D0A(B BBBG L L P% BHB G(D0A8G
-8D0A(B BBBK . K^ , [ AID t
-DAA , H[ AID t
-DAA < < x BBB A(A0(A BBB < ؼ ; BBB A(A0(A BBB D X
- BBB B(D0A8DP8A0A(B BBBL BEB B(A0A8D@y8A0A(B BBB D T p BBB B(A0D8D@8A0A(B BBBL # BEB B(A0A8D@y8A0A(B BBB $ h< AAN0fDA $ < AAN0fDA D < D BBB D(A0D@0A(A BBB T Pl ? BIE B(A0A8DY
-8D0A(B BBBA T hľ
BIE B(A0A8D
-8D0A(B BBBA T < ɾ BBE B(D0A8G
-8D0A(B BBBG T ξU BAA G0
+(A BBBG T H< Q0 BBE B(D0A8G
+8D0A(B BBBB D ! \ BBB D(A0D@0A(A BBB T | / BIB E(A0A8DF
+8D0A(B BBBA T 0 / BIB E(A0A8D6
+8D0A(B BBBA L " h BBB E(D0D8G@L
+8D0A(B BBBE < T" BLH D(A0
+(A BBBF T ! . BBB B(A0A8G 0
+8A0A(B BBBE T " 0 / BEB I(D0A8GM
+8D0A(B BBBH L l" h / BBB B(I0A8G
+8D0A(B BBBF L " / BBB B(I0A8G
+8D0A(B BBBF # ;. K^ # $ , T# [ {/ AID t
+DAA D \$ ڨ BBB D(A0D@0A(A BBB T # L / BIE B(A0A8D9
+8D0A(B BBBA T $$ . BJB B(A0D8G
+8D0A(B BBBA T% :. K^ l% , % , T $ , ?/ BBB B(I0A8D'
+8D0A(B BBBF D % BBB D(A0D@0A(A BBB T d% 0. . BIB E(A0A8G
+8D0A(B BBBA L % 4 . BBB B(A0D8Jo
+8D0A(B BBBE L & (8 . BBB B(A0D8Jo
+8D0A(B BBBE L \& ; m. BBB B(A0D8Jo
+8D0A(B BBBE L & ? M. BBB B(A0D8Jo
+8D0A(B BBBE L & XB -. BBB B(A0D8Jo
+8D0A(B BBBE T L' E
. BBB G(D0G8GN
+8D0A(B BBBF |( 7V Ko
+A ( 0R ( (R L ' 0R G. BBB B(D0A8DP
+8A0A(B BBBG L D( S - BBB B(D0A8DP
+8A0A(B BBBH L ( S - BBB B(I0A8G
+8D0A(B BBBF L ( V - BBB B(I0A8G
+8D0A(B BBBF D * Ʀ BBB D(A0D@0A(A BBB T |) X ;- BIE B(A0A8D
+8D0A(B BBBA T ) ] - BIE B(A0A8D
+8D0A(B BBBA T ,* cD , BBH E(A0A8L
+8D0A(B BBBE T * l g- BBB B(A0A8G
+8D0A(B BBBK T * s r- BBB B(A0A8G
+8D0A(B BBBE L 4+ @{ }- BFB B(A0A8JJ
+8D0A(B BBBA \, 4. K^ t, } , } L + } - BBB B(A0I8G
+8D0A(B BBBD T , , BEE E(D0A8G*
+8D0A(B BBBD T t, , BBE B(D0A8G
+8D0A(B BBBG D , H , BAA G@u
+ DABI
+ DABF D - BBB D(A0D@0A(A BBB T \- ȋ R, BIB E(A0A8D
+8D0A(B BBBA T - j +, BEE B(D0A8N[
+8D0A(B BBBG L . +, BHG B(A0D8G
+8D0A(B BBBJ 4/ x1. K^ L/ @ d/ 8 , . @[ , AID t
+DAA , . p[ , AID t
+DAA < / BBB A(A0(A BBB < D/ S, BBB A(A0(A BBB D \0
+ BBB B(D0A8DP8A0A(B BBBL / H + BEB B(A0A8D@y8A0A(B BBB D 0 BBB B(A0D8D@8A0A(B BBBL d0 @ ;+ BEB B(A0A8D@y8A0A(B BBB $ 1 < AAN0fDA $ 1 < AAN0fDA D 1 BBB D(A0D@0A(A BBB T L1 xl W* BIE B(A0A8DY
+8D0A(B BBBA T 1 %* BIE B(A0A8D
+8D0A(B BBBA T 1 ) BBE B(D0A8G
+8D0A(B BBBG T ,3 оU BAA G0
AABA
AABK f
- AABF | Ҿ BBB B(D0A8DP
+ AABF | 3 » BBB B(D0A8DP
8A0A(B BBBB U
8G0A(B BBBK
-8A0A(B BBBI L l 0վ BDE E(G0A8G
-8D0A(B BBBH L BDE E(G0A8G
-8D0A(B BBBH T 0] BBB J(D0A8M
-8D0A(B BBBE \
- X. K^ t
-
-
-
- , [ ' AID t
-DAA , P[ AID t
-DAA , < [ AID t
-DAA D d PB BBB D(A0D@0A(A BBB T h\ BIE B(A0A8DI
-8D0A(B BBBA T
- p M BIB E(A0A8G
-8D0A(B BBBA T d
- & BIB E(A0A8G
-8D0A(B BBBA L
- r BDB B(G0D8G
-8D0A(B BBBE L w BDB H(A0D8G
-8D0A(B BBBG T
. K^ l
x
p T x BBB B(A0D8R:
-8A0A(B BBBA , @> AID W
-DAA T , P BBE B(D0A8G
-8D0A(B BBBG D | A BBB D(A0D@0A(A BBB T , BIB E(A0A8D
-8D0A(B BBBA T $
H BEF B(G0D8GS
-8A0A(B BBBB T |
-b C BEF B(G0D8G
-8A0A(B BBBB L
9 BBB G(D0A8M
-8D0A(B BBBE . K^ 4 < L < d < | < , <[ ' AID t
-DAA , <[ AID t
-DAA D \@ BBB D(A0D@0A(A BBB T D <l BIE B(A0A8DY
-8D0A(B BBBA T Al m BIE B(A0A8DY
-8D0A(B BBBA T G- ; BDB E(G0A8G
-8D0A(B BBBD T L N w BDB E(G0A8G
-8D0A(B BBBE . K^ 0V (V V (V \ 0Vj ; BBB A(I0DW
+8A0A(B BBBI T ,3 XŻ ) BDE E(G0A8G
+8D0A(B BBBE T 3 ѻ ) BDE E(G0A8G
+8D0A(B BBBE T 3 he w* BBB J(D0A8M(
+8D0A(B BBBI 5 +. K^ $5 h <5 ` T5 X l5 ` , 4 h[ * AID t
+DAA , 4 [ ) AID t
+DAA , 5 [ ) AID t
+DAA D 6 ( BBB D(A0D@0A(A BBB T 5 \ _) BIE B(A0A8DI
+8D0A(B BBBA T 5 -) BIB E(A0A8G
+8D0A(B BBBA T 46 @ ) BIB E(A0A8G
+8D0A(B BBBA L 6 z ( BDE H(A0A8G
+8D0A(B BBBI L 6 / ) BDH B(A0D8G
+8D0A(B BBBD 8 ). K^ 8 48 T t7 ) BBB B(A0D8R:
+8A0A(B BBBA , 7 > ) AID W
+DAA T 7 ( BBE B(D0A8G
+8D0A(B BBBG D ,9 ޞ BBB D(A0D@0A(A BBB T 8 ( ( BIB E(A0A8D
+8D0A(B BBBA 8 E m( BEE E(G0G
+0A(A BBBK Q
+0A(A BBBG ,
+0A(A BBBD
+0A(A BBBG 9 `
i( BEE E(G0G>
+0A(A BBBK Q
+0A(A BBBG
+0A(A BBBE K
+0A(A BBBE T : $ g( BBB G(D0A8M5
+8D0A(B BBBG D; %. K^ \; p( t; h( ; `( ; h( , : p([ ' AID t
+DAA , ; ([ ' AID t
+DAA D < BBB D(A0D@0A(A BBB T ; (l s' BIE B(A0A8DY
+8D0A(B BBBA T ; -l A' BIE B(A0A8DY
+8D0A(B BBBA T << 2 ' BDB E(A0G8G
+8D0A(B BBBF T < :J C' BDB B(D0G8G
+8D0A(B BBBI = #. K^ = A = @ > @ $> @ \ d= Aj & BBB A(I0DW
0D(A BBBJ n
-0D(A BBBG D t ? BBB D(A0D@0A(A BBB T YL BIE B(A0A8D9
-8D0A(B BBBA L ^ BBB B(D0A8G
-8A0A(B BBBA d . K^ | h} `} X} P} H} @} H} $ P} < X} T `} , t h}[ AID t
-DAA , }[ AID t
-DAA , }[ AID t
-DAA , }[ AID t
-DAA , 4 (~[ AID t
-DAA D \ > BBB D(A0D@0A(A BBB T ~ BIB E(A0A8D6
-8D0A(B BBBA T H BIB E(A0A8D6
-8D0A(B BBBA T \ BIB E(A0A8D6
-8D0A(B BBBA T BIB E(A0A8D6
-8D0A(B BBBA T O BIB E(A0A8D6
-8D0A(B BBBA T d (Q
- BDB B(A0A8G
-8A0A(B BBBA T 0b n BDB B(A0A8G
-8A0A(B BBBA T HI
- BDB B(A0A8G
-8A0A(B BBBA T l @B 1 BDB B(A0A8G
-8A0A(B BBBA T 8"
- BDB B(A0A8G
-8A0A(B BBBA . K^ , ƿ D ƿ \ ƿ t ƿ ƿ ƿ ƿ ƿ ƿ ƿ ƿ 4 ƿ L ƿ d ƿ | ƿ ƿ ƿ ƿ ƿ ƿ \ ƿ, BBB A(I0Dp
+0D(A BBBG D >
+ BBB D(A0D@0A(A BBB T > DL z& BIE B(A0A8D9
+8D0A(B BBBA L d> I K& BBE B(A0A8G
+8A0A(B BBBG ? ". K^ ? 8h ? 0h ? (h ? h @ h @ h 4@ h L@ h d@ (h |@ 0h , ? 8h[ % AID t
+DAA , ? hh[ % AID t
+DAA , @ h[ w% AID t
+DAA , L@ h[ W% AID t
+DAA , |@ h[ 7% AID t
+DAA D A BBB D(A0D@0A(A BBB T @ h $ BIB E(A0A8D6
+8D0A(B BBBA T LA m $ BIB E(A0A8D6
+8D0A(B BBBA T A Pq k$ BIB E(A0A8D6
+8D0A(B BBBA T A u 9$ BIB E(A0A8D6
+8D0A(B BBBA T TB y $ BIB E(A0A8D6
+8D0A(B BBBA T B }A
+ # BDB B(A0A8G
+8A0A(B BBBA T C )
+ &$ BDB B(A0A8G
+8A0A(B BBBA T \C ȑ
+ {$ BDB B(A0A8G
+8A0A(B BBBA T C
+ $ BDB B(A0A8G
+8A0A(B BBBA T D 8e
+ %% BDB B(A0A8G
+8A0A(B BBBA <E . K^ TE 8 lE 0 E ( E E E E E F ,F DF 讼 \F tF 讼 F F F F F G G 4G ( LG 0 \ F 8, M# BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ t ǿ, BBB A(I0Dp
+0D(A BBBA \ F , # BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ ȿ, t BBB A(I0Dp
+0D(A BBBA \ LG ذ, " BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ 4 hɿ, 0 BBB A(I0Dp
+0D(A BBBA \ G , " BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ 8ʿ, BBB A(I0Dp
+0D(A BBBA \ H x, =" BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ ˿, BBB A(I0Dp
+0D(A BBBA \ lH H, ! BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ T ˿, d BBB A(I0Dp
+0D(A BBBA \ H , ! BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ ̿, BBB A(I0Dp
+0D(A BBBA \ ,I 贼, q! BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ xͿ, BBB A(I0Dp
+0D(A BBBA \ I , -! BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ t Hο, BBB A(I0Dp
+0D(A BBBA \ I , BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ Ͽ, T BBB A(I0Dp
+0D(A BBBA \ LJ X, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ 4 Ͽ, BBB A(I0Dp
+0D(A BBBA \ J (, a BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ п, BBB A(I0Dp
+0D(A BBBA \ K , BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ ѿ, BBB A(I0Dp
+0D(A BBBA \ lK ȹ, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ T Xҿ, D BBB A(I0Dp
+0D(A BBBA \ K , BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ (ӿ, BBB A(I0Dp
+0D(A BBBA \ ,L h, Q BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ ӿ, BBB A(I0Dp
+0D(A BBBA \ L 8,
BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ t Կ, x BBB A(I0Dp
+0D(A BBBA \ L , BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ տ, 4 BBB A(I0Dp
+0D(A BBBA \ LM ؽ, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ 4! hֿ, BBB A(I0Dp
+0D(A BBBA \ M , A BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA $ # 8^ AG b
-AA D # p BEE D(A0D`n
-0A(A BBBE $ # ؿ Az
+0D(A BBBA \ N x, BBB A(I0Dp
+0D(A BBBG ^
+0D(A BBBA \ lN H, BBB A(I0Dp
+0D(A BBBG ^
+0D(A BBBA $ O ^ AG b
+AA D O P BEE D(A0D`n
+0A(A BBBE $ P Az
E
-A D $$ ؿ BBB D(A0D@
-0A(A BBBA D t" ٿ BIB A(A0G`
-0D(A BBBA D " @ۿ BIB A(A0G`
-0D(A BBBA D # ܿ n BIB A(A0G`
-0D(A BBBA D L# ? BIB A(A0G`
-0D(A BBBA D # x߿ BIB A(A0G`
-0D(A BBBA D # BIB A(A0G`
-0D(A BBBA D $$ H BIB A(A0G`
-0D(A BBBA D l$ BIB A(A0G`
-0D(A BBBA D $ T BIB A(A0G`
-0D(A BBBA D $ % BIB A(A0G`
-0D(A BBBA D D% BIB A(A0G`#
-0D(A BBBA D % ` BIB A(A0G`#
-0D(A BBBA D % BIB A(A0G`#
-0D(A BBBA D & P i BIB A(A0G`#
-0D(A BBBA D d& : BIB A(A0G`#
-0D(A BBBA D & @ BIB A(A0G`#
-0D(A BBBA D & BIB A(A0G`#
-0D(A BBBA D <' 0 BIB A(A0G`#
-0D(A BBBA D ' ~ BIB A(A0G`#
-0D(A BBBA D ' O BIB A(A0G`#
-0D(A BBBA L * BOB A(A0]
+A D <P `¼ BBB D(A0D@
+0A(A BBBA D O ü BIB A(A0G`
+0D(A BBBA D O ż f BIB A(A0G`
+0D(A BBBA D <P Ƽ 7 BIB A(A0G`
+0D(A BBBA D P Ǽ BIB A(A0G`
+0D(A BBBA D P Xɼ BIB A(A0G`
+0D(A BBBA D Q ʼ BIB A(A0G`
+0D(A BBBA D \Q (̼ { BIB A(A0G`
+0D(A BBBA D Q ͼ L BIB A(A0G`
+0D(A BBBA D Q μ BIB A(A0G`
+0D(A BBBA D 4R `м BIB A(A0G`
+0D(A BBBA D |R Ѽ BIB A(A0G`
+0D(A BBBA D R 0Ӽ BIB A(A0G`#
+0D(A BBBA D S Լ a BIB A(A0G`#
+0D(A BBBA D TS ּ 2 BIB A(A0G`#
+0D(A BBBA D S BIB A(A0G`#
+0D(A BBBA D S ټ BIB A(A0G`#
+0D(A BBBA D ,T ڼ BIB A(A0G`#
+0D(A BBBA D tT ܼ v BIB A(A0G`#
+0D(A BBBA D T xݼ G BIB A(A0G`#
+0D(A BBBA D U BIB A(A0G`#
+0D(A BBBA D LU h BIB A(A0G`#
+0D(A BBBA D U BIB A(A0G`#
+0D(A BBBA L V X BOB A(A0]
(A BBBG Y
-(A BBBH L \* BBB B(D0D8G@H
-8A0A(B BBBG \ * X BBD D(G0G
-(E ABBD o
-(E ABBG L(A EBB D + E BAA GP
- AABB AAB L T+
+(A BBBH L W BBB B(D0D8G@H
+8A0A(B BBBG D TW E BAA GP
+ AABB AAB L W
BKB B(A0A8G}
-8A0A(B BBB $ + AD@
-AE $ + MSgv D + @E BAA GP
- AABB AAB L <, H
- BKB B(A0A8Gu 8A0A(B BBB D , E BAA GP
- AABB AAB L , * BKB B(A0A8G 8A0A(B BBB D $- E BAA GP
- AABB AAB L l- "
+8A0A(B BBB $ W AD@
+AE $ X MSgv D <X E BAA GP
+ AABB AAB L X BKB B(A0A8Gu 8A0A(B BBB D X E BAA GP
+ AABB AAB L Y * BKB B(A0A8G 8A0A(B BBB D lY E BAA GP
+ AABB AAB L Y
BKB B(A0A8G
-8A0A(B BBB D - ,E BAA GP
- AABB AAB $ . / AD@
-AE $ ,. h1 MSgv D T. 2E BAA GP
- AABB AAB L . (4 BHB B(A0A8D`
-8A0A(B BBBA $ . 5 IP c
-DH L / 7 BHB B(A0A8D`
-8A0A(B BBBA L d/ p9! BHB B(A0A8DX
-8A0A(B BBBA L / P; BHB B(A0A8D`
-8A0A(B BBBA L 0 =- BHB B(A0A8Dh
-8A0A(B BBBA L T0 ?5 BEB B(A0A8Dp
-8A0A(B BBBA , 0 @ BTA _DG L . PA
+8A0A(B BBB D Z XE BAA GP
+ AABB AAB $ LZ ` AD@
+AE $ tZ MSgv D Z E BAA GP
+ AABB AAB L Z BHB B(A0A8D`
+8A0A(B BBBA $ 4[ X" IP c
+DH L \[ $ BHB B(A0A8D`
+8A0A(B BBBA L [ %! BHB B(A0A8DX
+8A0A(B BBBA L [ ' BHB B(A0A8D`
+8A0A(B BBBA L L\ )- BHB B(A0A8Dh
+8A0A(B BBBA L \ `+5 BEB B(A0A8Dp
+8A0A(B BBBA , \ P- BTA _DG T D\ - # BBB B(A0A8G
+8A0A(B BBBG T \ 1 BEB H(D0A8Gk
+8A0A(B BBBA T \ 8 BEB H(D0A8Gk
+8A0A(B BBBA T L] (@ O BEB H(D0A8G
+8A0A(B BBBA T ] `L BEB H(D0A8G
+8A0A(B BBBA T ] X' BEH E(A0A8G5
+8A0A(B BBBA T T^ pf w BEB H(D0A8G
+8A0A(B BBBA _ r1 Dl \ _ r BBD D(G0G
+(E ABBD o
+(E ABBG L(A EBB L ,_ (s
- X BHB B(A0A8J
-8D0A(B BBBI T ,/ K ] BBB B(A0A8G
-8A0A(B BBBG L / xN BBB B(A0G8J
-8D0A(B BBBI T / T BEB H(D0A8Gk
-8A0A(B BBBA L ,0 \ BBB B(G0A8J
-8D0A(B BBBI T |0 b BEB H(D0A8Gk
-8A0A(B BBBA L 0 Hj BBB B(A0G8J
-8D0A(B BBBI T $1 p BEB H(D0A8G
-8A0A(B BBBA L |1 } > BBB B(A0G8J
-8D0A(B BBBI L 1 Pt = BBB B(A0G8J
-8D0A(B BBBI T 2 = BEB H(D0A8G
-8A0A(B BBBA L t2 BBB B(A0G8J
-8D0A(B BBBI T 2 8' BEH E(A0A8G5
-8A0A(B BBBA L 3 BBB B(G0A8J
-8D0A(B BBBI T l3 P BEB H(D0A8G
-8A0A(B BBBA L 3 c BBB B(G0A8J
-8D0A(B BBBI L 4 b BBB B(G0A8J
-8D0A(B BBBI \6 x. K^ D t6 BEB E(D0D8PP~8A0A(B BBB 6 X> Hu 6 x> Hu 6 > Hu 7 > Hu <7 E Hx \7 E Hx |7 8E Hx 7 hE Hx 7 \ AG RA< 7 x_ BBB D(A0
-(A BBBH 8 d T <6 BBE E(A0A8G
-8D0A(B BBBC T 6 ( L BBE E(A0A8G
-8D0A(B BBBA L 6 `k AAGPa
+ BHB B(A0A8J
+8D0A(B BBBI L |_ | BBB B(A0G8J
+8D0A(B BBBI L _ h BBB B(G0A8J
+8D0A(B BBBI L ` BBB B(G0A8J
+8D0A(B BBBI L l` 菽 BBB B(G0A8J
+8D0A(B BBBI L ` ( BBB B(G0A8J
+8D0A(B BBBI L a h BBB B(A0G8J
+8D0A(B BBBI L \a 袽 BBB B(A0G8J
+8D0A(B BBBI L a 8 BBB B(A0G8J
+8D0A(B BBBI L a BBB B(A0G8J
+8D0A(B BBBI L Lb BBB B(G0A8J
+8D0A(B BBBI tc . K^ D c 0 BEB E(D0D8PP~8A0A(B BBB c > Hu c > Hu d ؼ> Hu 4d > Hu Td E Hx td HE Hx d xE Hx d E Hx d nz\ AG RA< d _ BBB D(A0
+(A BBBH 4e ؾd T td 0
BBE E(A0A8G
+8D0A(B BBBC T d h
BBE E(A0A8G
+8D0A(B BBBA L $e ýk Y
AAGPa
DAA A
DAJ }
-DAA D 49 BBB D(A0D@
-0A(A BBBA D 7 BIB A(D0D`+
-0D(A BBBA D 7 r BIB A(D0D`+
-0D(A BBBA D 8 H P BIB A(D0D`+
-0D(A BBBA D \8 . BIB A(D0D`+
-0D(A BBBA D 8 BIB A(D0D`+
-0D(A BBBA D 8 p BIB A(D0D`2
-0D(A BBBA < ,; 8[ BBE H(A0
-(A BBBF T t9 X BBB B(K0A8Jo
-8A0A(B BBBI 4 9 q BAI A
-ABA 4 : q j BAI A
-ABA 4 <: `q F BAI A
-ABA 4 t: q " BAI A
-ABA 4 : q BAI A
-ABA 4 : 8q ڽ BAI A
-ABA , ; ADG0
-AAB < D= BDA k
+DAA D Lf Ľ BBB D(A0D@
+0A(A BBBA D e ƽ BIB A(D0D`+
+0D(A BBBA D f Ƚ BIB A(D0D`+
+0D(A BBBA D Lf ˽ BIB A(D0D`+
+0D(A BBBA D f @ν x BIB A(D0D`+
+0D(A BBBA D f н V BIB A(D0D`+
+0D(A BBBA D $g ӽ 4 BIB A(D0D`2
+0D(A BBBA < Dh xֽ[ BBE H(A0
+(A BBBF T g BBB B(K0A8Jo
+8A0A(B BBBI 4 h ݽq BAI A
+ABA 4 <h Xݽq BAI A
+ABA 4 th ݽq BAI A
+ABA 4 h ݽq l BAI A
+ABA 4 h 0q H BAI A
+ABA 4 i xq $ BAI A
+ABA , Ti ADG0
+AAB < \j @ BDA k
ABK @
-ABA $ = P A
+ABA $ j A
A e
-K $ = A
+K $ j H A
A e
-K $ = A
+K $ j A
A e
-K $ = x A
+K $ k A
A e
-K T ,< 0& BBB B(D0A8Gz
-8A0A(B BBBA T < U , BEE E(A0A8G>
-8D0A(B BBBA T < # v BBB B(A0A8Gl
-8A0A(B BBBA T 4= 5U BEE E(A0A8G9
-8D0A(B BBBA T = ;o# 8 BBB B(A0D8G
-8A0A(B BBBA T = ^U BEE E(A0A8G>
-8D0A(B BBBA T <> d BBB B(A0A8Gw
-8A0A(B BBBA T > U | BEE E(A0A8G<
-8D0A(B BBBA @ . K^ @ A ,A DA \A tA A A L ? u BBB B(A0I8Dp
-8D0A(B BBBF L @ 8u BBB B(A0I8Dp
-8D0A(B BBBF L d@ hu BBB B(A0I8Dp
-8D0A(B BBBF L @ u R BBB B(A0I8Dp
-8D0A(B BBBF D B BBB D(A0D@0A(A BBB T LA ֽ BIB B(A0D8Df
-8D0A(B BBBA T A BIB B(A0D8Df
-8D0A(B BBBA T A P r BIB B(A0D8D
-8D0A(B BBBA T TB ؗ @ BIB B(A0D8D
-8D0A(B BBBA T B `$ BBB B(D0A8G\
-8A0A(B BBBA T C 8$ BBB B(D0A8Ga
-8A0A(B BBBA T \C BGB B(D0A8G
-8A0A(B BBBA T C u BGB B(D0A8G
-8A0A(B BBBA F 0}. K^ F 4F LF 0 Hg lF 7 Hj , D [ ` AID t
-DAA L D 8 @ BBB J(A0A8G
-8D0A(B BBBA T E | + BBE E(D0A8G
-8D0A(B BBBF D dG T BBB D(A0D@0A(A BBB T E hb ɽ BIE B(A0A8DQ
-8D0A(B BBBA T F | BIE B(A0A8Di
-8D0A(B BBBA T dF R g BEE B(A0A8J
-8D0A(B BBBA T F X w BMB E(A0A8G
-8D0A(B BBBA I Xz. K^ $I <I $ TI
lI 1 I i I ( I 0 AL I DI I ( DI J 0 DI J 8 AL <J ( AL \J # TI tJ 0A I[
-L K , J `b D\
+K T dj p2 BBE B(A0A8G
+8A0A(B BBBA T j XE v
+ BEE E(A0A8G>
+8D0A(B BBBA T k P#
+ BBB B(A0A8G
+8A0A(B BBBA T lk &E 8 BEE E(A0A8G9
+8D0A(B BBBA T k ,! BBB B(A0A8J
+8A0A(B BBBA T l ME BEE E(A0A8G>
+8D0A(B BBBA T tl S D BBB B(A0A8G
+8A0A(B BBBA T l mE BEE E(A0A8G<
+8D0A(B BBBA m 0. K^ n s ,n s Dn s \n s tn xs n s n s n s L m su - BBB B(A0I8Dp
+8D0A(B BBBF L Ln tu BBB B(A0I8Dp
+8D0A(B BBBF L n uu BBB B(A0I8Dp
+8D0A(B BBBF L n (wu BBB B(A0I8Dp
+8D0A(B BBBF < p oD BBD A(D@0(A ABB T |o x BIB B(A0D8Df
+8D0A(B BBBA T o |
+ BIB B(A0D8Df
+8D0A(B BBBA T ,p 耾
+ BIB B(A0D8D
+8D0A(B BBBA T p p
+ BIB B(A0D8D
+8D0A(B BBBA T p U
+ BBB B(D0A8G\
+8A0A(B BBBA T 4q
+ BBB B(D0A8Ga
+8A0A(B BBBA T q X% I BGB B(D0A8G
+8A0A(B BBBA T q 0 BGB B(D0A8G
+8A0A(B BBBA s H. K^ ,s ా Ds ذ \s ా0 Hg |s 7 Hj , r [ AID t
+DAA L r @ } BBB J(A0A8G
+8D0A(B BBBA T Ds | h BBE E(D0A8G
+8D0A(B BBBF D s N BBE A(D0G
+0A(A BBBA D t &l BBB D(A0D@0A(A BBB T ,t V BIB E(A0A8DN
+8D0A(B BBBA T t @ $ BIB E(A0A8Df
+8D0A(B BBBA T t ž
+ BBE B(A0D8J
+8D0A(B BBBF T 4u Pʾ BGH B(A0D8G
+8D0A(B BBBH dv (. K^ |v pӾ v hӾ$ v Ӿ
v xӾ1 v Ӿi v Ӿ w Ծ AL ,w Ӿ DI Dw Ӿ DI \w Ծ DI tw Ծ AL w Ӿ AL w Ӿ# TI w ԾA I[
+L K , w 0Ծb D\
H P
H Q
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+G I , x pԾb D\
H P
H Q
-G I , J b D\
+G I , Lx Ծb D\
H P
H Q
-G I , $K b D\
+G I , |x Ծb D\
H P
H Q
-G I , TK `b D\
+G I , x 0վb D\
H P
H Q
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+G I , x pվb D\
H P
H Q
-G I , K b D\
+G I , y վb D\
H P
H Q
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+G I , <y վb D\
H P
H Q
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+G I , ly 0־b D\
H P
H Q
-G I , DL b D\
+G I , y p־b D\
H P
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-G I , tL b D\
+G I , y ־b D\
H P
H Q
-G I $ J r HY
-O A $ J xr HY
-O A $ J r _ HY
-O A $ $K (r ; HY
-O A $ LK r HY
-O A $ tK r HY
-O A K 0 n ϻ Ha K n Ha K n Ha K
-n { Ha L p
-n _ Ha <L
-n C Ha TN 2 \ AG RAT |L
-t BBE B(D0A8G
-8D0A(B BBBH $ L R AK
-D A < N D BBD A(D@0(A ABB T <M BIB E(A0A8D6
-8D0A(B BBBA T M H W BIB E(A0A8DF
-8D0A(B BBBA T M % BIB E(A0A8D6
-8D0A(B BBBA T DN BIB E(A0A8D6
-8D0A(B BBBA T N ! BIB E(A0A8DF
-8D0A(B BBBA T N H% BIB E(A0A8DF
-8D0A(B BBBA d DQ )B BBB B(A0A8G@x
+G I $ $y ־r H\
+L A $ Ly Hr H\
+L A $ ty r H\
+L A $ y r H\
+L A $ y Pؾr H\
+L A $ y ؾr [ H\
+L A z پq 7 Hd 4z `پq Hd Tz پq Hd tz ھq Hd z ھq Hd z ھq Hd { g\ AG RAT z ۾t o BBE B(D0A8G
+8D0A(B BBBH D L{ H U BBB A(A0G\
+0A(A BBBE $ { R h AI
+F A D | xfv BBB D(A0D@^0A(A BBB T | BIB E(A0A8DF
+8D0A(B BBBA T \| BIB E(A0A8D6
+8D0A(B BBBA T | BIB E(A0A8D6
+8D0A(B BBBA T } 8 f BIB E(A0A8DF
+8D0A(B BBBA T d} 4 BIB E(A0A8D6
+8D0A(B BBBA T } BIB E(A0A8DF
+8D0A(B BBBA d ~ B BBB B(A0A8G@x
8A0A(B BBBE
-8A0A(B BBBF D O x* BBB A(A0G\
-0A(A BBBE T O - BBB B(D0A8J (
-8A0A(B BBBG < TP hK BBI A(Ga
-(D ABBA < P K ` BBI A(Ga
-(D ABBA < P (L 7 BBI A(Ga
-(D ABBA < Q L BBI A(Ga
-(D ABBA < TQ L BBI A(Ga
-(D ABBA < Q HM BBI A(Ga
-(D ABBA S o. K^ S M
S M
T M ,T M DT M \T M < |R M BBB A(A0(A BBB < R O BBB A(A0(A BBB D R Ok s BBB A(A08
-(A BBBI D DS Q / BBB A(A0
-(A BBBA < S pS BBB A(A0(A BBB D S Tk BBB A(A08
-(A BBBI D T V k BBB A(A0
-(A BBBA < \T X ' BBB A(A0(A BBB < T Y BBB A(A0(A BBB D T Zk BBB A(A08
-(A BBBI < $U \ k BBB A(A0(A BBB < dU h] / BBB A(A0(A BBB < U ^ BBB A(A0(A BBB L U ^ BBB B(A0D8DP 8A0A(B BBB L 4V _ k BEB B(A0A8DP 8A0A(B BBB < V h`b BBA A(G0/
-(A ABBF < V bb BBA A(G0/
-(A ABBF D W ds BBB A(A0A
-(A BBBH < LW g c BBB A(A0(A BBB D W ph ' BBB A(A0
-(A BBBH < W j BBA A(G0(A ABB < X j BBA A(G0(A ABB < TX Xk k BBA A(G0(A ABB < X k / BBA A(G0(A ABB < X l BBB A(A0y(A BBB < Y m BBA A(G0(A ABB D TY n { BBB A(A0
-(A BBBH < Y p 7 BBB A(A0y(A BBB T Y pq BBE E(A0A8G
-8D0A(B BBBA T 4Z s BBB E(A0A8N
-M
-8A0A(B BBBA , Z > 8 AID W
-DAA < \ rD BBD A(D@0(A ABB T Z ` ر BIB E(A0A8D6
-8D0A(B BBBA T T[ \ BEE B(D0D8G*
-8A0A(B BBBA T [ \ BEE B(D0D8G*
-8A0A(B BBBA T \ 3 | BBB H(A0A8J
-8D0A(B BBBG T^ pe. K^ l^ ^ L \ r Q BBB B(E0A8J@
-8A0A(B BBBA T \ ( BEB E(A0A8G;
-8D0A(B BBBA T L] ٲ BEB E(A0A8G;
-8D0A(B BBBA L ] ) BBB B(A0I8G
-8D0A(B BBBA L ] ) BBB B(A0I8G
-8D0A(B BBBA D <` BBB D(A0D@
-0A(A BBBA L ^ ( BIB A(D0D
-0D(A BBBA L ^ p BIB A(D0D
-0D(A BBBA < $a Q BEH H(A0n(A BBE T l_ BEB B(A0D8J
-8A0A(B BBBA T _ 8 } BEB B(A0D8J
-8A0A(B BBBA T ` P BEB B(A0D8J
-8A0A(B BBBA T t` # BEB B(A0D8J
-8A0A(B BBBA T ` BEB B(A0D8J
-8A0A(B BBBA T $a ( ɰ BEB B(A0D8J
-8A0A(B BBBA T |a ` BEB B(A0D8J
-8A0A(B BBBA T a o BEB B(A0D8J
-8A0A(B BBBA T ,b B BEB B(A0D8J
-8A0A(B BBBA T b BEB B(A0D8J
-8A0A(B BBBA T b @ BEB B(A0D8J
-8A0A(B BBBA T 4c x BEB B(A0D8J
-8A0A(B BBBA T c BEB B(A0D8J
-8A0A(B BBBA T c a BEB B(A0D8J
-8A0A(B BBBA T <d P : BEB B(A0D8J
-8A0A(B BBBA T d
BEB B(A0D8J
-8A0A(B BBBA T d BEB B(A0D8J
-8A0A(B BBBA T De ( BEB B(A0D8J
-8A0A(B BBBA T e p BEB B(A0D8J
-8A0A(B BBBA T e ! Y BEB B(A0D8J
-8A0A(B BBBA T Lf & , BEB B(A0D8J
-8A0A(B BBBA T f H* BEB B(A0D8J
-8A0A(B BBBA T f . ҭ BEB B(A0D8J
-8A0A(B BBBA T Tg 2 BEB B(A0D8J
-8A0A(B BBBA T g 7 x BEB B(A0D8J
-8A0A(B BBBA T h X; K BEB B(A0D8J
-8A0A(B BBBA T \h ? BEB B(A0D8J
-8A0A(B BBBA T h C BEB B(A0D8J
-8A0A(B BBBA L i PHE ʬ BBH B(D0F8G
-8D0A(B BBBE Tk X. K^ lk 8Y k 0Y , i 8Y[ AID t
-DAA < k hY BBB A(A0(A BBB < j Y BBB A(A0(A BBB 4 Ll HZ BBA A(G0(A ABB< j Z # BBB D(A0n(A BBB $ l @[< AAN0fDA $ l X[< AAN0fDA D m V BBB D(A0D@0A(A BBB T dk ([l O BIE B(A0A8DY
-8D0A(B BBBA T k @`u) BDH E(A0A8G
-8D0A(B BBBG < l hp K BBD A(G
-(D ABBG Ln U. K^ dn |n x , l [ ݬ AID t
-DAA D n t BBB D(A0D@0A(A BBB T m hl u BIE B(A0A8DY
-8D0A(B BBBA T lm C BDE H(A0A8G.
-8D0A(B BBBI o T. K^ o o , n [ / AID t
-DAA D 4p BBB D(A0D@0A(A BBB T n K ǫ BIB E(A0A8G8
-8D0A(B BBBA L n I BDB B(D0A8M
-8D0A(B BBBD $q `S. K^ <q ȝO HF \q O HF |q (O HF q XO HF q O HF q O HF q O HF r O HF <r HO HF \r xO HF |r O HF r ؟O HF r O HF r 8O HF r hO HF s O HF <s ȠO HF \s O HF |s (O HF s XO HF s O HF s O HF s O HF t O HF <t HO HF \t xO HF |t V HI t V HI t (V HI t hV HI t V HI u V HI <u (V HI \u hV HI |u V HI u V HI u (V HI u hV HI u V HI v V HI <v (V HI \v hV HI |v V HI v V HI v (V HI v hV HI v V HI w V HI <w (V HI \w hV HI |w V HI w V HI $ w (^ AG b
-AA L u `T BBB B(A0A8JR
-8A0A(B BBBH 4 <v ps ( BAI C
-ABA 4 tv s BAI C
-ABA 4 v s BAI C
-ABA 4 v Hs BAI C
-ABA 4 w s BAI C
-ABA 4 Tw خs t BAI C
-ABA 4 w s P BAI C
-ABA 4 w hs , BAI C
-ABA 4 w s BAI C
-ABA 4 4x s BAI C
-ABA 4 lx @s BAI C
-ABA 4 x s BAI C
-ABA 4 x аs x BAI C
-ABA , y T AID
-AAA , Dy 6 AID
-AAA , ty AID
-AAA , y AID
-AAA , y ܢ AID
-AAA , z AID
-AAA , 4z AID
-AAA , dz AID
-AAA , z d AID
-AAA , z F AID
-AAA , z ( AID
-AAA , ${
- AID
-AAA , T{ AID
-AAA , { Ρ AID
-AAA , { AID
-AAA , { AID
-AAA , | t AID
-AAA , D| V AID
-AAA , t| 8 AID
-AAA , | AID
-AAA , | AID
-AAA , } ޠ AID
-AAA , 4} AID
-AAA , d} AID
-AAA , } AID
-AAA , } f AID
-AAA L K BBB B(A0A8G@d
-8A0A(B BBBA D < BBB D(A0D@
-0A(A BBBA < ~ p BHA GZ
- DABA < ~ BHA GZ
- DABA < z BHA GZ
- DABA < L P _ BHA GZ
- DABA < D BHA GZ
- DABA < ) BHA GZ
- DABA < 0 BHA GZ
- DABA < L BHA GZ
- DABA < p ؞ BHA GZ
- DABA 4 ̀ r BHA GP
- DABA 4 Xr BHA GP
- DABA 4 < r BHA GP
- DABA 4 t r ` BHA GP
- DABA 4 0r A BHA GP
- DABA 4 xr " BHA GP
- DABA 4 r BHA GP
- DABA 4 T r BHA GP
- DABA 4 Pr ŝ BHA GP
- DABA 4 Ă r BHA GP
- DABA 4 r BHA GP
- DABA 4 4 (r h BHA GP
- DABA 4 l pr I BHA GP
- DABA < * BHA Gi
- DABA < BHA Gi
- DABA < $ X BHA Gi
- DABA < d ( ٜ BHA Gi
- DABA < BHA Gi
- DABA < BHA Gi
- DABA < $ BHA Gi
- DABA < d h m BHA Gi
- DABA < 8 R BHA Gi
- DABA < 7 BHA Gi
- DABA < $ BHA Gi
- DABA < d BHA Gi
- DABA < x BHA Gi
- DABA < H ˛ BHA Gp
- DABA < $ BHA Gp
- DABA < d BHA Gp
- DABA < z BHA Gp
- DABA $ ܉ V ADD JAAT 7 BEE M(A0C8G[
-8A0A(B BBBG L d BEB E(I0H8D@
-8D0A(B BBBA < BFA D0}
- FABE D FAB T 5 ' BEE B(A0A8G
-8A0A(B BBBI T L BEB E(D0A8GK
-8A0A(B BBBA * A[
-L A L ĉ ] ǚ BEE E(D0G8GR
-8D0A(B BBBF T & BEG B(A0G8G
-8D0A(B BBBA T l , BBB B(F0G8G1
-8D0A(B BBBF L Ċ 08 BEE E(G0D8GR
-8D0A(B BBBF T 7 BEG B(A0G8G
-8D0A(B BBBA T l < BBB B(F0G8G1
-8D0A(B BBBF L ċ hA8 ! BEE E(G0D8GR
-8D0A(B BBBF T XH D BEG B(A0G8G
-8D0A(B BBBA T l M E BBB B(F0G8G1
-8D0A(B BBBF L Č 8RH K BEE E(G0D8GR
-8D0A(B BBBF T 8Y n BBG H(A0A8G
-8D0A(B BBBD T l _ p BBB B(F0G8G1
-8D0A(B BBBF L č dH v BEE E(G0D8GR
-8D0A(B BBBF T k BEG B(A0G8G
-8D0A(B BBBA T l `p BBB B(F0G8G1
-8D0A(B BBBF L Ď t BEE H(D0D8GL
-8D0A(B BBBD T z BEG B(A0G8G
-8D0A(B BBBA T l BBB B(F0G8G1
-8D0A(B BBBF L ď BEE H(D0D8GL
-8D0A(B BBBD T h BEG B(A0G8G
-8D0A(B BBBA T l BBB B(F0G8G1
-8D0A(B BBBF L Đ H8 BEE E(G0D8GR
-8D0A(B BBBF T 8 ۛ BEG B(A0G8G
-8D0A(B BBBA T l ܛ BBB B(F0G8G1
-8D0A(B BBBF L đ I BEE E(G0D8GR
-8D0A(B BBBF T BEG B(A0G8G
-8D0A(B BBBA T l ` BBB B(F0G8G1
-8D0A(B BBBF L Ē H BEE E(G0D8GR
-8D0A(B BBBF T / BEG B(A0G8G
-8D0A(B BBBA T l @ 0 BBB B(F0G8G1
-8D0A(B BBBF L ē 8 6 BEE E(G0D8GR
-8D0A(B BBBF T Y BBG H(A0A8G
-8D0A(B BBBD T l [ BBB B(F0G8G1
-8D0A(B BBBF L Ĕ 8 a BEE E(G0D8GR
-8D0A(B BBBF T BEG B(A0G8G
-8D0A(B BBBA T l BBB B(F0G8G1
-8D0A(B BBBF L ĕ xI BEE E(G0D8GR
-8D0A(B BBBF T x BEG B(A0G8G
-8D0A(B BBBA T l BBB B(F0G8G1
-8D0A(B BBBF +. K^ Ԙ @ 8 0 ( 4 0 L 8 L l @
BBB B(A0I8G
-8D0A(B BBBF T g BBB B(I0A8Gc
-8D0A(B BBBG T g BBB B(I0A8Gc
-8D0A(B BBBG D d pț BBB D(A0D@0A(A BBB T x Ǜ BIB E(A0A8D6
-8D0A(B BBBA T
- BIB E(A0A8DF
-8D0A(B BBBA T d c BIB E(A0A8D6
-8D0A(B BBBA L 0 BBB B(A0A8K`
-8A0A(B BBBH L BGB B(A0A8Gv
-8A0A(B BBBA L T @.] BBB B(A0A8G
-8A0A(B BBBA L P7m BMB E(A0A8G)
-8A0A(B BBBF d pC- BBB B(A0A8G
+8A0A(B BBBF T |~ ! h BBB B(D0A8J [
+8A0A(B BBBD < ~ b BBI A(Ga
+(D ABBA < 9 BBI A(Ga
+(D ABBA < T P BBI A(Ga
+(D ABBA < BBI A(Ga
+(D ABBA < ! BBI A(Ga
+(D ABBA < p! BBI A(Ga
+(D ABBA , . K^ D !
\ !
t ! ! ! ! < ! BBB A(A0(A BBB D < 8#k BBB A(A08
+(A BBBI < `% D BBB A(A0(A BBB L ā & BBB B(A0D8DP 8A0A(B BBB D &k BBB A(A08
+(A BBBI D \ ) x BBB A(A0
+(A BBBA D * 4 BBB A(A0
+(A BBBA < , BBB A(A0(A BBB < , x- BBB A(A0(A BBB < l . x BBB A(A0(A BBB < / < BBB A(A0(A BBB < 80 BBB A(A0(A BBB < , 0 BBB A(A0(A BBB L l 1 BEB B(A0A8DP 8A0A(B BBB D h2s < BBB A(A0A
+(A BBBH < 4b BBA A(G0/
+(A ABBF < D 6 BBB A(A0y(A BBB < 8 BBA A(G0(A ABB D ą 8 D BBB A(A0
+(A BBBH < h: BBA A(G0(A ABB < L ; BBA A(G0(A ABB D ; BBB A(A0
+(A BBBH < Ԇ @= D BBB A(A0y(A BBB < > BBA A(G0(A ABB < T 0? BBA A(G0(A ABB < ? BBB A(A0(A BBB T ԇ @A T BBE B(D0A8G
+8D0A(B BBBB T , E0
: BBE B(A0A8N
+I
+8A0A(B BBBB , R> AID W
+DAA < ZD BBD A(D@0(A ABB T R T BIB E(A0A8D6
+8D0A(B BBBA T L V. " BBB B(A0D8G
+8A0A(B BBBA T Z2 BBG E(A0A8G
+8A0A(B BBBC Ԋ ؖ. K^ L D r / BBB B(E0A8J@
+8A0A(B BBBA T @ BEB E(A0A8G;
+8D0A(B BBBA T BEB E(A0A8G;
+8D0A(B BBBA L D ) z BBB B(A0I8G
+8D0A(B BBBA L ) d BBB B(A0I8G
+8D0A(B BBBA T Ж N BBE E(A0A8G
+8D0A(B BBBH D BBB D(A0D@
+0A(A BBBA L ` BIB A(D0D
+0D(A BBBA L Ԍ BIB A(D0D
+0D(A BBBA < Q BEH H(A0n(A BBE T d @ K BEB B(A0D8J
+8A0A(B BBBA T x BEB B(A0D8J
+8A0A(B BBBA T BEB B(A0D8J
+8A0A(B BBBA T l 譿 BEB B(A0D8J
+8A0A(B BBBA T Ď BEB B(A0D8J
+8A0A(B BBBA T X j BEB B(A0D8J
+8A0A(B BBBA T t = BEB B(A0D8J
+8A0A(B BBBA T ̏ Ⱦ BEB B(A0D8J
+8A0A(B BBBA T $ ÿ BEB B(A0D8J
+8A0A(B BBBA T | 8ǿ BEB B(A0D8J
+8A0A(B BBBA T Ԑ p˿r BEB B(A0D8J
+8A0A(B BBBA T , Ͽ \ BEB B(A0D8J
+8A0A(B BBBA T ӿr / BEB B(A0D8J
+8A0A(B BBBA T ܑ BEB B(A0D8J
+8A0A(B BBBA T 4 `ܿ 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 ` ' BEB B(A0D8J
+8A0A(B BBBA T BEB B(A0D8J
+8A0A(B BBBA T D BEB B(A0D8J
+8A0A(B BBBA T BEB B(A0D8J
+8A0A(B BBBA T @ s BEB B(A0D8J
+8A0A(B BBBA T L xr F BEB B(A0D8J
+8A0A(B BBBA T r BEB B(A0D8J
+8A0A(B BBBA T
+ BEB B(A0D8J
+8A0A(B BBBA T T BEB B(A0D8J
+8A0A(B BBBA T 8 BEB B(A0D8J
+8A0A(B BBBA L k BBH B(D0F8G
+8D0A(B BBBD , ʖ. K^ D % \ % , %[ AID t
+DAA < % BBB A(A0(A BBB < X& 2 BBB A(A0(A BBB 4 $ & BBA A(G0(A ABB< ' BBB D(A0n(A BBB $ '< AAN0fDA $ ę '< AAN0fDA D K BBB D(A0D@0A(A BBB T \ 'l BIE B(A0A8DY
+8D0A(B BBBA T ,( BDH E(A0A8G
&
+8D0A(B BBBA < Tp BBD A(G
+(D ABBG $ (Ȗ. K^ < U T U , U[ M AID t
+DAA D K BBB D(A0D@0A(A BBB T Ul BIE B(A0A8DY
+8D0A(B BBBA T d Z BDE H(A0A8G<
+8D0A(B BBBK Ɩ. K^ 0_ Ĝ (_ , 0_[ AID t
+DAA D VL BBB D(A0D@0A(A BBB T | _K ' BIB E(A0A8G8
+8D0A(B BBBA L Ԝ eH BDB B(D0A8M
+8D0A(B BBBA Ŗ. K^ h- AG cA 4 iO HF T 8iO HF t hiO HF iO HF iO HF Ԟ iO HF (jO HF XjO HF 4 jO HF T jO HF t jO HF kO HF HkO HF ԟ xkO HF kO HF kO HF 4 lO HF T 8lO HF t hlO HF lO HF lO HF Ԡ lO HF (mO HF XmO HF 4 mO HF T mO HF t mV HI (nV HI hnV HI ԡ nV HI nV HI (oV HI 4 hoV HI T oV HI t oV HI (pV HI hpV HI Ԣ pV HI pV HI (qV HI 4 hqV HI T qV HI t qV HI (rV HI hrV HI ԣ rV HI rV HI (sV HI 4 hsV HI T sV HI t sV HI (tV HI L ܣ htT s BBB B(A0A8JR
+8A0A(B BBBH 4 , xxs BAI C
+ABA 4 d xs h BAI C
+ABA 4 ys D BAI C
+ABA 4 Ԥ Pys BAI C
+ABA 4 ys BAI C
+ABA 4 D ys BAI C
+ABA 4 | (zs BAI C
+ABA 4 pzs BAI C
+ABA 4 zs l BAI C
+ABA 4 $ {s H BAI C
+ABA 4 \ H{s $ BAI C
+ABA 4 {s BAI C
+ABA 4 ̦ {s BAI C
+ABA , | AID
+AAA , 4 | AID
+AAA , d } | AID
+AAA , } ^ AID
+AAA , ħ ~ @ AID
+AAA , ~ " AID
+AAA , $ AID
+AAA , T AID
+AAA , AID
+AAA , AID
+AAA , AID
+AAA , n AID
+AAA , D P AID
+AAA , t 2 AID
+AAA , AID
+AAA , ԩ AID
+AAA , AID
+AAA , 4 AID
+AAA , d AID
+AAA , ~ AID
+AAA , Ī ` AID
+AAA , B AID
+AAA , $ $ AID
+AAA , T AID
+AAA , AID
+AAA , AID
+AAA L K BBB B(A0A8G@d
+8A0A(B BBBA d BEB B(A0D8DPi
+8A0A(B BBBI
+8G0A(B BBBJ < t h BBD A(D@(A ABB 4 ܬ HB BHA G`
+ DABA 4 `B BHA G`
+ DABA 4 L xB v BHA G`
+ DABA 4 B W BHA G`
+ DABA 4 B 8 BHA G`
+ DABA 4 B BHA G`
+ DABA 4 , ؓB BHA G`
+ DABA 4 d B BHA G`
+ DABA 4 B BHA G`
+ DABA 4 Ԯ B BHA G`
+ DABA 4 8B ~ BHA G`
+ DABA 4 D PB _ BHA G`
+ DABA 4 | hB @ BHA G`
+ DABA < ! BHA GZ
+ DABA < BHA GZ
+ DABA < 4 BHA GZ
+ DABA < t ` BHA GZ
+ DABA < BHA GZ
+ DABA < BHA GZ
+ DABA < 4 @ BHA GZ
+ DABA < t d BHA GZ
+ DABA < I BHA GZ
+ DABA < . BHA GZ
+ DABA < 4 BHA GZ
+ DABA < t ` BHA GZ
+ DABA < BHA GZ
+ DABA < BHA GS
+ DABA < 4 @ BHA GS
+ DABA < t BHA GS
+ DABA < q BHA GS
+ DABA < V BHA GS
+ DABA < 4 ; BHA GS
+ DABA < t ` BHA GS
+ DABA < BHA GS
+ DABA < BHA GS
+ DABA < 4 @ BHA GS
+ DABA < t BHA GS
+ DABA < BHA GS
+ DABA < ~ BHA GS
+ DABA $ V ADD JAAT \ ; BEB M(A0C8G
+8A0A(B BBBF L BEB E(I0H8D@
+8D0A(B BBBA < ܷ 0 BFA D0}
+ FABE D FAB T D e ' BHB B(A0A8G
+8A0A(B BBBI T BEE E(A0A8GK
+8A0A(B BBBA T BBB B(D0A8J
+8A0A(B BBBE $ * A[
+L A < l G BBA D(G
+(A ABBG * A[
+L A D ̸ (W V BBA A(D
+(A ABBK @* A[
+L A D 4 PW BBA D(G
+(A ABBG D | hW BBA D(G
+(A ABBG D Ĺ g BBA D(G
+(A ABBH < W BBA D(G
+(A ABBG < L G { BBA D(G
+(A ABBG d * A[
+L A D B < BBA D(G
+(A ABBE ̻ * A[
+L A D g BBA D(G
+(A ABBH 4 (* A[
+L A < | 8W BBA D(G
+(A ABBG X* A[
+L A < ܻ hG o BBA D(G
+(A ABBG x* A[
+L A D < B 0 BBA D(G
+(A ABBE \ * A[
+L A D BBA A(Dq
+(A ABBA L (
J BEE H(D0D8GR
+8D0A(B BBBF T < ( BGB H(A0A8G
+8D0A(B BBBH T BBB G(G0A8G
+8D0A(B BBBH D BBA A(Dl
+(A ABBA L 4 J BEE H(D0D8GR
+8D0A(B BBBF T # BGB H(A0A8G
+8D0A(B BBBH T ܾ ( BBB G(G0A8G
+8D0A(B BBBH D 4 p, BBA A(Dm
+(A ABBA L | -J x BEE H(D0D8GR
+8D0A(B BBBF T ̿ 3 BGB H(A0A8G
+8D0A(B BBBH T $ P9 BBB G(G0A8G
+8D0A(B BBBH D | < } BBA A(Dq
+(A ABBA L `>J T BEE H(D0D8GR
+8D0A(B BBBF T `D k BGB H(A0A8G
+8D0A(B BBBH T l I b BBB G(G0A8G
+8D0A(B BBBH D @M Y BBA A(Dq
+(A ABBA L NJ 0 BEE H(D0D8GR
+8D0A(B BBBF T \ T G BGB H(A0A8G
+8D0A(B BBBH T Z > BBB G(G0A8G
+8D0A(B BBBH D ] 5 BBA A(Dq
+(A ABBA L T 0_J BEE H(D0D8GR
+8D0A(B BBBF T 0e # BGB H(A0A8G
+8D0A(B BBBH T j BBB G(G0A8G
+8D0A(B BBBH D T n BBA A(Dq
+(A ABBA L oJ BEE H(D0D8GR
+8D0A(B BBBF T u BGB H(A0A8G
+8D0A(B BBBH T D z BBB G(G0A8G
+8D0A(B BBBH D x~ BBA A(Dm
+(A ABBA L J BEE H(D0D8GR
+8D0A(B BBBF T 4 BGB H(A0A8G
+8D0A(B BBBH T X BBB G(G0A8G
+8D0A(B BBBH D BBA A(Dl
+(A ABBA L , hJ BEE H(D0D8GR
+8D0A(B BBBF T | h BGB H(A0A8G
+8D0A(B BBBH T BBB G(G0A8G
+8D0A(B BBBH D , H BBA A(Dq
+(A ABBA L t РJ | BEE H(D0D8GR
+8D0A(B BBBF T Ц BGB H(A0A8G
+8D0A(B BBBH T ( BBB G(G0A8G
+8D0A(B BBBH D t BBA A(Dq
+(A ABBA L 8J X BEE H(D0D8GR
+8D0A(B BBBF T 8 o BGB H(A0A8G
+8D0A(B BBBH T d f BBB G(G0A8G
+8D0A(B BBBH D ] BBA A(Dm
+(A ABBA L J 4 BEE H(D0D8GR
+8D0A(B BBBF T T K BGB H(A0A8G
+8D0A(B BBBH T B BBB G(G0A8G
+8D0A(B BBBH D 9 BBA A(Dl
+(A ABBA L L J BEE H(D0D8GR
+8D0A(B BBBF T ' BGB H(A0A8G
+8D0A(B BBBH T ` BBB G(G0A8G
+8D0A(B BBBH $ . K^ < T l L m BBB B(A0I8G
+8D0A(B BBBF T D g W BBB B(I0A8Gc
+8D0A(B BBBG T 8g c BBB B(I0A8Gc
+8D0A(B BBBG D d BBB D(A0D@0A(A BBB T < ' BIB E(A0A8D6
+8D0A(B BBBA T @ BIB E(A0A8D6
+8D0A(B BBBA T x BIB E(A0A8DF
+8D0A(B BBBA L BBB B(A0A8K`
+8A0A(B BBBH L l p BGB B(A0A8Gv
+8A0A(B BBBA L ] BBB B(A0A8G
+8A0A(B BBBA L m BMB E(A0A8G)
+8A0A(B BBBF d \ *- BBB B(A0A8G
8A0A(B BBBA =
-8A0A(B BBBE T d 8N% BEB H(D0A8G
-8A0A(B BBBA T s G BEB B(F0G8G
-8D0A(B BBBC T (} ߙ BBB B(G0F8G
-8D0A(B BBBC d &. K^ | 0 Hg 7 Hj 2 I $ Ԟ ^ AG b
-AA T < BBB B(I0A8GD
-8D0A(B BBBF T \ < BBB B(I0A8GD
-8D0A(B BBBF T ՙ BBB B(D0D8Gt
-8D0A(B BBBH T `| BBE E(D0A8G
-8D0A(B BBBI D \ Fě BBB D(A0D@0A(A BBB T @
BIB E(A0A8G
-8D0A(B BBBA T u BIB E(A0A8G
-8D0A(B BBBA T \ N BBE B(D0D8G
-8A0A(B BBBA T h r BBE B(D0D8G
-8A0A(B BBBA T BBE E(E0D8G
-8A0A(B BBBA L d : Ǚ BBB G(D0A8M
-8D0A(B BBBF h". K^ Ģ 0 Hg 7 Hj lÛ\ AG RAT , o BEE E(D0A8G'
-8D0A(B BBBG D | PÛ BBB D(A0D@0A(A BBB T ̡ \
BIE B(A0A8DI
-8D0A(B BBBA T $ b ۘ BIE B(A0A8DQ
-8D0A(B BBBA L | 0> BBB B(I0A8D
-8D0A(B BBBC T ̢ = BBB B(A0F8G
-8A0A(B BBBE L $ > 7 BBB B(I0A8D
-8D0A(B BBBC l . K^ , [ Ș AID t
-DAA L BEB E(A0A8G>
-8A0A(B BBBA T < X BBE B(D0A8G
-8D0A(B BBBG D BBB D(A0D@0A(A BBB T ܤ 0C BIB E(A0A8G0
-8D0A(B BBBA L 4 (
ӗ BDB B(D0G8G
-8D0A(B BBBF | . K^ 0 Hg 7 Hj ԧ \ AG RAT | BBE E(D0A8G
-8D0A(B BBBI T T | BBE E(D0A8G
-8D0A(B BBBI L BBB B(K0A8J
-8A0A(B BBBF 4 q BAI A
-ABA D , BBB D(A0D@0A(A BBB T | BIB E(A0A8D6
-8D0A(B BBBA d ̩ "B BBB B(A0A8G@x
+8A0A(B BBBE T 4% BEB H(D0A8G
+8A0A(B BBBA T D Z BEB B(F0A8G
+8A0A(B BBBE T xb% BBG B(A0G8G
+8D0A(B BBBH @. K^ 8k0 Hg Hk7 Hj $ hk2 I $ < k^ AG b
+AA T k< BBB B(I0A8GD
+8D0A(B BBBF T q< BBB B(I0A8GD
+8D0A(B BBBF T < w
BBB B(D0D8G
+8D0A(B BBBF T | & BBE E(D0A8G
+8D0A(B BBBI D : BBB D(A0D@0A(A BBB T 4
BIB E(A0A8G
+8D0A(B BBBA T x BIB E(A0A8G
+8D0A(B BBBA T @ v BEB B(D0D8G
+8A0A(B BBBA T < BEB B(D0D8G
+8A0A(B BBBA T 07 BEB B(D0D8G.
+8A0A(B BBBA L " BBB G(D0A8M
+8D0A(B BBBH (. K^ , 0 Hg L 7 Hj l `\ AG RAT w BEE E(D0A8G'
+8D0A(B BBBG D D BBB D(A0D@0A(A BBB T T @ BIB E(A0A8DF
+8D0A(B BBBA T BIB E(A0A8DN
+8D0A(B BBBA L к BBB B(I0A8D
+8D0A(B BBBD T T E BBB B(A0F8G
+8A0A(B BBBG L BBB B(I0A8D
+8D0A(B BBBD . K^ 0 ( , D 0[ AID t
+DAA L L ` BEB E(A0A8G>
+8A0A(B BBBA T 8 BBE B(D0A8G
+8D0A(B BBBG D BBB D(A0D@0A(A BBB T d C BIB E(A0A8G0
+8D0A(B BBBA L x
BDB B(A0D8M
+8D0A(B BBBA . K^ 0 Hg 7 Hj , < A|
+C K
+E R
+A $ l ^ AG b
+AA T | BBE E(D0A8G
+8D0A(B BBBI T | BBE E(D0A8G
+8D0A(B BBBI D D BBB D(A0D@0A(A BBB T C BIB E(A0A8D6
+8D0A(B BBBA d B BBB B(A0A8G@x
8A0A(B BBBE
-8A0A(B BBBF T < # BIB E(A0A8G
-8A0A(B BBBH T 8 BBB B(D0A8G}
-8D0A(B BBBB T @ BBB B(A0A8Lo
-8A0A(B BBBA < h. K^ T @R l 8R 4 @Rg ? BAD D
-ABA T ĩ xR BBB B(I0A8D%
-8D0A(B BBBH $ T^ AG b
-AA D < 8T BBB D(A0D@
-0A(A BBBA D U t BIB A(D0D`+
-0D(A BBBA 4 ̬ HXJ BDA q
+8A0A(B BBBF L L BEB A(D0l
+(A BBBG
+(A BBBA L BEB A(D0l
+(A BBBG
+(A BBBA L BEB A(D0l
+(A BBBG
+(A BBBA T d p BBE E(E0A8G
+8A0A(B BBBA L ( BEB A(D0l
+(A BBBG
+(A BBBA L BEB A(D0l
+(A BBBG
+(A BBBA L 4 BEB A(D0l
+(A BBBG
+(A BBBA T BBE B(E0D8G
+8A0A(B BBBA T P a BFE E(A0A8G
+8A0A(B BBBA T \ x BBB B(D0D8J}
+8D0A(B BBBD L % { BBB B(K0A8J
+8A0A(B BBBF 4 0-q BAI A
+ABA T < x- w BBB B(A0A8L
+
+8A0A(B BBBC l `. K^ > > 4 >g BAD D
+ABA T ? { BBB B(A0I8D
+8D0A(B BBBE D D @ BBB D(A0D@
+0A(A BBBA T @B BIB E(A0A8D6
+8D0A(B BBBA 4 xFJ BDA q
ABE A
-ABA d `Xg BLB B(A0A8D@q
+ABA d Fg BLB B(A0A8D@q
8A0A(B BBBE
-8A0A(B BBBF < t hYL BBA D(Jp
-(A ABBA L xZ BBB B(A0A8Gp8A0A(B BBB [% , \K BGA }AB L D 0\ BBB B(A0A8Gp8A0A(B BBB L ]M BBB B(A0D8Dp18A0A(B BBB L _ BBB B(A0A8Gp8A0A(B BBB < < 0aL BBA D(Jp
-(A ABBA L t @bM BBB B(A0D8Dp18A0A(B BBB D į @d BBB A(A0G@R
-0A(A BBBA L d BBB E(D0A8D`
-8A0A(B BBBA L \ xe BLB H(A0D8DPV
-8A0A(B BBBG fm , İ @g ADD X
-AAA d g BHD G0W
+8A0A(B BBBF < GF 2 BBA A(Gq
+(A ABBA L H BBB B(A0A8Gp8A0A(B BBB (J% L , @JM BBB B(A0D8Dp18A0A(B BBB L | @L BBB B(A0A8Gp8A0A(B BBB L MM BBB B(A0D8Dp18A0A(B BBB L O BBB B(A0A8Gp8A0A(B BBB < @QF n BBA A(Gq
+(A ABBA L PRM BBB B(A0D8Dp18A0A(B BBB L PT BBB B(A0A8D
+8A0A(B BBBA L L X BBB E(D0A8D`
+8A0A(B BBBA L Y BLB H(A0D8DPV
+8A0A(B BBBG [m , h[ ADD X
+AAA d 4 [ BHD G0W
AABH F
AABF g
AABE D
- FABK , \ hh ADD ]
-AAA d h BBB E(I0D8DP
+ FABK , \ ADD ]
+AAA d ] BBB E(I0D8DP
8D0A(B BBBC |
-8D0A(B BBBA , j ADD [
-AAA t $ j BIA D(G0C
+8D0A(B BBBA , 4 8^ ADD [
+AAA t d ^ BIA D(G0C
(A ABBH o
(A ABBC g
(A ABBC D
-(F ABBA d jE BBB B(A0A8DP
+(F ABBA d _E BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA l k c BBB E(I0D8D
-8A0A(B BBBG
-8D0A(B BBBB t nu L n BEB B(A0A8DP
-8A0A(B BBBH | ܳ xof BIB B(A0D8GPz
-8A0A(B BBBA v
-8A0A(B BBBH D
-8F0A(B BBBE T d hp + BBE D(I0
-(A BBBG _
-(A BEBG T qU BBB B(A0A8J
-8A0A(B BBBA ^ L $ 0 BEB B(A0A8DP
-8A0A(B BBBH | t f BIB B(A0D8GPz
-8A0A(B BBBA v
-8A0A(B BBBH D
-8F0A(B BBBE T BBE D(I0
-(A BBBG _
-(A BEBG T T ( G BBB B(A0A8J
-8A0A(B BBBA V L آ BEB B(A0A8DP
-8A0A(B BBBH l h S BBB E(D0I8D`:
+8F0A(B BBBA l l ` BBB E(I0D8D
+8A0A(B BBBG
+8D0A(B BBBE bu L c BEB B(A0A8DP
+8A0A(B BBBH l D c' BBB E(D0I8D`M
+8D0A(B BBBJ
+8D0A(B BBBB T pfU BBB B(A0A8J
+8A0A(B BBBA x}^ L } BEB B(A0A8DP
+8A0A(B BBBH l t P~' BBB E(D0I8D`M
+8D0A(B BBBJ
+8D0A(B BBBB T
w BBB B(A0A8JX
+8A0A(B BBBA ȗV L , BEB B(A0A8DP
+8A0A(B BBBH l BBB E(D0I8D`:
8D0A(B BBBE
-8D0A(B BBBJ T Z BBB B(A0A8J
-8A0A(B BBBA d Է ! BHF K(H0A8D`}
-8A0A(B BBBA d8C0A(B BBB L < BEB B(A0A8DP
-8A0A(B BBBH | Hf BIB B(A0D8GPz
+8D0A(B BBBJ T Z C BBB B(A0A8J
+8A0A(B BBBA d D (! BHF K(H0A8D`}
+8A0A(B BBBA d8C0A(B BBB L BEB B(A0A8DP
+8A0A(B BBBH | f BIB B(A0D8GPz
8A0A(B BBBA v
8A0A(B BBBH D
-8F0A(B BBBE d 8 S BBJ B(A0A8G`
+8F0A(B BBBE d p BBJ B(A0A8G`
8D0A(B BBBE N
-8A0A(B BBBH T | 4 BBB B(A0A8J]
-8A0A(B BBBA D Է % BGA D(M
-(D ABBF . K^ , 0 Hg L 7 Hj L t , BBB B(A0I8G
-8D0A(B BBBD L ĸ 8 BBB B(A0I8G
-8D0A(B BBBD D BBB D(A0D@0A(A BBB T \ P\ BIE B(A0A8DI
-8D0A(B BBBA T Xb BIE B(A0A8DQ
-8D0A(B BBBA T p W BEG E(A0G8G+
-8D0A(B BBBF \ d v BFB A(A0G`V
-0D(A BBBD W
-0D(A BBBF H . K^ Լ $ ^ AG b
-AA $ < , 4D BBD A(D@0(A ABB T t BIB E(A0A8DF
-8D0A(B BBBA T ̻ BIB E(A0A8DF
-8D0A(B BBBA T $ 8 N BIB E(A0A8DF
-8D0A(B BBBA L | X BBB E(A0D8J^
-8A0A(B BBBF < ̼ BEA A(Gc
-(D ABBA < BEA A(Gc
-(D ABBA < L P BEA A(Gc
-(D ABBA , Y ADG i
-AAA , Y l ADG i
-AAA N R < BBG A(G@
-(A ABBD T % T t BJB B(A0A8GZ
-8A0A(B BBBA T ̾ BJB B(A0A8G
-8A0A(B BBBA T $ H BJB B(A0A8Gf
-8A0A(B BBBA $ t "V ADD JAA4 " BEA A(D0(A ABBd ܿ P#1 BHB B(D0A8Dpv
+8A0A(B BBBH T (4 _ BBB B(A0A8J]
+8A0A(B BBBA D d BGA D(M
+(D ABBF xs. K^ 0 Hg 7 Hj L l BBB B(A0I8G
+8D0A(B BBBD L T 0 W BBB B(A0I8G
+8D0A(B BBBD T C BEG E(A0G8G-
+8D0A(B BBBD \ BFE A(A0D`W
+0D(A BBBK ^
+0D(A BBBG D 4 ^ BBB D(A0D@0A(A BBB T b BIE B(A0A8DQ
+8D0A(B BBBA T \ BIE B(A0A8DI
+8D0A(B BBBA , q. K^ D $ \ ^ AG b
+AA $ < D BBD A(D@0(A ABB T BIB E(A0A8DF
+8D0A(B BBBA T \ BIB E(A0A8DF
+8D0A(B BBBA T @ BIB E(A0A8DF
+8D0A(B BBBA L X BBB B(A0A8G
+8A0A(B BBBA < \ BEA A(Gc
+(D ABBA < H y BEA A(Gc
+(D ABBA < O BEA A(Gc
+(D ABBA , Y % ADG i
+AAA , L 8Y ADG i
+AAA T h N l p R < x BBG A(G@
+(A ABBD % T 0 U BHB B(A0A8GI
+8A0A(B BBBA T \
+ BHB B(A0A8J
+8A0A(B BBBA T BHB B(A0A8GU
+8A0A(B BBBA $ V ADD JAA4 BEA A(D0(A ABBd l X 1 A BHB B(D0A8Dpv
8C0A(B BBBG {
-8D0A(B BBBA T D ($ K BBB E(I0A8K}
-8D0A(B BBBF L `& # BEB B(D0A8Gpu
-8A0A(B BBBA L ' ܇ BEB B(D0A8Gpu
-8A0A(B BBBA T < ) BBB E(I0A8K}
-8D0A(B BBBF L +* o BEB B(D0A8Gp
-8A0A(B BBBA $ h-V ADD JAA, - JP
+8D0A(B BBBA T 0 BBB E(I0A8K}
+8D0A(B BBBF L , h BEB B(D0A8Gpu
+8A0A(B BBBA L | ! p BEB B(D0A8Gpu
+8A0A(B BBBA T # + BBB E(I0A8K}
+8D0A(B BBBF L $ %* BEB B(D0A8Gp
+8A0A(B BBBA $ L p'V ADD JAA, t ' JP
I ^
B j
F k
-A , 4 @0 JP
+A , 8* JP
I ^
B j
F k
-A L l 2 Æ BBB B(A0A8G}
-8A0A(B BBBH < pH 7 BLD D(G0
-(A ABBE D 0I BHI H(D0D@
-0A(A BBBA T D IK ӆ BJB B(A0A8G8
-8A0A(B BBBA L Y)
- BGB H(A0D8Gk
-8D0A(B BBBI T cT BBB B(G0D8L]
-8D0A(B BBBG , < n JP
+A L , W BBB B(A0A8G}
+8A0A(B BBBH < L hB BLD D(G0
+(A ABBE D d (C BHI H(D0D@
+0A(A BBBA T CX g BBB B(A0A8GC
+8A0A(B BBBA T , S
+ J BGE E(G0D8G<
+8D0A(B BBBB T P^
BBB H(D0A8L"
+8D0A(B BBBB , k JP
I ^
B j
F k
-A , l hq JP
+A , hn JP
I ^
B j
F k
-A L s - BBB B(A0A8G~
-8A0A(B BBBG L )
- BGB H(A0D8Gk
-8D0A(B BBBI T D xT Ӈ BBB B(G0D8L]
-8D0A(B BBBG , JP
+A L < p BBB B(A0A8G~
+8A0A(B BBBG T
+ y BGE E(G0D8G<
+8D0A(B BBBB T
BBB H(D0A8L"
+8D0A(B BBBB , x JP
I ^
B j
F k
-A , JP
+A , D JP
I ^
B j
F k
-A L BBB B(A0A8G~
-8A0A(B BBBG L L 0)
- BGB H(A0D8Gk
-8D0A(B BBBI T T BBB B(G0D8L]
-8D0A(B BBBG H. K^ 4 L d | , [ AID t
-DAA , 0[ e AID t
-DAA , , `[ E AID t
-DAA , \ [ % AID t
-DAA DN
-E A \ AG RAD BBB D(A0D@
-0A(A BBBA D } BIB A(D0D`+
-0D(A BBBA D \ [ BIB A(D0D`+
-0D(A BBBA D H 9 BIB A(D0D`+
-0D(A BBBA D BIB A(D0D`+
-0D(A BBBA D 4 S BEB A(A0Gh
-0A(A BBBA T | ׆ BEB B(A0G8J
-8A0A(B BBBD T hG dž BDB B(A0A8J
-8A0A(B BBBJ T , `W % BEB I(A0D8G
-8A0A(B BBBA T h 7
- 3 BDB B(A0A8J
-8A0A(B BBBA T P
- BEB B(A0G8JU
-8A0A(B BBBD T 4 M
- BDB B(A0A8J
-8A0A(B BBBF T e BEB B(A0G8J
-8A0A(B BBBD T BDB B(A0A8J
-8A0A(B BBBK 4 0. K^ L Π\ AG RAT t 85 և BBB I(A0A8G
-8A0A(B BBBA T 8 BBB I(A0A8GL
-8A0A(B BBBA x. K^ T < @< J BBB E(A0A8G
-8D0A(B BBBA 8. K^ ? x? p? h? `? X? 4 P? L H? d @? | 8? 0? 8? @? H? P? X? $ `? < h? T p? l x? , ?[ AID t
-DAA , ?[ AID t
-DAA , ?[ ӄ AID t
-DAA , @[ AID t
-DAA D L @@ BBB A(I0Dp
-0D(A BBBK D A g BBB A(I0Dp
-0D(A BBBK D B ; BBB A(I0Dp
-0D(A BBBK T $ D BBB B(I0A8D$
-8D0A(B BBBI T | F ߃ BBB B(I0A8D$
-8D0A(B BBBI T H BBB B(I0A8D$
-8D0A(B BBBI D $ PKJ BBB D(A0D@
-0A(A BBBA D t XM 7 BIB A(D0D`7
-0D(A BBBA T P BIB E(A0A8DF
-8D0A(B BBBA D hT BIB A(D0D`7
-0D(A BBBA D \ 0W BIB A(D0D`7
-0D(A BBBA D Y BIB A(D0D`7
-0D(A BBBA T \ } BIB E(A0A8DF
-8D0A(B BBBA D D a K BIB A(D0D`7
-0D(A BBBA T c ) BIB E(A0A8DF
-8D0A(B BBBA T h BIB E(A0A8DF
-8D0A(B BBBA T < `l Ł BIB E(A0A8DF
-8D0A(B BBBA L p BBB B(A0F8Gp
-8A0A(B BBBG < Xs BGG A(D0
-(D ABBJ L $ v BGE G(A0GJ
-0D(A BBBF L t Xzr BDB B(G0D8G
-8D0A(B BBBA L ~r 3 BDB B(G0D8G
-8D0A(B BBBA D o BGE G(A0G
-0D(A BBBA L \ Pv j BBH G(D0A8G
-8D0A(B BBBA T \ BDB E(A0G8G]
-8D0A(B BBBJ L H G BEG A(A0J]
-0D(A BBBF L T : BGH A(D0G
-0D(A BBBA X. K^ L @ AAG ]
+A L : BBB B(A0A8G~
+8A0A(B BBBG T (
+ BGE E(G0D8G<
+8D0A(B BBBB T D
BBB H(D0A8L"
+8D0A(B BBBB t d. K^ 4 , t [ AID t
+DAA , [ AID t
+DAA , P[ AID t
+DAA , [ AID t
+DAA DN
+E A , \ AG RAD L p BBB D(A0D@
+0A(A BBBA D BIB A(D0D`+
+0D(A BBBA D BIB A(D0D`+
+0D(A BBBA D L 8 BIB A(D0D`+
+0D(A BBBA D BIB A(D0D`+
+0D(A BBBA D S g BEB A(A0Gh
+0A(A BBBA T $ X I BEB B(A0G8J
+8A0A(B BBBD T | 9 BDB B(A0A8J
+8A0A(B BBBK T P BEB B(A0G8JM
+8A0A(B BBBD T , =
+ | BDB B(A0A8J
+8A0A(B BBBF T 7 BEB I(A0D8G
+8A0A(B BBBA T '
+ BDB B(A0A8J
+8A0A(B BBBA T 4 - BEB B(A0G8J
+8A0A(B BBBD T $7 BDB B(A0A8J
+8A0A(B BBBJ ^. K^ F\ AG RAT 7 C BBB I(A0A8G
+8A0A(B BBBA T t ; BBB I(A0A8GL
+8A0A(B BBBA ^. K^ T > BBB E(A0A8G
+8D0A(B BBBA ]. K^ , B D B \ B t A A A A A A A A 4 A L A d A | A A A A B B , 4 B[ AID t
+DAA , d @B[ ` AID t
+DAA , pB[ @ AID t
+DAA , B[ AID t
+DAA D B BBB A(I0Dp
+0D(A BBBK D <
+ D BBB A(I0Dp
+0D(A BBBK D
+ `E BBB A(I0Dp
+0D(A BBBK T
+ F | BBB B(I0A8D$
+8D0A(B BBBI T $ I L BBB B(I0A8D$
+8D0A(B BBBI T | xK BBB B(I0A8D$
+8D0A(B BBBI D MJ BBB D(A0D@
+0A(A BBBA D O BIB A(D0D`7
+0D(A BBBA T d R BIB E(A0A8DF
+8D0A(B BBBA T V P BIB E(A0A8DF
+8D0A(B BBBA T
@[ BIB E(A0A8DF
+8D0A(B BBBA T l
_ BIB E(A0A8DF
+8D0A(B BBBA D
c BIB A(D0D`7
+0D(A BBBA D f BIB A(D0D`7
+0D(A BBBA D T `i v BIB A(D0D`7
+0D(A BBBA D (l T BIB A(D0D`7
+0D(A BBBA T n 2 BIB E(A0A8DF
+8D0A(B BBBA L 8s BBB B(A0F8Gp
+8A0A(B BBBG L u BGE G(A0DG
+0D(A BBBD T (y BDB B(G0D8GE
+8D0A(B BBBA T 4 } BDB B(G0D8GE
+8D0A(B BBBA D 8 BGE G(A0D
+0D(A BBBA L d BBH G(D0A8G
+8D0A(B BBBA L $ І BDB D(G0D`
+0D(A BBBF L t BEG A(A0J]
+0D(A BBBF L r BGH A(D0G
+0D(A BBBA D BDG D(D
+(D ABBG 4 R. K^ L L AAG ]
CAF k
ACA R
-CAJ JCA $ ^ AG b
-AA D , BBE A(A0D@n
-0A(A BBBA D t P BBB D(A0D@
-0A(A BBBA Ȕ, L BBB B(D0A8DP
-8A0A(B BBBA $ AL d D [ BBB B(A0A8DpW
-8A0A(B BBBI
-8A0A(B BBBA T x ~ BBE B(D0A8G
-8D0A(B BBBC L i ~ BBE E(D0A8Dp3
-8D0A(B BBBI D T 0 BBB D(A0D@
-0A(A BBBA L " a~ BIB B(A0D8DpH
-8D0A(B BBBA T h 7~ BIB E(A0A8D^
-8D0A(B BBBA T L ~ BBB B(D0A8J
-8A0A(B BBBA , q } BDA TAT L Hb BLE B(A0A8D
-8A0A(B BBBF < h BBG A(G@
-(A ABBG T d ( 0} BEB B(A0A8JB
-8A0A(B BBBE ` L XR BLE B(A0A8D
-8A0A(B BBBF T $ h } BBB B(A0A8J
-8A0A(B BBBF D | & | BDA D@
+CAJ JCA $ ^ AG b
+AA D BBE A(A0D@n
+0A(A BBBA D BBB D(A0D@
+0A(A BBBA L T (h BBB B(D0A8DP9
+8A0A(B BBBA H AL H, d ` BBB B(A0A8D`
+8A0A(B BBBE d
+8A0A(B BBBA T l ؟ BBE B(D0A8G
+8D0A(B BBBC L pi { BBE E(D0A8Dp2
+8D0A(B BBBJ D BBB D(A0D@
+0A(A BBBA T \ BIB E(A0A8DV
+8D0A(B BBBA L @$ BIB B(A0D8DpO
+8D0A(B BBBA T BBB B(D0A8J
+8A0A(B BBBA , \ Xq BDA TAT L d BOB B(A0A8Dp
+8A0A(B BBBJ < 8 BBG A(G@
+(A ABBG T BEB B(A0A8JB
+8A0A(B BBBE L 0 L d ( BOB B(A0A8Dp
+8A0A(B BBBJ T BBB B(A0A8J
+8A0A(B BBBH D 4 & BDA D@
DABE L
- DABA L | BEB E(I0H8D@
-8D0A(B BBBA T hV | BBB B(A0D8G
-8A0A(B BBBA T l p | BBB B(A0D8G
-8A0A(B BBBA hT KHT +} BBB E(A0A8NH
-8A0A(B BBBC , 4 > 9} AID W
-DAA , d > } AID W
-DAA $ ^ AG b
-AA D BBB D(A0D@
-0A(A BBBA D XZ | BBE A(A0G@
-0A(A BBBC L D p BBB B(D0A8DPV
-8A0A(B BBBA @ AL L @ BEB A(A0V
-(A BBBE F
-(A BBBA L p BEB A(A0
-(A BBBE I
-(A BBBA D T BBB D(A0D@
-0A(A BBBA T hB { BIB B(A0D8Dg
-8D0A(B BBBA T `I z BIB B(A0D8Dn
-8D0A(B BBBA T T X z BBB B(A0A8J
-8A0A(B BBBA T F z BBB B(D0A8G
-8D0A(B BBBD L X z BFB A(D0J
-0A(A BBBE < l
- z BEA A(G@}
-(D ABBJ \ p b GLB E(A0A8DPV8A0A(B BBBBP T
Kz BBB B(A0D8LX
-8D0A(B BBBB L \ BEE B(A0A8D`
-8A0A(B BBBJ , BDA
-ABD T ! Oz BEB B(D0A8GH
-8A0A(B BBBA T < & Sz BBB B(A0A8J
-8A0A(B BBBA T 4$ z BBE B(K0D8G
-8D0A(B BBBA ݛT KHL H8 z BBE B(A0A8N
-8A0A(B BBBB , T ;> z AID W
-DAA , ;> z AID W
-DAA L ;r z BBB B(E0A8J@
-8A0A(B BBBA $ <^ AG b
-AA < , 0=^ /z BDA n
+ DABA L | _ BEB E(I0H8D@
+8D0A(B BBBA T 7 BBB B(A0D8G\
+8A0A(B BBBA T $ ; BBB B(A0D8G
+8A0A(B BBBI T KT KHT BBB E(A0A8NH
+8A0A(B BBBC , > AID W
+DAA , > AID W
+DAA $ AU D F $ \ ^ AG b
+AA D BBB D(A0D@
+0A(A BBBA D Z BBE A(A0G@
+0A(A BBBC d < BJE B(A0A8D@Y
+8C0A(B BBBB i8A0A(B BBB 4 | BDA D0
+ AABE L } ' BBB B(A0D8DP
+8A0A(B BBBA AL L L BEE B(A0D8G
+8A0A(B BBBA d t H" BBB B(A0A8DpO
+8A0A(B BBBI j
+8A0A(B BBBA d r BBB B(A0A8Dp
+8A0A(B BBBI j
+8A0A(B BBBA D D ( BBB D(A0D@
+0A(A BBBA T B m BIB B(A0D8Dg
+8D0A(B BBBA T xI ; BIB B(A0D8Dn
+8D0A(B BBBA T d p BBB B(A0A8J
+8A0A(B BBBA D 8 BFB A(D0J
+0A(A BBBB < # BEA A(G@}
+(D ABBJ d
+i BBB B(A0A8Dm
+8A0A(B BBBA
+8A0A(B BBBG L
+ BEB B(A0A8J
+8A0A(B BBBD L H BEE B(A0A8D`
+8A0A(B BBBB , $! BDA n
+ABH T! 5 T ; BBB B(A0A8J
+8A0A(B BBBG ! X( T ! P( y BBE E(D0A8D
+8D0A(B BBBE T \! *U ] BBB B(A0A8J
+8A0A(B BBBA T ! 9 BBB B(K0D8G
+8A0A(B BBBH " CT KHT $" @> BBE B(A0A8N
+8A0A(B BBBA , |" A> ս AID W
+DAA , " A> AID W
+DAA # A AU # AF # B AU $ BF $$ 8B A[ $ l# 8B BFA D@ , l$ Bb D\
+H P
+H Q
+G I , $ Bb D\
+H P
+H Q
+G I < $ C( BHE m
+ABI A
+ABD L 4$ Dr I BBB B(E0A8J@
+8A0A(B BBBA $ \% @E^ AG b
+AA , % xE( BHE u
+ABI < $ xF^ BDA n
EBD C
-EBA < d P=( BHE n
-ABH A
-ABD , @> BHI AB ? AL L ? oy BBB B(A0A8J
-8D0A(B BBBA D @ d \ @ BBB B(A0A8Dp
-8A0A(B BBBE D
-8A0A(B BBBA d B[ BLB B(A0A8Dq
-8A0A(B BBBE
-8A0A(B BBBF L , xF7 BLB B(A0A8Gv
-8A0A(B BBBE D | hN BBB D(A0D@
-0A(A BBBA L O$ w BIB B(A0D8DpO
-8D0A(B BBBA L RE w BEB E(D0A8Gp
-8A0A(B BBBD \ d Xm BBD D(J@
+EBA $ % Fb A]
+B A , D% F b BHI kAB L& @H AL L % @H BBB B(A0A8J
+8D0A(B BBBA & I 4 & IJ BDA o
+ABG A
+ABA d ' JA BLB B(A0A8Dq
+8A0A(B BBBE &
+8A0A(B BBBH D t' P BBB D(A0D@
+0A(A BBBA L & @R$ BIB B(A0D8DpO
+8D0A(B BBBA L 4' Ug ˹ BBE B(D0D8Gp
+8A0A(B BBBD \ \( @[m BBD D(J@
(A ABBD T
(A ABBF _
-(A ABBK D Y w BBB D(A0J
+(A ABBK D ' P\ BBB D(A0J
-0D(A BBBJ L xa BLB B(A0A8D
-8A0A(B BBBF \ fK L t h BBA D(D0
-(A ABBF
-(A ABBH T l w BBB B(A0A8G|
-8A0A(B BBBI d uD BBB B(A0A8D
+0D(A BBBJ L ,( dP ۹ BEB E(A0D8DP
+8D0A(B BBBA d |( f9 BLB B(D0A8D@q
+8A0A(B BBBJ
+8A0A(B BBBA L ( f
+ g BLB B(D0D8D@
+8A0A(B BBBG L * i BBD D(D0O
+(A ABBK \
+(A ABBE < \* `k BAD
+ABE
+ABJ T ) o BBB B(A0A8D~
+8A0A(B BBBJ d * w BBB B(A0A8D7
8A0A(B BBBI l
-8A0A(B BBBA 4 {J BDA l
+8A0A(B BBBA 4 \+ |J BDA l
ABJ A
-ABA \ {% GLE B(A0A8DPV8A0A(B BBBHP T $ | u BBB B(D0A8G
-8A0A(B BBBA T | u BBE B(A0A8G]
-8A0A(B BBBA ֛T KHT 0 u BBB B(A0D8NZ
-8A0A(B BBBA , D ȑ> u AID W
-DAA d l ؑ BBB B(A0D8Dp
-8D0A(B BBBJ D
-8A0A(B BBBA d BBB B(A0A8D
-8A0A(B BBBG g
-8A0A(B BBBA L D h t BFB B(A0D8J
-8A0A(B BBBJ d ( BBB B(A0A8D
-8A0A(B BBBK g
-8A0A(B BBBA L BBB B(A0A8D
-8A0A(B BBBE L L @ ?t BBE B(A0A8J
-8A0A(B BBBC < >t BEA D(DP
-(D ABBC T P ,t BBB B(A0A8J
-8A0A(B BBBH L 4 r 9t BBB B(E0A8J@
-8A0A(B BBBA $ | H^ AG b
-AA , BHI AB ` AL L ` s BBB B(A0A8J
-8D0A(B BBBA D D \ BBB D(A0D@
-0A(A BBBA L ` s BIB B(A0D8DpE
-8D0A(B BBBA L 0" r BIB B(A0D8DpL
-8D0A(B BBBA T L ! r BBB E(A0A8G9
-8A0A(B BBBI L r r BBE E(D0D8I{
-8D0A(B BBBI T r BBB B(D0A8JW
-8D0A(B BBBE T L P8 r BBB B(A0A8J
-8A0A(B BBBG 8& L Pd BLB B(A0A8G
-8A0A(B BBBD L p Qr BEB E(D0A8G
-8A0A(B BBBE T \ 0 Kr BBB B(A0D8G/
-8A0A(B BBBC ΛT KHT p r BBB B(A0D8N 2
-8A0A(B BBBI , $ > r AID W
-DAA , T > {r AID W
-DAA d | BBB B(A0D8Dp
-8D0A(B BBBJ D
-8A0A(B BBBA T { q BBF A(A0
-(A BBBK W
-(A BBBB d < 8& BBB B(A0A8DH
-8A0A(B BBBH g
-8A0A(B BBBA L D Cq BBE B(A0A8J
-8A0A(B BBBC D $ Bq BBE A(D0D`
-0D(A BBBD T D 4q BBB B(A0A8J[
-8D0A(B BBBD L @ AAG ]
+ABA \ + |% GLE B(A0A8DPV8A0A(B BBBHP T + h~ w BBB B(A0D8G
+8A0A(B BBBA T t+ BBB E(A0A8Ge
+8A0A(B BBBA , h:T KHT + 0 BBB B(A0D8N
+8A0A(B BBBA , <, ؕ> AID W
+DAA d D- BEB B(A0A8D@
+8A0A(B BBBE I
+8A0A(B BBBA T , BFB B(A0D8JT
+8A0A(B BBBA d . 8 BEB B(A0A8D@
+8A0A(B BBBE I
+8A0A(B BBBA L l. BBE B(A0A8D`
+8A0A(B BBBA L - [ BBE B(A0A8J
+8A0A(B BBBC < 4. P Z BEA D(DP
+(D ABBC T t. D H BBB B(A0A8J
+8D0A(B BBBA / AU / F / A[ , / b D\
+H P
+H Q
+G I L T/ 0r ͵ BBB B(E0A8J@
+8A0A(B BBBA $ |0 `^ AG b
+AA , / j BHI kAB 0 AL L 0 BBB B(A0A8J
+8D0A(B BBBA D1 T 0 M BBB E(A0A8GI
+8A0A(B BBBI L 0 r ' BBE E(D0D8I{
+8D0A(B BBBI D 2 ȳ BBB D(A0D@
+0A(A BBBA L t1 BIB B(A0D8DpE
+8D0A(B BBBA L 1 " BIB B(A0D8DpL
+8D0A(B BBBA T 2 к f BBB B(D0A8JW
+8D0A(B BBBE T l2 9 BBB B(A0A8J
+8A0A(B BBBA 3 L 3 BLB B(A0A8D
+8A0A(B BBBA L ,3 BEB B(D0D8G
+8A0A(B BBBE T |3 BBB B(A0D8G'
+8A0A(B BBBK 4 2T KHT 3 ó BBB B(D0A8N
+8A0A(B BBBB , D4 > K AID W
+DAA , t4 > + AID W
+DAA T 4 0 BBF A(A0
+(A BBBG W
+(A BBBJ d 5 h BBB B(A0A8DPB
+8A0A(B BBBE L
+8A0A(B BBBA L d5 h [ BBE B(A0A8J
+8A0A(B BBBC D 5 $ Z BBE A(D0D`
+0D(A BBBD T 5 L BBB B(A0A8J
+8A0A(B BBBA L ,7 ` AAG ]
CAF k
ACA R
-CAJ JCA $ ^ AG b
-AA D BBB D(A0D@
-0A(A BBBA 4 T 0 BDA D0
- AABC 5 = < ( BHE n
-ABH A
-ABD 4 o SGB A(A0D 4 < X o cGB A(A0D L t o BBB E(D0A8Sf
-8A0A(B BBBE \ X o BBB A(A0Do
-0A(A BBBE h
-0E(A BBBA L BOB A(A0]
+CAJ JCA $ |7 ^ AG b
+AA < 7 ( BHE m
+ABI A
+ABD , 7 ( BHE u
+ABI D 8 BBB D(A0D@
+0A(A BBBA \8 @5 t8 h= 4 7 SGB A(A0D 4 7 hX cGB A(A0D L $8 ͱ BHB B(G0G8Ga
+8A0A(B BBBB \ t8 0 ӱ BBB A(A0Do
+0A(A BBBE i
+0E(A BBBA L 9 ` BOB A(A0]
(A BBBG Y
-(A BBBH T t co BBE E(D0A8G)
-8D0A(B BBBH < &o ACG@s
+(A BBBH T $9 } BBE E(D0A8G)
+8D0A(B BBBH < |9 h @ ACG@s
DAE J
-DAA d P
BBB B(A0A8Dp
-8A0A(B BBBE D
-8A0A(B BBBA < t n BED A(G@=
-(D ABBG T h n BBB E(A0D8Jm
-8A0A(B BBBG T 0/ ]n BBB B(A0A8J
-8A0A(B BBBA \ \ < KLB E(A0A8DP|8A0A(B BBBFP L BLD A(D0S
-(A ABBH D(D ABB L ( Z BLB B(G0D8D@L
-8A0A(B BBBI T d 8 b Em BEB F(D0D8J
-8D0A(B BBBC T P% Dm BBB B(A0A8GQ
-8A0A(B BBBD < 1 BBG A(G@
-(A ABBG L L 1F BEE B(D0D8D`
-8A0A(B BBBF L 2 BEE B(G0D8G`C
-8A0A(B BBBC L H4 BLB B(A0A8D
-8A0A(B BBBF < 9K T \ : Dl BBE B(A0A8J/
-8D0A(B BBBE L (C _l BBB B(A0D8J
-8D0A(B BBBJ D G l BBB D(A0D
-0D(A BBBD L L K l BEE E(A0D8D`
-8D0A(B BBBK ÛT KH| Mm Nl BFE E(D0D8Gp
-8A0A(B BBBB T
+DAA 4 : (J BDA o
+ABG A
+ABA : @ FP
+A < : @ BEA D(G@)
+(D ABBK \ T: BBB D(D0J
+0A(A BBBF
+0A(A BBBG T :
/ { BBB B(A0A8J
+8A0A(B BBBA \ ; KOB B(A0A8Dpk8A0A(B BBBGp d D< X3 BOB B(A0A8D`E
+8A0A(B BBBF |
+8D0A(B BBBE L < 0 BLB E(A0A8DpC
+8A0A(B BBBH T $< b K BEB F(D0D8J
+8D0A(B BBBC T |< J BBB B(A0A8GS
+8A0A(B BBBA L = 0,F BEE B(D0D8D`
+8A0A(B BBBF L = 0- BEE B(G0D8G`C
+8A0A(B BBBC L t= . BLB B(A0D8D`
+8A0A(B BBBD L > 4 BBD D(D0O
+(A ABBK \
+(A ABBE T > 5R ; BBB B(A0A8J
+8A0A(B BBBH D l> <w k BBB A(D0J
+0D(A BBBD D > ? BBB D(A0D
+0D(A BBBI L > xB BEE E(A0D8D`
+8D0A(B BBBF $@ 'T KH| d? D} A BFE E(D0D8Gp
+8A0A(B BBBF T
8A0A(B BBBB |
-8A0A(B BBBA T 4 O k BEB E(D0D8D
-8A0A(B BBBE T @S
- k BBE E(G0A8GL
-8D0A(B BBBJ D X GBA A(G@H(A ABBH@ L $ @Z BEE E(D0A8G@p
-8D0A(B BBBF t . K^ Z Z Z Z , Z[ j AID t
-DAA D $ Z lj BBB A(I0Dp
-0D(A BBBK T l \ @j BBB B(I0A8D$
-8D0A(B BBBI T x^ j BBB B(I0A8D$
-8D0A(B BBBI D bg BBB D(A0D@0A(A BBB T d `\ i BIE B(A0A8DI
-8D0A(B BBBA T e fi BIE B(A0A8DH
-8D0A(B BBBA T Hj\ 4i BIE B(A0A8DI
-8D0A(B BBBA T l Pob i BIE B(A0A8DQ
-8D0A(B BBBA L htR h BGE A(A0M
-0D(A BBBG T xz h BDB H(A0D8G
-8D0A(B BBBE d н. K^ | f/ A Ѓ- AG cA L r h BBB B(E0A8J@
-8A0A(B BBBA D BBL A(A0D@p
-0A(A BBBA D L BBL A(A0D@x
-0A(A BBBA D P BBB D(A0D@
-0A(A BBBA L
- g BIB A(D0D
-0D(A BBBA T 4 r g BBB B(D0A8N
+8A0A(B BBBA T ? F BBE E(G0A8GJ
+8D0A(B BBBD D A O GBA A(G@H(A ABBH@ L \A `P BEE E(D0A8G@p
+8D0A(B BBBF A &. K^ A P A P A P B P | $B P BEB B(A0A8D@6
+8A0A(B BBBG @
+8A0A(B BBBF x8D0A(I BBB , A 8S[ AID t
+DAA L B hS AAG e
+CAF s
+ACA R
+CAJ JCA D LB S BBB A(I0Dp
+0D(A BBBK T B U BBB B(I0A8D$
+8D0A(B BBBI T B hW BBB B(I0A8D$
+8D0A(B BBBI D D Z BBB D(A0D@0A(A BBB T C Y ; BIB E(A0A8D6
+8D0A(B BBBA T C ] BIB E(A0A8DF
+8D0A(B BBBA T <D b ת BIB E(A0A8DF
+8D0A(B BBBA T D Pf BIB E(A0A8DN
+8D0A(B BBBA D E j BBE G(F0D@
+0D(A BBBI T 4E pn + BDB B(A0A8J
+8A0A(B BBBD dF !. K^ |F dz/ A F y- AG cA L E yr _ BBB B(E0A8J@
+8A0A(B BBBA D G { BBL A(A0D@p
+0A(A BBBA D LG { BBL A(A0D@x
+0A(A BBBA D G H| BBB D(A0D@
+0A(A BBBA L G } L BIB A(D0D
+0D(A BBBA T TG r - BBB B(D0A8N
E
-8A0A(B BBBF , > eh AID W
-DAA
0R HB
-F A$ pg %h AR
+8A0A(B BBBF , G > AID W
+DAA H (R HB
+F A$ G hg AR
M A
-A
W HJ T $ g BEB E(A0D8G+\
-8D0A(B BBBE T | P h BBG B(A0A8J)
-8A0A(B BBBB T z i BBB H(F0A8Jg
-8D0A(B BBBE $ @. K^ L < ( BBB B(A0A8GP8A0A(B BBB L BEB B(A0A8DP8A0A(B BBB , x0 AC
FK
- E $ x~ Al
+A H W HJ T DH a BEB E(A0D8G+\
+8D0A(B BBBE T H H u BBG B(A0A8J)
+8A0A(B BBBB T H L BBB G(A0D8M
+8D0A(B BBBA $J h . K^ L <J P BBB B(A0A8GP8A0A(B BBB L J BEB B(A0A8DP8A0A(B BBB , J 0 AC
FK
+ E $ K ~ Al
C V
-J < 4 ADG v
+J < 4K ADG v
GAF
-AAF t S L h+ ULB A(A0@(A BBBD0 T H g BBB E(D0A8G$
-8D0A(B BBBH T < g BBB E(D0A8G
+AAF tK HS L K + ULB A(A0@(A BBBD0 T K p r BBB E(D0A8G$
+8D0A(B BBBH T \K 8 / BBB E(D0A8G
-8D0A(B BBBB D U qg BFB A(A0GP
-0A(A BBBD d 9 g BBB B(A0A8G@s
+8D0A(B BBBB D K U BFB A(A0GP
+0A(A BBBD d K 9 BBB B(A0A8G@s
8A0A(B BBBB _
-8A0A(B BBBG T D -g BBB B(A0A8J
-8A0A(B BBBK T D g BEB B(A0A8G
-8A0A(B BBBA . K^ 4 L L l xf BBB J(A0A8G
-8D0A(B BBBA L uf BBB J(A0A8G
-8D0A(B BBBA D n^v BBB D(A0D@^0A(A BBB T T (" *f BIB E(A0A8D6
-8D0A(B BBBA T `& e BIB E(A0A8D6
-8D0A(B BBBA T * e BBE B(D0A8G
-8D0A(B BBBB T \ `/ e BBE B(D0A8G
-8D0A(B BBBB T (4| e BBE E(D0A8G
-8D0A(B BBBF L P9 BBB B(A0A8D
-8A0A(B BBBF D T ;. BEB B(A0A8
-0A(B BBBG L x=o
- d BBE B(A0G8L
-8D0A(B BBBD L G BBB B(A0A8D
-8A0A(B BBBF D < I BEB B(A0A8
-0A(B BBBG L Ko
- d BBE B(A0G8L
-8D0A(B BBBD . K^ U AQ [\ AG RA$ , U AG
-AA $ T @V AG
-AA D | [ BBB D(A0D@0A(A BBB T VL d BIE B(A0A8D9
-8D0A(B BBBA T $ [ Qd BBB B(A0I8G
-8D0A(B BBBK t . K^ 8a2 Hb
-F A Xa7 Hj F\\ AG RAT Xaf c BBB B(I0A8G
-8D0A(B BBBF T L pfH c BBE B(D0A8G
-8D0A(B BBBG D [ BBB D(A0D@0A(A BBB T u c BIB E(A0A8D6
-8D0A(B BBBA L D Xy
- c BBB G(D0A8G
-8A0A(B BBBA . K^ L BHB B(A0A8GI
-8A0A(B BBBA L ADD e
+8A0A(B BBBG T dL BBB B(A0A8J
+8A0A(B BBBK T L D BEB B(A0A8G
+8A0A(B BBBA M . K^ N N L 4N 9 BME F(A0A8Dp8A0A(B BBB < N BBD A(DP
+(A ABBA L N } BBB B(A0A8D@
+8A0A(B BBBJ L O AAG e
+CAF S
+ACA R
+CAJ JCA L dO X AAG e
+CAF S
+ACA R
+CAJ JCA L O AAG ]
+CAF c
+CAI R
+CAJ JCA L P AAG e
+CAF S
+ACA R
+CAJ JCA D |O xK BBB A(A0DP1
+0A(A BBBA P ֩\ AG RAL P `= BBA A(D0
+(A ABBG
+(A ABBH L 4P P O BBB B(A0A8Dt8A0A(B BBB T P BBE B(D0A8G
+8D0A(B BBBG T P X" BBE B(D0A8G
+8D0A(B BBBG D R ¨ BBB D(A0D@0A(A BBB T |Q & BIB E(A0A8D6
+8D0A(B BBBA L Q +" U BEB B(A0A8Q
+8A0A(B BBBB 4 $R ,q 0 BAI A
+ABA L \R 8-
+ BBB G(D0G8G
+8D0A(B BBBD D R x7 BBB D(A0D
+0A(A BBBA L R @9B BBB A(A0DD
+0A(A BBBA L DS @;1 T BEI B(A0A8JR
+8D0A(B BBBC lT V Ko
+A T O T O < T O. BAD
+ABG n
+ABG , $T R[ & AID t
+DAA L ,U 0R AAG e
+CAF {
+ACI R
+CAJ JCA L T R BBB B(A0A8D@~8A0A(B BBB D U Ц BBB D(A0D@0A(A BBB T <U UL " BIE B(A0A8D9
+8D0A(B BBBA T U Z BDE B(A0A8G r
+8A0A(B BBBF V . K^ V v V v , 4V v[ AID t
+DAA 4 <W v ADD 6
+AAG K
+AAK L tW hy ADD e
+CAF {
+ACI V
+CAF JCA L W z_ BBB B(A0A8D`
+8A0A(B BBBI D X V BBB D(A0D@0A(A BBB T W |L BIE B(A0A8D9
+8D0A(B BBBA L W ȁ) BEB B(D0A8J L
+8D0A(B BBBA Y x. K^ T DX BBE B(D0A8G
+8D0A(B BBBG T X ȯ BBE B(D0A8G
+8D0A(B BBBG T X BEE E(D0A8G
+8D0A(B BBBE T LY q BBE B(D0A8G
+8D0A(B BBBG T Y W BEE E(D0A8G
+8D0A(B BBBE T Y - ? BBB B(A0A8L Y
+8A0A(B BBBG ,[ T KH D[ \[ t[ [ D [ BBB D(A0D@0A(A BBB T [ _ BIB E(A0A8D6
+8D0A(B BBBA T l[ - BIB E(A0A8D6
+8D0A(B BBBA L [ BBB J(A0A8G
+8D0A(B BBBA L \ p BBB J(A0A8G
+8D0A(B BBBA T d\ BBE B(D0A8G
+8D0A(B BBBB T \ ۦ BBE B(D0A8G
+8D0A(B BBBB T ] P | BBE E(D0A8G
+8D0A(B BBBF L D^ x BBB B(A0A8D
+8A0A(B BBBF D ^ . BEB B(A0A8
+0A(B BBBG L ^
+ BBB B(D0G8L
+8D0A(B BBBE L ,_ BBB B(A0A8D
+8A0A(B BBBF D |_ BEB B(A0A8
+0A(B BBBG L ^
+ BBB B(D0G8L
+8D0A(B BBBE ` . K^ ,` 0+ AQ L` \ AG RA$ l` + AG
+AA $ ` + AG
+AA D ` BBB D(A0D@0A(A BBB T ,` 8,L BIE B(A0A8D9
+8D0A(B BBBA T ` 01 m BBB B(A0I8G
+8D0A(B BBBK a . K^ a 62 Hb
+F A a 67 Hj b $\ AG RAT Ta 6f ޤ BBB B(I0A8G
+8D0A(B BBBF T a ;i ¤ BBE B(D0A8G
+8D0A(B BBBG D b BBB D(A0D@0A(A BBB T Lb M ٤ BIB E(A0A8D6
+8D0A(B BBBA L b R
+ BBB G(D0A8G
+8A0A(B BBBA c . K^ L c H\ BHB B(A0A8GI
+8A0A(B BBBA L 4d c ADD e
CAF s
ACA V
-CAF JCA $ D m AG q
-AA $ l ؋u AG y
-AA [\ AG RAD [ BBB D(A0D@0A(A BBB T ȋ {b BIB E(A0A8DV
-8D0A(B BBBA T \ | Ib BBE E(D0A8G
-8D0A(B BBBE T HT /b BBB B(I0A8G
-8D0A(B BBBH < PW *b BAA G{
- AABF L L p{ b BBB H(A0D8G
-8D0A(B BBBA . K^ , oc AID
-AAA , Qc AID
-AAA , 3c AID
-AAA , D c AID
-AAA , t b AID
-AAA , b AID
-AAA , b AID
-AAA , b AID
-AAA , 4 b AID
-AAA , d ab AID
-AAA , Cb AID
-AAA , %b AID
-AAA , b AID
-AAA D ! J BBB D(A0D@
-0A(A BBBA L l a BIB A(D0D
-0D(A BBBA L ` a BIB A(D0D
-0D(A BBBA L ca BIB A(D0D
-0D(A BBBA T \ Da BIE B(A0A8D
-8D0A(B BBBA L x a BIB A(D0D
-0D(A BBBA T ! ` BIE B(A0A8D
-8D0A(B BBBA L \! @ ` BIB A(D0D
-0D(A BBBA L ! ` BIB A(D0D
-0D(A BBBA L ! ` BIB A(D0D
-0D(A BBBA T L" 0 z` BIE B(A0A8D
-8D0A(B BBBA L " S` BIB A(D0D
-0D(A BBBA T " 4` BIE B(A0A8D
-8D0A(B BBBA T L# p
` BIE B(A0A8D
-8D0A(B BBBA % 褛. K^ % % % % L $ q n_ BBB B(A0I8G
-8D0A(B BBBD T l$ X_ BBB B(A0I8G
-8D0A(B BBBD D & S BBB D(A0D@0A(A BBB T % L _ BIE B(A0A8D9
-8D0A(B BBBA T d% L ^ BIE B(A0A8D9
-8D0A(B BBBA T % ^ BBB E(F0G8G
-8D0A(B BBBE T & 8P ^ BBB E(F0G8Gn
-8D0A(B BBBF d( P. K^ |(
(
, &
[ ^ AID t
-DAA T & H
A ^ BEE E(D0A8GY
-8D0A(B BBBE D 4) S BBB D(A0D@0A(A BBB T ' | i^ BIE B(A0A8Di
-8D0A(B BBBA T ' E 7^ BGB H(A0D8Gh
-8D0A(B BBBD ,* . K^ $ D* # AG u
-AA $ l* # AG }
-AA D * nS BBB D(A0D@0A(A BBB T ( #L O^ BIE B(A0A8D9
-8D0A(B BBBA T <) (8 ^ BBB J(A0A8G>
-8D0A(B BBBA + . K^ $ + *m AG q
-AA $ + *u AG y
-AA + S\ AG RAT * 0+ w] BBB B(I0A8G6
-8D0A(B BBBA < l, SD BBD A(D@0(A ABB T * h7 ] BIB E(A0A8D6
-8D0A(B BBBA - @. K^ - ;0 Hg <- ;7 Hj T d+ ; 9] BBB B(A0I8G6
-8D0A(B BBBD D - S BBB D(A0D@0A(A BBB T , ?L \ BIE B(A0A8D9
-8D0A(B BBBA T \, DT \ BBB E(A0A8G!
-8A0A(B BBBA . Ȝ. K^ . Z . Z . Z / Z $/ T\ AG RAT L- Zi ] BBB B(A0D8R
-8A0A(B BBBF , - ^> ] AID W
-DAA T - ^i \ BBB B(A0D8R
-8A0A(B BBBF , ,. b> \ AID W
-DAA D T0 FS BBB D(A0D@0A(A BBB T . b \ BIB E(A0A8D6
-8D0A(B BBBA T . f c\ BIB E(A0A8D6
-8D0A(B BBBA 4 T/ j 1\ BAD v
-ABH < 1 k BBG A(G@
-(A ABBG T / 0lW [ BBB B(A0A8J
-8A0A(B BBBA T $0 8 4\ BBB B(A0A8J
-8A0A(B BBBA t2 0. K^ 2 X 2 P , 0 X[ c\ AID t
-DAA < 2 BBB A(A0
-(A BBBA D ,3 8= BEB B(A0A8
-0A(B BBBA D t3 0 BBB D(A0D@
-0A(A BBBA T 1 B s[ BIB B(A0D8Dg
-8D0A(B BBBA < 4 BBD D(G0b
-(A ABBE L T4 K BBB B(A0A8D@28A0A(B BBB d 4 S BBB E(D0D8D
+CAF JCA $ d (dm AG q
+AA $ d pdu AG y
+AA d \ AG RAT d d? ף BBB B(A0A8G
+8A0A(B BBBA D Le j BBB D(A0D@0A(A BBB T d Hl ; BIB E(A0A8DV
+8D0A(B BBBA T e p| BBE E(D0A8G
+8D0A(B BBBE T le uT BBB B(I0A8G
+8D0A(B BBBH T e z BBB B(A0G8J
+8D0A(B BBBE f . K^ , 4f G AID
+AAA , df ) AID
+AAA , f AID
+AAA , f AID
+AAA , f ϣ AID
+AAA , $g AID
+AAA , Tg AID
+AAA , g u AID
+AAA , g W AID
+AAA , g 9 AID
+AAA , h AID
+AAA , Dh AID
+AAA , th ߢ AID
+AAA D |i J BBB D(A0D@
+0A(A BBBA L h y BIB A(D0D
+0D(A BBBA L <i X Z BIB A(D0D
+0D(A BBBA T i ; BIE B(A0A8D
+8D0A(B BBBA L i BIB A(D0D
+0D(A BBBA T 4j p BIE B(A0A8D
+8D0A(B BBBA L j Ρ BIB A(D0D
+0D(A BBBA T j 8 BIE B(A0A8D
+8D0A(B BBBA T 4k BIE B(A0A8D
+8D0A(B BBBA T k ( a BIE B(A0A8D
+8D0A(B BBBA T k : BIE B(A0A8D
+8D0A(B BBBA T <l BIE B(A0A8D
+8D0A(B BBBA T l BIE B(A0A8D
+8D0A(B BBBA T l Š BIE B(A0A8D
+8D0A(B BBBA n . K^ 4n h Ln ` dn X |n ` L m hq & BBB B(A0I8G
+8D0A(B BBBD T n BBB B(A0I8G
+8D0A(B BBBD D <o H BBB D(A0D@0A(A BBB T n L ԟ BIE B(A0A8D9
+8D0A(B BBBA T o L BIE B(A0A8D9
+8D0A(B BBBA T \o xZ s BBB G(D0G8G
+8D0A(B BBBD T o
+ BBB G(D0G8G
+8D0A(B BBBE p . K^ p q , Tp [ { AID t
+DAA T p PA [ BEE E(D0A8GY
+8D0A(B BBBE D q BBB D(A0D@0A(A BBB T $q | BIE B(A0A8Di
+8D0A(B BBBA T |q ( h Ǟ BGH B(A0D8G
+8D0A(B BBBI r . K^ $ r ( AG u
+AA $ r AG }
+AA D s BBB D(A0D@0A(A BBB T r L Ӟ BIE B(A0A8D9
+8D0A(B BBBA T r 8 BBB J(A0A8G>
+8D0A(B BBBA t P. K^ $ $t m AG q
+AA $ Lt u AG y
+AA tt z\ AG RAT s X
BBB B(I0A8G
+8D0A(B BBBA < t ^D BBD A(D@0(A ABB T Tt G BIB E(A0A8D6
+8D0A(B BBBA u . K^ u $0 Hg u $7 Hj T u 0$ BBB B(A0I8G6
+8D0A(B BBBD D 4v Z BBB D(A0D@0A(A BBB T u 0(L BIE B(A0A8D9
+8D0A(B BBBA T u (-' S BBB E(A0A8Ga
+8A0A(B BBBA ,w . K^ Dw B \w B tw B w B < w B0 BEA A(G@
+(A ABBF < w D| BEA A(G@
+(A ABBI L $x G AAG e
+CAF S
+ACA R
+CAJ JCA L tx G AAG e
+CAF S
+ACA R
+CAJ JCA x \ AG RAL x G BBB B(A0A8DPV
+8A0A(B BBBA L 4y 8J BBB B(A0A8D`
+8A0A(B BBBA T x Li Ǜ BBB B(A0D8R
+8A0A(B BBBF , y Q> Λ AID W
+DAA T 4y Qi BBB B(A0D8R
+8A0A(B BBBF , y (U> AID W
+DAA D z $ BBB D(A0D@0A(A BBB T z T M BIB E(A0A8D6
+8D0A(B BBBA T \z (Y BIB E(A0A8D6
+8D0A(B BBBA < { `] BBG A(G@
+(A ABBG T z ^ BHB E(A0A8G
+8D0A(B BBBA T L{ p Q BHB E(A0A8G
+8D0A(B BBBA || p. K^ | | , { [ AID t
+DAA < | 8 BBB A(A0
+(A BBBA D 4} = BEB B(A0A8
+0A(B BBBA D |} BBB D(A0D@
+0A(A BBBA T | 8B BIB B(A0D8Dg
+8D0A(B BBBA < ~ 0 BBD D(G0b
+(A ABBE L \~ K BBB B(A0A8D@28A0A(B BBB d ~ S BBB E(D0D8D
8A0A(B BBBD
-8A0A(B BBBG L 5 BBB B(A0A8Dxv
-8A0A(B BBBA L \5 H
+8A0A(B BBBG L BBB B(A0A8Dxv
+8A0A(B BBBA L d
BIB B(A0A8G
-8A0A(B BBB < 3 Y AC
FM-
+8A0A(B BBB < ~ h AC
FM-
A X
- C T 3 }Y BBA A(Gp
+ C T H ˘ BBA A(Gp
(A ABBG u
-(A ABBE 4 L4 )Y AF
P
- G 4 |6 8 AC
BGM
+(A ABBE 4 t w AF
P
+ G 4 8 AC
BGM
H
- F l 4 X BBB B(A0D8Gc
+ F l BBB B(A0D8Gc
8A0A(B BBBG
-8A0A(B BBBG T ,5 X BEB B(D0D8Je
-8A0A(B BBBD T 5 NX BDB B(A0A8J
-8A0A(B BBBK 7 . K^ L 7 BBB E(A0A8D8A0A(B BBB L <8 x BEB B(A0A8GM
-8A0A(B BBBE L 8 ( BBB E(A0A8G
-8A0A(B BBBJ $ 8 AG r
-AA $ 9 ` AG z
-AA < 47 6W BBA I(D@e
-(D ABBA T t7 x W BBE B(D0A8G
-8D0A(B BBBB D 9 K BBB D(A0D@0A(A BBB T 8 V BIB E(A0A8D6
-8D0A(B BBBA T l8 0 ! V BBE B(A0A8L
-q
-8A0A(B BBBD : H. K^ T 8 W BBB B(A0I8G
-8D0A(B BBBD D ,;
-L BBB D(A0D@0A(A BBB T |9 $R W BIE B(A0A8DA
-8D0A(B BBBA ; h. K^ ; ) ; ) T : ) 1W BBB B(I0A8D*
-8D0A(B BBBC l< , < ," < < (, BGE D(A0j
-(A BFBH D < (L BBB D(A0D@0A(A BBB T ,; p, JV BIB E(A0A8GM
-8D0A(B BBBA T ; 2, /V BGB B(A0D8G%
-8A0A(B BBBH = . K^ = h8 > `8 > X8 4> `8 , T< h8[ U AID t
-DAA |> 8- AG cA L < 8r U BBB B(E0A8J@
-8A0A(B BBBA > 9} AG sA ? 8:} AG sA ,? : AG {A L? ; AG {A l? x; AG vA ? ; AG ~AT = H< T BBB B(A0I8GV
-8D0A(B BBBA L > > T BBB B(I0A8D
-8D0A(B BBBA L \> @ T BBB B(I0A8D
-8D0A(B BBBA L > B T BBB B(I0A8D
-8D0A(B BBBA L > C nT BBB B(I0A8D
-8D0A(B BBBA D DA 0E BBB D(A0D@
-0A(A BBBA 4 ? Fk T BHA GP
- DABA 4 ? Gk S BHA GP
- DABA 4 @ Hk S BHA GP
- DABA 4 <@ 0Jk S BHA GP
- DABA 4 t@ hKk S BHA GP
- DABA 4 @ Lk uS BHA GP
- DABA 4 @ Mz VS BHA GP
- DABA T A O 7S BBB E(D0A8G
-8A0A(B BBBK T tA Ww S BBG E(G0A8G+
-8D0A(B BBBI D A Z S BDB D(G0Dy
-0D(A BBBA T B \ S BBB J(A0A8G&
-8D0A(B BBBA | dD ^ BBB B(D0A8DP
-8A0A(B BBBB U
-8G0A(B BBBK
-8A0A(B BBBI T B ` {R BHB B(A0D8G
-8D0A(B BBBJ T DC oU !S BBB H(D0F8G
-8D0A(B BBBE E v AR E v AY E . K^ L C uu S BBB B(A0I8Dp
-8D0A(B BBBF L DD (wu R BBB B(A0I8Dp
-8D0A(B BBBF L D Xxu R BBB B(A0I8Dp
-8D0A(B BBBF D F C BBB D(A0D@0A(A BBB T ,E @y 'R BIB B(D0A8D
-8D0A(B BBBA T E ~ Q BIB B(D0A8D
-8D0A(B BBBA T E Q BIB B(D0A8D
-8D0A(B BBBA ,H . K^ DH ` \H X , |F `[ LQ AID t
-DAA D H C BBB D(A0D@0A(A BBB T F H\ P BIE B(A0A8DI
-8D0A(B BBBA T LG P P BDB E(G0A8G*
-8D0A(B BBBE I X. K^ I D DN
-E A $ I ^ AG b
-AA < I ؓ BEA A(D@
-(A ABBA < <J BEA A(D@
-(A ABBA |J H L J Pp BEA A(D0H
+8A0A(B BBBG T T ` Η BEB B(D0D8Je
+8A0A(B BBBD T v BDH B(A0A8J
+8D0A(B BBBI ܁ @. K^ L BBB E(A0A8D8A0A(B BBB L D Hx BEB B(A0A8GM
+8A0A(B BBBE L x BBB E(A0A8G
+8A0A(B BBBJ $ ( AG r
+AA $ AG z
+AA T \ H BBE B(D0A8G
+8D0A(B BBBB D BBB D(A0D@0A(A BBB T " BIB E(A0A8D6
+8D0A(B BBBA < T BBA I(D@e
+(D ABBA T '! ϕ BBE B(A0A8L
+
+8A0A(B BBBD Ą . K^ T @ + BBB B(A0I8G
+8D0A(B BBBD D 4 BBB D(A0D@0A(A BBB T 0R BIE B(A0A8DA
+8D0A(B BBBA ԅ . K^ T D u BBB B(I0A8D
+8D0A(B BBBE t " < BGE D(A0j
+(A BFBH D > BBB D(A0D@0A(A BBB T T BIB E(A0A8GM
+8D0A(B BBBA L " s BHB B(F0D8G
+8D0A(B BBBB ԇ . K^ ' ' ' 4 ' L ' DV , '[ C AID t
+DAA (} AG sA h(} AG sA Ԉ ( AG {A 8) AG {A ) AG vA 4 * AG ~AL | x*r c BBB B(E0A8J@
+8A0A(B BBBA $ +^ AG b
+AA T + BBB B(A0I8GV
+8D0A(B BBBA L L . BBB B(I0A8D
+8D0A(B BBBA L 0 BBB B(I0A8D
+8D0A(B BBBA L 1 ԓ BBB B(I0A8D
+8D0A(B BBBA L < 83 BBB B(I0A8D
+8D0A(B BBBA D d 4 BBB D(A0D@
+0A(A BBBA 4 Ԋ 6k ` BHA GP
+ DABA 4 X7k A BHA GP
+ DABA 4 D 8k " BHA GP
+ DABA 4 | 9k BHA GP
+ DABA 4 ;k BHA GP
+ DABA 4 8<k ΠBHA GP
+ DABA 4 $ p=z BHA GP
+ DABA T \ > BBB E(D0A8G
+8A0A(B BBBK T PGw h BBG E(G0A8G+
+8D0A(B BBBI D xJ o BDB D(G0Dy
+0D(A BBBA T T 0L i BBB J(A0A8G&
+8D0A(B BBBA T M K BHB B(A0D8G
+8D0A(B BBBF T @\U BBB H(D0F8G
+8D0A(B BBBE 4 Hc AR T Hc AY t hݕ. K^ L 0cu ے BBB B(A0I8Dp
+8D0A(B BBBF L `du BBB B(A0I8Dp
+8D0A(B BBBF L T eu s BBB B(A0I8Dp
+8D0A(B BBBF D | t BBB D(A0D@0A(A BBB T xf BIB B(D0A8D
+8D0A(B BBBA T D k Ƒ BIB B(D0A8D
+8D0A(B BBBA T Hq BIB B(D0A8D
+8D0A(B BBBA ̑ @ە. K^ v v , < v[ AID t
+DAA D D z BBB D(A0D@0A(A BBB T v\ BIE B(A0A8DI
+8D0A(B BBBA T { BDB E(G0A8G3
+8D0A(B BBBD < ڕ. K^ T l < ? AAG0
+AAD a
+AAE < AAG0
+AAD a
+AAE , \ H[ AID t
+DAA , x[ AID t
+DAA L AAG e
+CAF K
+ACI R
+CAJ JCA L AAG e
+CAF K
+ACI R
+CAJ JCA D 4 BBB A(A0DP
+0A(A BBBE D | BBB A(A0DPO
+0A(A BBBE D ĕ Ȇ BBB D(A0D@0A(A BBB T 4 BIB E(A0A8D6
+8D0A(B BBBA T ؏ э BIB E(A0A8D6
+8D0A(B BBBA T BBE B(D0A8G
+8D0A(B BBBG L < ؘ~ BBB B(A0A8G
+8A0A(B BBBI L ~ N BBB B(A0A8G
+8A0A(B BBBI T ܖ 8E BDB H(A0A8J
+8D0A(B BBBK T 4 0D K BDB H(A0A8J
+8D0A(B BBBG d Օ. K^ | ޅ DN
+E A $ ^ AG b
+AA < Ę ( BEA A(D@
+(A ABBA < BEA A(D@
+(A ABBA D L \ p BEA A(D0H
(A ABBE D
-(A ABBA T H p O BEE E(D0A8G
-8D0A(B BBBE T DI , O BBE B(D0A8G
-8D0A(B BBBK 4 K pJ BDA q
+(A ABBA T Ԙ s BEE E(D0A8G
+8D0A(B BBBE T , , Y BBE B(D0A8G
+8D0A(B BBBK 4 \ J BDA q
ABE A
-ABA d K g BLB B(A0A8D@q
+ABA d g BLB B(A0A8D@q
8A0A(B BBBE
-8A0A(B BBBF D 4L TB BBB D(A0D@0A(A BBB T J H N BIB E(A0A8D6
-8D0A(B BBBA < L BBG A(G@
-(A ABBF L M P~ BLB H(A0D8D@
-8A0A(B BBBD < dM BBG A(G@
-(A ABBF L M @m BBB H(A0D8DP
-8A0A(B BBBA T K ` 9M BBB B(H0A8J
-"
-8A0A(B BBBA , TL > oN AID W
-DAA L L Y ON BBB E(D0D8J
-8A0A(B BBBA T L ( UN BBE B(A0H8J
-8A0A(B BBBB L $O BLB H(A0D8DPV
-8A0A(B BBBG T |M P
- N BEE B(A0A8N
-8A0A(B BBBB L M N BBB G(D0G8G[
-8D0A(B BBBI P }. K^ 4P LP , lN [ M AID t
-DAA D P ? BBB D(A0D@0A(A BBB T N L M BIE B(A0A8D9
-8D0A(B BBBA $ 4Q V ADD JAAd dO 1 5M BHB B(D0A8Dpv
+8A0A(B BBBF D x BBB D(A0D@0A(A BBB T l W BIB E(A0A8D6
+8D0A(B BBBA < BBG A(G@
+(A ABBF L ܛ ~ BLB H(A0D8D@
+8A0A(B BBBD < , BBG A(G@
+(A ABBF L l m BBB H(A0D8DP
+8A0A(B BBBA T BBB B(H0D8G
+Z
+8A0A(B BBBA , < H> ; AID W
+DAA L l X BBB E(D0D8J
+8A0A(B BBBA T 2 BBE B(A0H8J
+8A0A(B BBBB L P BLB H(A0D8DPV
+8A0A(B BBBG T d
+ ܌ BEE B(A0A8N
+8A0A(B BBBB T h BBB G(D0A8M
+8D0A(B BBBC Ε. K^ , \ [ Ì AID t
+DAA D d ހ BBB D(A0D@0A(A BBB T Ԟ L [ BIE B(A0A8D9
+8D0A(B BBBA $ V ADD JAAd T 1 BHB B(D0A8Dpv
8C0A(B BBBG {
-8D0A(B BBBA T O L BBB E(I0A8K}
-8D0A(B BBBF d $P L BEB B(D0A8Gp
+8D0A(B BBBA T BBB E(I0A8K}
+8D0A(B BBBF d w BEB B(D0A8Gp
8E0A(B BBBC
-8E0A(B BBBA L P u OL BDB B(A0A8G
-8A0A(B BBBA R z. K^ R S S ?\ AG RAT DQ L BBB B(A0I8G
-8D0A(B BBBE D S BBB B(A0A8D`8A0A(B BBB S o T Q P< 'L BBB E(D0A8G
-8D0A(B BBBH \ TR 8N 8L BBB A(D0GP{
+8E0A(B BBBA T | 8l BDB B(A0A8Gp
+8A0A(B BBBA ̕. K^ ġ 8& ܡ 0& \ AG RAT < & o BBB B(A0I8G
+8D0A(B BBBE D l ) BBB B(A0A8D`8A0A(B BBB (*o T * BBB E(D0A8G
+8D0A(B BBBE \ L 8N ! BBB A(D0GP{
0D(A BBBH
-0A(A BBBA T R ( K BBB B(D0A8G
-8A0A(B BBBA D U |= BBB D(A0D@0A(A BBB T TS oK BIB E(A0A8DV
-8D0A(B BBBA T S ?K BBB B(A0G8J
-8D0A(B BBBF U w. K^ V P. ,V H. , LT P.[ L AID t
-DAA D tV = BBB D(A0D@0A(A BBB T T 8.l #L BIE B(A0A8DY
-8D0A(B BBBA L U P3 K BDB E(A0D8G
-8D0A(B BBBF dW Pv. K^ , |W 8j ADG`
-AAH W 9 W :Y l U H: K BBB B(D0A8G
+0A(A BBBA T 9 ֊ BBB B(D0A8G
+8A0A(B BBBA D ܣ ~ BBB D(A0D@0A(A BBB T L E X BIB E(A0A8DV
+8D0A(B BBBA T `IE ' BBB E(A0G8G>
+8D0A(B BBBK Ԥ (ɕ. K^ @^ 8^ , D @^[ ' AID t
+DAA D L ~ BBB D(A0D@0A(A BBB T (^l BIE B(A0A8DY
+8D0A(B BBBA L @c BDB B(D0D8G
+8D0A(B BBBC < Ǖ. K^ , T hj ADG`
+AAH i jY l ܥ Hj W BBB B(D0A8G
8A0A(B BBBD Z
-8A0A(B BBBD LX u. K^ dX P@ |X H@ X P@ X X@ X P@ X H@ L X P@g BEB B(A0A8J
-8A0A(B BBBA 4 LW pF J BHE n
-ABH 4 W H NJ BHE q
-ABM T W I J BEB B(A0D8J
-8A0A(B BBBA Z xU. T ,X U +J BBB B(A0A8J
-8A0A(B BBBB |Z s. K^ $ Z \^ AG b
-AA L X \ I BBE B(A0D8G
-8D0A(B BBBF [ 8s. K^ $[ a <[ a T[ a l[ a [ a [ ` [ ` [ ` [ ` [ ` \ ` HS 4\ ` L\ ` HS l\ ` \ `" D] , Z ` /H YX
+8A0A(B BBBD $ 8Ǖ. K^ < Pp T Hp l Pp Xp Pp Hp L ̧ Ppg BEB B(A0A8J
+8A0A(B BBBA 4 D pv BHE n
+ABH 4 | x BHE q
+ABM T y BEB B(A0D8J
+8A0A(B BBBA x. T $ BBB B(A0A8J
+8A0A(B BBBB T 8ŕ. K^ $ l ^ AG b
+AA L f BBE B(A0D8G
+8D0A(B BBBF ĕ. K^ , D \ t Ԫ ؐ А Ȑ 4 Ȑ HS T Ȑ l А HS А Ȑ" D] , { YX
G n
-A \ a \ a" AZ
-B L [ a G BBB A(A0D
-0A(A BBBH D \[ d$ G BBB A(A0Dg
-0A(A BBBA ] e" D] ] p. K^ ] e ] e , ] e BBB wBB ,^ 8i D^ 0i \^ (i t^ i ^ i ^ i ^ i ^ i ^ i _ i _ (i 4_ 0i L_ 8i , d_ @i AAD0[
-AAE _ i _ hj _ jS _ 8k _ @k ` k G ,` Xn D $ L` p AG a
-AF t` pF < ` p% BBF G(F0n
-(A BBDB , ` q> ADD c
-AAB d _ q iD BEB B(A0A8D@
+A " AZ
+B L L ' BBB A(A0D
+0A(A BBBA D $ BBB A(A0Dg
+0A(A BBBA Е" D] Ԭ . K^ Е ȕ , BBB wBB L d | ĭ ܭ $ < T l , ( AAD0[
+AAE ș ̮ P ؚS ( , G L @ D $ l AG a
+AF F < Р% BBF G(F0n
+(A BBDB , > ADD c
+AAB d D С BEB B(A0A8D@
8A0A(B BBBN D
-8A0A(B BBBA < da pr BBG A(G@
-(A ABBG L a 0si BBB B(A0A8Gp
+8A0A(B BBBA < X BBG A(G@
+(A ABBG L İ i BBB B(A0A8Gp
-8A0A(B BBBK , a Pu BOA
-cB| $b v <b Xwc Tb w* , lb w BJJ AB < b z BBG A(G@
-(A ABBG b x{5 , b {B BAA :AB 4 $c ~7 BKJ A( ABB 4 \c ȁU BBA A(I ABB D c BHB B(A0A8D`8A0A(B BBBD c BEB B(A0A8DP8A0A(B BBB $d 0 L <d BBB B(A0A8G8A0A(B BBB L d H BBB B(A0A8G8A0A(B BBB L d ؑ BBB E(A0A8J8A0A(B BBB L ,e h BBB E(A0A8J8A0A(B BBB |e I D e @ BBB D(A0J0A(A BBB D e آ BBE A(A0J0A(A BBB D $f BBE A(A0J0A(A BBB lf h f P $ f 8 ELE L f BBB E(A0C8G@
-8A0A(B BBBH L g P BDB B(A0D8DP8A0A(B BBB dg < D@w |g L g BEB E(A0A8Iv8A0A(B BBB L g BDB B(A0A8G`8A0A(B BBB 4h @< D@w 4 Lh h^ BTM F(( ABI L h BEB E(A0A8Iv8A0A(B BBB L h BDB B(A0D8D`8A0A(B BBB $i @< D@w 4 <i h BRA C( ABI L ti @ BBB E(A0C8G@
-8A0A(B BBBH L i BDB B(D0A8DP8A0A(B BBB j < D@w ,j H < Dj r BBA A(GP^(A ABB < j `X BBA A(GPD(A ABB j $ j BAG ABL k BEB B(A0A8J
+8A0A(B BBBK , 8 BOA
+cB| D \ @c t * , BJJ AB < BBG A(G@
+(A ABBG `5 , B BAA :AB 4 D 7 BKJ A( ABB 4 | U BBA A(I ABB D ش BHB B(A0A8D`8A0A(B BBBD BEB B(A0A8DP8A0A(B BBB D L \ BBB B(A0A8G8A0A(B BBB L 0 BBB B(A0A8G8A0A(B BBB L BBB E(A0A8J8A0A(B BBB L L P BBB E(A0A8J8A0A(B BBB I D ( BBB D(A0J0A(A BBB D BBE A(A0J0A(A BBB D D BBE A(A0J0A(A BBB P 8 $ ELE L BBB E(A0C8G@
+8A0A(B BBBH L 4 8 BDB B(A0D8DP8A0A(B BBB < D@w L x BEB E(A0A8Iv8A0A(B BBB L BDB B(A0A8G`8A0A(B BBB T (< D@w 4 l P^ BTM F(( ABI L x BEB E(A0A8Iv8A0A(B BBB L BDB B(A0D8D`8A0A(B BBB D (< D@w 4 \ P BRA C( ABI L ( BBB E(A0C8G@
+8A0A(B BBBH L BDB B(D0A8DP8A0A(B BBB 4 < D@w L 0 < d r BBA A(GP^(A ABB < HX BBA A(GPD(A ABB h $ BAG ABL $ x BEB B(A0A8J
g
-8A0A(B BBBA L Tk BEB B(A0A8G
-8A0A(B BBBA L i " 9 BBB B(A0A8Jy
-8A0A(B BBBA L k BBE B(A0A8G8A0A(B BBB $ Dl @ BAG ABL ll BEE B(A0A8G
-8A0A(B BBBA L l h BEB B(A0A8G
-8A0A(B BBBA L k $ y8 BBB B(A0A8Jy
-8A0A(B BBBA 4 \m BBA Gk
- ABBD L m P BBE B(A0A8G8A0A(B BBB $ m ADG uAAL n XO BEB B(A0A8J
-8A0A(B BBBA L \n X BEB B(A0A8G
-8A0A(B BBBA L l
" 6 BBB B(A0A8Jy
-8A0A(B BBBA L n BBE B(A0A8G8A0A(B BBB $ Lo ADG uAAL to BEE B(A0A8G
-8A0A(B BBBA L o BEB B(A0A8G
-8A0A(B BBBA L n P$$ 5 BBB B(A0A8Jy
-8A0A(B BBBA , dp 0({ BAGm
-ABF 4 n ) E5 AAG
-AAB L p + BBE B(A0A8G8A0A(B BBB D q .
BBB B(A0A8
-0A(B BBBA < dq P/ BBB A(A0
-(A BBBA L q / BIB B(A0A8OA
-8A0A(B BBBA < o A+ 3 AF
Pf
+8A0A(B BBBA L t BEB B(A0A8G
+8A0A(B BBBA L " x BBB B(A0A8Jy
+8A0A(B BBBA L x BBE B(A0A8G8A0A(B BBB $ d ( BAG ABL BEE B(A0A8G
+8A0A(B BBBA L ܻ P BEB B(A0A8G
+8A0A(B BBBA L T $ v BBB B(A0A8Jy
+8A0A(B BBBA 4 | # BBA Gk
+ ABBD L 8% BBE B(A0A8G8A0A(B BBB $ ' ADG uAAL , @(O BEB B(A0A8J
+8A0A(B BBBA L | @9 BEB B(A0A8G
+8A0A(B BBBA L <" Au BBB B(A0A8Jy
+8A0A(B BBBA L @ BBE B(A0A8G8A0A(B BBB $ l C ADG uAAL C BEE B(A0A8G
+8A0A(B BBBA L P BEB B(A0A8G
+8A0A(B BBBA L \ 8T$ s BBB B(A0A8Jy
+8A0A(B BBBA , X{ BAGm
+ABF 4 ܾ hY s AAG
+AAB L [ BBE B(A0A8G8A0A(B BBB D < p^
BBB B(A0A8
+0A(B BBBA < 8_ BBB A(A0
+(A BBBA < _ BBB A(A0
+(A BBBA L X` BIB B(A0A8OA
+8A0A(B BBBA < | q+ r AF
Pf
C
- A 4r FR D@M Lr XFW D@R dr FX D@S |r F, $ r G AA $ r I< VAA 4 r K] BED H(I@z(A ABB 4 s K BEA A(M
- ABBE 4 Ts PN BHA I(
- ABBG $ s O HN
+ A vR D@M vW D@R wX D@S `w, $ xw AA $ z< VAA 4 D |] BED H(I@z(A ABB 4 | @| BEA A(M
+ ABBE 4 ~ BHA I(
+ ABBG $ 0 HN
J W
-Y L s R BRI B(A0D8G!
-8A0A(B BBBJ t Y' t Y 4t PZw L Lt Z BIB B(A0A8Gd
-8A0A(B BBBA L t m BIB B(A0A8Gd
-8A0A(B BBBA d t xS BBB E(D0D8D
+Y L BRI B(A0D8G!
+8A0A(B BBBJ d 8' | P Ȋw L 0 BLE B(A0A8GQ
+8A0A(B BBBA T $ ko BEE B(A0D8J
+8A0A(B BBBA L T h BIB B(A0A8Gd
+8A0A(B BBBA L ȭ BIB B(A0A8Gd
+8A0A(B BBBA d (S BBB E(D0D8D
8A0A(B BBBD
-8A0A(B BBBG L Tu p BBB B(A0A8Gd
-8A0A(B BBBA d u ЉS BBB E(D0D8D
+8A0A(B BBBG L \ BBB B(A0A8Gd
+8A0A(B BBBA d S BBB E(D0D8D
8A0A(B BBBD
-8A0A(B BBBG L v ȍ BBB B(A0A8Gd
-8A0A(B BBBA d \v (S BBB E(D0D8D
+8A0A(B BBBG L x BBB B(A0A8Gd
+8A0A(B BBBA d d S BBB E(D0D8D
8A0A(B BBBD
-8A0A(B BBBG < t 4/ AC
BEO#
+8A0A(B BBBG < l AC
BEO
A +
- G T u / BBA A(Gp
+ G T 4 l BBA A(Gp
(A ABBE w
-(A ABBK 4 \w x8 AC
BGM
+(A ABBK 4 d 8 AC
BGM
H
- F L w - BBE B(A0A8GK
-8A0A(B BBBG , w `( AC
BN
- E T v ` - BBA A(Gp
+ F L - BBE B(A0A8GK
+8A0A(B BBBG , ( AC
BN
+ E T D k BBA A(Gp
(A ABBG u
-(A ABBE 4 lx ؽX AC
BGM
+(A ABBE 4 t xX AC
BGM
J
- F T v k- BKE E(D0A8J
-8A0A(B BBBA L x H- BBE B(A0A8GK
-8A0A(B BBBG , Ly (( AC
BN
- E D |y ( BBB B(A0A8
-0A(B BBBA < y BBB A(A0
-(A BBBA L z BEB B(A0A8Gy
-8A0A(B BBBA < \x + AC
BEO+
+ F T 7k BKE E(D0A8J
+8A0A(B BBBA L - BBE B(A0A8GK
+8A0A(B BBBG , T &( AC
BN
+ E D ' BBB B(A0A8
+0A(B BBBA < + BBB A(A0
+(A BBBA L 0, BEB B(A0A8Gy
+8A0A(B BBBA < < i AC
BEO+
A
- G T x + BBA A(Gp
+ G T pG ki BBA A(Gp
(A ABBG u
-(A ABBE 4 z HX AC
BGM
+(A ABBE 4 HX AC
BGM
J
- F T ,y p # + BEE E(D0D8M
-8A0A(B BBBG L |{ HZ BEB B(A0A8G3
-8A0A(B BBBG , { X(* AC
BN
- D D { X)
BBB B(A0A8
-0A(B BBBA < D| * BBB A(A0
-(A BBBA L | * BEB B(A0A8G
-8A0A(B BBBA < z P; n) AC
BEO+
- A
- E T { F =) BBA A(Gp
+ F T T M# h BEE E(D0D8M
+8A0A(B BBBG L SZ BEB B(A0A8G3
+8A0A(B BBBG , h* AC
BN
+ D D i
BBB B(A0A8
+0A(B BBBA L L j BEB B(A0A8G
+8A0A(B BBBA < `{{
+ g AC
BEO
+ A {
+ G T Sg BBA A(Gp
(A ABBE w
-(A ABBK 4 l} GX AC
BGM
- J
- F T { K ( BHE B(D0D8J,
-8A0A(B BBBG L } xSJ BEB B(A0A8G#
-8A0A(B BBBG , L~ xh* AC
BN
- D T | xi ' BHE B(D0D8J,
-8A0A(B BBBG ~ O. K^ ~ q q p L <} q A' BED A(Gpq
+(A ABBK < \ ( f AF
P
+ K j
+ A 4 t 8 AC
BGM
+ H
+ F T f BHE B(D0D8J,
+8A0A(B BBBG L J BEB B(A0A8G#
+8A0A(B BBBG , T * AC
BN
+ D T e BHE B(D0D8J,
+8A0A(B BBBG @. K^ ( $ L d &e BED A(Gpq
(C ABBD G
-(C ABBA D } q ' BAC G`^
+(C ABBA D d BAC G`^
AABA b
- CABA d } q & BEE B(D0A8Dj
+ CABA d d BEE B(D0A8Dj
8A0A(B BBBE I
-8C0A(B BBBA 4 r% < T~ r & ADD`y
+8C0A(B BBBA < з% < | gd ADD`y
AAD O
-CAE 4 ~ Xs `& BAA GPf
- AABK , ~ s^ ,& BKI ^
-ABA t AL t , s! < L t % ADD`y
+CAE 4 x Ed BAA GPf
+ AABK , ^ d BKI ^
+ABA AL 4 ! < t 0 c ADD`y
AAD O
-CAE 4 t % BAA GPf
- AABK , u^ f% BKI ^
-ABA Hu AL L. K^ $ 0u < (u T u l u , uI $ AEG yAA 0u AL Ԃ 0u (u u u5 Hl < 8u< Ho \ Xu4 t u4 xK. K^ L u # BEA D(F0k
+CAE 4 c BAA GPf
+ AABK , 8^ Kc BKI ^
+ABA h AL 8. K^ , P D H \ @ t 8 , 0I b AEG yAA P AL P H @ $ 85 Hl D X< Ho d x4 | 4 蛕. K^ L wa BEA D(F0k
(D ABBJ L
-(D ABBK u u8 Ho , u? Hr L v d v T v " BBE B(D0A8G`
-8D0A(B BBBD $ ܂ { " AG@q
-AA D |5 " BIC Q0v
+(D ABBK 8 Ho 4 ? Hr T 8 l 0 T ( ` BBE B(D0A8G`
+8D0A(B BBBD $ ` AG@q
+AA D , (5 ` BIC Q0v
MABK |
- CABF D } \ | t | | | |T l g ԅ `I. K^ } } } 4 } L } d } | } } (}- Ć @}- < X} BBA D(D`
-(D ABBG 4 L d AT L
O BBB F(A0D8G`
-8A0A(B BBBH L ܅ P 7 BBB B(A0A8JC
-8A0A(B BBBG $
- , D # }GA A(D $ l * ADG [AA T [ BBE B(D0A8G
-8D0A(B BBBF < = BBD D(D
-(A ABBA l 4 BBB E(A0A8G
+ CABF L d | T l g Й. K^ ( $ ( < T ( l 0 8 @ H- `- < x ^ BBA D(D`
+(D ABBG $ < T l AT L
3^ BBB F(A0D8G`
+8A0A(B BBBH L p ^ BBB B(A0A8JC
+8A0A(B BBBG ,
+ , l # ] }GA A(D $ t * ADG [AA T [ ] BBE B(D0A8G
+8D0A(B BBBF < = ] BBD D(D
+(A ABBA l \ ] BBB E(A0A8G
8A0A(B BBBC
-8A0A(B BBBB < h O BBD D(DU
-(A ABBA T Q BBE B(D0A8G
-8A0A(B BBBA T < b ; BIB B(A0A8G
-8A0A(B BBBA 4 R BDA r
+8A0A(B BBBB < 3] BBD D(DU
+(A ABBA T 5] BBE B(D0A8G
+8A0A(B BBBA T d b ] BIB B(A0A8G
+8A0A(B BBBA 4 R BDA r
ABL A
-ABA T ̈ Пg BBB E(A0A8J
-8A0A(B BBBA L BBB B(A0A8Gp8A0A(B BBB 4 t h BHH w
-ABA в AL \ ċ вh KLA A(G@(A ABBI@a
-(A ABBI 4 $ AAG d
+ABA T g k] BBB E(A0A8J
+8A0A(B BBBA L $ BBB B(A0A8Gp8A0A(B BBB 4 ] BHH w
+ABA AL \ h KLA A(G@(A ABBI@a
+(A ABBI 4 , AAG d
DAF K
-DAH T d X BEB B(D0A8J
-8A0A(B BBBI l $ KEB B(V0Q8L
-8A0A(B BBBH {CL $ 0 BBB B(A0A8Gp8A0A(B BBB d t BBB E(A0A8DP
+DAH T x \ BEB B(D0A8J
+8A0A(B BBBI l 0 ] KEB B(V0Q8L
+8A0A(B BBBH {CL , P BBB B(A0A8Gp8A0A(B BBB d | BBB E(A0A8DP
8A0A(B BBBE !
-8A0A(B BBBF T ( # BBB E(A0A8J
-8A0A(B BBBE L 4 BBF E(A0D8DPV
-8D0A(B BBBE T ; BBB B(A0A8JS
-8A0A(B BBBA D " BBA D(G
-(A ABBH L , BBB B(A0D8J
-8A0A(B BBBG T | 8 BEA A(D0
+8A0A(B BBBF T H \ BBB E(A0A8J
+8A0A(B BBBE L < # BBF E(A0D8DPV
+8D0A(B BBBE T $ ] BBB B(A0A8JS
+8A0A(B BBBA D 8' ] BBA D(G
+(A ABBH L T ( \ BBB B(A0D8J
+8A0A(B BBBG T . ] BEA A(D0
(D ABBK D
-(A ABBF l ̏ Hy BBF D(A0G@I
+(A ABBF l h/y BBF D(A0G@I
0A(A BBBI w
0A(A BBBA D
-0F(A BBBG T D X; BBB B(A0A8J$)
-8A0A(B BBBA T ' s! BBB B(A0A8J
-8A0A(B BBBG 80 L 00 ! BEB E(I0H8D@
-8D0A(B BBBA \ \ 1 ! BBB D(A0D`
+0F(A BBBG T l x1; g\ BBB B(A0A8J$)
+8A0A(B BBBA T m W_ BBB B(A0A8J
+8A0A(B BBBG Xu L 4 Pu _ BEB E(I0H8D@
+8D0A(B BBBA \ v c_ BBB D(A0D`
0D(A BBBF
-0C(A BBBB 3 AO ԑ 3 AP 3 3 $ @=. K^ < 3 DM \ 3 t 3 3 3 3 L ܐ 3G 0 BBE B(A0D8GD
-8D0A(B BBBA $ p<. K^ T D 4{ BBE B(D0A8G
-8D0A(B BBBH T 9 BEE E(D0A8G
-8D0A(B BBBE L >h BEB I(A0A8G}
-8D0A(B BBBK < A AO L \ A] BBB H(A0D8DP
-8A0A(B BBBG (B D ~
+0C(A BBBB y AO y AP y y , . K^ D x DM d x | x x x x L xG ^ BBE B(A0D8GD
+8D0A(B BBBA , . K^ T l y{ ] BBE B(D0A8G
+8D0A(B BBBH T ~ ] BEE E(D0A8G
+8D0A(B BBBE L h ] BEB I(A0A8G}
+8D0A(B BBBK D 8 AO L d 8] BBB H(A0D8DP
+8A0A(B BBBG H D ~
F b
-F ̔ :. K^ B xB pB , hB D `B \ XB t PB HB @B 8B ԕ 0B (B B B 4 B L B d B | A A A Ė A ܖ A A A $ A < A T A l A A A A ̗ A A xA pA , hA D `A \ XA t PA HA @A 8A Ԙ 0A (A A A 4 A L A d A | @ @ @ ę @ ܙ @ @ @ $ @ < @ T @ l @ @ @ @ ̚ @ @ x@ p@ , h@ D `@ \ X@ t P@ H@ @@ 8@ ԛ 0@ (@ @ @ 4 @ L @ d @ | ? ? ? Ĝ ? ܜ ? ? ? $ ? < ? T ? l ? ? ? ? ̝ ? ? x? p? , h? D `? \ X? t P? H? @? 8? Ԟ 0? (? ? ? 4 ? L ? d ? | > > > ğ > ܟ > > > $ ? < ? T ? l ? ? (? 0? ̠ 8? @? H? P? , X? D P? \ H? t @? 8? 0? (? ԡ ? ? ? ? 4 ? L > d >, AG bA ?* De ?* De 0?2 Dm ̢ X?" DN
-N A L h? AAG ]
+F h. K^ 4 L d x | p h ` X P H @ $ 8 < 0 T ( l , D ؆ \ І t Ȇ 4 L d x | p h ` X P H @ $ 8 < 0 T ( l , D \ Ѕ t ȅ 4 L d x | p h ` X P H @ $ 8 < 0 T ( l , D \ Є t Ȅ Ȅ Є 4 L d | ( 0 $ ( < T l , Є D Ȅ, AG bA d * De | * De " DN
+N A L AAG ]
CAF [
ACA R
-CAJ JCA L < ? AAG ]
+CAJ JCA L H AAG ]
CAF [
ACA R
-CAJ JCA ?S @6 Dq 8@6 Dq ԣ `@6 Dq @6 Dq @6 Dq @6 Dq 4 A6 Dq L (A6 Dq d PA: Dk
-I A 4 pAZ BGA w
+CAJ JCA T x6 Dq l 6 Dq ȅ6 Dq 6 Dq 6 Dq @6 Dq h6 Dq 6 Dq : Dk
+I A 4 4 ؆Z BGA w
AEI A
-ABA $ A^ ADD NAA4 Ay BDD q
-AEO aAB 4 By BDD q
-AEO aAB < T `B BEE A(D0c
-(J BBBL B
- Bv fN ĥ PC
- , HCg
BHH j
-ABA C $ , C
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+ABA $ l ^ ADD NAA4 8y BDD q
+AEO aAB 4 y BDD q
+AEO aAB < ȇ BEE A(D0c
+(J BBBL D X
+ \ Pv fN t
+ , g *N BHH j
+ABA $ M YX
G z
-A L C , l Cg
BHH j
-ABA D $ D K
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+A P , < Hg M BHH j
+ABA D $ M YX
G z
-A Ԧ D , xDg
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-ABA D 4 D^ cz L D d D | D D^ cz (E ħ E ܧ E E^ cz XE $ PE < HE T @E^ cz l E E xE^ cz E ̨ E E E^ cz E , E D E \ E^ cz t F F F F^ cz ԩ PF HF @F 8F^ cz 4 F L xF d pF | hF^ cz F F Ī F ܪ F^ cz F F $ F < F^ cz T G l G G F^ cz @G ̫ 8G 0G (G^ cz pG , hG D `G 4 \ XGz AAG r
-DAH dHA 4 G BGB A(A0D 4 Ԫ hH e BGB A(A0D < 0I_ J BJD D(G
-(D ABBC $ L PJ AFD@ $ t J AFD@ KD BDA A(DL
+A , g TM BHH j
+ABA $ M YX
+G z
+A , L xg L BHH j
+ABA T t l I DN
+F H
+H H
+H H
+H H
+H H
+H H
+H H
+H H
+H H
+H H
+H H
+H H
+H H
+H H
+A F z cV , D ؋ \ Ћz cV t 8 0 ( z cV x p 4 h L `z cV d Ȍ | z cV $ z cV < ` T X l P H @z cV z cV , D \ t ؍ Ѝz cV 8 0 ( z cV 4 x L p d h | `z cV Ȏ z cV $ < T l z cV X P H @ 8z cV , D , \ { AAG r
+DAH 4 Џ E BGB A(A0D 4 vE BGB A(A0D < $ `_ [E BJD D(G
+(D ABBC $ d -E AFD@ $ E AFD@ $ P D AFD@ D D BDA A(DL
(A ABBB l
(C ABBD m
(A ABBE }
(A ABBE }
(A ABBE }
(A ABBE }
-(A ABBE \ L N \ rEA A(Dx
-(A ABBE p T `R $ BBA A(Gl
+(A ABBE \ X QD rEA A(Dx
+(A ABBE p T D BBA A(Gl
(A ABBA D
-(F ABBA T S BBA A(GZ
+(F ABBA T D ` D BBA A(GZ
(A ABBC d
-(F ABBA L \ 0U ZBA A(Db(A ABBL W V ԯ V V V V 4 V L V L l V% & ZBA A(Dj(A ABBN X ̰ X X X X , xX D pX \ hX t `X XX PX HX Ա @X 8X 0X (X 4 X L T X$ ZBA A(DW(A ABBG X N D Y ; BEJ D(D0G
-0D(A BBBA D 8Z BEJ D(D0G
-0D(A BBBA D L p[ BGB D(M0B
-(A BBBK < [o BJD D(G
-(D ABBG L Ա ] k BEB E(I0H8D@
-8D0A(B BBBA L $ ^9 C BBB D(G0G
-0A(A BBBH l xa pa ha* De a* De ̴ a2 Dm a6 Dq a6 Dq b6 Dq , 8b6 Dq D `b6 Dq \ b6 Dq t b6 Dq b6 Dq c" DR
-J A ĵ c" DR
-J A 4 cZ BGA z
+(F ABBA T ȝ C BBA A(GZ
+(A ABBC t
+(F ABBA L P C ZBA A(Db(A ABBL 4 L d | L % C ZBA A(Dj(A ABBN , D L $ B ZBA A(DW(A ABBG N D B BEJ D(D0G
+0D(A BBBA D 4 Фw LB BEJ D(D0G
+0D(A BBBE $ T V ADD JAA< | @ BBG A(G@
+(A ABBH < BBG A(G@
+(A ABBH L $ y oA BBB B(A0A8G
+8A0A(B BBBA < t o LA BJD D(G
+(D ABBG L A BEB E(I0H8D@
+8D0A(B BBBA D J @ BBB D(G0G
+0A(A BBBE $ < T * De l * De Ȱ6 Dq 6 Dq 6 Dq @6 Dq h6 Dq 6 Dq 6 Dq , 6 Dq D " DR
+J A d " DR
+J A 4 (Z BGA z
AEF A
-ABA Hc" DR
-J A < Xc" DR
-J A \ hc" DR
-J A | xc" DR
-J A 4 cZ BGA z
+ABA P" DR
+J A `" DR
+J A p" DR
+J A " DR
+J A 4 < Z BGA z
AEF A
-ABA Զ c" DR
-J A c" DR
-J A T KH$ , cE AAG yAA T c AL t c c HS c ķ c HS c c HS c 4 d HS T d l d HS d d HS ĸ d ܸ d HS d d HS 4 d L (d HS l (d 0d HS 0d 8d HS ܹ 8d @d HS @d , Hd HS L Hd d Pd HS , Pd # YX
+ABA t " DR
+J A Ȳ" DR
+J A iT KH$ E AAG yAA AL , HS L d HS HS HS HS , D HS d | HS ( HS ( 0 HS 0 $ 8 HS D 8 \ @ HS | @ H HS H P HS P
+ X HS , L X : YX
G
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+B T
+ , : YX
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-B e , e YX
+B
+ д , ȴ : YX
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-B D f , d xf YX
+B
+ , $
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-B 8g , 0g O YX
+B , @ , l
+ 8 $: YX
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-B Ի g , g YX
+B t ,
+ 9 YX
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-B h , < h YX
+B ,
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-B d `i , Xi YX
+B h , D ` 9 YX
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-B j , ̺ j { YX
+B L , P9 YX
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-B j , j F YX
+B ع , й 9 YX
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-B < k , \ k YX
+B , 8 YX
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-B @l , 8l YX
+B $
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-B ̽ l , l YX
+B l
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-B m , 4 m r YX
+B
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A , Po AADP~
-AAJ 4 ̼ qu YKM
+B
p , <
h 8 AADP~
+AAJ 4 l
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HG
-HA 4 Hr1 YKM
-HG
-HA 4 < Psk YKpM
+HA 4
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+HG
+HA 4
k 7 YKpM
HG
-HA $ l tE AAG yAA t AL t ̿ t HS t t HS $ t < t HS \ t t t HS t t HS t t HS t t HS < t T t HS t t t HS t u HS u u HS u 4 u HS T u l u HS , u p YX
+HA $ E AAG yAA AL 4 L HS l HS HS ( HS ( , 0 HS L 0 d 8 HS 8 @ HS @ H HS H P HS , P D X HS d X | ` HS ` h HS h p HS , 4 p k4 YX
G
-B u , u ; YX
+B < 0 , | ( 64 YX
G
-B v , $ v YX
+B , 4 YX
G
-B L Hw , l @w YX
+B , 3 YX
G
-B x , w YX
+B X , T P 3 YX
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-B x , x g YX
+B \ , b3 YX
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-B $ py , D hy 2 YX
+B , -3 YX
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-B l (z , z YX
+B , , x 2 YX
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-B z , z YX
+B 4 8 , t 0 2 YX
G
-B { , { YX
+B | , 2 YX
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-B D P| , d H| ^ YX
+B , Y2 YX
G
-B } , } ) YX
+B ` , L X $2 YX
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-B } , } YX
+B T , 1 YX
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-B x~ , < p~ AADPn
-AAJ $ d E AAG yAA AL HS HS 4 HS T l HS HS ( HS ( 0 HS 4 0 L 8 HS l 8 @ HS @ H HS H P HS P , X HS L X d ` HS ` h HS h p HS p x HS , x D HS d | HS HS HS $ HS D \ HS | HS HS HS $ < Ȁ HS \ Ȁ t Ѐ HS Ѐ HS HS HS < T HS t HS HS HS 4 HS T l HS D ( { ZIA A(GP
-(H ABBG D 7 L ZIA A(GP
-(H ABBG 4 D T 7 ZIA A(GP
-(H ABBG D ؆ ZIA A(GP
-(H ABBG D 7 ZIA A(GP
-(H ABBG T D t x ZIA A(GP
-(H ABBG 0 D (( a ZIA A(GP
-(H ABBG D 4 2 ZIA A(GP
-(H ABBG t D ZIA A(GP
-(H ABBG ` D X ZIA A(GP
-(H ABBG 4 D T 7 ZIA A(GP
-(H ABBG D v ZIA A(GP
-(H ABBG D 7 G ZIA A(GP
-(H ABBG T D t x ZIA A(GP
-(H ABBG 0 D (7 ZIA A(GP
-(H ABBG D 4 ZIA A(GP
-(H ABBG t Н D ȝ ZIA A(GP
-(H ABBG D x \ ZIA A(GP
-(H ABBG 4 D T - ZIA A(GP
-(H ABBG Т D Ȣ ZIA A(GP
-(H ABBG D x ZIA A(GP
-(H ABBG T 0 D t ( ZIA A(GP
-(H ABBG Ч D ȧ q ZIA A(GP
-(H ABBG D 4 x B ZIA A(GP
-(H ABBG t D ZIA A(GP
-(H ABBG Ь 4 Ȭ YKM
+B $ E AAG yAA AL HS 4 L HS l HS HS HS , HS L d ( HS ( 0 HS 0 8 HS 8 @ HS , @ D H HS d H | P HS P X HS X ` HS D 4 ` b. ZIA A(GP
+(H ABBG T D 3. ZIA A(GP
+(H ABBG D . ZIA A(GP
+(H ABBG H D T @ - ZIA A(GP
+(H ABBG t D - ZIA A(GP
+(H ABBG D w- ZIA A(GP
+(H ABBG 4 ( D t H- ZIA A(GP
+(H ABBG D - ZIA A(GP
+(H ABBG h D 4 ` , ZIA A(GP
+(H ABBG T D , ZIA A(GP
+(H ABBG D , ZIA A(GP
+(H ABBG 8 D T 0 ], ZIA A(GP
+(H ABBG t D ., ZIA A(GP
+(H ABBG h D ` + ZIA A(GP
+(H ABBG 4 4 t + YKM
HG
-HA $ 5 A/ 4 L 0 BFA K`
- AABA t BBB A(A0JO
-0A(A BBBG
-0A(A BBBE `
-0A(A BBBH < BAD h
+HA $ 8E AAG yAA ` AL ` h HS h p HS < p T x HS t x HS HS HS 4 HS T l HS HS HS ! HS 4! L! HS l! ! HS ! ! HS ! ! HS " ," HS L" d" HS " " HS " " HS " # HS D T" ( ' ZIA A(GP
+(H ABBG t# D " ( & ZIA A(GP
+(H ABBG # D # 7 & ZIA A(GP
+(H ABBG 4$ D t# x& ZIA A(GP
+(H ABBG $ X D # P7 I& ZIA A(GP
+(H ABBG $ H D 4$ @ & ZIA A(GP
+(H ABBG T% D $ 7 % ZIA A(GP
+(H ABBG % D $ 7 % ZIA A(GP
+(H ABBG & D T% 7 % ZIA A(GP
+(H ABBG t& D % 7 ^% ZIA A(GP
+(H ABBG & & 5 A/ 4 4& % BFA K`
+ AABA 4 l& Hx % BAA G@
+ AABK < & $ BAD h
ADD A
-AFH 4 4 0Q BHD A(D0t(D ABB , l Xd BAA N
-ABK L x BBB B(A0A8D
-8A0A(B BBBA T T ) o BBB B(A0A8G
-8A0A(B BBBA AL L BBF B(A0A8D@
-8D0A(B BBBB d E BBB B(A0A8DP
+AFH 4 ' 0Q BHD A(D0t(D ABB , ' Xd BAA N
+ABK $( <( T( l( L ( x 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
8A0A(B BBBE \
-8F0A(B BBBA l hB G BBB J(D0D8D4
+8F0A(B BBBA l ,) hB {# BBB J(D0D8D4
8D0A(B BBBC i
-8A0A(B BBBE L H BFE B(H0D8M
-8D0A(B BBBD < AP < \ j BKE B
-ABI A
-ABA AL $ ^ AG b
-AA P DJ @ DJ $ 0 L D BEE E(A0I8G@
-8A0D(B BBBG X9 AX
-G X T x+ BBE E(D0A8G2
-8D0A(B BBBG L P BBB E(A0D8G`
-8A0A(B BBBH D \ " BEE A(A0
-(A BBBA < 8 BFB A(A0W
-(A EBBC L BEB E(I0H8D@
-8D0A(B BBBA < , h BBG A(G@
-(A ABBF D l BBE B(A0D8D@8A0A(B BBB " DR
-J A , BRE AE @ AL L $ @ BBE E(A0A8D@l
-8A0A(B BBBF l | 3 BBB E(I0D8DP
-8D0A(B BBBD
-8D0A(B BBBI < p BBA A(D]
-(A ABBA < , @
- BBA A(D[
-(A ABBA d $ A\
-C C < ; O BBA A(G
-(E AFEA T 3 BBB B(A0A8Jz
-8A0A(B BBBH T < + BBB B(A0D8GC
-8A0A(B BBBA T ( G BBB B(D0C8G
-8D0A(B BBBE ( AG [D T _ BEE E(D0D8J
-8A0A(B BBBA T d 8 | BEB E(A0A8G
-8D0A(B BBBD T U BBB B(D0D8G3
-8D0A(B BBBI L < BED A(G@x
+8A0A(B BBBE L ) H +# BFE B(H0D8M
+8D0A(B BBBD * ! AP $ * !^ AG b
+AA d + " BBB B(A0A8D`<
+8A0A(B BBBL d
+8A0A(B BBBA t+ # AL + # DJ + # DJ + # L + $ ! BEE B(A0D8GP
+8A0D(B BBBA <, %9 AX
+G X < + % ! BBA A(DU
+(E ABFA < + &
+ ! BBA A(D[
+(A ABBA , '$ AX
+G C , ' < <, '; ?! BBA A(G
+(E AFEA T |, ( #! BBB B(A0A8Jz
+8A0A(B BBBH L , ,V ! BBB A(A0J
+0A(A BBBI T $- /4 ! BBE E(D0F8J
+8D0A(B BBBA T |- 4+ ! BBE E(D0A8G2
+8D0A(B BBBG T - 6 BBB B(A0A8G
+8A0A(B BBBK T ,. ? 7! BBB B(D0D8G
+8D0A(B BBBA L . B ! BED A(G@x
(D ABBD V
-(D ABBA T d H
BEE B(D0D8JX
-8A0A(B BBBF T W BBE B(A0A8GR
-8A0A(B BBBH T %L BBB E(D0D8JG
-8D0A(B BBBA d 1 AL L 1\ + BEE E(F0D8GP
-8D0A(B BBBA . K^ 5 5 5 4 5 L x5 d 5 AV 5 AV 5 < 5 BBG A(G@
-(A ABBF l 6x BBF D(A0G@F
+(D ABBA T . 0C BEB E(A0A8G
+8D0A(B BBBD L 0 hG| BBE E(D0D8D.
+8A0A(B BBBF T |/ L J BEE E(D0D8J
+8A0A(B BBBA D / P g BEE A(A0
+(A BBBA < 0 Q BFB A(A0W
+(A EBBC T \0 (R BBB B(D0C8G
+8D0A(B BBBE 1 Y( AG [D T 0 Y BBE E(D0D8Gu
+8D0A(B BBBA 2 (f AL L L1 f BEB E(I0H8D@
+8D0A(B BBBA < t2 g BBG A(G@
+(A ABBF D 2 Xh BBE B(A0D8D@8A0A(B BBB 2 h" DR
+J A , D2 h BRE AE L3 i AL L l3 i BBE E(A0A8D@l
+8A0A(B BBBF l 2 Pj ; BBB E(I0D8DP
+8D0A(B BBBD
+8D0A(B BBBI T T3 m BEE B(D0D8JX
+8A0A(B BBBF 4 H<V Ko
+A 4 y 4 y 4 x 4 x 5 x 5 x AV <5 x AV \5 x < t5 hy BBG A(G@
+(A ABBF l 5 zx BBF D(A0G@F
0A(A BBBD v
0A(A BBBJ D
-0F(A BBBG T t 8 BBB B(D0A8K*
-8A0A(B BBBD . K^ L @w BBB E(A0A8DP[8A0A(B BBB L , ; BBB B(A0A8DT
-8A0A(B BBBA L | ? BBB B(A0A8DI
-8A0A(B BBBG T hE
- p BBB E(A0A8G
-8A0A(B BBBA $ p\ AG RAT L O% BBA A(D^
+0F(A BBBG T L5 (| m BBB B(D0A8K*
+8A0A(B BBBD |6 :. K^ L 6 tw BBB E(A0A8DP[8A0A(B BBB L 6 ~ BBB B(A0A8DT
+8A0A(B BBBA L 47 X BBB B(A0A8DI
+8A0A(B BBBG T 6 ؈A 7 BBB E(A0A8G
+8A0A(B BBBA 7 \ AG RAT $7 % BBA A(D^
(A ABBB P
-(A ABBA T hP| BBE E(D0A8G
-8D0A(B BBBE T U4 BBB B(D0D8G
-8A0A(B BBBK D T xX BFE D(A0G
-0D(A BBBH \ \ KBA A(G@(A ABBE@r
-(A ABBH ^ / BIB E(D0A8F`w
+(A ABBA T |7 | BBE E(D0A8G
+8D0A(B BBBE T 7 4 BBB B(D0D8G
+8A0A(B BBBK D ,8 g BFE D(A0G
+0D(A BBBH \ L9 KBA A(G@(A ABBE@r
+(A ABBH 8 0 BIB E(D0A8F`w
8A0A(B BBBB
8A0A(B BBBA V
-8A0A(B BBBA T b BEG E(A0A8J
-8D0A(B BBBA . K^ , iD BDD n
-DBA $ j^ AG b
-AA 4 L Pj BHE
-ABH 4 k { BHE
-ABE 4 kd BBD G(D0G(D ABB< lR BBA A(Jj
-(A ABBA d 4 8m BBB B(A0A8DP
+8A0A(B BBBA T \9 BEG E(A0A8J
+8D0A(B BBBA : 6. K^ , : D BDD n
+DBA $ : 8^ AG b
+AA 4 $: p w BHE
+ABH 4 \: 8 C BHE
+ABE 4 l; d BBD G(D0G(D ABBd : 8 BBB B(A0A8DP
8A0A(B BBBH D
-8A0A(B BBBA T m BJB B(A0A8G@
-8D0A(B BBBJ D HsJ BBB D(A0D@
-0A(A BBBA L < Pu G BJB A(A0W
-(A BBDH ^(A BBB< u BBG A(G@
-(A ABBG < `v BBG A(G@
-(A ABBG . K^ L ԩw BBB E(A0A8DP[8A0A(B BBB T t v BBB E(A0A8G
-8A0A(B BBBH AO , AAG@s
-DAA AS , < AAG@w
-DAA d AS , AAG@w
-DAA D p BBE A(A0D@z
-0A(A BBBA 4 (- ' BFD D0
- DABB L < m BBB B(D0A8GP
-8A0A(B BBBA @ L 81 BBB B(A0D8G^
-8A0A(B BBBA < ( BBG A(G@
-(A ABBF , ] D r
-J L (] D r
-J L t h + BEB E(I0H8DP
-8D0A(B BBBD L 8 BBJ B(D0A8DpS
-8D0A(B BBBA 4 BDD DPv
- AABA L L BBE J(A0A8DP9
-8A0A(B BBBD 4 BDD DPv
- AABA D BBE A(A0D@
-0A(A BBBA D $ BBB A(G0D`T
-0C(A BBBH D \ BBB D(A0D@0A(A BBB d Ъ BFB B(A0A8DP
+8A0A(B BBBA D < J BBB D(A0D@
+0A(A BBBA L |; + BJB A(A0W
+(A BBDH ^(A BBB< < BBG A(G@
+(A ABBG < < BBG A(G@
+(A ABBG T L< h _ BJB B(A0A8G@
+8D0A(B BBBJ |= 4. K^ L = Hw BBB E(A0A8DP[8A0A(B BBB T = BBB E(A0A8G
+8A0A(B BBBH <> AO , = AAG@s
+DAA > AS , = AAG@w
+DAA > ` AS , $> ` t AAG@w
+DAA D ,? BBE A(A0D@z
+0A(A BBBA 4 > - BFD D0
+ DABB L > m BBB B(D0A8GP
+8A0A(B BBBA ? L <? Y BBB B(A0D8G^
+8A0A(B BBBA < d@ BBG A(G@
+(A ABBF @ h] D r
+J @ ] D r
+J L @ BEB E(I0H8DP
+8D0A(B BBBD L \@ BBJ B(D0A8DpS
+8D0A(B BBBA 4 @ ( BDD DPv
+ AABA L @ BBE J(A0A8DP9
+8A0A(B BBBD 4 4A { BDD DPv
+ AABA D DB x BBE A(A0D@
+0A(A BBBA D A 0 BBB A(G0D`T
+0C(A BBBH D B v BBB D(A0D@^0A(A BBB d C P BFB B(A0A8DP
8A0A(B BBEK
-8D0A(B BBBH L ( K BBB E(I0D8DP^
-8D0A(B BBBA T d BBE E(A0A8D
-8D0A(B BBBA T @V
- BBE B(D0A8G
-8D0A(B BBBE d H BFB B(A0A8DP
+8D0A(B BBBH L B 3 BBB E(I0D8DP^
+8D0A(B BBBA T B BBE E(A0A8D
+8D0A(B BBBA T TC V BBE B(D0A8G
+8D0A(B BBBE d D BFB B(A0A8DP
8A0A(B BBEK
-8D0A(B BBBH L | g BEB E(I0H8DP
-8D0A(B BBBD d `{ ? BBB J(A0D8G`
+8D0A(B BBBH L D O BEB E(I0H8DP
+8D0A(B BBBD d dD { ' BBB J(A0D8G`
8F0A(B BBBF B
-8F0A(B BBBA T 4 x BEB B(A0A8G5
-8A0A(B BBBA T BEE B(A0A8M
-8D0A(B BBBI L H BEB E(H0J8G
-8D0A(B BBBB , ؚ. K^ 4 D BAD D0~
- AABC 4 | } AAG R
+8F0A(B BBBA T D BEB B(A0A8G
+8A0A(B BBBA T $E s BEE B(A0A8M
+8D0A(B BBBI L |E ( BEB E(H0J8G
+8D0A(B BBBB F +. K^ 4 F ` BAD D0~
+ AABC 4 F } AAG R
GAM D
-AAA T ` D BBB E(D0A8G^
-8A0A(B BBBA L ( BBB E(D0A8G
-8A0A(B BBBA T d X BFE E(D0E8D
-8C0A(B BBBH 4 BAD D0~
- AABC L 8 S BEB E(I0H8D@
-8D0A(B BBBD d D + BBB B(I0A8G`
+AAA T TF @ , BBB E(D0A8G^
+8A0A(B BBBA L F 0 BBB E(D0A8G
+8A0A(B BBBA T F 88 BFE E(D0E8D
+8C0A(B BBBH 4 ,H 9 BAD D0~
+ AABC L G : ; BEB E(I0H8D@
+8D0A(B BBBD d G ; BBB B(I0A8G`
8F0A(B BBBF r
-8F0A(B BBBG T P BBB B(A0A8G
-8A0A(B BBBG H֚. K^ < BBA A(DV
-(A ABBA T ֚. K^ l L AAG ]
+8F0A(B BBBG T DH 0= BBB B(A0A8G
+8A0A(B BBBG tI x(. K^ < H E BBA A(DV
+(A ABBA I P(. K^ I F I F L J F AAG ]
CAF [
ACA R
-CAJ JCA L AAG ]
+CAJ JCA L dJ F AAG ]
CAF k
ACA R
-CAJ JCA L < AAG ]
+CAJ JCA L J F AAG ]
CAF [
ACA R
-CAJ JCA L H AAG ]
+CAJ JCA L K (G AAG ]
CAF k
ACA R
-CAJ JCA Z\ AG RAL h BBB B(A0A8Ji
-8A0A(B BBBI L T BBB B(A0A8Ji
-8A0A(B BBBI L o BBB B(A0A8D
-8A0A(B BBBA < ( BBD D(G0d
-(A ABBA L 4 o BBB B(A0A8D
-8A0A(B BBBA , t ADG0N
-AAA Қ. K^ $ " DN
-N A L BBB E(A0A8DJ
+CAJ JCA TK v\ AG RAL J HG BBB B(A0A8Ji
+8A0A(B BBBI L J I BBB B(A0A8Ji
+8A0A(B BBBI L <K Ko BBB B(A0A8D
+8A0A(B BBBA < K T BBD D(G0d
+(A ABBA L K xTo h BBB B(A0A8D
+8A0A(B BBBA , L \ \ ADG0N
+AAA $M (%. K^ <M \ TM \ lM \ M \ M \" DN
+N A L \ BBB E(A0A8DJ
8C0A(B BBBI c
8C0A(B BBBA q
8D0A(B BBBB U
8A0A(B BBBA c
-8C0A(B BBBA T 8 6 BBD D(Ga
-(A ABBF
-(C ABBF T њT KH l &\ AG RA< # BBD D(I@
-(D ABBF % %$ A]
-B C 4 %Y ACG
-AAD DCA < 8% 4 T 0%a ACG E
-AAF DCA h% 4 `%Y ACG
-AAD DCA % DQ % DQ 4 %S AAF h
-CAD RCA 4 D %[ AAF n
-CAF RCA 4 | %S AAF h
-CAD RCA T & BBA A(Ir
+8C0A(B BBBA T M at BBE B(D0A8G
+8D0A(B BBBH T M @f BBD D(G
+(A ABBK
+(C ABBF $O X#T KH <O ݔ\ AG RA< \O i BBD D(I@
+(D ABBF O @k O 8k$ A]
+B C 4 O HkY ACG
+AAD DCA P pk 4 $P hka ACG E
+AAF DCA \P k 4 tP kY ACG
+AAD DCA P k DQ P k DQ 4 P kS AAF h
+CAD RCA 4 Q k[ AAF n
+CAF RCA 4 LQ lS AAF h
+CAD RCA T P Hl
BBA A(Ir
(C ABBG E
-(A ABBA < X'W BBA A(J
-(A ABBA D T x(" ~ BBB A(D0G
-0A(A BBBA T `+ BEE E(D0A8G
-8D0A(B BBBE < 0S w BBA A(Gx
-(A ABBA < 4 1B X BDA A(Gc
-(A ABBH l ͚. K^ 22 26 G n 2 AQ 2 2 < 2D BEE D(D0h(A BBB D L 2$ d BLB A(A0GS
-0A(A BBBD T 4t F BBB B(A0A8G
-8A0A(B BBBA ̚. K^ 7 7 L 4 7 BEB A(A0g
-(A BBDE _(A BBBT 88| BBE E(D0A8G
-8D0A(B BBBE D `= r BJD G@o
+(A ABBA < Q mW g
BBA A(J
+(A ABBA D DQ n" Q
BBB A(D0G
+0A(A BBBA < Q qR d
BDA A(G
+(A ABBH T Q r G
BEE E(D0A8G
+8D0A(B BBBE < $R wS -
BBA A(Gx
+(A ABBA <S . K^ TS x2 lS y6 G n S 8y AQ S 8y S 0y < S (yD BEE D(D0h(A BBB D <S 8y$ 6 BLB A(A0GS
+0A(A BBBD T S {k BBB B(A0A8G
+8A0A(B BBBA T X . K^ T ~ T ~ L $T ~ BEB A(A0g
+(A BBDE _(A BBBT tT p~| Y BBE E(D0A8G
+8D0A(B BBBE D T ? BJD G@o
DABC ]
- DABD D $ 8> = BJD G@l
+ DABD D U p
+ BJD G@l
DABF ]
- DABD D l ?{ BJD G@s
+ DABD D \U H{
+ BJD G@s
DABG @
- DABA T H@ BHE A(D0D@|
-0D(A BBBA m0D(A BBB T @ BHE A(D0D@y
-0D(A BBBD m0D(A BBB L d XA - BHE B(A0D8D`I
-8D0A(B BBBA ɚT KHD C` BJD JT
+ DABA T |V BHE A(D0D@|
+0D(A BBBA m0D(A BBB T V BHE A(D0D@y
+0D(A BBBD m0D(A BBB L TV BHE B(A0D8D`I
+8D0A(B BBBA |W T KHD V H` BJD JT
DABK `
- DABI D (D` BJD JT
+ DABI D W `` } BJD JT
DABK `
- DABI L T @E BBB B(A0A8Gp8A0A(B BBB $ FV ADD JAA< F! BLG A(G@
-(A ABBF d
- G BBB J(A0D8GP&
+ DABI L $X x BBB B(A0A8Gp8A0A(B BBB $ tX V ADD JAA< X 0! BLG A(G@
+(A ABBF d X BBB J(A0D8GP&
8F0A(B BBBG
-8D0A(B BBGA T | J ` BFB E(A0A8D
-8A0A(B BBBA <
- hK BFB A(A0W
-(A EBBC T L BBB D(C0GP
-0D(A BBBD n0D(A BBB T l LT BBB E(D0D8Gz
-8D0A(B BBBG < N1 BLG A(G@
-(A ABBH L
- O BEB E(I0H8D@
-8D0A(B BBBA L T
- 8Q BLB B(A0D8Dp#
-8A0A(B BBBA l
- T BBE B(A0A8DJ
+8D0A(B BBGA T lX H - BFB E(A0A8D
+8A0A(B BBBA < Y BFB A(A0W
+(A EBBC T Y @ BBB D(C0GP
+0D(A BBBD n0D(A BBB T \Y T V BBB E(D0D8Gz
+8D0A(B BBBG < Z 1 BLG A(G@
+(A ABBH L Y BEB E(I0H8D@
+8D0A(B BBBA L DZ p BLB B(A0D8Dp#
+8A0A(B BBBA l Z o BBE B(A0A8DJ
8A0A(B BBBA T
-8A0A(B BBBA
Ś. K^ T , @X8 C BBB A(A0Z
-(A BBBG h
-(H BBBJ T ([8 BBB A(A0Z
-(A BBBG h
-(H BBBJ T ^ BBE B(D0A8G
-8D0A(B BBBG T 4 b BBE E(D0A8G
-8D0A(B BBBK T h BEE E(D0A8G
-8D0A(B BBBE T 8m BEE E(A0D8J
-8A0A(B BBBA D <
r BBB A(D0J
-0A(A BBBC D
v
BBB A(D0J
-0A(A BBBC T
@{ U BEE E(A0D8J
-8A0A(B BBBA Ú. K^ $ < W AL n
-AA \ ( t , 1 AAG cCA (
- 4 L 2 d | $ `FD@ 4 x BAD F0q
- DABK T l T 4 BEB F(A0G
-(A BBDH ~(A BBB x 4 p PGB A(A0D T 4 H BEE E(D0A8GI
-8D0A(B BBBE < BEB D(A0
-(A EBBD L P% 1 BBB E(D0A8Gp
-8D0A(B BBBB T 0 BBE B(D0A8G
-8D0A(B BBBF L t ȏ BJB A(A0W
-(A BBDH ^(A BBB, s AAG0u
-AAA L h6 BBE B(A0A8DP
-8A0A(B BBBA < < X BGB A(A0
-(A BBBA < | ( BBG A(G@
-(A ABBF d BBB B(D0A8DP
+8A0A(B BBBA [ . K^ T [ x BBE B(A0D8J
+8A0A(B BBBA T t[ ) BBE B(A0D8J
+8A0A(B BBBA T [ h( C BBB A(A0U
+(A BBBD h
+(H BBBJ T $\ @( BBB A(A0U
+(A BBBD h
+(H BBBJ T |\ BBE B(D0A8G
+8D0A(B BBBG T \ BBE E(D0A8G
+8D0A(B BBBK T ,] BEE E(D0A8G
+8D0A(B BBBE D ] @ BBB A(D0J
+0A(A BBBD D ] BBB A(D0J
+0A(A BBBD ^ . K^ $ ,^ AL n
+AA ,_ D_ x , ^ p1 AAG cCA _ _ x _ p
+ _ h _ ` ` X ` P2 4` x L` p d` h |` ` $ _ X `FD@ ` ` 4 ` BAD F0q
+ DABK $a H <a @ T |` 8 BEB F(A0G
+(A BBDH ~(A BBB a 4 ` PGB A(A0D T $a v BEE E(D0A8GI
+8D0A(B BBBE < Tb BEB D(A0
+(A EBBD L a % BBB E(D0A8Gp
+8D0A(B BBBB T b BBE B(D0A8G
+8D0A(B BBBF L db BJB A(A0W
+(A BBDH ^(A BBB, c ps AAG0u
+AAA L c 6 BBE B(A0A8DP
+8A0A(B BBBA < d BGB A(A0
+(A BBBA < Ld BBG A(G@
+(A ABBF d d ` BBB B(D0A8DP
8A0A(B BBBC
-8A0A(B BBBH T , p ' BBB B(D0A8G\
-8A0A(B BBBA , (~ AID E
-DAA L x BEB E(I0H8D@
-8D0A(B BBBA D " BCD D0
- FABF D
- FABA T L BEE E(A0D8I
-8D0A(B BBBH , ADD ]
-AAA BBF D(A0G@I
+8A0A(B BBBH T d BBB B(D0A8G\
+8A0A(B BBBA , td ~ AID E
+DAA L d BEB E(I0H8D@
+8D0A(B BBBA D e P BCD D0
+ FABE D
+ FABA T <e (R S BEE E(A0D8I
+8D0A(B BBBG , lf 0 ADD ]
+AAA f BBF D(A0G@I
0A(A BBBI }
0D(A BBBH D
0F(A BBBG c
-0A(A BBBE d T BBB E(D0A8L`x
+0A(A BBBE d $g BBB E(D0A8L`x
8D0A(B BBBG
-8D0A(B BBBF T 6 : BBB B(D0D8G
-8D0A(B BBBA L BJE B(A0A8Dp
-8C0A(B BBBB T l BBB B(D0A8J
-8A0A(B BBBA $ 8^ AG b
-AA L p BEB E(I0H8D@
-8D0A(B BBBD T < BBB E(D0A8G
-8A0A(B BBBD T & P BJE B(A0A8G
-8A0A(B BBBA , `s AAG0u
-AAA L BEB E(I0H8D@
-8D0A(B BBBD d l 0O Ǿ BBB B(I0A8G`
-8F0A(B BBBF g8F0A(B BBB T o BEE E(F0A8Jt
-8A0A(B BBBA $ 0. K^ < ( T L l AAG e
+8D0A(B BBBF T f 6 BBB B(D0D8G
+8D0A(B BBBA L g BJE B(A0A8Dp
+8C0A(B BBBB T \g BBB B(D0A8J
+8A0A(B BBBA $ h P^ AG b
+AA L g w BEB E(I0H8D@
+8D0A(B BBBD T ,h O BBB E(D0A8G
+8A0A(B BBBD T h & BJE B(A0A8G
+8A0A(B BBBA , i xs AAG0u
+AAA L i BEB E(I0H8D@
+8D0A(B BBBD d \i HO BBB B(I0A8G`
+8F0A(B BBBF g8F0A(B BBB T i 0 7 BEE E(F0A8Jt
+8A0A(B BBBA j . K^ k @
+ $k 8
+ L <k 0
+ AAG e
CAF K
ACI R
-CAJ JCA L AAG e
+CAJ JCA L k
+ AAG e
CAF C
ACA R
-CAJ JCA $ ^ AG b
-AA \ 4 BEG O(G0
+CAJ JCA k DV $ k ^ AG b
+AA \ l @ 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 d H/ j BBB B(D0G8Dc
-8D0A(B BBBI L (2 5 BBB B(D0G8Dc
-8D0A(B BBBI < BBG A(G@
-(A ABBG L D ֻ BJB A(A0W
-(A BBDH ^(A BBBL ( BEB E(I0H8D@
-8D0A(B BBBA T c BBB B(A0A8J
-8A0A(B BBBA T < BBB B(A0A8G
-8A0A(B BBBE T KHT 0 C BBE B(D0D8G]
-8A0A(B BBBG 貚. K^ 4 BAD W
-ABG D L! BBB A(A0GPf
-0A(A BBBC < 0 ~ AAG @
+(E ABBE L k BBE B(D0D8FP
+8D0A(B BBBA $ l `
: ACG lAA L l x
5 V BBB E(D0A8Gp
+8D0A(B BBBG L ll h/ BBB B(D0G8Dc
+8D0A(B BBBI L l H2 BBB B(D0G8Dc
+8D0A(B BBBI < m 8 BBG A(G@
+(A ABBG L Lm BJB A(A0W
+(A BBDH ^(A BBBL m H ; BEB E(I0H8D@
+8D0A(B BBBA T m BBB B(A0A8J
+8A0A(B BBBA T Dn p5 k BBB B(A0A8G
+8A0A(B BBBE to T KHT n J BBE B(D0D8G]
+8A0A(B BBBG o . K^ 4 $o PN BAD `
+ABF D 4p P BBB A(D0DPa
+0A(A BBBH < o P R AAG @
AAE p
-AAB L B BBB A(D0H
-0D(E BBBH L , P ( BEE B(D0D8M
-8A0A(B BBBE D | BEB A(D06
-(D BBBB D BBB A(A0G
-0A(A BBBC T ! p BBE E(D0D8J%
-8A0A(B BBBA \# . K^ t# # # DV # 2z\ AG RAT ! BBA D(GPR
+AAB L o Q BBB A(D0H
+0D(E BBBH D 4p R BEB A(D0?
+(D BBBI T |p Tt BBE B(D0A8G
+8D0A(B BBBH L p Y BEE B(D0D8M
+8A0A(B BBBE D $q \ BBB A(A0G
+0A(A BBBC T lq X^ n BBE E(D0D8J
+8A0A(B BBBB r . K^ r b r b r \ AG RAT ,r xb BBA D(GPR
(D ABBE P
-(D ABBG L <" BEB B(A0A8G[
-8D0A(B BBBD d $ ' BBB B(A0A8DP
+(D ABBG L r c BEB B(A0A8G[
+8D0A(B BBBD d s `v BBB B(A0A8DP
8A0A(B BBBE D
-8A0A(B BBBA L $ H(8 BBE B(D0A8DP
-8D0A(B BBBE L D# 8) BEE B(D0D8D@
-8D0A(B BBBJ d % * BEE F(A0A8D@c
+8A0A(B BBBA L t v8 BBE B(D0A8DP
+8D0A(B BBBE L s w BEE B(D0D8D@
+8D0A(B BBBJ d t hy BEE F(A0A8D@c
8F0A(B BBBF
-8D0A(B BBGA L % p, BBB B(A0A8Gp8A0A(B BBB L D& - BLB B(A0A8DP
-8A0A(B BBBA L & . BLB B(A0A8DP
-8A0A(B BBBE L $ `/ BLB E(A0A8D`
-8A0A(B BBBF T <% 1 BBB B(A0D8G
-8A0A(B BBBF 4 % h= ˸ BEH G0x
- DABK ' T KH, ' =0 AC
FK
- E d ( > BBB E(A0A8G@p
+8D0A(B BBGA L u { BBB B(A0A8Gp8A0A(B BBB L lu | BLB B(A0A8DP
+8A0A(B BBBA L u 0} BLB B(A0A8DP
+8A0A(B BBBE L 4u } BLB E(A0A8D`
+8A0A(B BBBF T u 0 BBB B(A0D8G
+8A0A(B BBBF 4 u BEH G0x
+ DABK v T KH, w x0 AC
FK
+ E d 4w x BBB E(A0A8G@p
8A0A(B BBBB i
-8A0A(B BBBA T |& P? BBB A(D0o
+8A0A(B BBBA T v BBB A(D0o
(A BBBG E
-(A BBBD L & ? BGB E(A0K8Dp
-8D0A(B BBBA ) HAW 4) A $ T' A [ AG@q
-AA D |' B2 D BBB A(A0D
-0A(A BBBA ) C HP T ' C BEE E(D0A8G
-8D0A(B BBBG < <( H< BEA A(Gh
-(A ABBA < |( I7 Ķ BBA A(Gf
-(A ABBA * T KH$ ( xJ AL b
-AA D * J BBE A(A0D@n
-0A(A BBBA <+ K $ \) K AG@c
-AA |+ 8L + 0L + 8L0 Dk + PLH ) L AR + L A , hM $ <* pM [ AG0S
-AD d* M K K IN L * hN C BBB B(D0A8D`
-8D0A(B BBBH T * Q 7 BEB B(A0A8D
-8A0A(B BBBD L ,+ W + BBB E(D0D8FS
-8A0A(B BBBJ T |+ X; BBD A(J`
+(A BBBD L w X BGB E(A0K8Dp
+8D0A(B BBBA Dx ؏W \x $ w ( s AG@q
+AA D w 2 \ BBB A(A0D
+0A(A BBBA x HP T ,x BEE E(D0A8G
+8D0A(B BBBG < x < BEA A(Gh
+(A ABBA < x 7 BBA A(Gf
+(A ABBA y pT KH$ y AL b
+AA D z BBE A(A0D@n
+0A(A BBBA dz $ y 7 AG@c
+AA z Ț z z Ț0 Dk z H ,z AR ${ <{ ' DR
+J F $ z 0 s AG0S
+AD z c K IN L z ( [ BBB B(D0A8D`
+8D0A(B BBBH T { O BEB B(A0A8D
+8A0A(B BBBD L t{ C BBB E(D0D8FS
+8A0A(B BBBJ T { p; BBD A(J`
(A ABBD U
-(A ABBA D + Y1 BBB A(D0GW
-0A(A BBBG \ , ^ BBB D(D0G`
+(A ABBA L | XN BBB A(D0G
+0A(A BBBE \ l| X BBB D(D0G`
0A(A BBBG U
-0A(A BBBK t. . K^ $ . _^ AG b
-AA T , _T g BBB E(A0A8G
-8D0A(B BBBD \ / d BBG A(D0d
+0A(A BBBK } . K^ } p $ } x^ AG b
+AA , } AAG0q
+AAA T T} 0 + BBB E(A0D8GJ
+8D0A(B BBBB \ ~ س BBG A(D0d
(D ABBC ]
-(D ABBJ f(D ABB L t- he$ ADG`t
-DAK
-DAH n
-DAE < - Hi AAJ`B
-DAE y
-DAJ < . (k AAJ`B
-DAE y
-DAJ < <0 m BBE D(D0_
-(A BBBI |0 m? T 0 m GBB A(D0D@G0A(A BBBB@ 0 n AV 1 n $1 n <1 n A T d/ n 8 BEE E(D0A8G
-8D0A(B BBBH D / pt ADGpm
+(D ABBJ f(D ABB < ~ X BBE D(D0_
+(A BBBI $ ? T < GBB A(D0D@G0A(A BBBB@ h AV h p T h BEE E(D0A8G
+8D0A(B BBBH T d BEE E(D0A8G
+8D0A(B BBBE 4 ADG
+DAH V
+DAA T ` W BEE E(D0A8G
+8D0A(B BBBE $ ` 4 < g ADG
+DAH G
+DAA T < BBE B(D0A8G
+8D0A(B BBBB T BBB B(A0D8D}
+8D0A(B BBBE L L H AAJm
DAB
DAI ]
-DAF D 0 v ADGpm
-DAB
+DAF T BBB B(A0D8D}
+8D0A(B BBBE L ` R AAJm
+DAB
DAI ]
-DAF < L0 y> ʱ ADGpm
-DAB }
-DAF < 0 {> ADGpm
-DAB }
-DAF < 0 } ADGpm
+DAF T D 0 + BBB B(A0D8D}
+8D0A(B BBBE < AAGm
+DAE
+DAF T ܂ BBB B(A0D8D}
+8D0A(B BBBE < 4 ` AAGm
+DAE
+DAF T t BBB B(A0D8D}
+8D0A(B BBBE < ̃ 8 v AAJm
DAB
-DAK < 1 ` d ADGpm
+DAK T _ BBB B(A0D8D}
+8D0A(B BBBE < d ` 2 AAJm
DAB
-DAK D L1 B ADGpm
-DAB
+DAK T 0 BBB B(A0D8D}
+8D0A(B BBBE L AAJm
+DAB
DAG ]
-DAF D 1 ADGpm
+DAF T L X BBB B(A0D8D}
+8D0A(B BBBE L AAJm
DAB
DAD ]
-DAF D 1 P ADGpm
-DAB
+DAF T p s BBB B(A0D8D}
+8D0A(B BBBE L L F AAJm
+DAB
DAD ]
-DAF D $2 İ ADGpm
-DAB
+DAF T BBB B(A0D8D}
+8D0A(B BBBE L 8 AAJm
+DAB
DAF ]
-DAF T l2 BHB E(D0D8J
-8D0A(B BBBD 4 4 X ADG
-DAH O
-DAA 4 4 ADG
-DAH V
-DAA ,5 T KHT L3 k BBB B(A0D8G
-8D0A(B BBBB 5 5 $ 5 ^ AG b
-AA , 5 Э0 AC
FK
- E T ,4 Ю BBB B(A0I8Dr
-8A0A(B BBBA T 4 8 BBB B(I0A8D
-8A0A(B BBBA , 6 f AG d
+DAF T D BBB B(A0D8D}
+8D0A(B BBBE L 8
AAGp
+DAC
+DAH n
+DAE T l BBB B(A0D8D}
+8D0A(B BBBE < D ? AAJp,
+DAC f
+DAE T @ BBB B(A0D8D}
+8D0A(B BBBE < ܈ AAJp,
+DAC f
+DAE T BHB E(D0D8JD
+8D0A(B BBBD L T KHT H0 BBB B(A0D8G
+8D0A(B BBBB 2 Ԋ 2 $ 2^ AG b
+AA , 20 AC
FK
+ E T l 3 4 BBB B(A0I8Dr
+8A0A(B BBBA T Ċ `6 BBB B(I0A8D
+8A0A(B BBBA , 8f AG d
AC D
-CA 7 0 L 7 (8 BBE B(D0A8DP
-8D0A(B BBBE l7 AY
-F a, 5 # ACG0
-AAI L 5 BBE B(D0A8Ds
-8D0A(B BBBD T 6 V BEB B(A0A8G
-8A0A(B BBBI T l6 F BEB B(A0A8J
-8A0A(B BBBA T 6 H [ BEE B(D0A8G
-8D0A(B BBBC D 7 7 { BBB D(D0IPg
-0D(A BBBG L d7 k BBE E(D0D8J
-8D0A(B BBBD L 9 8 BLB B(G0D8DP
-8A0A(B BBBD T 8 K BBE B(A0A8J
-8A0A(B BBBA T: 1 AG0dD L |8 R BEB E(D0A8Gl
-8A0A(B BBBH : КT KH : : $ ; $; <; Z HQ L \; AAG e
-CAF s
-ACA R
-CAJ JCA L ; 8 AAG ]
+CA $ X8 L < P88 BBE B(D0A8DP
+8D0A(B BBBE @9 AY
+F a, ԋ 9# & ACG0
+AAI L : BBE B(D0A8Ds
+8D0A(B BBBD T T < BEB B(A0A8G
+8A0A(B BBBI T A BEB B(A0A8J
+8A0A(B BBBA T pF BEE B(D0A8G
+8D0A(B BBBC D \ N, BBB D(D0IPb
+0D(A BBBD T Q BBE E(D0D8J
+8D0A(B BBBG L Ԏ HV~ BOB B(A0A8Dp~
+8A0A(B BBBE T L xZ
BEE B(A0A8J
+8A0A(B BBBK | h1 AG0dD L Ď 0hF BBE E(D0A8Gl
+8A0A(B BBBH PT KH o o$ 4 8o L 0o d 8oZ HQ L xo AAG e
+CAF {
+ACI R
+CAJ JCA L Ԑ o AAG ]
CAF [
ACA R
-CAJ JCA L ; h AAG e
+CAJ JCA L $ p AAG e
CAF s
ACA R
-CAJ JCA L< d< Pa HT $ < ^ AG b
-AA $ < ; F^
-D K 4 < M BDF o
-ABJ AAB L ; " BJB A(A0G
-0A(A BBBA < d; $
- BBA A(D
-(A ABBA < ; BBA A(D
-(E ABGA < ; 3 Ƭ BBA A(D
-(E AFEA < $< " BBA A(Dq
-(A ABBA < d< x BBA A(D
-(E ABGA L < Xn f BEB E(A0F8G`
-8A0A(B BBBA < < xM * BBA A(D
-(A ABBA D 4=
BBA A(D
-(A ABBF T |= H BBB B(A0D8JR
-8A0A(B BBBE d ? ( BLB B(A0A8D@c
+CAJ JCA t Xp pa HT $ @q^ AG b
+AA $ ԑ xq; F^
+D K 4 qM BDF o
+ABJ AAB L \ q f BJB A(A0G
+0A(A BBBA < Xt$ N BBA A(D
+(A ABBA < Hu , BBA A(D
+(E ABGA < , (v3
+ BBA A(D
+(E AFEA < l (w BBA A(D
+(E ABGA L x$ BBB H(D0D8J
+8A0A(B BBBJ l zY BEB E(A0F8DW
+8A0A(B BBBA
+8A0A(B BBBA < l |M ] BBA A(D
+(A ABBA T } @ BBB B(A0A8J
+8A0A(B BBBE D BBA A(D
+(A ABBF d $ BLB B(A0A8D@c
8A0A(B BBBK |
-8A0A(B BBBJ T <> ̫ BBE E(A0A8G
-8D0A(B BBBA l > ( BBB B(A0D8D;
+8A0A(B BBBJ T BBE E(A0A8G
+8D0A(B BBBA l BBB B(A0D8D;
8A0A(B BBBJ W
-8A0A(B BBBA T ? 3 BBE E(A0A8G
-8D0A(B BBBE 4 TA J BDA q
+8A0A(B BBBA T | x I BBE E(A0A8G
+8D0A(B BBBE 4 J BDA q
ABE A
-ABA L ? (n BBB B(D0A8D
-8D0A(B BBBE T ? H BBE B(A0A8G
-8A0A(B BBBA l <@
a BEB B(A0A8D
+ABA L n BBB B(D0A8D
+8D0A(B BBBE T \ ( BBE B(A0A8G
+8A0A(B BBBA l w BEB B(A0A8D
8D0A(B BBBA C
-8A0A(B BBBJ T @ / BBE E(G0A8GF
-8D0A(B BBBH d B h BEE F(A0A8D@c
+8A0A(B BBBJ T $ p/ BBE E(G0A8GF
+8D0A(B BBBH d T H BEE F(A0A8D@c
8F0A(B BBBF
-8D0A(B BBGA L lA BBE F(A0A8F`
-8D0A(B BBBH D C 0 GBA A(G@Q(A ABBG@ \ C m KLA A(G@(A ABBI@n
-(A ABBD \D BBD G(R@
+8D0A(B BBGA L П BBE F(A0A8F`
+8D0A(B BBBH D GBA A(G@Q(A ABBG@ \ T xm KLA A(G@(A ABBI@n
+(A ABBD BBD G(R@
(D ABBD Q
(D ABBF Q
(E ABBE Q
(E ABBE Q
-(E ABBA L B C BBB B(A0A8Dpk
-8A0A(B BBBE d <C ! BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB < E 8"1 BLG A(G@
-(A ABBH D C 8#g g BBE A(A0J`g
-0D(A BBBI < $F `$ BBG A(G@
-(A ABBF L lD % BEB E(I0H8DP
-8D0A(B BBBA L F &
+(E ABBA L d
Y BBB H(A0A8Dp-
+8D0A(B BBBB d ! BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB < 1 BLG A(G@
+(A ABBH D \ g BBE A(A0J`g
+0D(A BBBI < | @ BBG A(G@
+(A ABBF L BEB E(I0H8DP
+8D0A(B BBBA L
BBB E(D0D8G
-8A0A(B BBBE T E 0T BBB B(A0I8G
-8A0A(B BBBF L dE ; Ŧ BBB E(D0A8Dp
-8D0A(B BBBH L E (?4 BEE E(D0D8L
-8D0A(B BBBA G T KH H @ ,H @ DH . K^ \H ? A[ |H ? H ? L H ? AAG ]
+8A0A(B BBBE T | BBB B(A0I8G
+8A0A(B BBBF L ܛ BBB E(D0A8Dp
+8D0A(B BBBH L , (4 BEE E(D0D8L
+8D0A(B BBBA T HٔT KH l Hٔ. K^ ̝ A[ L AAG ]
CAF [
ACA R
-CAJ JCA L H @ AAG ]
+CAJ JCA L l AAG ]
CAF [
ACA R
-CAJ JCA L LI H@ AAG ]
+CAJ JCA L P AAG ]
CAF [
ACA R
-CAJ JCA I T\ AG RA$ G X@ AG Y
-AA T G @` BBE E(D0A8G
-8A0A(B BBBA , DH K Ǥ AID J
-DAA , tH 8L AAG0i
-AAA L H L BEB E(I0H8D@
-8D0A(B BBBD L H hN _ BJE B(A0A8D`8A0A(B BBB T DI N9 BBE E(A0A8J
-8D0A(B BBBA T I S BEE E(A0D8J
-8D0A(B BBBA K . K^ L Z0 Hg $L Z7 Hj L DL @Z AAG e
+CAJ JCA Ҏ\ AG RA$ T ` AG Y
+AA T | ` BBE E(D0A8G
+8A0A(B BBBA , Ԟ AID J
+DAA , @ AAG0i
+AAA L 4 BEB E(I0H8D@
+8D0A(B BBBD L p _ BJE B(A0A8D`8A0A(B BBB T ԟ 9 BBE E(A0A8J
+8D0A(B BBBA T , BEE E(A0D8J
+8D0A(B BBBA \ Ք. K^ t 0 Hg (7 Hj L H AAG e
CAF s
ACA R
-CAJ JCA d L Z BBB B(A0A8DP
+CAJ JCA d BBB B(A0A8DP
8A0A(B BBBE D
-8A0A(B BBBA $ L [^ AG b
-AA , $M P[ ADD0e
-AAA \ \K [ d BBB A(D0GPw
+8A0A(B BBBA $ l ^ AG b
+AA , X ADD0e
+AAA \ d BBB A(D0GPw
0A(A BBBG w
-0A(A BBBA L M \ BOB A(A0]
+0A(A BBBA L $ BOB A(A0]
(A BBBG Y
-(A BBBH T L \| ѡ BBE E(D0A8G
-8D0A(B BBBE D \N b BBE A(A0D@d
-0A(A BBBA T L b1 o BEE E(D0A8GI
-8D0A(B BBBE < M O U BBA D(Gp
-(D ABBA L DM hh + BBB B(A0D8D`b
-8A0A(B BBBC < O j BBG A(G@
-(A ABBG T M k~ à BBE B(A0A8G
-8A0A(B BBBA T ,N Ђ2 ӡ BBB B(A0A8DI
-8A0A(B BBBG |P 8. K^ < N W BGD A(K0
-(A ABBE D P e BBE A(A0D@
-0A(A BBBA T $O 3 BEE B(D0A8J
-8A0A(B BBBF D tQ t BBB D(A0D@
-0A(A BBBA 4 Q țY BEA A(G0(D ABB Q R \ $R `M BBB B(A0A8DP
-8A0A(B BBBE D8A0A(B BBB$ R ^ AG b
-AA R 8. K^ T P ! BBB B(A0D8J
-8A0A(B BBBA S h 4S ` DV L LS h, BEB B(A0A8DP8A0A(B BBB L S H3 BBE B(A0A8DP8A0A(B BBB < S 8 BEH D(A0H@0A(A EBBT 4R ȾJ # BHG B(A0D8J
-8D0A(B BBBF T T KHT R | BBE E(D0A8G
-8D0A(B BBBE T . K^ T S J x BBE B(D0A8G
-8A0A(B BBBJ dU @. K^ T S w
o RIB B(A0A8GP
-8A0A(B BBBA U U F , T D % AHGP
-AAA , <T A AHG`
-AAA dV 0 , |V (b D\
+(A BBBH T | BBE E(D0A8G
+8D0A(B BBBE D ̣ BBE A(A0D@d
+0A(A BBBA T < 1 o BEE E(D0A8GI
+8D0A(B BBBE < Ή U BBA D(Gp
+(D ABBA L ԣ p + BBB B(A0D8D`b
+8A0A(B BBBC < BBG A(G@
+(A ABBG T d BBE B(A0A8G
+8A0A(B BBBA T ( 2 BBB B(A0A8DI
+8A0A(B BBBG pє. K^ < , W BGD A(K0
+(A ABBE D D e BBE A(A0D@
+0A(A BBBA T @ ; BEE B(D0A8J
+8A0A(B BBBF D !t BBB D(A0D@
+0A(A BBBA 4 , @"Y BEA A(G0(D ABB d h" | `" \ BBB B(A0A8DP
+8A0A(B BBBE D8A0A(B BBB$ "^ AG b
+AA pϔ. K^ T \ ("K$ BBB B(A0D8J
+8A0A(B BBBA F F DV L F, BEB B(A0A8DP8A0A(B BBB L G3 BBE B(A0A8DP8A0A(B BBB < \ G BEH D(A0H@0A(A EBBT Ĩ HJ 7 BHG B(A0D8J
+8D0A(B BBBF ͔T KHT 4 `_| BBE E(D0A8G
+8D0A(B BBBE d ͔. K^ T pdJ BBE B(D0A8G
+8A0A(B BBBJ Ԫ x͔. K^ T Pkw
RIB B(A0A8GP
+8A0A(B BBBA D xx \ pxF , xD 9 AHGP
+AAA , ̪ yA # AHG`
+AAA ԫ { , {b D\
H P
H Q
-G I , V hb D\
+G I , |b D\
H P
H Q
-G I , V b D\
+G I , L `|b D\
H P
H Q
-G I , W b D\
+G I , | |b D\
H P
H Q
-G I , <W (b D\
+G I , |b D\
H P
H Q
-G I , lW hb D\
+G I , ܬ }b D\
H P
H Q
-G I , W b D\
+G I , `}b D\
H P
H Q
-G I , W b D\
+G I , < }b D\
H P
H Q
-G I , W (b D\
+G I , l }b D\
H P
H Q
-G I , ,X hb D\
+G I , ~b D\
H P
H Q
-G I , \X b D\
+G I , ̭ `~b D\
H P
H Q
-G I , X b D\
+G I , ~b D\
H P
H Q
-G I , X (b D\
+G I , , ~b D\
H P
H Q
-G I , X hb D\
+G I , \ b D\
H P
H Q
-G I , Y b D\
+G I , `b D\
H P
H Q
-G I , LY b D\
+G I , b D\
H P
H Q
-G I , |Y (b D\
+G I , b D\
H P
H Q
-G I , Y hb D\
+G I , b D\
H P
H Q
-G I , Y b D\
+G I , L `b D\
H P
H Q
-G I , Z b D\
+G I , | b D\
H P
H Q
-G I , <Z (b D\
+G I , b D\
H P
H Q
-G I , lZ hb D\
+G I , ܯ b D\
H P
H Q
-G I , Z b D\
+G I , `b D\
H P
H Q
-G I Z m A~
-A l L X 8 { BBB F(A0A8J@X
-8A0A(B BBBA 4 <[ ADG X
-CAH [AA L |Y BBB B(A0A8DW
-8D0A(B BBBF < Y ` BEA G(GPE
-(A ABBG < Z AAGPQ
+G I < m A~
+A l L BBB F(A0A8J@X
+8A0A(B BBBA 4 p ADG X
+CAH [AA L " # BBB B(A0A8DW
+8D0A(B BBBF < \ BEA G(GPE
+(A ABBG < h AAGPQ
AAD O
-AAG D\ + A[ d\ ( A[ \ ( A[ \ \ ( A[ < Z u ! BBA C(JU
-(A ABBC L $[ Hr < BDB B(I0A8G
-8A0A(B BBBG \ t[ x ˛ BDB A(A0LP~
+AAG (+ A[ Ա 8( A[ H( A[ X , P( A[ L t ` 5 BBB B(A0C8JY
+8A0A(B BBBG L ı Z L BDB B(A0I8G
+8A0A(B BBBG \ BDB A(A0LP~
0A(A BBBD
-0A(A BBBD < [ B AADPU
+0A(A BBBD < t 0B AADPU
AAC
-AAA 4 ^ ACO a
+AAA 4 @ ACO a
AAJ
-GAO 4 D^ !) AAJ P
+GAO 4 ij ) AAJ P
AAB
-GAO D \ x" BAA G@
+GAO D $ BAA G@
AABH
- AABH L \ $5 BDB B(A0C8OZ
-8A0A(B BBBG L ] ) BCA LP
+ AABH L l E
+ BDB J(A0A8Ib
+8A0A(B BBBG L BCA LP
AABH )
- AABK D l] p. BED D(G$
-(D ABBE D ] / d BED D(G$
-(D ABBE \ ] @1 / BDB A(A0LP^
+ AABK T BBB B(A0A8G
+8A0A(B BBBG T d ` BBB B(A0A8G
+8A0A(B BBBG \ m BDB A(A0LP^
0A(A BBBD 8
-0A(A BBBH D \^ 3 BED D(G$
-(D ABBE D ^ H5 љ BED D(G$
-(D ABBE D ^ 6 BED D(G$
-(D ABBE < 4_ 8a g BBA A(JP
-(A ABBI D t_ H9 ? BED D(G$
-(D ABBE L _ :
- BDB B(A0C8LZ
-8A0A(B BBBB T ` p?w BDB B(A0A8O+
-8A0A(B BBBA \b xuT KHT |` D BBA A(G^
+0A(A BBBH T X D BBB B(A0A8G
+8A0A(B BBBG D t BED D(G$
+(D ABBE < a BBA A(JP
+(A ABBI D H BED D(G$
+(D ABBE L D | BDB B(A0C8LZ
+8A0A(B BBBB T pp W BDB B(A0I8G
+8A0A(B BBBA ķ T KHT p ) BBA A(G^
(A ABBG
-(A ABBA D ` S$
- 8 BAA Gj
- AABG L
- AABH T a ]fx T BBB B(A0A8G
-8A0A(B BBBB L ta BDB M(A0A8Gi
-8A0A(B BBBG T a Ġ BBB B(A0A8J
-8A0A(B BBBA L b `d BDB B(A0A8L
-8A0A(B BBBD L lb d BDB B(A0A8L
-8A0A(B BBBD L b C BDJ B(A0A8GZ
-8A0A(B BBBA l c H BDB J(A0A8D\
-8A0A(B BBBB
-8A0A(B BBBD , te b D\
+(A ABBA D \ BAA Gw
+ AABJ
+ AABI T ~{ BBB B(A0A8G]
+8A0A(B BBBH L u BDB M(A0A8Gi
+8A0A(B BBBG T L H| BBB B(A0A8J
+8A0A(B BBBA L d BDB B(A0A8L
+8A0A(B BBBD L d BDB B(A0A8L
+8A0A(B BBBD L D 0C BDJ B(A0A8GZ
+8A0A(B BBBA L 0Y BDB J(A0A8G\
+8A0A(B BBBG , @b D\
H P
H Q
-G I , c AALPd
-DAA T c Pe BEE B(A0A8G
-8A0A(B BBBF $ 4d h JFD@ $ \d @ GFD@ $ d џ GFD@ < d AAD@j
-AAF
-FAA $ d JFD@ T e Xu z BEE B(A0A8G
-8A0A(B BBBE T le e r BEE B(A0A8G
-8A0A(B BBBF g xpT KHL e R BBB A(A0M
-0A(A BBBH $h @ <h 8 Th 0 lh ( h 0 h 8
h 0 h ( AX h ( F i ` i h 4i p , Tg x \ AAG o
-AAA , g b ADG r
-AAA , i b D\
+G I , Z AALPd
+DAA T D e > BEE B(A0A8G
+8A0A(B BBBF $ ( 6 JFD@ $ ĺ ! GFD@ $ ș GFD@ $ s BFA D@ $ < h JFD@ T d @u BEE B(A0A8G
+8A0A(B BBBE T he BEE B(A0A8G
+8A0A(B BBBF T KHL , h BBB A(A0M
+0A(A BBBH T l ̽
AX F 4 Ȳ L в d ز , \ AAG o
+AAA , Խ b ADG r
+AAA < PP BBD A(D0|(A ABB , `b D\
H P
H Q
-G I , i ( b D\
+G I , L b D\
H P
H Q
-G I L j h AAG ]
+G I L | AAG ]
CAF [
ACA R
-CAJ JCA \j Y , tj { BFA i
-ABA 4 h l BKE
-AJA 4 h ! 8 BKE
-AJE k "J n N L 4i X"r BBB B(E0A8J@
-8A0A(B BBBA $ |k #^ AG b
-AA k #B Av
-I A k # k #4 Af
-I C k $ l $% ,l $G DB4 Dl P$M AAG0o
+CAJ JCA ̿ Y , { BFA i
+ABA 4 < h BKE
+AJA 4 t T BKE
+AJE J n N L Ŀ еr BBB B(E0A8J@
+8A0A(B BBBA $ ^ AG b
+AA 8B Av
+I A 4 h L `4 Af
+I C l x% G DB4 ȷM AAG0o
AAF D
-AAJ $ |l h%V AD s
-AG l % l %
- l % l % m % m x% 4m p%
- Lm h%
- dm `% AO m `% m X% m P% < m H% BDD R
+AAJ $ V AD s
+AG ,
+ D \ t
+
+ ظ AO ظ и $ ȸ < < BDD R
ABA h
-CEH n % $ $n %C I^
-I K Ln % dn % |n % n % n % n % n % DR n % o % ,o % Do % \o % L |m % BJB A(A0_
-(A BBDH _(A BBBd o & BBE B(D0A8G@
+CEH | 0 ( $ DR D \ t L BJB A(A0_
+(A BBDH _(A BBBd P BBE B(D0A8G@
8A0A(B BBBJ
-8A0A(B BBBK $ ,p ' AIK0AA Tp @(e , tn ) ŗ ADD@n
-AAG L n h*2 BBB B(A0D8D
-8D0A(B BBBH p X, q P, q H, T <o @, O BBB E(A0A8G
-8A0A(B BBBA q /2 D o / BBB D(A0G
-0A(A BBBE D o 2 ͖ BBB D(A0G
-0A(A BBBE T <p 59 BBA A(Dq
+8A0A(B BBBK $ t AIK0AA e , ADD@n
+AAG L 2 BBB B(A0D8D
+8D0A(B BBBH 4 L d T BBB E(A0D8GY
+8D0A(B BBBC H2 D p = BBB D(A0G
+0A(A BBBE D \ BBB D(A0G
+0A(A BBBE T 9 BBA A(Dq
(C ABBE L
-(A ABBA < r 6 BEB D(A0M
-(A BBBF T p 7 ( BBE E(D0A8G
-8D0A(B BBBI 4 $s :Z BDA y
+(A ABBA < BEB D(A0M
+(A BBBF D < o l BEE D(D0G
+0D(A BBBB 4 \ 0Z BDA y
ABM A
-ABA \s H:Q , |q ;Y ADG i
-AAA T q ; w BBB B(A0A8G
-8A0A(B BBBD L r 8?J A BEB B(A0A8G
-8D0A(B BBBA 4 Lt 8AJ BDA l
+ABA XQ , Y ADG i
+AAA T BBB B(A0A8G
+8A0A(B BBBD L \ HJ BEB B(A0A8G
+8D0A(B BBBA 4 HJ BDA l
ABJ A
-ABA , r PAY ADG i
-AAA T r AF Ɣ BBB B(A0A8G>
-8A0A(B BBBG T s xE BBE E(D0A8G7
-8D0A(B BBBB T ls H S BEE E(D0D8G9
-8D0A(B BBBJ L u I BBB B(A0A8Gp8A0A(B BBB v (K+ Ae 4 ,v 8KI ADG q
-AAA DCA T lt PKX f BBB E(A0A8G
-8A0A(B BBBA D v XN BBE A(A0D@l
-0A(A BBBA $ w N ADD AA4 4u O BFA b
-ABA $ dw PRV ADD JAAL u R BJB A(A0W
-(A BBDH ^(A BBB< u R W BBD A(G0L
-(A ABBA $ x xTc AG g
-AA D Dx T BBE A(A0D@l
-0A(A BBBA D x XU BBE A(A0D@l
-0A(A BBBA < v U BBD A(G0T
-(A ABBA d y W BBE F(D0A8D@
+ABA , `Y 5 ADG i
+AAA T F BBB B(A0A8G>
+8A0A(B BBBG T l BEE E(D0D8G
+8D0A(B BBBD T BEE E(D0D8G9
+8D0A(B BBBJ L H BBB B(A0A8Gp8A0A(B BBB D + Ae 4 d I ADG q
+AAA DCA T BEB B(A0D8Gh
+8A0A(B BBBA D h BBE A(A0D@l
+0A(A BBBA $ < ADD AA4 BFA b
+ABA $ `V ADD JAAL BJB A(A0W
+(A BBDH ^(A BBB< < BBD A(G0L
+(A ABBA $ T c AG g
+AA D | BBE A(A0D@l
+0A(A BBBA < h + BBD A(G0T
+(A ABBA d BBE F(D0A8D@
8F0A(B BBBC
-8D0A(B BBGH T w 8Y G BEB B(A0D8J
-8A0A(B BBBA L w ] J BJB A(A0W
-(A BBDH ^(A BBB< $z P] BBG A(G@
-(A ABBF < dz ^ BBG A(G@
-(A ABBF $ z ^V ADD JAA4 z _U BDA w
-ABO AAB < { 0_ BBG A(G@
-(A ABBG D{ _T d dy ` Ǐ BBB E(A0A8DPD
+8D0A(B BBGH T BEB B(A0D8J
+
+8A0A(B BBBA L h BJB A(A0W
+(A BBDH ^(A BBB< BBG A(G@
+(A ABBF < T x BBG A(G@
+(A ABBF $ 8V ADD JAA4 pU BDA w
+ABO AAB T d 4 BBB E(A0A8DPD
8A0A(B BBBA x
-8A0A(B BBBF D { @b BBE A(A0D@l
-0A(A BBBA < z b W BBA D(K@C
-(A ABBA L Tz Xc 0 BJB A(A0W
-(A BBDH ^(A BBBL | c] BBB H(A0D8DP
-8A0A(B BBBG , | dv BAA i
-ABA D } e BBE A(A0D@l
-0A(A BBBA d} e5 L |} e BBB B(A0D8D@
-8A0A(B BBBB L { hf BJB A(A0W
-(A BBDH ^(A BBB, $| fm k AID t
-DAA < T| f W BBD A(G0$
-(A ABBA L ~ Hh] BBB H(A0D8DP
-8A0A(B BBBG L | Xi BJE B(A0A8D@8A0A(B BBB , 4} j AID0|
-DAA L \ xj] BBB H(A0D8DP
-8A0A(B BBBG L } k W BBB B(D0D8Dp
-8D0A(B BBBD L o BFE B(D0A8D@v
-8D0A(B BBBE L L Hp BBB B(D0A8D@m
-8A0A(B BBBH T ~ p | BBB B(D0A8G
-8A0A(B BBBE < u BBG A(G@
-(A ABBG L < uc ? BBB B(A0A8Dp#
-8A0A(B BBBA l w BNB B(A0D8G
+8A0A(B BBBF D t BBE A(A0D@l
+0A(A BBBA < BBA D(K@C
+(A ABBA L $ BJB A(A0W
+(A BBDH ^(A BBBL L p] BBB H(A0D8DP
+8A0A(B BBBG , v BAA i
+ABA D BBE A(A0D@l
+0A(A BBBA h5 L , BBB B(A0D8D@
+8A0A(B BBBB L 0 x BJB A(A0W
+(A BBDH ^(A BBB, m / AID t
+DAA < $ BBD A(G0$
+(A ABBA L < ] BBB H(A0D8DP
+8A0A(B BBBG L BJE B(A0A8D@8A0A(B BBB , w AID0|
+DAA L @] BBB H(A0D8DP
+8A0A(B BBBG L P BBB B(D0D8Dp
+8D0A(B BBBD L BFE B(D0A8D@v
+8D0A(B BBBE L BBB B(D0A8D@m
+8A0A(B BBBH T t R @ BEB E(A0A8G
+8A0A(B BBBF < BBG A(G@
+(A ABBG L Xc BBB B(A0A8Dp#
+8A0A(B BBBA l \ x BNB B(A0D8G
8A0A(B BBBA n
-8A0A(B BBBA D x, ˊ BJB K(A0j
-(_ aBBA D D hy BBJ A(D0
-(A BBEA L @z] BBB H(A0D8DP
-8A0A(B BBBG < ܀ P{ 3 BBA D(GP
-(D ABBG < | BBG A(G@
-(A ABBG L \ `} Ӊ BEB E(I0H8DP
-8D0A(B BBBD L BJE B(A0A8D@
-8A0A(B BBBA < BBG A(G@
-(A ABBG L 4 BBB B(A0D8DP
-8A0A(B BBBA < p BED A(D0U
-(A ABBE < Ą BED A(D0Y
-(A ABBA < P BBG A(G@
-(A ABBG L L 3 BEB E(I0H8DP
-8D0A(B BBBD T Єz BEE I(A0A8D
-8D0A(B BBBA < BED D(GPf
-(A ABBF 4 4 XY AIG t
-AAI DCA T l X BHB B(A0A8J
-8A0A(B BBBE \ H BBE B(A0D8D@r
-8D0A(B BBBE D8C0A(B BBB4 $ i ؆ ADD N
-AAG DCAL \ Њ BEB E(I0H8DP
-8D0A(B BBBD < BLD D(G0
-(A ABBE \ P BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB< L e BID ~
-AFD AEF < Ѝ Dž BGD A(G@
-(A ABBH T ̆ BJB B(A0A8G
-8A0A(B BBBA , (< BDG kAB < T 8 s BGB D(A0~
-(A BBBK L ؕ [ BEB E(I0H8DP
-8D0A(B BBBD T * 3 BLB B(A0A8Gq
-8A0A(B BBBA L < p C BEB E(I0H8D@
-8D0A(B BBBA < BFA D0}
- FABE D FAB Ċ ` N L h Ä BEB E(I0H8D@
-8D0A(B BBBA < , BFA D0}
- FABE D FAB L l X BBB B(A0D8G@j
-8A0A(B BBBH L ĉ BEB E(I0H8DP
-8D0A(B BBBD d , BGE E(A0A8D@
+8A0A(B BBBA D , BJB K(A0j
+(_ aBBA D o BBJ A(D0
+(A BBEA L 4 ] BBB H(A0D8DP
+8A0A(B BBBG < BBA D(GP
+(D ABBG <
+ BBG A(G@
+(A ABBG L ,
+ BEB E(I0H8DP
+8D0A(B BBBD L | o BJE B(A0A8D@
+8A0A(B BBBA < h
BBG A(G@
+(A ABBG L ( BBB B(A0D8DP
+8A0A(B BBBA < 4 BED A(D0Y
+(A ABBA < t X BBG A(G@
+(A ABBG L 7 BEB E(I0H8DP
+8D0A(B BBBD T , z BEE I(A0A8D
+8D0A(B BBBA < \ BED D(GPf
+(A ABBF 4 `Y AIG t
+AAI DCA T \ BHB B(A0A8J
+8A0A(B BBBE \ , P BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB\ BBE B(A0D8D@r
+8D0A(B BBBE D8C0A(B BBB4 i | ADD N
+AAG DCAL L ( K BEB E(I0H8DP
+8D0A(B BBBD < # BLD D(G0
+(A ABBE \ BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB4 < Y AIG t
+AAI DCA < t e s BID ~
+AFD AEF < P 7 BGB D(A0~
+(A BBBK L BEB E(I0H8DP
+8D0A(B BBBD T D * BLB B(A0A8Gq
+8A0A(B BBBA L # BEB E(I0H8D@
+8D0A(B BBBA < % BFA D0}
+ FABE D FAB x% N L D % BEB E(I0H8D@
+8D0A(B BBBA < l ' BFA D0}
+ FABE D FAB L p' BBB B(A0D8G@j
+8A0A(B BBBH L $ ) BEB E(I0H8DP
+8D0A(B BBBD d t *, BGE E(A0A8D@
8F0A(B BBBI g
-8F0A(B BBBA < t p BBG A(G@
-(A ABBF < c BLA D(G@
-(D ABBC T BBE B(A0A8G
-8A0A(B BBBH d AO L " BLE I(D0D8D
-8A0A(B BBBC T ܋ 2 BBB E(A0D8G
-8A0A(B BBBA T 4 M BHB B(D0D8G
-8A0A(B BBBA T Ԃ BEB B(A0A8Gl
-8A0A(B BBBF 8 E
-D < BKD A(G0(A ABB < h E
-K < \ H BKD A(G0(A ABB T BEB B(A0A8GU
-8A0A(B BBBA L BBL E(H0A8D`
-8D0A(B BBBA D AO d 1 Dl HV Ko
-A $ Y AK Y
-AA < Ԏ ߁ BED I(K@c
-(D ABBE D BGB D(A0G@~
-0A(A BBBC T `1 Ak < | BED D(G
-(A ABBH < Y BED D(G
-(A ABBI D + BEE A(D0G
-0A(A BBBI T D x' BBB B(A0D8G
-8A0A(B BBBB D P BBE A(A0D@u
-0A(A BBBA L ܒ BBB B(A0A8D`
-8A0A(B BBBA L , X BEB B(A0A8D
-8A0A(B BBBA L | BDB E(A0A8DP
-8A0A(B BBBK T ԑ H y BBE E(A0A8GZ
-8A0A(B BBBA T , X BBJ B(A0A8G
-8A0A(B BBBA T h ] BBB B(A0D8D
-8D0A(B BBBF < ܒ 1 R BGA D(G@
-(A ABBB d
BBB F(D0D8J`g
+8F0A(B BBBA < + BBG A(G@
+(A ABBF < 8, ' BLA D(G@
+(D ABBC 4 - T t - BBE B(A0A8G
+8A0A(B BBBH ; AO L ; BLE I(D0D8D
+8A0A(B BBBC T < (=2 BBB E(A0D8G
+8A0A(B BBBA T ?M BHB B(D0D8G
+8A0A(B BBBA T D BEB B(A0A8Gd
+8A0A(B BBBF @J T \ 8J BBB B(A0E8M
+8A0A(B BBBH O E
+K < P BKD A(G0(A ABB T R BEB B(A0A8Gu
+8A0A(B BBBA L l i | BBL E(H0A8D`
+8D0A(B BBBA Xm AO Xm T Pm BBE B(A0A8G
+8A0A(B BBBA , $ {< BDG kAB T {1 Dl t ȓV Ko
+A $ {Y AK Y
+AA < { BED I(K@c
+(D ABBE D $ | BGB D(A0G@~
+0A(A BBBC D X}1 Ak < x} g BED D(G
+(A ABBH < ~ 9 BED D(G
+(A ABBI D ~ BEE A(D0G
+0A(A BBBI T T p' ֽ BBB B(A0D8G
+8A0A(B BBBB D H BBE A(A0D@u
+0A(A BBBA L BBB B(A0A8D`
+8A0A(B BBBA L P BEB B(A0A8D
+8A0A(B BBBA L l Ї BDB E(A0A8DP
+8A0A(B BBBK T @y Y BBE E(A0A8GZ
+8A0A(B BBBA T < h 2 BBJ B(A0A8G
+8A0A(B BBBA T BBB B(A0D8D
+8D0A(B BBBF < X1 BGA D(G@
+(A ABBB d , X ػ BBB F(D0D8J`g
8A0A(B BBBA K
-8A0A(B BBBK | " DR
-J A L & BBE B(A0A8DP
-8A0A(B BBBA T Q~ BBB B(A0D8G:
-8D0A(B BBBE L D @ BBB L(A0A8D`f
-8A0A(B BBBA AL , AAG f
-AAG T P( } RHB B(A0A8G{
-8A0A(B BBBD L D (b K} BEB B(A0A8GW
-8D0A(B BBBH L H)g } BBB B(A0D8JS
-8D0A(B BBBI 4 h- } BAA G@
- DABB L . l} BBB B(D0A8KP
-8A0A(B BBBA $ d `2Y AK Y
-AA L 2 } BEB E(I0H8D@
-8D0A(B BBBD L 42 | BJE B(A0A8DP
-8A0A(B BBBA T 4 5 | BKD A(G`z
-(A ABBG
-(A ABBC D 6 BBE A(A0D@u
-0A(A BBBA T ԗ (7 7| BEE E(D0D8G
-8A0A(B BBBA , , L | AAD@s
-AAE T \ M | BBE E(D0A8L
-8A0A(B BBBB l O s| BGB B(D0A8GF
-8A0A(B BBBG
-8A0A(B BBBA T $ xQ O| BEE E(A0H8G;
-8A0A(B BBBJ T | 0ZU 7| BBB B(A0A8J}
-8D0A(B BBBA D ̛ 8`y BBB B(A0G8GPW8A0A(B BBBT p`7 { BEB B(D0A8T7
-8D0A(B BBBH T t Xc { BBB B(A0A8G
-8A0A(B BBBA T ̚ j { BBE B(A0A8G
-8A0A(B BBBA L $ x | BEB B(A0A8G
-8A0A(B BBBJ T t 8! | BBB B(A0A8G
-8A0A(B BBBA 4 ̛ {} BDA G0x
- DABC T z ^} BBB E(A0A8GE
-8A0A(B BBBE L \ d} BBB B(A0D8J_
-8D0A(B BBBE 0 AN T ̜ } BBB E(A0A8G
-8A0A(B BBBA D $ h{ M} BBB A(D0D@
-0A(A BBBA 4 d D BEA A(D0p(A ABB T | BBB B(D0A8J
-8A0A(B BBBA d C BBE B(A0A8DPQ
-8A0A(B BBBD
-8A0A(B BBBA \ , A^
-A K D | A
+8A0A(B BBBK l " DR
+J A L & BBE B(A0A8DP
+8A0A(B BBBA T BBB B(A0D8G:
+8D0A(B BBBE L 4 x BBB L(A0A8D`@
+8A0A(B BBBA AL , AAG f
+AAG T ( N RHB B(A0A8G{
+8A0A(B BBBD L T
BEB B(A0A8G\
+8D0A(B BBBC L p W BBB B(A0D8JS
+8D0A(B BBBI 4 N BAA G@
+ DABB $ HY AK Y
+AA L T BEB E(I0H8D@
+8D0A(B BBBD L 2 BJE B(A0A8DP
+8A0A(B BBBA D BGF D(A0JI
+0A(A BBBA D H BBE A(A0D@u
+0A(A BBBA T _ BEE E(D0D8G
+8A0A(B BBBA , x AADP}
+AAK T H BBE E(D0A8L!
+8A0A(B BBBF l d BGB B(A0D8GF
+8A0A(B BBBG (
+8A0A(B BBBF T w BEE E(A0H8G;
+8A0A(B BBBJ T , U _ BBB B(A0A8J}
+8D0A(B BBBA D \ y BBB B(A0G8GPW8A0A(B BBBT 7 " BEB B(D0A8T7
+8D0A(B BBBH T $ BBB B(A0A8G
+8A0A(B BBBA T | XS ۸ BBE B(A0A8G
+8A0A(B BBBA L ` # BEB B(A0A8G
+8A0A(B BBBJ D $ s . BBB A(D0D@
+0A(A BBBA 4 D X D BEA A(D0p(A ABB T p BBB B(D0A8J
+8A0A(B BBBA ## HZ L #$ BBB E(D0A8KPI
+8A0A(B BBBA T l (p [ BJB B(A0A8G
+8A0A(B BBBA 4 6 ӹ BDA G0x
+ DABC T (7 BBE B(D0A8G?
+8A0A(B BBBH L T p> BBB B(A0D8J_
+8D0A(B BBBE | D AN T D ݹ BBB E(A0A8G
+8A0A(B BBBA (G, A^
+A K D 8G A
A O
A O
E G
I K
A ]
A G
-A S 4 Ġ д ADD H
-AAE HAA L H~ BED D(G0Z
-(C ABBH m(G ABB L T x { BEB A(A0g
-(A BBDE _(A BBB4 صW BDH q
-AGI AAD D ܟ ) z BBB A(A0D
-0A(A BBBA D $ ) z BBB A(A0D
-0A(A BBBA L d з BEB B(A0A8D`\
-8A0A(B BBBA T `, Zz BBE B(D0A8G
-8D0A(B BBBK T 8A @z BEE E(D0A8Go
-8D0A(B BBBG T l 0 &z BBA A(JK
+A S \ \ HL ADD
+AAD N
+AAH N
+AAH N
+AAA `
+AAA 4 I ADD H
+AAE HAA L xI~ BED D(G0Z
+(C ABBH m(G ABB L l I U BEB A(A0g
+(A BBDE _(A BBB4 JW BDH q
+AGI AAD 0J d (K BEL B(A0A8D`
+8C0A(B BBBF L8A0A(B BBB d t K ^ BEL B(A0A8D`
+8C0A(B BBBF L8A0A(B BBB L Lp BEH A(D0y
+(D ABBJ D(C DBB 8L' DQ
+K F D L HL) BBB A(A0D
+0A(A BBBA D 0M) i BBB A(A0D
+0A(A BBBA L N BEB B(A0A8D`\
+8A0A(B BBBA T , N, BBE B(D0A8G
+8D0A(B BBBK T SA յ BEE E(D0A8Go
+8D0A(B BBBG T xY BBA A(JK
(A ABBG K
-(A ABBA T ġ ) y BEE B(A0D8Dn
-8C0A(B BBBA T ` y RBE B(G0A8Dv
-8A0A(B BBBA T t _y BBB B(A0A8G
-8C0A(B BBBA Ĥ P0 AP
-G W \ `? BEA C(GPf
+(A ABBA L Z BOB A(A0]
+(A BBBG Y
+(A BBBH T @[) . BEE B(A0D8Dn
+8C0A(B BBBA T ^ RBE B(G0A8Dv
+8A0A(B BBBA T 4 _ BBB B(A0A8G#
+8C0A(B BBBA d f0 AP
+G W \ f? BEA C(GPf
(A ABBB {
(A ABBG
-(A ABBF D @ DR \ H AW L | Hc BKB B(A0A8Dpa
-8A0A(B BBBF T ԣ h x BKB B(D0A8Dz
-8D0A(B BBBA L , p Px BBE B(D0A8D]
-8A0A(B BBBA L | @} x BBB B(D0A8Dn
-8A0A(B BBBA L ̤ pV w BBB B(D0A8D
-8D0A(B BBBA L } w BBB B(D0H8Kp
-8A0A(B BBBA , d 7 BDD eAB T <w BBB B(A0A8G
-8A0A(B BBBA D xc w BBB A(D0GB
-0A(A BBBD < v BBB E(A0D8D
+(A ABBF h DR h AW < h{ BDD G0V
+ AABE D CAB < \ i BBG A(G@
+(A ABBG T ia BBB E(A0G8J
+8A0A(B BBBA L nc BKB B(A0A8Dpa
+8A0A(B BBBF T l q W BKB B(D0A8Dz
+8D0A(B BBBA L rw BBB B(D0A8Dg
+8A0A(B BBBA L v BBB B(D0A8Dr
+8A0A(B BBBA L d @yV BBB B(D0A8D
+8D0A(B BBBA L Pz} n BBB B(D0H8Kp
+8A0A(B BBBA , {7 BDD eAB T 4 { BBB B(A0A8G
+8A0A(B BBBA D Hc ı BBB A(D0GB
+0A(A BBBD p BBB E(A0D8D
8F0A(B BBBE r
8C0A(B BBBA u
-8A0A(B BBDA 80T KHT ܦ b ov BBB B(A0A8G_
-8A0A(B BBBF T 4 w Zv BBB B(D0D8J
-8F0A(B BBBA T 0 ov BBB I(A0A8G
-8A0A(B BBBJ L uv BBB B(D0A8Jr
-8A0A(B BBBE T 4 ( nv BEB B(A0A8G
-8D0A(B BBBA $ ) AGD ZAA L AAG ]
+8A0A(B BBDA 4 hyT KH L " DR
+J A T ЃU BBB B(A0A8G_
+8A0A(B BBBF T ؈K BBB B(D0D8J
+8F0A(B BBBA T D Ѝ BBB I(A0A8G
+8A0A(B BBBJ L h BBB B(D0A8Jr
+8A0A(B BBBE T 4 BEB B(A0A8G
+8D0A(B BBBA $ ) AGD ZAA L D ȝ AAG ]
CAF [
ACA R
-CAJ JCA , (b D\
+CAJ JCA , b D\
H P
H Q
-G I , h
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- \ X
- t P
- H P H ԫ @ L 8 t BJB A(A0_
-(A BBDH _(A BBB< D R t BBA A(Gm
-(A ABBA $ | N AD p
-AJ T it BBB B(A0D8G
-8A0A(B BBBA X,. K^ , x D p \ h t ` X `" x ԭ - 4 - L \ AG RA l ̮ , 4 D k BDD D0u
- DABF | D AB E AC Z AK
-L AL X _q BJE B(A0A8D@
-8A0A(B BBBA T 4 ( 7q BBB J(H0D8J
-8A0A(B BBBA L ` sq BJE B(A0A8D@
-8A0A(B BBBA L ܮ 0 Kq BEB E(I0H8D@
-8D0A(B BBBD L , " #q BJE B(A0A8D@
-8A0A(B BBBA < | p BAD '
-ABG O
-ABA T 0 p BEB B(D0A8N.
-8A0A(B BBBJ < * q BED I(D@x
-(A ABBA t T * q BBB D(A0G`
-0D(A BBBA Z
-0D(A BBBK K
-0D(A BBBJ IJ &T KH< H-7 ?q BBD A(JX
-(D ABBD T $ H.n #q BBD D(G
+G I 8
+ 0
+ (
+ $ < T l L ¯ BJB A(A0_
+(A BBDH _(A BBB< XB v BBA A(Gg
+(A ABBA $ hN AD p
+AJ T d . BBB B(A0D8G
+8A0A(B BBBA hu. K^ 0 ( $ " < 0 T 8 l @- $ X^ AG b
+AA $ < T x l p 4 hk BDD D0u
+ DABF D AB ФZ AK
+L AL $ BJE B(A0A8D@
+8A0A(B BBBA T t w BBB J(H0D8JY
+8A0A(B BBBA L H BJE B(A0A8D@
+8A0A(B BBBA T | BBE E(D0A8G
+8D0A(B BBBE T t @| q BBE E(D0A8G
+8D0A(B BBBE T h W BBE B(D0A8G
+8D0A(B BBBH L $ 0 ? BEB E(I0H8D@
+8D0A(B BBBD L t " BJE B(A0A8D@
+8A0A(B BBBA T BBE B(D0A8N1
+8A0A(B BBBG D BEE D(I0D@{
+0A(A BBBA L d @ s BBB B(D0A8Gp
+8D0A(B BBBG
+ oT KH< 7 [ BBD A(JX
+(D ABBD T
+ n ? BBD D(G
(D ABBJ
-(D ABBA L | `1F q BBB E(A0D8G
-8D0A(B BBBC T ̱ `4~ p BBE E(D0A8J
-
-8D0A(B BBBG \ AG RAl D hB /q BEE E(D0H8D`i
+(D ABBA D d
+ # BAD Dh
+ DABF r
+ DABA <
+ BED A(J
+(D ABBG $\ AG RAl 8 BEE E(D0H8D`i
8A0A(B BBBD
-8A0A(B BBBB d D p BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d E _p BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB L | E BBB B(A0D8G@
-8A0A(B BBBB ̵ hG_ L G o BEB E(I0H8DP
-8D0A(B BBBD L < pI ko BJE B(A0A8D@
-8A0A(B BBBA L @J9 Co BEB F(G0D8D`
-8D0A(B BBBA L ܴ 0K o BBB I(I0A8DPv
-8D0A(B BBBH d , Ls n BBB J(A0A8G`
+8A0A(B BBBB d | x w BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB d BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB L $
h BBB B(A0D8G@
+8A0A(B BBBB t
8_ L G BEB E(I0H8DP
+8D0A(B BBBD L
@ BJE B(A0A8D@
+8A0A(B BBBA L T
9 BEB F(G0D8D`
+8D0A(B BBBA L
BBB I(I0A8DPv
+8D0A(B BBBH d
s BBB J(A0A8G`
8F0A(B BBBF B
-8F0A(B BBBA d M] n BBB B(I0A8G`
+8F0A(B BBBA d \ ] S BBB B(I0A8G`
8F0A(B BBBF A
-8F0A(B BBBA T N Dn BBB B(A0I8N
-8D0A(B BBBF | L hS BBB B(D0A8DP
+8F0A(B BBBA T BBB B(A0I8N
+8D0A(B BBBF | 8 BBB B(D0A8DP
8A0A(B BBBB U
8G0A(B BBBK
-8A0A(B BBBI L ̸ U BEB B(A0A8DP4
-8A0A(B BBBA , 8Y BAH D` AAB, L Y BAH D` AAB | h . K^ l ZY l BBB J(D0A8DR
+8A0A(B BBBI L t BEB B(A0A8DP4
+8A0A(B BBBA , BAH D` AAB, BAH D` AAB $ hi. K^ l d Y BBB J(D0A8DR
8F0A(B BBBA b
-8D0A(B BBGA L h BBE B(A0A8D`
-8A0A(B BBBA L T j BBE B(A0A8D`
-8A0A(B BBBA T m0 k BBB B(A0D8J
-8A0A(B BBBA AL \ $ ؉ l BBE D(A0P@|
+8D0A(B BBGA L BBE B(A0A8D`
+8A0A(B BBBA L ` BBE B(A0A8D`
+8A0A(B BBBA T t
0 BBB B(A0D8J
+8A0A(B BBBA * AL \ * c BBE D(A0P@|
0D(A BBBK d
-0D(A BBBA L x ll BBE E(D0C8J
-8A0A(B BBBF 4 Թ y 7l BKA Dp
- AABA . K^ 8 4 0 L ( d , (b k ADG r
-AAA h L ļ ` AAG ]
+0D(A BBBA L L H+ BBE E(D0C8J
+8A0A(B BBBF 4 0y BKA Dp
+ AABA g. K^ 6 6 5 5 , L 5b N ADG r
+AAA T 86 L l 06 AAG ]
CAF [
ACA R
-CAJ JCA $ ^ AG b
-AA , < ȕ0 AF
FK
- D l Ȗ 4 = BDA Y
-JBL AAB < Ȗ BDD k
-ABH V
-ABA L (6 BEB B(A0A8D`h
-8A0A(B BBBA < L 4 BBB A(A0d
-(A BBBE T i BBE E(A0A8G
-8D0A(B BBBD PR DG
-E AD BBE A(A0D@l
-0A(A BBBA d T ( h BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d h BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB 4 J BDA o
+CAJ JCA $ `6^ AG b
+AA , 60 AF
FK
+ D 7 4 , 7= BDA Y
+JBL AAB 4 d 7J BDA o
ABG A
-ABA L \ 0 g BEB E(I0H8DP
-8D0A(B BBBD L g BJE B(A0A8D@
-8A0A(B BBBA 5 \ BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB< t 8 +g BGD A(G0~
-(A ABBG T b f BEB B(D0D8GF
-8A0A(B BBBA @T KH, AAK0i
-AAA L (" DN
-N A < l 8[ BBA A(K@C(A ABB X AL L X
BEE A(A0]
+ABA < 7 BDD k
+ABH V
+ABA L 86 BEB B(A0A8D`h
+8A0A(B BBBA < , 94 BBB A(A0d
+(A BBBE T : BBE E(A0A8G
+8D0A(B BBBD D 8< BBE A(A0D@l
+0A(A BBBA d 4 < BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB d H= ) BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB L = Ǣ BEB E(I0H8DP
+8D0A(B BBBD L T ? BJE B(A0A8D@
+8A0A(B BBBA | P@5 \ x@ BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBBL @ BHF E(D0D8GP`
+8A0A(B BBBB < l B BGD A(G0~
+(A ABBG T Cb BEB B(D0D8GF
+8A0A(B BBBA aT KH, F AAK0i
+AAA $ xF" DN
+N A < D FB BBA A(K@*(A ABB G AL L G
BEE A(A0]
(A BBBF
-(E BBBK T $ e BEB B(A0A8G
-8A0A(B BBBG T | ]f BBB B(A0I8D
-8D0A(B BBBA L BBB E(A0A8DP
-8A0A(B BBBA ȼ" DR
-J A T D ؼ e BBB E(A0A8J
-
-8A0A(B BBBA D p f BBB A(I0GP
-0D(A BBBA d H| Pf BGB E(D0A8Dp
+(E BBBK T XH o BEB B(A0A8G
+8A0A(B BBBG T t X BBB B(A0I8D
+8D0A(B BBBA L [ BBB E(A0A8DP
+8A0A(B BBBA _" DR
+J A d < _| BGB E(D0A8Dp
8A0A(B BBBH k
-8A0A(B BBBA T L ` f BBE B(A0D8J ^
-8A0A(B BBBA D # g BBB A(D0D@
-0A(A BBBA 4 ZHA G HAB 4 $ XF 3g BAA G0_
- AABB L \ p f BEH B(D0D8Dp`
-8D0A(B BBBA ML , f AAD@l
-AAA 4 f BAA GP
- AABA D , X BBB D(D0G@T
-0A(A BBBG t DP DP < f BBA A(G@z
-(A ABBC T (! e BBB B(A0D8G
-8A0A(B BBBA T DI T t
- f BEB B(A0A8G
-8A0A(B BBBA , @ f OGA A(D 4 ! f BJD K@g
- AABJ $ , !c AK c
-AA L \ "$ }f BBB B(A0A8GpH
-8A0A(B BBBA T $s 3f BLB B(D0A8G
-8A0A(B BBBA T (' f BHF E(D0A8G
-8A0A(B BBBD T \ ) e BEL B(D0D8J
-8A0A(B BBBE . K^ D֙\ AG RA 2 2 3p cL , X3 D L `3q e BBB A(D0Gpt
-0D(A BBBG D 6 ve BBB D(D0D`
-0A(A BBBA D 8 _e BBB D(D0De
-0A(A BBBA 9 4 9 L 9 d 9 | 9 L 9 AAG ]
+8A0A(B BBBA T 0`6 G BBE I(D0A8J6
+8A0A(B BBBA D p BBB D(I0GP
+0D(A BBBA T D p BBE B(D0D8G
+8A0A(B BBBA D x# { BBB A(D0D@
+0A(A BBBA 4 ` ZHA G HAB 4 F BAA G0_
+ AABB L T 0 ˡ BEH B(D0D8Dp`
+8D0A(B BBBA | Г ML , Г r AAD@l
+AAA 4 ` Y BAA GP
+ AABA D BBB D(D0G@T
+0A(A BBBG L p d h DP | p DP < x ۠ BBA A(G@z
+(A ABBC T ! BBB B(A0D8G
+8A0A(B BBBA T T
+ BEB B(A0A8G
+8A0A(B BBBA < á BBA A(DO
+(A ABBA , OGA A(D 4 BJD K@g
+ AABJ $ ,! pc AK c
+AA L | $ > BBB B(A0A8GpH
+8A0A(B BBBA T s BLB B(D0A8G
+8A0A(B BBBA T $! נ BHF E(D0A8G
+8A0A(B BBBD T |! BEL B(D0D8J
+8A0A(B BBBE " X. K^ " (\ AG RA " " # p cL ,# D l" q W BBB A(D0Gpt
+0D(A BBBG D " 0 6 BBB D(D0D`
+0A(A BBBA D " BBB D(D0De
+0A(A BBBA $ @ 4$ 8 L$ 0 d$ ( |$ 0 L $ 8 AAG ]
+CAF k
+ACA R
+CAJ JCA L $ x AAG ]
CAF c
CAI R
-CAJ JCA L 9 AAG ]
+CAJ JCA L 4% AAG ]
CAF c
CAI R
-CAJ JCA L 4 : AAG ]
+CAJ JCA L % AAG ]
CAF c
CAI R
-CAJ JCA `:- x: U : W 4 : AAD {
+CAJ JCA % 8- % P U & X W , & `U BAA H
+ABA 4 L& AAD {
AAE p
-AAB , : BHE s
-ABK < < `; 0c AAG `
+AAB , & BHE s
+ABK < % h p AAG `
AAE p
-AAB 4 | ; b BHE X
-ABF $ h< Ag
+AAB 4 & 4 BHE X
+ABF $ ,' p Ag
H l
E U
-E , <= b AAG`
-AAK = =1 Ae
-A T D = Db BBB B(D0A8Gy
-8A0A(B BBBI d @ )b BBB B(A0A8DP
+E , |& = AAG`
+AAK ' ' 1 Ae
+A T & BBB B(D0A8Gy
+8A0A(B BBBI T <' i BAA DP
+ AABH k
+ AABA l
+ AABH d ' 0 5 BBB B(A0A8DP
8A0A(B BBBE D
-8A0A(B BBBA T 8A a BBB B(D0C8J
-8A0A(B BBBA T I D T T t I va BFE E(D0A8D
-8C0A(B BBBA T Q 2a BEB B(A0K8G
-8A0A(B BBBH Xd 4 Pd
L Hd
d @d | 8d 0d 8d @d" Xd Pd 4 Hd BAD D0
- AABA D d \ d dh _ BEB D(D0Gp
+8A0A(B BBBA T ' ћ BBB B(D0C8J
+8A0A(B BBBA ,) D T T l( ( BFE E(D0A8D
+8C0A(B BBBA T ( > BEB B(A0K8G
+8A0A(B BBBH ) *
$*
<* T* l* * * " $ * G AG a
+DC RD *
+ 4 *
+ BAD D0
+ AABA ,+
+ D l* x
+ ę BBE D(D0D
+0D(A BBBD \ * Ph BEB D(D0Gp
0D(A BBBK L
-0D(A BBBA D f _ BAD GPl
+0D(A BBBA D + ` s BAD GPl
DABG
- DABA D h X_ BBE D(D0D
-0D(A BBBD T T ka G_ BEE I(A0A8G ^
-8A0A(B BBBJ , r ?_ AAI@T
-DAD , rT BKI _
-ABH , rD BHD ^
-ABA D < rC ^ BBB D(D0Gpn
-0D(A BBBB L ts ^ BBB B(D0A8Gi
-8A0A(B BBBI , { ^ YX
+ DABA T \+ a C BEE I(A0A8G ^
+8A0A(B BBBJ , + ; AAI@T
+DAD , , `T BKI _
+ABH , , D BHD ^
+ABA D D, C BBB D(D0Gpn
+0D(A BBBB L , c BBB B(D0A8G_
+8A0A(B BBBC L , h BBB B(D0A8GI8A0A(B BBB L ,- BFH B(A0D8Jl
+8D0A(B BBBF , |- X6 s YX
G Z
-A T { }^ BEE E(D0A8G
-8D0A(B BBBF L \ ' c^ BBE B(D0A8G
-8D0A(B BBBE L 9 ^ BEE B(D0D8D
-8A0A(B BBBF Д 4 Ȕ ^ BAA D@O
- AABA D @ \ 8 t 0 4 (h 1^ BAA G[
- AABA 4 ` /^ BHA G@h
- DABA ( , D \ t < G BEE D(D0Y(Q BBB HT # DT 4 L d | # D ^ , 8 k\ YX
+A T - 6< Q BBE B(D0A8G
+8D0A(B BBBB T . ;< 7 BBE B(D0A8G
+8D0A(B BBBB T \. @"( BEB B(A0A8G
+8A0A(B BBBG T . h< BBB B(A0A8G
+8A0A(B BBBA L / 9 BEE B(D0D8D
+8A0A(B BBBF 40 4 t/ BAA D@O
+ AABA 0 0 0 4 / h M BAA G[
+ AABA 4 ,0 8 K BHA G@h
+ DABA <1 T1 l1 1 1 1 ب 1 Ш 1 Ȩ < 1 ШJ BEE D(D0\(Q BBB <2 HT \2 # DT t2 2 2 2 2 # D ^ , 2 YX
G H
-A $ H
-A 4 L ؙv D d @X BIB B(A0A85
-0A(B BBBA 4 X BFA A(
- ABBA D , BIE B(A0A8
-0A(B BBBA , , ȡ AC
TS
- A \ Xe G@]L t { BEE E(A0D8J
-8A0A(B BBBI d BJB B(D0D8G
-8A0A(B BBBD B8A0A(B BBB , D D h BFB B(A0A8H
-0A(B BBBA 4 8 BFA A(
- ABBA L BEB B(A0A8G
-8A0A(B BBBA , R AC
T
- A D нd G@\L \ ({ BEE E(A0D8J
-8A0A(B BBBI d X BHB B(D0D8G
-8A0A(B BBBF B8A0A(B BBB L BEB B(A0A8Dj
-8A0A(B BBBA L d ` BBB B(A0A8J
-8A0A(B BBBH L BEB A(A0z
+A 3 $ 43 x H
+A \3 Ы t3 H 3 v D 3 X BIB B(A0A85
+0A(B BBBA 4 3 0 BFA A(
+ ABBA D $4 , BIE B(A0A8
+0A(B BBBA , l4 AC
TS
+ A 4 0e G@]L 4 { BEE E(A0D8J
+8A0A(B BBBI d 5 BJB B(D0D8G
+8A0A(B BBBD B8A0A(B BBB l5 p D 5 h BFB B(A0A8H
+0A(B BBBA 4 5 BFA A(
+ ABBA L 6 BEB B(A0A8G
+8A0A(B BBBA , T6 xR AC
T
+ A 6 d G@\L 6 { BEE E(A0D8J
+8A0A(B BBBI d 6 0 BHB B(D0D8G
+8A0A(B BBBF B8A0A(B BBB L T7 BEB B(A0A8Dj
+8A0A(B BBBA L 7 8 BBB B(A0A8J
+8A0A(B BBBH L 7 BEB A(A0z
(A BBGG [
-(A BBBF L P BBB B(A0A8D
-8A0A(B BBBA L T BBB B(D0A8G
-8A0A(B BBBH L @ BEB A(A0*
+(A BBBF L D8 ( BBB B(A0A8D
+8A0A(B BBBA L 8 BBB B(D0A8G
+8A0A(B BBBH L 8 BEB A(A0*
(A BBGG [
-(A BBBF 4 A AC
Tv
- B , , AC
Tf
- B L \ h BDB B(A0D8G
-8A0A(B BBBB L BME B(A0A8DX
-8A0A(B BBBG L 8 BNE B(A0G8DQ
-8A0A(B BBBB L L x BEE B(A0A8G:
-8A0A(B BBBA M G EL BBE B(A0A8J
-8A0A(B BBBH d @p BBB B(A0A8G`
+(A BBBF 4 49 hA AC
Tv
+ B , l9 AC
Tf
+ B L 9 @ BDB B(A0D8G
+8A0A(B BBBB L 9 BME B(A0A8DX
+8A0A(B BBBG L <: BNE B(A0G8DQ
+8A0A(B BBBB L : P BEE B(A0A8G:
+8A0A(B BBBA : M G EL : BBE B(A0A8J
+8A0A(B BBBH d D; p BBB B(A0A8G`
8A0A(B BBBI ~
-8A0A(B BBBH D l H BEB E(A0A80A(B BBB D BEB B(D0A8
-0A(B BBBD L h BEB B(A0A8D
-8A0A(B BBBB D L BBB K(A0A8
-0A(B BBBD L 2 BBB B(A0D8DD
-8A0A(B BBBD L p BEB B(A0A8D
-8A0A(B BBBE 4 L L d p BBB E(A0A8GBb
-8A0A(B BBBA L @ BBB B(A0D8GA
-8A0A(B BBBE L BBE B(A0A8GA
-8A0A(B BBBA L T ` BBB B(A0A8G@6
-8A0A(B BBBG < P BBA A(G0(A ABB L @
BBB B(A0A8Gv
-8A0A(B BBBG L 4 d BBB B(A0A8G=
-8A0A(B BBBA L u BBB B(A0A8JG
-8A0A(B BBBA L BBB B(A0D8J8A0A(B BBB L $ BBB B(A0D8G8A0A(B BBB t p L h BBE B(A0C8G;
-8A0A(B BBBE L p BBB B(D0A8J
-8A0A(B BBBH L , $ BBB B(A0C8J
-8A0A(B BBBG L | X5
+8A0A(B BBBH D ; BEB E(A0A80A(B BBB D ; BEB B(D0A8
+0A(B BBBD L << @ BEB B(A0A8D
+8A0A(B BBBB D < BBB K(A0A8
+0A(B BBBD L < 2 BBB B(A0D8DD
+8A0A(B BBBD L $= p BEB B(A0A8D
+8A0A(B BBBE t= =
+ L = H BBB E(A0A8GBb
+8A0A(B BBBA L = @ BBB B(A0D8GA
+8A0A(B BBBE L D> x BBE B(A0A8GA
+8A0A(B BBBA L > 8 BBB B(A0A8G@6
+8A0A(B BBBG < > BBA A(G0(A ABB L $? BBB B(A0A8Gv
+8A0A(B BBBG L t? #d BBB B(A0A8G=
+8A0A(B BBBA L ? %u BBB B(A0A8JG
+8A0A(B BBBA L @ ( BBB B(A0D8J8A0A(B BBB L d@ + BBB B(A0D8G8A0A(B BBB @ H- L @ @- BBE B(A0C8G;
+8A0A(B BBBE L A 0p BBB B(D0A8J
+8A0A(B BBBH L lA 6 BBB B(A0C8J
+8A0A(B BBBG L A 0G
BBB B(A0C8J_
-8A0A(B BBBA L (? BBB B(A0C8J
-8A0A(B BBBA < I BFB A(A0m
-(A BBBH < \ _ BBA A(G?
-(A ABBF , (a AAG
-AAC a KIA D(G@(A ABBF@
+8A0A(B BBBA L B Q BBB B(A0C8J
+8A0A(B BBBA < \B [ BFB A(A0m
+(A BBBH < B pq BBA A(G?
+(A ABBF , B s AAG
+AAC C s KIA D(G@(A ABBF@
(A ABBO
-(G EBBH D(G EBB \ h t j j `l 4 Xl BDD G0 AAB 0p < , b K BBA D(D0N(A ABB L d @ AAG ]
+(G EBBH D(G EBB C z C h| C `| C 8~ 4 C 0~ BDD G0 AAB 4D LD < C b ! BBA D(D0N(A ABB L D AAG ]
CAF c
CAI R
-CAJ JCA D + yJ BAA FP
+CAJ JCA D D X+ BAA FP
AABG z
- AABJ 6 AG lA 4 > AG tA , T D AKD i
-DAA , 0{ BFA i
-ABA L I BBB F(A0A8J@X
-8A0A(B BBBA , `p ^I AHK s
-AAA L < G >I BEE D(D0G
-0D(A BBBA T q I BEE E(D0D8G0
-8D0A(B BBBA T q H BEE E(D0D8G0
-8D0A(B BBBA T < q tH BEE E(D0D8G0
-8D0A(B BBBA T q .H BEE E(D0D8G0
-8D0A(B BBBA T @q G BEE E(D0D8G0
-8D0A(B BBBA T D hq G BEE E(D0D8G0
-8D0A(B BBBA < p \G BBA A(J
-(A ABBC T Pq G BEE E(D0D8G0
-8D0A(B BBBA L 4 xG mG BEE D(D0G
-0D(A BBBA T xZ /G BEE E(D0D8G
-8D0A(B BBBA T z F BEE E(D0D8G9
-8D0A(B BBBA L 4 G F BEE D(D0G
-0D(A BBBA L G eF BEE D(D0G
-0D(A BBBA L G 'F BEE D(D0G
-0D(A BBBA L $ G E BEE D(D0G
-0D(A BBBA L t E BEE E(D0D8G
-8D0A(B BBBA L 8 mE BEE E(D0D8G
-8D0A(B BBBA T Z /E BEE E(D0D8G
-8D0A(B BBBA T l 0 D BEB B(A0A8G
-8A0A(B BBBH T xv E BEE E(D0D8G5
-8D0A(B BBBA L >E BBE D(A0G
-0A(A BBBA L l PG =E BEE D(D0G
-0D(A BBBA L P G D BEE D(D0G
-0D(A BBBA T P
-v D BEE E(D0D8G5
-8D0A(B BBBA L d {D BBE A(D0G
-0A(A BBBA T ( q D BEE E(D0D8G0
-8D0A(B BBBA L P G 9D BEE D(D0G
-0D(A BBBA L \ P
G C BEE D(D0G
-0D(A BBBA L PG C BEE D(D0G
-0D(A BBBA L PG C BEE D(D0G
-0D(A BBBA d L @ AC BBA A(J
+ AABJ <E TE 6 AG lA tE > AG tA , E D AKD i
+DAA , E { BFA i
+ABA L E X BBB F(A0A8J@X
+8A0A(B BBBA , lE 8p z AHK s
+AAA L E xG Z BEE D(D0G
+0D(A BBBA T E xq BEE E(D0D8G0
+8D0A(B BBBA T DF q ֆ BEE E(D0D8G0
+8D0A(B BBBA T F
+q BEE E(D0D8G0
+8D0A(B BBBA T F q J BEE E(D0D8G0
+8D0A(B BBBA T LG
q BEE E(D0D8G0
+8D0A(B BBBA T G @q BEE E(D0D8G0
+8D0A(B BBBA < G H x BBA A(J
+(A ABBC T <H (q υ BEE E(D0D8G0
+8D0A(B BBBA L H PG BEE D(D0G
+0D(A BBBA T H PZ K BEE E(D0D8G
+8D0A(B BBBA T <I Xz BEE E(D0D8G9
+8D0A(B BBBA L I G BEE D(D0G
+0D(A BBBA L I G BEE D(D0G
+0D(A BBBA L 4J G C BEE D(D0G
+0D(A BBBA L J G BEE D(D0G
+0D(A BBBA L J ǃ BEE E(D0D8G
+8D0A(B BBBA L $K BEE E(D0D8G
+8D0A(B BBBA T tK Z K BEE E(D0D8G
+8D0A(B BBBA T K BEB B(A0A8G
+8A0A(B BBBH T $L Pv BEE E(D0D8G5
+8D0A(B BBBA L |L x Z BBE D(A0G
+0A(A BBBA L L (G Y BEE D(D0G
+0D(A BBBA L M (G BEE D(D0G
+0D(A BBBA T lM ( v ݂ BEE E(D0D8G5
+8D0A(B BBBA L M BBE A(D0G
+0A(A BBBA T N q BEE E(D0D8G0
+8D0A(B BBBA L lN ( G U BEE D(D0G
+0D(A BBBA L N ( G BEE D(D0G
+0D(A BBBA L O ( G ف BEE D(D0G
+0D(A BBBA L \O (!G BEE D(D0G
+0D(A BBBA d O ] BBA A(J
(A ABBA U
(A ABBA U
-(A ABBA T q PC BEE E(D0D8G0
-8D0A(B BBBA T q
-C BEE E(D0D8G0
-8D0A(B BBBA L d 8G B BEE D(D0G
-0D(A BBBA L 8G B BEE D(D0G
-0D(A BBBA L 8G HB BEE D(D0G
-0D(A BBBA T T 8q
-B BEE E(D0D8G0
-8D0A(B BBBA T `q A BEE E(D0D8G0
-8D0A(B BBBA L G A BEE D(D0G
-0D(A BBBA L T G LA BEE D(D0G
-0D(A BBBA T q A BEE E(D0D8G0
-8D0A(B BBBA T q @ BEE E(D0D8G0
-8D0A(B BBBA L T G @ BEE D(D0G
-0D(A BBBA L G D@ BEE D(D0G
-0D(A BBBA L G @ BEE D(D0G
-0D(A BBBA L D G ? BEE D(D0G
-0D(A BBBA L G ? BEE D(D0G
-0D(A BBBA L G L? BEE D(D0G
-0D(A BBBA , ! 4 D !i ADG S
+(A ABBA T P !q l BEE E(D0D8G0
+8D0A(B BBBA T lP "q & BEE E(D0D8G0
+8D0A(B BBBA L P $G BEE D(D0G
+0D(A BBBA L Q %G BEE D(D0G
+0D(A BBBA L dQ &G d BEE D(D0G
+0D(A BBBA T Q 'q & BEE E(D0D8G0
+8D0A(B BBBA T R 8(q BEE E(D0D8G0
+8D0A(B BBBA L dR `)G BEE D(D0G
+0D(A BBBA L R `*G h BEE D(D0G
+0D(A BBBA T S `+q * BEE E(D0D8G0
+8D0A(B BBBA T \S ,q ~ BEE E(D0D8G0
+8D0A(B BBBA L S -G ~ BEE D(D0G
+0D(A BBBA L T .G `~ BEE D(D0G
+0D(A BBBA L TT /G "~ BEE D(D0G
+0D(A BBBA L T 0G } BEE D(D0G
+0D(A BBBA L T 1G } BEE D(D0G
+0D(A BBBA L DU 2G h} BEE D(D0G
+0D(A BBBA lV 3 4 V 3i ADG S
CAE ]
-FCB T "d ? BEB E(A0C8J
-8A0A(B BBBA | ' BIE B(A0A8GP
+FCB T U 3d } BEB E(A0C8J
+8A0A(B BBBA | W 8 BIE B(A0A8GP
8A0A(B BBBD B
8D0A(B BBBA
-8A0A(B BBBG T \ 0e > BBE B(A0A8GV
-8A0A(B BBBD T H K? BBE B(A0A8G
-8A0A(B BBBF L ? BBB D(A0D
-0A(A BBBG T \ pi [@ BBB B(A0A8G&
-8A0A(B BBBG < " @ BJA A(D
-(A ABBA T x @ BBB E(A0A8G
-8A0A(B BBBA D ۙ. K^ T d (1 @ BBB B(A0A8G
-8A0A(B BBBF %m A~
-A l T Po @ BBB B(A0A8G
-8A0A(B BBBF L 4 s @ BBB B(A0A8JW
-8A0A(B BBBK T H @ BEJ E(D0D8G
-8D0A(B BBBD < | @ AAD`
+8A0A(B BBBG T V e | BBE B(A0A8GV
+8A0A(B BBBD T W g} BBE B(A0A8G
+8A0A(B BBBF L lW } BBB D(A0D
+0A(A BBBG T W Hi w~ BBB B(A0A8G&
+8A0A(B BBBG < X `" ~ BJA A(D
+(A ABBA T TX P ~ BBB E(A0A8G
+8A0A(B BBBA Y ". K^ T X 1 ~ BBB B(A0A8G
+8A0A(B BBBF Y 7m A~
+A l T <Y ( ~ BBB B(A0A8G
+8A0A(B BBBF L Y ~ BBB B(A0A8JW
+8A0A(B BBBK T Y BEJ E(D0D8G
+8D0A(B BBBD < <Z X| ~ AAD`
AAH p
-AAF T ! ~@ BEJ E(D0D8Gn
-8D0A(B BBBH L t 9@ BBB B(D0A8Gi
-8A0A(B BBBI T (1 @ BEJ E(D0D8G
-8D0A(B BBBH 4 $ ? BCA GPc
- AABA L L ؋ BBB A(A0
+AAF T |Z ! ~ BEJ E(D0D8Gn
+8D0A(B BBBH L Z p U~ BBB B(D0A8Gi
+8A0A(B BBBI T $[ 1 0~ BEJ E(D0D8G
+8D0A(B BBBH 4 |[ 5 } BCA GPc
+ AABA L \ BBB A(A0
(A BBGG A
-(A BBDA d 8M Z? BLB B(A0A8DPq
+(A BBDA d \ M v} BLB B(A0A8DPq
8A0A(B BBBE
-8A0A(B BBBG T ! ? BEJ E(D0D8Go
-8D0A(B BBBG < \ BBG A(G@
-(A ABBG L > BLB B(D0A8DP
-8A0A(B BBBG L ^ W> BLH B(D0A8G@
-8A0A(B BBBB < 5 < T @ BBG A(G@
-(A ABBG ֙. K^ L ; [? BIB B(A0A8Gpf
-8A0A(B BBBA T "R C? BIE B(A0A8G
-8D0A(B BBBD T \ & 3? BIE B(A0A8G<
-8D0A(B BBBD T * #? BIE B(A0A8G\
-8D0A(B BBBD T 0/ ? BBE B(A0A8G
-8A0A(B BBBA T d J @ BIB E(A0A8G)
-8A0A(B BBBB T Zu
c@ BEB B(A0A8G
-8A0A(B BBBB Ȕm A~
-A l T 4
@ BIE B(A0A8G
-8A0A(B BBBD T P @ BEJ E(D0D8GP
-8D0A(B BBBF T @ BEJ E(D0D8GP
-8D0A(B BBBF 4 < @ BCA GPc
- AABA < t Q @ BDD v
-ABE AAB ҙ. K^ T P hB BIB B(D0A8G
-8D0A(B BBBA T $ 5B BIB B(D0A8G
-8D0A(B BBBA T | B BIB B(D0A8G
-8D0A(B BBBA T 80 A BIB B(D0A8G8
-8D0A(B BBBA 4 $
- D BEA A(D0p(A ABB L d dA BBB E(A0C8GP#
-8A0A(B BBBE T خ 'A BBB B(A0A8JZ
-8A0A(B BBBH T A BEB B(A0A8G
-8A0A(B BBBA T d X B BEB B(A0A8G
-8A0A(B BBBB D 0 BBE A(A0D@I
-0A(A BBBA <
- B YHD M
+8A0A(B BBBG T l\ ! } BEJ E(D0D8Go
+8D0A(B BBBG < ] Р BBG A(G@
+(A ABBG L ] | BLB B(D0A8DP
+8A0A(B BBBG L T] ^ s| BLH B(D0A8G@
+8A0A(B BBBB |^ 5 < ^ BBG A(G@
+(A ABBG ^ . K^ L ^ ; w} BIB B(A0A8Gpf
+8A0A(B BBBA T d^ 3R _} BIE B(A0A8G
+8D0A(B BBBD T ^ 7 O} BIE B(A0A8G<
+8D0A(B BBBD T _ p< ?} BIE B(A0A8G\
+8D0A(B BBBD T l_ A /} BBE B(A0A8G
+8A0A(B BBBA T _ `\ $~ BIB E(A0A8G)
+8A0A(B BBBB T ` lu
~ BEB B(A0A8G
+8A0A(B BBBB La m A~
+A l T `
~ BIE B(A0A8G
+8A0A(B BBBD T ` ( BEJ E(D0D8GP
+8D0A(B BBBF T Da ~ BEJ E(D0D8GP
+8D0A(B BBBF 4 a ~ BCA GPc
+ AABA < a `Q ~ BDD v
+ABE AAB b . K^ T ,b ( BIB B(D0A8G
+8D0A(B BBBA T b ` Q BIB B(D0A8G
+8D0A(B BBBA T b BIB B(D0A8G
+8D0A(B BBBA T 4c 0 BIB B(D0A8G8
+8D0A(B BBBA 4 dd D BEA A(D0p(A ABB L c BBB E(A0C8GP#
+8A0A(B BBBE T d C BBB B(A0A8JZ
+8A0A(B BBBH T ld x BEB B(A0A8G
+8A0A(B BBBA T d 0 Ԁ BEB B(A0A8G
+8A0A(B BBBB D e ) BBE A(A0D@I
+0A(A BBBA < de YHD M
HAE
-HAA < L \
- ZB BBB B(A0A8G@
-8D0A(B BBBA
4
L
d
& PT |
/ SZ
L
e BEA A(D0s
-(C ABBH D(G ABB
' AR
-E N L 4 m BEA A(D0~
-(C ABBE D(G ABB ' AR
-E N $ 0! AAG SCA 8 @ $ H1 ADJ [AA $ $ ` AAG`AA L B d | AZ L
> BJB A(A0W
-(A BBDH ^(A BBBT < ? BGB I(A0A8G
-8A0A(B BBBD $ 8 ? YX
+HAA |f L e v BBB B(A0A8G@
+8D0A(B BBBA f x f g x ,g p Dg h \g ` tg h g ` g h& PT g / SZ g L g e BEA A(D0s
+(C ABBH D(G ABB <h ' AR
+E N \h L th m BEA A(D0~
+(C ABBE D(G ABB h ' AR
+E N $ h ! AAG SCA i $i $ <i 1 ADJ [AA $ di 8 AAG`AA i B i i AZ i i j L Li ( | BJB A(A0W
+(A BBDH ^(A BBBT i /} BGB I(A0A8G
+8A0A(B BBBD $ i
+ } YX
G z
-A $ v ACG eDAT '
- ? BBB E(A0D8D
-8D0A(B BBBG L < ? BEB A(A0d
-(A BBDH [(A BBGL 0/ ? BBE B(A0A8DP
-8A0A(B BBBA ə. K^ D h BBE A(A0D@~
-0A(A BBBA T < t c? BBB B(A0G8G
-8A0A(B BBBF T b? BIB B(A0D8DH
-8D0A(B BBBC , U D? ADD`v
-AAA , 0b D\
+A $ j x
+v ACG eDAT Dj
+'
+ } BBB E(A0D8D
+8D0A(B BBBG L j } BEB A(A0d
+(A BBDH [(A BBGL j / } BBE B(A0A8DP
+8A0A(B BBBA l . K^ D ,l @' BBE A(A0D@~
+0A(A BBBA T k t } BBB B(A0G8G
+8A0A(B BBBF T k ~} BIB B(A0D8DH
+8D0A(B BBBC , Ll x!U `} ADD`v
+AAA , Tm 'b D\
H P
H Q
-G I , D pb D\
+G I , m H'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 , m 'b D\
H P
H Q
-G I , 0b D\
+G I , n (b D\
H P
H Q
-G I , pb D\
+G I , Dn H(b D\
H P
H Q
-G I , 4 b D\
+G I , tn (b D\
H P
H Q
-G I , d b D\
+G I , n (b D\
H P
H Q
-G I , 0b D\
+G I , n )b D\
H P
H Q
-G I 4 p w= BAD D
-ABA , j [= AAG@D
-AAA , 4 hk == AAG@D
-AAA \ l% AQ
-F L | l. Aa
-F E 4 (ly C= BDD G0y
- AABA < pl X= ACDP
+G I 4 ,n H) { BAD D
+ABA , dn | w{ AAG@D
+AAA , n @} Y{ AAG@D
+AAA 4 n }y { BDD G0y
+ AABA < n (~ { ACDP
AAA O
-AAA T H= BBB B(A0A8J
-8A0A(B BBBA < t l ;= AADP
+AAA T <o ( { BBB B(A0A8J
+8A0A(B BBBA < o ~ { AADP
AAA O
-AAA D m K= BJD D(G$
-(D ABBH D @o = BJD D(G$
-(D ABBH < D p < AADP
+AAA D o { BJD D(G$
+(D ABBH D p r{ BJD D(G$
+(D ABBH < dp ` ={ AADP
AAI O
-AAA T r < BEJ E(D0D8GT
-8D0A(B BBBJ T Pt < BEJ E(D0D8GT
-8D0A(B BBBJ T 4 v M< BEJ E(D0D8GT
-8D0A(B BBBJ T w < BED D(G`~
+AAA T p @ 3{ BEJ E(D0D8GT
+8D0A(B BBBJ T p z BEJ E(D0D8GT
+8D0A(B BBBJ T Tq Ї z BEJ E(D0D8GT
+8D0A(B BBBJ T q dz BED D(G`~
(A ABBF
-(A ABBA \ n ; BBB A(A0GP
+(A ABBA \ r +n Nz BBB A(A0GP
0A(A BBBD j
-0A(A BBBA \ D n ; BBB A(A0GP
+0A(A BBBA \ dr -n
+z BBB A(A0GP
0A(A BBBD j
-0A(A BBBA D xy k; BEE E(J0J@X
-0A(A BBBK D @z S; BJD D(G$
-(D ABBH D 4 hW ; BBB A(A0GP
-0A(A BBBH 4 t `{J BDA l
+0A(A BBBA D r 0 y BEE E(J0J@X
+0A(A BBBK D s y BJD D(G$
+(D ABBH D Ts -W zy BBB A(A0GP
+0A(A BBBH 4 tt J BDA l
ABJ A
-ABA 4 x{ AAG }
+ABA D s 0 (y BJD D(G$
+(D ABBH 4 t AAG }
ADE o
-AAG @|m A~
-A l |m A~
-A l T , | g: BBB B(A0A8G
-8A0A(B BBBG 4 8 1: AAD
-DAA 4 : AAD$
-DAA 4 : AAD
-DAA L , 9 BDB A(A0L
-0A(A BBBE T | ` 9 BBB B(A0A8J*
-8A0A(B BBBA ؾ. K^ 4 pg : BDD G~
- AABA 4 $ H& : BAD G
-ABA 4 T pJ BDA o
+AAG ,u `m A~
+A l Lu m A~
+A l T t {x BBB B(A0A8G
+8A0A(B BBBG 4 t X Ex AAD
+DAA 4 $u 1x AAD$
+DAA 4 \u x AAD
+DAA L u - x BDB A(A0L
+0A(A BBBE T u 01 w BBB B(A0A8J*
+8A0A(B BBBA w 8. K^ 4 Tv g x BDD G~
+ AABA 4 v 8 x BAD G
+ABA 4 w J BDA o
ABG A
-ABA L h&j p; BBB I(A0G
-0D(A BBBA < ( K; BAI G|
- DABA < $ 8* $; BAI G
- DABA < d ,0 : BAI G
- DABA < .0 : BAI G
- DABA T /4 : BBB B(I0A8Gg
-8D0A(B BBBA T < 24 : BBB B(I0A8Gg
-8D0A(B BBBA < 5; U: BAI G
- DABA 4 Hv BDA D0a
- AABA L BBB B(A0A8Dp
-8A0A(B BBBA L \ z 9 BBB E(D0D8GP
-8A0A(B BBBC < 6 9 BBK A(DPn
-(A ABBA < 7 9 BBK A(DPn
-(A ABBA T , 8_ ~9 BBB B(A0D8G
-8A0A(B BBBD T @ 9 BBB B(A0A8G 4
-8A0A(B BBBA D p[| '; BBI A(G
-(D ABBA , " b D\
+ABA L v 88j y BBB I(A0G
+0D(A BBBA < Lw X: _y BAI G|
+ DABA < w < 8y BAI G
+ DABA < w =0 y BAI G
+ DABA < x ?0 x BAI G
+ DABA T Lx A4 x BBB B(I0A8Gg
+8D0A(B BBBA T x D4 x BBB B(I0A8Gg
+8D0A(B BBBA < x G; ix BAI G
+ DABA 4 z hv BDA D0a
+ AABA L Lz BBB B(A0A8Dp
+8A0A(B BBBA L y @z w BBB E(D0D8GP
+8A0A(B BBBC < z H w BBK A(DPn
+(A ABBA < Tz PI w BBK A(DPn
+(A ABBA T z I_ w BBB B(A0D8G
+8A0A(B BBBD T z Q w BBB B(A0A8G 4
+8A0A(B BBBA D D{ @m| ;y BBI A(G
+(D ABBA , d| b D\
H P
H Q
-G I , L" b D\
+G I , | 8b D\
H P
H Q
-G I T X : BBB B(A0A8G
-8A0A(B BBBJ T e }: BEE B(A0A8G
-8A0A(B BBBF T 4! 4 u: BBB B(A0A8G{
-8A0A(B BBBJ # P. K^ T ! h4 < BBB B(A0A8G
-8A0A(B BBBA T ! P < BBB B(A0A8Gu
-8A0A(B BBBA L L$ AAG ]
+G I T { x x BBB B(A0A8G
+8A0A(B BBBJ T D| e x BEE B(A0A8G
+8A0A(B BBBF T | 4 x BBB B(A0A8G{
+8A0A(B BBBJ } . K^ T } 4 +z BBB B(A0A8G
+8A0A(B BBBA T d} p z BBB B(A0A8Gu
+8A0A(B BBBA L ~ AAG ]
CAF [
ACA R
-CAJ JCA , $ b D\
+CAJ JCA , ~ b D\
H P
H Q
-G I , $ b D\
+G I , b D\
H P
H Q
-G I L # 8 ; BBB A(A0N
-0A(A BBBA , T# صe l; ADG r
-DAA D # /6 8< BCA GPg
+G I L l~ X y BBB A(A0N
+0A(A BBBA , ~ e y ADG r
+DAA D ~ B6 Lz BCA GPg
AABH C
- AABI T # 2e < BEE B(A0A8G
-8A0A(B BBBF < & 8 BBG A(G@
-(A ABBG \& . K^ T |$ = BIB B(D0A8G
-8D0A(B BBBA T $ غ y= BIB B(D0A8G
-8D0A(B BBBA T ,% c= BBB B(I0A8G'
-8D0A(B BBBC T % ( g= BIE B(A0A8G
-8D0A(B BBBF T % 7 = BBB B(A0A8G
-8A0A(B BBBA T 4&
= BIB B(A0A8G
-8A0A(B BBBA T & 0 o= BIB B(A0A8G
-8A0A(B BBBA T & h
= BIB B(A0A8G
-8A0A(B BBBA T <' = BBB B(A0A8M
-8A0A(B BBBA L ' # = BBB B(A0A8Mi
-8A0A(B BBBA L ) 6 AAG ]
+ AABI T 4 Ee 0z BEE B(A0A8G
+8A0A(B BBBF < d K BBG A(G@
+(A ABBG . K^ T @ { BIB B(D0A8G
+8D0A(B BBBA T < { BIB B(D0A8G
+8D0A(B BBBA T w{ BBB B(I0A8G'
+8D0A(B BBBC T H {{ BIE B(A0A8G
+8D0A(B BBBF T D 7 { BBB B(A0A8G
+8A0A(B BBBA T
{ BIB B(A0A8G
+8A0A(B BBBA T P { BIB B(A0A8G
+8A0A(B BBBA T L
{ BIB B(A0A8G
+8A0A(B BBBA T - { BBB B(A0A8M
+8A0A(B BBBA L 6 { BBB B(A0A8Mi
+8A0A(B BBBA L $ 8I AAG ]
CAF [
ACA R
-CAJ JCA L ,* H6 AAG ]
+CAJ JCA L t hI AAG ]
CAF [
ACA R
-CAJ JCA , |* x6b D\
+CAJ JCA , Ą Ib D\
H P
H Q
-G I D ( 6 < BBA A(N
-(A ABBA L ( 9 < BBB A(A0N
-0A(A BBBA , L) ;e < ADG r
-DAA , |) ;e < ADG r
-DAA D ) 6 < BCA GPg
+G I D I z BBA A(N
+(A ABBA L d L z BBB A(A0N
+0A(A BBBA , Ne z ADG r
+DAA , Oe z ADG r
+DAA D 6 z BCA GPg
AABH C
- AABI + H. K^ T * ;
> BIB B(A0A8G
-8A0A(B BBBA T d* (I
> BIB B(A0A8G
-8A0A(B BBBA T * V
s> BIB B(A0A8G
-8A0A(B BBBA T + c
]> BIB B(A0A8G
-8A0A(B BBBA T l+ `q
G> BIB B(A0A8G
-8A0A(B BBBA T + ~
1> BIB B(A0A8G
-8A0A(B BBBA T , 0
> BIB B(A0A8G
-8A0A(B BBBA T t,
> BIB B(A0A8G
-8A0A(B BBBA T ,
= BIB B(A0A8G
-8A0A(B BBBA L $- h = BBB B(A0A8Mi
-8A0A(B BBBA D t- x = BBA K(D|
-(A ABBA L / @ AAG ]
+ AABI 4 . K^ T t N
| BIB B(A0A8G
+8A0A(B BBBA T ̅ H\
| BIB B(A0A8G
+8A0A(B BBBA T $ i
| BIB B(A0A8G
+8A0A(B BBBA T | w
q| BIB B(A0A8G
+8A0A(B BBBA T Ԇ
[| BIB B(A0A8G
+8A0A(B BBBA T ,
E| BIB B(A0A8G
+8A0A(B BBBA T P
/| BIB B(A0A8G
+8A0A(B BBBA T ܇
| BIB B(A0A8G
+8A0A(B BBBA T 4
| BIB B(A0A8G
+8A0A(B BBBA L { BBB B(A0A8Mi
+8A0A(B BBBA D ܈ { BBA K(D|
+(A ABBA L ` AAG ]
CAF [
ACA R
-CAJ JCA , 0 pb D\
+CAJ JCA , L b D\
H P
H Q
-G I L 40 AAG ]
+G I L | AAG ]
CAF c
CAI R
-CAJ JCA , 0 b D\
+CAJ JCA , ̊ b D\
H P
H Q
-G I , 0 0b D\
+G I , Pb D\
H P
H Q
-G I , 0 pb D\
+G I , , b D\
H P
H Q
-G I L 1 AAG ]
+G I L \ AAG ]
CAF [
ACA R
-CAJ JCA , d1 b D\
+CAJ JCA , b D\
H P
H Q
-G I , 1 b D\
+G I , ܋ @b D\
H P
H Q
-G I , 1 `b D\
+G I , b D\
H P
H Q
-G I , 1 b D\
+G I , < b D\
H P
H Q
-G I , $2 b D\
+G I , l b D\
H P
H Q
-G I , T2 b D\
+G I , @b D\
H P
H Q
-G I , 2 `b D\
+G I , ̌ b D\
H P
H Q
-G I , 2 b D\
+G I , b D\
H P
H Q
-G I 2 m A~
-A l T 1 0 u: BBB B(K0A8G
-8A0A(B BBBA L d1 | w: BBB B(A0A8DW
-8D0A(B BBBF L 1 8 O: BLE A(D0D
-0D(A BBBA , 2 (e -: ADG r
-DAA , 42 hh : BDD y
-ABA , d2 e 9 ADG r
-DAA L 2 L: BLB B(A0A8G\
-8A0A(B BBBG T 2 X g: BBB L(A0A8G
-8A0A(B BBBA T <3 : BBB B(A0A8G
-8A0A(B BBBG L 3 8 M: BDB J(A0A8Ib
-8A0A(B BBBG T 3 ]: BBB B(A0A8G
-8A0A(B BBBG T <4 ': BBB B(A0A8G
-8A0A(B BBBG T 4 H 9 BBB B(A0A8G
-8A0A(B BBBG 6 6 h. K^ 4 5 p ; BKA G
- DABA 4 T5 ( ; BKA G
- DABA T 5 e ; BBB B(I0A8GM
-8D0A(B BBBA 4 5 U; BKA G
- DABA 4 6 4; BKA G
- DABA < T6 h ; BAD GC
- DABA T 6 : BBB B(D0A8G
-8D0A(B BBBA < 6 @ : BAD GC
- DABA < ,7 : BAD GC
- DABA < l7 ]: BAD GC
- DABA < 7 0 1: BAD GC
- DABA < 7 : BAD GC
- DABA < ,8 9 BAD Gk
- DABA < l8 P 9 BAD Gk
- DABA < 8 9 BAD G
- DABA < 8 p U9 BAD Gk
- DABA < ,9 )9 BAD GC
- DABA < l9 @ 8 BAD GC
- DABA < 9 8 BAD GC
- DABA D 9 8 BBA I(G)
-(D ABBI D 4: v 8 BBA D(G
-(D ABBA < |: f8 BAD GC
- DABA < : :8 BAD G
- DABA 4 : 8 BKA G
- DABA 4 4; x 7 BKA G
- DABA 4 l; 0 7 BKA G
- DABA 4 ; 7 BKA G
- DABA < ; 7 BAD G
- DABA 4 < ` ^7 BRA G
- DABA 4 T< =7 BRA G
- DABA 4 < 7 BRA G
- DABA < < 6 BAD GC
- DABA < = 6 BAD GC
- DABA D D= ( 6 BBA I(G
-(D ABBF < = 6 BAI Gp
- DABA T = 0
- s6 BBB B(I0A8G
-8D0A(B BBBA < $>
G6 BAD GC
- DABA < d> H 6 BAD GC
- DABA < > 5 BAD GC
- DABA < > 5 BAD GC
- DABA < $? 8 5 BAD GC
- DABA < d? k5 BAD GC
- DABA T ? y ?5 BBB B(I0A8GQ
-8D0A(B BBBI < ? 35 BAD GC
- DABA < <@ P 5 BAD GC
- DABA < |@ 4 BAD GC
- DABA < @ 4 BAD GC
- DABA < @ @! 4 BAD GC
- DABA T <A " W4 BBB B(D0A8G
-8D0A(B BBBA T A % %4 BBB B(D0A8G
-8D0A(B BBBA < A 'Y 3 BAI G
- DABA < ,B * 3 BAI G"
- DABA < lB -5 3 BAD G
- DABA < B / y3 BAD GC
- DABA < B `0 M3 BAI G<
- DABA T ,C pn &3 BDB E(A0C8G
-8A0A(B BBBA < C 3 BEA D(D`s
-(A ABBG D C 81 2 BDB A(I0F`K
-0A(A BBBE T D 2V 2 BDA C(L`^
+G I , m A~
+A l T t P x BBB B(K0A8G
+8A0A(B BBBA L ̌ x BBB B(A0A8DW
+8D0A(B BBBF L X cx BLE A(D0D
+0D(A BBBA , l He Ax ADG r
+DAA , h x BDD y
+ABA , ̍ e w ADG r
+DAA L `x BLB B(A0A8G\
+8A0A(B BBBG T L x {x BBB L(A0A8G
+8A0A(B BBBA T x BBB B(A0A8G
+8A0A(B BBBG L X ax BDB J(A0A8Ib
+8A0A(B BBBG T L qx BBB B(A0A8G
+8A0A(B BBBG T ;x BBB B(A0A8G
+8A0A(B BBBG T h x BBB B(A0A8G
+8A0A(B BBBG , D . K^ 4 y BKA G
+ DABA 4 H y BKA G
+ DABA T e y BBB B(I0A8GM
+8D0A(B BBBA 4 L iy BKA G
+ DABA 4 Hy BKA G
+ DABA < 'y BAD GC
+ DABA T x BBB B(D0A8G
+8D0A(B BBBA < T ` x BAD GC
+ DABA < x BAD GC
+ DABA < Ԓ qx BAD GC
+ DABA < P Ex BAD GC
+ DABA < T x BAD GC
+ DABA < w BAD Gk
+ DABA < ԓ p w BAD Gk
+ DABA < w BAD G
+ DABA < T iw BAD Gk
+ DABA < =w BAD GC
+ DABA < Ԕ ` w BAD GC
+ DABA < v BAD GC
+ DABA D T v BBA I(G)
+(D ABBI D v v BBA D(G
+(D ABBA <
+ zv BAD GC
+ DABA < $ @ Nv BAD G
+ DABA 4 d
"v BKA G
+ DABA 4 v BKA G
+ DABA 4 Ԗ P u BKA G
+ DABA 4 u BKA G
+ DABA < D u BAD G
+ DABA 4 ru BRA G
+ DABA 4 8 Qu BRA G
+ DABA 4 0u BRA G
+ DABA < , u BAD GC
+ DABA < l t BAD GC
+ DABA D H t BBA I(G
+(D ABBF < t BAI Gp
+ DABA T 4 P t BBB B(I0A8G
+8D0A(B BBBA < ! [t BAD GC
+ DABA < ̙ h" /t BAD GC
+ DABA < # t BAD GC
+ DABA < L % s BAD GC
+ DABA < X& s BAD GC
+ DABA < ̚ ' s BAD GC
+ DABA T (y Ss BBB B(I0A8GQ
+8D0A(B BBBI < d / Gs BAD GC
+ DABA < p0 s BAD GC
+ DABA < 1 r BAD GC
+ DABA < $ 3 r BAD GC
+ DABA < d `4 r BAD GC
+ DABA T 5 kr BBB B(D0A8G
+8D0A(B BBBA T 88 9r BBB B(D0A8G
+8D0A(B BBBA < T :Y r BAI G
+ DABA < = q BAI G"
+ DABA < ԝ 0@5 q BAD G
+ DABA < 0B q BAD GC
+ DABA < T C aq BAI G<
+ DABA T n :q BDB E(A0C8G
+8A0A(B BBBA < %q BEA D(D`s
+(A ABBG D , XD p BDB A(I0F`K
+0A(A BBBE T t EV p BDA C(L`^
(A ABBF
-(A ABBE T dD 4V r2 BDA C(L`^
+(A ABBE T ̟ GV p BDA C(L`^
(A ABBF
-(A ABBE T D 6V 92 BDA C(L`^
+(A ABBE T $ IV Mp BDA C(L`^
(A ABBF
-(A ABBE L E 8Q 2 BDB B(A0A8OZ
-8A0A(B BBBA L dE @Q 2 BDB B(A0A8Or
-8A0A(B BBBI T E P W2 BDJ B(A0A8G
-8A0A(B BBBA L F `V
G2 BBB B(D0G8JP
-8A0A(B BBBI TH d AZ 4 |F dT m2 BAA J@
- AABB < F (fa n2 BBA A(JP
-(A ABBI L F G2 BBB A(K0D
-0A(A BBBA D DG 2 BBB A(A0Np
-0A(A BBBA L I ` AAG ]
+(A ABBE L | KQ p BDB B(A0A8OZ
+8A0A(B BBBA L ̠ SQ p BDB B(A0A8Or
+8A0A(B BBBI T c kp BDJ B(A0A8G
+8A0A(B BBBA L t i
[p BBB B(D0G8JP
+8A0A(B BBBI w AZ 4 wT p BAA J@
+ AABB < Hya p BBA A(JP
+(A ABBI L \ [p BBB A(K0D
+0A(A BBBA D /p BBB A(A0Np
+0A(A BBBA L ̣ AAG ]
CAF c
CAI R
-CAJ JCA L I AAG ]
+CAJ JCA L AAG ]
CAF [
ACA R
-CAJ JCA L $J Ї AAG ]
+CAJ JCA L l AAG ]
CAF [
ACA R
-CAJ JCA , tJ b D\
+CAJ JCA , b D\
H P
H Q
-G I , J @b D\
+G I , `b D\
H P
H Q
-G I , J b D\
+G I , b D\
H P
H Q
-G I , K b D\
+G I , L b D\
H P
H Q
-G I , 4K b D\
+G I , | b D\
H P
H Q
-G I , dK @b D\
+G I , `b D\
H P
H Q
-G I K m A~
-A l T I Љh / BBL B(A0A8G\
-8A0A(B BBBG , J h 0 BDD y
-ABA , DJ (e / ADG r
-DAA , tJ he / ADG r
-DAA J 80 BDB B(A0C8Lpo
+G I ܥ m A~
+A l T $ h m BBL B(A0A8G\
+8A0A(B BBBG , | h n BDD y
+ABA , He m ADG r
+DAA , ܥ e m ADG r
+DAA Ȥ Ln BDB B(A0C8Lpo
8A0A(B BBBE 4
8A0A(B BBBJ /
-8A0A(B BBBG T ,K @
Y0 BBB B(A0A8G
-8A0A(B BBBG L K ( #0 BDB B(A0C8OZ
-8A0A(B BBBG L K ș( !0 BDB B(A0C8OZ
-8A0A(B BBBG N x. K^ 4 <L 2 BRA G
- DABA 4 tL H 1 BRA G
- DABA 4 L 1 BRA G
- DABA 4 L 1 BKA G
- DABA 4 M p 1 BKA G
- DABA 4 TM ( c1 BKA G
- DABA 4 M B1 BKA G
- DABA 4 M !1 BKA G
- DABA 4 M P 1 BKA G
- DABA 4 4N 0 BKA G
- DABA 4 lN 0 BKA G
- DABA D N x 0 BBA D(G8
-(D ABBA 4 N - u0 ADG
-DAA 4 $O - P0 ADG
-DAA 4 \O - +0 ADG
-DAA 4 O ت- 0 ADG
-DAA 4 O Ы- / ADG
-DAA 4 P Ȭ- / ADG
-DAA 4 <P - / ADG
-DAA 4 tP U r/ ADG0
-DAA 4 P } M/ ADGX
-DAA 4 P (U (/ ADG0
-DAA 4 Q P- / ADG
-DAA 4 TQ H- . ADG
-DAA 4 Q @- . ADG
-DAA 4 Q 8a . ADG<
-DAA < Q p o. BAD Gw
- DABA D <R v C. BBA D(G
-(D ABBA 4 R 8- . ADG
-DAA 4 R 0} - ADGX
-DAA 4 R x0 - ADG
-DAA 4 ,S p0 - ADG
-DAA 4 dS h0 - ADG
-DAA 4 S `0 b- ADG
-DAA 4 S Xi =- ADGD
-DAA 4 T B - ADG
-DAA < DT , BAI Gr
- DABA D T H , BBA I(G
-(D ABBA D T , BBA I(G
-(D ABBA < U , BAD GF
- DABA < TU ( _, BAD GF
- DABA < U x 3, BAD GF
- DABA < U , BAD GF
- DABA < V + BAI Gp
- DABA < TV I + BAI G
- DABA < V \ + BAI G
- DABA < V h+ BAD G
- DABA L W + BDB B(A0A8O|
-8A0A(B BBBG L dW X + BDB B(A0A8O|
-8A0A(B BBBG T W + BFB B(A0A8J
-8A0A(B BBBD Z P Z X L <X , BBB A(A0Nf
-0A(A BBBA D X + BBA A(N
-(A ABBA D X 5 + BBA A(N
-(A ABBA D Y q + BBA A(N
-(A ABBA D dY 8 + BBA K(DU
-(A ABBA D Y N + BBA K(D
-(A ABBA D Y D + BBA A(N
-(A ABBA L 4\ AAG e
+8A0A(B BBBG T ` mn BBB B(A0A8G
+8A0A(B BBBG L ( 7n BDB B(A0C8OZ
+8A0A(B BBBG L < ( 5n BDB B(A0C8OZ
+8A0A(B BBBG d ԓ. K^ 4 p BRA G
+ DABA 4 ܧ h o BRA G
+ DABA 4 o BRA G
+ DABA 4 L ز o BKA G
+ DABA 4 o BKA G
+ DABA 4 H wo BKA G
+ DABA 4 Vo BKA G
+ DABA 4 , 5o BKA G
+ DABA 4 d p o BKA G
+ DABA 4 ( n BKA G
+ DABA 4 ԩ n BKA G
+ DABA D n BBA D(G8
+(D ABBA 4 T - n ADG
+DAA 4 - dn ADG
+DAA 4 Ī - ?n ADG
+DAA 4 - n ADG
+DAA 4 4 - m ADG
+DAA 4 l - m ADG
+DAA 4 - m ADG
+DAA 4 ܫ U m ADG0
+DAA 4 } am ADGX
+DAA 4 L HU <m ADG0
+DAA 4 p- m ADG
+DAA 4 h- l ADG
+DAA 4 `- l ADG
+DAA 4 , Xa l ADG<
+DAA < d l BAD Gw
+ DABA D v Wl BBA D(G
+(D ABBA 4 X- /l ADG
+DAA 4 $ P}
+l ADGX
+DAA 4 \ 0 k ADG
+DAA 4 0 k ADG
+DAA 4 ̮ 0 k ADG
+DAA 4 0 vk ADG
+DAA 4 < xi Qk ADGD
+DAA 4 t B ,k ADG
+DAA < k BAI Gr
+ DABA D h j BBA I(G
+(D ABBA D 4 j BBA I(G
+(D ABBA < | j BAD GF
+ DABA < H sj BAD GF
+ DABA < Gj BAD GF
+ DABA < < j BAD GF
+ DABA < | 8 i BAI Gp
+ DABA < I i BAI G
+ DABA < \ i BAI G
+ DABA < < |i BAD G
+ DABA L | i BDB B(A0A8O|
+8A0A(B BBBG L ̲ x i BDB B(A0A8O|
+8A0A(B BBBG T j BFB B(A0A8J
+8A0A(B BBBD L p d x L j BBB A(A0Nf
+0A(A BBBA D j BBA A(N
+(A ABBA D < ( 5 i BBA A(N
+(A ABBA D q i BBA A(N
+(A ABBA D ̴ X" i BBA K(DU
+(A ABBA D #N i BBA K(D
+(A ABBA D \ %D i BBA A(N
+(A ABBA L | ( AAG e
CAF s
ACA R
-CAJ JCA L \ 0 AAG ]
+CAJ JCA L ̶ P( AAG ]
CAF Z
ACB R
-CAJ JCA , \ `b D\
+CAJ JCA , (b D\
H P
H Q
-G I L ] AAG ]
+G I L L ( AAG ]
CAF [
ACA R
-CAJ JCA , T] b D\
+CAJ JCA , (b D\
H P
H Q
-G I L ] AAG ]
+G I L ̷ 0) AAG ]
CAF [
ACA R
-CAJ JCA , ] @b D\
+CAJ JCA , `)b D\
H P
H Q
-G I L ^ AAG ]
+G I L L ) AAG ]
CAF [
ACA R
-CAJ JCA , T^ b D\
+CAJ JCA , )b D\
H P
H Q
-G I , ^ b D\
+G I , ̸ *b D\
H P
H Q
-G I , ^ 0b D\
+G I , P*b D\
H P
H Q
-G I L ^ p AAG ]
+G I L , * AAG ]
CAF [
ACA R
-CAJ JCA , 4_ b D\
+CAJ JCA , | *b D\
H P
H Q
-G I L d_ AAG ]
+G I L + AAG ]
CAF c
CAI R
-CAJ JCA , _ b D\
+CAJ JCA , @+b D\
H P
H Q
-G I L _ ` AAG ]
+G I L , + AAG ]
CAF c
CAI R
-CAJ JCA , 4` b D\
+CAJ JCA , | +b D\
H P
H Q
-G I , d` b D\
+G I , ,b D\
H P
H Q
-G I , ` b D\
+G I , ܺ @,b D\
H P
H Q
-G I , ` `b D\
+G I , ,b D\
H P
H Q
-G I , ` b D\
+G I , < ,b D\
H P
H Q
-G I , $a b D\
+G I , l -b D\
H P
H Q
-G I , Ta b D\
+G I , @-b D\
H P
H Q
-G I , a `b D\
+G I , ̻ -b D\
H P
H Q
-G I a m A~
-A l 4 _ % BAD D
-ABA < ` H % BBD A(G@a
-(A ABBA L T` % BBB F(A0A8J@X
-8A0A(B BBBA D ` h ~ Z% BEE D(D0A
-(A BBBA , ` e % ADG r
-DAA , a e $ ADG r
-DAA , La e $ ADG r
-DAA , |a ` e $ ADG r
-DAA , a h $ BDD y
-ABA , a h j$ BDD y
-ABA d A`
-G b $d A`
-G b Dd A`
-G b< lb p $ ACLPo
+G I -m A~
+A l 4 D . c BAD D
+ABA < | h. c BBD A(G@a
+(A ABBA L / c BBB F(A0A8J@X
+8A0A(B BBBA D 0~ nc BEE D(D0A
+(A BBBA , T 0e 2c ADG r
+DAA , 1e c ADG r
+DAA , @1e b ADG r
+DAA , 1e b ADG r
+DAA , 1h b BDD y
+ABA , D 2h ~b BDD y
+ABA L @2 A`
+G b l 2 A`
+G b 3 A`
+G b< Խ 3 b ACLPo
AAG
-AAE D b $ BED D(G$
-(D ABBE D b X $ BED D(G$
-(D ABBE D <c $ BED D(G$
-(D ABBE L |e ( BBB B(A0A8Gp8A0A(B BBB , e ADD Z
-AAA $ d v $ A
-E D ,d $ BPR M(D0c
-(A BBBA 4 td x & B\A G
- DABA < d `! % B_A G5
- DABA < d " % BAI G,
- DABA 4 ,e % % BKA G
- DABA 4 de % w% BKA G
- DABA 4 e & V% BKA G
- DABA D e H' 5% BBA I(G4
-(D ABBA D f + % BBA I(G
-(D ABBA 4 df (/ $ BKA G
- DABA 4 f / $ BRA G
- DABA 4 f 0 $ BRA G
- DABA 4 g P1 $ BRA G
- DABA T Dg 2 _$ BBB B(A0A8G
-8A0A(B BBBJ < g 79 3$ BAI G
- DABA < g 9 $ BAI G
- DABA D h @; # BBA I(G2
-(D ABBA T dh = # BBE B(A0A8G<
-8D0A(B BBBC T h C # BBJ B(A0A8G
-8D0A(B BBBE < i hH& # BAD G
- DABA < Ti XJ |# BAI G~
- DABA T i M U# BBB B(A0I8G
-8D0A(B BBBA T i PPR g# BBE E(D0D8L
-8A0A(B BBBK , <l Xf BCA x
-ABO ll f( A[ l f( A[ 4 j fd # BAA J@
- AABJ < j 0ia # BBA A(JP
-(A ABBA T ,k `j- # BBB B(A0D8G
-8A0A(B BBBG |m 8} L k 0}% 3$ BFB B(A0C8J]
-8A0A(B BBBG m d l { @$ BFB B(A0A8Gp_
+AAE D b BED D(G$
+(D ABBE D \ x b BED D(G$
+(D ABBE D b BED D(G$
+(D ABBE L Ŀ H BBB B(A0A8Gp8A0A(B BBB , ADD Z
+AAA $ l ( c A
+E D b BPR M(D0c
+(A BBBA 4 ܿ 3 d B\A G
+ DABA < 4 c B_A G5
+ DABA < T 5 c BAI G,
+ DABA 4 @8 c BKA G
+ DABA 4 8 c BKA G
+ DABA 4 9 jc BKA G
+ DABA D < h: Ic BBA I(G4
+(D ABBA D > c BBA I(G
+(D ABBA 4 HB b BKA G
+ DABA 4 C b BRA G
+ DABA 4 < C b BRA G
+ DABA 4 t pD b BRA G
+ DABA T (E sb BBB B(A0A8G
+8A0A(B BBBJ < J9 Gb BAI G
+ DABA < D L b BAI G
+ DABA D `N a BBA I(G2
+(D ABBA T P a BBE B(A0A8G<
+8D0A(B BBBC T $ V a BBJ B(A0A8G
+8D0A(B BBBE < | [& a BAD G
+ DABA < x] a BAI G~
+ DABA T (` ia BBB B(A0I8G
+8D0A(B BBBA T T pcR {a BBE E(D0D8L
+8A0A(B BBBK , xy BCA x
+ABO y( A[ z( A[ 4 zd a BAA J@
+ AABJ < T P|a a BBA A(JP
+(A ABBA T }- a BBB B(A0D8G
+8A0A(B BBBG X L P% Gb BFB B(A0C8J]
+8A0A(B BBBG , 0 d l ({ Tb BFB B(A0A8Gp_
8A0A(B BBBB
-8A0A(B BBBA dn |n T l 1 # BBA A(MT
+8A0A(B BBBA @ 8 T 01 a BBA A(MT
(A ABBC \
-(A ABBF L l # BAA JV
+(A ABBF L \ a BAA JV
AABH
- AABD L Dm ȏ # BFB B(A0C8J]
-8A0A(B BBBG o T m $ BBE E(A0C8G^
-8D0A(B BBBD L n ( # BDB B(A0A8O\
-8A0A(B BBBG T Tn # BBB E(A0G8G
-8D0A(B BBBJ T n # BIB B(A0A8G
-8A0A(B BBBD D o 8 [% BBA A(N
-(A ABBA L Lo I% BBB A(A0Nf
-0A(A BBBA D o ` 0% BBA A(N
-(A ABBA D o 5 % BBA A(N
-(A ABBA D ,p q % BBA A(N
-(A ABBA L lr AAG ]
+ AABD L a BFB B(A0C8J]
+8A0A(B BBBG T -b BBE E(A0C8G^
+8D0A(B BBBD L l H a BDB B(A0A8O\
+8A0A(B BBBG T a BBB E(A0G8G
+8D0A(B BBBJ T 0 b BIB B(A0A8G
+8A0A(B BBBD D l X oc BBA A(N
+(A ABBA L ]c BBB A(A0Nf
+0A(A BBBA D Dc BBA A(N
+(A ABBA D L 5 2c BBA A(N
+(A ABBA D q c BBA A(N
+(A ABBA L AAG ]
CAF k
ACA R
-CAJ JCA L r 8 AAG e
+CAJ JCA L X AAG e
CAF s
ACA R
-CAJ JCA , s b D\
+CAJ JCA , T b D\
H P
H Q
-G I L <s AAG ]
+G I L AAG ]
CAF Z
ACB R
-CAJ JCA , s b D\
+CAJ JCA , b D\
H P
H Q
-G I L s 8 AAG ]
+G I L X AAG ]
CAF [
ACA R
-CAJ JCA , t hb D\
+CAJ JCA , T b D\
H P
H Q
-G I L <t AAG ]
+G I L AAG ]
CAF [
ACA R
-CAJ JCA , t b D\
+CAJ JCA , b D\
H P
H Q
-G I L t AAG ]
+G I L 8 AAG ]
CAF [
ACA R
-CAJ JCA , u Hb D\
+CAJ JCA , T hb D\
H P
H Q
-G I , <u b D\
+G I , b D\
H P
H Q
-G I , lu b D\
+G I , b D\
H P
H Q
-G I , u b D\
+G I , (b D\
H P
H Q
-G I , u Hb D\
+G I , hb D\
H P
H Q
-G I u m A~
-A l < $t { s! BED D(G0z
-(D ABBA D dt ~ ?! BEE D(D0A
-(A BBBA , t Pe ! ADG r
-DAA , t e ADG r
-DAA , u e ADG r
-DAA D <u 2 $! BCA L@Z
+G I D m A~
+A l < { _ BED D(G0z
+(D ABBA D 8~ S_ BEE D(D0A
+(A BBBA , pe _ ADG r
+DAA , D e ^ ADG r
+DAA , t e ^ ADG r
+DAA D 02 8_ BCA L@Z
AABH
- AABA D u 2 BCA L@Z
+ AABA D (2 _ BCA L@Z
AABH
- AABA T u [ BEJ E(D0D8GP
-8D0A(B BBBF x xe. K^ 4 <v " BYA G
- DABA 4 tv H " BYA G
- DABA 4 v 0 ^" B\A G
- DABA 4 v =" BYA G
- DABA < w t " BYA G
- DABA < \w ! B_A G5
- DABA < w p ! BAI G,
- DABA L w ! BIB A(D0G
-0D(A BBBA 4 ,x m! BKA G
- DABA 4 dx H L! BKA G
- DABA 4 x +! BKA G
- DABA 4 x 7
-! ADG
-DAA T y BDB I(A0A8Np
-8A0A(B BBBE 4 dy H ADG
-DAA 4 y ADG
-DAA < y X _ BAD G
- DABA D z 8 3 BBA D(G
-(D ABBA T \z V BBB B(A0A8G
-8A0A(B BBBD < z BAI Ge
- DABA T z 8X BBJ B(A0A8G
-8D0A(B BBBI T L{ @ BBB B(A0A8G.
-8A0A(B BBBG T { P x BEE B(D0D8L
-8A0A(B BBBK , } & ACL d
-JAQ , $~ `' BCA x
-ABO T~ '( A[ t~ '( A[ T | (" BBB B(A0D8G
-8A0A(B BBBE ~ ; L } ; L BFB B(A0C8J]
-8A0A(B BBBG T K L t} KM BDB B(A0A8O\
-8A0A(B BBBG L } OM BDB B(A0A8O\
-8A0A(B BBBG , Ub D\
+ AABA T 4 n _ BEJ E(D0D8GP
+8D0A(B BBBF d ث. K^ 4 ` BYA G
+ DABA 4 h ` BYA G
+ DABA 4 P r` B\A G
+ DABA 4 L 8 Q` BYA G
+ DABA < t 0` BYA G
+ DABA < 0 ` B_A G5
+ DABA < _ BAI G,
+ DABA L D _ BIB A(D0G
+0D(A BBBA 4 _ BKA G
+ DABA 4 h `_ BKA G
+ DABA 4 ?_ BKA G
+ DABA 4 < 7 _ ADG
+DAA T t ^ BDB I(A0A8Np
+8A0A(B BBBE 4 h
+ ^ ADG
+DAA 4 ^ ADG
+DAA < < x
s^ BAD G
+ DABA D | X G^ BBA D(G
+(D ABBA T V ^ BBB B(A0A8G
+8A0A(B BBBD < ] BAI Ge
+ DABA T \ XX ] BBJ B(A0A8G
+8D0A(B BBBI T ` ] BBB B(A0A8G.
+8A0A(B BBBG T "P ] BEE B(D0D8L
+8A0A(B BBBK , < : ACL d
+JAQ , l : BCA x
+ABO ;( A[ ;( A[ T ;" ] BBB B(A0D8G
+8A0A(B BBBE 4 N L t N `^ BFB B(A0C8J]
+8A0A(B BBBG ^ L ^M ^ BDB B(A0A8O\
+8A0A(B BBBG L , bM ^ BDB B(A0A8O\
+8A0A(B BBBG , T hb D\
H P
H Q
-G I , < Ub D\
+G I , hb D\
H P
H Q
-G I T t~ HE e BBE E(A0A8G6
-8A0A(B BBBI T ~ @ ^ BBE E(A0A8G
-8A0A(B BBBK T $ ȞE V BBE E(A0A8G6
-8A0A(B BBBI t P\. K^ T Tp 7 BBB B(A0A8G
-8A0A(B BBBA T e7 ) BBB B(A0A8G
-8A0A(B BBBA T D s{ BDB B(A0A8J
-8A0A(B BBBH <
-! BBA A(J`x
-(A ABBB HQ Az
-E P , b D\
+G I T hE y^ BBE E(A0A8G6
+8A0A(B BBBI T 4 ` r^ BBE E(A0A8G
+8A0A(B BBBK T E j^ BBE E(A0A8G6
+8A0A(B BBBI . K^ T hp K^ BBB B(A0A8G
+8A0A(B BBBA T T 0x7 =^ BBB B(A0A8G
+8A0A(B BBBA T { )^ BDB B(A0A8J
+8A0A(B BBBH @ < 8 _ BBA A(J`x
+(A ABBB 4 hQ Az
+E P , T b D\
H P
H Q
-G I < Z. K^ T \ 7 } BBB B(A0A8G
-8A0A(B BBBA , b D\
+G I . K^ T з7 ^ BBB B(A0A8G
+8A0A(B BBBA , b D\
H P
H Q
-G I , ܃ سb D\
+G I , $ b D\
H P
H Q
-G I 4 BAD D
-ABA D L p #! BAA Jp
+G I 4 | 8 ^ BAD D
+ABA D 7_ BAA Jp
AABH
- AABC D 8l &! BAA JPV
+ AABC D Xl :_ BAA JPV
AABH
- AABC Ԅ PY. K^ T H " BBB B(A0I8G_
-8D0A(B BBBA T L " BBB B(A0A8G
-8A0A(B BBBA T HA # BIB B(A0A8G+"
-8A0A(B BBBI L AAG ]
+ AABC . K^ T \ h ` BBB B(A0I8G_
+8D0A(B BBBA T ` BBB B(A0A8G
+8A0A(B BBBA T hA a BIB B(A0A8G+"
+8A0A(B BBBI L < 0# AAG ]
CAF [
ACA R
-CAJ JCA , D @b D\
+CAJ JCA , `#b D\
H P
H Q
-G I L t AAG ]
+G I L # AAG ]
CAF [
ACA R
-CAJ JCA , Ć b D\
+CAJ JCA , #b D\
H P
H Q
-G I T
- & BBB L(A0A8G
-8A0A(B BBBA T T ( & BBB L(A0A8G
-8A0A(B BBBI , 'e '& ADG r
-DAA , ܅ 'e & ADG r
-DAA T u & BEE B(A0A8G
-8A0A(B BBBD T d șe & BEE B(A0A8G
-8A0A(B BBBF L ' & BBB B(A0G8Dh
-8A0A(B BBBJ T , & BBB B(G0A8Gx
-8A0A(B BBBA \ T. K^ T | 5 ( BIB B(D0A8GV
-8D0A(B BBBA T ԇ X; l( BIB B(D0A8GV
-8D0A(B BBBA T , @ U( BIB B(D0A8GV
-8D0A(B BBBA T (F >( BIB B(D0A8GV
-8D0A(B BBBA T ܈ K '( BIB B(D0A8GV
-8D0A(B BBBA T 4 P ( BIB B(D0A8GV
-8D0A(B BBBA T `V ' BIB B(D0A8GV
-8D0A(B BBBA T [ ' BIB B(D0A8GV
-8D0A(B BBBA T < 0a ' BIB B(D0A8GV
-8D0A(B BBBA T f|
' BIB B(A0A8GH
-8A0A(B BBBA T s|
' BIB B(A0A8GH
-8A0A(B BBBA T D |
' BIB B(A0A8GH
-8A0A(B BBBA L AAG ]
+G I T d $
+ #d BBB L(A0A8G
+8A0A(B BBBA T H. /d BBB L(A0A8G
+8A0A(B BBBI , :e ;d ADG r
+DAA , D ;e d ADG r
+DAA T t u d BEE B(A0A8G
+8A0A(B BBBD T e d BEE B(A0A8G
+8A0A(B BBBF L $ : d BBB B(A0G8Dh
+8A0A(B BBBJ T t ? d BBB B(G0A8Gx
+8A0A(B BBBA X. K^ T I f BIB B(D0A8GV
+8D0A(B BBBA T < xN f BIB B(D0A8GV
+8D0A(B BBBA T S if BIB B(D0A8GV
+8D0A(B BBBA T HY Rf BIB B(D0A8GV
+8D0A(B BBBA T D ^ ;f BIB B(D0A8GV
+8D0A(B BBBA T d $f BIB B(D0A8GV
+8D0A(B BBBA T i
f BIB B(D0A8GV
+8D0A(B BBBA T L n e BIB B(D0A8GV
+8D0A(B BBBA T Pt e BIB B(D0A8GV
+8D0A(B BBBA T y|
e BIB B(A0A8GH
+8A0A(B BBBA T T |
e BIB B(A0A8GH
+8A0A(B BBBA T |
e BIB B(A0A8GH
+8A0A(B BBBA L AAG ]
CAF [
ACA R
-CAJ JCA , 0b D\
+CAJ JCA , , Pb D\
H P
H Q
-G I L p AAG ]
+G I L \ AAG ]
CAF [
ACA R
-CAJ JCA , d b D\
+CAJ JCA , b D\
H P
H Q
-G I T
- o& BBB L(A0A8G
-8A0A(B BBBA T {& BBB L(A0A8G
-8A0A(B BBBI , L e & ADG r
-DAA , | e c& ADG r
-DAA L 5 /' BBB B(A0G8Dh
-8A0A(B BBBJ T _ ' BBB B(G0A8Gx
-8A0A(B BBBA L 8N. K^ T l ( BIB B(D0A8GV
-8D0A(B BBBA T Ď h ( BIB B(D0A8GV
-8D0A(B BBBA T ( BIB B(D0A8GV
-8D0A(B BBBA T t 8 ( BIB B(D0A8GV
-8D0A(B BBBA T ̏ s( BIB B(D0A8GV
-8D0A(B BBBA T $ \( BIB B(D0A8GV
-8D0A(B BBBA T | p E( BIB B(D0A8GV
-8D0A(B BBBA T Ԑ |
.( BIB B(A0A8GH
-8A0A(B BBBA T , |
( BIB B(A0A8GH
-8A0A(B BBBA T ( |
( BIB B(A0A8GH
-8A0A(B BBBA T ܑ P
|
' BIB B(A0A8GH
-8A0A(B BBBA T 4 x|
' BIB B(A0A8GH
-8A0A(B BBBA , 'b D\
+G I T
+ d BBB L(A0A8G
+8A0A(B BBBA T \ 8 d BBB L(A0A8G
+8A0A(B BBBI , e d ADG r
+DAA , e wd ADG r
+DAA L @5 Ce BBB B(A0G8Dh
+8A0A(B BBBJ T d 0_ $e BBB B(G0A8Gx
+8A0A(B BBBA . K^ T f BIB B(D0A8GV
+8D0A(B BBBA T , f BIB B(D0A8GV
+8D0A(B BBBA T f BIB B(D0A8GV
+8D0A(B BBBA T X f BIB B(D0A8GV
+8D0A(B BBBA T 4 f BIB B(D0A8GV
+8D0A(B BBBA T ( pf BIB B(D0A8GV
+8D0A(B BBBA T Yf BIB B(D0A8GV
+8D0A(B BBBA T < |
Bf BIB B(A0A8GH
+8A0A(B BBBA T |
+f BIB B(A0A8GH
+8A0A(B BBBA T H|
f BIB B(A0A8GH
+8A0A(B BBBA T D p |
e BIB B(A0A8GH
+8A0A(B BBBA T -|
e BIB B(A0A8GH
+8A0A(B BBBA , :b D\
H P
H Q
-G I , ' ' AAN`V
-AAA , *_ }' AAG o
-DAA I. K^ T 4 *_ S* BIB B(A0A8GF
+G I , $ ; e AAN`V
+AAA , T =_ e AAG o
+DAA \ . K^ T =_ gh BIB B(A0A8GF
-8A0A(B BBBA T =; w* BIB B(A0A8Gu
-8A0A(B BBBA T M m* BIB B(A0A8G
-8A0A(B BBBA T < \5 c* BBB B(D0A8G
-8A0A(B BBBB , rb D\
+8A0A(B BBBA T P; h BIB B(A0A8Gu
+8A0A(B BBBA T L ` h BIB B(A0A8G
+8A0A(B BBBA T p5 wh BBB B(D0A8G
+8A0A(B BBBB , b D\
H P
H Q
-G I , sb D\
+G I , (b D\
H P
H Q
-G I , Hs + AANP4
-AAA , $ u + AAN`}
-AAA , T x_ * AAG o
-DAA , x_ * AAG o
-DAA 8G. K^ T ̕ x' S- BIB B(A0A8G
-8A0A(B BBBA T $ w =- BIB B(A0A8GA
-8A0A(B BBBA T | F '- BIB B(A0A8G
-8A0A(B BBBA T Ԗ أ - BIB B(A0A8G
-8A0A(B BBBA T , +- BBB B(A0A8J N
-8A0A(B BBBD | A C C ę A L 8 . BEE B(D0D8K`
-8A0A(B BBBA , D \ t Ԛ ^ cz
- , < , AAG@
-DAF d | AP AL , ę h @, ADDP~
-AAA $ $ ACK ODA 4 r + BDD DPA
- AABA L AP , t h + ADDP~
-AAA X P ̜ X AZ L Xp BBB B(A0A8G@
-8A0A(B BBBB T D x * BBB E(D0A8D
-8D0A(B BBBK < @ + YHD M
+G I , \ h 3i AANP4
+AAA , ؈ i AAN`}
+AAA , _ i AAG o
+DAA , _ h AAG o
+DAA . K^ T 4 ' gk BIB B(A0A8G
+8A0A(B BBBA T ؚw Qk BIB B(A0A8GA
+8A0A(B BBBA T F ;k BIB B(A0A8G
+8A0A(B BBBA T < 'k BIB B(A0A8G
+8A0A(B BBBA T ?k BBB B(A0A8J N
+8A0A(B BBBD A V V A L L X l BEE B(D0D8K`
+8A0A(B BBBA t 4 ^ cz L
+ d , j AAG@
+DAF AP AL , , h Tj ADDP~
+AAA $ 4 $ ACK ODA 4 r j BDD DPA
+ AABA 8 AP , 8h i ADDP~
+AAA x p x AZ L 4 xp BBB B(A0A8G@
+8A0A(B BBBB T i BBB E(D0A8D
+8D0A(B BBBK < ` i YHD M
HAE
-HAA ԝ D BBE E(G0D8DP~8D0A(B BBB, 4 0? BAA
-ABA L l P E* BEB B(D0D8KPQ
-8A0A(B BBBA T p?_ * BJE E(D0D8G
-8D0A(B BBBA x@ N T , @{ ) BJE E(D0D8G5
-8D0A(B BBBA T A{ ^) BJE E(D0D8G5
-8D0A(B BBBA T ܝ B ) BJE E(D0D8GZ
-8D0A(B BBBA T 4 D{ ( BJE E(D0D8G5
-8D0A(B BBBA T @E ( BJE E(D0D8GZ
-8D0A(B BBBA T F{ F( BJE E(D0D8G5
-8D0A(B BBBA L < G ( BJE D(D0G!
-0D(A BBBD T Hc ' BJE E(D0D8G
-8D0A(B BBBA L J }' BJE D(D0G!
-0D(A BBBD L 4 HK @' BEE I(D0G
-0D(A BBBE L L ' BJE D(D0G!
-0D(A BBBD L Ԡ ML & BJE D(D0G
-0D(A BBBA N AL < N0 AG fA $ \ NF AAG0zAA O D O & BBJ A(D0
-(A BBEA T p & BDD u
+HAA @ D 4 8 BBE E(G0D8DP~8D0A(B BBB, | PR BAA
+ABA L p Yh BEB B(D0D8KPQ
+8A0A(B BBBA T $ R_ h BJE E(D0D8G
+8D0A(B BBBA T S N T S{ g BJE E(D0D8G5
+8D0A(B BBBA T T{ rg BJE E(D0D8G5
+8D0A(B BBBA T D U ,g BJE E(D0D8GZ
+8D0A(B BBBA T 8W{ f BJE E(D0D8G5
+8D0A(B BBBA T `X f BJE E(D0D8GZ
+8D0A(B BBBA T L Y{ Zf BJE E(D0D8G5
+8D0A(B BBBA L Z f BJE D(D0G!
+0D(A BBBD T \c e BJE E(D0D8G
+8D0A(B BBBA L L (] e BJE D(D0G!
+0D(A BBBD L h^ Te BEE I(D0G
+0D(A BBBE L _ e BJE D(D0G!
+0D(A BBBD L < `L d BJE D(D0G
+0D(A BBBA d a AL a0 AG fA $ aF AAG0zAA b D b d BBJ A(D0
+(A BBEA T T d BDD u
ADD
ABE U
ABH I
-ABA T D 8 J& BDD u
+ABA T X ^d BDD u
ADD
ABE U
ABH I
-ABA < & BDA G
- DABG < ܢ ` % BDA G
- DABG L O % BHE B(D0D8DP
-8D0A(B BBBB L d pP BLB B(A0A8Dz
-8A0A(B BBBA L ) ;% ZHA DPM
+ABA < d BDA G
+ DABG < D c BDA G
+ DABG L @b c BHE B(D0D8DP
+8D0A(B BBBB L c BLB B(A0A8Dz
+8A0A(B BBBA L $ @) Oc ZHA DPM
HABA v
- HABA T C% BBB B(A0A8G
-8A0A(B BBBA \ \ XR BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 Ĥ RY O& AIG t
-AAI DCA L R & BEB E(I0H8D@
-8D0A(B BBBA T L T % BBB B(A0A8G
-8A0A(B BBBA D F % BLB A(A0Gp@
-0A(A BBBA $ SF AAG0zAA L S % BEB E(I0H8D@
-8D0A(B BBBD \ d XU] % BBB I(D0D@
+ HABA T t Wc BBB B(A0A8G
+8A0A(B BBBA \ xe BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB4 , eY cd AIG t
+AAI DCA L d e /d BEB E(I0H8D@
+8D0A(B BBBA T T d BBB B(A0A8G
+8A0A(B BBBA D
F c BLB A(A0Gp@
+0A(A BBBA $ , fF AAG0zAA L | f c BEB E(I0H8D@
+8D0A(B BBBD \ xh] c BBB I(D0D@
0F(A BBBK U
-0F(A BBBA T Ħ ! W% BLH B(A0A8G
-8D0A(B BBBA T C% BLB B(A0A8G
-8A0A(B BBBE L t UU ?% BBB E(E0E8G`
-8A0A(B BBBA l ħ V= % BEB B(A0A8D
+0F(A BBBA T , (! kc BLH B(A0A8G
+8D0A(B BBBA T Wc BLB B(A0A8G
+8A0A(B BBBE L hU Sc BBB E(E0E8G`
+8A0A(B BBBA l , i= #c BEB B(A0A8D
8A0A(B BBBG U
-8A0A(B BBBA T 4 @ $ BEE B(A0D8G
-8D0A(B BBBD L 7 $ BEB F(A0D8D
-8A0A(B BBBA T ܨ p
- u$ BEE B(A0D8Gf
-8D0A(B BBBC L 4
7 2$ BEB F(A0D8D
-8A0A(B BBBA | 3. K^ $ XM AAK0}AA D HX BEB B(A0A8DP8A0A(B BBBL Y
BEE A(A0]
+8A0A(B BBBA T @ b BEE B(A0D8G
+8D0A(B BBBD L 7 b BEB F(A0D8D
+8A0A(B BBBA T D b BEE B(A0D8Gf
+8D0A(B BBBC L ( 7 Fb BEB F(A0D8D
+8A0A(B BBBA y. K^ $ @kM AAK0}AA D hk BEB B(A0A8DP8A0A(B BBBL L 0l
BEE A(A0]
(A BBBF
-(E BBBJ T
D Q T t (
0 $ BBE B(A0A8J]
-8D0A(B BBBA < ̪ x$ BBD A(G
-(D ABBA L K$ BHF E(D0A8Dm
-8A0A(B BBBA T \ `u $ BBB B(A0A8GM
-8D0A(B BBBE L # BHE E(D0A8Dm
-8A0A(B BBBA T u # BBB B(A0A8GM
-8D0A(B BBBE L \ @ }# BHE E(D0A8Dm
-8A0A(B BBBA T D# BBB B(A0A8G]
-8D0A(B BBBE L # BHE E(D0A8Dm
-8A0A(B BBBA T T " BBB B(A0A8GR
-8D0A(B BBBH T " BBB B(A0A8GR
-8D0A(B BBBH T 8 " BBB B(A0A8G
-8A0A(B BBBA T \ X" BBB B(A0A8G
-8A0A(B BBBA T 8"T /" BBB B(A0A8G
-8A0A(B BBBA T @% " BBB B(A0A8G
-8A0A(B BBBA T d ' ! BBB B(A0A8G
-8A0A(B BBBA D p* ! BBB A(A0G
-0D(A BBBA $ (T3 AAJ ZGA , $ @Tf AAJ m
-GAO , T Tb D\
+(E BBBJ @ D Q T H 0 b BBE B(A0A8J]
+8D0A(B BBBA < 4 " b BBD A(G
+(D ABBA L t " _b BHF E(D0A8Dm
+8A0A(B BBBA T #u &b BBB B(A0A8GM
+8D0A(B BBBE L % a BHE E(D0A8Dm
+8A0A(B BBBA T l 8&u a BBB B(A0A8GM
+8D0A(B BBBE L `( a BHE E(D0A8Dm
+8A0A(B BBBA T ( Xa BBB B(A0A8G]
+8D0A(B BBBE L l 8+ *a BHE E(D0A8Dm
+8A0A(B BBBA T + ` BBB B(A0A8GR
+8D0A(B BBBH T . ` BBB B(A0A8GR
+8D0A(B BBBH T l X0 ` BBB B(A0A8G
+8A0A(B BBBA T 2 l` BBB B(A0A8G
+8A0A(B BBBA T
+ X5T C` BBB B(A0A8G
+8A0A(B BBBA T t
+ `8 ` BBB B(A0A8G
+8A0A(B BBBA T
+ : _ BBB B(A0A8G
+8A0A(B BBBA D $ = _ BBB A(A0G
+0D(A BBBA $ D Hg3 AAJ ZGA , l `gf AAJ m
+GAO , gb D\
H P
H Q
-G I , Tb D\
+G I , gb D\
H P
H Q
-G I , Ub D\
+G I , hb D\
H P
H Q
-G I , @Ub D\
+G I , ,
`hb D\
H P
H Q
-G I , Ub D\
+G I , \
hb D\
H P
H Q
-G I , D Ub D\
+G I ,
hb D\
H P
H Q
-G I , t Vb D\
+G I ,
ib D\
H P
H Q
-G I , @Vb D\
+G I ,
`ib D\
H P
H Q
-G I L ܱ > BBB B(A0A8GZ
-8D0A(B BBBH T , e BEE B(A0A8G
-8A0A(B BBBF 4 U" { BAA GP
- AABG T e r BEE B(A0A8G
-8A0A(B BBBF T e j BEE B(A0A8G
-8A0A(B BBBF T l b BBE E(A0A8G
-8A0A(B BBBH T ij X Z BBE E(A0A8G
-8A0A(B BBBH T e R BEE B(A0A8G
-8A0A(B BBBF T t e J BEE B(A0A8G
-8A0A(B BBBF T ̴ B BBE E(A0A8G
-8A0A(B BBBF T $ e BEE B(A0A8G
-8A0A(B BBBF T | 0 BBE E(A0A8G2
-8A0A(B BBBE L Ե TU { BBB B(A0A8J
-8A0A(B BBBB | BBB B(A0D8DP
+G I L D
0> ] BBB B(A0A8GZ
+8D0A(B BBBH T
e ] BEE B(A0A8G
+8A0A(B BBBF 4
h" ] BAA GP
+ AABG T $ e ] BEE B(A0A8G
+8A0A(B BBBF T | e ~] BEE B(A0A8G
+8A0A(B BBBF T 0 v] BBE E(A0A8G
+8A0A(B BBBH T , x n] BBE E(A0A8G
+8A0A(B BBBH T e f] BEE B(A0A8G
+8A0A(B BBBF T e ^] BEE B(A0A8G
+8A0A(B BBBF T 4 V] BBE E(A0A8G
+8A0A(B BBBF T 8e ] BEE B(A0A8G
+8A0A(B BBBF T P ] BBE E(A0A8G2
+8A0A(B BBBE L < gU ] BBB B(A0A8J
+8A0A(B BBBB | d BBB B(A0D8DP
8A0A(B BBBB
8A0A(B BBBE "
-8A0A(B BBBD L HZ4 BBB B(A0A8M
-8A0A(B BBBH X&. K^ T f /! BBJ B(A0A8G_
-8D0A(B BBBK T d hj, #! BBJ B(A0A8G
-8D0A(B BBBF T @q ?! BBB B(I0A8G%
-8D0A(B BBBE T w K! BBJ B(A0A8Gd
-8D0A(B BBBF T l ~v g! BBJ B(A0A8G
-8D0A(B BBBD T ĸ [! BBJ B(A0A8G
-8D0A(B BBBK T O! BBJ B(A0A8GT
-8D0A(B BBBF T t u W! BBJ B(A0A8G
-8D0A(B BBBC T ̹ , [! BBB B(A0A8M
-8A0A(B BBBG 4 ADD K
-AAJ ]DA T \ P5 BEB B(A0A8J
-8A0A(B BBBG ļ
, ܼ b D\
+8A0A(B BBBD L hm4
] BBB B(A0A8M
+8A0A(B BBBH 4 l. K^ T t @y C_ BBJ B(A0A8G_
+8D0A(B BBBK T }, 7_ BBJ B(A0A8G
+8D0A(B BBBF T $ ` S_ BBB B(I0A8G%
+8D0A(B BBBE T | Ȋ __ BBJ B(A0A8Gd
+8D0A(B BBBF T v {_ BBJ B(A0A8G
+8D0A(B BBBD T , ( o_ BBJ B(A0A8G
+8D0A(B BBBK T c_ BBJ B(A0A8GT
+8D0A(B BBBF T u k_ BBJ B(A0A8G
+8D0A(B BBBC T 4 @, o_ BBB B(A0A8M
+8A0A(B BBBG 4 d ADD K
+AAJ ]DA T p5 _ BEB B(A0A8J
+8A0A(B BBBG
, $ b D\
H P
H Q
-G I , b D\
+G I , T b D\
H P
H Q
-G I , < b D\
+G I , (b D\
H P
H Q
-G I , l Hb D\
+G I , hb D\
H P
H Q
-G I , b D\
+G I , b D\
H P
H Q
-G I , ̽ b D\
+G I , b D\
H P
H Q
-G I , b D\
+G I , D ( b D\
H P
H Q
-G I , , Hb D\
+G I , t h b D\
H P
H Q
-G I , \ b D\
+G I , b D\
H P
H Q
-G I , b D\
+G I , b D\
H P
H Q
-G I , b D\
+G I , (b D\
H P
H Q
-G I , Hb D\
+G I , 4 hb D\
H P
H Q
-G I , b D\
+G I , d b D\
H P
H Q
-G I , L b D\
+G I , b D\
H P
H Q
-G I , | b D\
+G I , (b D\
H P
H Q
-G I , Hb D\
+G I , hb D\
H P
H Q
-G I , ܿ b D\
+G I , $ b D\
H P
H Q
-G I , b D\
+G I , T b D\
H P
H Q
-G I , < b D\
+G I , (b D\
H P
H Q
-G I , l Hb D\
+G I , hb D\
H P
H Q
-G I , b D\
+G I , b D\
H P
H Q
-G I , b D\
+G I , b D\
H P
H Q
-G I , b D\
+G I , D (b D\
H P
H Q
-G I , , Hb D\
+G I , t hb D\
H P
H Q
-G I , \ b D\
+G I , b D\
H P
H Q
-G I , b D\
+G I , b D\
H P
H Q
-G I , b D\
+G I , (b D\
H P
H Q
-G I , Hb D\
+G I , 4 hb D\
H P
H Q
-G I , b D\
+G I , d b D\
H P
H Q
-G I , L b D\
+G I , b D\
H P
H Q
-G I | m A~
-A l 4 X AAJ v
-AAD fAA L BBB F(A0A8J@X
-8A0A(B BBBA < $ ` BAA M
+G I (m A~
+A l 4 x AAJ v
+AAD fAA L D X BBB F(A0A8J@X
+8A0A(B BBBA < l BAA M
ABD
-ABD T l ^ BDB B(A0I8G
-8A0A(B BBBH H A`
-G b AW A`
-G b ( A`
-G mT D e BDB B(I0A8G
-8A0A(B BBBA , ! ADDP
-AAE < AAJPh
+ABD T
+ rY BDB B(A0I8G
+8A0A(B BBBH h A`
+G b $ AW D A`
+G b d H A`
+G mT e X BDB B(I0A8G
+8A0A(B BBBA , ! X ADDP
+AAE < 4 X AAJPh
AAJ
-AAH < P v AAJPh
+AAH < t p X AAJPh
AAJ
-AAH L L 7 BAA N
+AAH L 7 X BAA N
AABH
- AABF D BED D(G$
-(D ABBE D H BED D(G$
-(D ABBE L , @ BDB B(A0A8L`\
-8A0A(B BBBB L | p BDB J(A0A8GL
-8A0A(B BBBA D BED D(G$
-(D ABBE D x ^ BED D(G$
-(D ABBE T \ ) BDB B(A0I8D(
-8A0A(B BBBF D BED D(G$
-(D ABBE < BDA A(LP`
-(A ABBF T < 0 BEB B(A0A8Jn
-8A0A(B BBBA T ) ) BDB B(A0A8O
-8A0A(B BBBA < . AEL@Z
+ AABF D X BED D(G$
+(D ABBE D L h X BED D(G$
+(D ABBE L ` X BDB B(A0A8L`\
+8A0A(B BBBB L # X BDB J(A0A8GL
+8A0A(B BBBA D 4 0 X BED D(G$
+(D ABBE D | rX BED D(G$
+(D ABBE T * =X BDB B(A0I8D(
+8A0A(B BBBF D ! X BED D(G$
+(D ABBE < d! / W BDA A(LP`
+(A ABBF T ! P1 W BEB B(A0A8Jn
+8A0A(B BBBA T ! = =X BDB B(A0A8O
+8A0A(B BBBA < T" A "X AEL@Z
AAB
-AAE D , BED D(G$
-(D ABBE D t ( BED D(G$
-(D ABBE L 0 BDB B(A0A8L
-8A0A(B BBBJ L P4 j BBB B(A0C8JA
-8A0A(B BBBG T \ : ^ BFB B(A0D8Gd
-8A0A(B BBBA A T A W BDB B(A0I8G
-8A0A(B BBBF L $ hM
- s BDB J(A0A8G
-8A0A(B BBBA l 1 Dl L W X BBB B(A0A8Je
-8D0A(B BBBJ . K^ < @[ p BAI G
- DABA L < \ I BBB I(A0G
-0D(A BBBA L @_ $ BBB I(A0G
-0D(A BBBA L a BBB I(A0G
-0D(A BBBA T , c BBB B(I0A8G
+AAE D "
X BED D(G$
+(D ABBE D " H W BED D(G$
+(D ABBE L $# C W BDB B(A0A8L
+8A0A(B BBBJ L t# pG ~W BBB B(A0C8JA
+8A0A(B BBBG T # @M rW BFB B(A0D8Gd
+8A0A(B BBBA $ T T 4$ T kW BDB B(A0I8G
+8A0A(B BBBF L $ `
+ X BDB J(A0A8G
+8A0A(B BBBA L $ k X BBB B(A0A8Je
+8D0A(B BBBJ & Z. K^ < D% n Y BAI G
+ DABA L % 0p }Y BBB I(A0G
+0D(A BBBA L % r XY BBB I(A0G
+0D(A BBBA L $& t 3Y BBB I(A0G
+0D(A BBBA T t& w Y BBB B(I0A8G
-8D0A(B BBBA L f BBB I(A0G
-0D(A BBBA L hW BBB I(A0G
-0D(A BBBA L $ l} c BBB I(A0G
-0D(A BBBA T t 8n5 > BBB B(I0A8Gn
-8D0A(B BBBA L q BBB I(A0G
-0D(A BBBA T s( BBB B(I0A8Ga
-8D0A(B BBBA T t xv BBB B(I0A8G-
-8D0A(B BBBA T y BBB B(I0A8G-
-8D0A(B BBBA T $ { e BBB B(I0A8G
-8D0A(B BBBA L | ~ 8 BBB I(A0G
-0D(A BBBA L BBB I(A0G
-0D(A BBBA T BBB B(I0A8G
-8D0A(B BBBA L t BBB I(A0G
-0D(A BBBA T BBB B(I0A8G/
-8D0A(B BBBA T ЊE { BBJ B(A0A8G
-8D0A(B BBBA T t ȏ ` BBB B(D0A8G
-8D0A(B BBBA L P . BBB I(A0G
-0D(A BBBA T V BBJ B(A0A8G
-8D0A(B BBBA < t BAI G^
- DABA < x BAI G
- DABA L X BBB I(A0G)
-0D(A BBBA T D BBB B(I0A8G
-8D0A(B BBBA T a BBB B(I0A8G?
-8D0A(B BBBA T Xa 4 BBB B(I0A8G
-8D0A(B BBBA L L p BBB I(A0G
-0D(A BBBA T C BBB B(I0A8G^
-8D0A(B BBBA L BBB B(A0A8J
-8A0A(B BBBA < ( L \ L E BDB B(A0C8R_
-8A0A(B BBBA T A BFB B(A0D8G
-8A0A(B BBBH T = BFB B(A0D8G
-8A0A(B BBBH l , b D\
+8D0A(B BBBA L & y X BBB I(A0G
+0D(A BBBA L ' 8|W X BBB I(A0G
+0D(A BBBA L l' H} X BBB I(A0G
+0D(A BBBA T ' x5 rX BBB B(I0A8Gn
+8D0A(B BBBA L ( ` EX BBB I(A0G
+0D(A BBBA T d( ( X BBB B(I0A8Ga
+8D0A(B BBBA T ( W BBB B(I0A8G-
+8D0A(B BBBA T ) ` W BBB B(I0A8G-
+8D0A(B BBBA T l) W BBB B(I0A8G
+8D0A(B BBBA L ) Б lW BBB I(A0G
+0D(A BBBA L * GW BBB I(A0G
+0D(A BBBA T d* 0 "W BBB B(I0A8G
+8D0A(B BBBA L * ؘ V BBB I(A0G
+0D(A BBBA T + H V BBB B(I0A8G/
+8D0A(B BBBA T d+ E V BBJ B(A0A8G
+8D0A(B BBBA T + V BBB B(D0A8G
+8D0A(B BBBA L , bV BBB I(A0G
+0D(A BBBA T d, V =V BBJ B(A0A8G
+8D0A(B BBBA < , ( 4V BAI G^
+ DABA < ,
V BAI G
+ DABA L <- U BBB I(A0G)
+0D(A BBBA T - 8 U BBB B(I0A8G
+8D0A(B BBBA T - U BBB B(I0A8G?
+8D0A(B BBBA T <. a hU BBB B(I0A8G
+8D0A(B BBBA L . ;U BBB I(A0G
+0D(A BBBA T . C U BBB B(I0A8G^
+8D0A(B BBBA L </ T BBB B(A0A8J
+8A0A(B BBBA d0 h L / `L yU BDB B(A0C8R_
+8A0A(B BBBA T / ` uU BFB B(A0D8G
+8A0A(B BBBH $1 T d0 qU BFB B(A0D8G
+8A0A(B BBBH 1 8 1 1 , 1 b D\
H P
H Q
-G I , 0b D\
+G I , 2 0b D\
H P
H Q
-G I , pb D\
+G I , <2 pb D\
H P
H Q
-G I , D b D\
+G I , l2 b D\
H P
H Q
-G I , t b D\
+G I , 2 b D\
H P
H Q
-G I , 0b D\
+G I , 2 0b D\
H P
H Q
-G I , pb D\
+G I , 2 pb D\
H P
H Q
-G I , b D\
+G I , ,3 b D\
H P
H Q
-G I , 4 b D\
+G I , \3 b D\
H P
H Q
-G I , d 0b D\
+G I , 3 0b D\
H P
H Q
-G I , pb D\
+G I , 3 pb D\
H P
H Q
-G I , b D\
+G I , 3 b D\
H P
H Q
-G I , b D\
+G I , 4 b D\
H P
H Q
-G I , $ 0b D\
+G I , L4 0b D\
H P
H Q
-G I , T pb D\
+G I , |4 pb D\
H P
H Q
-G I , b D\
+G I , 4 b D\
H P
H Q
-G I m A~
-A l L @ BBB F(A0A8J@X
-8A0A(B BBBA $ < A`
-G bT d ( BDB B(A0A8O
-8A0A(B BBBG 4 BAA N
- AABD T 8 & BEB B(A0A8Jn
-8A0A(B BBBA L L = k BDB B(I0A8GL
-8A0A(B BBBA T BBB J(A0A8G
-8A0A(B BBBI L x
BDJ A(A0Ge
-0A(A BBBJ T D 8 BDB B(A0I8D(
-8A0A(B BBBF T e BDB B(A0A8Oo
-8A0A(B BBBD L BBB B(A0C8JA
-8A0A(B BBBG T D $ BBB B(A0A8J=
-8D0A(B BBBB L @* BDB J(A0A8D
-8A0A(B BBBA . K^ 4 /R ADG-
-DAA < < 0 w BAI G
- DABA 4 | 20 P ADG
-DAA L x3 + BBB I(A0G
-0D(A BBBA 4 5 ADG`
-DAA < < 7 BHD Gt
- DABA 4 | 8K ADG&
-DAA < 93 BAI G
- DABA < ; m BHD Gz
- DABA T 4 8= E BBB B(I0A8G
-8D0A(B BBBA T ? BBB B(I0A8G'
-8D0A(B BBBA T B BBB B(I0A8G'
-8D0A(B BBBA L < PE BBB I(A0G
-0D(A BBBA L GW BBB I(A0G
-0D(A BBBA T J( BBB B(I0A8Ga
-8D0A(B BBBA L 4 M Y BBB I(A0G
-0D(A BBBA T O 4 BBB B(I0A8G-
-8D0A(B BBBA L R} BBB I(A0G
-0D(A BBBA L , T BBB I(A0G
-0D(A BBBA T | VV BBJ B(A0A8G
-8D0A(B BBBA L [ BIE A(A0G
-0D(A BBBA L $ x^ BBB I(A0G
-0D(A BBBA < t `R a BAI G
- DABA L cB @ BBB B(A0A8J
-8A0A(B BBBA z T z3 BFB B(A0D8G0
-8A0A(B BBBF l D { BBE E(A0A8D@\8A0A(B BBBT 5 BBE E(A0A8G$
-8A0A(B BBBK L , BEB E(A0A8F
-8A0A(B BBBB L | ^ BEB E(A0A8F
-8A0A(B BBBB T 0% ( BBE E(A0A8G"
-8A0A(B BBBE T $ e ! BEE B(A0A8G
-8A0A(B BBBF T | e i BEE B(A0A8G
-8A0A(B BBBF T 8e a BEE B(A0A8G
-8A0A(B BBBF L , Y BBB E(A0A8JX
-8A0A(B BBBG \ | @ BDB A(A0Js
+G I 4 m A~
+A l L $4 @ R BBB F(A0A8J@X
+8A0A(B BBBA L5 d5 A`
+G bT 4 ( R BDB B(A0A8O
+8A0A(B BBBG 4 5 R BAA N
+ AABD T <5 8 ZS BEB B(A0A8Jn
+8A0A(B BBBA L 5 = S BDB B(I0A8GL
+8A0A(B BBBA T 5 S BBB J(A0A8G
+8A0A(B BBBI L <6 x
T BDJ A(A0Ge
+0A(A BBBJ T 6 8% T BDB B(A0I8D(
+8A0A(B BBBF T 6 )e S BDB B(A0A8Oo
+8A0A(B BBBD L <7 2 T BBB B(A0C8JA
+8A0A(B BBBG T 7 7 S BBB B(A0A8J=
+8D0A(B BBBB L 7 @= T BDB J(A0A8D
+8A0A(B BBBA 9 @G. K^ 4 L8 BR U ADG-
+DAA < 8 C U BAI G
+ DABA 4 8 E0 U ADG
+DAA L 8 xF _U BBB I(A0G
+0D(A BBBA 4 L9 H :U ADG`
+DAA < 9 J U BHD Gt
+ DABA 4 9 KK T ADG&
+DAA < 9 L3 T BAI G
+ DABA < <: N T BHD Gz
+ DABA T |: 8P yT BBB B(I0A8G
+8D0A(B BBBA T : R RT BBB B(I0A8G'
+8D0A(B BBBA T ,; U +T BBB B(I0A8G'
+8D0A(B BBBA L ; PX T BBB I(A0G
+0D(A BBBA L ; ZW S BBB I(A0G
+0D(A BBBA T $< ]( S BBB B(I0A8Ga
+8D0A(B BBBA L |< ` S BBB I(A0G
+0D(A BBBA T < b hS BBB B(I0A8G-
+8D0A(B BBBA L $= e} ;S BBB I(A0G
+0D(A BBBA L t= g S BBB I(A0G
+0D(A BBBA T = iV R BBJ B(A0A8G
+8D0A(B BBBA L > n R BIE A(A0G
+0D(A BBBA L l> xq R BBB I(A0G
+0D(A BBBA < > sR R BAI G
+ DABA L > vB tR BBB B(A0A8J
+8A0A(B BBBA $@ T d? 3 S BFB B(A0D8G0
+8A0A(B BBBF @ D @ { BBE E(A0A8D@\8A0A(B BBBT @ 5 R BBE E(A0A8G$
+8A0A(B BBBK L t@ R BEB E(A0A8F
+8A0A(B BBBB L @ R BEB E(A0A8F
+8A0A(B BBBB T A 0% \R BBE E(A0A8G"
+8A0A(B BBBE T lA e UR BEE B(A0A8G
+8A0A(B BBBF T A e R BEE B(A0A8G
+8A0A(B BBBF T B 8e R BEE B(A0A8G
+8A0A(B BBBF L tB R BBB E(A0A8JX
+8A0A(B BBBG \ B @ R BDB A(A0Js
0A(A BBBA
-0A(A BBBE . K^ L BBB A(A0
+0A(A BBBE C <. K^ L D BBB A(A0
(A BBBG Y
-(A EBBE | < BBB B(D0A8DP
+(A EBBE | dD BBB B(D0A8DP
8A0A(B BBBB U
8G0A(B BBBK
-8A0A(B BBBI L BBB B(A0A8GpN
-8D0A(B BBBD L H BBB B(A0A8GpN
-8D0A(B BBBD | \ h BBB B(D0A8DP
+8A0A(B BBBI L D R BBB B(A0A8GpN
+8D0A(B BBBD L \D H Q BBB B(A0A8GpN
+8D0A(B BBBD | E h BBB B(D0A8DP
8A0A(B BBBB U
8G0A(B BBBK
-8A0A(B BBBI L X BBB B(A0A8GpN
-8D0A(B BBBD | , BBB B(D0A8DP
+8A0A(B BBBI L ,E X 5Q BBB B(A0A8GpN
+8D0A(B BBBD | TF BBB B(D0A8DP
8A0A(B BBBB U
8G0A(B BBBK
-8A0A(B BBBI L h L BBB B(A0A8GpW
-8D0A(B BBBK . K^ , } ADG0A
-DAF , L @m ADG r
-DAE , | m ADG q
-DAF , m ADG s
-DAD , ADG
-DAH , ` ` ADG0M
-DAB 4 < A BDA
-ABF L t BBB B(D0D8DP
-8D0A(B BBBG L BBB E(A0H8DP
-8D0A(B BBBA L BBB B(D0D8DP
-8D0A(B BBBG L d g BBB B(D0D8DP
-8D0A(B BBBG L ( + BBB B(D0D8DP
-8D0A(B BBBG L BBB B(D0D8DP(
-8D0A(B BBBG D T BEB A(A0Q
-(A BBBA , 0b D\
+8A0A(B BBBI L E h P BBB B(A0A8GpW
+8D0A(B BBBK $G 9. K^ , dF } 4P ADG0A
+DAF , F @m P ADG r
+DAE , F m O ADG q
+DAF , F m O ADG s
+DAD , $G O ADG
+DAH , TG ` O ADG0M
+DAB 4 G uO BDA
+ABF L G PO BBB B(D0D8DP
+8D0A(B BBBG L H O BBB E(A0H8DP
+8D0A(B BBBA L \H N BBB B(D0D8DP
+8D0A(B BBBG L H N BBB B(D0D8DP
+8D0A(B BBBG L H ( _N BBB B(D0D8DP
+8D0A(B BBBG L LI #N BBB B(D0D8DP(
+8D0A(B BBBG D I M BEB A(A0Q
+(A BBBA , J 0b D\
H P
H Q
-G I , pb D\
+G I , J pb D\
H P
H Q
-G I , b D\
+G I , K b D\
H P
H Q
-G I , $ b D\
+G I , LK b D\
H P
H Q
-G I , T 0b D\
+G I , |K 0b D\
H P
H Q
-G I , pb D\
+G I , K pb D\
H P
H Q
-G I , b D\
+G I , K b D\
H P
H Q
-G I , b D\
+G I , L b D\
H P
H Q
-G I D 0 AAJ X
+G I D <L 0 AAJ X
AAJ O
AAG X
-AAF 4 \ AAJ o
+AAF 4 L AAJ o
AAC O
-AAG D 0g
BAA G@
+AAG D K 0g K BAA G@
AABD b
- AABB T X K
BBB L(A0A8D
-8A0A(B BBBB T < % '
BBB L(A0A8D
-8A0A(B BBBB T j
BBB L(A0A8D
-8A0A(B BBBI T 2e 7 BEE B(A0A8G
-8A0A(B BBBF T D G BBB B(A0D8Gg
-8A0A(B BBBK . K^ 4 C ADG
-DAA 4 7 ADG
-DAA D < & BBA I(G
-(D ABBA < s BAI G
- DABA < ( R BAI G
- DABA 4 + ADGw
-DAA < < `d BAI G
- DABA D | k BBI A(G
-(D ABBA < BHD G
- DABA < BHD G,
- DABA 4 D t l ADGO
-DAA 4 | @K G ADG&
-DAA D X? " BBA I(G1
-(D ABBA L P ? BIB D(A0Gi
-0D(A BBBA < L @s BAI G
- DABA D BIA D(G6
-(D ABBA < H BAI Gx
- DABA T Y BIB B(A0D8G
-8D0A(B BBBA T l C BIB B(D0A8G7
-8A0A(B BBBD , D P3 AAJ X
+ AABB T ,L X K BBB L(A0A8D
+8A0A(B BBBB T L % [K BBB L(A0A8D
+8A0A(B BBBB T L j 7K BBB L(A0A8D
+8A0A(B BBBI T 4M Ee kL BEE B(A0A8G
+8A0A(B BBBF T M G M BBB B(A0D8Gg
+8A0A(B BBBK N N 2. K^ 4 N C P ADG
+DAA 4 LN 7 O ADG
+DAA D N & O BBA I(G
+(D ABBA < N O BAI G
+ DABA < O ( O BAI G
+ DABA 4 LO
_O ADGw
+DAA < O `d :O BAI G
+ DABA D O k O BBI A(G
+(D ABBA < P N BHD G
+ DABA < LP N BHD G,
+ DABA 4 P t N ADGO
+DAA 4 P @K {N ADG&
+DAA D P X? VN BBA I(G1
+(D ABBA L DQ P ? 2N BIB D(A0Gi
+0D(A BBBA < Q @"s N BAI G
+ DABA D Q & M BIA D(G6
+(D ABBA < R H* M BAI Gx
+ DABA T \R + M BIB B(A0D8G
+8D0A(B BBBA T R 1 wM BIB B(D0A8G7
+8A0A(B BBBD S ? D S PF AAJ X
AAJ O
AAG X
-AAF , 3b D\
+AAF , DT Fb D\
H P
H Q
-G I L . K^ T l 3 # BBB B(A0A8G
-8A0A(B BBBA 8w @w! Xw Pw L $ Xw BJB A(A0W
-(A BBDH _(A BBBD l w BBB A(D0D@
-0A(A BBBA $ `{i ! aFA Dp $ |c [FA Dp $ }~ vFA Dp L 4 A1 BBB A(D0JH
-0A(A BBBK T ~e BEE B(A0A8G
-8A0A(B BBBF T BBE E(A0A8G
-8A0A(B BBBI T 4 BBE E(A0A8G
-8A0A(B BBBE T BBE E(A0A8G
-8A0A(B BBBE < HE
AAD`
+G I tT ,. K^ T S F WM BBB B(A0A8G
+8A0A(B BBBA T 8 T @! U X ,U P L lT X L BJB A(A0W
+(A BBDH _(A BBBD U BBB A(D0D@
+0A(A BBBA $ U `i UL aFA Dp $ ,U c LL [FA Dp $ TU ~ CL vFA Dp L |U T1 ;L BBB A(D0JH
+0A(A BBBK T U e PL BEE B(A0A8G
+8A0A(B BBBF T $V HL BBE E(A0A8G
+8A0A(B BBBI T |V @L BBE E(A0A8G
+8A0A(B BBBE T V 8L BBE E(A0A8G
+8A0A(B BBBE < ,W HX 0L AAD`
AAK Q
-AAE T $ 8
BBE E(A0A8G
-8A0A(B BBBC D | `F
BBE A(A0G
-0A(A BBBD D 8I* BDA
+AAE T lW 8 L BBE E(A0A8G
+8A0A(B BBBC D W `Y L BBE A(A0G
+0A(A BBBD D X 8\* BDA
ABM A
-ABD EAG T 0% n
BBE E(A0A8G"
-8A0A(B BBBE D \ BBE A(A0D@l
-0A(A BBBA D BBB D(A0D@
-0A(A BBBA D BBE A(A0D@
-0A(A BBBA < 4 Ш BBG A(G@
-(A ABBG D t BBE A(A0D@l
-0A(A BBBA < ( BBD A(G0T
-(A ABBA d ث BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d l P 3 BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB L Ȭ BEB E(I0H8DP
-8D0A(B BBBD L $ BJE B(A0A8D@
-8A0A(B BBBA L t X BEB E(I0H8DP
-8D0A(B BBBD l W BBB E(A0D8D\
+ABD EAG T TX 0% K BBE E(A0A8G"
+8A0A(B BBBE D Y BBE A(A0D@l
+0A(A BBBA D Y BBB D(A0D@
+0A(A BBBA D Z BBE A(A0D@
+0A(A BBBA < \Z л BBG A(G@
+(A ABBG D Z BBE A(A0D@l
+0A(A BBBA < Z ( J BBD A(G0T
+(A ABBA d LZ ؾ J BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB d Z P gJ BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB L [ ȿ J BEB E(I0H8DP
+8D0A(B BBBD L l[ I BJE B(A0A8D@
+8A0A(B BBBA L [ X I BEB E(I0H8DP
+8D0A(B BBBD l \ I BBB E(A0D8D\
8A0A(B BBBF k
-8A0A(B BBBA L 4 K BEB E(I0H8DP
-8D0A(B BBBD L X # BJE B(A0A8D@
-8A0A(B BBBA L ( s BEB E(I0H8DP
-8D0A(B BBBD d $ K BBB J(A0D8G`
+8A0A(B BBBA L |\ I BEB E(I0H8DP
+8D0A(B BBBD L \ X WI BJE B(A0A8D@
+8A0A(B BBBA L ] ( I BEB E(I0H8DP
+8D0A(B BBBD d l] I BBB J(A0D8G`
8F0A(B BBBF `
-8F0A(B BBBA T @ BBB B(D0A8D
-8D0A(B BBBH , h
- BGD c
-ABA ۘ. K^ T , D
- BBB E(A0A8G
-8A0A(B BBBA T XT + BBE B(D0D8G
-8A0A(B BBBJ T ` w BBE G(D0A8D
-8A0A(B BBBA T 4 a3 3 BBB B(A0A8G`
-8A0A(B BBBE T d BBE B(D0A8G
-8D0A(B BBBA ٘. K^ 4 0
- BDD H
-ABK 4 4
- BDD H
-ABK T l
- BED A(G0Q
+8F0A(B BBBA T ] @ ;I BBB B(D0A8D
+8D0A(B BBBH , ,^ h I BGD c
+ABA 4_ ". K^ T t^ W H BBB E(A0A8G
+8A0A(B BBBA T ^ Xg _I BBE B(D0D8G
+8A0A(B BBBJ T $_ s I BBE G(D0A8D
+8A0A(B BBBA T |_ t3 gI BBB B(A0A8G`
+8A0A(B BBBE T _ w OI BBE B(D0A8G
+8D0A(B BBBA a h . K^ 4 D` 0 "I BDD H
+ABK 4 |` H BDD H
+ABK T ` H BED A(G0Q
(D ABBC k
-(D ABBA 4 H X
- BDD Q
-ABB 4 2
- BDD Q
-ABB L 4
- BBB B(D0D8GP-
-8D0A(B BBBG L BBB B(D0D8GP%
-8D0A(B BBBG D BBB D(D06
-(A BBBA < P ] BBD D(G0
-(D ABBA < \ 0 0 BBD D(G0
-(D ABBA < BED A(G0N
-(D ABBF D BEE A(D0DPt
-0D(A BBBA L $ 8 BLB B(A0A8D`g
-8A0A(B BBBA d t h BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d / BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB < . K^ T \ [ BHE B(A0A8GA
+(D ABBA 4 a H H BDD Q
+ABB 4 Da fH BDD Q
+ABB L |a @H BBB B(D0D8GP-
+8D0A(B BBBG L a H BBB B(D0D8GP%
+8D0A(B BBBG D b G BBB D(D06
+(A BBBA < db P G BBD D(G0
+(D ABBA < b 0 dG BBD D(G0
+(D ABBA < b 7G BED A(G0N
+(D ABBF D $c G BEE A(D0DPt
+0D(A BBBA L lc 8 F BLB B(A0A8D`g
+8A0A(B BBBA d c h F BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB d $d cF BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB de 8 . K^ T d F BHE B(A0A8GA
-8A0A(B BBBK ` ` ` $ < T l x p h ` X P H
@ ,
8 D
0 \
( t
^
^ ^ ^ 4 ^ L ^ d ^ | x^ p^ h^ `^ X^ P^ H^ $ @^ < 8^ T 0^ l (^ ^ ^ " AN ND 0" AN ND @" AN ND P" AN ND 4 `" AN ND T p" AN ND t " AN ND " AN ND " AN ND " AN ND " AN ND " AN ND 4 " AN ND T " AN ND t " AN ND " AN ND " AN ND 0" AN ND @" AN ND P" AN ND 4 [ L [ d [ | x[ p[ h[ `[ X[ P[ H[ $ @[ < 8[ T 0[ l ([ [ [ [ [ [ Z < P BBD A(D0|(A ABB 4 T S BBA D(A0B(A ABB4 ,S BBA D(A0B(A ABB< g BBA D(D0Y
-(A ABBD 4 S BBA D(A0B(A ABB4 < $S BBA D(A0B(A ABB< t g BBA D(D0Y
-(A ABBD 4 S BBA D(A0B(A ABB< g BBA D(D0Y
-(A ABBD < , g BBA D(D0Y
-(A ABBD 4 l S BBA D(A0B(A ABB4 S BBA D(A0B(A ABB, BDD h
-AEH , (u BDA g
-AEL , < xu BDA g
-AEL , l ADG l
-DAK , 8t BDA g
-AEL , W ADG _
-DAH , t BDA g
-AEL , , t BDA g
-AEL , \ Xu BDA g
-AEL , ADG J
-DAE , u BDA g
-AEL < BBB A(D0
-(D BBBG < , e BBB D(D0
-(A EBBK < l BBD D(D0
-(D ABBI < x BEA D(D0R
-(D ABBE < BEA D(D0Z
-(D ABBM < , H BBB A(D0
-(D BBBH < l
BBB D(D0
-(D BBBK < t BEA D(F0H
-(D ABBE L TY BEE E(D0A8DP
-8A0A(B BBBA < (UJ n N L T ` BBB B(D0A8DPY
-8A0A(B BBBD $ U< H\
+8A0A(B BBBK e s e s f s f 4f Lf df |f f x f p f h f ` f X g P $g H <g @ Tg 8 lg 0 g ( g g g g g q h q ,h q Dh q \h q th q h q h xq h pq h hq h `q i Xq i Pq 4i Hq Li @q di 8q |i 0q i (q i q i q i " AN ND i 0" AN ND j @" AN ND <j P" AN ND \j `" AN ND |j p" AN ND j " AN ND j " AN ND j " AN ND j " AN ND k " AN ND <k " AN ND \k " AN ND |k " AN ND k " AN ND k " AN ND k " AN ND k 0" AN ND l @" AN ND <l P" AN ND \l n tl n l n l xn l pn l hn l `n m Xn m Pn 4m Hn Lm @n dm 8n |m 0n m (n m n m n m n m n n n $n m < dm P > BBD A(D0|(A ABB 4 |n 4ɒS BBA D(A0B(A ABB4 n PɒS BBA D(A0B(A ABB< n g BBA D(D0Y
+(A ABBD 4 ,o ,ɒS BBA D(A0B(A ABB4 do HɒS BBA D(A0B(A ABB< o g BBA D(D0Y
+(A ABBD 4 o $ɒS BBA D(A0B(A ABB< p g BBA D(D0Y
+(A ABBD < Tp g BBA D(D0Y
+(A ABBD 4 p ȒS BBA D(A0B(A ABB4 p ȒS BBA D(A0B(A ABB, q BDD h
+AEH , 4q (u BDA g
+AEL , dq xu BDA g
+AEL , q ADG l
+DAK , q 8t BDA g
+AEL , q W ADG _
+DAH , $r t BDA g
+AEL , Tr t BDA g
+AEL , r Xu BDA g
+AEL , r ADG J
+DAE , r u BDA g
+AEL < s BBB A(D0
+(D BBBG < Ts e BBB D(D0
+(A EBBK < s BBD D(D0
+(D ABBI < s x BEA D(D0R
+(D ABBE < t BEA D(D0Z
+(D ABBM < Tt H BBB A(D0
+(D BBBH < t
BBB D(D0
+(D BBBK < t t BEA D(F0H
+(D ABBE L u gY BEE E(D0A8DP
+8A0A(B BBBA du (hJ n N L |u ` BBB B(D0A8DPY
+8A0A(B BBBD $ u h< H\
D O
-A (UI D c
-I X XU8 HZ
-F G xUM < $ [ BBB D(D0\
-(D BBBL l pU t $ | D
+A u (hI D c
+I X v Xh8 HZ
+F G 4v xhM < Lv [ BBB D(D0\
+(D BBBL u ph 6 $ v D
A K
-I @ 8 0 ( 2 4 @[ 4 L BAA G0
- AABA ` X$ VM p x 4 BAA G0q
- AABA , $ 8 AAG0u
-AAA L AL l $ VM 4 BAD D0
- AABA 0 ($ VM 4 @ L H9 4 d p BAA G0
- AABA $ VM 4 BDA D0X
- AABA , < AAG0u
-AAA d AL | Ep
-A h$ VM x $ ! pg AD n
-AA < 4 BBA A(G@O
-(A ABBF l! AL ! < ! BEA A(D0
-(D ABBA ! $ VM ! " 4 ," BDA D0^
- AABA d" |" $ VM " " " , AAG0u
-AAA # 8 AL ,# 8 D# 0h E\
-A d# $ VM |# # 3 4 # ` BAA G0
- AABA , ! AAG0
-AAF $ AL 4$ < L$ BBA D(D0
-(D ABBJ $ X$ VM $ p $ x $ 2 Ha
-G A , " AAG0K
-AAJ $% @ AL D% @ , \% 8 BAA I
-DBE % $ VM % % % $ % Ex
+I v @
+ v 8
+ v 0
+ w (
+ ,w
+ Dw
+2 \w @
+[ 4 tw
+ BAA G0
+ AABA w ` w X $ VM w p w x 4 x BAA G0q
+ AABA , lw 8
5 AAG0u
+AAA tx
AL x
x
$ VM x
x
4 x
BAD D0
+ AABA ,y 0 Dy ($ VM \y @ ty H9 4 y p BAA G0
+ AABA y y $ VM y z 4 $z BDA D0X
+ AABA , y 2 AAG0u
+AAA z AL z z | Ep
+A z h$ VM z { x $ ,{ pg AD n
+AA < |z 1 BBA A(G@O
+(A ABBF { AL { < { BEA A(D0
+(D ABBA | $ VM $| <| 4 T| BDA D0^
+ AABA | | $ VM | | | , ,| P0 AAG0u
+AAA 4} 8 AL T} 8 l} 0h E\
+A } $ VM } } 3 4 } ` BAA G0
+ AABA , 4} L/ AAG0
+AAF <~ AL \~ < t~ BBA D(D0
+(D ABBJ ~ X$ VM ~ p ~ x ~ 2 Ha
+G A , D~ @. AAG0K
+AAJ L @ AL l @ , 8 BAA I
+DBE $ VM $ Ex
C c
-A & `
-$ VM 4 ,& x
- AAG0Q
+A < ` $ VM 4 T x AAG0Q
AAD v
-AAA , d&
-{ ADD0}
-AAA , $ p AAG0u
-AAA & AL & & ' < 4% x BBA A(G@O
-(A ABBF l' H AL ' H < ' @ BEA A(D0h
-(D ABBA ' $ VM ' ( ,( " , L& AAG0u
-AAA t( 0
AL ( 0
, ( (
AAG Q
-DAA (
$ VM (
)
$)
" , D'
AAG0u
-AAA l) ( AL ) ( ) $ VM ) 8 ) @ ) 8" , ( P AAG0u
-AAA 4* AL T* l* $ VM * * * " , ( L AAG0u
-AAA * X AL + X , 4+ P AAG Q
-DAA d+ $ VM |+ + 4 + BDA D0X
- AABA , ) P 8 AAG0u
-AAA , AL 4, L, | Ep
-A l, $ VM , 0 , ( , 0 , , ( AAG
-DAE , , AAG Q
-DAA , ,- H AAG
-DAC < \- BBA A(G0e
-(D ABBA < - (H BBA A(G0q
-(D ABBI < - 8 BEA A(D0
-(D ABBA , . _ AAG J
-DAA L. $ VM d. 8 Av $ . ( ADG YAA $ . (( ADG YAA $ . 0( ADG YAA $ . 8( ADG YAA $ $/ @( ADG YAA , L/ H{ ADD0}
-AAA , - AAG0u
-AAA / AL / / $ / g AD n
-AA , ,. P AAG0u
-AAA T0 AL t0 0 r Ef
-A 0 $ VM 0 0 0 ( ( 4 0 @ BDA D0[
- AABA ,1 < D1 BBD A(D0
-(D ABBA < 1 ( BBA D(D0
-(D ABBD 1 $ VM 4 1 @ AAG0R
+AAA , { ADD0}
+AAA , , AAG0u
+AAA AL $ < < | x , BBA A(G@O
+(A ABBF H AL H < ́ @ BEA A(D0h
+(D ABBA $ VM $ < T " , * AAG0u
+AAA 0 AL 0 , Ԃ ( AAG Q
+DAA $ VM 4 L " , * AAG0u
+AAA (! AL (! ̃ !$ VM 8! @! 8!" , T P! D) AAG0u
+AAA \ ! AL | ! !$ VM ! Ą ! ܄ !" , ! ( AAG0u
+AAA $ X" AL D X" , \ P" AAG Q
+DAA "$ VM " " 4 ԅ " BDA D0X
+ AABA , 4 P# l' AAG0u
+AAA < # AL \ # t #| Ep
+A $$ VM 0$ Ć ($ ܆ 0$ , ($ AAG
+DAE , $ $ AAG Q
+DAA , T H% AAG
+DAC < % BBA A(G0e
+(D ABBA < ć ((H BBA A(G0q
+(D ABBI < 8+ BEA A(D0
+(D ABBA , D ,_ AAG J
+DAA t ,$ VM -8 Av $ -( ADG YAA $ Ԉ (-( ADG YAA $ 0-( ADG YAA $ $ 8-( ADG YAA $ L @-( ADG YAA , t H-{ ADD0}
+AAA , ̈ - # AAG0u
+AAA ԉ . AL . . $ $ .g AD n
+AA , t P. 4# AAG0u
+AAA | . AL . .r Ef
+A Ԋ /$ VM 0/ (/( 4 @/ BDA D0[
+ AABA T / < l / BBD A(D0
+(D ABBA < 1( BBA D(D0
+(D ABBD 3$ VM 4 S AAG0R
AAA q
-AAA , 0 ` t AAG }
-AAA D2 ; Au < l0 AAG y
+AAA , d `3t H! AAG }
+AAA l 3; Au < 3 ! AAG y
AAD Y
-DAA 2 ? 2 ? , 0 t p AAG }
-AAA 3 @!; Au < ,1 `! , AAG y
+DAA ̌ R R , $ 3t AAG }
+AAA , @4; Au < t `4 ` AAG y
AAD Y
-DAA d3 @? |3 8? , 1 !t AAG }
-AAA 3 !; Au < 1 ! AAG y
+DAA @R 8R , 4t AAG }
+AAA 4; Au < 4 4 AAG y
AAD Y
-DAA $4 > <4 > , \2 "t ( AAG }
-AAA 4 `"; Au < 2 " AAG y
+DAA L Q d Q , 5t \ AAG }
+AAA `5; Au < 5 AAG y
AAD Y
-DAA 4 > 4 = , 3 "t AAG }
-AAA D5 "; Au < l3 # @ AAG y
+DAA Q $ P , d 5t AAG }
+AAA l 5; Au < 6 t AAG y
AAD Y
-DAA 5 `= 5 X= , 3 0#t AAG }
-AAA < 4 # AAG y
+DAA ̏ `P XP , $ 06t AAG }
+AAA < T 6 AAG y
AAD Y
-DAA D6 < \6 < , |4 #t \ AAG }
-AAA < 4 # 8 AAG y
+DAA l O O , ď 6t AAG }
+AAA < 6 l AAG y
AAD Y
-DAA 6 `< 6 X< , 5 $t AAG }
-AAA D7 `$; Au < l5 $ AAG y
+DAA `O $ XO , d 7t AAG }
+AAA l `7; Au < 7 AAG y
AAD Y
-DAA 7 ; 7 ; , 5 $t 4 AAG }
-AAA 8 $; Au < ,6 % AAG y
+DAA ̑ N N , $ 7t h AAG }
+AAA , 7; Au < t 8 $ AAG y
AAD Y
-DAA d8 ; |8 ; , 6 0%t AAG }
-AAA $ 8 % ARD hAA< 6 % D AAG y
+DAA N N , 08t AAG }
+AAA $ 8 ARD hAA< < 8 x AAG y
AAD Y
-DAA ,9 x: D9 p: , d7 &t AAG }
-AAA 9 X&; Au < 7 x& AAG y
+DAA T xM l pM , 9t AAG }
+AAA X9; Au < x9 AAG y
AAD Y
-DAA 9 9 : 9 < $8 & @ BBA A(G0
-(A ABBK \: (( AL , 8 (( AAG
-AAA < 8 ( AAG y
+DAA L , L < l 9 t BBA A(G0
+(A ABBK (; AL , ̓ (; AAG
+AAA < ; AAG y
AAD Y
-DAA : 8 ; 8 < $9 ) c BBA A(G0b
-(A ABBK \; ) AL , 9 ) AAG [
-AAA ; *; Au < 9 (* AAG y
+DAA K , K < l < BBA A(G0b
+(A ABBK < AL , ̔ < ; AAG [
+AAA ԕ =; Au < (= AAG y
AAD Y
-DAA < 7 $< 7 , D: H* d AAG
-AAA l< +; Au < : 8+ # AAG y
+DAA 4 J L J , H= AAG
+AAA >; Au < ܕ 8> W AAG y
AAD Y
-DAA < X7 < P7 < ; X+ BBA A(G0o
-(A ABBF <= (, AL , d; (, g AAG
-AAA = -; Au < ; 8- & AAG y
+DAA XJ PJ < L X> BBA A(G0o
+(A ABBF d (? AL , (? AAG
+AAA @; Au < 8@ Z AAG y
AAD Y
-DAA = X6 > P6 < $< X- BBA A(G0o
-(A ABBF \> - AL , < - j AAG 0
-AAA < < / I AAG y
+DAA XI , PI < l X@ BBA A(G0o
+(A ABBF @ AL , ̗ @ AAG 0
+AAA < B } AAG y
AAD Y
-DAA > x5 ? p5 < $= /G BBA A(G0
-(A ABBD \? 0 AL , = 0 AAG [
-AAA ? (1; Au < = H1 J AAG y
+DAA xH , pH < l BG BBA A(G0
+(A ABBD C AL , ̘ C AAG [
+AAA ԙ (D; Au < HD ~ AAG y
AAD Y
-DAA @ x4 $@ p4 <@ h4% T@ 4% , t> 81 AAG S
-AAA @ 1; Au < > 1 w AAG y
+DAA 4 xG L pG d hG% | G% , 8D AAG S
+AAA Ě D; Au < D AAG y
AAD Y
-DAA @ 4 A 4 , 4? 1p AAG
-AAA $ \A (3 ARD hAA< ? 3 AAG y
+DAA $ G < G , | Dp K AAG
+AAA $ (F ARD hAA< Ԛ F AAG y
AAD Y
-DAA A `3 A X3 A P3% B h3% $B 3% , D@ h3 & AAG
-AAA lB 4; Au < @ 5 AAG y
+DAA `F XF PF% 4 hF% L F% , hF Z AAG
+AAA G; Au < ܛ H AAG y
AAD Y
-DAA B 3 B 3 , B 2 ADD c
-ADG ,C x32 W V LA 30 = D]
-E , dC 4 AG s
+DAA F F , $ E ADD c
+ADG T xF2 W V F0 q D]
+E , G AG s
AD X
-AE C `7 $ A h3 AG0y
-AA < A 3a AAG0_
+AE `J $ hF AG0y
+AA < $ Fa AAG0_
AAF Q
-AAA L B 7 BBB B(D0A8K
-8C0A(B BBBI 4 dD 3j BAD a
-ADC rAB D B (8 BBB D(A0Kp
-0C(A BBBB D B 9P BBB D(A0Kp
-0C(A BBBG L 4C ; BBB B(D0A8K
-8C0A(B BBBI D C = BBB D(A0Kp
-0C(A BBBH T C >a j BBB B(D0A8K~
-8C0A(B BBBA T $D @ 0 BBB B(D0A8K>
-8C0A(B BBBF T |D B BBB B(D0A8K
-8C0A(B BBBE T D HD BBB B(D0A8K
-8C0A(B BBBE D ,E F BBB D(A0Kp
-0C(A BBBG L tE G T BBB B(D0A8N
-8C0A(B BBBK 4 G 0j BAD a
-ADC rAB L E K BBB B(D0A8K
-8C0A(B BBBH 4 DH 0j BAD a
-ADC rAB D F (MQ BBB D(A0Kp
-0C(A BBBB , H @N AG C
+AAA L d J BBB B(D0A8K
+8C0A(B BBBI 4 Fj BAD a
+ADC rAB D (K O BBB D(A0Kp
+0C(A BBBB D 4 LP $ BBB D(A0Kp
+0C(A BBBG L | N BBB B(D0A8K
+8C0A(B BBBI D ̞ P BBB D(A0Kp
+0C(A BBBH T Qa BBB B(D0A8K~
+8C0A(B BBBA T l S d BBB B(D0A8K>
+8C0A(B BBBF T ğ U ) BBB B(D0A8K
+8C0A(B BBBE T HW BBB B(D0A8K
+8C0A(B BBBE D t Y BBB D(A0Kp
+0C(A BBBG L Z BBB B(D0A8N
+8C0A(B BBBK 4 Cj BAD a
+ADC rAB L D ^ BBB B(D0A8K
+8C0A(B BBBH 4 l Cj BAD a
+ADC rAB D ̡ (`Q BBB D(A0Kp
+0C(A BBBB , @a AG C
AD X
-AE H N , I N AG s
+AE a , 4 a AG s
AD X
-AE <I O , TI O AG C
+AE d b , | b AG C
AD X
-AE I O , I O AG C
+AE b , ģ b AG C
AD X
-AE I P , I P AG
+AE c , c AG
AH X
-AE J P , ,J P AG S
+AE < c , T c AG S
AD X
-AE \J Q , tJ Q AG
+AE d , d AG
AH X
-AE J Q , J Q AG
+AE ̤ d , d AG
AH X
-AE J Q , K R AG C
+AE d , , e AG C
AD X
-AE 4K pR , LK xR AG O
+AE \ pe , t xe AG O
AH X
-AE |K R , K S AG O
+AE e , f AG O
AH X
-AE K S , K S AG K
+AE f , f AG K
AD X
-AE L T , $L T AG [
+AE 4 g , L g AG [
AD X
-AE TL T , lL T AG k
+AE | g , g AG k
AD X
-AE L HU , L PU= AG
+AE Ħ Hh , ܦ Ph= AG
AH X
-AE L `V , L hV AG 3
+AE `i , $ hi AG 3
AD X
-AE ,M W < LK W BBA D(G0d
-(A ABBA , M 0X} AG #
+AE T j < j
+ BBA D(G0d
+(A ABBA , 0k} AG #
AD X
-AE M Y , M Y~ ADG0h
-AAJ M Y , N Y= AG
+AE ܧ l , l~ ADG0h
+AAJ $ l , < l= AG
AD X
-AE DN [ 4 \N [ BDD D0U
- AABI N \ , N x\ AG S
+AE l n 4 n BDD D0U
+ AABI o , Ԩ xo AG S
AD X
-AE N ^ T N _ BDA G0e
+AE q T r BDA G0e
AABI D
AABH T
- FABE LO _ T dO _ BDA G0e
+ FABE t r T r BDA G0e
AABI D
AABH T
- FABE O (` < M ` R BBE D(G0Z
-(A ABBA D P `& ADG0p
+ FABE (s < $ s BBE D(G0Z
+(A ABBA D < s& ADG0p
DAO a
AAE Q
-DAJ \P a < |N a BFA D(G0p
-(A ABBA < N b BFA D(G0p
-(A ABBA N bL \ 4 Q b7 ADG N
-GAN DAA T LO b BFA D(G0
+DAJ t < ĩ t BFA D(G0p
+(A ABBA < u BFA D(G0p
+(A ABBA D uL 4 4 u7 ADG N
+GAN DAA T u D BFA D(G0
(A ABBI [
-(A ABBA $ O cU Ac
-D d 4 Q c7 ADG N
-GAN DAA 4 P c k AAG i
-AAD KAA4 4R @d7 ADG N
-GAN DAA 4 tP Hd AAG i
-AAD KAA4 R d7 ADG N
-GAN DAA $ P dU Ac
-D d 4 S d7 ADG N
-GAN DAA $ DQ dU ; Ac
-D d 4 lQ e AAG o
-AAF [AA$ Q eE A[
-D \ $ Q eM Ab
-E \ L Q ey BEA A(D0@
-(A ABBE V(A ABB $ DR fM c Ab
-E \ $ lR @fM C Ab
-E \ L R hf # BEA A(D0G
-(A ABBF z(A ABB T R f BFA H(D0
+(A ABBA $ vU Ac
+D d 4 v7 ADG N
+GAN DAA 4 L v AAG i
+AAD KAA4 \ @w7 ADG N
+GAN DAA 4 Hw 3 AAG i
+AAD KAA4 ̬ w7 ADG N
+GAN DAA $ , wU Ac
+D d 4 , w7 ADG N
+GAN DAA $ wU o Ac
+D d 4 x O AAG o
+AAF [AA$ xE A[
+D \ $ xM Ab
+E \ L < xy BEA A(D0@
+(A ABBE V(A ABB $ yM Ab
+E \ $ @yM w Ab
+E \ L ܭ hy W BEA A(D0G
+(A ABBF z(A ABB T , y BFA H(D0
(A ABBE p
-(A ABBJ 4 <S hz AAG p
-AAE sAA T tS HhL ` BFA H(D0
+(A ABBJ 4 {z AAG p
+AAE sAA T H{L BFA H(D0
(A ABBE ~
-(A ABBL L U @i BBA D(D0
+(A ABBL L @| BBA D(D0
(A ABBJ d
-(E ABBJ T T j BFA H(D0
+(E ABBJ T d } BFA H(D0
(A ABBA \
-(E ABBJ T tT l' v BFA H(D0
+(E ABBJ T ' BFA H(D0
(A ABBB z
-(A ABBH T T m & BBA D(D0
+(A ABBH T Z BBA D(D0
(A ABBG g
-(E ABBO L W H" BBB B(A0D8G`c
-8A0A(B BBBG L lW # BBA D(G0a
-(C ABBG (F ABB T U Xn 7 BBB B(D0A8Nz
-8D0A(B BBBF 4 X $j BAD a
-ADC rAB L TV BBB B(D0A8K
-8C0A(B BBBE 4 X $j BAD a
-ADC rAB T V @U a BBB B(D0A8N=
-8D0A(B BBBA T 4W Ht - BBB B(D0A8K
-8C0A(B BBBE 4 Y #j BAD a
-ADC rAB T W 8 BBB B(D0A8K
-8C0A(B BBBA 4 Z p#j BAD a
-ADC rAB T TX I BBB B(D0A8K
-8D0A(B BBBK L X BBB B(D0A8N
-8A0A(B BBBG d Z # BBB B(A0A8D`P
+(E ABBO L D H5 BBB B(A0D8G`c
+8A0A(B BBBG L 6 BBA D(G0a
+(C ABBG (F ABB T X k BBB B(D0A8Nz
+8D0A(B BBBF 4 < 7j BAD a
+ADC rAB L BBB B(D0A8K
+8C0A(B BBBE 4 IJ 7j BAD a
+ADC rAB T $ @U BBB B(D0A8N=
+8D0A(B BBBA T | Ht a BBB B(D0A8K
+8C0A(B BBBE 4 6j BAD a
+ADC rAB T 8 BBB B(D0A8K
+8C0A(B BBBA 4 < p6j BAD a
+ADC rAB T } BBB B(D0A8K
+8D0A(B BBBK L C BBB B(D0A8N
+8A0A(B BBBG d 6 BBB B(A0A8D`P
8A0A(B BBBH
-8A0A(B BBBA \[ h$ t[ `$ | [ BEB B(D0A8D`E
+8A0A(B BBBA h7 `7 | BEB B(D0A8D`E
8A0A(B BBBM D
8A0A(B BBBB
-8F0A(B BBBE \ H 4 $\ @7 ADG N
-GAN DAA L dZ H! u BBB E(D0A8D`
-8A0A(B BBBK L \ ( BBB B(A0D8G`
-8A0A(B BBBG \ X 4 ] P7 ADG N
-GAN DAA D L] XR BAD G0
+8F0A(B BBBE 4 H 4 L @7 ADG N
+GAN DAA L H! BBB E(D0A8D`
+8A0A(B BBBK L Զ ( BBB B(A0D8G`
+8A0A(B BBBG $ X 4 < P7 ADG N
+GAN DAA D t XR BAD G0
AABF D
- AABH ] p 4 ] h7 ADG N
-GAN DAA ] " ] ! T \ @) BEB E(A0A8Dy
-8A0A(B BBBI T t\ BIF E(D0d
+ AABH p 4 Է h7 ADG N
+GAN DAA 5 T L X) + BEB E(A0A8Dy
+8A0A(B BBBI T 0 2 BIF E(D0d
(A BBBK
-(A BBBA ^ @! d ^ Hf BBB B(A0A8G`^
+(A BBBA Ը H4 d `f BBB B(A0A8G`^
8A0A(B BBBG
-8A0A(B BBBA D_ P 4 \_ H7 ADG N
-GAN DAA 4 _ P BDD D0G
- AABG _ 4 _ 7 ADG N
-GAN DAA 4 ` BDD D0G
- AABG T` 4 l` x7 ADG N
-GAN DAA d ` ADG0
+8A0A(B BBBA T h 4 l `7 ADG N
+GAN DAA 4 h BDD D0G
+ AABG ܹ 4 7 ADG N
+GAN DAA 4 , BDD D0G
+ AABG d 4 | 7 ADG N
+GAN DAA d ADG0
DAH {
DAH {
DAH {
DAH {
DAH D
-AAJ 4 a BDD D0G
- AABG Da 4 \a x7 ADG N
-GAN DAA D a / BAD G0
+AAJ 4 BDD D0G
+ AABG T 4 l 7 ADG N
+GAN DAA D / BAD G0
AABL D
- AABH a h 4 a `7 ADG N
-GAN DAA ,b | Db P BBB B(A0D8G`
+ AABH 4 x7 ADG N
+GAN DAA | < BBB B(A0D8G`
8A0A(B BBBM D
8A0A(B BBBB
-8H0A(B BBBE b н 4 b Ƚ7 ADG N
-GAN DAA c L ,c BBB B(A0D8G`w
-8A0A(B BBBK |c ( 4 c 7 ADG N
-GAN DAA c h | c BBB B(A0D8Gp
+8H0A(B BBBE 4 Լ 7 ADG N
+GAN DAA 0 L $ BBB B(A0D8G`w
+8A0A(B BBBK t X 4 P7 ADG N
+GAN DAA Ľ x/ | ܽ @ BBB B(A0D8Gp
8A0A(B BBBH D
8A0A(B BBBB
-8E0A(B BBBE dd 4 |d 7 ADG N
-GAN DAA L d BBB B(D0A8Gp
-8A0A(B BBBH e P 4 e H7 ADG N
-GAN DAA Te L le 8
BBB B(A0D8G`X
-8A0A(B BBBJ e 4 e 7 ADG N
-GAN DAA L d 8 s BBB E(A0A8G`?
-8A0A(B BBBC L dd S
- x BBB E(A0A8G`
-8A0A(B BBBG L d { n BBB E(A0A8G`h
-8A0A(B BBBJ L e ( U BBB B(D0A8GpK
-8A0A(B BBBG L Te Y f BBB E(A0A8G`1
-8A0A(B BBBA 4 g ADG0j
+8E0A(B BBBE \ 4 t 7 ADG N
+GAN DAA L BBB B(D0A8Gp
+8A0A(B BBBH 4 x7 ADG N
+GAN DAA L . L d h
BBB B(A0D8G`X
+8A0A(B BBBJ ( 4 ̿ 7 ADG N
+GAN DAA L , (8 BBB E(A0A8G`?
+8A0A(B BBBC L | S
+ BBB E(A0A8G`
+8A0A(B BBBG L ̿ ({ BBB E(A0A8G`h
+8A0A(B BBBJ L X BBB B(D0A8GpK
+8A0A(B BBBG L l Y BBB E(A0A8G`1
+8A0A(B BBBA 4
ADG0j
AAH D
-AAJ g 4 g 7 ADG N
-GAN DAA $h 4 <h 7 ADG N
-GAN DAA |f Y z BBE D(G0
+AAJ @ 4 87 ADG N
+GAN DAA @ 4 4 87 ADG N
+GAN DAA @Y BBE D(G0
(A ABBA y
(A ABBA y
(A ABBA y
(A ABBA u
-(A ABBE T g BFA D(G0
+(A ABBE T e BFA D(G0
(A ABBC a
-(A ABBA L \g ó BBE D(G0}
+(A ABBA L t ' BBE D(G0}
(A ABBI a
-(A ABBA < g @ BBE D(G0C
-(A ABBC l g e BBB B(D0A8D`d
+(A ABBA < p BBE D(G0C
+(A ABBC l BBB B(D0A8D`d
8A0A(B BBBA "
-8A0A(B BBBA < \h ` BBE D(G0C
-(A ABBC < h BBE D(G0C
-(A ABBC j Pv. K^ j x D i p BEB D(A0GpY
-0A(A BBBJ $ Lk ([ ADG LAA4 tk ` BDA G0x
- AABF k k 6 $ k . ACD `DA < l L BBA A(D0t
-(D ABBA , Dl 0< BDD kAE 4 tl @ BEA A(D0(A ABB l l # Aa j Ұ AL m D , m AFG |
-AAD $ Lm h+ AAG \DA 4 tm p BDD D0I
- AABE m Q Ad
-A c m (. Ac
-A D < m 8o ADG x
+8A0A(B BBBA < t u BBE D(G0C
+(A ABBC < N BBE D(G0C
+(A ABBC . K^ x( D $ p( BEB D(A0GpY
+0A(A BBBJ $ D ()[ ADG LAA4 l `) BDA G0x
+ AABF ) )6 $ *. ACD `DA < *L BBA A(D0t
+(D ABBA , < 0*< BDD kAE 4 l @* BEA A(D0(A ABB * *# Aa * 6 AL *D , * AFG |
+AAD $ D h++ AAG \DA 4 l p+ BDD D0I
+ AABE +Q Ad
+A c (,. Ac
+A D < 8,o ADG x
AAA P
-GAE ADA 4 ,n hv BDD D0|
- AABA L dn BED D(D@Q
+GAE ADA 4 $ h,v BDD D0|
+ AABA L \ , BED D(D@Q
(A ABBA S
-(G ABEE 4 n B ADG b
-AAA JAA D n 8x BDD z
+(G ABEE 4 -B ADG b
+AAA JAA D 8-x BDD z
ABA Y
-ABE AGB 4 4o pO AAG n
-DDI DAA lo " o q. K^ o x o pJ n N o 4 o 8 Ds d p BBB B(A0A8DP
+ABE AGB 4 , p-O AAG n
+DDI DAA d -" | . K^ x p-J n N -4 -8 Ds d - BBB B(A0A8DP
8A0A(B BBBA h
-8A0A(B BBBA lp ~M p p p h p ` $ n X t QQ
+8A0A(B BBBA d ȑM | p1 h1 `1 $ X1t R QQ
F b
-S p q x~Z $q ~k 4 <q ADG `
+S 1 xZ k 4 4 ADG `
AAJ D
-AAJ tq P q H DT , o H l ATDP}
-AAA q q r x $r p <r h Tr ` lr X r ` % , r x 4 AAG a
-AAD < p E
- ADD`r
+AAJ l P1 H1 DT , H1l ~ ATDP}
+AAA 1 1 x1 p1 4 h1 L `1 d X1 | `1% , x14 AAG a
+AAD < 1E n ADD`r
AAC ~
-AAA , s } AG w
-DL ED L Dq h - BBB B(A0A8G
-8A0A(B BBBA s L AT , s (~ ADG
-AAA s ~ 4 s LL AAG d
-DAF NDA D ,t x~' BDD G0a
+AAA , AG w
+DL ED L \ h2- BBB B(A0A8G
+8A0A(B BBBA _ AT , ( ADG
+AAA 4 _L AAG d
+DAF NDA D $ x' BDD G0a
IABR t
- GABE , tt `~ ADG0R
-AAH L r L, BBB B(A0A8D
-8A0A(B BBBG t x@ u H( < $u x BDD G0
- AABH | DAB du l. K^ |u FR u C AA s 8 ɨ u @ u 5 v EN $ $v AAG QAC Lv AL lv & v M v & , t | է ARDPO
-AAA 4 v p BAA S
+ GABE , l `~ ADG0R
+AAH L _, BBB B(A0A8D
+8A0A(B BBBG @ H( < Ћ BDD G0
+ AABH | DAB \ в. K^ t FR C AA 8 - @ 5 В EN $ В AAG QAC D Ȓ AL d & | ВM & , | 9 ARDPO
+AAA 4 p BAA S
CBD |
-CBG < $u } BBA A(Gn
-(A ABBG < \w (l BEE A(D0S(A BBB , w XN AAG0f
-AAG , w xN AAG0h
-AAE $ w P AG ]
-AJ L $x BED C(G0T
+CBG < < ؓ BBA A(Gn
+(A ABBG < T (l BEE A(D0S(A BBB , XN AAG0f
+AAG , xN AAG0h
+AAE $ P AG ]
+AJ L BED C(G0T
(C ABBG L
-(A ABBA $ tx D X
+(A ABBA $ l D X
D Y
-G x AO $ x D b
+G AO $ D b
J
E K
-E $ x T Ag
+E $ T Ag
H F
J K
-E y " D] $y , <y x Ac
+E " D] , 4 x Ac
D O
I o
-A G ly N D w 0 4 BEB D(A0
-(A BBBA < y s FAD I
-ABI AAB z (+ Ai 4x 8 Dz 0 AY $ dz 0L AK e
-AF RA4 z XQ ADF r
-FAD DCA $ z H AG Y
-AF 4 z e BBD C(G0L(A ABB$ ${ J AG p
-AG DA L{ 9 d T d{ 0: d U |{ X? F x { A G y { K Hr
-F J {
- { 5 { e. K^ | ` ,| X , Lz b ARDPs
-CAA t| pe. K^ | X | : | ( | : | H D } @b BBB A(C0G B
-0C(A BBBA L} h d} ` |} x } p } h } ` } h } p ~ x 4 ,| b BAC G`a
- AABF 4 d| BAC G`k
- AABD 4 | `b BAC G`a
- AABF , | AAG`T
-AAA D } BAC G`q
+A G d N D 0 BEB D(A0
+(A BBBA < s FAD I
+ABI AAB (+ Ai L 8 < 0 AY $ \ 0L AK e
+AF RA4 XQ ADF r
+FAD DCA $ H AG Y
+AF 4 e BBD C(G0L(A ABB$ J AG p
+AG DA D 9 d T \ 0: d U t X? F x A G y K Hr
+F J
+ 5 h. K^ ` $ X , d b ARDPs
+CAA l . K^ X : ( : H D @b BBB A(C0G B
+0C(A BBBA D h \ ` t x p h ` h p x 4 D b BAC G`a
+ AABF 4 | BAC G`k
+ AABD 4 `b BAC G`a
+ AABF , AAG`T
+AAA D BAC G`q
AABF ]
- AABG D L} @ BAC G`b
+ AABG D d @ BAC G`b
AABE
- AABA 4 } d BAD G`k
- AABC l } В BBE E(A0D8Du
+ AABA 4 d BAD G`k
+ AABC l Х BBE E(A0D8Du
8F0A(B BBBE U
-8C0A(B BBBG 4 M HD T AL D |~
- BAC G`a
+8C0A(B BBBG , M HD L AL D n BAC G`a
AABF e
- AABG , Ԁ , ؔ ؔF 4 , AK
-D W , \ I AAG`h
-AAE L BBA A(G0O
+ AABG , , ا اF , , AK
+D W , t AAG`h
+AAE L | BBA A(G0O
(C ABBD z
-(A ABBH ԁ ? 8, AK
-D W H" , $ `f AI V
+(A ABBH ? 8, AK
+D W H" , `f AI V
AG e
-AB \ R _ HI t AL 4 ؖV BDD v
-ABE JAB 0_. K^ x p , x" AN ND L ! $ d 3 ADG SDK L B + BBB B(D0D8Jq
-8A0A(B BBBK D BGB A(K0G`
-0D(A BBBA L , y ל BEE D(A0Dj
-0A(A BBBA 4 | 0 BAD D`P
- CABG 4 BAD G`\
- CABH $ ' ADG WAA , y AAGp
-CAA , D X ] AAGp
-CAA 4 t A BDD Dp
- CABG 4 BDD Dp
- CABG < h BBA A(J
-(D ABBJ , $ ء ԛ AAGp
-CAA < T x BEA A(J
-(D ABBA , أ ADGp
-CAA \ Ą hM l BBB D(A0G`}
+AB t R HI l AL 4 ةV BDD v
+ABE JAB ॒. K^ x p $ x" AN ND D ! $ \ 3 ADG SDK L B BBB B(D0D8Jq
+8A0A(B BBBK D d BGB A(K0G`
+0D(A BBBA L D y ; BEE D(A0Dj
+0A(A BBBA 4 0 G BAD D`P
+ CABG 4 & BAD G`\
+ CABH $ ' ADG WAA , , AAGp
+CAA , \ X AAGp
+CAA 4 BDD Dp
+ CABG 4 BDD Dp
+ CABG < h ] BBA A(J
+(D ABBJ , < ش 8 AAGp
+CAA < l x BEA A(J
+(D ABBA , ض ADGp
+CAA \ hM BBB D(A0G`}
0A(A BBBI
-0C(A BBBA X , < PK ADDPl
-AAA , l pK ADDPl
-AAA T Ț BBD A(G`j
+0C(A BBBA X , T PK l ADDPl
+AAA , pK L ADDPl
+AAA T , BBD A(G`j
(D ABBE d
-(D ABBA H , @0 j AFJ RGA , < @0 B AFJ RGA L d @t BEE A(C0R
-(C BBBE z(C BBB p , Ԇ h_ AAM0n
-DAA , _ AAM0n
-DAA , Ȧ D \ Ȧ t $ , AAG ]DA X. K^ ̉ L BBB A(A0
+(D ABBA H , $ @0 AFJ RGA , T @0 AFJ RGA L \ @t BEE A(C0R
+(C BBBE z(C BBB p , h_ AAM0n
+DAA , _ AAM0n
+DAA $ ȹ < T ȹ l $ , AAG ]DA @. K^ L BBB A(A0
(A BBGG Q
-(A FBGG 4 L B 4 d ЧM ADG T
+(A FBGG , D B 4 \ кM ADG T
AAF
-AAG W. K^ ̊ ب Ш ب Ш , DZ D D[ \ AL | AL B Es 4 X7 AAG Z
-AAK DAA $ g AG k
-AA AL , < PAQqDA l hl D ]
-A AP $ - ADJ XDA , ܊ AAG@C
-DAG , P AAG@o
-DAA T < BBE B(A0A8G
-8D0A(B BBBH , ( AVGaDA , ATGaDA , ATGaDA , H AUGaDA , L AUGaDA , | AUGaDA , h AUGaDA , Ȳ AUGaDA , ( AUGaDA < T K o N 4 t ȳ BAA S
-ABA L č g Y BEB B(D0A8FY
-8A0A(B BBBG ) $ $ C Am
+AAG . K^ ػ л ػ л $ DZ < D[ T AL t AL B Es 4 X7 AAG Z
+AAK DAA $ g AG k
+AA AL , 4 PAQqDA d hl D ]
+A AP $ - ADJ XDA , & AAG@C
+DAG , $ P AAG@o
+DAA T T BBE B(A0A8G
+8D0A(B BBBH , ( AVGaDA , ATGaDA , ATGaDA , H AUGaDA , D AUGaDA , t AUGaDA , h AUGaDA , AUGaDA , ( AUGaDA 4 L K o N 4 BAA S
+ABA L g BEB B(D0A8FY
+8A0A(B BBBG ) $ C Am
E H
-A $ T C Am
+A $ l C a Am
E H
-A t $ 4 l RAA
-AIH < ܐ x BBG A(G@
-(A ABBF D $ Z @ BEE A(A0D@
-0A(A BBBF d 0 | (L DC Q. K^ l o ő BEE E(D0D8G
+A l $ 4 l
RAA
+AIH < x BBG A(G@
+(A ABBF D < Z BEE A(A0D@
+0A(A BBBF \ 0 t (L DC . K^ l o ) BEE E(D0D8G
8A0A(B BBBJ
-8A0A(B BBBE _ AG0UA < ` AG0VAT d ( ' BBE B(A0D8G
-8A0A(B BBBF P. K^ ̒ K o N p 4 x BAA G@Y
- AABH 4 L ( AY l (5 fN < P BBA A(G`p
-(D ABBJ $ ̑ m AG0q
-DA , O BOA AB 4 L h d | , 0 AG
-AA aA Ĕ k Ae
-A 4 AFD0{
+8A0A(B BBBE _ AG0UA 4 ` AG0VAT | ( BBE B(A0D8G
+8A0A(B BBBF Ж. K^ K o N p 4 x l BAA G@Y
+ AABH , D ( AY d (5 fN < P BBA A(G`p
+(D ABBJ $ AG0q
+DA , ȕ BOA AB , D h \ t , 0 AG
+AA aA k Ae
+A 4 AFD0{
AAH I
-FAH x
- 4 p 4 L h ADG Z
+FAH x
+ , p 4 D h ADG Z
AAH D
-AFA < BIA A(FPK
-(C ABBJ ĕ ܕ $ < T l , AM
-AA aD , ̖ AN
-AA cG , 8 AM }
-AD ^F , , AN
-AB ^G , \ h AM
-AK iF , ( AN
-AA dG , AM F
-AK VC , 8 AN N
-AJ VD , AM F
-AK VC , L AN N
-AJ VD 4 | X AAN0Q
-AAM LFA , z AAN0L
-GAL , AM Z
-AG ^G , p AM
-AA aD 4 L M BHD G`
- AABB D (m BHD G`
+AFA < ! BIA A(FPK
+(C ABBJ 4 L d | , AM
+AA aD , AN
+AA cG , 8 AM }
+AD ^F , $ AN
+AB ^G , T h AM
+AK iF , ( AN
+AA dG , AM F
+AK VC , 8 AN N
+AJ VD , AM F
+AK VC , D AN N
+AJ VD 4 t X AAN0Q
+AAM LFA , z AAN0L
+GAL , AM Z
+AG ^G , p AM
+AA aD 4 d M v BHD G`
+ AABB D (m ^ BHD G`
AABK I
- AABC 4 ̗ PM Ҋ BHD G`
- AABB D hm BHD G`
+ AABC 4 PM 6 BHD G`
+ AABB D hm BHD G`
AABK I
- AABC 4 D BDJ
-ABA TAB , | ( AKG
-AAB < BIJ
+ AABC 4 < BDJ
+ABA TAB , t ( AKG
+AAB < BIJ
ABE _
-ABN 4 : AKG0
+ABN 4 : AKG0
AAK R
-AAL D $ BAD h
+AAL D BAD h
ADD L
ADG L
-CDE T l X$ BAA G0|
+CDE T d X$ BAA G0|
CABC J
FADK O
- CADI T ̙ 0 BBE B(A0C8D:
-8C0A(B BBBA C 4 1 L H; d pC | 4 4 B L Ĝ 0 BBE D(A0j
+ CADI T 0 v BBE B(A0C8D:
+8C0A(B BBBA C , 1 D H; \ pC t 4 4 B L 0 BBE D(A0j
(A BBDG
-(A BBGH d BBE E(D0D8GPj
+(A BBGH d BBE E(D0D8GPj
8C0A(B BBBE
-8F0A(B BBBF | HF. K^ xt ĝ ܝ x Pt HS P AL 4 4 0tw ADD [
-DAG DAA l xt% t% t% t% ̞ t% t% u% < ut BBA D(D0]
-(A ABBH $ T `uE D
+8F0A(B BBBF t . K^ x x P HS P AL 4 , 0w ADD [
+DAG DAA d x% | % % % ؇% % % < t BBA D(D0]
+(A ABBH $ L `E D
I A
-G L | v BBB B(D0A8DP^
-8A0A(B BBBG L ̟ 8w BBB B(D0A8DPk
-8A0A(B BBBJ $ h XY
-G p D \ , t s AAD y
-CAA < P W BED H(G`o
-(A ABBA T _ BBA A(G0q
+G L t BBB B(D0A8DP^
+8A0A(B BBBG L 8 BBB B(D0A8DPk
+8A0A(B BBBJ $ h XY
+G p < T , l s AAD y
+CAA < P BED H(G`o
+(A ABBA T _ v BBA A(G0q
(A ABBD M
-(A ABBE , T m AAD x
-AAA < 0
BBD D(G`
-(A ABBF < ġ 2 BED A(D0
-(A ABBA 4 0 BAD DP
- DABE , < H{ BAA |
-DBA < t ނ AADPs
+(A ABBE , L m AAD x
+AAA < 0
BBD D(G`
+(A ABBF < 2 BED A(D0
+(A ABBA 4 $ BAD DP
+ DABE , 4 H{ BAA |
+DBA < B AADPs
AAE J
-AAD | j BBB B(A0A8DPf
+AAD | j BBB B(A0A8DPf
8A0A(B BBBB j
8A0A(B BBBD n
-8A0A(B BBBH < , ADD Z
+8A0A(B BBBH < $ ADD Z
AAK Z
-AAD HAA4 l X ADD
+AAD HAA4 d X ADD
AAF D
-FAA N ԣ p' DR
-J F ' DQ
-K F , H AADP[
-AAE D 0 AZ 4 l 0 BAD D`\
- AABE A] 4 Ģ ހ BAD D`]
- AABD
- AZ 4
- BAD D`\
- AABE L
- A] 4 t
- p BAD D`]
- AABD ) D d 4 ģ ( A BAD D`^
- AABC ) D d 4 BAD D`^
- AABC D 0 A[ 4 l 0 BAD D`]
- AABD AQ
-F D AZ 4 BAD D`\
- AABE
A] 4 <
M BAD D`]
- AABD $ l
5 AW
+FAA N p' DR
+J F ' DQ
+K F , 4 AADP[
+AAE < 0 AZ 4 0 y BAD D`\
+ AABE A] 4 B BAD D`]
+ AABD AZ 4 4 BAD D`\
+ AABE D A] 4 Ծ BAD D`]
+ AABD ) D d 4 ( BAD D`^
+ AABC ) D d 4 , v BAD D`^
+ AABC < 0 A[ 4 0 ? BAD D`]
+ AABD AQ
+F D AZ 4 BAD D`\
+ AABE A] 4 T BAD D`]
+ AABD $ d 5 AW
H G
-I D 4
BAD D`]
- AABD ̧ (N ` % DQ
-K D < $ ~ BBD D(Dp]
-(A ABBE < d X ~ BBD D(Dp]
-(A ABBE < i~ BBD D(Dp]
-(A ABBE < 8 J~ BBD D(Dp]
-(A ABBE 4 $ +~ BAD D`j
- AABG 4 \ 0 ~ BAD D`j
- AABG < } BBD D(Dp]
-(A ABBE , ԧ ( } AAD`T
-AAD < } BBD D(Dp]
-(A ABBE 4 D } BAD D`]
- AABD 4 | } BAD D`]
- AABD , } AAD`D
-AAD , {} AAD`C
-AAE T n} BBD D(Dp[
+I D 4 r BAD D`]
+ AABD (!N `! !% DQ
+K D < < ! BBD D(Dp]
+(A ABBE < | X" BBD D(Dp]
+(A ABBE < " ͼ BBD D(Dp]
+(A ABBE < 8# BBD D(Dp]
+(A ABBE 4 < # BAD D`j
+ AABG 4 t 0$ y BAD D`j
+ AABG < $ c BBD D(Dp]
+(A ABBE , (% D AAD`T
+AAD < % 2 BBD D(Dp]
+(A ABBE 4 \ & BAD D`]
+ AABD 4 & BAD D`]
+ AABD , & AAD`D
+AAD , ' AAD`C
+AAE T , ( һ BBD D(Dp[
(A ABBG _
-(A ABBC d d [ BBB B(D0A8D
+(A ABBC d \ )[ BBB B(D0A8D
8A0A(B BBBB
-8A0A(B BBBL , ԩ | YX
+8A0A(B BBBL , . 7 YX
G
-A , | YX
+A , / YX
G T
-A 4 , kw ADD [
-DAG DAA $ d 0lE D
+A 4 $ ~w ADD [
+DAG DAA $ \ 0E D
I A
-G L Xm BBB B(D0A8DPk
-8A0A(B BBBJ L ܬ n BBB B(A0A8Gp8A0A(B BBB D 4 H { BAD G
+G L X BBB B(D0A8DPk
+8A0A(B BBBJ L BBB B(A0A8Gp8A0A(B BBB D L H/ BAD G
ABG D
-ABE KAB , t Po= BDA rAB L `o BBB A(A0
+ABE KAB , l P= BDA rAB L ` BBB A(A0
(A BBBK Y
-(A EBBM L p BBB A(A0~
+(A EBBM L BBB A(A0~
(A BBBC Q
-(A EBBE d D pQ BBB E(D0E8D@O
+(A EBBE d < Q BBB E(D0E8D@O
8F0A(B BBBA
-8D0A(B BBGA D \ BDC D`
+8D0A(B BBGA D .\ BDC D`
AABA U
- AABA $ $ ADG QCC $ 2 AFF0bAA D D BHE D(D0D@
-0A(A BBBK $ x 4 AFG0cAA D BHE D(D0D@
-0A(A BBBK $ H 2 AFF0bAA D $ ` BHE D(D0D@
-0A(A BBBK $ l 4 AFG0cAA D 0 BHE D(D0D@
-0A(A BBBK ܰ > AI0rA < BHD D(DP
-(A ABBE < > AI0rA < \ BHD D(DP
-(A ABBE $ !2 AFF0bAA D ı ! BHE D(D0D@
-0A(A BBBK < x"B BEG D(D@
-(A ABBJ $ L %2 AFF0bAA D t % BHE D(D0D@
-0A(A BBBK 4 X& BGA G0f
- AABE D & BGD D0
+ AABA $ /$ ADG QCC $ /2 AFF0bAA D < / BHE D(D0D@
+0A(A BBBK $ x04 AFG0cAA D 0 BHE D(D0D@
+0A(A BBBK $ H12 AFF0bAA D
+ `1 BHE D(D0D@
+0A(A BBBK $ d
+ 24 AFG0cAA D
+ 02 BHE D(D0D@
+0A(A BBBK
+ 2> AI0rA <
+ 3 BHD D(DP
+(A ABBE 4 3> AI0rA < T 3 BHD D(DP
+(A ABBE $ 42 AFF0bAA D 4 BHE D(D0D@
+0A(A BBBK < x5B BEG D(D@
+(A ABBJ $ D 82 AFF0bAA D l 8 BHE D(D0D@
+0A(A BBBK 4 X9 BGA G0f
+ AABE D 9 BGD D0
AAEG o
- AABE \ < h( BBG D(G@e
+ AABE \ 4
h; BBG D(G@e
(A ABBG r
-(A ABBH P(A ABB d (0 BBB B(D0C8GP}
+(A ABBH P(A ABB d
;0 BBB B(D0C8GP}
8A0A(B BBBC A
-8A0A(B BBBE D p) BAD G0s
+8A0A(B BBBE D
p< BAD G0s
AABK y
- AABC L L ll BBE F(A0A8D@
-8A0A(B BBBJ , ) AG r
+ AABC L D l BBE F(A0A8D@
+8A0A(B BBBJ , < AG r
AA G
-AA < ̴ 8* BED D(D0D
-(A ABBC < *t BED D(D0h
-(A ABBG 4 L xl
ADG H
+AA < 8= BED D(D0D
+(A ABBC < =t BED D(D0h
+(A ABBG 4 D x
ADG H
AAJ k
-AAC L *5 BBG D(GP
+AAC L | =5 BBG D(GP
(A ABBB T
-(A ABBF L ܳ +8 r BBE E(D0D8GO
-8A0A(B BBBJ T $ <} BHE D(A0Gpv
-0A(A BBBG T0A(A BBB 4 <} Yr BAA DPT
- AABA 4 < -r BAA DPY
- AABA L H=e BBB B(A0D8G`p
-8A0A(B BBBB L < hO BBB B(D0D8D@
-8A0A(B BBBK D Q aq BAD G@P
+(A ABBF L >8 P BBE E(D0D8GO
+8A0A(B BBBJ T O} BHE D(A0Gpv
+0A(A BBBG T0A(A BBB 4 O} BAA DPT
+ AABA 4 O BAA DPY
+ AABA L HPe BBB B(A0D8G`p
+8A0A(B BBBB L 4 hb BBB B(D0D8D@
+8A0A(B BBBK D d ů BAD G@P
AABF t
- AABA L ܵ Q +q BBB B(D0D8Dp
-8A0A(B BBBE L , j p BEB E(I0H8D@
-8D0A(B BBBD \ | RK p BBB A(C0G*
+ AABA L d BBB B(D0D8Dp
+8A0A(B BBBE L D } S BEB E(I0H8D@
+8D0A(B BBBD \ eK + BBB A(C0G*
0A(A BBBE
-0A(A BBBE L ܶ U p BHE E(D0D8F
-8A0A(B BBBA L , `V bp BEE E(D0D8F
-8A0A(B BBBA L | W *p BBE E(D0D8Fc
-8A0A(B BBBG Ĺ 0*. K^ ܹ k k k $ k < hj- T k" AN ND t k8 Ds k Uo k
k 4 ܸ je o BHA D`~
- AABA $ ku AG y
-AA $ 4 l DG
+0A(A BBBE L h BHE E(D0D8F
+8A0A(B BBBA L D `i Ʈ BEE E(D0D8F
+8A0A(B BBBA L j BBE E(D0D8Fc
+8A0A(B BBBG p. K^ ~ ~ ~ ~ 4 h}- L ~" AN ND l ~8 Ds ~ ~
~ 4 }e u BHA D`~
+ AABA $ ~u AG y
+AA $ , DG
E p
-A $ \ lm AG q
-AA is D d
-A (. K^ L l BBE B(A0D8JW
-8A0A(B BBBE , Hm m AAJ
-DAA , D m m AAJDA l xn G D n2 m BEA D(J?
-(D ABBA Լ '. K^ \ q BIB E(A0A8I
+A $ T m AG q
+AA | |s D d
+A 0o. K^ L BBE B(A0D8JW
+8A0A(B BBBE , , H M AAJ
+DAA , \ , AAJDA d x G D 2 BEA D(J?
+(D ABBA 0n. K^ \ Ȅ BIB E(A0A8I
0A(B BBBA
-0A(B IBBK d L Xs BGB B(A0C8D`S
+0A(B IBBK d D X BGB B(A0C8D`S
8A0A(B BBBA
-8A0A(B BBBG s> QR YA Խ t8 T c D 8t BBE E(A0A8D`8D0A(B BBB 4 P&6 Kj $ L tF An
+8A0A(B BBBG > QR YA 8 T c D 8 BBE E(A0A8D`8D0A(B BBB , m6 Kj $ D F An
A L
-D $ t tF An
+D $ l F An
A L
-D t D< Pu# k AAJ@j
+D ؇ D< P# AAJ@j
DAE Y
-DAB L dk BBF B(D0E8GPr
-8A0A(B BBBH L L /k BBB B(E0A8Gp
-8A0A(B BBBK L u k BLB B(A0A8Jm
-8A0A(B BBBK `|# DZ x|I L | j BBB B(A0A8J
-8A0A(B BBBD T l j BBB E(D0A8J
-8A0A(B BBBE 4 ľ ؔ Ik BAF GP
- AABH 4 k BFA GP
- AABA 4 4 X j BFA G@
- AABA 4 l j BAA G0x
- AABA $ n AG Z
-AA - KQJ $ n AG Z
-AA $ $ @n AG Z
-AA L T i BBB B(D0D8DP
-8D0A(B BBBE l , i BBB E(A0A8D_
+DAB L ȩ BBF B(D0E8GPr
+8A0A(B BBBH L d BBB B(E0A8Gp
+8A0A(B BBBK L k BLB B(A0A8Jm
+8A0A(B BBBK `# DZ xI L 4 ? BBB B(A0A8J
+8A0A(B BBBD T 5 BBB E(D0A8J
+8A0A(B BBBE 4 ا BAF GP
+ AABH 4 BFA GP
+ AABA 4 L X [ BFA G@
+ AABA 4 2 BAA G0x
+ AABA $ n AG Z
+AA - KQJ $ n AG Z
+AA $ @n AG Z
+AA L l X BBB B(D0D8DP
+8D0A(B BBBE l , BBB E(A0A8D_
8A0A(B BBBF D
-8C0A(B BBBA L Z* li BBB B(A0D8G[
-8D0A(B BBBD L d ^ j BEB B(A0A8GZ
-8A0A(B BBBH 6 AU ^A (3 AR ^A , H Dl
+8C0A(B BBBA L , دZ* Ч BBB B(A0D8G[
+8D0A(B BBBD L | ^ ^ BEB B(A0A8GZ
+8A0A(B BBBH 6 AU ^A (3 AR ^A , H Dl
H _
A Z
-A Z L b L l @ Bj BEB B(D0A8G
-8A0A(B BBBG T j BBB E(G0D8G
-8C0A(B BBBC T X Zj BBB E(A0A8J#
-8A0A(B BBBD T l j BBE E(G0D8G
-8A0A(B BBBF H @. LW X `& x& 4 & L L R BBB B(A0A8GK
-8A0A(B BBBJ L d BBB B(A0A8GK
-8A0A(B BBBJ $ > AG _
-AH LA$ > AG ^
-AI LA$ < = AG ^
-AI KA$ d = AG ]
-AJ KAL 8R BBB B(A0A8GK
-8A0A(B BBBJ L Hd BBB B(A0A8GK
-8A0A(B BBBJ D , h BBB D(A0GN
-0A(A BBBH D t BBB D(A0GN
-0A(A BBBH
-
-
-
- 4
- L
- d |
-
-
-
-
-
- $ < x
- T p
- l h
- ` X
- P
- H
- @ 8
- 0
- , (
- D \
- t
-
- L BBB B(A0A8D
-8A0A(B BBBA L x BBB B(A0A8D
-8A0A(B BBBA 4 d X -c BAA G0
- AABG L BBB B(A0A8Gp8A0A(B BBB l `x BBF D(A0G@F
+A Z D b L @ BEB B(D0A8G
+8A0A(B BBBG T BBB E(G0D8G
+8C0A(B BBBC T , X BBB E(A0A8J#
+8A0A(B BBBD T { BBE E(G0D8G
+8A0A(B BBBF H @. LW X `& x& , & L D R BBB B(A0A8GK
+8A0A(B BBBJ L d BBB B(A0A8GK
+8A0A(B BBBJ $ > AG _
+AH LA$ ! > AG ^
+AI LA$ 4! = AG ^
+AI KA$ \! = AG ]
+AJ KAL ! 8R BBB B(A0A8GK
+8A0A(B BBBJ L ! Hd BBB B(A0A8GK
+8A0A(B BBBJ D $" h BBB D(A0GN
+0A(A BBBH D l" BBB D(A0GN
+0A(A BBBH "
+ "
+ "
+ " #
+ ,#
+ D#
+ \# t#
+ #
+ #
+ # #
+ #
+ $
+ $ 4$ x
+ L$ p
+ d$ h
+ |$ ` $ X
+ $ P
+ $ H
+ $ @ $ 8
+ % 0
+ $% (
+ <% T%
+ l%
+ %
+ % L % BBB B(A0A8D
+8A0A(B BBBA L & x BBB B(A0A8D
+8A0A(B BBBA 4 |% X BAA G0
+ AABG L & BBB B(A0A8Gp8A0A(B BBB l & `x BBF D(A0G@F
0A(A BBBD v
0A(A BBBJ D
-0F(A BBBG T \ p 9b BLB E(A0A8D
-8C0A(B BBBA $ 7 HTTF X{ `{ X{ 4 P{ L x^ AW
-A L t ; ;a BDB B(A0A8GQ
-8A0A(B BBBB T z 1a BEB B(A0A8G:
-8D0A(B BBBE T a BEB B(A0A8G:
-8D0A(B BBBE T t ( a BEB B(A0A8G:
-8D0A(B BBBE Ћ DM
-A
-; D
- BDA
+0F(A BBBG T t& p BLB E(A0A8D
+8C0A(B BBBA ' $ ' 7 HTTF ' X ' ` ( X ,( P D( x^ AW
+A L ' ; BDB B(A0A8GQ
+8A0A(B BBBB T ' ؍ BEB B(A0A8G:
+8D0A(B BBBE T 4( BEB B(A0A8G:
+8D0A(B BBBE T ( ( o BEB B(A0A8G:
+8D0A(B BBBE ) О DM
+A ) ; D ) BDA
ABA O
-ABF ]AB , L P D x` BDH _
-ABA | p X` BBB E(A0A8F
+ABF ]AB , d) P D ܞ BDH _
+ABA ) p BBB E(A0A8F
8A0A(B BBBD
8A0A(B BBBF
8D0A(B BBBH W
-8A0A(B BBBG ` , h D p < d x` _ BEI C(D0l
-(D ABBA < c u_ BEJ D(C0p
-(A BBBA , D E_ BDH _
-ABA d %_ BED A(D0O
+8A0A(B BBBG + `! $+ h! <+ p! < |* x!` BEI C(D0l
+(D ABBA < * !c ٝ BEJ D(C0p
+(A BBBA , * !D BDH _
+ABA d ,+ ! BED A(D0O
(D ABBH F
(C ABBJ a
-(D ABBA , | PF ^ BDI `
-ABA pn AL@_A 4 AS [D \ # y^ BBB A(D0G`u
+(D ABBA , + P"F = BDI `
+ABA , p"n AL@_A , "4 AS [D \ , "# ݜ BBB A(D0G`u
0D(A BBBF K
-0D(A BBBA D \ 8 t A L w ] BBB B(A0A8G`
-8D0A(B BBBA @2 Gj h $ p A
-A D C < \ 8s 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 BEB A(D0l
+0D(A BBBA <- # T- #8 l- #A - $ L , $w 5 BBB B(A0A8G`
+8D0A(B BBBA - @%2 Gj . h% . p% A
+A <. &C < T. 8&s BBE E(A0X
+(A BBBA L . BBB B(A0A8Gp8A0A(B BBB L . (& BBB B(A0A8Je
+8A0A(B BBBE 4/ ,! A_ < T/ ( BBG A(G@
+(A ABBG L / BEB A(D0l
(A BBBG
-(A BBBA L
- [ BBB E(D0A8Gd
-8A0A(B BBBK T D h [ BBB B(A0A8J
-8A0A(B BBBG < u [ BHE L
-AEO AAB < @u [ BHE L
-AEO AAB < | U[ BEA A(D0a(D ABB < \ | [ BEA A(D0a(D ABB < Z BHE |
-AEO AAB < p Z BBD A(D0(D ABB < eZ BHE |
-AEO AAB < \ P )Z BBD A(D0(D ABB d 2 BBB E(D0E8D@P
+(A BBBA L / ,
+ 9 BBB E(D0A8Gd
+8A0A(B BBBK T \/ h D BBB B(A0A8J
+8A0A(B BBBG < / u 1 BHE L
+AEO AAB < / @u BHE L
+AEO AAB < 40 | BEA A(D0a(D ABB < t0 | } BEA A(D0a(D ABB < 0 A BHE |
+AEO AAB < 0 p BBD A(D0(D ABB < 41 ɘ BHE |
+AEO AAB < t1 P BBD A(D0(D ABB d 2 2 BBB E(D0E8D@P
8F0A(B BBBA
-8D0A(B BBGA d BBE B(A0A8FP[
+8D0A(B BBGA d 2 3 BBE B(A0A8FP[
8C0A(B BBBA W
-8F0A(B BBBA , d "; BDE lAB d 2 BBB E(D0E8D@P
+8F0A(B BBBA , \3 5; BDE lAB d 3 2 BBB E(D0E8D@P
8F0A(B BBBA
-8D0A(B BBGA L h"? BBB B(A0C8DP
-8A0A(B BBBA l L y BBF D(A0G@I
+8D0A(B BBGA L 3 h5? BBB B(A0C8DP
+8A0A(B BBBA l D4 y BBF D(A0G@I
0A(A BBBI w
0A(A BBBA D
-0F(A BBBG L "W W BBB B(A0A8GPQ
-8C0A(B BBBB L # W BBB B(A0A8G`U
-8D0A(B BBBE d d x& IW BEB B(F0D8Je
+0F(A BBBG L 3 5W ) BBB B(A0A8GPQ
+8C0A(B BBBB L ,4 6 BBB B(A0A8G`U
+8D0A(B BBBE d |4 x9 BEB B(F0D8Je
8A0A(B BBBB S
-8C0A(B BBBA L BBB B(A0A8Gp8A0A(B BBB d P)
V BBB B(A0A8GPZ
+8C0A(B BBBA L 5 BBB B(A0A8Gp8A0A(B BBB d 45 P<
BBB B(A0A8GPZ
8A0A(B BBBK D
-8A0A(B BBBJ T , SV BBB B(A0A8JS
-8A0A(B BBBA T 04 WV BBD C(G0J
+8A0A(B BBBJ T 5 ? BBB B(A0A8JS
+8A0A(B BBBA T 5 0G BBD C(G0J
(A ABBF d
-(D ABBA , 4 D 4 \ 4 l t x BBF D(A0G@F
+(D ABBA $7 G <7 G T7 G l l7 x BBF D(A0G@F
0A(A BBBD v
0A(A BBBJ D
-0F(A BBBG L BBB B(A0C8D`I
-8C0A(B BBBK < 4 ` BBG A(G@
-(A ABBF T | 3 T BLB E(A0A8D
-8A0A(B BBBG D / T BJB K(A0j
-(c `BBA D M T BFA Dp
+0F(A BBBG L 7 BBB B(A0C8D`I
+8C0A(B BBBK < ,8 ` BBG A(G@
+(A ABBF T 7 F 8 BLB E(A0A8D
+8A0A(B BBBG D 7 / BJB K(A0j
+(c `BBA D 48 M BFA Dp
AABH O
- AABA d \ E BBB B(A0A8DP
+ AABA d T9 E BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA l B S BBB J(D0D8D4
+8F0A(B BBBA l 8 B [ BBB J(D0D8D4
8D0A(B BBBC i
-8A0A(B BBBE L 4 pN BBB B(A0A8DV
-8A0A(B BBBB L p2 BBB B(A0D8G@@
-8A0A(B BBBB T P4
S BBB B(A0D8Jc
-8A0A(B BBBD < 4 C NS BBD A(DP
-(D ABBA T t C S BBB B(D0A8J
-8A0A(B BBBD < 0U lS BBA D(DP
-(D ABBA T U >S BBB B(A0D8J+
-8A0A(B BBBD < d f S BBA D(DP
-(D ABBA + Ai < ZBA A(%
- DBFA @) 4 , X bGHX
-AG d X | h AS h 0 D0W B , (K BAC G0w CAB L , H| BAC G0L
+8A0A(B BBBE L ,: pN BBB B(A0A8DV
+8A0A(B BBBB L |: p2 BBB B(A0D8G@@
+8A0A(B BBBB T 9 PG
k BBB B(A0D8Jc
+8A0A(B BBBD < L: V BBD A(DP
+(D ABBA T : V BBB B(D0A8J
+8A0A(B BBBD < : 0h Б BBA D(DP
+(D ABBA T $; h BBB B(A0D8J+
+8A0A(B BBBD < |; y BBA D(DP
+(D ABBA < + Ai < < < ZBA A(%
+ DBFA = @) 4 $= X bGHX
+AG \= X t= h AS = h = 0 = D0W = B , = (K BAC G0w CAB L $> H| BAC G0L
AABC D
- CABF D DAB L | x BEE B(A0A8G
-8A0A(B BBBF L H BBB E(A0A8GP
-8D0A(B BBBA , AAG j
-AAA , T X P AAJ
-DAA , P AAJAA G s , `_ AAG N
-AAA L P BBB E(A0D8GG
-8A0A(B BBBH T l 0 O BDB B(A0D8G
+ CABF D DAB L t> x BEE B(A0A8G
+8A0A(B BBBF L > H BBB E(A0A8GP
+8D0A(B BBBA , ? AAG j
+AAA , l> X AAJ
+DAA , > AAJAA ? G ? s , ? `_ AAG N
+AAA L 4? f BBB E(A0D8GG
+8A0A(B BBBH T ? 0 ` BDB B(A0D8G
-8A0A(B BBBH T BBB A(D0F
+8A0A(B BBBH T @ BBB A(D0F
(A BBBH :
-(E BBBH L ` 3P BEE B(D0D8G
-8C0A(B BBBF d | x p h L P O BBE B(A0A8D`s
-8D0A(B BBBG L fO BBE B(A0A8D`s
-8D0A(B BBBG d | A < h BGD A(D0T
-(A ABBD < BBA D(D0
-(A ABBG , D DM T d
eN BEB B(A0A8GG
-8A0A(B BBBK \ p BIH D(G0Q
+(E BBBH L 4@ ` BEE B(D0D8G
+8C0A(B BBBF \A tA x A p A h L @ P BBE B(A0A8D`s
+8D0A(B BBBG L 4A ʍ BBE B(A0A8D`s
+8D0A(B BBBG \B tB B A < B h BGD A(D0T
+(A ABBD < B BBA D(D0
+(A ABBG $C <C DM T |B
Ɍ BEB B(A0A8GG
+8A0A(B BBBK \ C p BIH D(G0Q
(A ABBK s
-(C ABEB I(C AEB L BEA D(G0O
+(C ABEB I(C AEB L D BEA D(G0O
(A ABBH E
-(G AFEH L d @ BEB I(D0^
-(A BFEF a(A BBB A\ L q BEE I(D0v
-(A BFEC I(A BBB 4 $ _ AID i
-JDK LAA L \ BBA D(G0^
+(G AFEH L \D @ BEB I(D0^
+(A BFEF a(A BBB D A\ L D q BEE I(D0v
+(A BFEC I(A BBB 4 E _ AID i
+JDK LAA L TE BBA D(G0^
(A ABBD a
-(A ABBA 4 hE BID a
-ABE IAB 4 ] BID n
-EEI IAB \ BBB B(A0D8G@p
-8G0A(B BFEE L8A0A(B BBB$ | l ADD `AA4 Pb BNA o
-ABE TCB 5 T ( J BBB E(D0D8J
-8A0A(B BBBC | d `, BBB B(D0C8GP
+(A ABBA 4 E hE BID a
+ABE IAB 4 E ] BID n
+EEI IAB \ F BBB B(A0D8G@p
+8G0A(B BFEE L8A0A(B BBB$ tF l ADD `AA4 F Pb BNA o
+ABE TCB F F 5 T ,F ( c BBB E(D0D8J
+8A0A(B BBBC | \G `, BBB B(D0C8GP
8A0A(B BBBH
8C0A(B BBBD A
-8D0A(B BBBJ D BAA L
+8D0A(B BBBJ D G BAA L
ABE G
ABF K
-ABJ , X D PB X^ 4 d BPD A(K`Z(A ABB| BBB A(A0GPU
+ABJ $H X <H PB X^ 4 \H BPD A(K`Z(A ABB| H BBB A(A0GPU
0C(A BBBB R
0A(A BBBF o
-0D(A BBBF N0A(A BBB Hn 4 4 AAG
-AAB tCA l H$ Ab T H BBB B(A0A8J+
-8A0A(B BBBG < Pu H BHE L
-AEO AAB < , | H BEA A(D0a(D ABB < l GH BHH
-AEG AAB P AL L p G BEB E(I0H8D@
-8D0A(B BBBA L 4 BIF A(R0
-(A ABBA K(D ABB d d W BDB B(A0A8Dpq
+0D(A BBBF N0A(A BBB I Hn 4 ,I AAG
+AAB tCA dI H$ Ab T H 6 BBB B(A0A8J+
+8A0A(B BBBG < I Pu # BHE L
+AEO AAB < DI | BEA A(D0a(D ABB < I BHH
+AEG AAB J P AL L I p O BEB E(I0H8D@
+8D0A(B BBBA L K 4 BIF A(R0
+(A ABBA K(D ABB d \K W BDB B(A0A8Dpq
8A0A(B BBBE
-8A0A(B BBBA T x F BBB B(A0A8J
-8A0A(B BBBG D D U +G OMB L(E0Y
-(g ~BBA D M G BFA Dp
+8A0A(B BBBA K T K x W BBB B(A0A8J
+8A0A(B BBBG D \K U OMB L(E0Y
+(g ~BBA D K M o BFA Dp
AABH O
- AABA 4 F BAA
-ABA L
- BBB B(A0D8G@
-8A0A(B BBBD T \ p
- ~F BBB E(D0H8J
-8A0A(B BBBG L HW F BBE D(C0G
-0A(A BBBG X
- P
L 4 Hc F BBE D(D0G
-0D(A BBBK | h L E BBB F(A0E8GPg
-8A0A(B BBBA P DM
-A l ` E BFB B(A0D8G`"
+ AABA 4 K @ BAA
+ABA L L BBB B(A0D8G@
+8A0A(B BBBD T tL p
+ BBB E(D0H8J
+8A0A(B BBBG L L H W BBE D(C0G
+0A(A BBBG M X
+ N P
L LM H
c ~ BBE D(D0G
+0D(A BBBK tN h L M ( + BBB F(A0E8GPg
+8A0A(B BBBA N P DM
+A l $N `* BFB B(A0D8G`"
8A0A(B BBBD V
-8A0A(B BBBH , | e {E AAD I
-AAA , [ [E AAD
-AAA < ;E AAD R
+8A0A(B BBBH , N .e ߃ AAD I
+AAA , N .[ AAD
+AAA < N / AAD R
AAF d
-AAA 4 ` E BKD D0
- DABA < T b D BEA D(DP
-(D ABBE $ ADD AA, + D ADD
-AAA % DW
-A $ 5 AJ
+AAA 4 4O `/ z BKD D0
+ DABA < lO /b P BEA D(DP
+(D ABBE $ P 1 ADD AA, O 1+ ADD
+AAA P 2% DW
+A $ P 25 AJ
E ]
-A , DP
-A L K AI 4 l ADG Z
-AAP bAA P X < , ( AG o
+A $Q 2 DP
+A DQ 2K AI 4 dQ 2 ADG Z
+AAP bAA Q P3 Q X3< , Q 3( AG o
AH V
-AI !" DQ
-K A $ ! T D !M C BBB H(A0A8Jr
-8A0A(B BBBJ < ) BEE D(D0e(A BBB T )f B BBB E(D0A8GP
-8A0A(B BBBG 4 , 1 BED D(F0(A ABB$ d V ADD JAAL ( gB BEB E(I0H8D@
-8D0A(B BBBA < BDI ~
+AI Q 4" DQ
+K A R 4 T \Q 4M s BBB H(A0A8Jr
+8A0A(B BBBJ < R < BEE D(D0e(A BBB T Q <f , BBB E(D0A8GP
+8A0A(B BBBG 4 $S D BED D(F0(A ABB$ \S V ADD JAAL R ( ˀ BEB E(I0H8D@
+8D0A(B BBBA < S BDI ~
AGK K
-ABB L $ ( A BEB E(I0H8D@
-8D0A(B BBBD d l h1? BBB B(H0G8Fp0
+ABB L <S ( c BEB E(I0H8D@
+8D0A(B BBBD d dT hD? BBB B(H0G8Fp0
8D0A(B BBBF W
-8A0A(B BBBG L @4M oA BBB B(A0D8GQ
-8A0A(B BBBA L $ BBB B(A0D8GP>
-8A0A(B BBBD t 0 E
-G < BHD A(D0(A ABB d 9 BBB B(K0D8Fpb
+8A0A(B BBBG L S @GM BBB B(A0D8GQ
+8A0A(B BBBA L U BBB B(A0D8GP>
+8A0A(B BBBD lU 0 E
+G < U BHD A(D0(A ABB d U L BBB B(K0D8Fpb
8A0A(B BBBG j
-8A0A(B BBBA T D 8? Q@ BBB B(D0A8Jo
-8A0A(B BBBH < Of @ BED D(H0m
-(D ABBA T `Z @ BHB E(A0A8GP
-8A0A(B BBBD , O T L O l@ BBB E(A0A8G
-8A0A(B BBBF V V V V T U ? BBB E(A0A8G
-8A0A(B BBBF T P\ l H\ @\ 8\ L 0\ v? BBB B(A0D8I@L
-8C0A(B BBBB T ]A
4? BBB B(A0A8J
-8D0A(B BBBH L d i d? BBB B(A0A8Ji
-8A0A(B BBBI L Xkm BBB E(A0D8GR
-8A0A(B BBBE L xm > BBE E(A0A8GV
-8D0A(B BBBF , L s, BDD WAB L s
- > BBE B(A0A8IR
-8C0A(B BBBA H~ AO , BDA AK T $ X
- W> BDB B(A0A8G$
-8A0A(B BBBA T | $ Z> BBB B(A0A8Gs
-8A0A(B BBBA < Ȥ X> BED C(G@
-(D ABBC T Xs &> BBB B(A0A8J
-8A0A(B BBBE d 2 Ap < @ > BBB A(C0Z
-(A BBBA < \> BBA A(G0d
-(A ABBA L 7> BEH E(D0D8Gr
-8A0A(B BBBA T `2 Ap T | @ = BLE B(A0A8G
-8C0A(B BBBC T
- = BLB E(A0A8G
-8A0A(B BBBA $ j < ( T \ z = BBB B(A0D8G
-8A0A(B BBBG d ' BBB B(A0A8GPe
+8A0A(B BBBA T \U 8R ~ BBB B(D0A8Jo
+8A0A(B BBBH < U bf 2 BED D(H0m
+(D ABBA T U `Z ~ BHB E(A0A8GP
+8A0A(B BBBD $W b T dV b ~ BBB E(A0A8G
+8A0A(B BBBF W i W i W i W i T W h U~ BBB E(A0A8G
+8A0A(B BBBF LX Po dX Ho |X @o X 8o L W 0o } BBB B(A0D8I@L
+8C0A(B BBBB T $X pA
} BBB B(A0A8J
+8D0A(B BBBH L |X | } BBB B(A0A8Ji
+8A0A(B BBBI L Y X~m BBB E(A0D8GR
+8A0A(B BBBE L Y x ;} BBE E(A0A8GV
+8D0A(B BBBF , DZ , BDD WAB L Y
+ | BBE B(A0A8IR
+8C0A(B BBBA Z H AO , Z BDA AK T <Z X
+ | BDB B(A0A8G$
+8A0A(B BBBA T Z $ | BBB B(A0A8Gs
+8A0A(B BBBA < Z ȷ | BED C(G@
+(D ABBC T ,[ Xs | BBB B(A0A8J
+8A0A(B BBBE \\ 2 Ap < [ @ | BBB A(C0Z
+(A BBBA < [ | BBA A(G0d
+(A ABBA L $\ | BEH E(D0D8Gr
+8A0A(B BBBA L] `2 Ap T \ @ =| BLE B(A0A8G
+8C0A(B BBBC T \
+ 4| BLB E(A0A8G
+8A0A(B BBBA ^ j 4^ ( T t] z | BBB B(A0D8G
+8A0A(B BBBG d ^ ' BBB B(A0A8GPe
8D0A(B BBBM
-8A0A(B BBBB L >= BBB B(A0A8Jk
-8C0A(B BBBE d A , | BDD E
-ABF (% L @ BBD A(D@|
+8A0A(B BBBB L 4^ { BBB B(A0A8Jk
+8C0A(B BBBE \_ A , t_ BDD E
+ABF _ (% L _ @ BBD A(D@|
(A ABBA D
-(F ABBA T < BBB B(A0A8G3
-8C0A(B BBBH L t H < BEB B(A0A8I_
-8A0A(B BBBA : < < BBA A(D@z
-(A ABBA u T 4 H I< BEB B(A0A8G<
-8A0A(B BBBF h (; H^
-J F H @S L ; BDA }
+(F ABBA T 4_ 7{ BBB B(A0A8G3
+8C0A(B BBBH L _ H
+ ${ BEB B(A0A8I_
+8A0A(B BBBA ` : < _ { BBA A(D@z
+(A ABBA a u T L` H z BEB B(A0A8G<
+8A0A(B BBBF |a h a (; H^
+J F a H a @S L a 7z BDA }
ABI \
ABA G
-ABF < (y L \ |; BBB B(A0D8I
-8A0A(B BBBG d @@ BBB B(D0A8D@l
+ABF 4b (y L ta y BBB B(A0D8I
+8A0A(B BBBG d b @!@ BBB B(D0A8D@l
8A0A(B BBBI
-8C0A(B BBBA < , ; BDA D(L0u
-(A ABGI , T G : BDI ]
-ABE | ( 0 4 8B AAG L
-FAL WAA < P r: BAA x
+8C0A(B BBBA < ,b ", y BDA D(L0u
+(A ABGI , lb #G ^y BDI ]
+ABE tc (# c 0# 4 c 8#B AAG L
+FAL WAA < c P# x BAA x
ABI A
-ABJ < , L: BBD A(L@e
-(A ABBH , l PQ &: AID c
-DDE T b : BBD D(D0m
+ABJ < Dc # x BBD A(L@e
+(A ABBH , c P$Q x AID c
+DDE T c $b jx BBD D(D0m
(A ABBE
-(D ABBG D
- BND `
+(D ABBG D d % BND `
GBE o
-IBL _AB L 4 BBB B(A0A8DPj
-8D0A(B BBBK L BEB B(A0A8Gp
-8C0A(B BBBH \ P BEB E(D0D8G@i
-8A0A(B BBBH T8C0A(B BBBT <
- 8 BEB B(A0D8G
-8A0A(B BBBG D
- x#| 8 BDE A(G0DP\0A(A BBB 4 #l AAD Y
+IBL _AB L ,e & BBB B(A0A8DPj
+8D0A(B BBBK L |e & BEB B(A0A8Gp
+8C0A(B BBBH \ e P+ BEB E(D0D8G@i
+8A0A(B BBBH T8C0A(B BBBT Te + v BEB B(A0D8G
+8A0A(B BBBG D e x6| v BDE A(G0DP\0A(A BBB 4 f 6l AAD Y
DAD v
-DAE d #1 8 BBA D(G0
+DAE d ,f 61 ~v BBA D(G0
(A ABGC `
(A ABBJ S
-(F ABBB l | $; 7 BEB B(A0A8D`
+(F ABBB l f 7; )v BEB B(A0A8D`
8A0A(B BBBO C
-8A0A(B BBBA , &k i7 AAG @
-IAE $ & Aw
+8A0A(B BBBA , g 9k u AAG @
+IAE $ h 9 Aw
H h
-H < h'( Ab 4 \ x' ADD0|
+H 4h h:( Ab 4 Th x: ADD0|
AAI }
-GAK (N Ad
-K ] 0( 6 BBE D(A0Gp
+GAK h ;N Ad
+K ] g 0;
u BBE D(A0Gp
0A(A BBBH o
0A(A BBBI
0A(A BBBG d
-0A(A BBBA d < 8* BGB B(A0A8FI
+0A(A BBBA d 4i 8= BGB B(A0A8FI
8A0A(B BBBH Y
-8F0A(B BBBH L
- 5 BBB B(D0A8JG
-8A0A(B BBBH <
ou 5 BHE L
-AEO AAB < < o| 5 BEA A(D0a(D ABB L t p, BLB B(D0A8D`
-8A0A(B BBBJ < q1 BBG A(GP
-(A ABBF L 2s BBB B(A0A8G
-8A0A(B BBBF L T P41 BBB E(A0C8I
-8A0A(B BBBA T @6,
- A4 BBB B(A0A8G
-8A0A(B BBBA T @ ]4 BBB B(A0A8G
-8D0A(B BBBA T \ q8 X5 BEE B(A0C8J
-8A0A(B BBBG L Y \5 BBE E(D0C8Dp
-8C0A(B BBBK T [s +5 BBB E(D0C8G
-8D0A(B BBBD T y$ Ab D t y' BKK
+8F0A(B BBBH L h @ 1t BBB B(D0A8JG
+8A0A(B BBBH < i u It BHE L
+AEO AAB < Ti | t BEA A(D0a(D ABB L lj , BLB B(D0A8D`
+8A0A(B BBBJ < j 1 BBG A(GP
+(A ABBF L j Es BBB B(A0A8G
+8A0A(B BBBF L Lk PG1 BBB E(A0C8I
+8A0A(B BBBA T j @I,
+ r BBB B(A0A8G
+8A0A(B BBBA T k S r BBB B(A0A8G
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_ZZN11onnxruntime21WritableSliceIteratorIfE4InitERKSt6vectorIlSaIlEEN3gsl4spanIKlEESA_E19__PRETTY_FUNCTION__ _ZN12_GLOBAL__N_119ZeroOutSliceAtIndexIfEEvRN11onnxruntime6TensorElllRKSt6vectorIlSaIlEES8_l _ZN12_GLOBAL__N_110CopySlicesIfEEvRKN11onnxruntime6TensorERS2_RKSt6vectorIlSaIlEESA_SA_SA_l _ZN12_GLOBAL__N_19SumSlicesIfEEvRKN11onnxruntime6TensorERS2_RKSt6vectorIlSaIlEESA_SA_SA_SA_l _GLOBAL__sub_I_cumsum.cc det.cc _ZZN11onnxruntime21BuildKernelCreateInfoINS_43kCpuExecutionProvider_Det_kOnnxDomain_ver11EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZN5Eigen8internal13gemm_pack_lhsIflNS0_22const_blas_data_mapperIflLi0EEELi8ELi4EU8__vectorfLi0ELb0ELb0EEclEPfRKS3_llll.constprop.439 _ZN5Eigen8internal13gemm_pack_rhsIflNS0_22const_blas_data_mapperIflLi0EEELi4ELi0ELb0ELb0EEclEPfRKS3_llll.constprop.440 _ZNKSt14default_deleteIN11onnxruntime9KernelDefEEclEPS1_.isra.162 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_ZZN11onnxruntime21BuildKernelCreateInfoINS_41kCpuExecutionProvider_Or_kOnnxDomain_ver7EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_42kCpuExecutionProvider_Xor_kOnnxDomain_ver7EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_48kCpuExecutionProvider_Add_kOnnxDomain_ver7_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_49kCpuExecutionProvider_Add_kOnnxDomain_ver7_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Add_kOnnxDomain_ver7_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Add_kOnnxDomain_ver7_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_48kCpuExecutionProvider_Sub_kOnnxDomain_ver7_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_49kCpuExecutionProvider_Sub_kOnnxDomain_ver7_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Sub_kOnnxDomain_ver7_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Sub_kOnnxDomain_ver7_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_48kCpuExecutionProvider_Mul_kOnnxDomain_ver7_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_49kCpuExecutionProvider_Mul_kOnnxDomain_ver7_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Mul_kOnnxDomain_ver7_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Mul_kOnnxDomain_ver7_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_48kCpuExecutionProvider_Div_kOnnxDomain_ver7_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_49kCpuExecutionProvider_Div_kOnnxDomain_ver7_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Div_kOnnxDomain_ver7_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Div_kOnnxDomain_ver7_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_48kCpuExecutionProvider_Abs_kOnnxDomain_ver6_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_49kCpuExecutionProvider_Abs_kOnnxDomain_ver6_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_49kCpuExecutionProvider_Abs_kOnnxDomain_ver6_int8_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Abs_kOnnxDomain_ver6_int16_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Abs_kOnnxDomain_ver6_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Abs_kOnnxDomain_ver6_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Abs_kOnnxDomain_ver6_uint8_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_Abs_kOnnxDomain_ver6_uint16_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_Abs_kOnnxDomain_ver6_uint32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_Abs_kOnnxDomain_ver6_uint64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_48kCpuExecutionProvider_Neg_kOnnxDomain_ver6_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_49kCpuExecutionProvider_Neg_kOnnxDomain_ver6_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ 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_ZZN11onnxruntime21BuildKernelCreateInfoINS_63kCpuExecutionProvider_ReduceLogSumExp_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_65kCpuExecutionProvider_ReduceLogSumExp_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_61kCpuExecutionProvider_ReduceLogSumExp_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_63kCpuExecutionProvider_ReduceLogSumExp_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver1_10_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver11_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver1_10_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver11_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver1_10_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver11_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver1_10_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver11_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_63kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_65kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_61kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_63kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_64kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver1_10_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_62kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver11_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ArgMax_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ArgMax_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_ArgMax_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ArgMax_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ArgMin_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ArgMin_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_ArgMin_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ArgMin_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime16ReduceKernelBaseILb1EEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime16ReduceKernelBaseILb0EEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime16PrepareForReduceIlEEbPNS_15OpKernelContextERSt6vectorIT_SaIS4_EEPPNS_6TensorERlSB_RKS3_IlSaIlEEbbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime16PrepareForReduceIiEEbPNS_15OpKernelContextERSt6vectorIT_SaIS4_EEPPNS_6TensorERlSB_RKS3_IlSaIlEEbbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime16PrepareForReduceIfEEbPNS_15OpKernelContextERSt6vectorIT_SaIS4_EEPPNS_6TensorERlSB_RKS3_IlSaIlEEbbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime16PrepareForReduceIdEEbPNS_15OpKernelContextERSt6vectorIT_SaIS4_EEPPNS_6TensorERlSB_RKS3_IlSaIlEEbbE19__PRETTY_FUNCTION__ _GLOBAL__sub_I_reduction_ops.cc rnn_helpers.cc 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_ZTSZN4onnx24RoiPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx21ConvOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx21ConvOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZTSZN4onnx11GetOpSchemaINS_22QLinearConv_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_22ConvInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZZN4onnx30ConvTransposeOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx30ConvTransposeOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_ _ZTSZZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_ _ZTSZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_31InstanceNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_25LpNormalization_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_25TfIdfVectorizer_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_27StringNormalizer_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_23PoolOpSchemaGenerator_9EPKcS5_S5_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_23PoolOpSchemaGenerator_9EPKcS5_S5_ENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx23PoolOpSchemaGenerator_9EPKcS3_S3_ENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_24PoolOpSchemaGenerator_10EPKcS5_S5_bEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_24PoolOpSchemaGenerator_10EPKcS5_S5_bENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx24PoolOpSchemaGenerator_10EPKcS3_S3_bENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_26LpPoolOpSchemaGenerator_10EPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_26LpPoolOpSchemaGenerator_10EPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _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_ _ZNSt14_Function_base13_Base_managerIZN4onnx24PoolOpSchemaGenerator_10EPKcS3_S3_bEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_bEUlRNS_8OpSchemaEE_ _ZTIZZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTIZZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_bENKUlRNS_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_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.357 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28BatchNormalization_Onnx_ver6EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28BatchNormalization_Onnx_ver7EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19MaxUnpool_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Flatten_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Flatten_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_EUlRNS_8OpSchemaEE_ _ZTSZZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_bENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_bEUlRNS_8OpSchemaEE_ _ZTSZN4onnx11GetOpSchemaINS_19MaxUnpool_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZZN4onnx26LpPoolOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx26LpPoolOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx24ConvOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx24ConvOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ _ZTSZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19RoiAlign_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28NonMaxSuppression_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19RoiAlign_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28NonMaxSuppression_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.201 _ZTSZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28NonMaxSuppression_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28NonMaxSuppression_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25QuantizeLinear_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_27DequantizeLinear_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt6vectorISsSaISsEEC2ESt16initializer_listISsERKS0_.constprop.235 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25QuantizeLinear_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_27DequantizeLinear_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx18ReduceDocGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx18ReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx18ReduceDocGeneratorEPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx18ReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_18ReduceDocGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_18ReduceDocGeneratorEPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_21ArgReduceDocGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_21ArgReduceDocGeneratorEPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZZN4onnx18ReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx18ReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx25ReduceDocGenerator_opset1EPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx25ReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx25ReduceDocGenerator_opset1EPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx25ReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_25ReduceDocGenerator_opset1EPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_25ReduceDocGenerator_opset1EPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_28ArgReduceDocGenerator_opset1EPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_28ArgReduceDocGenerator_opset1EPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZZN4onnx25ReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx25ReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx15RNNDocGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx15RNNDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_15RNNDocGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx15RNNDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx18RNNDocGeneratorOldEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx16RNNDocGenerator1EPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_18RNNDocGeneratorOldEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_16RNNDocGenerator1EPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_ _ZTSZN4onnx16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_ schema.cc _ZNSt17_Function_handlerIFbiEZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_E9_M_invokeERKSt9_Any_datai _ZNSt17_Function_handlerIFbiEZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_E9_M_invokeERKSt9_Any_datai _ZGVZN4onnx16OpSchemaRegistry8InstanceEvE8instance _ZZN4onnx16OpSchemaRegistry8InstanceEvE8instance _ZGVZN4onnx16OpSchemaRegistry33GetMapWithoutEnsuringRegistrationEvE3map _ZZN4onnx16OpSchemaRegistry33GetMapWithoutEnsuringRegistrationEvE3map _ZNSt14_Function_base13_Base_managerIZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_E10_M_managerERSt9_Any_dataRKSA_St18_Manager_operation _ZTIZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_ _ZNSt14_Function_base13_Base_managerIZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_E10_M_managerERSt9_Any_dataRKSA_St18_Manager_operation _ZTIZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_ _ZGVZN4onnx16OpSchemaRegistry20DomainToVersionRange8InstanceEvE23domain_to_version_range _ZZN4onnx16OpSchemaRegistry20DomainToVersionRange8InstanceEvE23domain_to_version_range _GLOBAL__sub_I_schema.cc _ZTSZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_ _ZTSZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_24SequenceErase_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_21SequenceAt_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_24SequenceEmpty_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28SequenceConstruct_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25SequenceInsert_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_21SequenceAt_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_24SequenceErase_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25SequenceLength_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26SplitToSequence_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_29ConcatFromSequence_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_24SequenceEmpty_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_29ConcatFromSequence_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_26SplitToSequence_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28SequenceConstruct_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25SequenceInsert_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25SequenceLength_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19Upsample_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Resize_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Cast_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Reshape_Onnx_ver5EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15Shape_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Size_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Concat_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16Split_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16Slice_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19Transpose_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18Scatter_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_20ScatterND_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26ScatterElements_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Gather_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25GatherElements_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18Squeeze_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_20Unsqueeze_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_22SpaceToDepth_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_23DepthToSpace_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Tile_Onnx_ver6EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19Upsample_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Resize_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19Compress_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17OneHot_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15IsNaN_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16IsInf_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15Where_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17NonZero_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26ReverseSequence_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Unique_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19GatherND_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Pad_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZN6google8protobuf8internal20RepeatedPtrFieldBase3AddINS0_16RepeatedPtrFieldIN4onnx26TensorShapeProto_DimensionEE11TypeHandlerEEEPNT_4TypeESB_.constprop.541 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Size_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt18basic_stringstreamIcSt11char_traitsIcESaIcEEC1ESt13_Ios_Openmode.constprop.542 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_15Shape_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Tile_Onnx_ver6EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Scatter_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_20ScatterND_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_26ScatterElements_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25GatherElements_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Squeeze_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17NonZero_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_15IsNaN_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_16IsInf_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Cast_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Unique_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Concat_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Gather_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19Compress_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17OneHot_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Pad_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_26ReverseSequence_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19GatherND_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_16Split_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ 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_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_ 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logistic.cpp tanh.cpp erf.cpp SconvKernelAvx.S.o MlasConvNchwFloatSingleAvxFilter4 MlasConvNchwFloatSingleAvxFilter3 MlasConvNchwFloatSingleAvxFilter2 MlasConvNchwFloatSingleAvxFilter1 MlasConvNchwcFloatSingleAvxFilter4 MlasConvNchwcFloatSingleAvxFilter3 MlasConvNchwcFloatSingleAvxFilter2 MlasConvNchwcFloatSingleAvxFilter1 MlasConvDepthwiseFloatSingleAvxFilter1 SpoolKernelAvx.S.o MlasPoolMaximumFloatSingleAvx MlasPoolAverageExcludePadFloatSingleAvx MlasPoolAverageIncludePadFloatSingleAvx SconvKernelFma3.S.o MlasConvNchwFloatSingleFma3Filter4 MlasConvNchwFloatSingleFma3Filter3 MlasConvNchwFloatSingleFma3Filter2 MlasConvNchwFloatSingleFma3Filter1 MlasConvNchwcFloatSingleFma3Filter4 MlasConvNchwcFloatSingleFma3Filter3 MlasConvNchwcFloatSingleFma3Filter2 MlasConvNchwcFloatSingleFma3Filter1 MlasConvDepthwiseFloatSingleFma3Filter1 LogisticKernelFma3.S.o LogisticConstants_LowerRange LogisticConstants_UpperRange LogisticConstants_alpha_9 LogisticConstants_alpha_7 LogisticConstants_alpha_5 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_ZZN11onnxruntime21BuildKernelCreateInfoINS_62kCpuExecutionProvider_TfIdfVectorizer_kOnnxDomain_ver9_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_61kCpuExecutionProvider_TfIdfVectorizer_kOnnxDomain_ver9_stringEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_62kCpuExecutionProvider_TfIdfVectorizer_kOnnxDomain_ver9_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime15TfIdfVectorizerC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _GLOBAL__sub_I_tfidfvectorizer.cc reduction_ops.cc _ZNK11onnxruntime9ReduceSumIlE7ComputeEPNS_15OpKernelContextE._omp_fn.1 _ZNK11onnxruntime9ReduceSumIfE7ComputeEPNS_15OpKernelContextE._omp_fn.3 _ZNK11onnxruntime9ReduceSumIdE7ComputeEPNS_15OpKernelContextE._omp_fn.0 _ZNK11onnxruntime9ReduceSumIiE7ComputeEPNS_15OpKernelContextE._omp_fn.2 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_ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceL2_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceL2_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ReduceL2_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceL2_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceLogSum_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_62kCpuExecutionProvider_ReduceLogSum_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ 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_ZZN11onnxruntime21BuildKernelCreateInfoINS_63kCpuExecutionProvider_ReduceLogSumExp_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver1_10_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ 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_ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver1_10_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver11_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ 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_ZNSt14_Function_base13_Base_managerIZZN4onnx26LpPoolOpSchemaGenerator_10EPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_24ConvOpSchemaGenerator_10EPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_24ConvOpSchemaGenerator_10EPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx24ConvOpSchemaGenerator_10EPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_33ConvTransposeOpSchemaGenerator_10EPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_33ConvTransposeOpSchemaGenerator_10EPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt14_Function_base13_Base_managerIZN4onnx23PoolOpSchemaGenerator_9EPKcS3_S3_EUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_EUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx24PoolOpSchemaGenerator_10EPKcS3_S3_bEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_bEUlRNS_8OpSchemaEE_ _ZTIZZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTIZZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_bENKUlRNS_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_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.357 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28BatchNormalization_Onnx_ver6EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28BatchNormalization_Onnx_ver7EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19MaxUnpool_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Flatten_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Flatten_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_EUlRNS_8OpSchemaEE_ _ZTSZZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_bENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_bEUlRNS_8OpSchemaEE_ _ZTSZN4onnx11GetOpSchemaINS_19MaxUnpool_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZZN4onnx26LpPoolOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx26LpPoolOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx24ConvOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx24ConvOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ _ZTSZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19RoiAlign_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28NonMaxSuppression_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19RoiAlign_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28NonMaxSuppression_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.201 _ZTSZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28NonMaxSuppression_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28NonMaxSuppression_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25QuantizeLinear_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_27DequantizeLinear_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt6vectorISsSaISsEEC2ESt16initializer_listISsERKS0_.constprop.235 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25QuantizeLinear_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_27DequantizeLinear_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx18ReduceDocGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx18ReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx18ReduceDocGeneratorEPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx18ReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_18ReduceDocGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_18ReduceDocGeneratorEPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_21ArgReduceDocGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_21ArgReduceDocGeneratorEPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZZN4onnx18ReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx18ReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx25ReduceDocGenerator_opset1EPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx25ReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx25ReduceDocGenerator_opset1EPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx25ReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_25ReduceDocGenerator_opset1EPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_25ReduceDocGenerator_opset1EPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_28ArgReduceDocGenerator_opset1EPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_28ArgReduceDocGenerator_opset1EPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZZN4onnx25ReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx25ReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx15RNNDocGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx15RNNDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_15RNNDocGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx15RNNDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx18RNNDocGeneratorOldEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx16RNNDocGenerator1EPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_18RNNDocGeneratorOldEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_16RNNDocGenerator1EPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_ _ZTSZN4onnx16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_ schema.cc _ZNSt17_Function_handlerIFbiEZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_E9_M_invokeERKSt9_Any_datai _ZNSt17_Function_handlerIFbiEZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_E9_M_invokeERKSt9_Any_datai _ZGVZN4onnx16OpSchemaRegistry8InstanceEvE8instance _ZZN4onnx16OpSchemaRegistry8InstanceEvE8instance _ZGVZN4onnx16OpSchemaRegistry33GetMapWithoutEnsuringRegistrationEvE3map _ZZN4onnx16OpSchemaRegistry33GetMapWithoutEnsuringRegistrationEvE3map _ZNSt14_Function_base13_Base_managerIZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_E10_M_managerERSt9_Any_dataRKSA_St18_Manager_operation _ZTIZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_ _ZNSt14_Function_base13_Base_managerIZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_E10_M_managerERSt9_Any_dataRKSA_St18_Manager_operation _ZTIZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_ _ZGVZN4onnx16OpSchemaRegistry20DomainToVersionRange8InstanceEvE23domain_to_version_range _ZZN4onnx16OpSchemaRegistry20DomainToVersionRange8InstanceEvE23domain_to_version_range _GLOBAL__sub_I_schema.cc _ZTSZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_ _ZTSZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_24SequenceErase_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_21SequenceAt_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_24SequenceEmpty_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28SequenceConstruct_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25SequenceInsert_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_21SequenceAt_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_24SequenceErase_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25SequenceLength_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26SplitToSequence_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_29ConcatFromSequence_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_24SequenceEmpty_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_29ConcatFromSequence_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_26SplitToSequence_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28SequenceConstruct_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25SequenceInsert_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25SequenceLength_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19Upsample_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Resize_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Cast_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Reshape_Onnx_ver5EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15Shape_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Size_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Concat_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16Split_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16Slice_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19Transpose_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18Scatter_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_20ScatterND_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26ScatterElements_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Gather_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25GatherElements_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18Squeeze_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_20Unsqueeze_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_22SpaceToDepth_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_23DepthToSpace_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Tile_Onnx_ver6EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19Upsample_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Resize_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19Compress_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17OneHot_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15IsNaN_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16IsInf_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15Where_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17NonZero_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26ReverseSequence_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Unique_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19GatherND_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Pad_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZN6google8protobuf8internal20RepeatedPtrFieldBase3AddINS0_16RepeatedPtrFieldIN4onnx26TensorShapeProto_DimensionEE11TypeHandlerEEEPNT_4TypeESB_.constprop.541 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Size_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt18basic_stringstreamIcSt11char_traitsIcESaIcEEC1ESt13_Ios_Openmode.constprop.542 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_15Shape_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Tile_Onnx_ver6EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Scatter_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_20ScatterND_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_26ScatterElements_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25GatherElements_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Squeeze_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17NonZero_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_15IsNaN_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_16IsInf_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Cast_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Unique_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Concat_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Gather_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19Compress_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17OneHot_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Pad_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_26ReverseSequence_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19GatherND_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_16Split_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.517 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19Transpose_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Reshape_Onnx_ver5EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_20Unsqueeze_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_ENKUlPKNS_11TensorProtoEE_clES8_.isra.520 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_16Slice_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_22SpaceToDepth_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_23DepthToSpace_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_15Where_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.530 _ZZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.531 _ZZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.532 _ZTSZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _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_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Upsample_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ 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_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_ 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_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_ 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