vendor/onnxruntime.dll in onnxruntime-0.4.0 vs vendor/onnxruntime.dll in onnxruntime-0.5.0

- old
+ new

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7HHK8HuxEHH@?EHL$ ,HL$ B7HHK8HuxEHH@ +EHL$ HL$ 6HHK8Hu{xEHH@~EHL$ ±HL$ H6HHK8HuFxEHH@~EHL$ 荱HL$ CM<HHK8HuxEHH@k~EHL$ XHL$ O<HHK8HuwEHH@6~EHL$ #HL$ &6HHK8HuwEHH@~EHL$ HL$ I<HHK8HurwEHH@}EHL$ 蹰HL$ 16HHK8Hu=wEHH@}EHL$ 脰HL$ Z;HHK8HuwEHH@b}EHL$ OHK8HtHH;H@ :}EHC8H$(H]H\$HL$UH$HHHL$ i;HHK8HuwvEHH@|EHL$ 辯HL$ 贜8HHK8HuBvEHH@|EHL$ 良HL$ 46HHK8Hu vEHH@g|EHL$ THL$ 7HHK8HuuEHH@2|EHL$ HL$ 7HHK8HuuEHH@{EHL$ HL$ 7HHK8HunuEHH@{EHL$ 赮HL$ 苿:HHK8Hu9uEHH@{EHL$ 耮HL$ &/<HHK8HuuEHH@^{EHL$ KHL$ 9HHK8HutEHH@){EHL$ HL$ <\5HHK8HutEHH@zEHL$ HL$ g:HHK8HuetEHH@zEHL$ 謭HL$  :HHK8Hu0tEHH@zEHL$ wHL$ =:HHK8HusEHH@UzEHL$ BHL$ 9HHK8HusEHH@ zEHL$ HL$ :HHK8HusEHH@yEHL$ جHL$ 9HHK8Hu\sEHH@yEHL$ 裬HL$ Y7HHK8Hu'sEHH@yEHL$ nHL$ :HHK8HurEHH@LyEHL$ 9HL$ R;HHK8HurEHH@yEHL$ HL$ :HHK8HurEHH@xEHL$ ϫHL$ 56HHK8HuSrEHH@xEHL$ 蚫HL$ :HHK8HurEHH@xxEHL$ eHL$ 苩:HHK8HuqEHH@CxEHL$ 0HL$ 膯:HHK8HuqEHH@xEHL$ HL$ A:HHK8HuqEHH@wEHL$ ƪHL$ ;HHK8HuJqEHH@wEHL$ 葪HL$ W;HHK8HuqEHH@owEHL$ \HL$ k;HHK8HupEHH@:wEHL$ 'HL$ l;HHK8HupEHH@wEHL$ HL$ Hh;HHK8HuvpEHH@vEHL$ 轩HL$ i;HHK8HuApEHH@vEHL$ 舩HL$ 辇;HHK8Hu pEHH@fvEHL$ SHL$ d;HHK8HuoEHH@1vEHL$ HL$ Ĉ;HHK8HuoEHH@uEHL$ HL$ e;HHK8HumoEHH@uEHL$ 质HL$ Ja;HHK8Hu8oEHH@uEHL$ HL$ b;HHK8HuoEHH@]uEHL$ JHL$  6HHK8HunEHH@(uEHL$ HL$ :HHK8HunEHH@tEHL$ HL$ F9HHK8HudnEHH@tEHL$ 諧HL$ 9HHK8Hu/nEHH@tEHL$ vHL$ 9HHK8HumEHH@TtEHL$ AHL$ /5HHK8HumEHH@tEHL$ HL$ 2:HHK8HumEHH@sEHL$ צHL$ M6HHK8Hu[mEHH@sEHL$ 袦HL$ (9:HHK8Hu&mEHH@sEHL$ mHL$ c6HHK8HulEHH@KsEHL$ 8HL$ n6HHK8HulEHH@sEHL$ HL$ 6HHK8HulEHH@rEHL$ ΥHL$ 6HHK8HuRlEHH@rEHL$ 虥HL$ ;HHK8HulEHH@wrEHL$ dHL$ ;HHK8HukEHH@BrEHL$ /H;HHK8HukEHH@ rEHHL$ ;HHK8HuzkEHH@qEHL$ H;HHK8HuCkEHH@qEH舤HL$ .;HHK8Hu kEHH@fqEHL$ SHG;HHK8HujEHH@/qEHHL$ <HHK8HujEHH@pEHL$ H:HHK8HugjEHH@pEH謣HL$ G9HHK8Hu0jEHH@pEHL$ wH+;HHK8HuiEHH@SpEH>HL$ 负;HHK8HuiEHH@pEHL$ HK8HtHH;H@ oEHC8H$H]H\$HL$UH$HHHL$ i;HHK8Hu7iEHH@oEHL$ ~HL$ j;HHK8HuiEHH@\oEHL$ IHL$ /B9HHK8HuhEHH@'oEHL$ HL$ :B8HHK8HuhEHH@nEHL$ ߡHL$ 6HHK8HuchEHH@nEHL$ 誡HL$ `W;HHK8Hu.hEHH@nEHL$ uHL$ X;HHK8HugEHH@SnEHL$ @HL$ :HHK8HugEHH@nEHL$ HL$ /8HHK8HugEHH@mEHL$ ֠HL$ 9HHK8HuZgEHH@mEHL$ 衠HL$ w9HHK8Hu%gEHH@mEHL$ lHL$ bb7HHK8HufEHH@JmEHL$ 7HL$ 79HHK8HufEHH@mEHL$ HL$ 99HHK8HufEHH@lEHL$ ͟HL$ #8HHK8HuQfEHH@lEHL$ 蘟HL$ ޛ8HHK8HufEHH@vlEHL$ cHL$ V9HHK8HueEHH@AlEHL$ .HL$ .9HHK8HueEHH@ lEHL$ HK8HtHH;H@ kEHC8H$(H]H\$HL$UH$HHHL$ 虭8HHK8Hu'eEHH@kEHL$ nHL$ 8HHK8HudEHH@LkEHL$ 9HL$ σ8HHK8HudEHH@kEHL$ HL$ 蚆8HHK8HudEHH@jEHL$ ϝHL$ U(7HHK8HuSdEHH@jEHL$ 蚝HL$ )7HHK8HudEHH@xjEHL$ eHL$ ;.7HHK8HucEHH@CjEHL$ 0HL$ v/7HHK8HucEHH@jEHL$ HL$ 7HHK8HucEHH@iEHL$ ƜHL$ L7HHK8HuJcEHH@iEHL$ 葜HL$ 臄7HHK8HucEHH@oiEHL$ \HL$ )7HHK8HubEHH@:iEHL$ 'HL$ /7HHK8HubEHH@iEHL$ HL$ 17HHK8HuvbEHH@hEHL$ 轛HL$ 47HHK8HuAbEHH@hEHL$ 舛HL$ 67HHK8Hu bEHH@fhEHL$ SHL$ 87HHK8HuaEHH@1hEHL$ HL$ :7HHK8HuaEHH@gEHL$ HL$ <7HHK8HumaEHH@gEHL$ 贚HL$ _7HHK8Hu8aEHH@gEHL$ HL$ a7HHK8HuaEHH@]gEHL$ JHL$ b7HHK8Hu`EHH@(gEHL$ HL$ d7HHK8Hu`EHH@fEHL$ HL$ &j7HHK8Hud`EHH@fEHL$ 諙HL$ !r7HHK8Hu/`EHH@fEHL$ vHL$ t7HHK8Hu_EHH@TfEHL$ AHL$ 7w7HHK8Hu_EHH@fEHL$ HL$ x7HHK8Hu_EHH@eEHL$ טHL$ mz7HHK8Hu[_EHH@eEHL$ 袘HL$ H7HHK8Hu&_EHH@eEHL$ mHL$ Ӑ7HHK8Hu^EHH@KeEHL$ 8HL$ N7HHK8Hu^EHH@eEHL$ HL$ ٶ7HHK8Hu^EHH@dEHL$ ΗHL$ Ĺ7HHK8HuR^EHH@dEHL$ 虗HL$ _Z8HHK8Hu^EHH@wdEHL$ dHL$ j6HHK8Hu]EHH@BdEHL$ /HL$ 6HHK8Hu]EHH@ dEHL$ HL$ 6HHK8Hu~]EHH@cEHL$ ŖHL$ 6HHK8HuI]EHH@cEHL$ 萖HL$ V6HHK8Hu]EHH@ncEHL$ [HL$ !X6HHK8Hu\EHH@9cEHL$ &HL$ \Y6HHK8Hu\EHH@cEHL$ HL$ Z6HHK8Huu\EHH@bEHL$ 輕HL$ [6HHK8Hu@\EHH@bEHL$ 臕HL$ ]6HHK8Hu \EHH@ebEHL$ RHL$ H^6HHK8Hu[EHH@0bEHL$ HL$ _6HHK8Hu[EHH@aEHL$ HL$ `6HHK8Hul[EHH@aEHL$ 賔HL$ a6HHK8Hu7[EHH@aEHL$ ~HL$ h6HHK8Hu[EHH@\aEHL$ IHL$ i6HHK8HuZEHH@'aEHL$ HL$ Zw5HHK8HuZEHH@`EHL$ ߓHz5HHK8HuaZEHH@`EH覓HL$ L5HHK8Hu*ZEHH@`EHL$ qH赈5HHK8HuYEHH@M`EH8HL$ ΋5HHK8HuYEHH@`EHL$ HW5HHK8HuYEHH@_EHʒHL$ 谛5HHK8HuNYEHH@_EHL$ 蕒H蹫5HHK8HuYEHH@q_EH\HL$ b5HHK8HuXEHH@:_EHL$ 'H 5HHK8HuXEHH@_EHHL$ D5HHK8HurXEHH@^EHL$ 蹑Hݽ5HHK8Hu;XEHH@^EH耑HL$ F5HHK8HuXEHH@^^EHL$ KH5HHK8HuWEHH@'^EHHL$ 5HHK8HuWEHH@]EHL$ ݐHQ5HHK8Hu_WEHH@]EH褐HL$ 5HHK8Hu(WEHH@]EHL$ oH5HHK8HuVEHH@K]EH6HL$ 5HHK8HuVEHH@]EHL$ Hu5HHK8HuVEHH@\EHȏHL$ n5HHK8HuLVEHH@\EHL$ 蓏H6HHK8HuVEHH@o\EHZHL$ P 6HHK8HuUEHH@8\EHL$ %H96HHK8HuUEHH@\EHHL$ !6HHK8HupUEHH@[EHL$ 跎Hk75HHK8Hu9UEHH@[EH~HL$ :5HHK8HuUEHH@\[EHL$ IH=5HHK8HuTEHH@%[EHHL$ 5HHK8HuTEHH@ZEHL$ ۍHK8HtHH;H@ ZEHC8H$H]H\$HL$UH$HHHL$ 評4HHK8HuTEHH@aZEHL$ NHL$ 蔗4HHK8HuSEHH@,ZEHL$ HL$ _4HHK8HuSEHH@YEHL$ HL$ *4HHK8HuhSEHH@YEHL$ 诌HL$ 4HHK8Hu3SEHH@YEHL$ zHL$ 蠣4HHK8HuREHH@XYEHL$ EHL$ k4HHK8HuREHH@#YEHL$ HL$ F4HHK8HuREHH@XEHL$ ۋHL$ 4HHK8Hu_REHH@XEHL$ 見HL$ ̸4HHK8Hu*REHH@XEHL$ qHL$ 74HHK8HuQEHH@OXEHL$ <HL$ B4HHK8HuQEHH@XEHL$ HL$ }4HHK8HuQEHH@WEHL$ ҊHL$ 4HHK8HuVQEHH@WEHL$ 蝊HL$ 4HHK8Hu!QEHH@{WEHL$ hHL$ 4HHK8HuPEHH@FWEHL$ 3HL$ I4HHK8HuPEHH@WEHL$ HL$ 4HHK8HuPEHH@VEHL$ ɉHK8HtHH;H@ VEHC8H$(H]H\$ UVWAVAWH@IIHHHL$0HT$8D$8?E3DxfDxDxL8LxLxDxLx Lx(Lx0HL$0H;t HHHt +H3HHL>HD$0Dt$ LLD$0HH%H>HtHt2H?HH$H@A_A^_^]H\$ VWAVH0IHHT$ LD$ MHA(Lq ML;tSA{I9zu>AK9Ju5AK9Ju,HRMKL+DB +D+uH…uEt IL;uI%I;FtL GDH(u2LLHT$(I&3HHNHt*HH\$hH0A^_^@SVWATAUAVAWHMILHHT$PL$E3D$L*DŽ$I;t%IM(H9HHtH2AUH%?$CAAWAG $H @FH$HHEH$)L$`)T$p$$x]2HE3LD$`H$'HHSH;t&HL)H:HHtH苚20Hr?H$HtHi20HP?AO(AW8AGH$H 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F`F`$F00FLFppF0vFFF@F FF0F@FFGPG(GP,GpCG`HGVGWGPfG nGPGPGGG@GGGG0GG@G0GGGG0G0GG GG HH`KHH@HHH HHH`HH`HHHHHHH`HHHHH`HHHICI``I`I`IIIII I@I`IIIPIIIIIII :J:J`NJ XJXJiJiJsJsJJ JJJPJPJPJ`J@JPJJJ J@JJJJpJ`J0JPJpaMaMaMbM bM0bM@bMbMbM0cM@cMcMcM`dMdM@eMeMeMeMfMgMhMiMjMkMlMplMlMPmMmM0nMnMoMoM0pMpMqMqMqMrM@rMrMrMsM@sMsMsMtMtM tM0tM@tMPtM`tMptMtMtMtMtMtMtMtM uM`uMuMuM vM`vMvMvM wM`wMwMwM xM`xMxMxM yM`yMyMyM zM`zMzMzM {M`{M{M{M |M`|M|M|M }M`}M}M}M ~M`~M~M~M M`MMM M`MpMMMhMp:V1`1@V1`1xBV1`1V     @JTp0   `Y0(cannot determine missing fields for lite message)D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\protobuf\src\google\protobuf\message_lite.ccCan't message of type "" because it is missing required fields: parse exceeded maximum protobuf size of 2GB: JTAGD:\a\ml-builds\ml-builds\onnxruntime\cmake\external\protobuf\src\google\protobuf\stubs\common.ccThis 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 "%d.%d.%dINFOWARNINGERRORFATAL[libprotobuf %s %s:%d] %s -AVP  PpP`@`0@pV%I64u00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899>?456789:;<= -  !"#$%&'()*+,-./0123>456789:;<= - ? !"#$%&'()*+,-./0123ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-_falsetrue\x%02xt10@D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\protobuf\src\google\protobuf\arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits<size_t>::max() - kBlockHeaderSize): D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug.Can't happen8KTswt`zwKT t y0vw`Y0D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream_impl_lite.ccCHECK failed: (count) >= (0): CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: 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(). Parameter to BackUp() can't be negative.CHECK failed: (backup_bytes_) == (0): CHECK failed: (buffer_used_) == (buffer_size_): 8LT~ yLT~ MT0~Ђ(MTNTp~(NT``Y0D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream_impl.ccclose() failed: CHECK failed: !is_closed_: INVALID_ARGUMENT  pMpNtNNV  @5 '   3   ' 'D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\protobuf\src\google\protobuf\repeated_field.ccCHECK failed: (new_size) <= ((std::numeric_limits<size_t>::max() - kRepHeaderSize) / sizeof(old_rep->elements[0])): Requested size is too large to fit into size_t. - - (OTp 0pШ...\\?\D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\protobuf\src\google\protobuf\generated_message_util.ccCHECK failed: (scc->visit_status.load(std::memory_order_relaxed)) == (SCCInfoBase::kRunning): api-ms-win-core-synch-l1-2-0.dllkernel32.dllSleepConditionVariableCSWakeAllConditionVariablePTPbad allocationp^^Q;PQ;н`0p`pP0p  `Pp`  @`@y;Pz;@z;`z;z;f; 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?`:?0?? :?0????0p?p?:`?0??0:?0???p?0??:?0??:?0????0??`:?0P?P?@?0?0??:?0??:?0??`:?0`?`?P?@?00?0?0: ?00?0?: ?0??:?0P?P?:@?0??0:?0??:?00?0?: ?0p?p?:`?00?0? ??0p?p?:`?0P?P?@?0?0????0??p?`?0??:?0 ? ???0P?P?:@?0??p:?0??:?0????0p?p?`:`?0??:?0????0??0:?0??:?0????0P@P@:@@00@0@p@`@0@@ @ @00@0@ @@0`@`@:P@0@@@p@0`@`@P@@@0@@@@0@@@@00@0@: @0@!@@!@ @p @0p!@p!@`!@P!@0`@`@P@@@0@@@@0@@Е@@0@@@Ж@0@@@p@0@@@@0@@@@00@0@ @@0@@)=@0@@)=г@0@@@@0@@@@0p@p@@@0@@@@00@0@0g5 @0@@Pf5@0`@`@P@@@0 @ @@@0P@P@@@0@0aAbA bAbAcAcApdAfA@Ay8BUBAA=B4NBKBлA>BEB B2BAA"B40.B*BлA#B'BAB`APA`B4 B +BлA`B`B2B07BAмAp3B46B6BлA3B 5BBpBAнAnB4rB`qBлAnBppBBBApAB4B`BлABBUB]B AAWB4\B[BлA@XB`ZB`BpBAAB4B`BлABBAA AAA4 APAлAAAsBxBAA uB4wBwBлApuBvBBBAAB4BBлA@BBЎBB@A0AB4BpBлABBAA@A0AA4A0AлA@AAyB~BAAzB40~B}BлA {B|BgBmB`APAiB4PlBkBлAPiBjBAAAAA4A0AлAPA0AmBrBApAnB4rB`qBлAnBppBApAAAA4pA0AлA@AA^BgB@A0A`B4eB`dBлAPaBPcBPB B AA B4B BлAB`B@B0BPB@BB4B@BлABB`CC(C(CC*C(C(C(CPvD4C0ICPJCpHCICZCpWCWCWCPvD*CjCkC(CkCCCCC0CPCpCCEDODODFDODEDWDGDFDGDEDWDWDWDPvD`C`[D^D(CUnknown exceptionbad array new lengthstring too longC:\a\5\s\cmake\external\protobuf\src\google/protobuf/parse_context.h(cannot determine missing fields for lite message)C:\a\5\s\cmake\external\protobuf\src\google\protobuf\message_lite.cc.Can't message of type "" because it is missing required fields: parse exceeded maximum protobuf size of 2GB: vector too longC:\a\5\s\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug.C:\a\5\s\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream_impl.ccclose() failed: CHECK failed: !is_closed_: C:\a\5\s\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream_impl_lite.ccCHECK failed: (count) >= (0): CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: 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(). Parameter to BackUp() can't be negative.CHECK failed: (backup_bytes_) == (0): CHECK failed: (buffer_used_) == (buffer_size_): INFOWARNINGERRORFATAL[libprotobuf %s %s:%d] %s +%I64uMKMKMKMKC:\a\5\s\cmake\external\protobuf\src\google\protobuf\arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits<size_t>::max() - kBlockHeaderSize): Can't happenfalsetrue\\x%02xty1invalid string position...\\?\\/INVALID_ARGUMENT:C:\a\5\s\cmake\external\protobuf\src\google\protobuf\repeated_field.ccCHECK failed: (new_size) <= ((std::numeric_limits<size_t>::max() - kRepHeaderSize) / sizeof(old_rep->elements[0])): Requested size is too large to fit into size_t., C:\a\5\s\cmake\external\protobuf\src\google\protobuf\generated_message_util.ccCHECK failed: (scc->visit_status.load(std::memory_order_relaxed)) == (SCCInfoBase::kRunning): map/set too longunordered_map/set too longinvalid hash bucket count/\ was false. Stacktrace: -0RT  0RT Ъ0!=D:\a\ml-builds\ml-builds\onnxruntime\include\onnxruntime\core/framework/tensor.hTrying to get a Tensor, but got: IsTensor()OrtValue::GetD:\a\ml-builds\ml-builds\onnxruntime\include\onnxruntime\core/framework/ml_value.hOrtValue::GetMutableTrying to get a TensorSeq, but got: IsTensorSequence()CPUExecutionProviderDmlExecutionProviderInteger overflowSafeIntExceptionHandler<class onnxruntime::OnnxRuntimeException>::SafeIntOnOverflowD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/common/safeint.hTensor sequence must contain only primitive typeselem_type_ != nullptronnxruntime::TensorSeq::SetTypeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/framework/TensorSeq.hi < tensors_.size()onnxruntime::TensorSeq::Gettried creating tensor with negative value in shapesize overflow, got not enough space: expected is not supported in this functiontype RegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryMem pattern should be disabled when using DML execution provider.Sequential execution should be enabled when using DML execution provider.onnxruntime_profile_input name cannot be emptyoutput name cannot be emptyinput array is too shortshape is invalidspace is not enoughout of indexinternal errorindex out of rangeInput is not of one of the supported sequence types.Input is not of type sequence or map.Unsupported tensor element type in the input: Input is not of one of the supported map types.Expecting all elements to be tensors. Got: in[idx]->IsTensor()OrtCreateValueImplSeqHelperD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\session\onnxruntime_c_api.ccSequences must have tensors of the same data type. There was at least one tensor in the input that was different.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.Unsupported input typeFor map type num_values MUST be 2Either the key tensor or the value tensor has NumDimensions > 1Key and value tensors have unequal number of elements.Key type is not supported yet.Number of values should be at least 1.Specified domain and type names combination does not refer to a registered opaque typeml_type != nullptrOrtApis::CreateOpaqueValueOpaque type is not a non_tensor type!!!non_tensor_base != nullptrOrtApis::GetOpaqueValue1.4.0(ST`'T Tensor type mismatch. utils::IsPrimitiveDataType<T>(dtype_)onnxruntime::Tensor::MutableDataonnxruntime::Tensor::DataInvalid index requested for map type.Tensor must always contain primitive types. Found: value_type != nullptrOrtCreateValueImplMapHelperValue type is not supported yet: Map is missing type entry for its value++index < c.size()onnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<__int64,double,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,double> > > >::checkD:\a\ml-builds\ml-builds\onnxruntime\include\onnxruntime\core/framework/data_types_internal.honnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<__int64,float,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,float> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<__int64,__int64,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,__int64> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<__int64,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,double,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,double> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,float> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,__int64> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > > > > >::checkCheck for duplicate types in MLTypeCallDispatcherRetcalled_ < 2onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper<struct OrtStatus *,struct c_api_internal::UnsupportedReturnFailStatus>::GetSequence is missing type entry for its elementonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::vector<class std::map<__int64,float,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,float> > >,class std::allocator<class std::map<__int64,float,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,float> > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::vector<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,float> > >,class std::allocator<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,float> > > > > >::checkinvalid vector subscriptonnxruntime::DataTypeImpl::GetType<T>() == type_len >= 0 && static_cast<uint64_t>(len) < std::numeric_limits<size_t>::max()OrtCreateMapMLValueST`Encountered nullptr.:V1`1not implementedexecution_mode is not validgraph_optimization_level is not valid(TT`pxUT?V1`1VT1`1VT1`1 is not implementedWT -AAhWT Ponnxruntime::OpKernel::ComputeAsyncResult buffer is not large enoughXTP P' in custom op 'Unsupported version 'Descriptiononnxruntime::CreateCustomRegistryD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\session\custom_ops.cc8YT0`'YT` '8ZT`'`@V%%$%$$;V$$$$$$ZT``$$HV1`1Attempt to use DefaultLogger but none has been registered.V& @%  % p# " P" `    P p  @  8[TCpu\T00H]T< p<00'<<<<<<CUDAExecutionProvider has already been registered.Provider onnxruntime::ExecutionProviders::AddD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/framework/execution_providers.hTrying to get a SparseTensor, but got: IsSparseTensor()onnxruntime::FeedsFetchesInfo::FeedsFetchesInfoD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/framework/feeds_fetches_manager.h8^T@} Compute_Create_State_Release_State_]T ^T@0_T@_TMemcpyTransformerx`T<p<@0'<<<<<<`T8] [json.exception.xaT8other_errorORT_LOAD_CONFIG_FROM_MODEL%Y-%m-%d_%H-%M-%SThe environment variable contained the value: are '0' and '1'. The only supported values for the environment variable Reading the provided model for the ORT configonnxruntime::FinalizeSessionOptionsD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\session\inference_session.ccModelProto needs to be parsed to check for ORT config within itis_model_proto_parsedCould not finalize session options while constructing the inference session. Error Message: status.IsOK()onnxruntime::InferenceSession::ConstructorCommongraph_transformation_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK()Creating and using per session threadpools since use_per_session_threads_ is trueintra-opFailed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIALUsing global/env threadpools since use_per_session_threads_ is falseWhen the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API.session_env.EnvCreatedWithGlobalThreadPools()CastFloat16TransformerGiven model could not be parsed while creating inference session. Error message: onnxruntime::InferenceSession::InferenceSessionCould not parse model successfully while constructing the inference sessionst.IsOK()resultError during EndProfiling(): onnxruntime::InferenceSession::~InferenceSessionUnknown error during EndProfiling()Received nullptr for exec providerExecution providers must be registered before the session is initialized. onnxruntime::InferenceSession::RegisterExecutionProviderExecution providers must be registered before the session is initialized.Memory pattern must be disabled before registering DMLExecutionProviderSequential execution must be enabled before registering DMLExecutionProvideronnxruntime::InferenceSession::AddCustomOpDomainsReceived nullptr for custom registryThis session already contains a loaded model.onnxruntime::InferenceSession::LoadException during loading: Unknown exception in Load()Encountered unknown exception in 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_protoonnxruntime::InferenceSession::TransformGraphCould not find an implementation for the node (Node placementsAll nodes have been placed on [: [ Provider: [Main Graph instance should have populated all subgraphs when being resolved.subgraphonnxruntime::InferenceSession::CreateSubgraphSessionStateCreateSubgraphSessionState should have created an entry earlier.subgraph_session_stateonnxruntime::InferenceSession::InitializeSubgraphSessionsp_op_kernelInitializing session.onnxruntime::InferenceSession::InitializeModel was not loadedModel was not loaded.Session has already been initialized.Adding default CPU execution provider.Parallel execution mode doesn't support CUDA Execution Provider currently.Serializing Optimized ONNX model with Graph Optimization level greater than ORT_ENABLE_EXTENDED. The generated model may contain hardware and execution provider specific optimizations, and should only be used in the same environment the model was optimized for.Session successfully initialized.Exception during initialization: Encountered unknown exception in Initialize()session_initialization Please fix either the inputs or the model. Expected: Got: Invalid rank for input: for the following indices -Got invalid dimensions for input: index: ) , expected: (Unexpected input data type. Actual: ( elements.elements, but feeds has Size mismatch: feed_names has Invalid Feed Input Name: is not expected to be of type tensor.Input with name: onnxruntime::InferenceSession::ValidateInputs is not expected to be of type sparse tensor. is not expected to be of type tensor sequence.Output vector pointer is NULLAt least one output should be requested.p_fetches->size(): Output vector incorrectly sized: output_names.size(): Invalid Output Name:Session was not initializedonnxruntime::InferenceSession::RunSession not initialized.Running with tag: Encountered unknown exception in Run()model_runonnxruntime::InferenceSession::GetModelMetadataonnxruntime::InferenceSession::GetModelInputsonnxruntime::InferenceSession::GetOverridableInitializersonnxruntime::InferenceSession::GetModelOutputsProfiler is disabled.onnxruntime::InferenceSession::EndProfilingCould 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)onnxruntime::InferenceSession::CreateLoggerForRunInvalid session log severity level. Not a valid onnxruntime::logging::Severity value: session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast<int>(logging::Severity::kFATAL)onnxruntime::InferenceSession::InitLoggerExceeded max transformer level. Current level is set to graph_optimization_level <= TransformerLevel::MaxLevelonnxruntime::InferenceSession::AddPredefinedTransformers961c151d2e87f2686a955a9be24d316f1362bf21 3.7.1PbT,`0'[T .json_list too longbT,,0-p2$$localtime_s(&local_tm, &in_time_t) == 0onnxruntime::`anonymous-namespace'::GetCurrentTimeStringPcT,,-2$$ dT} } @?dT} } @Denv_ptr == p_instance_OrtEnv::ReleaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\session\ort_env.ccp_providerpRNsession_options: eT8parse errorparse_error, column at line fT8invalid_iteratorfT8type_errorgT8out_of_rangegT`} ort_configUnsupported value for intra_op_num_threads: onnxruntime::SetIntraOpNumThreadsD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\session\inference_session_utils.ccSetting intra_op_num_threads to Unsupported value for inter_op_num_threads: onnxruntime::SetInterOpNumThreadsSetting inter_op_num_threads to Unsupported execution_mode value in ORT config: onnxruntime::SetExecutionModeSequential modeParallel modeSetting execution_mode to Setting graph_optimization_level to ORT_DISABLE_ALLonnxruntime::SetGraphOptimizationLevelSetting graph_optimization_level to ORT_ENABLE_BASICSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_ALLUnsupported graph_optimization_level value in ORT config: Unsupported value for enable_profiling option: onnxruntime::SetEnableProfilingSetting enable_profiling to 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 modelonnxruntime::InferenceSessionUtils::ParseOrtConfigJsonInModelProtoORT config json from the model: Json stored in the `ort_config` key cannot be parsed. Error message: 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 modelonnxruntime::InferenceSessionUtils::ParseSessionOptionsFromModelProtointra_op_num_threadsintra_op_num_threads option in the model file must be an integerinter_op_num_threadsinter_op_num_threads option in the model file must be an integerexecution_modeexecution_mode option in the model file must be an integergraph_optimization_levelgraph_optimization_level option in the model file must be an integerenable_profilingenable_profiling option in the model file must be an integerIgnoring unsupported session option in ORT config: ' not foundkey 'cannot use at() with nullcannot use key() for non-object iteratorsinvalid map<K, T> keyobjectarraybooleandiscardednumbercannot compare iterators of different containerscannot get valuesyntax error while parsing - ; last read: 'unexpected ; expected <U+%.4X>invalid BOM; must be 0xEF 0xBB 0xBF if giveninvalid literal<uninitialized>true literalfalse literalnull literalstring literalnumber literal'[''{'']''}'':'','<parse error>end of input'[', '{', or a literalunknown tokeninvalid number; expected digit after '-'invalid number; expected digit after '.'invalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after exponent signinvalid string: missing closing quoteinvalid string: '\u' must be followed by 4 hex digitsinvalid string: surrogate U+DC00..U+DFFF must be followed by U+DC00..U+DFFFinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: forbidden character after backslashinvalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001F (US) must be escaped to \u001Finvalid string: ill-formed UTF-8 byteobject keyobject separatornumber overflow parsing 'iterator does not fit current valueiterator out of rangecannot use erase() with phT `'type must be number, but is com.microsoftcom.microsoft.nchwccom.microsoft.mlfeaturizersGV 65>V 65AV1inter-opD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\session\environment.ccMemcpyFromHostMemcpyToHostException caught: FV0^0^ ^^$$@iTxx ^x$$onnxruntime::GraphTransformer::ApplyD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\graph_transformer.cconnxruntime::GraphTransformerManager::ApplyTransformersD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\graph_transformer_mgr.ccThis transformer is already registered D:\a\ml-builds\ml-builds\onnxruntime\include\onnxruntime\core/graph/graph.h Max:Validating no unexpected access using an invalid node_index. Got:node_index < nodes_.size()onnxruntime::Graph::NodeAtIndexImplonnxruntime::GraphTransformer::RecurseD:\a\ml-builds\ml-builds\onnxruntime\include\onnxruntime\core/optimizer/graph_transformer.hCastDef_%dcast node to cast from float16 to float32 on cpuiTpRemoveDuplicateCastTransformeronnxruntime::RemoveDuplicateCastTransformer::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\insert_cast_transformer.cconnxruntime::InsertCastTransformer::ApplyImplDnnlExecutionProviderNGRAPHExecutionProviderOpenVINOExecutionProviderNupharExecutionProviderVitisAIExecutionProviderTensorrtExecutionProviderNnapiExecutionProviderACLExecutionProviderArmNNExecutionProvideronnxruntime::Node::ForEachWithIndexonnxruntime::MemcpyTransformer::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\transformer_memcpy.cconnxruntime::TransformerMemcpyImpl::ProcessDefs' doesn't support memcpy Execution type 'MemcpyCopy from/to host memorydup_replacements.find(&arg) == dup_replacements.end()onnxruntime::TransformerMemcpyImpl::ProcessInitializers::<lambda_73d151dd9f087a4786e14d26c37f9f1f>::operator ()onnxruntime::TransformerMemcpyImpl::ProcessInitializersjT   0p$kT `$kT@@@`$lT   lTPNmTp~AttentionFusionmT`BiasGeluFusionnTDpCastEliminationnTPConstantFoldingoT ConvActivationFusionoTD`ConvAddFusionpTD!ConvBNFusionpTDE:ConvMulFusionqT@E0IpEliminateDropoutqTJDynamicQuantizeMatMulFusionrThEmbedLayerNormFusionrT GpExpandEliminationsTpFastGeluFusionsT GeluApproximationtT GeluFusiontT3GemmActivationFusionuTE YpEliminateIdentityuT@YLayerNormFusionvT0MatMulAddFusionvTNchwcTransformerwTG&FuseReluClipwT/ReshapeFusionxT`HZ`VShapeToInitializerxT[SkipLayerNormFusionyTFypEliminateSliceyTI0UnsqueezeElimination_RuleBasedTransformerLevelUnsupported levelonnxruntime::optimizer_utils::GenerateRewriteRulesD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\graph_transformer_utils.ccUnsupported level onnxruntime::optimizer_utils::GenerateTransformersonnxruntime::RuleBasedGraphTransformer::ApplyRulesOnNodeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\rule_based_graph_transformer.cconnxruntime::RuleBasedGraphTransformer::ApplyImplonnxruntime::Initializer::InitializerD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/optimizer/initializer.hsize is differentsize_ == sizeunsupported data type: ReadExternalRawData() failed: input_indices.size() == expected_values.size() && input_indices.size() > 0onnxruntime::AttentionFusionHelper::CheckSliceParametersD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/optimizer/attention_fusion_helper.hSlice does not have enough number of inputsSlice ends is less than INT_MAXExpected value:Slice parameter is not expected. Input index:Start MatchGemmSubgraphonnxruntime::AttentionFusionHelper::MatchGemmSubgraphFaild to match gemm pathInput of reshape_before_gemm is not the input of subgraphOutput edge count not expected for nodes in gemm pathGemm does not have 3 inputsGemm bias is not constantGemm bias shape not expectedCheckSliceParameters return falseconcat first input value is not -1Faild to match concat node for Gather pathsconcat_after_gather does not have expected number of inputs or output edgesconcat_after_gather input 2 does not have expected valueFaild to match gemm gather pathOutput edge count not expected for nodes in gemm gather pathunsqueeze_after_gather axes value not expectedgather axis value not expectedgather input 1 value is not expectedPass MatchGemmSubgraphStart ValidateGemmInitializeronnxruntime::AttentionFusionHelper::ValidateGemmInitializerGemm bias is not constant initializerGemm bias shape is not expectedGemm weight is not constant initializerGemm weight shape is not expectedPass ValidateGemmInitializerStart MatchUnidirMaskSubgraphonnxruntime::AttentionFusionHelper::MatchUnidirMaskSubgraphFaild to match the path (Div-->Where-->Add) for unidirectional maskFaild to match path 1 for unidirectional maskOutput edge count not expected for nodes in path 1 of unidirectional maskDiv and Shape1 does not have edgeCheckSliceParameters returns false for last_sliceCheckSliceParameters returns false for mask_sliceCheckSliceParameters returns false for slice1Faild to match path 2 for unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 3 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskFaild to match path 4 for unidirectional maskDiv and Shape does not have edgeOutput edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskCheckSliceParameters return false for slice2Pass MatchUnidirMaskSubgraphStart MatchInputMaskSubgraphonnxruntime::AttentionFusionHelper::MatchInputMaskSubgraphFailed to find Softmax nodeOutput edge count not expected for SoftmaxFailed to find path for maskOutput edge count not expected for mask nodesSoftmax attribute axis is expected to be 3mask_unsqueeze_1 axes not matched. Expect: 1mask_unsqueeze_2 axes not matched. Expect: 2mask_sub const input not matchedmask_mul const input not matchedPass MatchInputMaskSubgraphStart MatchPastSubgraphonnxruntime::AttentionFusionHelper::MatchPastSubgraphFailed to find path for past_kFailed to find path for present_kFailed to find path for present_v and past_vFailed to match v_concatpast_k_transpose perm attribute not matchedpresent_k_transpose perm attribute not matchedpresent_k_unsqueeze axes value not expectedpresent_v_unsqueeze axes value not expectedpast_v_gather indices != 1past_k_gather indices != 0past_v_gather and past_k_gather does not have same past inputOutput edge count not expected for nodes in past subgraphPass MatchPastSubgraphStart CheckNodesInPathVonnxruntime::AttentionFusionHelper::CheckNodesInPathVOutput edge count not expected for nodes in path vFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3hidden_size != num_heads * head_sizev_reshape initializer value is not expectedreshape initializer value is not expectedPass CheckNodesInPathVStart CheckNodesInPathQonnxruntime::AttentionFusionHelper::CheckNodesInPathQq_reshape const not matchedqk_div const not matched.q_transpose perm attribute not matchedPass CheckNodesInPathQStart CheckNodesInPathKonnxruntime::AttentionFusionHelper::CheckNodesInPathKk_transpose perm attribute not matchedk_reshape const not matchedPass CheckNodesInPathKMask_Int32Cast mask from int64 to int32MaskCastStart FuseGptAttentiononnxruntime::AttentionFusionHelper::FuseGptAttentionFaild to find path to qkv_matmulFaild to find path v to SplitCheckNodesInPathV return falseMatchInputMaskSubgraph returns falseMatchUnidirMaskSubgraph returns NULLFailed to find path for qq and v are not from same Split nodeCheckNodesInPathQ returns falseFailed to find path for kk and v are not from same Split nodeCheckNodesInPathK returns falseMatchPastSubgraph returns falseFused Attention subgraphs Attentionnum_headsunidirectionalFused an attention node for GPT.qkv_weightsqkv_biasMaskIndex_Int32Count number of wordsMaskIndexMask shape is unknown or not 2D, or data type unknownonnxruntime::ProcessMaskD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\attention_fusion.ccMask data type is not int32 or int64 or float32onnxruntime::AttentionFusion::ApplyImplLayerNormalizationshape of layer norm bias tensor not expectedTotal fused Attention node count: Faild to find path vonnxruntime::AttentionFusion::FuseSubGraphOutput edge count not expected for Add or MatMul in path vFailed in match v_matmul and v_add input shapeFailed in match input mask subgraphq root should be layer normalizationq_matmul and q_add shape not matchedk root is not layer normk_matmul and k_add shape not matchedFailed 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.Failed to create mask indexFused an attention node.@ - onnxruntime::BiasGeluFusion::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\bias_gelu_fusion.ccGeluFastGeluBiasGelufused Add and GeluCould not find OrtValue with name 'onnxruntime::ConstantFolding::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\constant_folding.cc node 'can't constant fold Could not find a CPU kernel and hence fetches.size() == node->OutputDefs().size(). Can't constant fold Unsupported output type of Unexpected data type for Clip input of onnxruntime::`anonymous-namespace'::GetClipConstantMinMax::<lambda_f71fb383bad23dbc1e591f014db6e22d>::operator ()D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\conv_activation_fusion.cconnxruntime::ConvActivationFusion::ApplyImplwith activation fused Conv FusedConvfused activationactivation_paramsdata type is not supportedonnxruntime::Initializer::ToProtoconv_W_tensor_protoonnxruntime::ConvAddFusion::ApplyD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\conv_add_fusion.ccadd_B_tensor_protoconv_B_tensor_protoConvAddFusion_B_ConvAddFusion_Add_B_bn_scale_tensor_protoonnxruntime::ConvBNFusion::ApplyD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\conv_bn_fusion.ccbn_B_tensor_protobn_mean_tensor_protobn_var_tensor_protoConvBnFusion_W_ConvBnFusion_BN_B_onnxruntime::ConvMulFusion::ApplyD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\conv_mul_fusion.ccmul_B_tensor_protoConvMulFusion_W_ConvMulFusion_Mul_B_ -onnxruntime::DynamicQuantizeMatMulFusion::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\dynamic_quantize_matmul_fusion.ccDynamicQuantizeMatMulMatMulIntegerToFloatnullptr != fused_node̼+_Int32Cast Input from int64 to int32_CastInput shape is unknown or not 2D, or data type unknownonnxruntime::CheckInputD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\embed_layer_norm_fusion.ccInput data type is not int32 or int64Failed to find path 1 of position shape.onnxruntime::MatchInputToConcatSubgraphOutput edge count not expected for nodes in path 1 of position shape.Second input of Gather in path 1 of position shape should be a constant with value 0.Failed to find path 2 of position shape.Output edge count not expected for nodes in path 2 of position shape.Gather node in path 2 is not linked to another subgraph.Second input of Gather in path 2 of position shape should be a constant with value 1.The parent of two shape nodes are expected to be input_ids.Output edge count not expected for nodes in path1.onnxruntime::MatchPositionEmbeddingSubgraphsFromGatherThe first input of Range should be a constant with value 0.The third input of Range should be a constant with value 1.Second input of Gather should be a constant with value 1. Failed to match Shape node. The parent of shape nodes are expected to be input_ids.Optional position subgraph nodes number of outputs unexpected.Optional position subgraph nodes Where node is expected to be the parent of Reshape.Failed to match position subgraph.position_embeddingsonnxruntime::EmbedLayerNormFusion::ApplyImplWord embedding shape not expected.Input is expected to have dim value in all dimensions.Failed to get initializer tensor.Position embedding shape not matched.Position embedding data type shall be float or float16.Failed to match position embedding subgraph.Failed to get position embedding weights.Position embedding shape is not expected.Input id is not valid. Segment id is not valid. Mask is not valid. 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 EmbedLayerNormalizationonnxruntime::FastGeluFusion::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\fast_gelu_fusion.ccfast_gelu_outputfused GPT2Gelu subgraphs GPT2Gelu'7=*BL?@@zT@FreeDimensionOverrideTransformerInvalid free dimension override.onnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformerD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\free_dim_override_transformer.ccConflicting free dimension overrides.which does not equal the specified override of with a fixed dimension size ' The model has input 'onnxruntime::FreeDimensionOverrideTransformer::ApplyImplonnxruntime::GeluApproximation::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\gelu_approximation.ccGelu approximationTotal Gelu Approximation (FastGelu) node count: onnxruntime::GeluFusion::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\gelu_fusion.ccfused Gelu subgraphs ??onnxruntime::GemmActivationFusion::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\gemm_activation_fusion.ccfused Gemm FusedGemmactivation_'7onnxruntime::LayerNormFusion::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\layer_norm_fusion.ccfused LayerNorm subgraphs invalid unordered_map<K, T> keyonnxruntime::MatMulAddFusion::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\matmul_add_fusion.ccfused Matmul and Add gemmreorderReorderInput_nchwcbn_scalebn_B_bn_nchwcReorderOutputchannelschannels_lastasymmetricflooronnxruntime::NchwcTransformer::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\nchwc_transformer.ccdeque<T> too long -  - is not supported.data type Unexpected data type for Clip 'min' input of onnxruntime::FuseReluClip::ApplyD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\relu_clip_fusion.cc_min_zero_constantFuseReluClip_onnxruntime::ReshapeFusion::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\reshape_fusion.ccFused reshape node: Total fused reshape node count: Cannot replace concat node with initializer:onnxruntime::ReshapeFusion::Fuse_Subgraph̼+onnxruntime::SkipLayerNormFusion::ApplyImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\skip_layer_norm_fusion.ccfused SkipLayerNorm subgraphs SkipLayerNormalizationUnsqueezeElimination_UnsqueezeElimination cannot remove node onnxruntime::UnsqueezeElimination::ApplyD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\unsqueeze_elimination.ccExternal data type must not be UNDEFINED or STRING.onnxruntime::Initializer::ReadExternalRawDataD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\initializer.ccNot satisfied: tensor_proto.data_type() != ONNX_NAMESPACE::TensorProto_DataType_UNDEFINED && tensor_proto.data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING +Cpuai.onnx.mlai.onnx.trainingai.onnx.preview.training!=C:\a\5\s\include\onnxruntime\core/framework/tensor.h)Trying to get a Tensor, but got: IsTensor()OrtValue::GetC:\a\5\s\include\onnxruntime\core/framework/ml_value.hOrtValue::GetMutableTrying to get a TensorSeq, but got: IsTensorSequence()VIWEFCPUExecutionProviderInteger overflowSafeIntExceptionHandler<class onnxruntime::OnnxRuntimeException>::SafeIntOnOverflowC:\a\5\s\onnxruntime\core/common/safeint.hORT_LOAD_CONFIG_FROM_MODELTensor sequence must contain only primitive typeselem_type_ != nullptronnxruntime::TensorSeq::SetTypeC:\a\5\s\onnxruntime\core/framework/TensorSeq.hi < tensors_.size()onnxruntime::TensorSeq::Gettried creating tensor with negative value in shapesize overflow, got not enough space: expected is not supported in this functiontype RegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryonnxruntime_profile_input name cannot be emptyoutput name cannot be emptylengths allocation failedstring buffer allocation failedOutput buffer allocation failedinput array doesn't equal tensor sizeelement index is out of boundsshape is invalidoffsets buffer is not equal to tensor sizeoutput buffer is too smallbuffer size is too small for stringout of indexinternal errorindex out of rangeInput is not of one of the supported sequence types.Input is not of type sequence or map.input array is too shortUnsupported tensor element type in the input: Input is not of one of the supported map types.Expecting all elements to be tensors. Got: in[idx]->IsTensor()OrtCreateValueImplSeqHelperC:\a\5\s\onnxruntime\core\session\onnxruntime_c_api.ccSequences must have tensors of the same data type. There was at least one tensor in the input that was different.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.Unsupported input typeFor map type num_values MUST be 2Either the key tensor or the value tensor has NumDimensions > 1Key and value tensors have unequal number of elements.Key type is not supported yet.Number of values should be at least 1.opaque(,Specified domain and type names combination does not refer to a registered opaque typeml_type != nullptrOrtApis::CreateOpaqueValueOpaque type is not a non_tensor type!!!non_tensor_base != nullptrOrtApis::GetOpaqueValuethis API does not support stringslocation dimensions do not match shape sizeinvalid location range1.5.1T Tensor type mismatch. utils::IsPrimitiveDataType<T>(dtype_)onnxruntime::Tensor::MutableDataonnxruntime::Tensor::DataInvalid index requested for map type.Tensor must always contain primitive types. Found: value_type != nullptrOrtCreateValueImplMapHelperValue type is not supported yet: Map is missing type entry for its value++index < c.size()onnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<__int64,double,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,double> > > >::checkC:\a\5\s\include\onnxruntime\core/framework/data_types_internal.honnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<__int64,float,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,float> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<__int64,__int64,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,__int64> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<__int64,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,double,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,double> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,float> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,__int64> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > > > > >::checkCheck for duplicate types in MLTypeCallDispatcherRetcalled_ < 2onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper<struct OrtStatus *,struct c_api_internal::UnsupportedReturnFailStatus>::GetSequence is missing type entry for its elementonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::vector<class std::map<__int64,float,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,float> > >,class std::allocator<class std::map<__int64,float,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,float> > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType<class std::vector<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,float> > >,class std::allocator<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,float> > > > > >::checkinvalid vector subscript != onnxruntime::DataTypeImpl::GetType<T>() == type_len >= 0 && static_cast<uint64_t>(len) < std::numeric_limits<size_t>::max()OrtCreateMapMLValueEncountered nullptr.SKnot implementedgraph_optimization_level is not validexecution_mode is not validWARNING: Since openmp is enabled in this build, this API cannot be used to configure intra op num threads. Please use the openmp environment variables to control the number of threads. +unknown is not implementedai.onnxonnxruntime::OpKernel::ComputeAsync' in custom op 'Result buffer is not large enoughonnxruntime::CustomOpKernel::CustomOpKernelUnsupported version 'C:\a\5\s\onnxruntime\core\session\custom_ops.cconnxruntime::CreateCustomRegistryANo requested allocator availableEnv is nullReceived invalid value for arena extend strategy. Valid values can be either 0, 1 or -1.OrtMemoryInfo is nullOnly CPU devices are supported for now.env_ptr == p_instance_OrtEnv::ReleaseC:\a\5\s\onnxruntime\core\session\ort_env.cconnxruntime::IOBinding::BindInputC:\a\5\s\onnxruntime\core\session\IOBinding.ccp_providerbad castonnxruntime::logging::LoggingManager::DefaultLoggerC:\a\5\s\include\onnxruntime\core/common/logging/logging.hAttempt to use DefaultLogger but none has been registered.session.load_model_formatsession.save_model_formatsession.use_env_allocators]ORTMCUDAExecutionProviderDmlExecutionProvideronnxruntime::ExecutionProviders::AddC:\a\5\s\onnxruntime\core/framework/execution_providers.h has already been registered.Provider onnxruntime::FeedsFetchesInfo::FeedsFetchesInfoC:\a\5\s\onnxruntime\core/framework/feeds_fetches_manager.hTrying to get a SparseTensor, but got: IsSparseTensor()Release_State_Compute_Create_State_MemcpyTransformer] [json.exception.other_error1.4.0The environment variable contained the value: %Y-%m-%d_%H-%M-%SReading the provided model for the ORT configonnxruntime::FinalizeSessionOptions are '0' and '1'. The only supported values for the environment variable is_model_proto_parsedCould not finalize session options while constructing the inference session. Error Message: C:\a\5\s\onnxruntime\core\session\inference_session.ccModelProto needs to be parsed to check for ORT config within itgraph_transformation_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK()Creating and using per session threadpools since use_per_session_threads_ is truestatus.IsOK()onnxruntime::InferenceSession::ConstructorCommonUsing global/env threadpools since use_per_session_threads_ is falseWhen the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API.intra-opFailed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIALGiven model could not be parsed while creating inference session. Error message: onnxruntime::InferenceSession::InferenceSessionsession_env.EnvCreatedWithGlobalThreadPools()CastFloat16TransformerresultError during EndProfiling(): Could not parse model successfully while constructing the inference sessionst.IsOK()onnxruntime::InferenceSession::~InferenceSessionReceived nullptr for exec provideronnxruntime::InferenceSession::{dtor}::<lambda_08a0ec150ea654d65e73b6777af410b8>::operator ()Unknown error during EndProfiling()Execution providers must be registered before the session is initialized.So disabling it for this session since it uses the DML Execution Provider.Execution providers must be registered before the session is initialized. onnxruntime::InferenceSession::RegisterExecutionProviderParallel execution mode does not support the DML Execution Provider. So making the execution mode sequential for this session since it uses the CUDA Execution Provider.Having memory pattern enabled is not supported while using the DML Execution Provider. So making the execution mode sequential for this session since it uses the DML Execution Provider.Parallel execution mode does not support the CUDA Execution Provider. onnxruntime::InferenceSession::AddCustomOpDomainsReceived nullptr for custom registryException during loading: onnxruntime::InferenceSession::SaveToOrtFormatUnknown exception in Load()Encountered unknown exception in Load()This session already contains a loaded model.onnxruntime::InferenceSession::LoadFailed to load model because protobuf parsing failed.model_loading_arrayORTModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().ModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.model_loading_from_saved_proto( (onnxruntime::InferenceSession::TransformGraphCould not find an implementation for the node All nodes have been placed on [: [Node placements].onnxruntime::InferenceSession::LoadOrtModel::<lambda_24792f6403ccaba9765cde80932bdcdd>::operator ()onnxruntime::InferenceSession::LoadOrtModel Provider: [Satisfied, but should not be: nullptr == fbs_session +Serialized version info is null. Invalid ORT format model.This session has already been initialized.InferenceSession is null. Invalid ORT format model.The ORT format model version [Not satisfied: IsOrtModelVersionSupported(fbs_ort_model_version->str()) +Satisfied, but should not be: fbs_ort_model_version == nullptr +] is not supported this build SessionState is null. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_sess_state +Missing Model. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_model +onnxruntime::InferenceSession::Initialize::<lambda_0e8157b2f9aa777002543862aaf58d84>::operator ()Initializing session.Exception during initialization: onnxruntime::InferenceSession::Initialize::<lambda_f38b967eeccf82d76b4de04a1bccb559>::operator ()Model was not loaded.Session has already been initialized.onnxruntime::InferenceSession::InitializeModel was not loadedSession successfully initialized.Encountered unknown exception in Initialize()Adding default CPU execution provider.Serializing optimized model with Graph Optimization level greater than ORT_ENABLE_EXTENDED. The generated model may contain hardware and execution provider specific optimizations, and should only be used in the same environment the model was optimized for. Expected: Got: session_initialization Please fix either the inputs or the model.Got invalid dimensions for input: index: Invalid rank for input: for the following indices +elements, but feeds has Size mismatch: feed_names has Unexpected input data type elements.Input with name: onnxruntime::InferenceSession::ValidateInputsInvalid Feed Input Name: is not expected to be of type tensor.Output vector pointer is NULLAt least one output should be requested. is not expected to be of type sparse tensor. is not expected to be of type tensor sequence.Invalid Output Name:Session was not initializedp_fetches->size(): Output vector incorrectly sized: output_names.size(): Running with tag: Encountered unknown exception in Run()onnxruntime::InferenceSession::RunSession not initialized.onnxruntime::InferenceSession::GetModelInputsonnxruntime::InferenceSession::GetOverridableInitializersmodel_runonnxruntime::InferenceSession::GetModelMetadataProfiler is disabled.onnxruntime::InferenceSession::EndProfilingonnxruntime::InferenceSession::GetModelOutputsonnxruntime::InferenceSession::NewIOBindingCould not write a profile because no model was loaded.onnxruntime::InferenceSession::CreateLoggerForRunInvalid 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)session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast<int>(logging::Severity::kFATAL)onnxruntime::InferenceSession::InitLoggerInvalid session log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::AddPredefinedTransformersExceeded max transformer level. Current level is set to graph_optimization_level <= TransformerLevel::MaxLevel/961c151d2e87f2686a955a9be24d316f1362bf21 3.7.1.json_Load model from onnxruntime::LoadOrtModelBytesmodel_loading_uri failed:list too long bytes were able to be read. failed. Only localtime_s(&local_tm, &in_time_t) == 0onnxruntime::`anonymous-namespace'::GetCurrentTimeStringcom.microsoft.mlfeaturizerscom.microsoft.nchwccom.microsoft[TypeInferenceError] Element type of input unknownInput expected to have tensor type expected to have type but instead is null expected to have sequence typeOutput tensor(int32)tensor(uint64)tensor(uint32)tensor(double)tensor(float)tensor(float16)tensor(int64)tensor(int8)tensor(uint16)tensor(uint8)tensor(bfloat16)tensor(int16)tensor(complex128)tensor(complex64)tensor(bool)tensor(string) version: ) from file , but it is already registered from file line , but it its domain is not known by the checker.Trying to register schema with name (domain: ] (usually, this means you bumped the operator version but forgot to update the version range in DomainToVersionRange in onnx/defs/schema.h).Schema error: , but it its version is not in the inclusive range [inter-opException caught: Allocator with this OrtMemoryInfo is already registered.inputTMemcpyFromHostC:\a\5\s\onnxruntime\core\session\environment.ccConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.YoutputXMemcpyToHost: value'parse_errorparse errorinvalid_iterator at line , column session_optionsort_configout_of_rangetype_errorSetting intra_op_num_threads to C:\a\5\s\onnxruntime\core\session\inference_session_utils.cconnxruntime::SetIntraOpNumThreadsUnsupported value for intra_op_num_threads: Unsupported execution_mode value in ORT config: Setting inter_op_num_threads to onnxruntime::SetInterOpNumThreadsUnsupported value for inter_op_num_threads: Setting execution_mode to Parallel modeSequential modeonnxruntime::SetExecutionModeSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_BASIConnxruntime::SetGraphOptimizationLevelSetting graph_optimization_level to ORT_DISABLE_ALLonnxruntime::SetEnableProfilingUnsupported value for enable_profiling option: Unsupported graph_optimization_level value in ORT config: Setting graph_optimization_level to ORT_ENABLE_ALLThe Model Proto has already been checked for the ORT config json.onnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProto::<lambda_abffc64b9fb5bbedac1e1a9bc3d497ab>::operator ()Json stored in the `ort_config` key cannot be parsed. Error message: Setting enable_profiling to The Model Proto hasn't been checked for the ORT config json.ORT config json from the model: onnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProtoFound session/run/environment configuration in the model file to be used while running the modelintra_op_num_threads option in the model file must be an integerintra_op_num_threadsonnxruntime::inference_session_utils::JsonConfigParser::ParseSessionOptionsFromModelProtoDid not find session options in the model file to be used while running the modelexecution_mode option in the model file must be an integerexecution_modeinter_op_num_threads option in the model file must be an integerinter_op_num_threadsenable_profiling option in the model file must be an integerenable_profilinggraph_optimization_level option in the model file must be an integergraph_optimization_levelkey '' not foundIgnoring unsupported session option in ORT config: cannot use at() with cannot use key() for non-object iteratorsnullarrayobjectinvalid map<K, T> keynumberdiscardedbooleanstringcannot get valuecannot compare iterators of different containerssyntax error unexpected ; last read: '- while parsing invalid literalinvalid BOM; must be 0xEF 0xBB 0xBF if given<U+%.4X>; expected null literalfalse literaltrue literal<uninitialized>'{''['number literalstring literal','':''}'']'unknown token'[', '{', or a literalend of input<parse error>invalid number; expected digit after exponent signinvalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after '.'invalid number; expected digit after '-'invalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: surrogate U+DC00..U+DFFF must be followed by U+DC00..U+DFFFinvalid string: '\u' must be followed by 4 hex digitsinvalid string: missing closing quoteinvalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: forbidden character after backslashinvalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001B (ESC) must be escaped to \u001Bvector<bool> too longinvalid string: ill-formed UTF-8 byteinvalid string: control character U+001F (US) must be escaped to \u001Fnumber overflow parsing 'object separatorobject keycannot use erase() with iterator out of rangeiterator does not fit current valuetype must be number, but is Max:C:\a\5\s\include\onnxruntime\core/graph/graph.honnxruntime::Graph::NodeAtIndexImplnode_index < nodes_.size()Validating no unexpected access using an invalid node_index. Got:onnxruntime::GraphTransformer::RecurseCastcast node to cast from float16 to float32 on cpuInserted_CastC:\a\5\s\include\onnxruntime\core/optimizer/graph_transformer.hC:\a\5\s\onnxruntime\core\optimizer\insert_cast_transformer.cconnxruntime::RemoveDuplicateCastTransformer::ApplyImplRemoveDuplicateCastTransformertodtype_attribute->second.has_i()InsertCastTransformer works on the assumption that `dtype` attribute holds an integer.dtypeonnxruntime::InsertCastTransformer::ApplyImplACLExecutionProviderAttentionFusionCommonSubexpressionEliminationCastEliminationBiasGeluFusionConvActivationFusionConvMulFusionConvBNFusionConvConvAddFusionEmbedLayerNormFusionDynamicQuantizeMatMulFusionDropoutEliminateDropoutGeluApproximationFastGeluFusionExpandExpandEliminationIdentityEliminateIdentityGemmActivationFusionGeluFusionNchwcTransformerMatMulScaleFusionMatMulAddFusionLayerNormFusionShapeToInitializerReshapeFusionReluFuseReluClipSliceEliminateSliceSkipLayerNormFusionShapeLevel_RuleBasedTransformerUnsqueezeUnsqueezeEliminationUnsupported level C:\a\5\s\onnxruntime\core\optimizer\graph_transformer_utils.cconnxruntime::optimizer_utils::GenerateRewriteRulesUnsupported levelonnxruntime::optimizer_utils::GenerateTransformersOpenVINOExecutionProviderNupharExecutionProviderVitisAIExecutionProviderTensorrtExecutionProviderDnnlExecutionProviderNGRAPHExecutionProvideronnxruntime::Node::ForEachWithIndexNnapiExecutionProviderArmNNExecutionProvideronnxruntime::MemcpyTransformer::ApplyImplC:\a\5\s\onnxruntime\core\optimizer\transformer_memcpy.ccCopy from/to host memorydup_replacements.find(&arg) == dup_replacements.end()onnxruntime::TransformerMemcpyImpl::ProcessInitializers::<lambda_c1e5f027a83cae628bf95cd7b44eb192>::operator ()onnxruntime::TransformerMemcpyImpl::ProcessInitializersonnxruntime::TransformerMemcpyImpl::ProcessDefs' doesn't support memcpy Execution type 'Memcpyonnxruntime::GraphTransformer::ApplyC:\a\5\s\onnxruntime\core\optimizer\graph_transformer.cconnxruntime::GraphTransformerManager::ApplyTransformersC:\a\5\s\onnxruntime\core\optimizer\graph_transformer_mgr.ccThis transformer is already registered onnxruntime::Initializer::InitializerC:\a\5\s\onnxruntime\core/optimizer/initializer.hsize is differentsize_ == sizeunsupported data type: ReadExternalRawData() failed: Could not find OrtValue with name 'C:\a\5\s\onnxruntime\core\optimizer\constant_folding.cc node 'can't constant fold Could not find a CPU kernel and hence ConstantFoldingonnxruntime::ConstantFolding::ApplyImplfetches.size() == node->OutputDefs().size(). Can't constant fold Unsupported output type of C:\a\5\s\onnxruntime\core\optimizer\matmul_scale_fusion.ccDivdiv_inputs.size() == 2onnxruntime::`anonymous-namespace'::GetScaleFromNodeConstant initializer NodeArg shape should not be null. NodeArg: shapeonnxruntime::`anonymous-namespace'::GetScalarConstantInitializeroutput_node.OutputDefs().size() == 1onnxruntime::`anonymous-namespace'::GetOutputNodeMergesMatMulTransposeMatMulMulmul_inputs.size() == 2input_node.InputDefs().size() == 2 && scale_and_index.value().second < 2onnxruntime::`anonymous-namespace'::GetInputNodeMergeshas_value_C:\a\5\s\include\onnxruntime\core/common/optional.honnxruntime::optional<float>::valuealphaFused MatMul and Scale_FusedMatMulAndScaleonnxruntime::MatMulScaleFusion::ApplyImplUnsupported data type: onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy<class onnxruntime::common::Status>::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget<struct onnxruntime::BFloat16>::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget<double>::operator ()onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper<class onnxruntime::common::Status,struct onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy<class onnxruntime::common::Status> >::Getonnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget<unsigned __int64>::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget<unsigned int>::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget<float>::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget<union onnxruntime::MLFloat16>::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget<__int64>::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget<int>::operator ()Slice ends is less than INT_MAXSlice does not have enough number of inputsSlice parameter is not expected. Input index:Expected value:input_indices.size() == expected_values.size() && input_indices.size() > 0C:\a\5\s\onnxruntime\core/optimizer/attention_fusion_helper.honnxruntime::AttentionFusionHelper::CheckSliceParametersSqueezeConcatInput of reshape_before_gemm is not the input of subgraphFaild to match gemm pathonnxruntime::AttentionFusionHelper::MatchGemmSubgraphStart MatchGemmSubgraphGemmReshapeconcat first input value is not -1CheckSliceParameters return falseconcat_after_gather does not have expected number of inputs or output edgesFaild to match concat node for Gather pathsGemm does not have 3 inputsOutput edge count not expected for nodes in gemm pathGemm bias shape not expectedGemm bias is not constantunsqueeze_after_gather axes value not expectedaxesgather axis value not expectedaxisGatherconcat_after_gather input 2 does not have expected valueOutput edge count not expected for nodes in gemm gather pathFaild to match gemm gather pathGemm bias shape is not expectedGemm bias is not constant initializerGemm weight shape is not expectedGemm weight is not constant initializerPass MatchGemmSubgraphgather input 1 value is not expectedonnxruntime::AttentionFusionHelper::ValidateGemmInitializerStart ValidateGemmInitializerSubFaild to match the path (Div-->Where-->Add) for unidirectional maskOutput edge count not expected for nodes in path 1 of unidirectional maskFaild to match path 1 for unidirectional maskStart MatchUnidirMaskSubgraphPass ValidateGemmInitializerWhereonnxruntime::AttentionFusionHelper::MatchUnidirMaskSubgraphOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 2 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskFaild to match path 3 for unidirectional maskCheckSliceParameters returns false for last_sliceDiv and Shape1 does not have edgeCheckSliceParameters returns false for slice1CheckSliceParameters returns false for mask_sliceStart MatchInputMaskSubgraphPass MatchUnidirMaskSubgraphSoftmaxonnxruntime::AttentionFusionHelper::MatchInputMaskSubgraphDiv and Shape does not have edgeFaild to match path 4 for unidirectional maskCheckSliceParameters return false for slice2Output edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskSoftmax attribute axis is expected to be 3Output edge count not expected for mask nodesmask_unsqueeze_2 axes not matched. Expect: 2mask_unsqueeze_1 axes not matched. Expect: 1Output edge count not expected for SoftmaxFailed to find Softmax nodeFailed to find path for maskAddFailed to find mask pathEqualFailed to find shape pathwhere const not matched.mask_mul const input not matchedmask_sub const input not matchedStart MatchInputMaskSubgraphDistilBertPass MatchInputMaskSubgraphStart MatchPastSubgraphPass MatchInputMaskSubgraphDistilBertTransposeonnxruntime::AttentionFusionHelper::MatchPastSubgraphFailed to find reshape shape path 1equal const not matched.gather indices not matched.Failed to find reshape shape path 2past_k_transpose perm attribute not matchedpermpresent_k_unsqueeze axes value not expectedpresent_k_transpose perm attribute not matchedFailed to find path for present_kFailed to find path for past_kFailed to match v_concatFailed to find path for present_v and past_vPass MatchPastSubgraphOutput edge count not expected for nodes in past subgraphStart CheckNodesInPathVonnxruntime::AttentionFusionHelper::CheckDistilBertReshapeShapepast_v_gather indices != 1present_v_unsqueeze axes value not expectedpast_v_gather and past_k_gather does not have same past inputpast_k_gather indices != 0v_reshape initializer value is not expectedhidden_size != num_heads * head_sizereshape initializer value is not expectedPass CheckNodesInPathVOutput edge count not expected for nodes in path vonnxruntime::AttentionFusionHelper::CheckNodesInPathVFailed in match v_transpose attribute perm. Expected: 0, 2, 1, 3Failed in match Transpose attribute perm. Expected: 0, 2, 1, 3Pass CheckNodesInPathQq_transpose perm attribute not matchedonnxruntime::AttentionFusionHelper::CheckNodesInPathKStart CheckNodesInPathKonnxruntime::AttentionFusionHelper::CheckNodesInPathQStart CheckNodesInPathQqk_div const not matched.q_reshape const not matchedCast mask from int64 to int32onnxruntime::AttentionFusionHelper::FuseGptAttentionStart FuseGptAttentionk_reshape const not matchedk_transpose perm attribute not matchedMask_Int32Pass CheckNodesInPathKMatchUnidirMaskSubgraph returns NULLMatchInputMaskSubgraph returns falseq and v are not from same Split nodeFailed to find path for qSplitFaild to find path to qkv_matmulCheckNodesInPathV return falseFaild to find path v to SplitFused Attention subgraphs MatchPastSubgraph returns falsenum_headsAttentionFailed to find path for kCheckNodesInPathQ returns falseCheckNodesInPathK returns falsek and v are not from same Split nodeonnxruntime::ConvertMaskToInt32Mask shape is unknown or not 2D, or data type unknownMask data type is not int32 or int64 or float32C:\a\5\s\onnxruntime\core\optimizer\attention_fusion.ccFused an attention node for GPT.unidirectionalqkv_biasqkv_weightsq root should be layer normalizationonnxruntime::FuseSubGraphQKImplk root is not layer normq_matmul and q_add shape not matchedLayerNormalizationonnxruntime::AttentionFusion::ApplyImplTotal fused Attention node count: shape of layer norm bias tensor not expectedFused an attention node.onnxruntime::FuseSubGraphQKFaild to find path vonnxruntime::FuseSubGraphQKDistilBertFailed to load Q, K and V weights, or data type is not float or float16.k_matmul and k_add shape not matchedFailed to convert mask to int32Failed to load Q, K and V bias tensors, or data type is not float or float16.Output edge count not expected for Add or MatMul in path vonnxruntime::AttentionFusion::FuseSubGraphFailed in match input mask subgraphFailed in match v_matmul and v_add input shapedata type is not supportedonnxruntime::ConvBNFusion::Applybn_scale_tensor_protobn_B_tensor_protoC:\a\5\s\onnxruntime\core\optimizer\conv_bn_fusion.cconnxruntime::Initializer::ToProtoepsilonConvBnFusion_BN_B_ConvBnFusion_W_BatchNormalizationbn_var_tensor_protobn_mean_tensor_protoconv_B_tensor_protoconv_W_tensor_protoonnxruntime::`anonymous-namespace'::GetClipConstantMinMax::<lambda_7eb2d31997266b46b2969f0bb5580cca>::operator ()Unexpected data type for Clip input of ClipC:\a\5\s\onnxruntime\core\optimizer\conv_activation_fusion.ccLeakyReluTanhfused Conv with activation maxminSigmoidonnxruntime::ConvActivationFusion::ApplyImplfused FusedConvactivation_paramsactivationUnsqueezeElimination_onnxruntime::UnsqueezeElimination::ApplyUnsqueezeElimination cannot remove node C:\a\5\s\onnxruntime\core\optimizer\unsqueeze_elimination.ccis not supported.data type FuseReluClip__min_zero_constantUnexpected data type for Clip 'min' input of C:\a\5\s\onnxruntime\core\optimizer\relu_clip_fusion.cconnxruntime::FuseReluClip::ApplyGeluBiasGeluC:\a\5\s\onnxruntime\core\optimizer\gelu_approximation.cconnxruntime::GeluApproximation::ApplyImplFastGeluGelu approximationTotal Gelu Approximation (FastGelu) node count: Fused reshape node: C:\a\5\s\onnxruntime\core\optimizer\reshape_fusion.ccCannot replace concat node with initializer:Total fused reshape node count: onnxruntime::ReshapeFusion::ApplyImplonnxruntime::ReshapeFusion::Fuse_Subgraphonnxruntime::CheckInputC:\a\5\s\onnxruntime\core\optimizer\embed_layer_norm_fusion.cc_CastInput shape is unknown or not 2D, or data type unknown_Int32Cast Input from int64 to int32Output 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 1 of position shape should be a constant with value 0.Failed to find path 2 of position shape.onnxruntime::MatchInputToConcatSubgraphOutput edge count not expected for nodes in path 1 of position shape.Input data type is not int32 or int64Failed to find path 1 of position shape.Output edge count not expected for nodes in path1.onnxruntime::MatchPositionEmbeddingSubgraphsFromGatherConstantOfShapeRangetwo paths share the same shapeNonZeroSecond 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.Optional position subgraph nodes Where node is expected to be the parent of Reshape.Failed to match position subgraph.The parent of shape nodes are expected to be input_ids.Optional position subgraph nodes number of outputs unexpected.Second input of Gather should be a constant with value 1. Failed to match Shape node. The first input of Range should be a constant with value 0.The third input of Range should be a constant with value 1.Input is expected to have dim value in all dimensions.Failed to get initializer tensor.Word embedding shape not expected.onnxruntime::FuseSubGraphfused EmbedLayerNorm subgraphs EmbedLayerNormalizationposition_embeddingsmask_indexSegment id is not valid. Input_ids and segment id should have the same shape. Position embedding shape is not expected.Input id is not valid. Failed to match position embedding subgraph.Failed to get position embedding weights.Position embedding shape not matched.Position embedding data type shall be float or float16.onnxruntime::FuseSubGraphDistilBertonnxruntime::EmbedLayerNormFusion::ApplyImplGamma should be of shape (hidden_size). Beta should be of shape (hidden_size). ̼+MatMulIntegerToFloatnullptr != fused_nodeDynamicQuantizeLinearDynamicQuantizeMatMulC:\a\5\s\onnxruntime\core\optimizer\dynamic_quantize_matmul_fusion.ccMatMulIntegeronnxruntime::DynamicQuantizeMatMulFusion::ApplyImplstartsendsConflicting free dimension overrides.which does not equal the specified override of onnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformerC:\a\5\s\onnxruntime\core\optimizer\free_dim_override_transformer.ccFreeDimensionOverrideTransformerInvalid free dimension override.The model has input 'onnxruntime::FreeDimensionOverrideTransformer::ApplyImplwith a fixed dimension size ' C:\a\5\s\onnxruntime\core\optimizer\matmul_add_fusion.ccfused Matmul and Add onnxruntime::MatMulAddFusion::ApplyImplSoftplusSoftsignHardSigmoidSeluEluFusedGemmactivation_C:\a\5\s\onnxruntime\core\optimizer\gemm_activation_fusion.ccfused Gemm ThresholdedReluonnxruntime::GemmActivationFusion::ApplyImplScaledTanhParametricSoftplusConvMulFusion_Mul_B_ConvMulFusion_W_mul_B_tensor_protoC:\a\5\s\onnxruntime\core\optimizer\conv_mul_fusion.cconnxruntime::ConvMulFusion::ApplySkipLayerNormalizationfused SkipLayerNorm subgraphs C:\a\5\s\onnxruntime\core\optimizer\skip_layer_norm_fusion.cconnxruntime::SkipLayerNormFusion::ApplyImpl̼+stridespadsReorderInputreordergroupVALIDSAME_LOWERSAME_UPPERNOTSETauto_padkernel_shapedilationschannels_lastchannelsReorderOutput_bn_nchwcspatial_nchwcscalesUpsamplefloornearest_modeasymmetriccoordinate_transformation_modenearestmodeC:\a\5\s\onnxruntime\core\optimizer\nchwc_transformer.cconnxruntime::NchwcTransformer::ApplyImplGlobalAveragePoolGlobalMaxPoolResizeSumAveragePoolMaxPooldeque<T> too long'7ReduceMeanC:\a\5\s\onnxruntime\core\optimizer\layer_norm_fusion.cconnxruntime::LayerNormFusion::ApplyImplinvalid unordered_map<K, T> keyfused LayerNorm subgraphs PowSqrtonnxruntime::CommonSubexpressionElimination::ApplyImplNot eliminating output of node [] because it's the graph's output.representative.output_index != kInvalidOutputIndexC:\a\5\s\onnxruntime\core\optimizer\common_subexpression_elimination.ccC:\a\5\s\onnxruntime\core\optimizer\rule_based_graph_transformer.cconnxruntime::RuleBasedGraphTransformer::ApplyRulesOnNodeonnxruntime::RuleBasedGraphTransformer::ApplyImplonnxruntime::GeluFusion::ApplyImplC:\a\5\s\onnxruntime\core\optimizer\gelu_fusion.ccErffused Gelu subgraphs onnxruntime::BiasGeluFusion::ApplyImplC:\a\5\s\onnxruntime\core\optimizer\bias_gelu_fusion.ccfused Add and Gelufast_gelu_outputfused GPT2Gelu subgraphs onnxruntime::FastGeluFusion::ApplyImplC:\a\5\s\onnxruntime\core\optimizer\fast_gelu_fusion.ccConvAddFusion_B_ConvAddFusion_Add_B_onnxruntime::ConvAddFusion::ApplyC:\a\5\s\onnxruntime\core\optimizer\conv_add_fusion.ccadd_B_tensor_protoonnxruntime::Initializer::ReadExternalRawDataC:\a\5\s\onnxruntime\core\optimizer\initializer.ccNot satisfied: tensor_proto.data_type() != ONNX_NAMESPACE::TensorProto_DataType_UNDEFINED && tensor_proto.data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING model_path must not be empty. Ensure that a path is provided when the model is created or loaded.Satisfied, but should not be: model_path.IsEmpty() -, external_data.length: Computed size: TensorProto external data size mismatch. Not satisfied: external_data_length == 0 || external_data_length == actual_tensor_data_length -w+2zTPRPm>pm>Pn>n>X{T]>onnxruntime::OptimizerExecutionFrame::Info::{ctor}::<lambda_963868dc1eb2d80ff6ea67a629ea4e1f>::operator ()D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\optimizer\optimizer_execution_frame.ccProvided CPU execution provider is a nullptrcpu_execution_provideronnxruntime::OptimizerExecutionFrame::Info::InfoFailed to get allocator for optimizerallocator_ptr_Tried to allocate without valid type information, ort_value index=(|T`$|T`x}T`0'P 0 `  P~@ILpGKE'&(*Ќ0srPtpu879;P0pP#!0"wv{xyVP4pp`G@F`a0`lmde0|0+p}]@\pX W0ABC0<>?p=0h`i0p5p,p$.pз*Ц0`+Ya0w@0P0Emnp@qprstu{|} w0xPyzPpP P0PpУ0Pвp @`pdepkl@R!!`"N"K"@L"M"I"F"@G"H"@B"D"C"A"pY## ##Х# $$$#$,$$$`$P$$P%D%pN%O%P%KJ`PM N@OTQRSIH@L&p&`& 'P-'I'Y[^ `e'P" '}'@'' E''p'''' ''`'@''(P6-%/()))))))@* ** *`*Z0pPpH*&pС**0***`* *****)$@#+B+O+M++ `++0++P],0\,[,Pe,_afhPp&$P(PXP'PX#@#*$$ $pC%pn,)+(p@*0&p,,,,,p,f-,l-P-0302#---(@)P'P''''''p'''0'`***0bPc0iPj`jbЄ0 @`-l..@...onnxruntime::RegisterOnnxOperatorKernelsD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\cpu_execution_provider.ccp.P.... .@.`/1/@0///-/,/@+/`M/0]/o//@000B0A0`@0C0 x0v0z0Py0P00 1 -1`11Е223s/0}/@v/q/z//x/onnxruntime::ml::RegisterOnnxMLOperatorKernelsonnxruntime::RegisterCPUKernelsonnxruntime::CPUExecutionProvider::GetKernelRegistryp33п3P303333onnxruntime::contrib::RegisterNchwcKernelsD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\cpu_contrib_kernels.cc3"4!4`%4 $4(4`'4:484o44Й4onnxruntime::contrib::RegisterQuantizationKernels`4444444[50-Ч5-P55P5#0555@6 636T6@k6m6x666`6U6W6@X6n6 66 666onnxruntime::contrib::RegisterCpuContribKernelsD:\a\ml-builds\ml-builds\onnxruntime\include\onnxruntime\core/framework/op_kernel.h}T@Px~TPmin_ <= max_onnxruntime::clip_internal::Clip_6Base<float>::Clip_6BaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/math/clip.hMissing Input: onnxruntime::OpKernelContext::Inputonnxruntime::utils::MLTypeCallDispatcher<struct onnxruntime::Clip::ComputeImpl,float,double,signed char,unsigned char,__int64,unsigned __int64>::InvokeUnsupported data type: helper.called_ == 1xT   ^@$$min should be a scalar.min->Shape().NumDimensions() == 0onnxruntime::Clip::ComputeImpl<unsigned __int64>::operator ()D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\clip.ccmax should be a scalar.max->Shape().NumDimensions() == 0onnxruntime::Clip::ComputeImpl<__int64>::operator ()onnxruntime::Clip::ComputeImpl<unsigned char>::operator ()onnxruntime::Clip::ComputeImpl<signed char>::operator ()onnxruntime::Clip::ComputeImpl<double>::operator ()onnxruntime::Clip::ComputeImpl<float>::operator ()'is defined.No attribute with name:'Attibute name and type don't matchT    HT3pOpM`6@Tp5@O`N`6T.OK06@T56PT@/OPK06@T58PT0 OK06@T5:PT1@O`L`6@T6=PT2`OM06@T5>PT6 AP@T30OM`6T6CP@T4@ON`6T6EP@T6FPT5@ON06@T5HPinput_size < std::numeric_limits<std::ptrdiff_t>::max()onnxruntime::ElementWiseKernel<struct onnxruntime::functors::ThresholdedRelu<float> >::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/activation/element_wise_ranged_transform.honnxruntime::ElementWiseKernel<struct onnxruntime::functors::ThresholdedRelu<float> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Tanh<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Softsign<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Softplus<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Sigmoid<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Selu<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Selu<float> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Relu<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::LeakyRelu<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::LeakyRelu<float> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::HardSigmoid<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::HardSigmoid<float> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Elu<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Elu<float> >::ElementWiseKernelonnxruntime::functors::Selu<float>::InitD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/activation/activations.honnxruntime::functors::HardSigmoid<float>::InitT`00OL06@T@2POL06unknown kernel typeonnxruntime::functors::ScaledTanh<float>::InitD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops/cpu/activations.honnxruntime::functors::ParametricSoftplus<float>::InitTbb0ccb$@Tpbpb0ccc$T0b0b0ccc$@Taa0ccc$Taa0ccc$@Tpapa0ccb$T0a0a0ccc$@T``0cpcb$T``0c`cb$@Tp`p`0cPcb$~3a*7.A\3Ӡ5JCy7֕89 ':;Y;Y;??@@.@9@>@@@O@pAAA%O~3a*~3a*~3a*~3a*A\3A\3A\3A\3Ӡ5Ӡ5Ӡ5Ӡ5JCy7JCy7JCy7JCy7֕8֕8֕8֕8giP9giP9giP9giP99999 ': ': ': ':C:C:C:C:;;;;Y;Y;Y;Y;Y;Y;Y;Y;<<<<*=*=*=*=*>*>*>*>????1?1?1?1?????;?;?;?;?@@O@O@O@O@BBBBBBBB%%%%7777^^^^OOOO¥¥¥Please fetch output tensor with specified shape.p_ml_valueonnxruntime::OpKernelContext::Output((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOT`P@TPTPP@TPT P@T0PTpP by Attempting to broadcast an axis by a dimension other than 1. axis == 1 || axis == largestonnxruntime::BroadcastIterator::InitD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/math/element_wise_ops.honnxruntime::BroadcastIterator::Append is invalid.Can broadcast 0 by 0 or 1. largest <= 1onnxruntime::Broadcaster::Broadcaster@TPT P@TPTPP@TPTP@TPPTP@TPTP@TPPTP@TPTP@T`PTP@TPTP@TPTP@T@PTP@T`PTP@TPPTP@TPTP@TPTP@TPT P@TPTP@T0PT@P@TpPTP@TPTP@T PTP@TPT P@TPPTP@TPT`P@TPT0P@TPTP@T PTP@TPPTP@TPT`P@TPTP@TPUnsupported X type: Must have 1 or more inputsinput_count >= 1onnxruntime::Sum_6<float>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\element_wise_ops.ccAll inputs must have the same shapedata_1.Shape() == shapedata_n.Shape() == shapeinputCount >= 1onnxruntime::Min_6<float>::Computeonnxruntime::Max_6<float>::Computeonnxruntime::Mean_6<float>::ComputeTP@TPTP@TpPTP@TPPTP@TPTP@T0PTP@TPTP@T0PTеP@TPTРP@TPT`P@TPTP@TPPTPP@T@PTpP@TPPfmod must have value either 0 or 1(fmod == 0) || (fmod == 1)onnxruntime::Mod::Modfailed to get first output!onnxruntime::mod_internal::BroadCastMFloat16FMod vs X and Y input types do not match: fmod attribute must be true for float, float16 and double typesfmod_onnxruntime::Mod::Compute) for tensor of length:Invalid start/ending offset [start_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenonnxruntime::TBroadcastOutput<union onnxruntime::MLFloat16>::TBroadcastOutput) are not at boundary of span with size:Broadcast Output range [start_offset % span_size == 0 && real_end % span_size == 0onnxruntime::BitShift<unsigned char>::BitShiftLEFTRIGHT'. Valid values are 'LEFT' or 'RIGHT'.Invalid direction value of 'cur1 == end1onnxruntime::BitShift<unsigned char>::Compute::<lambda_ac8b3aa46bdc34e87838846ea9179d0f>::operator ()cur_out == end_outonnxruntime::BitShift<unsigned int>::BitShiftonnxruntime::BitShift<unsigned int>::Compute::<lambda_9afb059b7118f89efd2aa9603c93ffc4>::operator ()onnxruntime::BitShift<unsigned __int64>::BitShiftonnxruntime::BitShift<unsigned __int64>::Compute::<lambda_d87f71d4ffbd51b78b631bb95d353b06>::operator ()Unsupported Y type: onnxruntime::BroadcastVariadicShape must be 1 dimensional as it's tensor data is a shapetensor_shape.Shape().GetDims().size() == 1onnxruntime::Expand_8<float>::Computeonnxruntime::Expand_8<double>::Computeonnxruntime::Expand_8<signed char>::Computeonnxruntime::Expand_8<short>::Computeonnxruntime::Expand_8<int>::Computeonnxruntime::Expand_8<__int64>::Computeonnxruntime::Expand_8<unsigned char>::Computeonnxruntime::Expand_8<unsigned short>::Computeonnxruntime::Expand_8<unsigned int>::Computeonnxruntime::Expand_8<unsigned __int64>::Computeonnxruntime::Expand_8<bool>::Computeonnxruntime::Expand_8<union onnxruntime::MLFloat16>::Computeonnxruntime::mod_internal::BroadCastFModonnxruntime::utils::MLTypeCallDispatcher<struct onnxruntime::mod_internal::CallModImpl,unsigned char,signed char,unsigned short,short,unsigned int,int,unsigned __int64,__int64>::Invokeonnxruntime::TBroadcastOutput<unsigned __int64>::TBroadcastOutputonnxruntime::TBroadcastOutput<unsigned int>::TBroadcastOutputonnxruntime::TBroadcastOutput<unsigned short>::TBroadcastOutputonnxruntime::TBroadcastOutput<unsigned char>::TBroadcastOutputonnxruntime::TBroadcastOutput<__int64>::TBroadcastOutputonnxruntime::TBroadcastOutput<int>::TBroadcastOutputonnxruntime::TBroadcastOutput<short>::TBroadcastOutputonnxruntime::TBroadcastOutput<signed char>::TBroadcastOutputonnxruntime::TBroadcastOutput<double>::TBroadcastOutputonnxruntime::TBroadcastOutput<bool>::TBroadcastOutputonnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper<class onnxruntime::common::Status,struct onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy<class onnxruntime::common::Status> >::Getonnxruntime::TBroadcastOutput<float>::TBroadcastOutputonnxruntime::TensorAllocator<float>::TensorAllocatoronnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy<class onnxruntime::common::Status>::operator ()onnxruntime::mod_internal::BroadCastModonnxruntime::TensorAllocator<double>::TensorAllocatoronnxruntime::TensorAllocator<union onnxruntime::MLFloat16>::TensorAllocatoronnxruntime::TensorAllocator<int>::TensorAllocatoronnxruntime::TensorAllocator<unsigned int>::TensorAllocatoronnxruntime::TensorAllocator<__int64>::TensorAllocatoronnxruntime::TensorAllocator<unsigned __int64>::TensorAllocatorT $$@T@@`  $$T $$@TP $$T`` @$$@T` $$T00 -$$@T` $$T $$@T  ` -$$Tp $$@TPPp0$$T   $$@T0 $$T@@ $$@T $$T $$@TppP $$T$$@T@ $$TP`$$@T  $$TP $$@T@@` $$T $$@T $$T $$@T $$TPP0 $$@T $$T$$@T``@ $$Tp$$@TppP$$TppP$$@T``@$$TPP0$$@T$$T` $$@T$$T00$$@T$$T00 $$@T $$Tp $$@T  $$Tp $$@Tp $$-DT!<@C,FFt.t.t.t..4.4.4.4 s7 s7 s7 s7Ӆ<Ӆ<Ӆ<Ӆ<*=*=*=*=!=!=!=!=%=%=%=%=>>>>L>L>L>L>>>>>ʫy>ʫy>_6.>_6.>K͉ ?K͉ ?"?"?"?"?5?5?5?5?9d?9d?~, ?~, ?????t ?t ???@.?@.???+eG?+eG?@@"~+@"~+@BBBB,F,F,F,FFFFF@K@K@K@KpMpMpMpMnnnn뽸뽸뽸O]O]O]O]****P*P*P*P*ɿɿɿɿW6}+W6}+TPinfo.GetAttr<float>("mean", &mean_).IsOK()onnxruntime::RandomNormal::RandomNormalD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/generator/random.hinfo.GetAttr<float>("scale", &scale_).IsOK()info.GetAttr<int64_t>("dtype", &dtype).IsOK()Invalid dtype of ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDinfo.GetAttrs<int64_t>("shape", shape).IsOK()@T`Ponnxruntime::RandomNormalLike::RandomNormalLikeTPinfo.GetAttr<float>("high", &high_).IsOK()onnxruntime::RandomUniform::RandomUniforminfo.GetAttr<float>("low", &low_).IsOK()@TPonnxruntime::RandomUniformLike::RandomUniformLikeTPinfo.GetAttr<int64_t>("sample_size", &num_samples_).IsOK()onnxruntime::Multinomial::MultinomialONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDinput count mismatchD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\generator\random.ccCould not infer data type from input tensor with data type Empty dimensions for input tensorbatch_size is < 1num_classes is < 1num_samples is < 1Invalid data type of FLOAT16 is not supportedonnxruntime::RandomNormalComputeonnxruntime::RandomUniformComputeonnxruntime::MultinomialComputeJ@AAOleft.NumDimensions() == 2 || left.NumDimensions() == 1onnxruntime::GemmHelper::GemmHelperD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\gemm_helper.hright.NumDimensions() == 2 N: K: GEMM: Dimension mismatch, W: Gemm: Invalid bias shape for broadcastM_ >= 0 && K_ > 0 && N_ >= 0@TPPinfo.GetAttr<int64_t>("transA", &temp).IsOK()onnxruntime::Gemm<float>::GemmD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/math/gemm.hinfo.GetAttr<int64_t>("transB", &temp).IsOK()info.GetAttr<float>("alpha", &alpha_).IsOK()info.GetAttr<float>("beta", &beta_).IsOK()c_shape is required if c_data is providedc_shape != nullptronnxruntime::Gemm<float>::ComputeGemmTг$$ is not in valid range [-axis axis >= -tensor_rank && axis <= tensor_rank - 1onnxruntime::HandleNegativeAxisD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/common.h@TP, D=. N=Hardmax inputs N, D and N * D must be < TP@TPonnxruntime::MatMulComputeHelper::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\matmul_helper.hNot satisfied: left_num_dims >= 1 && right_num_dims >= 1 -MatMul dimension mismatchNot satisfied: K_ == right_shape[right_num_dims - 2] || transb && K_ == right_shape[right_num_dims - 1] -left operand cannot broadcast on dim Not satisfied: left_padded_dims_[idx_dim] == 1 -right operand cannot broadcast on dim Not satisfied: right_padded_dims_[idx_dim] == 1 -Not satisfied: K_ == right_shape[transb ? right_num_dims - 1 : right_num_dims - 2] -Not satisfied: num_dims_with_pad == num_output_dims -Not satisfied: M_ == 1 && N_ == 1 -Not satisfied: num_dims_with_pad - 1 == num_output_dims -Not satisfied: num_dims_with_pad - 2 == num_output_dims -Not satisfied: K_ == right_shape[0] -TP@T0PTP@TPT`P@TPonnxruntime::MatMul<float>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\matmul.cconnxruntime::MatMul<double>::Computeonnxruntime::MatMul<int>::Computeonnxruntime::MatMul<unsigned int>::Computeonnxruntime::MatMul<__int64>::Computeonnxruntime::MatMul<unsigned __int64>::ComputeTPop_kernel_info.GetAttr<int64_t>("k", &k_temp).IsOK()onnxruntime::TopK<9,float>::TopKD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\top_k.cck_temp > 0op_kernel_info.GetAttr<int64_t>("axis", &axis_temp).IsOK()input count mismatch, expected 1 input - the tensor to be processedTPonnxruntime::TopK<10,float>::TopKinput count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valuek tensor should be a 1D tensor of size 1value of k must not be negativeonnxruntime::TopkOpset11ConstructorCommonop_kernel_info.GetAttr<int64_t>("largest", &largest_temp).IsOK()op_kernel_info.GetAttr<int64_t>("sorted", &sorted_temp).IsOK()TPT@P] should not be greater than specified axis dim value [k argument [output count mismatch, expected 2 outputs to be present for TopK operatorT'С$T@'$T'p$T'PС$TP'$T'$T'УС$T0'0$T'@$T' С$T`'`$T'Х$333333?Invalid input X: The rank of input X must be atleast 2. Got rank: Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != dimension != Invalid input scale: Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input B: Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input mean: Invalid input var: NumDimensions() != Invalid input var: 0th dimension != Invalid input var: TPT@Ponnxruntime::BatchNorm<double>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/batch_norm.honnxruntime::BatchNorm<double>::BatchNormonnxruntime::BatchNorm<float>::Computeonnxruntime::BatchNorm<float>::BatchNormUnknown AutoPadType Stringonnxruntime::StringToAutoPadType W: kernel_shape: kernel_shape num_dims is not compatible with W num_dims.kernel_shape is not compatible with W shape. X: X num_dims does not match W num_dims. group: kernel channels: C: Input channels C is not equal to kernel channels * group. M: Output channels M is not divisible by group.T `Ponnxruntime::Conv<float>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\nn\conv.ccd_iter[d_i] < d_maxonnxruntime::math::Im2colNd<float,2>::operator ()D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/util/math.hOut of bound access to arrayonnxruntime::ConvAttributes::InferOutputShapeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/conv_attributes.hInvalid input shape: Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.dilation == 1onnxruntime::ComputePadAndOutputShapepad type not supported.group count is <= 0 num_input_channels: filter_number: filter number not equal to input channel number.Input channels is not divisible by group.onnxruntime::ConvTransposeAttributes::PrepareForComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/conv_transpose_attributes.hdim_size > 0onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape*out_size >= 0onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShapepad type not supportedT22Ponnxruntime::ConvTranspose<float>::DoConvTransposeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\nn\conv_transpose.ccT`]Pinfo.GetAttr<int64_t>("axis", &axis_).IsOK()onnxruntime::Flatten::FlattenD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/flatten.hThe rank of input tensor must be >= axisgsl::narrow_cast<int64_t>(X_shape.NumDimensions()) >= axisonnxruntime::Flatten::ComputeInvalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: vs. 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 T@hPonnxruntime::InstanceNorm<float>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\nn\instance_norm.ccop_kernel_info.GetAttr<float>("epsilon", &epsilon_).IsOK()onnxruntime::InstanceNorm<float>::InstanceNormD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/instance_norm.hT|Pop_kernel_info.GetAttr<int64_t>("axis", &axis_).IsOK()onnxruntime::LpNorm<float>::LpNormD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/lp_norm.hop_kernel_info.GetAttr<int64_t>("p", &p_).IsOK()p_ == 1 || p_ == 2TPX->Shape().NumDimensions() == 4onnxruntime::LRN<float>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\nn\lrn.ccinfo.GetAttr<int64_t>("size", &size).IsOK()onnxruntime::LRN<float>::LRND:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/lrn.hsize_ > 0size_ % 2 == 1alpha_ > 0.0fbeta_ > 0.0fTPPpp$t@No kernel shape is set.info.GetAttrs<int64_t>("kernel_shape", kernel_shape).IsOK()onnxruntime::PoolAttributes::PoolAttributesD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/pool_attributes.hinfo.GetAttr<std::string>("auto_pad", &auto_padding).IsOK()info.GetAttr<int64_t>("count_include_pad", &temp).IsOK()info.GetAttr("storage_order", &storage_order).IsOK()kernel_shape[dim] > 0Pad should be smaller than kernel.pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]strides.size() == kernel_shape.size()Dilations dimensions should match kernel shapedilations.size() == kernel_shape.size()Invalid input shape. Only N can be zero. Got:input_shape.Size() > 0 || input_shape[0] == 0onnxruntime::PoolAttributes::SetOutputSizeinput_dims.size() >= 2onnxruntime::PoolAttributes::InferOutputSizeUnsupported AutoPad Type.onnxruntime::PoolAttributes::ComputeSizePadDilationsinfo.GetAttr<int64_t>("p", &p_).IsOK()onnxruntime::PoolProcessContext::initD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/pool_base.hT#PInput dimension cannot be less than 3.onnxruntime::PoolBase::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\nn\pool.ccNot satisfied: input_dims >= 3 -Unsupported pooling size.kernel_shape num_dims is not compatible with X num_dims.Not satisfied: pooling_dims == pool_attrs_.kernel_shape.size() -T0PTPPTPonnxruntime::Pool<float,class onnxruntime::LpPool>::ComputeNot satisfied: x_shape.NumDimensions() >= 3 -Unsupported pooling size : onnxruntime::MaxPoolV8::ComputeImpl(T:'@`CP;$Tp:'BpC;$(T:'BC;$T05'<BP;$(T6'p@B`;$T8'@0Cp;$(T4';BP;$TP6'`@B`;$(T8'@ Cp;$T5' ?BP;$(T7'@C`;$T9'@PCp;$(T5'>BP;$T07'@C`;$(T9'@@Cp;$TKKPR->Shape()[1] == 5onnxruntime::RoiPool<float>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\nn\roi_pool.ccroi_batch_id >= 0roi_batch_id < batch_sizeinfo.GetAttrs<int64_t>("pooled_shape", pooled_shape).IsOK()onnxruntime::RoiPool<float>::RoiPoolD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/roi_pool.hpooled_shape.size() == 2pooled_height_ > 0pooled_width_ > 0info.GetAttr<float>("spatial_scale", &spatial_scale_).IsOK()spatial_scale_ > 0TP Ы PhTP P PTP  PTP P PUP  PUP P P8UP p PUP  PXUP  PUP  PxUP ` PUP  PUP  P(UP P PUP  PHUP  PUP  PhUP  PUP ` PUP p P UP P!P UP  P8 -UP !P -UP  !PX UP !P UP !Px UP !P UP !P UP p!P(UP p!PUP p PHUP Ў PUP  PhUP  Pinfo.GetAttr("keepdims", &keepdims).IsOK()onnxruntime::ReduceKernelBase<0>::ReduceKernelBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/reduction/reduction_ops.honnxruntime::ReduceKernelBase<1>::ReduceKernelBasehas_value_onnxruntime::optional<__int64>::valueD:\a\ml-builds\ml-builds\onnxruntime\include\onnxruntime\core/common/optional.honnxruntime::GetAllocatorD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/containers.hInput to be reduced is nullinput_tensor_ptr != nullptronnxruntime::PrepareForReduceD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\reduction\reduction_ops.ccThe input shape override's size does not match the input tensor's shape sizeinput_tensor_ptr->Shape().Size() == input_shape_override->Size()Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:in_dim != 0U04!04!5!9!4!$xUp4!p4!p6!9!4!$U4!4!8!9!4!$xU4!4!8!9!4!$UP4!P4!5!9!4!$xU4!4!@7!9!4!$U1`1'. Must be one of 'forward', 'reverse', or 'bidirectional'.Invalid 'direction' argument of 'onnxruntime::rnn::detail::MakeDirectionD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/rnn/rnn_helpers.hxU!P!Pinfo.GetAttr("direction", &direction).IsOK()onnxruntime::DeepCpuGruOp::DeepCpuGruOpD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.hinfo.GetAttr("linear_before_reset", &int64_value).IsOK()info.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0clip_ > 0.fsigmoidtanhactivation_func_names.size() == static_cast<size_t>(num_directions_) * 2GRU operator does not support double yetInvalid data type for GRU operator of onnxruntime::DeepCpuGruOp::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.cconnxruntime::DeepCpuGruOp::ComputeImplonnxruntime::Tensor::DataAsSpanonnxruntime::Tensor::MutableDataAsSpanlda >= K && ldb >= K && ldc >= Nonnxruntime::rnn::detail::ComputeGemmA + (M * lda - (lda - K)) <= A_endB + (N * ldb - (ldb - K)) <= B_endC + (M * ldc - (ldc - N)) <= C_endcur + size <= endonnxruntime::rnn::detail::SafeRawConstPointeroffset + size <= size_t(span.size())onnxruntime::rnn::detail::SafeRawPointerU!!!`!0!$U! !!p!0!$U!!Ponnxruntime::DeepCpuLstmOp::DeepCpuLstmOpD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/rnn/deep_cpu_lstm.hactivation_func_names.size() == static_cast<size_t>(num_directions_) * 3LSTM operator does not support double yetInvalid data type for LSTM operator of onnxruntime::DeepCpuLstmOp::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\rnn\deep_cpu_lstm.cconnxruntime::DeepCpuLstmOp::ValidateInputs}. Actual:Input initial_c must have shape {Input P must have shape {onnxruntime::DeepCpuLstmOp::ComputeImpl.. rows[U!!!`!`$U*"*"Ponnxruntime::RNN<float>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\rnn\rnn.ccinfo.GetAttr("direction", &direction_).IsOK()onnxruntime::RNN<float>::RNND:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/rnn/rnn.hallowed_directions.find(direction_) != allowed_directions.end()info.GetAttrs("activations", activations_).IsOK()info.GetAttr("hidden_size", &hidden_size_).IsOK()activations_.size() == static_cast<size_t>(num_directions)RNN op: Invalid activation attribute - allowed_activations.find(activations_[direction]) != allowed_activations.end()hU ?" ?"@?"`?"$$invalid stoi argumentstoi argument out of rangeinvalid stol argumentstol argument out of rangeinvalid stoul argumentstoul argument out of rangeinvalid stoll argumentstoll argument out of rangeinvalid stoull argumentstoull argument out of rangeinvalid stof argumentstof argument out of rangeinvalid stod argumentstod argument out of rangeTensor type mismatch.type == dtype_onnxruntime::Tensor::MutableDataRawonnxruntime::Tensor::DataRawU"PCast op must have 'to' argument of type DataTypeonnxruntime::Cast<unsigned char>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\cast_op.ccUnexpected 'to' argument value: hU #Ponnxruntime::Cast<unsigned short>::ComputeU`#Ponnxruntime::Cast<unsigned int>::ComputehUD#Ponnxruntime::Cast<unsigned __int64>::ComputeUp"Ponnxruntime::Cast<signed char>::ComputehU#Ponnxruntime::Cast<short>::ComputeUP#Ponnxruntime::Cast<int>::ComputehU=#Ponnxruntime::Cast<__int64>::ComputeUJ#Ponnxruntime::Cast<bool>::ComputehU #Ponnxruntime::Cast<float>::ComputeU##Ponnxruntime::Cast<double>::ComputehU-#PCasting from 'float16' to 'string' is not supported yet.onnxruntime::Cast<union onnxruntime::MLFloat16>::ComputeU3#PInput is missing. The operator Cast expects one and only one inputonnxruntime::Cast<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::ComputeAttribute to is not set.onnxruntime::Cast<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::Castonnxruntime::Cast<union onnxruntime::MLFloat16>::Castonnxruntime::Cast<double>::Castonnxruntime::Cast<float>::Castonnxruntime::Cast<bool>::Castonnxruntime::Cast<__int64>::Castonnxruntime::Cast<int>::Castonnxruntime::Cast<short>::Castonnxruntime::Cast<signed char>::Castonnxruntime::Cast<unsigned __int64>::Castonnxruntime::Cast<unsigned int>::Castonnxruntime::Cast<unsigned short>::Castonnxruntime::Cast<unsigned char>::Castonnxruntime::Cast<unsigned char>::CastFloat16Dataonnxruntime::Cast<unsigned short>::CastFloat16Dataonnxruntime::Cast<unsigned int>::CastFloat16Dataonnxruntime::Cast<unsigned __int64>::CastFloat16Dataonnxruntime::Cast<signed char>::CastFloat16Dataonnxruntime::Cast<short>::CastFloat16Dataonnxruntime::Cast<int>::CastFloat16Dataonnxruntime::Cast<__int64>::CastFloat16Dataonnxruntime::Cast<bool>::CastFloat16Dataonnxruntime::Cast<double>::CastFloat16Dataonnxruntime::Cast<union onnxruntime::MLFloat16>::CastFloat16Dataallocator != nullptronnxruntime::CastFloat16Datalen > 0bufferonnxruntime::CastToStringDataNaN-INFINFonnxruntime::CastFromStringDataMust have valid 'axis' attributeonnxruntime::ConcatBase::ConcatBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/concat.hhU`_#Ponnxruntime::ConcatBase::PrepareForComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\concat.ccNot satisfied: input_count >= 1 -Cannot concatenate scalarsNot satisfied: inputs_0_rank > 0 - got: Ranks of input data are different, cannot concatenate them. expected rank: inputs_n_rank == inputs_0_rank has mismatched dimensions of Non concat axis dimensions must match: Axis Not satisfied: dim_value == inputs_0_dims[axis_index] -Not satisfied: data_n.DataType() == p.output_tensor->DataType() -Missing/Invalid 'axis' attribute valueonnxruntime::GatherBase::GatherBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/gather.hh U#Ponnxruntime::Gather::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\gather.ccType for Tind not supported yet in Gather. must be within the inclusive range [indices element out of data bounds, idx=h!Up#p#Е##$$!UP#P###$$h"U#P"U@#PX != nullptronnxruntime::IdentityOp<0>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/identity_op.honnxruntime::IdentityOp<1>::ComputereflectedgeInvalid 'mode' attribute valueonnxruntime::PadBase::PadBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/pad.hInvalid 'pads' attribute valueh#U#p#Pdims.size() == extents.size() && dims.size() >= steps.size()onnxruntime::SliceSkips::SliceSkipsD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/utils.hdims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()onnxruntime::SliceIteratorBase::InitCannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Unexpected mode of Unsupported input data type of onnxruntime::PadValueFromFloatD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\pad.ccPads tensor should be an INT64 tensorpads_tensor.IsDataType<int64_t>()onnxruntime::Pad::ComputePads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]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 pads_size == 2 * data_rankValue tensor should be a 1D tensor of size 1 with the same type as that of the input tensorvalue_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1Input tensor has no dimensionsdata_rank > 0onnxruntime::PadImpl'pads' has wrong number of valuesdata_rank * 2 == pads.size()onnxruntime::PadInputWithDimValueOfZeroA dimension cannot be less than -1.requested_shape[i] >= -1onnxruntime::ReshapeHelper::ReshapeHelperD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\reshape_helper.hAt most one dimension can be -1.unknown_dim == -1The dimension with value zero exceeds the dimension size of the input tensor.i < input_shape.NumDimensions(), requested shape:The input tensor cannot be reshaped to the requested shape. Input shape:size != 0 && (input_shape.Size() % size) == 0gsl::narrow_cast<int64_t>(input_shape.Size()) == sizeh$U0$PA shape tensor must be a vector tensor.shapeTensor->Shape().NumDimensions() == 1onnxruntime::Reshape::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/reshape.h$U$$PAttribute shape is not set.onnxruntime::Reshape_1::Reshape_1Reshape_1h%U $P%U&$PMissing or invalid starts and ends attributehas_starts && has_ends && attr_starts_.size() == attr_ends_.size()onnxruntime::SliceBase::SliceBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/slice.hInvalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributes!has_axes || attr_axes_.size() == attr_starts_.size()h&UW$X$Ph'UW$X$PUnexpected element size of onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStep'axes' has an axis outside of the tensor dimension count'step' value cannot be 0Starts must be a 1-D arraystart_tensor.Shape().NumDimensions() == 1onnxruntime::SliceBase::FillVectorsFromInputD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\slice.ccEnds must be a 1-D arrayends_tensor.Shape().NumDimensions() == 1Starts and ends shape mismatchstart_tensor.Shape() == ends_tensor.Shape()Starts and axes shape mismatchnullptr == axes_tensor || start_tensor.Shape() == axes_tensor->Shape()Starts and steps shape mismatchnullptr == 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 Missing input tensor to be processedonnxruntime::SliceBase::ComputeCannot slice scalarsoutput == output_endonnxruntime::SliceImpl::<lambda_a7fa78876120f6f0e3a4822a74c861f7>::operator ()onnxruntime::SliceImpl::<lambda_91a5101865787e348c9977481d2061f5>::operator ()onnxruntime::SliceImpl::<lambda_2c9fcc3dbfd39962463b4575f00cd700>::operator ()onnxruntime::SliceImpl::<lambda_dd077fb7fd419d09444afebd23b229a7>::operator ()onnxruntime::SliceImpl::<lambda_c6a334d182cebe65e532e6ed235e7624>::operator ()'U PAttribute blocksize is not set.info.GetAttr("blocksize", &blocksize_).IsOK()onnxruntime::SpaceDepthBase::SpaceDepthBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/space_depth_ops.hp(U$P(U$Pinput_shape.NumDimensions() == 4onnxruntime::SpaceToDepth<float>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\space_depth_ops.ccinput_height % this->blocksize_ == 0input_width % this->blocksize_ == 0input.Shape().NumDimensions() == 4onnxruntime::DepthToSpace<float>::Computeinput_depth % (blocksize_ * blocksize_) == 0DepthToSpace op: only 'DCR' and 'CRD' modes are supportedonnxruntime::DepthToSpace<float>::DepthToSpace*>e@Invalid value in 'split' attribute. All values must be > 0std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })onnxruntime::SplitBase::SplitBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/split.h)U$$P NumOutputs= Axis=Input cannot be split evenly on selected axis. Input shape= Sum of sizes in 'split' (must equal size of selected axis) was Num entries in 'split' (must equal number of outputs) was Input shape=Cannot split using values in 'split' attribute. Axis= yetSplit operator does not support onnxruntime::Split::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\split.cconnxruntime::Split::ComputeImpl. shape=input_shape[i] == 1onnxruntime::SqueezeBase::ComputeOutputShapeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/squeeze.h*U$ $P+U%PInput count of Tile OP mismatch, the first one is emptyInput count of Tile OP mismatch, the second one is emptythe 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' tensorTile doesn't have an implementation yet for the type: onnxruntime::Tile::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\tile.ccv >= 0 && static_cast<uint64_t>(v) <= std::numeric_limits<size_t>::max()onnxruntime::TransposeBase::TransposeBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/transpose.h is outside range.Attribute perm of Transpose has an invalid value. Value is repeated.[ does not align with rank of input data: perm: ,U%%PMismatched data types between input and output Tensors. onnxruntime::Transpose::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\transpose.ccMissing/Invalid 'axes' attribute valueinfo.GetAttrs("axes", axes_).IsOK()onnxruntime::UnsqueezeBase::UnsqueezeBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/unsqueeze.h-U$pG%Ponnxruntime::UnsqueezeBase::PrepareComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\unsqueeze.cc'axes' has an out of range axis'axes' has a duplicate axisonnxruntime::Unsqueeze::Computelinearcubicinfo.GetAttr<std::string>("mode", &mode).IsOK()onnxruntime::UpsampleBase::UpsampleBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/upsample.hinfo.GetAttrs<float>("scales", scales_).IsOK()exclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to or (default) or . It can only be mode attribute is onnxruntime::UpsampleBase::StringToUpsampleModepytorch_half_pixeltf_half_pixel_for_nnalign_cornerstf_crop_and_resize] is not supportted!coordinate_transform_mode:[onnxruntime::UpsampleBase::StringToCoordinateTransformationModeround_prefer_ceilceil] is not supported!nearest_mode:[onnxruntime::UpsampleBase::StringToNearestModeScale value should be greater than or equal to 1.scale >= 1onnxruntime::UpsampleBase::ScalesValidationScale value should be greater than 0.scale > 0Resize operatorUpsample operator'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)scales size should be greater than 0.scales_size > 0onnxruntime::UpsampleBase::ParseScalesData Output dim value: Input dim value: Dimension: Cannot scale 0 by any factor to generate a non-zero value. Input dim is zero but required output dim is non-zero. output_dims[i] == 0onnxruntime::UpsampleBase::ParseScalesDataFromOutputSize.U%`,&P/U%"&P(0U%&Ponnxruntime::Upsample<float>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\upsample.ccInvalid roi input index.roi_input_idx_ > 0scales != nullptrOnly one of scales or sizes must be provided as input.sizes == nullptrEither scales or sizes MUST be provided as input.sizes != nullptr && sizes->Shape().Size() != 0Resize: input tensor's rank does not match the output tensor's rank.X->Shape().GetDims().size() == output_dims.size()onnxruntime::Upsample<int>::Computeonnxruntime::Upsample<unsigned char>::Computeonnxruntime::Upsample<float>::BaseComputeRank of input and output tensor should be same.output_dims.size() == dims.size()Resize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales.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 Resize: unexpected modeUpsample: unexpected modeonnxruntime::Upsample<int>::BaseComputeonnxruntime::Upsample<unsigned char>::BaseCompute0UB&B&D&H&$$01U C& C&D&0H&$$1UB&B&C&G&$$02UC&C&D& H&$$2UB&B&C&G&$$03UB&B&`D&G&$$3UB&B& D&G&$$04UC&C&D&H&$$4U0C&0C& E&@H&$$05UB&B&@D&G&$$5UB&B&@C&G&$$Resize: input/output value is nullptrUpsample: input/output value is nullptrResize: input/output value's dimension mismatchUpsample: input/output value's dimension mismatchResize: input shape needs to be at least a single dimensionUpsample: input shape needs to be at least a single dimension.A@8U@l&s&P&Scan<8> spec does not support transpose of output. This should never be called.info.GetAttr<ONNX_NAMESPACE::GraphProto>("body", &proto).IsOK()onnxruntime::Scan<8>::ScanD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\controlflow\scan_8.ccinfo.GetAttr<int64_t>("num_scan_inputs", &num_scan_inputs_).IsOK()SetupSubgraphExecutionInfo should only be called once for each subgraph.info_ == nullptronnxruntime::Scan<8>::SetupSubgraphExecutionInfoCreateFeedsFetchesManager must be called prior to execution of graph.feeds_fetches_manager_ && info_onnxruntime::Scan<8>::ComputeSubgraph SessionState was not found for 'body' attribute.session_stateonnxruntime::Scan8Impl::Initialize dimensions or more but input had shape of Expected Invalid scan input: has batch size of but Scan inputs have inconsistent batch size. Previous value was has length of Scan inputs have inconsistent sequence lengths. Previous value was onnxruntime::Scan8Impl::ValidateInput did not match batch size of sequence_lens length of Invalid entries in sequence_lens. Max sequence length was outputs but Scan expects Subgraph in 'body' produces onnxruntime::Scan8Impl::AllocateOutputTensors7Ul&>Output OrtValue has not been created for loop state variable output p_mlvalueonnxruntime::Scan8Impl::CreateLoopStateVariablesonnxruntime::Scan8Impl::Executeposition_ >= 0 && position_ < sequence_length_onnxruntime::OrtValueTensorSlicer<struct OrtValue const >::Iterator::operator *D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/framework/ort_value_tensor_slicer.h8U&&&&$$H9U&&&&$$9UК&К&@&&$$H:U&&&О&$$:U&&P`& outputs. outputs which doesn't match the subgraph's 'If' node has num_subgraph_outputs == static_cast<size_t>(num_outputs)onnxruntime::If::Info::InfoD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\controlflow\if.ccinfo.GetAttr<ONNX_NAMESPACE::GraphProto>("then_branch", &proto).IsOK()onnxruntime::If::Ifinfo.GetAttr<ONNX_NAMESPACE::GraphProto>("else_branch", &proto).IsOK()info == nullptronnxruntime::If::SetupSubgraphExecutionInfothen_feeds_fetches_manager_ && else_feeds_fetches_manager_onnxruntime::If::Compute' attribute.Subgraph SessionState was not found for 'onnxruntime::IfImpl::InitializeFailed to create output tensor for Failed to create output tensor for If output onnxruntime::IfImpl::Execute;U&&`&&4!$ <U&`&PP' inputs. Found:Graph in 'body' attribute of Loop should have static_cast<size_t>(num_subgraph_inputs) == subgraph_inputs.size()onnxruntime::Loop::Info::InfoD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\controlflow\loop.cc but has outputs so the subgraph requires 'Loop' node has num_subgraph_outputs - 1 == num_outputs Got: Expected:Inconsistent shape in loop output for output. onnxruntime::Loop::Looponnxruntime::Loop::SetupSubgraphExecutionInfoonnxruntime::Loop::Computefeeds_fetches_manager_'Loop' input 'M' should be a scalar tensor. Got shape of 'Loop' input 'cond' should be a scalar tensor. Got shape of onnxruntime::LoopImpl::ConcatenateLoopOutputonnxruntime::LoopImpl::Execute was not found. Defaulting to a rank 1 shape of {0}.Loop had zero iterations and the shape of subgraph output <U ' '''$$x=U"'Paxes_right_stride >= 0 && static_cast<uint64_t>(axes_right_stride) < std::numeric_limits<size_t>::max()onnxruntime::Compress::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\compress.ccUnsupported value attribute datatype with sizeof=: onnxruntime::ConstantOfShapeBase::SetValueD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/generator/constant_of_shape.h=U`4'Putils::HasDataType(t_proto)onnxruntime::ConstantOfShapeBase::SetValueFromTensorProtoD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\generator\constant_of_shape.ccTensorProto::DataType_IsValid(t_proto.data_type())Value attribute unpacking failed:unpack_status.IsOK()Unsupported value attribute datatype: Must have a single dimensiont_proto.dims_size() == 1onnxruntime::ConstantOfShapeBase::ConstantOfShapeBaseMust have a single dimension of 1t_proto.dims()[0] == 1Must have a valid input shape.input_shape.NumDimensions() > 0onnxruntime::ConstantOfShapeBase::PrepareComputeonnxruntime::ConstantOfShape::Compute?UPW'P>UPW'PInput is expected to have four dimensions corresponding to [N,C,H,W]across_channelsinfo.GetAttr<int64_t>("across_channels", &across_channels_).IsOK()onnxruntime::MeanVarianceNormalization_0<float>::MeanVarianceNormalization_0D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/tensor/mean_variance_normalization.hnormalize_varianceinfo.GetAttr<int64_t>("normalize_variance", &normalize_variance_).IsOK()@UKpz'PT1 != nullptronnxruntime::EyeLike::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\eye_like.ccUnsupported 'dtype' value: EyeLike : Input tensor dimension is not 2@U`'PAUЂ'PNull input ptrAU 'PCheck for duplicate types in MLTypeCallDispatcherhelper.called_ < 2onnxruntime::utils::MLTypeCallDispatcher<struct onnxruntime::sign_internal::CallSignImpl,float,double,signed char,unsigned char,short,unsigned short,int,unsigned int,__int64,unsigned __int64>::InvokeBU'Pop_kernel_info.GetAttr<float>("bias", &bias_temp).IsOK()onnxruntime::Shrink::ShrinkD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/shrink.hop_kernel_info.GetAttr<float>("lambd", &lambd_temp).IsOK()BUJ(PCU@#(PCUC(PDU(PDUP=(PEU -(PEUp(PFU(PFU)(PGU6(PGU0(PInvalid argument for depth; it's not a scalar.Invalid argument for values; either it's rank is more than 1 or it has more than 2 elementsonnxruntime::OneHotOp<__int64,__int64,__int64>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\onehot.ccDepth is negative.onnxruntime::OneHotOp<float,__int64,__int64>::Computeonnxruntime::OneHotOp<__int64,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64>::Computeonnxruntime::OneHotOp<float,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64>::Computeonnxruntime::OneHotOp<__int64,float,__int64>::Computeonnxruntime::OneHotOp<int,float,int>::Computeonnxruntime::OneHotOp<int,float,float>::Computeonnxruntime::OneHotOp<float,float,float>::Computeonnxruntime::OneHotOp<__int64,int,float>::Computeonnxruntime::OneHotOp<__int64,float,float>::Computeonnxruntime::OneHotOp<__int64,float,int>::Compute> @HU@(p(Pinfo.GetAttrs<int64_t>("kernel_shape", kernel_shape_).IsOK()onnxruntime::MaxUnpool::MaxUnpoolD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/unpool.hkernel_shape_[dim] > 0pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]strides_.size() == kernel_shape_.size()onnxruntime::MaxUnpool::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\nn\Unpool.ccNot satisfied: X_shape.NumDimensions() >= 3 -Index tensor shape should be same as that of the input data tensor to unpool.Not satisfied: I_shape == X_shape -Shape must be 1 dimensional as it's tensor data of a shapeNot satisfied: tensor_shape->Shape().GetDims().size() == 1 - inferred output shape:output_shape is smaller than minimum required. output_shape:Not satisfied: given_shape.Size() >= shape.Size() -Attempt to retrieve final output before it was set.final_output_mlvalue_onnxruntime::scan::detail::OutputIterator::GetOutputD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/controlflow/scan_utils.hHUP(@(P)onnxruntime::Scan<9>::ScanD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\controlflow\scan_9.cc but expected 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_outputsonnxruntime::Scan<9>::SetupSubgraphExecutionInfoonnxruntime::Scan<9>::Computeonnxruntime::ScanImpl::Initialize' dimension but input '. Input tensor rank was of Invalid value in scan_input_axes for input onnxruntime::ScanImpl::ValidateInputonnxruntime::ScanImpl::SetupInputsonnxruntime::ScanImpl::AllocateOutputTensorsonnxruntime::ScanImpl::CreateLoopStateVariablesoutput_mlvalueonnxruntime::ScanImpl::Execute. Output tensor rank was Invalid value in scan_output_axes for output Outputs from Scan are not optional and should never be null.onnxruntime::ScanImpl::TransposeOutputIU)))0)$$IU))&@)$$JU|)Ponnxruntime::Scatter::ScatterD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\scatter.ccdata type is different from updates typeIndices and updates must have the same rankIndices vs updates dimensions differs at position=. Input rank=Indices must have the same rank as Input. Indices rank= is greater than input dim= at pos=Indices dim=Unknown tensor type of onnxruntime::Scatter::ComputeExpecting indices to be either int32_t or int64_tJU ))Pmode is requiredonnxruntime::TfIdfVectorizer::TfIdfVectorizerD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.ccTFIDF is unrecognized, acceptable values are TF,IDF,TFIDFmode: impl_->weighting_criteria_ != kNonemin_gram_length is requiredRequired min_gram_length must be positive: impl_->min_gram_length_ > 0 >= min_gram_length >= max_gram_length required: impl_->max_gram_length_ >= impl_->min_gram_length_max_skip_count is requiredmax_skip_count must be non-negative: impl_->max_skip_count_ >= 0Non-empty ngram_counts is requiredstatus.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()Non-empty ngram_indexes is requiredstatus.IsOK() && !impl_->ngram_indexes_.empty()Negative ngram_indexes values are not allowedstd::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; }) must be of equal size but ngram_indexes size: Got weights of size: impl_->weights_.size() == impl_->ngram_indexes_.size()pool_strings must not be empty if specified!impl_->pool_strings_.empty()non-empty pool_int64s is required if pool_strings not providedstatus.IsOK() && !pool_int64s.empty()-gramsn-gram counts out of bounds for end_idx >= start_idx && end_idx <= total_itemsNumber of items must compose whole (items % ngram_size == 0)Input shape must have either [C] or [B,C] dimensions with B > 0. id: Duplicate ngram detected, size: p.first->second->id_ == 0onnxruntime::ngram_details::PopulateGramsKU))) )p$KU)))0)4!$OOOOLU)PLU)PMU)PMU)PNU)PX input is required!onnxruntime::NonZero<bool>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\nonzero_op.cconnxruntime::NonZero<unsigned char>::Computeonnxruntime::NonZero<int>::Computeonnxruntime::NonZero<__int64>::Computeonnxruntime::NonZero<float>::ComputeNU/*POU4*POUC*PPU9*PPU>*Pcondition, X, and Y inputs are required!condition && X && Yonnxruntime::Where<unsigned char>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\where_op.cconnxruntime::Where<int>::Computeonnxruntime::Where<__int64>::Computeonnxruntime::Where<float>::Computeonnxruntime::Where<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::Computeonnxruntime::TBroadcastOutput<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::TBroadcastOutputonnxruntime::TensorAllocator<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::TensorAllocatoronnxruntime::TensorAllocator<unsigned char>::TensorAllocatorConversion ErrorConversion Erroren-USQU1`1*SUP**P:Please, install necessary language-pack-XX and configure localesFailed to construct locale with name:onnxruntime::string_normalizer::Locale::LocaleD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\nn\string_normalizer.ccTUЀ*JJ0*'****TU*attribute is_case_sensitive is not setonnxruntime::StringNormalizer::StringNormalizerattribute case_change_action is not setLOWERUPPERattribute case_change_action has invalid valueEmpty stopwords not allowed!sw.empty()Duplicate stopwords not allowedp.secondStopword contains invalid utf8 charswstr != wconv_errorSingle dimension value must be greater than 0Input dimensions are either[C > 0] or [1][C > 0] allowedInput contains invalid utf8 chars at: bad conversion(UU%*PUU%`*PHVU%@*PVU*PXWU`*PWU *PXXU*Px_scale must be 1D tensor with size x_scale.Shape().NumDimensions() == 1 && x_scale.Shape().Size() == broadcast_dimonnxruntime::DequantizeLinear<unsigned char>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\quantize_linear.ccx_zero_point must be 1D tensor with size x_zero_point != nullptr && x_zero_point->Shape().NumDimensions() == 1 && x_zero_point->Shape().Size() == broadcast_dimx_scale must be a scalar or 1D tensor or size 1.IsScalarOr1ElementVector(&x_scale)x_zero_point must be a scalar or 1D tensor or size 1.x_zero_point == nullptr || IsScalarOr1ElementVector(x_zero_point)onnxruntime::DequantizeLinear<signed char>::Computey_scale must be 1D tensor with size y_scale.Shape().NumDimensions() == 1 && y_scale.Shape().Size() == broadcast_dimonnxruntime::QuantizeLinear<unsigned char>::Computey_zero_point must be 1D tensor with size y_zero_point != nullptr && y_zero_point->Shape().NumDimensions() == 1 && y_zero_point->Shape().Size() == broadcast_dimy_scale must be a scalar or 1D tensor or size 1.IsScalarOr1ElementVector(&y_scale)y_zero_point must be a scalar or 1D tensor or size 1.y_zero_point == nullptr || IsScalarOr1ElementVector(y_zero_point)onnxruntime::QuantizeLinear<signed char>::ComputeXU *Ponnxruntime::QLinearMatMul::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\quantize_linear_matmul.ccQLinearMatmul : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_offset)QLinearMatmul : weight zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_offset)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)QLinearMatmul : weight scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_scale)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_scale)XYUp*Ponnxruntime::MatMulInteger::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\matmul_integer.ccMatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_zero_point)MatmulInteger : input2 zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_zero_point)YU**PMust be a scalar or 1D tensor or size 1.IsScalarOr1ElementVector(X_Zero_Point)onnxruntime::ConvInteger::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\nn\conv_integer.ccNon per-tensor quantization is not supported now.IsScalarOr1ElementVector(W_Zero_Point)onnxruntime::math::Im2colNd<unsigned char,2>::operator ()XZU*P*PQLinearConv : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_zero_point)onnxruntime::QLinearConv::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\nn\qlinearconv.ccQLinearConv : filter zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(W_zero_point)QLinearConv : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_zero_point)QLinearConv : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_scale)QLinearConv : filter scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(W_scale)QLinearConv : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_scale)center_point_box only support 0 or 10 == center_point_box_ || 1 == center_point_box_onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.hZU+Pboxes_tensoronnxruntime::NonMaxSuppressionBase::PrepareComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.ccscores_tensorboxes must be a 3D tensor.Not satisfied: boxes_shape.NumDimensions() == 3 -scores must be a 3D tensor.Not satisfied: scores_shape.NumDimensions() == 3 -boxes and scores should have same num_batches.Not satisfied: boxes_dims[0] == scores_dims[0] -boxes and scores should have same spatial_dimension.Not satisfied: boxes_dims[1] == scores_dims[2] -The most inner dimension in boxes must have 4 data.Not satisfied: boxes_dims[2] == 4 -iou_threshold must be in range [0, 1].onnxruntime::NonMaxSuppressionBase::GetThresholdsFromInputsNot satisfied: (iou_threshold >= 0 && iou_threshold <= 1.f) -onnxruntime::NonMaxSuppression::ComputeNot satisfied: ret.IsOK() -output != nullptr[UK+PFailed to obtain detect_positiveonnxruntime::IsInf::IsInfD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\isinf.ccFailed to obtain detect_negativeData type X must be float or double, but instead got onnxruntime::IsInf::ComputeX\U+@+PX]U+0+PNull input X ptrNull rois_ptrNull batch_indices_ptrNumber of dimensions for batch indices should be exactly 1Number of dimensions for rois should be exactly Second dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't match specified. It should be either avg or maxInvalid mode of value onnxruntime::RoiAlignBase<double>::RoiAlignBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\object_detection\roialign.hSampling ratio should be >=0, but it was sampling_ratio_ >= 0onnxruntime::RoiAlignBase<float>::RoiAlignBaseX^U +'+++$^U+'+ ++$X_U+Pinfo.GetAttr<int64_t>("batch_axis", &batch_axis).IsOK()onnxruntime::ReverseSequenceOp::ReverseSequenceOpD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\reverse_sequence.hinfo.GetAttr<int64_t>("time_axis", &time_axis).IsOK(). Must be 0 or 1Invalid batch_axis of batch_axis < 2Invalid time_axis of time_axis < 2time_axis and batch_axis must have different values but both are batch_axis != time_axis. batch_size=sequence_lens shape must be {batch_size}. Got:onnxruntime::ReverseSequenceOp::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\reverse_sequence.ccAxis tensor must be provided to the CumSum opAxis tensor should be 0D or 1DAxis tensor should be of type `int32_t` or `int64_t`_U$,PX`U1,P`U,PXaU>,PD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\cumsum.ccCannot apply CumSum operator on a scalaronnxruntime::CumSum<float>::Computeonnxruntime::CumSum<double>::Computeonnxruntime::CumSum<int>::Computeonnxruntime::CumSum<__int64>::Computestarts.size()=dims.size()=dims.size() == starts.size()onnxruntime::WritableSliceIterator<__int64>::Initextents.size()=dims.size() == extents_.size()steps.size()=dims.size() == steps.size()onnxruntime::WritableSliceIterator<int>::Initonnxruntime::WritableSliceIterator<double>::Initonnxruntime::WritableSliceIterator<float>::InitaUpc,PXbU_,PbUa,PXcUpg,Px_ptr != nullptronnxruntime::DynamicQuantizeLinear<unsigned char>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\dynamicquantizelinear.cccU,Ponnxruntime::Det<float>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\det.ccInput tensor should have a rank of at least 2Matrix dimensions are not equal. Square matrix is expected) != new size (Tensor size (shape_.Size() == new_shape.Size()onnxruntime::Tensor::ReshapeXdUQ-PdU;-PXeUD-PeU@J-PXfUF-PfUA-PXgUK T-PGot nullptr for sequence input.onnxruntime::SequenceLength::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\sequence\sequence_ops.ccSequenceLength: Got nullptr for output tensorY != nullptronnxruntime::GetSeqIdxonnxruntime::SequenceAt::ComputeGot nullptr input for index tensorI != nullptr) specified for sequence of size (Invalid sequence index (SequenceAt: Got nullptr for output tensorSequenceEmpty: Got nullptr for output sequenceonnxruntime::SequenceEmpty::Computeonnxruntime::CreateCopyAndAppendCpuTensorS != nullptronnxruntime::SequenceInsert::ComputeGot nullptr for input tensor.), input tensor data type (Data type of the input tensor MUST be same as that of the input sequence. Sequence data type (SequenceInsert: Got nullptr for output sequenceonnxruntime::SequenceErase::ComputeSequenceErase: Got nullptr for output sequencenum_inputs >= 1onnxruntime::SequenceConstruct::ComputeSequenceConstruct: Got nullptr for output sequenceViolation of the requirment that all input tensors must have the same data type.SplitToSequence operator does not support onnxruntime::SplitToSequence::Compute) != split_dim_size (split_size_sum (Invalid data type for split tensor onnxruntime::GetScalarSplitInputonnxruntime::GetSplitSizesInputSplit should be > 0split_scalar > 0onnxruntime::SplitToSequence::ComputeImplInvalid value in 'split' input. All values must be >= 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })gUi-Ponnxruntime::ConcatFromSequence::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\sequence\concat_from_sequence.cc`hU`-Ponnxruntime::ScatterND::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\scatter_nd.ccinput_tensor != nullptronnxruntime::ScatterNDBase::PrepareForComputeindice_tensor != nullptrupdate_tensor != nullptr, indices shape: input shape: input tensor and indices tensor must has rank larger than 0. last dimension of indices must not be larger than rank of input tensor, data shape: updates shape: updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. invalid indice found, indice = `iU----$$iU----$$`jUp-Ponnxruntime::GatherElements::GatherElementsD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\gather_elements.hGatherElements op: Data type for 'indices' tensor must be 'int32_t' and 'int64_t'onnxruntime::parse_and_validate_indices_tensorD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\gather_elements.cc]. Actual value is GatherElements op: Value in indices must be within bounds [GatherElements op: Cannot operate on scalar inputGatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Data type of input 'data' should match the data type of the outputjU-PGatherNDBase PrepareForCompute: Input count mismatchinput_tensor != nullptr && indices_tensor != nullptronnxruntime::GatherND::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\tensor\gather_nd.ccindices tensor data type not supportedindices tensor must has rank larger than 0invalid index found, index = kU0-0---$$`lUP-P- --$$lU--p--$$`mU----$$mU-Pstart in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:delta in Range operator should be scalar like tensor, yet got shape:delta in Range operator can not be zero!`nUe.PUnsupported tensor type of pU ..P.Missing 'equation' attributeinfo.GetAttr<std::string>("equation", &equation_).IsOK()onnxruntime::Einsum::EinsumD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\einsum.hEinsum op: There must be atleast one inputThere was a problem acquiring temporary memory allocator in Einsum oponnxruntime::Einsum::DeviceComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\einsum.cc is not supported yetEinsum op: An implementation for the input type qUp.p.p..$$`rU.. .И.$$rU....$$`sUP.P..0.$$sU....$$`tUД.Д...$$tU....$$`uU0.0.. .$$uUp..P`vUp..PvUp.`.P`wUp..PX and mask should have the same shape!mask || mask->Shape() == X_shapeonnxruntime::Dropout<float,float,0>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/nn/dropout_op.honnxruntime::Dropout<float,double,0>::Computeonnxruntime::Dropout<double,float,0>::Computeonnxruntime::Dropout<double,double,0>::Computeratio input should have a single value.ratio_tensor->Shape().Size() == 1onnxruntime::`anonymous-namespace'::GetRatioOrDefaultratio must be in the range [0, 1)0.0f <= ratio_value && ratio_value < 1.0fwU.P`xU.PxUp.P`yU0.PyU.PInvalid argument: X input has empty dimensions.Invalid Y argument: num_indices = 0) >=] (Invalid Y argument: index is out of range: Y[`zU .P is NaNInput data with index: %f Expected TO_FLOAT, TO_STRING or TO_INT64Invalid CAST_TO value of onnxruntime::ml::MakeCastD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/ml/ml_common.hSPARSE Expected DENSE or SPARSEInvalid PACK_MAP value of onnxruntime::ml::MakePackzUp /Pinfo.GetAttr<std::string>("cast_to", &attr).IsOK()onnxruntime::ml::CastMap::CastMapD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/ml/cast_map.hinfo.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 SPARSEmap_form_ != PACK_MAP::SPARSE || max_map_ > 0 Expected std::map<int64_t, float> or std::map<int64_t, std::string>Invalid input type of value: 0.fUnexpected CAST_TO value of Negative index values are not permitted. First entry in map has index value of cur_input == end_input || cur_input->first >= 0onnxruntime::ml::CastMap::ComputeImplD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\cast_map.cc`{UP$/%/Pinfo.GetAttrs<std::string>("cats_strings", string_categories).IsOK()onnxruntime::ml::CategoryMapper::CategoryMapperD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/ml/category_mapper.hinfo.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()Input of string must have output of int64Input of int64 must have output of string {U @/@D/P`|U @/`@/P|U @/PB/P`}U$ J/P}U$F/P`~U$pH/Pinfo.GetAttrs(std::is_same<AttrType, std::string>::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()onnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOpD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::DictVectorizerOponnxruntime::ml::DictVectorizerOp<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,double>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64>::DictVectorizerOp~UP V/Pinputdimensions attribute must be providedstatus.IsOK() && !input_dimensions_.empty()onnxruntime::ml::FeatureVectorizer::FeatureVectorizerD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/ml/feature_vectorizer.h) does not match number of inputdimensions values (Number of inputs (input_count >= 0 && static_cast<size_t>(input_count) == input_dimensions_.size()onnxruntime::ml::FeatureVectorizer::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\feature_vectorizer.ccInvalid input type:`U j/k/PExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedonnxruntime::ml::ImputerOp::ImputerOpD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\imputer.ccExpected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedMust provide imputed_values_float_ or imputed_values_int64_ but not both.imputed_values_float_.empty() ^ imputed_values_int64_.empty()onnxruntime::ml::ImputerOp::ComputeInvalid typeEmpty value of imputed values.Empty input dimensions.UP$/0/Pinfo.GetAttrs<std::string>("classes_strings", string_classes).IsOK()onnxruntime::ml::LabelEncoder::LabelEncoderD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/ml/label_encoder.hInput of tensor(string) must have output of tensor(int64)Input of tensor(int64) must have output of tensor(string)`U //PU//P`U//PU/@/P`U //PU//P`U//Pinfo.GetAttrs<TKey>(_key_field_name, keys).IsOK()onnxruntime::ml::LabelEncoder_2<__int64,__int64>::LabelEncoder_2info.GetAttrs<TValue>(_value_field_name, values).IsOK()values is and the number of However, the number of key is ) must have the same length. (name: attribtues in LabelEncoder The num_keys == num_valuesonnxruntime::ml::LabelEncoder_2<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<float,__int64>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<float,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::LabelEncoder_2LOGISTICSOFTMAXSOFTMAX_ZEROD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\ml_common.hU//P!coefficients_.empty()onnxruntime::ml::LinearClassifier::LinearClassifierD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\linearclassifier.cc Found:Scores output is incorrect size. Expected:scores_output_data.length() >= scores_output_sizeonnxruntime::ml::LinearClassifier::ComputeImplInput shape needs to be at least a single dimension.Unsupported input element type of scores.size() == static_cast<size_t>(expected_num_scores)onnxruntime::ml::batched_update_scores_inplaceUnexpected value for 'add_second_class' of shape_size == out.length()onnxruntime::ml::CastInputToFloatU0000`00$$0U@0@000$$UP0P000$$3@@ֳ0U00Pinfo.GetAttr<int64_t>("targets", &num_targets_).IsOK()onnxruntime::ml::LinearRegressor::LinearRegressorD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\linearregressor.ccinfo.GetAttrs<float>("coefficients", coefficients_).IsOK()Input shape had more than 2 dimension. Dims=Unsupported data type of Invalid normalize value of onnxruntime::ml::MakeNormalizeU`>0Pinfo.GetAttr<std::string>("norm", &norm).IsOK()onnxruntime::ml::Normalizer::NormalizerD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/ml/normalizer.hRank of input to Normalized must be less than 2. Got Unexpected NORMALIZE value of 0Ub0p0PUb0 c0P0Ub0g0PUb0l0PUnknown Category and zeros = 0.One and only one of the 'cats_*' attributes must be definedtmp_cats_int64s.empty() || tmp_cats_strings.empty()onnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOpD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\onehotencoder.ccnum_categories_ > 0onnxruntime::ml::OneHotEncoderOp<float>::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp<double>::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::OneHotEncoderOp0Up00PUp000P0Up0`0PUp0P0PEmpty scale in attributes!scale_.empty()onnxruntime::ml::ScalerOp<float>::ScalerOpD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\scaler.cc) != (Scale size: (scale_.size() == offset_.size()Invalid argument: input has empty dimensions.) or 1Either both scale and offset can be of feature size (onnxruntime::ml::ScalerOp<double>::ScalerOponnxruntime::ml::ScalerOp<__int64>::ScalerOponnxruntime::ml::ScalerOp<int>::ScalerOpPOLYinfo.GetAttrs<float>("kernel_params", kernel_params).IsOK()onnxruntime::ml::SVMCommon::SVMCommonD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/providers/cpu/ml/svmclassifier.h0U00Pinfo.GetAttrs<float>("rho", rho_).IsOK()onnxruntime::ml::SVMClassifier::SVMClassifierD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\svmclassifier.ccproba_.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() > 0coefficients_.size() > 0onnxruntime::ml::SVMClassifier::Compute0U000 04!$ -ף;?U000Ponnxruntime::ml::SVMRegressor<float>::SVMRegressorD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\svmregressor.ccnum_features == feature_count_onnxruntime::ml::SVMRegressor<float>::ComputeUnexpected mode:8U<2 @2PU<2@C2P8U<2`F2PU<2=2PX dims is empty.n_targets_or_classes > 0onnxruntime::ml::detail::TreeEnsembleCommon<float,float>::TreeEnsembleCommonD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\tree_ensemble_common.hnodes_falsenodeids.size() == nodes_featureids.size()nodes_falsenodeids.size() == nodes_modes.size()nodes_falsenodeids.size() == nodes_nodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()nodes_falsenodeids.size() == nodes_truenodeids.size()nodes_falsenodeids.size() == nodes_values.size()target_class_ids.size() == target_class_nodeids.size()target_class_ids.size() == target_class_treeids.size() is already there. in tree Node (truenode).-Unable to find node One falsenode is pointing either to itself, either to another tree. (falsenode). (weights).onnxruntime::ml::detail::TreeEnsembleCommon<double,float>::TreeEnsembleCommononnxruntime::ml::detail::TreeEnsembleCommon<__int64,float>::TreeEnsembleCommononnxruntime::ml::detail::TreeEnsembleCommon<int,float>::TreeEnsembleCommon8U`<2'predictions.size() == 2onnxruntime::ml::detail::TreeAggregatorClassifier<int,float>::FinalizeScoresD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\tree_ensemble_aggregator.hpredictions.size() == predictions2.size()onnxruntime::ml::detail::TreeAggregatorSum<int,float>::MergePredictionit->i < (int64_t)predictions.size()onnxruntime::ml::detail::TreeAggregatorSum<int,float>::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum<__int64,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum<__int64,float>::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorClassifier<double,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum<double,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum<double,float>::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorClassifier<float,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum<float,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum<float,float>::ProcessTreeNodePredictionclasses.size() == 2 || classes.size() == 1onnxruntime::ml::detail::TreeAggregatorClassifier<int,float>::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float>::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier<double,float>::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier<float,float>::_set_score_binaryUpu2pu2@{2`2@w2$8U u2'0{2P2С$U`t2`t2@x22@w2$8Uu2'P{2p2С$Uw2w222@w2$8Uv2'}22С$Ut2t2Px22@w2$8Uv2'|22С$Ut2t2y2024!$8U`v2`v2|224!$Uu2u2`z2@24!$8Ut2t2`x2 24!$U@t2@t2pw224!$8Uu2u2`{224!$Uv2v22Ѐ24!$8Uv2v2}224!$U{3@{3P8U{3|3PUnknown aggregation function in TreeEnsemble.onnxruntime::ml::detail::TreeEnsembleCommon<float,float>::computeonnxruntime::ml::detail::TreeEnsembleCommon<double,float>::computeonnxruntime::ml::detail::TreeAggregatorMax<double,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorMin<double,float>::MergePredictionthis->base_values_.size() == predictions.size()onnxruntime::ml::detail::TreeAggregatorAverage<double,float>::FinalizeScorespredictions.size() == (size_t)n_targets_or_classes_onnxruntime::ml::detail::TreeAggregator<double,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorMax<float,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorMin<float,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorAverage<float,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregator<float,float>::FinalizeScoresU3333@w2$8U@3'@33С$U030333@w2$8Up3'3@3С$UP3P33@3@w2$8U3'303С$U33`33@w2$8U3'Ў33С$U33 3Й3@w2$8U3'33С$U33 3`3@w2$8U3'033С$U`3`333@w2$8U3'p33С$UP3P3p33@w2$8U3'03p3С$U33y234!$8U333P34!$UP3P3y234!$8U33334!$U333 34!$8U33334!$U33`3`34!$8U03033 34!$U33`z234!$8U33Ћ3P34!$UЅ3Ѕ3`z2p34!$8Up3p3@334!$UP3P303034!$8U33З334!$U03033К34!$8U33P334!$UЬ3`3PMust provide classlabels_strings or classlabels_int64s but not both.classlabels_strings_.empty() ^ classlabels_int64s_.empty()onnxruntime::ml::ZipMapOp::ZipMapOpD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\ml\zipmap.ccZipmap does not support empty dim countZipmap only supports 1D or 2D input tensors] != number of classlabels[Input features_per_batch[8U 3PU3Pinfo.GetAttr<int64_t>("channels", &channels_).IsOK()onnxruntime::contrib::ReorderOutput::ReorderOutputD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\nchwc_ops.hinvalid channel countchannels_ > 0info.GetAttr<int64_t>("channels_last", &channels_last_).IsOK()8U3 3PGetFusedActivationAttr(info, activation_).IsOK()onnxruntime::contrib::NchwcConv::NchwcConvpool_attrs_.kernel_shape.size() == 2onnxruntime::contrib::NchwcPoolBase::NchwcPoolBaseU`3PȫU3PXU$3Pinfo.GetAttrs<int64_t>("scales", scales_).IsOK()onnxruntime::contrib::NchwcUpsample::NchwcUpsamplescales_.size() == 4scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1X_shape.NumDimensions() == 4onnxruntime::contrib::ReorderInput::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\nchwc_ops.cc(X_shape[1] % MlasNchwcGetBlockSize()) == 0X_rank == 4onnxruntime::contrib::ReorderOutput::Computechannels_ <= X_shape[1]onnxruntime::contrib::NchwcConv::Compute(static_cast<size_t>(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.output and sum shape must matchNot satisfied: Y->Shape() == sum_shape -onnxruntime::contrib::NchwcPoolBase::NchwcPoolonnxruntime::contrib::NchwcUpsample::ComputeجU4PMatMulInteger16A != nullptr && B != nullptronnxruntime::contrib::MatMulInteger16<short,short,int>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\matmul_integer16.ccXU 44P44PQLinearLeakyReluحU 44064PQLinearLeakyRelu : input X_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_x_scale)onnxruntime::contrib::BuildQLinearLeakyReluLookupTableD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\qlinear_lookup_table.ccQLinearLeakyRelu : input X_zero_point must be a scalar or 1D tensor of size 1tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)QLinearLeakyRelu : input Y_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_y_scale)QLinearLeakyRelu : input Y_zero_point must be a scalar or 1D tensor of size 1tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)XUZ4PQLinearAddخUZ4PMatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_a_scale)onnxruntime::contrib::QLinearBroadcastTwoD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\qlinear_binary_op.ccMatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if giventensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point)MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_b_scale)MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if giventensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point)MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_c_scale)MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if giventensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point)TBroadcaster can only start at span boundary!offset % span_size_ == 0onnxruntime::TBroadcaster<unsigned char,unsigned char>::AdvanceByonnxruntime::TBroadcaster<signed char,signed char>::AdvanceByXU]4]40b4d4@w2$دU\4\4_4pd4@w2$XU@\4'@_4`d4$ذU\4'a4d4$XU[4'@]4 d4$رU[4'@^4@d4$XUP[4']40d4$زU[4'^4Pd4$XU@44PQAttentiononnxruntime::contrib::QAttention<float>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\quantization\attention_quant.ccinput scale must be a scalar or 1D tensor of size 1Not satisfied: IsScalarOr1ElementVector(input_scale_tensor) -weight must be a scalar or 1D tensor of size 1Not satisfied: IsScalarOr1ElementVector(weight_scale_tensor) -input zero point must be a scalar or 1D tensor of size 1.Not satisfied: IsScalarOr1ElementVector(i_zp_tensor) -weight zero point must be a scalar or 1D tensor of size 1.Not satisfied: IsScalarOr1ElementVector(w_zp_tensor) -onnxruntime::contrib::AttentionCPUBase::ApplyAttentionD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops/cpu/bert/attention_cpu_base.hU4'4З4p4$`U4'44;$UP4'44;$@F@@@@`U 4 Ponnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommonD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_matmul.ccU 4`4PhU 44PDynamicQuantizeMatMul : input B scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(b_scale_tensor)onnxruntime::contrib::DynamicQuantizeMatMul::ComputeDynamicQuantizeMatMul : input B zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(b_zero_point_tensor)MatMulIntegerToFloat : input A scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(a_scale_tensor)onnxruntime::contrib::MatMulIntegerToFloat::ComputeMatMulIntegerToFloat : input B scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.MatMulIntegerToFloat : input A zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(a_zero_point_tensor)MatMulIntegerToFloat : input B zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.U4PSampleOppUp4Pinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0onnxruntime::contrib::AttentionBase::AttentionBaseD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\bert\attention.ccInput 'input' is expected to have 3 dimensions, got Input 0 dimension 2 should be divisiable by value of the num_heads attribute.Input 'weights' is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0Input 'weights' dimension 1 should be 3 times of dimension 0Input 'bias' is expected to have 1 dimension, got Input 'bias' dimension 0 should have same length as dimension 1 of input 'weights'Input 'past' is only allowed for unidirectionalInput 'past' is expected to have 5 dimension, got Inputs 'past' dimension 0 shall have length of 2Inputs 'past' dimension 1 shall have same length as dimension 0 of input 0Inputs 'past' dimension 2 shall have length of num_headsInputs 'past' dimension 2 shall have length of Inputs 'mask_index' dimension 0 shall have length of batch_size or 2 * batch_sizeInputs 'mask_index' with raw attention mask shall have shape batch_size x (past_sequence_length + sequence_length)Input 'mask_index' is expected to have 1 or 2 dimensions, got Expect to have present state output when past state input is givenonnxruntime::contrib::AttentionBase::GetPresentonnxruntime::contrib::Attention<float>::ComputeU4'04@4С$U4Ponnxruntime::contrib::EmbedLayerNorm<float>::EmbedLayerNormD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\bert\embed_layer_norm.ccepsilon_ >= 0onnxruntime::contrib::EmbedLayerNorm<float>::Computeinput index out of rangeU44444!$U4PAn axis tensor must be a scalar tensor.axis_tensor->Shape().IsScalar()onnxruntime::contrib::ExpandDims::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\expand_dims.h Axis is Axis must be within range [axis <= X_NumDims && axis >= -X_NumDimsExpandDimsU `Ponnxruntime::contrib::FusedConvFloat::FusedConvFloatD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\fused_conv.ccUPPonnxruntime::contrib::FusedGemm<float>::FusedGemmD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\fused_gemm.ccU!65Ponnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOpD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.hstatic_cast<int>(activation_func_names.size()) == num_directions_ * 3AttnLSTMonnxruntime::contrib::DeepCpuAttnLstmOp::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.ccInput X must have 3 dimensions only. Actual:Input W must have shape {Input R must have shape {Input B must have shape {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 {}, actural: Attention mechanism memory shape error! Expected: {Attention mechanism memory sequence lengths must have shape { found!], while Attention mechanism memory sequence lengths value must in (0, , am_attn_size}, Got:Attention memory layer weight shape error! Expected:{}, Got: Attention query layer weight shape error! Expected:{}. Got: Attention v weight shape error! Expected:{, aw_attn_size}. Got:Attention layer weight shape error! Expected: {onnxruntime::contrib::DeepCpuAttnLstmOp::ValidateInputsonnxruntime::contrib::DeepCpuAttnLstmOp::ComputeImplUs5}5PTokenizermarkattribute mark is not setonnxruntime::contrib::Tokenizer::TokenizerD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\tokenizer.ccpad_valueattribute pad_value is not setmincharnumattribute mincharnum is not setattribute mincharnum must have a positive valuemincharnum_ > 0separatorstokenexpEither one of the separators OR tokenexp attributes required but none is setExpecting a non-empty tokenexp!tokenexp.empty()separators must not be empty!separators.empty()mincharnum is too big for char level tokenezation!char_tokenezation_ || mincharnum_ < 2Can not digest separators: Can not digest tokenexp: Input string contains invalid utf8 chars: Match contains invalid utf8 chars: tensor(string) expected as inputInput dimensions are either [C] or [N][C] allowedUK5Pembedding_sizeconv_window_sizechar_embedding_size conv filter size: embedding_size attribute: Conv filter size does not match embedding_size attribute. conv kernal size 1: conv_window_size attribute: Conv kernal size 1 does not match conv_window_size attribute . Char embedding size: char_embedding_size attribute: Char embedding size does not match char_embedding_size attribute. Conv kernal size 2 : Char embedding size does not match conv kernal size 2.onnxruntime::contrib::WordConvEmbedding::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\word_conv_embedding.ccWordConvEmbeddingU05PpositiveMurmurHash3keysonnxruntime::contrib::MurmurHash3::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\murmur_hash3.ccInvalid assumption of output element sizesizeof(uint32_t) == output_element_bytesinput_num_bytes % 4 == 0U5PTransposeMatMulinfo.GetAttr("transA", &trans_a_attr_).IsOK()onnxruntime::contrib::TransposeMatMul::TransposeMatMulD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\transpose_matmul.ccinfo.GetAttr("transB", &trans_b_attr_).IsOK()onnxruntime::contrib::TransposeMatMul::ComputeU`5Ponnxruntime::contrib::MaxpoolWithMask::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\maxpool_with_mask.hMaxpoolWithMaskU5'55P;$U5'55;$U@5'55;$UP5PInput tensor to Unique op should be 1DU25PConvTransposeWithDynamicPadsU 6 6PCropAndResizebilinear specified. It should be either bilinear or nearestonnxruntime::contrib::CropAndResize<float>::CropAndResizeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops/cpu/crop_and_resize.hNull crop_size_ptrNumber of dimensions for crop size should be exactly 1 U666P64!$U)6PCDist U,6Pmetricinfo.GetAttr<std::string>("metric", &metric).IsOK()onnxruntime::contrib::CDist<double>::CDistD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\cdist.hsqeuclideaneuclideanonnxruntime::contrib::CDist<float>::CDistThe first input of CDist kernel has wrong shape: The second input of CDist kernel has wrong shape: Input shape dimensions mismatch:UL6P UH6Ponnxruntime::contrib::BiasGelu<float,0>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\bert\bias_gelu.ccuse_approximationonnxruntime::contrib::BiasGelu<float,1>::ComputeU@Q6@Q6Q6S64!$ U`Q6`Q6pR6S64!$UQ6Q6R6S64!$y"=5?y"=y"=y"=y"=*BL?*BL?*BL?*BL? U5c6PU5e6P Uh6Ponnxruntime::ElementWiseKernel<struct onnxruntime::functors::ScaledTanh<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::ScaledTanh<float> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::ParametricSoftplus<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::ParametricSoftplus<float> >::ElementWiseKernelD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\activations.hU j6 j60ck6b$ Ui6i60ck6b$U`j6`j6j6 k64!$@ U@u6PUv6Pinfo.GetAttr("scale", &scale_).IsOK()onnxruntime::contrib::Scale<float>::ScaleD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\element_wise_ops.hinfo.GetAttr("alpha", &alpha_).IsOK()onnxruntime::contrib::Affine<float>::Affineinfo.GetAttr("beta", &beta_).IsOK()borderAttribute border needs to be specified with four border elements, got input dimensions insteadInput is expected to have four dimensions corresponding to [N,C,H,W], got ) + bottomBorder () needs to be greater than or equal to the topBorder (Input's height () + rightBorder () needs to be greater than or equal to the leftBorder (Input's width () + scale_[0] () + scale_[1] ( UP}6}6Ponnxruntime::contrib::Crop<float>::ComputeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\crop.h U66P) does not match the number of channels (Bias size (onnxruntime::contrib::ImageScaler<float>::ImageScalerD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\image_scaler.hinfo.GetAttrs<float>("bias", bias_).IsOK()U6P U6Pop_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::contrib::LayerNorm<float>::LayerNormD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\layer_norm.cconnxruntime::contrib::LayerNorm<float>::Computeonnxruntime::contrib::LayerNorm<double>::LayerNormonnxruntime::contrib::LayerNorm<double>::ComputeU666064!$ U666 64!$U6P U 6Ponnxruntime::contrib::SkipLayerNorm<float>::SkipLayerNormD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\skip_layer_norm.ccinput is expected to have 3 dimensions, got skip is expected to have same shape as inputgamma is expected to have 1 dimension, got Last dimension of gamma and input does not matchbeta is expected to have 1 dimension, got Last dimension of beta and input does not matchbias is expected to have 1 dimension, got Last dimension of bias and input does not matchonnxruntime::contrib::SkipLayerNorm<double>::SkipLayerNormU0606p664!$ UP6P6664!$UТ7PInverseonnxruntime::utils::MLTypeCallDispatcher<struct onnxruntime::contrib::Inverse::ComputeImpl,float,double,union onnxruntime::MLFloat16>::Invoke U@7@777p7$SoftmaxCPU inputs N, D and N * D must be < ;affinereluleakyreluthresholdedreluscaledtanhhardsigmoidelusoftsignsoftplusExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCountD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\rnn\rnn_helpers.ccInvalid activation function of onnxruntime::rnn::detail::deepcpu::ActivationFuncByNameInvalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNameInvalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameInvalid GRU hidden gate activation function: onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByName%% AA %%%%%%%%7.7.7.7. A A A AAAAA inputs but Scan was only given The subgraph in 'body' requires num_variadic_inputs == num_subgraph_inputsonnxruntime::scan::detail::Info::InfoD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\controlflow\scan_utils.cc' was Number of entries in 'num_entries < 0 || gsl::narrow_cast<int64_t>(directions.size()) == num_entriesonnxruntime::scan::detail::ReadDirections'. 0 == forward. 1 == reverse.Invalid values in 'valid did not.Subgraph must have the shape set for all outputs but onnxruntime::scan::detail::CreateFeedsFetchesManageronnxruntime::scan::detail::IterateSequence::<lambda_8fb056035962b4b1c96ec8d77794334f>::operator ()onnxruntime::scan::detail::IterateSequenceMisuse of LoopStateVariable. Attempt to move beyond end of sequenceiteration_num_ < sequence_len_onnxruntime::scan::detail::LoopStateVariable::Next is not compatible with Mismatch between expected shape and shape from first outputonnxruntime::scan::detail::OutputIterator::InitializeUl&>Failed to create output tensor for output #onnxruntime::scan::detail::OutputIterator::AllocateFinalBufferIf shape was concrete we shouldn't be using a custom allocator!is_concrete_shape_onnxruntime::scan::detail::OutputIterator::AllocateFinalOutputcur_iteration_ < num_iterations_onnxruntime::scan::detail::OutputIterator::operator *Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.is_concrete_shape_Expected AllocateFinalOutput to have been called to before we increment the iteratoronnxruntime::scan::detail::OutputIterator::operator ++onnxruntime::OrtValueTensorSlicer<struct OrtValue>::Iterator::operator *U+9+9+9 ,94!$Einsum op: The candidate output does not match the actual output's shapeoutput.SizeInBytes() == input.SizeInBytes()onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopyD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_auxiliary_ops.ccThe innermost dims should have the same dim value to parse the diagonal elementsinput_dims[rank - 2] == input_dims[rank - 1]onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDimsEinsum op: Unsupported data type for Diagonal for input shape Cannot parse the diagonal elements along dims rank >= 2 && dim_1 != dim_2 && input_dims[dim_1] == input_dims[dim_2]onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalThe rank of the input must match permutation size for Transposeinput_rank == permutation.size()onnxruntime::EinsumOp::IsTransposeRequiredLength of permutation must match the rank of the input to be permutatedonnxruntime::EinsumOp::TransposeEinsum op: Transpose failed: Data types of the inputs must match for MatMulinput_1.DataType() == input_2.DataType()onnxruntime::EinsumOp::MatMulOnly 1 batch dimension is allowed for MatMulinput_shape_1_override.size() == 3 && input_shape_2_override.size() == 3Batch dimension should match for MatMul;input_shape_1_override[0] == input_shape_2_override[0]Incompatible matrix dimensions for matMulinput_shape_1_override[2] == input_shape_2_override[1]Einsum op: Exception during MatMul operation: onnxruntime::EinsumComputePreprocessor::RunD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_compute_preprocessor.ccNumber of subscripts in the input equation does not match number of input tensorsFound a '.' not part of an ellipsis in input: Einsum subscripts string contains too many subscript labels when compared to the rank of the inputEllipsis must indicate a fixed number of dimensions across all inputsFound '.' not part of an ellipsis in input: The only subscript labels allowed are lowercase letters (a-z) in the same dimensionAnother operand has a dim value of . The shape is: is incompatible in the dimension Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand Einsum subscripts string contains too many subscript labels when compared to the rank of the input Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input num_broadcasted_indices < num_of_ellipsis_dims_onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDimsThe broadcasted dimensions of the inputs are incompatibledim_iter == rankInputs have ellipses in them but the provided output subscript does not contain an ellipsisFound a '.' not part of an ellipsis in the output subscript providedFound '.' not part of an ellipsis in the output subscript providedThe only subscript labels allowed in the output subscript are lowercase letters (a-z)Output subscript contains repeated lettersOutput subscript contains letters not seen in the inputsRank of the input must match number of subscript labels corresponding to the input Left shape override: Left shape: The override dims are not compatible with given tensor's shape. left.Shape().Size() == left_shape_override.Size()onnxruntime::EinsumTypedComputeProcessor<float>::PairwiseOperandProcessD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_typed_compute_processor.cc Right shape override: Right shape: right.Shape().Size() == right_shape_override.Size() Right shape: Ranks of pair-wise operands must be equal. left_rank == right_rankEinsum op: Input dimensions must be equal along an axis to be reduced across all inputsleft_dim == right_dimEinsum op: Input shapes do not alignEinsum op: The candidate output cannot be reshaped into the op's outputcandidate_output.Shape().Size() == output_shape.Size()onnxruntime::EinsumTypedComputeProcessor<float>::FinalizeOutputNot all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: candidate_output_dims[iter] == 1Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: onnxruntime::EinsumTypedComputeProcessor<int>::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor<int>::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor<double>::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor<double>::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor<__int64>::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor<__int64>::FinalizeOutputunimplemented activation: activation_params count mismatchInput 0 and 1 shall have same shapeInput 0 and 7 (mask) shall have same shapeinput_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 1word_embedding and segment_embedding shall have same dimension 1beta 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 U:`:P::@:0:0not support normalize yet.!normalize_onnxruntime::contrib::BahdanauAttention<float>::BahdanauAttentionD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.cc is not in (0, Real memory steps mem_steps <= max_memory_steps_ && mem_steps > 0onnxruntime::contrib::BahdanauAttention<float>::PrepareMemoryhU: [seqno= row[Input 0 is expected to have 1 or more dimensions, got Input 1 is expected to have 1 dimensions, got Input 1 dimension 0 should have same length as the last dimension of input 0Gemv found an unexpected CBLAS_TRANSPOSE input ofonnxruntime::math::GemvD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\util\math_cpu.cc DeviceId: MemoryType:DeviceType:Device:[ OrtAllocatorType: OrtMemType: id:name:OrtMemoryInfo:[CudaCudaPinnedonnxruntime::IAllocator::CalcMemSizeForArrayWithAlignmentD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\allocator.ccSpecified device is not supported.OrtValue is TensorSequence type but has no element Tensor DataType.Tensor types should have been handled already is not supportedNot implementedOrtValue is not a Tensortype != nullptrOrtApis::GetTensorTypeAndShapeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\tensor_type_and_shape.ccArgument is not a tensortype_proto is not of type map!type_proto is not of type sequence! dimensions. for SizeFromDimension. Tensor has Invalid dimension of dimension <= num_dimsonnxruntime::TensorShape::SizeToDimensionD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\tensor_shape.cconnxruntime::TensorShape::SizeFromDimensionInvalid tensor shape slice argument.dimstart <= dimend && dimend <= size()onnxruntime::TensorShape::Slice{0U;`;;;$00Q''''p;onnxruntime::TensorTypeBase::GetElementTypeU@; ;;;00$''Q''0;onnxruntime::SparseTensorTypeBase::GetElementTypeU`;   000'''Q' P;;0U;;;;0$0'Q''';onnxruntime::SequenceTensorTypeBase::GetElementTypeonnxruntime::data_types_internal::IsCompatibleD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\data_types.ccOnly ONNX MLDataType can be registeredproto != nullptronnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypeWe do not expect duplicate registration of types for: thisProto->value_case() == TypeProto::ValueCase::kTensorTypeonnxruntime::TensorTypeBase::IsCompatibleutils::HasElemType(thisProto->tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeonnxruntime::SparseTensorTypeBase::IsCompatibleutils::HasElemType(thisProto->sparse_tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSequenceTypeonnxruntime::SequenceTensorTypeBase::IsCompatibleutils::HasElemType(thisProto->sequence_type())thisProto->value_case() == TypeProto::ValueCase::kMapTypeonnxruntime::NonTensorTypeBase::IsMapCompatibleutils::HasKeyType(thisProto->map_type())onnxruntime::NonTensorTypeBase::IsSequenceCompatibleonnxruntime::NonTensorTypeBase::FromDataContaineronnxruntime::NonTensorTypeBase::ToDataContainerU;`;;;$00Q''''P;`U;`;;;$00Q''''p;U;`;;;$00Q'''';pU;`;;;$00Q'''';U;`;;;$00Q'''';U;`;;;$00Q'''' ;U;`;;;$00Q''''@;U;`;;;$00Q''''0;U;`;;;$00Q'''';U;`;;;$00Q'''';(U;`;;;$00Q''''`;U;`;;;$00Q'''';8U;`;;;$00Q'''';U;`;;;$00Q'''';HU@; ;;;00$''Q''P;U@; ;;;00$''Q''p;XU@; ;;;00$''Q'';U@; ;;;00$''Q'';hU@; ;;;00$''Q'' ;U@; ;;;00$''Q''@;xU@; ;;;00$''Q''0;U@; ;;;00$''Q'';U@; ;;;00$''Q'';U@; ;;;00$''Q''`;U@; ;;;00$''Q''; U@; ;;;00$''Q'';U@; ;;;00$''Q'';0U`;;0;@;000'''Q';P;; U`;;0;0;000'''Q';P;;U`;;0;0;000'''Q';P;;U`;;0;0;000'''Q';P;;U`;;0;`;000'''Q';P;;U`;;0;P;000'''Q';P;;U`;;0;P;000'''Q';P;;U`;;0;P;000'''Q';P;;U;;;;0$0'Q'''p;8U;;;;0$0'Q''';U;;;;0$0'Q''';HU;;;;0$0'Q''' ;U;;;;0$0'Q'''0;XU;;;;0$0'Q'''@;U;;;;0$0'Q'''P;hU;;;;0$0'Q'''`;U;;;;0$0'Q''';xU;;;;0$0'Q''';U;;;;0$0'Q''';U;;;;0$0'Q''';U;;;;0$0'Q''';U;;;;0$0'Q'''; U`;;@;p;000'''Q';P;;U`;;@;;000'''Q';P;;(null)tensor(bfloat16)tensor type is not currently registered or supportedMLDataType for: U`0`;;'000''''QU`0`;;'000''''QU`0;'000''''QU`0;;'000''''Q U`0;'000''''QU`0;'000''''Q0U`0P;;'000''''QU`0P;;'000''''Q@U`0p;;'000''''QU`0p;;'000''''QPU`0`;;'000''''QU`0p;;'000''''Q`U`0P;;'000''''QU`0P;;'000''''QInvalid DataTypeImpl TypeProto definitiononnxruntime::utils::ContainerChecker::ContainerChecker expected to be a registered ONNX typevalue_proto != nullptronnxruntime::data_types_internal::SetMapTypes<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::SetD:\a\ml-builds\ml-builds\onnxruntime\include\onnxruntime\core/framework/data_types.honnxruntime::data_types_internal::SetMapTypes<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64>::Setonnxruntime::data_types_internal::SetMapTypes<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float>::Setonnxruntime::data_types_internal::SetMapTypes<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,double>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::Setonnxruntime::data_types_internal::SetMapTypes<__int64,__int64>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,float>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,double>::Setelem_proto != nullptronnxruntime::data_types_internal::SetSequenceType<float>::Setonnxruntime::data_types_internal::SetSequenceType<double>::Setonnxruntime::data_types_internal::SetSequenceType<signed char>::Setonnxruntime::data_types_internal::SetSequenceType<unsigned char>::Setonnxruntime::data_types_internal::SetSequenceType<short>::Setonnxruntime::data_types_internal::SetSequenceType<unsigned short>::Setonnxruntime::data_types_internal::SetSequenceType<int>::Setonnxruntime::data_types_internal::SetSequenceType<unsigned int>::Setonnxruntime::data_types_internal::SetSequenceType<__int64>::Setonnxruntime::data_types_internal::SetSequenceType<unsigned __int64>::Setonnxruntime::data_types_internal::SetSequenceType<bool>::Setonnxruntime::data_types_internal::SetSequenceType<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::Setonnxruntime::data_types_internal::SetSequenceType<union onnxruntime::MLFloat16>::Setonnxruntime::data_types_internal::SetSequenceType<struct onnxruntime::BFloat16>::Setonnxruntime::data_types_internal::SetSequenceType<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,float> > > >::Setonnxruntime::data_types_internal::SetSequenceType<class std::map<__int64,float,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,float> > > >::SetpU<< <0<$$p_type != nullptronnxruntime::Tensor::TensorD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\tensor.ccshape.Size() must >=0tensor failed memory size calculationtensor size overflowonnxruntime::Tensor::SizeInBytesonnxruntime::Tensor::InitTensor is expected to contain one of the primitive data types. Got: dtype_ != nullptrRknpuExecutionProvider@U} >invalid allocatorFailed to find allocator for device onnxruntime::utils::BatchOrCopyMLValueD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\utils.cconnxruntime::utils::FindMemoryInfoForValueexec_plan_ptronnxruntime::utils::CalculateStaticCopyInfoForFeedonnxruntime::utils::CalculateStaticCopyInfoForFeedsonnxruntime::utils::InitializeFeedFetchCopyInfofeed_locations.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFeedsfetch_alloc_info.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFetchescopy_info.size() == num_feedsonnxruntime::utils::CopyInputsAcrossDevicesonnxruntime::utils::CopyOutputsAcrossDevicesOnly one thread was configured for parallel execution. Hence will use sequential execution.onnxruntime::utils::ExecuteGraphImplonnxruntime::utils::ExecuteGraphcannot find allocatoronnxruntime::OpKernelInfo::GetMemoryInfoD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\op_kernel_info.ccUpf<node_offsets_index < node_offsets_size_onnxruntime::NodeIndexInfo::GetNodeOffsetD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/framework/node_index_info.hExecution frame was nullframe != nullptronnxruntime::OpKernelContext::OpKernelContextD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\op_kernel.ccOpKernel was nullkernel != nullptronnxruntime::OpKernelContext::OutputMLValue. Num args is Invalid arg_num of arg_num < arg_counts.size()onnxruntime::OpKernelContext::NumVariadicInputsTempSpace allocator not foundonnxruntime::OpKernelContext::GetOrCreateOutputMLValueNo attribute with this name is defined.duplicated allocatoronnxruntime::IExecutionProvider::InsertAllocatorD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\execution_provider.cc (node The node is not placed on any Execution Provider, therefore, can't find a suitable kernel for Failed to find kernel for in KernelRegistryManagerfound duplicated provider The node is not placed on any Execution Providerdata_transfer registered is nullptr.Tensor size mismatch to There's no data transfer registered for copying tensors from onnxruntime::DataTransferManager::CopyTensorsD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\data_transfer_manager.cconnxruntime::FeedsFetchesInfo::MapNamesToMLValueIdxsD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\feeds_fetches_manager.ccError mapping feeds: Error mapping output names: input_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknownonnxruntime::FeedsFetchesManager::SetDeviceCopyChecksUnloading DSO onnxruntime::ExLibLoader::~ExLibLoaderD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\ex_lib_loader.ccFailed to unload DSO: Caught exception while destructing CustomOpsLoader with message: has already been loaded.A dso with name onnxruntime::ExLibLoader::LoadExternalLibCaught exception while loading custom ops with message: Could not find OrtValue with idx '. Ignoring allocator from Allocator already registered for onnxruntime::SessionState::SetupAllocatorsD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\session_state.ccSaveMLValueNameIndexMappingonnxruntime::SessionState::CreateGraphInfoDone saving OrtValue mappings. as there's no execution provider allocated.Could not create kernel for node: with error: Kernel creation failed for node: . Do you have duplicated calls to SessionState::AddInitializedTensor function?duplicated ort_value index:) and node is used by node Using an input in multiple nodes on different devices is not supported currently. Input:Failed to find input name in the mapping: Only one node should produce an output. Existing entry for output_names_to_nodeinfo.empty()onnxruntime::SessionState::AddOutputNameToNodeInfoMapping for attribute Entry exists in node existing_entries.find(attribute_name) == existing_entries.cend()onnxruntime::SessionState::AddSubgraphSessionStateSetGraphAndCreateKernels must be called prior to GetExecutionInfo.node_index_info_onnxruntime::SessionState::GetNodeIndexInfoonnxruntime::SessionState::UpdateToBeExecutedNodesU$=$=%=&=$$XU$=$=0%= &=$$U'=`} (=XV'=`} @(=V-=.=Ponnxruntime::FunctionKernel::FunctionKernelD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/framework/func_kernel.hcreate_func(&context, &func_state_) == 01 == capability->nodes.size()onnxruntime::PlaceNodeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\graph_partitioner.ccNo provider specified.onnxruntime::GraphPartitioner::PartitionProvider doesn't return correct number of compiled functionsnode_compute_funcs.size() == nodes_need_compile.size()nullptr != type_protoonnxruntime::utils::GetMLDataTypeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\mldata_type_utils.ccVs=s=onnxruntime::FinalizeSessionStateD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\finalize_session_state.ccstring tensor is not supported for copying between allocatorsonnxruntime::DeserializeTensorProto, Got Internal error. The preallocated buffer is too small. Requires Failed to copy tensor to onnxruntime::SaveInputOutputNamesToNodeMapping::<lambda_ada137c713f75d17ee1b5d2458dd6464>::operator ()onnxruntime::SaveInputOutputNamesToNodeMapping::<lambda_961b61fbb63e724a84bcc8c8b754584a>::operator ()onnxruntime::SaveInputOutputNamesToNodeMapping is not used by any node. input with name SubgraphGraphSaving initialized tensors.onnxruntime::SaveInitializedTensorsOrtValue indexes should have been populated.ort_value_name_idx_map.MaxIdx() > -1 failed.Deserialize tensor Done saving initialized tensors(VЃ='`== =$V==0==@w2$) in proto) does not match the data size(corrupted protobuf data: tensor shape size(UnpackTensor: the pre-allocate size does not match the size in protodata overflowonnxruntime::utils::GetFileContentD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\tensorprotoutils.ccstring tensor can not have raw dataonnxruntime::utils::TensorProtoToMLValuetensor can't contain negative dimsThe buffer planner is not consistent with tensor buffer size, expected initialize preallocated buffer failedInitialized tensor with unexpected type: onnxruntime::utils::ConstantNodeProtoToTensorProto in 'Constant' node 'Unsupported attribute value type of onnxruntime::utils::SparseTensorProtoToDenseTensorProtoUnsupported sparse tensor data type of UnpackTensor: the pre-allocated size does not match the raw data size, expected `anonymous-namespace'::UnpackTensorWithRawDataInvalid TensorProtorank == dims.size() && rank > 0onnxruntime::utils::CopySparseDatacur_index == &*indices_data.cend()Invalid SparseTensor indices. Should be rank 0 or 1. Got:source and destination buffer size mismatchonnxruntime::utils::ReadLittleEndianD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/framework/endian_utils.hNot satisfied: source_bytes.size_bytes() == destination.size_bytes() -xV>>>>4!$V>>>>4!$xV>> >>`$offset >= 0 && static_cast<size_t>(offset) < node_values_size_onnxruntime::NodeIndexInfo::GetMLValueIndexV.>]>   ort_value_index >= 0 && static_cast<size_t>(ort_value_index) < all_values_size_onnxruntime::IExecutionFrame::GetMLValueD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/framework/execution_frame.h V.>`\>`L>J>J>node_index_info and ort_value_idx_map are out of sync and cannot be usednode_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()onnxruntime::IExecutionFrame::IExecutionFrameD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\execution_frame.cc Requested shape:OrtValue shape verification failed. Current shape:shape && tensor.Shape() == *shapeonnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValueinvalid index feeds.size() == feed_mlvalue_idxs.size()onnxruntime::IExecutionFrame::Initfetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size() entries which doesn't match the number of fetches the frame was initialized with of Fetches vector passed to GetOutputs contains buffers_.find(location) == buffers_.end()onnxruntime::ExecutionFrame::ExecutionFrame returned nullptrAllocation of memory pattern buffer for failed. Error:Trying to allocate memory for unused optional inputs/outputsTensor shape cannot contain any negative valueort_value.Fence() == nullptronnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper, fall back to default allocation behavior but the actually size is: , block in memory pattern size is: For ort_value with index: . 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.Shape mismatch attempting to re-use buffer. onnxruntime::ExecutionFrame::AllocateMLValueTensorPreAllocateBuffermlvalue.Fence() == nullptronnxruntime::AllocateSparseTensorort_value_index >= 0 && static_cast<size_t>(ort_value_index) < alloc_plan.size()onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlanWe don't expect custom allocators for non-tensor types, so a shape is mandatory here.Allocation of tensor types requires a shape.Invalid allocation kind: onnxruntime::ExecutionFrame::ReleaseMLValueImplort_value_idx >= 0 && static_cast<size_t>(ort_value_idx) < alloc_plan.size()onnxruntime::ExecutionFrame::GetAllocationPlan failed: size=TraceAllocation for ort_value_idx=onnxruntime::ExecutionFrame::TraceAllocateonnxruntime::ExecutionFrame::TraceFreeTraceFree for ort_value_idx=Memory pattern planner is not enabled on this execution framework.model format error! Need a key for the external data infomodel format error! Need a value for the external data info failedparsing checksummodel format error!model format error! Missing 'location'onnxruntime::IDataTransfer::CopyTensorsD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\data_transfer.ccsrc.SizeInBytes() == dst.SizeInBytes()onnxruntime::CPUDataTransfer::CopyTensor kernel_end_version: kernel start version: node_version: Version mismatch. and type (Op with name ( This op has been implemented only for the following types ( However the types are incompatible. (node_version: in the supported version rangeFound kernel for Op with name (), but the node in the model has the following type (kernel def can't be NULL: Conflicting with a registered kernel with op versions.Failed to add kernel for onnxruntime::KernelRegistry::TryCreateKernelD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\kernel_registry.cc Encountered following errors: ( kernel is not supported in Kernel not foundlen <= op_schema.inputs().size()onnxruntime::`anonymous-namespace'::TraverseFormalParametersWithTypeProtoonnxruntime::NodeIndexInfo::Init::<lambda_a111c4770711d91fc3a579047c6acca0>::operator ()D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\node_index_info.cconnxruntime::NodeIndexInfo::Init::<lambda_1f56f922ddb679acf583be4c09ddce92>::operator ()V>>>>$$pV>>`>Ц>4!$VУ>У>>>$$pV>>>>4!$Can't slice a non-tensor OrtValue. Type was ort_value.IsTensor()onnxruntime::OrtValueTensorSlicer<struct OrtValue>::CreateD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\ort_value_tensor_slicer.ccOrtValue has not been allocated so can't be sliced.ort_value.IsAllocated(). Shape:Insufficient dimensions to slice on gsl::narrow_cast<int64_t>(tensor_shape.NumDimensions()) >= slice_dimension. Dimension 0 is Invalid dim0_offset of dim0_offset < dim0_sizeonnxruntime::OrtValueTensorSlicer<struct OrtValue const >::Create@V>0>V@> does not.All implicit inputs should have OrtValue instances by now. entry != nullptronnxruntime::OpKernelContextInternal::OpKernelContextInternalD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/framework/op_kernel_context_internal.hMultiple errors were found.onnxruntime::ParallelExecutor::ExecuteD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\parallel_executor.ccParallelExecutor::ExecuteBegin executiononnxruntime::ParallelExecutor::RunNodeAsyncExiting due to terminate flag being set to true.Got nullptr from GetKernel for node: op_name_fence_before' Status Message: node. Name:'Non-zero status code returned while running provider_kernel_time_fence_afterUnknown exception was caught by catch-all handler.'. Exception running nodes starting at @ V`>`>>>p$onnxruntime::SequentialExecutor::ExecuteD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\sequential_executor.ccgraph_indexexec_plan_indexactivation_sizeparameter_sizeoutput_sizeSequentialExecutor::Executeonnxruntime::ReleaseNodeMLValues already exist.func info for node: Can't use func with null ptr not found.onnxruntime::FuncManager::GetFuncsD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\fuse_nodes_funcs.cc -V /? /?p/?0?$$0 V@/?@/?/?0?$$ V/?/?`/?/?$$MaxAllocSize: NumAllocs: MaxInUse: TotalAllocated: InUse: Limit: VPJ?K?@?PS?0?`?`  | in_use: | Requested Size: Size: , prev: , next: 0 == memory_size % kMinAllocationSizeonnxruntime::BFCArena::AllocationRegion::AllocationRegionD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core/framework/bfc_arena.hp_int >= base_intonnxruntime::BFCArena::AllocationRegion::IndexForp_int < base_int + memory_size_Could not find Region for onnxruntime::BFCArena::RegionManager::RegionForCreating BFCArena for onnxruntime::BFCArena::BFCArenaD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\bfc_arena.cc bins of max chunk size Creating BinForSize(bin_size) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size * 2) != BinFromIndex(b)h < chunks_.size()onnxruntime::BFCArena::ChunkFromHandlecudaMallocIncorrect arena extend strategy.onnxruntime::BFCArena::Extend::<lambda_eaadf1f483df5eff49a5fccd707cb071>::operator () is smaller than requested bytes of Available memory of Failed to allocate memory for requested buffer of size bytes.Extended allocation by onnxruntime::BFCArena::ExtendTotal allocated bytes: Allocated memory at reserved_chunks_.find(ptr) == reserved_chunks_.end()onnxruntime::BFCArena::Reserveh != kInvalidChunkHandletried to allocate 0 bytesonnxruntime::BFCArena::AllocateRawInternal rounded_bytes:. bin_num:Extending BFCArena for Failed to find a free memory block despite calling Extend. rounded_bytes=. Current allocation summary follows.BFC Arena ran out of memory trying to allocate !chunk->in_use()onnxruntime::BFCArena::FindChunkPtr!c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::SplitChunkonnxruntime::BFCArena::DeallocateRawInternal!c1->in_use() && !c2->in_use()onnxruntime::BFCArena::Mergec2->prev == h1onnxruntime::BFCArena::InsertFreeChunkIntoBin!c->in_use() && (c->bin_num != kInvalidBinNum)onnxruntime::BFCArena::RemoveFreeChunkIterFromBinonnxruntime::BFCArena::RemoveFreeChunkFromBinCould not find chunk in binBinFromIndex(c->bin_num)->free_chunks.erase(h) > 0c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::FreeAndMaybeCoalescebin->free_chunks.count(h) == 1onnxruntime::BFCArena::get_bin_debug_infoc->bin_num == bin_numAllocator:onnxruntime::BFCArena::DumpMemoryLogBin size: Chunks in_use/total (if not zero). Allocated bytes in_use/total. Requested bytes.b->free_chunks.size() == bin_info.total_chunks_in_bin - bin_info.total_chunks_in_useRequested . /. Bytes : Chunks Diff between in-use and requested bytes is , Chunk State: bytes has max bytes of Bin for Overall chunks summary: of size at Chunk Free Summary of in-use chunks by size: . Total chunks of size Sum Total of in-use chunks: Stats: -Divide by zeroSafeIntExceptionHandler<class onnxruntime::OnnxRuntimeException>::SafeIntOnDivZero` V?Ч??@?Failed to get allocator for location: duplicated locationonnxruntime::TensorAllocatorWithMemPattern::FinalizePlanD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\tensor_allocator_with_mem_pattern.hInternal error. is not foundMem pattern for initializer ' is not foundWeight buffer for initializer '' failedGet preallocated buffer for initializer 'onnxruntime::TensorAllocatorWithMemPattern::Trace V<"@+@?V@?@??) D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\allocation_planner.cc[onnxruntime::PlannerImpl::Indexn >= 0 && static_cast<size_t>(n) < ort_value_info_.size()onnxruntime::PlannerImpl::UseCountonnxruntime::PlannerImpl::Buffern >= 0 && static_cast<size_t>(n) < plan_.allocation_plan.size()onnxruntime::PlannerImpl::AllocPlanid >= 0 && static_cast<size_t>(id) < ort_value_info_.size()onnxruntime::PlannerImpl::ProcessDefreused != reused_foronnxruntime::PlannerImpl::Reusenullptr != tensor_type_baseonnxruntime::PlannerImpl::GetElementSizeCan not find the node onnxruntime::PlannerImpl::ComputeUseCountsNo suitable kernel definition found for op Can not find the execution provider onnxruntime::PlannerImpl::GetLocationForNodeInputkernel_create_info != nullptr && kernel_create_info->kernel_def != nullptronnxruntime::PlannerImpl::GeneratePlanForWeightsonnxruntime::PlannerImpl::ComputeReusePlanonnxruntime::PlannerImpl::CreatePlan0V@@@@` @p7$V@@`@p @$$0V @ @@ @p$invalid ort_value_index:onnxruntime::SimpleTensorAllocator::GetPreallocatedBufferD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\framework\simple_tensor_allocator.cc', location: Failed to get allocator for initializer 'V       } V   @(VAAA@@NoOpVЎ@. Falling back to lenient merge. target:' source:Error merging shape info for output. 'onnxruntime::MergeShapeInfoD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\graph\graph.ccOutput: Input=Type mismatch. Current=Tensor element type mismatch. onnxruntime::NodeArg::UpdateTypeAndShapeSparseTensor element type mismatch. This is an invalid model. The sum of input arg count is not equal to size of input defs in node (graph_proto cannot be nullgraph_proto != nullptronnxruntime::Graph::GraphThis is an invalid model. Tensor does not have type information.or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.by either re-generating the model with latest exporter/converter Move it out of graph inputs if there is no need to override it, This may prevent some of the graph optimizations, like const folding. appears in graph inputs and will not be treated as constant value/weight. Initializer Graph state to be loaded into must be empty.graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()onnxruntime::Graph::InitializeStateFromModelFileGraphProtoGraph ctor should have created NodeArg for initializer. Missing:node_arg) does not exist in the graph.This is an invalid model. Graph output (This is an invalid model. Error: two nodes with same node name (This is an invalid model. Error: Duplicate definition of name (onnxruntime::Graph::SetOuterScopeNodeArgsInvalid node indexes specified when adding edge.onnxruntime::Graph::AddEdgeInvalid 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 removing edge.onnxruntime::Graph::RemoveEdgeInvalid source node arg slot specified when removing edge.Invalid destination node arg slot specified when removing edge.Argument mismatch when removing edge.onnxruntime::Graph::BuildConnections Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.This is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name=This is an invalid model. Failed to find NodeArg in all parent graphs. Name=This is an invalid model. Error: the graph is not acyclic. V`AGraph attribute inferencing failed: VA`AA`AAA0A@ in node No Graph instance was found for attribute inputs. Either provide all subgraph inputs, or just the required inputs. inputs and requires inputs but subgraph has Size mismatch validating subgraph inputs. Got Node:Subgraph input missing type.onnxruntime::Graph::InferAndVerifySubgraphTypesUpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsnode.GetAttributeNameToMutableSubgraphMap().empty()onnxruntime::Graph::UpdateShapeInference) does not have type information set by parent node.) input arg (This is an invalid model. Node () is invalid.) in node () of operator (' of input parameter (This is an invalid model. Type Error: Type ' in node () bound to different types (Type Error: Type parameter () Op () type inference failed) output arg () does not match expected type () of node () of output arg (Type Error: Type () does not have type information.This is an invalid model. Model input ( but usage of initializer in graph expects ' has element type Type Error: Data in initializer ' does not match. Type Error: Shape of initializer This is an invalid model. Error in Node: is not a registered function/opFatal error: onnxruntime::Graph::VerifyNodeAndOpMatch) is required but not specified.) attribute (Error: Duplicate definition-site for (onnxruntime::Graph::InitInputsInitializersOutputsonnxruntime::Graph::PerformTypeAndShapeInferencingonnxruntime::Graph::ForThisAndAllSubgraphsonnxruntime::Graph::ResolveShouldn't be possible to have NodeArgs that haven't been handled already.outer_scope_node_args_consumed.empty() but different TensorProto.AddInitializedTensor already has tensor with name existing->second == &tensoronnxruntime::Graph::AddInitializedTensorgraph_proto_ is not in sync with name_to_initial_tensor_.!foundonnxruntime::Graph::RemoveInitializedTensor_token_ as it still has output edges.Can't remove node node->GetOutputEdgesCount() == 0onnxruntime::Graph::RemoveNode'was added but does not exist. Outer scope node arg name 'onnxruntime::Graph::ToGraphProtoInternal'. It is not used by any node and should be removed from the model.Removing initializer 'onnxruntime::Graph::CleanUnusedInitializers'. It is no longer used by any node. must be either specified in graph inputs or graph initializers.nodes_.size() < static_cast<unsigned int>(std::numeric_limits<int>::max())onnxruntime::Graph::AllocateNodenullptr != sub_graph && nullptr != sub_graph->GetMetaDef()onnxruntime::Graph::FuseSubGraph's number of inputs is different from function body graph's number of input.'s number of outputs is different from function body graph's number of outputs.node.MutableOutputDefs().size() == subgraph.GetOutputs().size()onnxruntime::Graph::InlineFunctionInput to set must exist.input->Exists()onnxruntime::Graph::SetInputsindex < data_.size()onnxruntime::ConstPointerContainer<class std::vector<class onnxruntime::NodeArg *,class std::allocator<class onnxruntime::NodeArg *> > >::atD:\a\ml-builds\ml-builds\onnxruntime\include\onnxruntime\core/common/const_pointer_container.hVpApAAA$$@VAApAA$$VAAPAA$$@VAAAA$$Domain already set in registryonnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSetD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\graph\schema_registry.cconnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSchemaInternalknown by the checker.than the operator set version , but it its version is higher@VB -`AAModelProto 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.Unknown model file format version. model may run depending upon legacy support of some older opset version operators.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 onnxruntime::Model::ModelD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\graph\model.ccNo graph was found in the protobuf.Failed to load model with error: onnxruntime::Model::LoadProtobuf parsing failed.<p_fd> less than 0.<p_fd> is less than 0.onnxruntime::Model::SaveProtobuf serialization failed.HV`PB'onnxruntime::SaveModelVB`} ' failed. File doesn't existLoad model system error number ̼+̼+'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Input data type does not match the expected data typeInput data type does not match the expected data type. Current data type is Scale and Zero-point must be a scalarScale and Zero-point must be of rank 1Scale and Zero-point must be of rank 1 and the number of elements should be equal to the number of rows of the corresponding input. +, external_data.length: Computed size: TensorProto external data size mismatch. External data type must not be UNDEFINED or STRING.Not satisfied: external_data_length == 0 || external_data_length == actual_tensor_data_length +RandomUniformRandomNormalRandomUniformLikeRandomNormalLikeMultinomialonnxruntime::OptimizerExecutionFrame::Info::{ctor}::<lambda_cf5443266bce5a977569b32702ff438d>::operator ()C:\a\5\s\onnxruntime\core\optimizer\optimizer_execution_frame.ccFailed to get allocator for optimizerallocator_ptr_onnxruntime::OptimizerExecutionFrame::Info::InfoTried to allocate without valid type information, ort_value index=C:\a\5\s\onnxruntime\core\providers\cpu\cpu_execution_provider.cconnxruntime::RegisterOnnxOperatorKernelsonnxruntime::RegisterCPUKernelsonnxruntime::ml::RegisterOnnxMLOperatorKernelsonnxruntime::CPUExecutionProvider::GetKernelRegistryC:\a\5\s\include\onnxruntime\core/framework/op_kernel.h is not in valid range [-axis >= -tensor_rank && axis <= tensor_rank - 1axis C:\a\5\s\onnxruntime\core/providers/common.honnxruntime::HandleNegativeAxisx_zero_point must be null or 1D tensor with size scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimDequantizeLinearzero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim)T2T1QuantizeLinearx_zero_point must be null or a scalar or 1D tensor or size 1.onnxruntime::PrepareForQDQzero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)scale must be 1D tensor with size C:\a\5\s\onnxruntime\core\providers\cpu\tensor\quantize_linear.cconnxruntime::OpKernelContext::InputMissing Input: helper.called_ < 2Check for duplicate types in MLTypeCallDispatcherhelper.called_ == 1onnxruntime::utils::MLTypeCallDispatcher<struct onnxruntime::sign_internal::CallSignImpl,float,double,signed char,unsigned char,short,unsigned short,int,unsigned int,__int64,unsigned __int64>::InvokeSigninvalid stol argumentstoi argument out of rangeinvalid stoul argumentstol argument out of rangeinvalid stoll argumentstoul argument out of rangeinvalid stoull argumentstoll argument out of rangeinvalid stoi argumentinvalid stof argumentstoull argument out of rangeinvalid stod argumentstof argument out of rangestod argument out of rangeonnxruntime::Cast::ComputeAttribute to is not set.C:\a\5\s\onnxruntime\core\providers\cpu\tensor\cast_op.cconnxruntime::Cast::Castonnxruntime::utils::MLTypeCallDispatcher<struct onnxruntime::Cast::StringDispatcher,float,double,union onnxruntime::MLFloat16,signed char,unsigned char,short,unsigned short,int,unsigned int,__int64,unsigned __int64,bool>::Invokeonnxruntime::utils::MLTypeCallDispatcher<struct onnxruntime::Cast::SrcDispatcher,float,double,signed char,unsigned char,short,unsigned short,int,unsigned int,__int64,unsigned __int64,bool>::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType<unsigned short,struct onnxruntime::Cast::Dispatcher,float,double,signed char,unsigned char,short,unsigned short,int,unsigned int,__int64,unsigned __int64,bool>::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType<int,struct onnxruntime::Cast::Dispatcher,float,double,signed char,unsigned char,short,unsigned short,int,unsigned int,__int64,unsigned __int64,bool>::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType<unsigned char,struct onnxruntime::Cast::Dispatcher,float,double,signed char,unsigned char,short,unsigned short,int,unsigned int,__int64,unsigned __int64,bool>::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType<short,struct onnxruntime::Cast::Dispatcher,float,double,signed char,unsigned char,short,unsigned short,int,unsigned int,__int64,unsigned __int64,bool>::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType<double,struct onnxruntime::Cast::Dispatcher,float,double,signed char,unsigned char,short,unsigned short,int,unsigned int,__int64,unsigned __int64,bool>::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType<signed char,struct onnxruntime::Cast::Dispatcher,float,double,signed char,unsigned char,short,unsigned short,int,unsigned int,__int64,unsigned __int64,bool>::InvokeUnsupported type in cast oponnxruntime::utils::MLTypeCallDispatcherWithCarriedType<float,struct onnxruntime::Cast::Dispatcher,float,double,signed char,unsigned char,short,unsigned short,int,unsigned int,__int64,unsigned __int64,bool>::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType<unsigned __int64,struct onnxruntime::Cast::Dispatcher,float,double,signed char,unsigned char,short,unsigned short,int,unsigned int,__int64,unsigned __int64,bool>::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType<bool,struct onnxruntime::Cast::Dispatcher,float,double,signed char,unsigned char,short,unsigned short,int,unsigned int,__int64,unsigned __int64,bool>::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType<unsigned int,struct onnxruntime::Cast::Dispatcher,float,double,signed char,unsigned char,short,unsigned short,int,unsigned int,__int64,unsigned __int64,bool>::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType<__int64,struct onnxruntime::Cast::Dispatcher,float,double,signed char,unsigned char,short,unsigned short,int,unsigned int,__int64,unsigned __int64,bool>::InvokeNaNonnxruntime::`anonymous-namespace'::CastFromStringDataINF-INFonnxruntime::Tensor::MutableDataAsSpanonnxruntime::Tensor::DataAsSpanSizeinput count mismatchseed!mask || mask->Shape() == X_shapeonnxruntime::Dropout<float,float,0>::ComputeX and mask should have the same shapeonnxruntime::Dropout<double,float,0>::Computeonnxruntime::Dropout<double,double,0>::ComputeC:\a\5\s\onnxruntime\core/providers/cpu/nn/dropout_op.honnxruntime::Dropout<float,double,0>::Computeonnxruntime::`anonymous-namespace'::GetRatioOrDefaultratio must be in the range [0, 1)ratio input should have a single value.ratio_tensor->Shape().Size() == 10.0f <= ratio_value && ratio_value < 1.0f((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOPlease fetch output tensor with specified shape.p_ml_valueonnxruntime::OpKernelContext::Output by onnxruntime::BroadcastIterator::InitC:\a\5\s\onnxruntime\core/providers/cpu/math/element_wise_ops.hAttempting to broadcast an axis by a dimension other than 1. axis == 1 || axis == largestExpLogCeilReciprocalLessGreaterMaxMinCan broadcast 0 by 0 or 1. largest <= 1onnxruntime::BroadcastIterator::Append is invalid.NegFlooronnxruntime::Broadcaster::BroadcasterAbsC:\a\5\s\onnxruntime\core\providers\cpu\math\element_wise_ops.ccAll inputs must have the same shapeinput_count >= 1onnxruntime::Sum_6<float>::ComputeinputCount >= 1onnxruntime::Min_6<float>::Computedata_1.Shape() == shapedata_n.Shape() == shapeNotAndMeanBitShiftUnsupported X type: Must have 1 or more inputsOrXorAsinhAcoshSinhCoshfmodfmod must have value either 0 or 1AtanhPReluSinCosonnxruntime::Max_6<float>::Computeonnxruntime::Mean_6<float>::ComputeAcosAtanTanAsin) for tensor of length:Invalid start/ending offset [fmod_onnxruntime::Mod::Compute) are not at boundary of span with size:Broadcast Output range [start_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenModfailed to get first output!(fmod == 0) || (fmod == 1)onnxruntime::Mod::ModX and Y input types do not match: fmod attribute must be true for float, float16 and double typesonnxruntime::mod_internal::BroadCastMFloat16FMod vs onnxruntime::ElementWiseKernel<struct onnxruntime::functors::Sqrt<double> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Sqrt<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Exp<float> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Sqrt<double> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Reciprocal<float> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Ceil<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Sqrt<float> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Reciprocal<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Log<float> >::ComputeC:\a\5\s\onnxruntime\core/providers/cpu/element_wise_ranged_transform.hstart_offset % span_size == 0 && real_end % span_size == 0input_size < std::numeric_limits<std::ptrdiff_t>::max()onnxruntime::ElementWiseKernel<struct onnxruntime::functors::Exp<double> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Exp<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Log<float> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Exp<double> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Neg<double> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Neg<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Neg<signed char> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Neg<double> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Abs<unsigned __int64> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Abs<unsigned int> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Neg<float> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Abs<unsigned __int64> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Floor<float> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Neg<__int64> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Ceil<float> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Floor<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Neg<int> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Neg<signed char> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Neg<__int64> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Neg<int> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Abs<short> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Abs<signed char> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Abs<int> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Abs<short> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Abs<double> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Abs<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Abs<signed char> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Abs<double> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Abs<unsigned short> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Abs<unsigned char> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Abs<unsigned int> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Abs<unsigned short> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Abs<__int64> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Abs<int> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Abs<unsigned char> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Abs<__int64> >::ComputeLEFTRIGHTdirectiononnxruntime::BitShift<unsigned char>::BitShiftonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Abs<float> >::ElementWiseKernelonnxruntime::BroadcastVariadic(output_size % span_size) == 0onnxruntime::BitShift<unsigned __int64>::Compute::<lambda_e1144b6f829ee474c7c699722dafbcf1>::operator ()Unsupported Y type: tensor_shape.Shape().GetDims().size() == 1onnxruntime::Expand_8<float>::Computeonnxruntime::BroadcastTwoShape must be 1 dimensional as it's tensor data is a shapecur1 == end1onnxruntime::BitShift<unsigned char>::Compute::<lambda_328421e389380781bdf85427dc641d39>::operator ()'. Valid values are 'LEFT' or 'RIGHT'.Invalid direction value of 'onnxruntime::BitShift<unsigned int>::Compute::<lambda_5f622be7c3cff5b70a164b165930ca87>::operator ()onnxruntime::BitShift<unsigned __int64>::BitShiftcur_out == end_outonnxruntime::BitShift<unsigned int>::BitShiftonnxruntime::Expand_8<union onnxruntime::MLFloat16>::Computeonnxruntime::mod_internal::BroadCastFModonnxruntime::Expand_8<unsigned __int64>::Computeonnxruntime::Expand_8<bool>::Computeonnxruntime::TBroadcastOutput<unsigned int>::TBroadcastOutputonnxruntime::utils::MLTypeCallDispatcher<struct onnxruntime::mod_internal::CallModImpl,unsigned char,signed char,unsigned short,short,unsigned int,int,unsigned __int64,__int64>::Invokeonnxruntime::TBroadcastOutput<unsigned __int64>::TBroadcastOutputonnxruntime::Expand_8<short>::Computeonnxruntime::Expand_8<int>::Computeonnxruntime::Expand_8<double>::Computeonnxruntime::Expand_8<signed char>::Computeonnxruntime::Expand_8<unsigned short>::Computeonnxruntime::Expand_8<unsigned int>::Computeonnxruntime::Expand_8<__int64>::Computeonnxruntime::Expand_8<unsigned char>::Computeonnxruntime::TBroadcastOutput<float>::TBroadcastOutputonnxruntime::TBroadcaster<float,float>::AdvanceByoffset % span_size_ == 0onnxruntime::TBroadcaster<bool,bool>::AdvanceByonnxruntime::BroadcastOneSpaninput0_size == output_sizeonnxruntime::TensorAllocator<float>::TensorAllocatorinput1_size == output_sizeonnxruntime::TBroadcastOutput<int>::TBroadcastOutputonnxruntime::TBroadcastOutput<unsigned char>::TBroadcastOutputonnxruntime::TBroadcastOutput<__int64>::TBroadcastOutputonnxruntime::TBroadcastOutput<bool>::TBroadcastOutputTBroadcaster can only start at span boundary!onnxruntime::TBroadcastOutput<signed char>::TBroadcastOutputonnxruntime::TBroadcastOutput<double>::TBroadcastOutputonnxruntime::TensorAllocator<unsigned int>::TensorAllocatoronnxruntime::TensorAllocator<__int64>::TensorAllocatoronnxruntime::TensorAllocator<union onnxruntime::MLFloat16>::TensorAllocatoronnxruntime::TensorAllocator<int>::TensorAllocatoronnxruntime::TensorAllocator<unsigned __int64>::TensorAllocatoronnxruntime::TBroadcaster<unsigned char,unsigned char>::AdvanceByonnxruntime::TBroadcaster<__int64,__int64>::AdvanceByonnxruntime::TBroadcaster<unsigned __int64,unsigned __int64>::AdvanceByonnxruntime::TBroadcaster<unsigned int,unsigned int>::AdvanceByonnxruntime::mod_internal::BroadCastModonnxruntime::TensorAllocator<double>::TensorAllocatoronnxruntime::TBroadcaster<int,int>::AdvanceByonnxruntime::TBroadcaster<double,double>::AdvanceByMissing/Invalid 'axes' attribute valueinfo.GetAttrs("axes", axes_).IsOK()onnxruntime::UnsqueezeBase::UnsqueezeBaseC:\a\5\s\onnxruntime\core/providers/cpu/tensor/unsqueeze.hX != nullptr'axes' has an out of range axis'axes' has a duplicate axisonnxruntime::UnsqueezeBase::PrepareComputeC:\a\5\s\onnxruntime\core\providers\cpu\tensor\unsqueeze.ccnullptr != p.output_tensoronnxruntime::Unsqueeze::Computeequation->C:\a\5\s\onnxruntime\core\providers\cpu\math\einsum.honnxruntime::Einsum::Einsuminfo.GetAttr<std::string>("equation", &equation_).IsOK()Missing 'equation' attributeonnxruntime::Einsum::DeviceComputeThere was a problem acquiring temporary memory allocator in Einsum opEinsum op: There must be atleast one inputEinsumEinsum op: An implementation for the input type is not supported yetC:\a\5\s\onnxruntime\core\providers\cpu\math\einsum.ccC:\a\5\s\onnxruntime\core\providers\cpu\tensor\gather_nd.cconnxruntime::GatherND::Computeinput_tensor != nullptr && indices_tensor != nullptrGatherNDBase PrepareForCompute: Input count mismatchindices tensor data type not supportedlast dimension of indices must not be larger than rank of input tensorGatherNDTindbatch_dimsinvalid index found, index = indices tensor must has rank larger than 0ReduceL1ReduceMaxReduceLogSumExpReduceLogSumReduceL2C:\a\5\s\onnxruntime\core/providers/cpu/reduction/reduction_ops.honnxruntime::ReduceKernelBase<0>::ReduceKernelBaseinfo.GetAttr("keepdims", &keepdims).IsOK()keepdimsReduceSumSquareReduceSumReduceProdReduceMinArgMinArgMaxInput to be reduced is nullC:\a\5\s\onnxruntime\core/providers/cpu/containers.honnxruntime::GetAllocatorThe input shape override's size does not match the input tensor's shape sizeC:\a\5\s\onnxruntime\core\providers\cpu\reduction\reduction_ops.cconnxruntime::PrepareForReduceinput_tensor_ptr != nullptronnxruntime::optional<__int64>::valueonnxruntime::ReduceKernelBase<1>::ReduceKernelBaseselect_last_indexnoop_with_empty_axesin_dim != 0Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:input_tensor_ptr->Shape().Size() == input_shape_override->Size()onnxruntime::StringToAutoPadTypeUnknown AutoPadType Stringinfo.GetAttr<int64_t>("count_include_pad", &temp).IsOK()count_include_padceil_modeinfo.GetAttr<std::string>("auto_pad", &auto_padding).IsOK()Pad should be smaller than kernel.kernel_shape[dim] > 0info.GetAttr("storage_order", &storage_order).IsOK()storage_orderGlobalLpPoolC:\a\5\s\onnxruntime\core/providers/cpu/nn/pool_attributes.honnxruntime::PoolAttributes::PoolAttributesinfo.GetAttrs<int64_t>("kernel_shape", kernel_shape).IsOK()No kernel shape is set.ponnxruntime::PoolAttributes::ComputeSizePadDilationsUnsupported AutoPad Type.onnxruntime::PoolAttributes::InferOutputSizeInput dimension cannot be less than 3.C:\a\5\s\onnxruntime\core/providers/cpu/nn/pool_base.honnxruntime::PoolProcessContext::initinfo.GetAttr<int64_t>("p", &p_).IsOK()dilations.size() == kernel_shape.size()Dilations dimensions should match kernel shapestrides.size() == kernel_shape.size()pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]input_dims.size() >= 2onnxruntime::PoolAttributes::SetOutputSizeinput_shape.Size() > 0 || input_shape[0] == 0Invalid input shape. Only N can be zero. Got:Unsupported pooling size.Not satisfied: input_dims >= 3 +C:\a\5\s\onnxruntime\core\providers\cpu\nn\pool.cconnxruntime::PoolBase::ComputeLpPoolINot satisfied: pooling_dims == pool_attrs_.kernel_shape.size() +kernel_shape num_dims is not compatible with X num_dims.onnxruntime::Pool<float,class onnxruntime::LpPool>::Computeonnxruntime::MaxPoolV8::ComputeImplUnsupported pooling size : Not satisfied: x_shape.NumDimensions() >= 3 +C:\a\5\s\onnxruntime\core/providers/cpu/math/clip.honnxruntime::clip_internal::Clip_6Base<float>::Clip_6Basemin_ <= max_C:\a\5\s\onnxruntime\core\providers\cpu\math\clip.cconnxruntime::Clip::ComputeImpl<unsigned __int64>::operator ()min->Shape().NumDimensions() == 0min should be a scalar.onnxruntime::Clip::ComputeImpl<unsigned char>::operator ()onnxruntime::Clip::ComputeImpl<__int64>::operator ()max->Shape().NumDimensions() == 0max should be a scalar.onnxruntime::utils::MLTypeCallDispatcher<struct onnxruntime::Clip::ComputeImpl,float,double,signed char,unsigned char,__int64,unsigned __int64>::Invokeonnxruntime::Clip::ComputeImpl<float>::operator ()onnxruntime::Clip::ComputeImpl<double>::operator ()onnxruntime::Clip::ComputeImpl<signed char>::operator ()extrapolation_valueinfo.GetAttrs<float>("scales", scales_).IsOK()C:\a\5\s\onnxruntime\core/providers/cpu/tensor/upsample.honnxruntime::UpsampleBase::UpsampleBaseexclude_outsidecubic_coeff_around_prefer_floorhalf_pixelinfo.GetAttr<std::string>("mode", &mode).IsOK()cubiclinearcoordinate_transform_mode:[] is not supportted!tf_crop_and_resizealign_corners] is not supported!ceilround_prefer_ceilonnxruntime::UpsampleBase::StringToCoordinateTransformationMode. It can only be (default) or or exclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to tf_half_pixel_for_nnpytorch_half_pixelonnxruntime::UpsampleBase::StringToUpsampleModemode attribute is scales size should be greater than 0.scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1)'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 Upsample operatoronnxruntime::UpsampleBase::ParseScalesDatascales_size > 0scale >= 1Scale value should be greater than or equal to 1.onnxruntime::UpsampleBase::StringToNearestModenearest_mode:[Resize operatorscale > 0Scale value should be greater than 0.onnxruntime::UpsampleBase::ScalesValidationbad allocationT3onnxruntime::OneHotOp<__int64,__int64,__int64>::ComputeC:\a\5\s\onnxruntime\core\providers\cpu\tensor\onehot.ccOneHotInvalid argument for depth; it's not a scalar.Invalid argument for values; either it's rank is more than 1 or it has more than 2 elementsonnxruntime::OneHotOp<__int64,int,float>::Computeonnxruntime::OneHotOp<__int64,float,float>::Computeonnxruntime::OneHotOp<__int64,float,int>::Computeonnxruntime::OneHotOp<__int64,float,__int64>::Computeonnxruntime::OneHotOp<int,float,int>::Computeonnxruntime::OneHotOp<int,float,float>::Computeonnxruntime::OneHotOp<float,float,float>::ComputeDepth is negative.onnxruntime::OneHotOp<float,__int64,__int64>::Computeonnxruntime::OneHotOp<__int64,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64>::Computeonnxruntime::OneHotOp<float,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64>::Computeonnxruntime::TopK<10,float>::TopKinput count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valuek tensor should be a 1D tensor of size 1value of k must not be negativeC:\a\5\s\onnxruntime\core\providers\cpu\math\top_k.cck_temp > 0op_kernel_info.GetAttr<int64_t>("axis", &axis_temp).IsOK()input count mismatch, expected 1 input - the tensor to be processedkop_kernel_info.GetAttr<int64_t>("k", &k_temp).IsOK()onnxruntime::TopK<9,float>::TopKop_kernel_info.GetAttr<int64_t>("sorted", &sorted_temp).IsOK()TopKonnxruntime::TopkOpset11ConstructorCommonlargestop_kernel_info.GetAttr<int64_t>("largest", &largest_temp).IsOK()sorted] should not be greater than specified axis dim value [k argument [output count mismatch, expected 2 outputs to be present for TopK operatorUniqueUnsupported tensor type of limit in Range operator should be scalar like tensor, yet got shape:delta in Range operator should be scalar like tensor, yet got shape:delta in Range operator can not be zero!start in Range operator should be scalar like tensor, yet got shape:C:\a\5\s\onnxruntime\core/providers/cpu/tensor/pad.hInvalid 'pads' attribute valuereflectedgeInvalid 'mode' attribute valueonnxruntime::PadBase::PadBaseconstantUnexpected mode of Unsupported input data type of onnxruntime::PadValueFromFloatC:\a\5\s\onnxruntime\core\providers\cpu\tensor\pad.ccPadCannot use 'reflect' mode to pad dimension with a value of 0. Input shape:onnxruntime::SliceSkips::SliceSkipsC:\a\5\s\onnxruntime\core/providers/cpu/tensor/utils.hdims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()onnxruntime::SliceIteratorBase::Initdims.size() == extents.size() && dims.size() >= steps.size()value_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1pads_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 pads_size == 2 * data_rankValue tensor should be a 1D tensor of size 1 with the same type as that of the input tensorPads tensor should be an INT64 tensorpads_tensor.IsDataType<int64_t>()onnxruntime::Pad::ComputePads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]data_rank * 2 == pads.size()onnxruntime::PadInputWithDimValueOfZeroInput tensor has no dimensionsdata_rank > 0onnxruntime::PadImpl'pads' has wrong number of valuesinfo.GetAttr<int64_t>("axis", &axis_).IsOK()onnxruntime::GatherElements::GatherElementsC:\a\5\s\onnxruntime\core\providers\cpu\tensor\gather_elements.hGatherElementsMissing/Invalid 'axis' attribute valueGatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Data type of input 'data' should match the data type of the output , GatherElements op: Value in indices must be within bounds [GatherElements op: Cannot operate on scalar inputGatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: Data type for 'indices' tensor must be 'int32_t' and 'int64_t'onnxruntime::parse_and_validate_indices_tensorC:\a\5\s\onnxruntime\core\providers\cpu\tensor\gather_elements.cc]. Actual value is C:\a\5\s\onnxruntime\core\providers\cpu\math\gemm_helper.hright.NumDimensions() == 2 N: K: left.NumDimensions() == 2 || left.NumDimensions() == 1onnxruntime::GemmHelper::GemmHelperinfo.GetAttr<float>("alpha", &alpha_).IsOK()betainfo.GetAttr<float>("beta", &beta_).IsOK()onnxruntime::Gemm<float>::GemmC:\a\5\s\onnxruntime\core/providers/cpu/math/gemm.htransBinfo.GetAttr<int64_t>("transB", &temp).IsOK()transAinfo.GetAttr<int64_t>("transA", &temp).IsOK()GEMM: Dimension mismatch, W: Gemm: Invalid bias shape for broadcastM_ >= 0 && K_ > 0 && N_ >= 0c_shape != nullptronnxruntime::Gemm<float>::ComputeGemmc_shape is required if c_data is providedScatterNDC:\a\5\s\onnxruntime\core\providers\cpu\tensor\scatter_nd.cconnxruntime::ScatterND::Computeindice_tensor != nullptronnxruntime::ScatterNDBase::PrepareForCompute, indices shape: update_tensor != nullptrinput_tensor != nullptrinvalid indice found, indice = updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. input tensor and indices tensor must has rank larger than 0. input shape: updates shape: , data shape: ) != new size (shape_.Size() == new_shape.Size()Tensor size (onnxruntime::Tensor::ReshapeY != nullptrSequenceLength: Got nullptr for output tensoronnxruntime::GetSeqIdxSequenceAtGot nullptr for sequence input.SequenceLengthC:\a\5\s\onnxruntime\core\providers\cpu\sequence\sequence_ops.cconnxruntime::SequenceLength::ComputeSonnxruntime::SequenceInsert::ComputeS != nullptr), input tensor data type (Got nullptr for input tensor.Unsupported 'dtype' value: onnxruntime::SequenceEmpty::Computeonnxruntime::CreateCopyAndAppendCpuTensorSequenceInsertSequenceAt: Got nullptr for output tensorInvalid sequence index (SequenceEmpty: Got nullptr for output sequenceSequenceEmptyGot nullptr input for index tensoronnxruntime::SequenceAt::Compute) specified for sequence of size (I != nullptronnxruntime::SplitToSequence::ComputeSplitToSequence operator does not support split_size_sum () != split_dim_size (Violation of the requirment that all input tensors must have the same data type.SequenceConstruct: Got nullptr for output sequence yetSplitToSequenceSequenceConstructSequenceErase: Got nullptr for output sequenceonnxruntime::SequenceConstruct::Computenum_inputs >= 1SequenceInsert: Got nullptr for output sequenceData type of the input tensor MUST be same as that of the input sequence. Sequence data type (onnxruntime::SequenceErase::ComputeSequenceErasesplit_scalar > 0Split should be > 0onnxruntime::GetScalarSplitInputInvalid data type for split tensor onnxruntime::GetSplitSizesInputInvalid value in 'split' input. All values must be >= 0onnxruntime::SplitToSequence::ComputeImplstd::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })onnxruntime::ConcatBase::ConcatBaseMust have valid 'axis' attributeConcatFromSequenceC:\a\5\s\onnxruntime\core\providers\cpu\sequence\concat_from_sequence.cconnxruntime::ConcatFromSequence::Computenew_axisC:\a\5\s\onnxruntime\core/providers/cpu//tensor/concat.honnxruntime::If::Ifinfo.GetAttr<ONNX_NAMESPACE::GraphProto>("then_branch", &proto).IsOK()info.GetAttr<ONNX_NAMESPACE::GraphProto>("else_branch", &proto).IsOK()else_branchonnxruntime::If::Info::Infonum_subgraph_outputs == static_cast<size_t>(num_outputs)then_branchC:\a\5\s\onnxruntime\core\providers\cpu\controlflow\if.cc outputs.If'If' node has outputs which doesn't match the subgraph's VBonnxruntime::IfImpl::Executeonnxruntime::IfImpl::Initializesession_stateFailed to create output tensor for If output Failed to create output tensor for onnxruntime::If::Computethen_feeds_fetches_manager_ && else_feeds_fetches_manager_Subgraph SessionState was not found for '' attribute.info == nullptrSetupSubgraphExecutionInfo should only be called once for each subgraph.CreateFeedsFetchesManager must be called prior to execution of graph.onnxruntime::If::SetupSubgraphExecutionInfo X: kernel_shape is not compatible with W shape. group: X num_dims does not match W num_dims. W: kernel_shape num_dims is not compatible with W num_dims. kernel_shape: output_paddinggroup count is <= 0output_shapeInvalid input shape: onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape*out_size >= 0pad type not supportedC:\a\5\s\onnxruntime\core/providers/cpu/nn/conv_transpose_attributes.honnxruntime::ConvTransposeAttributes::PrepareForComputeonnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShapedim_size > 0 filter_number: num_input_channels: Input channels is not divisible by group.filter number not equal to input channel number.ConvTransposeC:\a\5\s\onnxruntime\core\providers\cpu\nn\conv_transpose.cconnxruntime::ConvTranspose<float>::DoConvTransposeonnxruntime::ComputePadAndOutputShapeComputePad: pad type not supported. C: kernel channels: Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.C:\a\5\s\onnxruntime\core/providers/cpu/nn/conv_attributes.hNot enough elements in dilations. Expected: Not enough elements in kernel shape. Expected: onnxruntime::ConvAttributes::InferOutputShapeNot enough elements in pads. Expected: M: Input channels C is not equal to kernel channels * group.Not enough elements in strides. Expected: Output channels M is not divisible by group.d_iter[d_i] < d_maxC:\a\5\s\onnxruntime\core\providers\cpu\nn\conv.ccC:\a\5\s\onnxruntime\core/util/math.honnxruntime::math::Im2colNd<float,2>::operator ()onnxruntime::Conv<float>::Computepads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]kernel_shape_[dim] > 0strides_.size() == kernel_shape_.size()info.GetAttrs<int64_t>("kernel_shape", kernel_shape_).IsOK()C:\a\5\s\onnxruntime\core/providers/cpu/nn/unpool.honnxruntime::MaxUnpool::MaxUnpoolNot satisfied: X_shape.NumDimensions() >= 3 +C:\a\5\s\onnxruntime\core\providers\cpu\nn\Unpool.ccNot satisfied: I_shape == X_shape +Index tensor shape should be same as that of the input data tensor to unpool.onnxruntime::MaxUnpool::ComputeMaxUnpoolNot satisfied: given_shape.Size() >= shape.Size() +Not satisfied: tensor_shape->Shape().GetDims().size() == 1 +Shape must be 1 dimensional as it's tensor data of a shapeoutput_shape is smaller than minimum required. output_shape: inferred output shape:boxes_tensoronnxruntime::NonMaxSuppressionBase::PrepareComputeC:\a\5\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.hNonMaxSuppression0 == center_point_box_ || 1 == center_point_box_onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBasecenter_point_boxcenter_point_box only support 0 or 1Not satisfied: (iou_threshold >= 0 && iou_threshold <= 1.f) +onnxruntime::NonMaxSuppression::Computeiou_threshold must be in range [0, 1].onnxruntime::NonMaxSuppressionBase::GetThresholdsFromInputsThe most inner dimension in boxes must have 4 data.Not satisfied: boxes_dims[2] == 4 +boxes and scores should have same spatial_dimension.Not satisfied: boxes_dims[1] == scores_dims[2] +boxes and scores should have same num_batches.Not satisfied: boxes_dims[0] == scores_dims[0] +scores must be a 3D tensor.Not satisfied: scores_shape.NumDimensions() == 3 +boxes must be a 3D tensor.Not satisfied: boxes_shape.NumDimensions() == 3 +C:\a\5\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.ccscores_tensorNot satisfied: ret.IsOK() +output != nullptrExpecting indices to be either int32_t or int64_tUnknown tensor type of onnxruntime::Scatter::Compute at pos=Indices dim=Indices must have the same rank as Input. Indices rank= is greater than input dim=Indices vs updates dimensions differs at position=. Input rank=data type is different from updates typeIndices and updates must have the same rankScatterScatterElementsonnxruntime::Scatter::ScatterC:\a\5\s\onnxruntime\core\providers\cpu\tensor\scatter.cc must be within the inclusive range [indices element out of data bounds, idx=DetMatrix dimensions are not equal. Square matrix is expectedC:\a\5\s\onnxruntime\core\providers\cpu\math\det.ccInput tensor should have a rank of at least 2onnxruntime::Det<float>::ComputeDimension of input input_shape[i] == 1. shape= must be 1 instead of onnxruntime::SqueezeBase::ComputeOutputShapeC:\a\5\s\onnxruntime\core/providers/cpu/tensor/squeeze.honnxruntime::SplitBase::SplitBaseC:\a\5\s\onnxruntime\core/providers/cpu/tensor/split.hInvalid value in 'split' attribute. All values must be > 0std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })split Sum of sizes in 'split' (must equal size of selected axis) was Num entries in 'split' (must equal number of outputs) was Axis=Input cannot be split evenly on selected axis. Input shape= NumOutputs=C:\a\5\s\onnxruntime\core\providers\cpu\tensor\split.ccSplit operator does not support onnxruntime::Split::Compute Input shape=Cannot split using values in 'split' attribute. Axis=onnxruntime::Split::ComputeImpl!has_axes || attr_axes_.size() == attr_starts_.size()C:\a\5\s\onnxruntime\core/providers/cpu/tensor/slice.hInvalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributeshas_starts && has_ends && attr_starts_.size() == attr_ends_.size()onnxruntime::SliceBase::SliceBaseMissing or invalid starts and ends attributeC:\a\5\s\onnxruntime\core\providers\cpu\tensor\slice.ccEnds must be a 1-D arraystart_tensor.Shape().NumDimensions() == 1onnxruntime::SliceBase::FillVectorsFromInput'step' value cannot be 0Starts must be a 1-D array'axes' has an axis outside of the tensor dimension count'axes' has duplicatesUnexpected element size of onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStepCannot slice scalarsMissing input tensor to be processedonnxruntime::SliceBase::Computenullptr == 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 nullptr == axes_tensor || start_tensor.Shape() == axes_tensor->Shape()Starts and steps shape mismatchstart_tensor.Shape() == ends_tensor.Shape()Starts and axes shape mismatchends_tensor.Shape().NumDimensions() == 1Starts and ends shape mismatchonnxruntime::SliceImpl::<lambda_677d89789726a57908cfd3af9ddaccb9>::operator ()onnxruntime::SliceImpl::<lambda_7f21084dd80ad634e566f81e536a8eea>::operator ()onnxruntime::SliceImpl::<lambda_933430d96dbc33fbe1779bc021ba9b41>::operator ()onnxruntime::SliceImpl::<lambda_ae2825aaba0b4d524d357f23ce125d7f>::operator ()output == output_endonnxruntime::SliceImpl::<lambda_fc5bf7ac25837de4124a325624e631d6>::operator ()Type for Tind not supported yet in Gather.C:\a\5\s\onnxruntime\core\providers\cpu\tensor\gather.cconnxruntime::Gather::ComputeC:\a\5\s\onnxruntime\core/providers/cpu/tensor/gather.honnxruntime::GatherBase::GatherBase and Not satisfied: data_n.DataType() == p.output_tensor->DataType() +Not satisfied: dim_value == inputs_0_dims[axis_index] +Non concat axis dimensions must match: Axis has mismatched dimensions of inputs_n_rank == inputs_0_rankRanks of input data are different, cannot concatenate them. expected rank: got: Not satisfied: inputs_0_rank > 0 +Cannot concatenate scalarsNot satisfied: input_count >= 1 +C:\a\5\s\onnxruntime\core\providers\cpu\tensor\concat.cconnxruntime::ConcatBase::PrepareForComputeC:\a\5\s\onnxruntime\core\providers\cpu\tensor\compress.cconnxruntime::Compress::Computeaxes_right_stride >= 0 && static_cast<uint64_t>(axes_right_stride) < std::numeric_limits<size_t>::max()CompressFlattenonnxruntime::Flatten::Computegsl::narrow_cast<int64_t>(X_shape.NumDimensions()) >= axisThe rank of input tensor must be >= axisC:\a\5\s\onnxruntime\core/providers/cpu/nn/flatten.honnxruntime::Flatten::FlattenC:\a\5\s\onnxruntime\core/providers/cpu/controlflow/scan_utils.honnxruntime::scan::detail::OutputIterator::GetOutputfinal_output_mlvalue_Attempt to retrieve final output before it was set.num_scan_inputsC:\a\5\s\onnxruntime\core\providers\cpu\controlflow\scan_9.cconnxruntime::Scan<9>::Scaninfo.GetAttr<ONNX_NAMESPACE::GraphProto>("body", &proto).IsOK()bodyonnxruntime::ScanImpl::InitializeSubgraph SessionState was not found for 'body' attribute.onnxruntime::Scan<9>::Computefeeds_fetches_manager_ && info_onnxruntime::Scan<9>::SetupSubgraphExecutionInfoinfo_ == nullptrgsl::narrow_cast<int64_t>(output_axes_.size()) == num_scan_outputsNumber of entries in 'scan_output_axes' was scan_output_axesgsl::narrow_cast<int64_t>(input_axes_.size()) == num_scan_inputs_Number of entries in 'scan_input_axes' was but expected scan_input_axesscan_output_directionsscan_input_directionsinfo.GetAttr<int64_t>("num_scan_inputs", &num_scan_inputs_).IsOK()onnxruntime::ScanImpl::CreateLoopStateVariablesonnxruntime::ScanImpl::AllocateOutputTensorsSubgraph in 'body' produces outputs but Scan expects onnxruntime::ScanImpl::SetupInputsonnxruntime::ScanImpl::ValidateInputInvalid value in scan_input_axes for input of . Input tensor rank was Scan inputs have inconsistent sequence lengths. Previous value was but input '' dimension has length of Invalid scan input: Expected dimensions or more but input had shape of Scanonnxruntime::ScanImpl::TransposeOutputOutputs from Scan are not optional and should never be null.Invalid value in scan_output_axes for output . Output tensor rank was onnxruntime::ScanImpl::Executeoutput_mlvalueOutput OrtValue has not been created for loop state variable output C:\a\5\s\onnxruntime\core/providers/cpu/tensor/space_depth_ops.honnxruntime::SpaceDepthBase::SpaceDepthBaseinfo.GetAttr("blocksize", &blocksize_).IsOK()Attribute blocksize is not set.blocksizeDCRCRDinput_depth % (blocksize_ * blocksize_) == 0onnxruntime::DepthToSpace<float>::Computeinput.Shape().NumDimensions() == 4input_width % this->blocksize_ == 0input_height % this->blocksize_ == 0C:\a\5\s\onnxruntime\core\providers\cpu\tensor\space_depth_ops.cconnxruntime::SpaceToDepth<float>::Computeinput_shape.NumDimensions() == 4DepthToSpaceSpaceToDepthonnxruntime::DepthToSpace<float>::DepthToSpaceDepthToSpace op: only 'DCR' and 'CRD' modes are supportedC:\a\5\s\onnxruntime\core\providers\cpu\controlflow\loop.cconnxruntime::Loop::Info::Infostatic_cast<size_t>(num_subgraph_inputs) == subgraph_inputs.size()Graph in 'body' attribute of Loop should have inputs. Found:Loop was not found. Defaulting to a rank 1 shape of {0}.onnxruntime::LoopImpl::Executeonnxruntime::LoopImpl::ConcatenateLoopOutput'Loop' input 'cond' should be a scalar tensor. Got shape of 'Loop' input 'M' should be a scalar tensor. Got shape of feeds_fetches_manager_onnxruntime::Loop::Computeonnxruntime::Loop::SetupSubgraphExecutionInfoonnxruntime::Loop::LoopInconsistent shape in loop output for output. Expected: Got:num_subgraph_outputs - 1 == num_outputs'Loop' node has outputs so the subgraph requires but has Loop had zero iterations and the shape of subgraph output LogSoftmax, D=. N=Hardmax inputs N, D and N * D must be < Hardmaxx_ptr != nullptronnxruntime::DynamicQuantizeLinear<unsigned char>::ComputeC:\a\5\s\onnxruntime\core\providers\cpu\tensor\dynamicquantizelinear.ccRoundexclusiveC:\a\5\s\onnxruntime\core\providers\cpu\math\cumsum.ccreverseCannot apply CumSum operator on a scalaronnxruntime::CumSum<float>::Computeonnxruntime::CumSum<double>::Computeonnxruntime::CumSum<int>::ComputeAxis tensor must be provided to the CumSum opAxis tensor should be 0D or 1DAxis tensor should be of type `int32_t` or `int64_t`CumSumonnxruntime::CumSum<__int64>::Computestarts.size()=dims.size()=dims.size() == starts.size()onnxruntime::WritableSliceIterator<__int64>::Initextents.size()=dims.size() == extents_.size()steps.size()=dims.size() == steps.size()onnxruntime::WritableSliceIterator<int>::Initonnxruntime::WritableSliceIterator<double>::Initonnxruntime::WritableSliceIterator<float>::Initbatch_axisReverseSequence. batch_size=sequence_lens shape must be {batch_size}. Got:onnxruntime::ReverseSequenceOp::ComputeC:\a\5\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.ccinfo.GetAttr<int64_t>("batch_axis", &batch_axis).IsOK()onnxruntime::ReverseSequenceOp::ReverseSequenceOpC:\a\5\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.htime_axisinfo.GetAttr<int64_t>("time_axis", &time_axis).IsOK(). Must be 0 or 1Invalid batch_axis of batch_axis < 2Invalid time_axis of time_axis < 2time_axis and batch_axis must have different values but both are batch_axis != time_axisavg specified. It should be either avg or maxInvalid mode of value onnxruntime::RoiAlignBase::RoiAlignBaseC:\a\5\s\onnxruntime\core\providers\cpu\object_detection\roialign.houtput_heightoutput_widthNull input X ptrNull rois_ptrNull batch_indices_ptrNumber of dimensions for batch indices should be exactly 1Number 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 matchsampling_ratioSampling ratio should be >=0, but it was sampling_ratio_ >= 0spatial_scaleRoiAlignIsInfFailed to obtain detect_positivedetect_positiveC:\a\5\s\onnxruntime\core\providers\cpu\tensor\isinf.cconnxruntime::IsInf::IsInfFailed to obtain detect_negativedetect_negativeonnxruntime::IsInf::ComputeData type X must be float or double, but instead got Tensor type mismatch.onnxruntime::Tensor::MutableDataRawtype == dtype_onnxruntime::Tensor::DataRawC:\a\5\s\onnxruntime\core/providers/cpu/tensor/identity_op.honnxruntime::IdentityOp<0>::Computeonnxruntime::IdentityOp<1>::Computeonnxruntime::math::Im2colNd<unsigned char,2>::operator ()QLinearConvT4IsScalarOr1ElementVector(X_zero_point)QLinearConv : input zero point must be a scalar or 1D tensor of size 1C:\a\5\s\onnxruntime\core\providers\cpu\nn\qlinearconv.cconnxruntime::QLinearConv::ComputeIsScalarOr1ElementVector(W_zero_point)QLinearConv : filter zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_zero_point)QLinearConv : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_scale)QLinearConv : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(W_scale)QLinearConv : filter scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_scale)QLinearConv : result scale must be a scalar or 1D tensor of size 1ConvIntegerIsScalarOr1ElementVector(X_Zero_Point)Must be a scalar or 1D tensor or size 1.C:\a\5\s\onnxruntime\core\providers\cpu\nn\conv_integer.cconnxruntime::ConvInteger::ComputeIsScalarOr1ElementVector(W_Zero_Point)Non per-tensor quantization is not supported now.onnxruntime::MatMulComputeHelper::ComputeNot satisfied: left_num_dims >= 1 && right_num_dims >= 1 +C:\a\5\s\onnxruntime\core/providers/cpu/math/matmul_helper.hNot satisfied: K_ == right_shape[right_num_dims - 2] || transb && K_ == right_shape[right_num_dims - 1] +MatMul dimension mismatchNot satisfied: left_padded_dims_[idx_dim] == 1 +left operand cannot broadcast on dim Not satisfied: right_padded_dims_[idx_dim] == 1 +right operand cannot broadcast on dim onnxruntime::MatMulInteger::ComputeMatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1C:\a\5\s\onnxruntime\core\providers\cpu\math\matmul_integer.ccMatmulInteger : input2 zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_zero_point)IsScalarOr1ElementVector(b_zero_point)Not satisfied: num_dims_with_pad == num_output_dims +Not satisfied: K_ == right_shape[transb ? right_num_dims - 1 : right_num_dims - 2] +Not satisfied: num_dims_with_pad - 1 == num_output_dims +Not satisfied: M_ == 1 && N_ == 1 +Not satisfied: K_ == right_shape[0] +Not satisfied: num_dims_with_pad - 2 == num_output_dims +QLinearMatmul : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_scale)IsScalarOr1ElementVector(y_scale)onnxruntime::QLinearMatMul::ComputeQLinearMatMulQLinearMatmul : input zero point must be a scalar or 1D tensor of size 1C:\a\5\s\onnxruntime\core\providers\cpu\math\quantize_linear_matmul.ccQLinearMatmul : weight zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_offset)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_offset)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : weight scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)onnxruntime::ElementWiseKernel<struct onnxruntime::functors::Softsign<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Tanh<float> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Softplus<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Softsign<float> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Sigmoid<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Softplus<float> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Selu<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Sigmoid<float> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Relu<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Selu<float> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::LeakyRelu<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Relu<float> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::HardSigmoid<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::LeakyRelu<float> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::Elu<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::HardSigmoid<float> >::ElementWiseKernelNo attribute with name:''is defined.onnxruntime::ElementWiseKernel<struct onnxruntime::functors::ThresholdedRelu<float> >::ComputeAttribute name and type don't match for 'onnxruntime::ElementWiseKernel<struct onnxruntime::functors::Tanh<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::ThresholdedRelu<float> >::ElementWiseKernelonnxruntime::functors::ScaledTanh<float>::Initunknown kernel typeonnxruntime::functors::ParametricSoftplus<float>::InitC:\a\5\s\onnxruntime\contrib_ops/cpu/activations.honnxruntime::ElementWiseKernel<struct onnxruntime::functors::Elu<float> >::ElementWiseKernelonnxruntime::functors::Selu<float>::InitgammaC:\a\5\s\onnxruntime\core/providers/cpu/activation/activations.honnxruntime::functors::HardSigmoid<float>::Init*NONEFailed to construct locale with name:onnxruntime::string_normalizer::Locale::LocaleConversion Error:Please, install necessary language-pack-XX and configure localesis_case_sensitiveattribute is_case_sensitive is not setC:\a\5\s\onnxruntime\core\providers\cpu\nn\string_normalizer.ccen-USattribute case_change_action is not setLOWERonnxruntime::StringNormalizer::StringNormalizercase_change_actionlocalestopwordsUPPERattribute case_change_action has invalid valueStringNormalizerConversion Errorbad conversionDuplicate stopwords not allowedp.secondEmpty stopwords not allowed!sw.empty()Single dimension value must be greater than 0Input dimensions are either[C > 0] or [1][C > 0] allowedStopword contains invalid utf8 charswstr != wconv_errorInput contains invalid utf8 chars at: onnxruntime::MatMul<__int64>::Computeonnxruntime::MatMul<double>::Computeonnxruntime::MatMul<int>::Computeonnxruntime::MatMul<float>::ComputeC:\a\5\s\onnxruntime\core\providers\cpu\math\matmul.cccondition, X, and Y inputs are required!condition && X && Yonnxruntime::Where<int>::Computeonnxruntime::Where<__int64>::Computeonnxruntime::Where<unsigned char>::ComputeC:\a\5\s\onnxruntime\core\providers\cpu\tensor\where_op.cconnxruntime::TensorAllocator<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::TensorAllocatoronnxruntime::Where<float>::Computeonnxruntime::Where<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::Computeonnxruntime::TensorAllocator<unsigned char>::TensorAllocatorX input is required!onnxruntime::NonZero<bool>::Computeonnxruntime::NonZero<int>::Computeonnxruntime::NonZero<__int64>::ComputeC:\a\5\s\onnxruntime\core\providers\cpu\tensor\nonzero_op.cconnxruntime::NonZero<unsigned char>::Computeonnxruntime::NonZero<float>::ComputeTfIdfVectorizerC:\a\5\s\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.ccTFmode is requiredonnxruntime::TfIdfVectorizer::TfIdfVectorizer is unrecognized, acceptable values are TF,IDF,TFIDFmode: IDFTFIDFmin_gram_length is requiredRequired min_gram_length must be positive: impl_->weighting_criteria_ != kNonemin_gram_lengthNon-empty ngram_indexes is requiredstatus.IsOK() && !impl_->ngram_indexes_.empty()size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()ngram_indexesweights must be of equal sizeNegative ngram_indexes values are not allowedstd::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 but ngram_indexes size: Got weights of size: pool_int64snon-empty pool_int64s is required if pool_strings not providedpool_strings must not be empty if specified!impl_->pool_strings_.empty() >= min_gram_length >= max_gram_length required: impl_->min_gram_length_ > 0max_gram_lengthmax_skip_count is requiredmax_skip_count must be non-negative: impl_->max_gram_length_ >= impl_->min_gram_length_max_skip_countNon-empty ngram_counts is requiredstatus.IsOK() && !impl_->ngram_counts_.empty()impl_->max_skip_count_ >= 0ngram_countssize_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()max_gram_length must be inbounds of ngram_counts: <= min_gram_length must be inbounds of ngram_counts: id: Duplicate ngram detected, size: p.first->second->id_ == 0onnxruntime::ngram_details::PopulateGramsn-gram counts out of bounds for end_idx >= start_idx && end_idx <= total_itemsstatus.IsOK() && !pool_int64s.empty()-gramsInput shape must have either [C] or [B,C] dimensions with B > 0.Number of items must compose whole (items % ngram_size == 0)biasC:\a\5\s\onnxruntime\core/providers/cpu/nn/shrink.hlambdop_kernel_info.GetAttr<float>("bias", &bias_temp).IsOK()onnxruntime::Shrink::ShrinkShrinkop_kernel_info.GetAttr<float>("lambd", &lambd_temp).IsOK()Null input ptrIsNaNC:\a\5\s\onnxruntime\core\providers\cpu\tensor\eye_like.cconnxruntime::EyeLike::ComputeT1 != nullptrEyeLikeEyeLike : Input tensor dimension is not 2Input is expected to have four dimensions corresponding to [N,C,H,W]MeanVarianceNormalizationC:\a\5\s\onnxruntime\core/providers/cpu/tensor/mean_variance_normalization.honnxruntime::MeanVarianceNormalization_0<float>::MeanVarianceNormalization_0info.GetAttr<int64_t>("across_channels", &across_channels_).IsOK()across_channelsinfo.GetAttr<int64_t>("normalize_variance", &normalize_variance_).IsOK()normalize_varianceMust have a single dimensionUnsupported value attribute datatype: unpack_status.IsOK()Value attribute unpacking failed:t_proto.dims()[0] == 1Must have a single dimension of 1onnxruntime::ConstantOfShapeBase::ConstantOfShapeBaset_proto.dims_size() == 1onnxruntime::ConstantOfShape::Computeonnxruntime::ConstantOfShapeBase::PrepareComputeinput_shape.NumDimensions() > 0Must have a valid input shape.C:\a\5\s\onnxruntime\core/providers/cpu/generator/constant_of_shape.honnxruntime::ConstantOfShapeBase::SetValueUnsupported value attribute datatype with sizeof=: TensorProto::DataType_IsValid(t_proto.data_type())C:\a\5\s\onnxruntime\core\providers\cpu\generator\constant_of_shape.cconnxruntime::ConstantOfShapeBase::SetValueFromTensorProtoutils::HasDataType(t_proto)onnxruntime::Scan8Impl::AllocateOutputTensorsInvalid entries in sequence_lens. Max sequence length was sequence_lens length of did not match batch size of position_ >= 0 && position_ < sequence_length_onnxruntime::Scan8Impl::Executeonnxruntime::Scan8Impl::CreateLoopStateVariablesp_mlvalueC:\a\5\s\onnxruntime\core/framework/ort_value_tensor_slicer.honnxruntime::OrtValueTensorSlicer<struct OrtValue const >::Iterator::operator *C:\a\5\s\onnxruntime\core\providers\cpu\controlflow\scan_8.cconnxruntime::Scan<8>::ScanScan<8> spec does not support transpose of output. This should never be called.onnxruntime::Scan8Impl::Initializeonnxruntime::Scan<8>::Computeonnxruntime::Scan<8>::SetupSubgraphExecutionInfodirectionsonnxruntime::Scan8Impl::ValidateInputScan inputs have inconsistent batch size. Previous value was but has batch size of roi_input_idx_ > 0Invalid roi input index.C:\a\5\s\onnxruntime\core\providers\cpu\tensor\upsample.cconnxruntime::Upsample<float>::ComputeCannot scale 0 by any factor to generate a non-zero value. Dimension: Input dim value: Output dim value: onnxruntime::UpsampleBase::ParseScalesDataFromOutputSizeoutput_dims[i] == 0Input dim is zero but required output dim is non-zero. onnxruntime::Upsample<unsigned char>::BaseComputeonnxruntime::Upsample<int>::BaseComputeUpsample: unexpected modeResize: unexpected modeUpsample: input/output value's dimension mismatchResize: input/output value's dimension mismatchUpsample: input/output value is nullptrResize: input/output value is nullptrUpsample: input shape needs to be at least a single dimension.Resize: input shape needs to be at least a single dimensionEither scales or sizes MUST be provided as input.sizes == nullptrOnly one of scales or sizes must be provided as input.scales != nullptronnxruntime::Upsample<int>::ComputeX->Shape().GetDims().size() == output_dims.size()Resize: input tensor's rank does not match the output tensor's rank.sizes != nullptr && sizes->Shape().Size() != 0output_dims.size() == dims.size()Rank of input and output tensor should be same.onnxruntime::Upsample<float>::BaseComputeonnxruntime::Upsample<unsigned char>::Compute'Linear' mode only support 2-D inputs ('Bilinear') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the Resize: size of roi array should be 2 * N where N is the rank of input tensor X.Upsample: input tensor's dimension does not match the scales.Resize: input tensor's dimension does not match the scales.onnxruntime::TransposeBase::TransposeBasev >= 0 && static_cast<uint64_t>(v) <= std::numeric_limits<size_t>::max() is repeated.Attribute perm of Transpose has an invalid value. Value is outside range.C:\a\5\s\onnxruntime\core/providers/cpu/tensor/transpose.hperm: does not align with rank of input data: [ C:\a\5\s\onnxruntime\core\providers\cpu\tensor\transpose.cconnxruntime::Transpose::ComputeMismatched data types between input and output Tensors. 'repeat' input tensor must be 1 dimensional'repeat' input tensor must have the same length as the 'input' tensorTile doesn't have an implementation yet for the type: onnxruntime::Tile::ComputeTileInput count of Tile OP mismatch, the first one is emptyInput count of Tile OP mismatch, the second one is emptythe tensor to be tiled using Tile OP must be atleast 1 dimensionalC:\a\5\s\onnxruntime\core\providers\cpu\tensor\tile.ccgsl::narrow_cast<int64_t>(input_shape.Size()) == sizeshapeTensor->Shape().NumDimensions() == 1onnxruntime::Reshape::ComputeC:\a\5\s\onnxruntime\core/providers/cpu/tensor/reshape.hAttribute shape is not set.A shape tensor must be a vector tensor.A dimension cannot be less than -1, got requested_shape[i] >= -1onnxruntime::ReshapeHelper::ReshapeHelperi < input_shape.NumDimensions(), requested shape:The input tensor cannot be reshaped to the requested shape. Input shape:size != 0 && (input_shape.Size() % size) == 0C:\a\5\s\onnxruntime\core\providers\cpu\tensor\reshape_helper.hAt most one dimension can be -1.unknown_dim == -1The dimension with value zero exceeds the dimension size of the input tensor.onnxruntime::Reshape_1::Reshape_1Reshape_1clipinfo.GetAttr("direction", &direction_).IsOK()onnxruntime::RNN<float>::RNNC:\a\5\s\onnxruntime\core/providers/cpu/rnn/rnn.hRNNonnxruntime::RNN<float>::ComputeC:\a\5\s\onnxruntime\core\providers\cpu\rnn\rnn.ccAffineinfo.GetAttrs("activations", activations_).IsOK()hidden_sizeinfo.GetAttr("hidden_size", &hidden_size_).IsOK()activations_.size() == static_cast<size_t>(num_directions)allowed_directions.find(direction_) != allowed_directions.end()activation_alphaactivation_betaactivationsforwardbidirectionalRNN op: Invalid activation attribute - allowed_activations.find(activations_[direction]) != allowed_activations.end()C:\a\5\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_lstm.cconnxruntime::DeepCpuLstmOp::ValidateInputs}. Actual:Input initial_c must have shape {LSTMLSTM operator does not support double yetInvalid data type for LSTM operator of onnxruntime::DeepCpuLstmOp::ComputeInput P must have shape {Invalid 'direction' argument of 'onnxruntime::rnn::detail::MakeDirectionC:\a\5\s\onnxruntime\core/providers/cpu/rnn/rnn_helpers.hinfo.GetAttr("direction", &direction).IsOK()'. Must be one of 'forward', 'reverse', or 'bidirectional'.input_forgetsigmoidtanhactivation_func_names.size() == static_cast<size_t>(num_directions_) * 3onnxruntime::DeepCpuLstmOp::DeepCpuLstmOpC:\a\5\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_lstm.hinfo.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0clip_ > 0.foffset + size <= size_t(span.size())onnxruntime::rnn::detail::SafeRawConstPointeronnxruntime::DeepCpuLstmOp::ComputeImpl..B + (N * ldb - (ldb - K)) <= B_endC + (M * ldc - (ldc - N)) <= C_endcur + size <= endonnxruntime::rnn::detail::SafeRawPointer rows[lda >= K && ldb >= K && ldc >= Nonnxruntime::rnn::detail::ComputeGemmA + (M * lda - (lda - K)) <= A_endonnxruntime::DeepCpuGruOp::DeepCpuGruOpC:\a\5\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.hlinear_before_resetonnxruntime::DeepCpuGruOp::ComputeImplInvalid data type for GRU operator of onnxruntime::DeepCpuGruOp::ComputeC:\a\5\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.ccinfo.GetAttr("linear_before_reset", &int64_value).IsOK()activation_func_names.size() == static_cast<size_t>(num_directions_) * 2GRUGRU operator does not support double yetonnxruntime::RoiPool<float>::RoiPoolC:\a\5\s\onnxruntime\core/providers/cpu/nn/roi_pool.hpooled_shape.size() == 2pooled_height_ > 0roi_batch_id >= 0roi_batch_id < batch_sizepooled_shapeinfo.GetAttrs<int64_t>("pooled_shape", pooled_shape).IsOK()pooled_width_ > 0info.GetAttr<float>("spatial_scale", &spatial_scale_).IsOK()spatial_scale_ > 0MaxRoiPoolR->Shape()[1] == 5onnxruntime::RoiPool<float>::ComputeC:\a\5\s\onnxruntime\core\providers\cpu\nn\roi_pool.ccalpha_ > 0.0fsize_ % 2 == 1beta_ > 0.0fonnxruntime::LRN<float>::LRNinfo.GetAttr<int64_t>("size", &size).IsOK()size_ > 0C:\a\5\s\onnxruntime\core/providers/cpu/nn/lrn.hC:\a\5\s\onnxruntime\core\providers\cpu\nn\lrn.cconnxruntime::LRN<float>::ComputesizeLRNX->Shape().NumDimensions() == 4op_kernel_info.GetAttr<int64_t>("p", &p_).IsOK()C:\a\5\s\onnxruntime\core/providers/cpu/nn/lp_norm.hp_ == 1 || p_ == 2LpNormalizationonnxruntime::LpNorm<float>::LpNormop_kernel_info.GetAttr<int64_t>("axis", &axis_).IsOK()InstanceNormalizationC:\a\5\s\onnxruntime\core\providers\cpu\nn\instance_norm.cconnxruntime::InstanceNorm<float>::Computeonnxruntime::InstanceNorm<float>::InstanceNormop_kernel_info.GetAttr<float>("epsilon", &epsilon_).IsOK()C:\a\5\s\onnxruntime\core/providers/cpu/nn/instance_norm.hInvalid input scale: number of dimensions is not 1: Invalid input data: number of dimensions is less than 3: Mismatch between input data and scale: size of scale != input channel count vs. Mismatch between input data and B: size of B != input channel count Invalid input B: number of dimensions is not 1: Invalid input scale: 0th dimension != Invalid input scale: NumDimensions() != Invalid input scale: dimension != Invalid input X: The rank of input X must be atleast 2. Got rank: Invalid input mean: Invalid input mean: 0th dimension != Invalid input var: 0th dimension != Invalid input var: NumDimensions() != Invalid input B: 0th dimension != Invalid input B: NumDimensions() != Invalid input mean: NumDimensions() != Invalid input B: onnxruntime::BatchNorm<float>::BatchNormonnxruntime::BatchNorm<float>::Computeonnxruntime::BatchNorm<double>::ComputeInvalid input var: onnxruntime::BatchNorm<double>::BatchNormC:\a\5\s\onnxruntime\core/providers/cpu/nn/batch_norm.hinfo.GetAttr<float>("mean", &mean_).IsOK()meanInvalid dtype of info.GetAttr<int64_t>("dtype", &dtype).IsOK()info.GetAttrs<int64_t>("shape", shape).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDC:\a\5\s\onnxruntime\core/providers/cpu/generator/random.honnxruntime::RandomNormal::RandomNormalinfo.GetAttr<float>("scale", &scale_).IsOK()scaleCould not infer data type from input tensor with data type C:\a\5\s\onnxruntime\core\providers\cpu\generator\random.ccEmpty dimensions for input tensoronnxruntime::RandomUniformLike::Computeonnxruntime::RandomNormalLike::Computeinfo.GetAttr<float>("low", &low_).IsOK()lowsample_sizeonnxruntime::RandomUniformLike::RandomUniformLikehighonnxruntime::RandomNormalLike::RandomNormalLikeonnxruntime::RandomUniform::RandomUniforminfo.GetAttr<float>("high", &high_).IsOK()onnxruntime::Multinomial::Multinomialinfo.GetAttr<int64_t>("sample_size", &num_samples_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDonnxruntime::MultinomialComputeonnxruntime::RandomNormalComputeFLOAT16 is not supportedonnxruntime::RandomUniformComputenum_classes is < 1batch_size is < 1Invalid data type of num_samples is < 1C:\a\5\s\onnxruntime\contrib_ops\cpu\cpu_contrib_kernels.cconnxruntime::contrib::RegisterNchwcKernelsonnxruntime::contrib::RegisterCpuContribKernelsonnxruntime::contrib::RegisterQuantizationKernelsinfo.GetAttr<std::string>("default_string", &default_string_).IsOK()default_stringinfo.GetAttr<int64_t>("default_int64", &default_int_).IsOK()default_int64info.GetAttrs<std::string>("classes_strings", string_classes).IsOK()classes_stringsC:\a\5\s\onnxruntime\core/providers/cpu/ml/label_encoder.honnxruntime::ml::LabelEncoder::LabelEncoder_Unusedvalues_stringsvalues_floatskeys_stringsInput of tensor(string) must have output of tensor(int64)LabelEncoderkeys_floatsInput of tensor(int64) must have output of tensor(string)onnxruntime::ml::LabelEncoder_2<__int64,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<float,__int64>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<float,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float>::LabelEncoder_2info.GetAttrs<TValue>(_value_field_name, values).IsOK()onnxruntime::ml::LabelEncoder_2<__int64,__int64>::LabelEncoder_2 and the number of values is keys_int64sdefault_floatinfo.GetAttrs<TKey>(_key_field_name, keys).IsOK()values_int64snum_keys == num_valuesThe onnxruntime::ml::LabelEncoder_2<__int64,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64>::LabelEncoder_2) must have the same length. However, the number of key is attribtues in LabelEncoder (name: Input features_per_batch[ZipMapclasslabels_int64sZipmap only supports 1D or 2D input tensors] != number of classlabels[C:\a\5\s\onnxruntime\core\providers\cpu\ml\zipmap.ccZipmap does not support empty dim countclasslabels_strings_.empty() ^ classlabels_int64s_.empty()onnxruntime::ml::ZipMapOp::ZipMapOpclasslabels_stringsMust provide classlabels_strings or classlabels_int64s but not both.BRANCH_LEQLEAFTreeEnsembleRegressorSOFTMAX_ZEROAVERAGELOGISTICSOFTMAXBRANCH_GTBRANCH_EQBRANCH_LTBRANCH_GTESUMMINnodes_featureidsnodes_falsenodeidsnodes_missing_value_tracks_truenodes_hitratesnodes_nodeidsnodes_modesnodes_truenodeidsnodes_treeidsC:\a\5\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_common.hnodes_falsenodeids.size() == nodes_featureids.size()n_targets_or_classes > 0onnxruntime::ml::detail::TreeEnsembleCommon<float,float>::TreeEnsembleCommonaggregate_functionInput shape needs to be at least a single dimension.n_targetsbase_valuespost_transformnodes_valuestarget_nodeidstarget_idstarget_weightstarget_treeidsC:\a\5\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_aggregator.honnxruntime::ml::detail::TreeAggregatorMin<double,float>::MergePredictionpredictions.size() == predictions2.size()onnxruntime::ml::detail::TreeAggregatorMax<double,float>::MergePredictiononnxruntime::ml::detail::TreeEnsembleCommon<double,float>::TreeEnsembleCommononnxruntime::ml::detail::TreeEnsembleCommon<double,float>::computeUnknown aggregation function in TreeEnsemble.onnxruntime::ml::detail::TreeEnsembleCommon<float,float>::computeonnxruntime::ml::detail::TreeAggregator<double,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorMax<float,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum<double,float>::ProcessTreeNodePredictionpredictions.size() == (size_t)n_targets_or_classes_onnxruntime::ml::detail::TreeAggregatorSum<double,float>::MergePredictionit->i < (int64_t)predictions.size()this->base_values_.size() == predictions.size()onnxruntime::ml::detail::TreeAggregatorAverage<double,float>::FinalizeScorestarget_class_ids.size() == target_class_treeids.size() is already there.nodes_falsenodeids.size() == nodes_values.size()target_class_ids.size() == target_class_nodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()nodes_falsenodeids.size() == nodes_truenodeids.size()nodes_falsenodeids.size() == nodes_modes.size()nodes_falsenodeids.size() == nodes_nodeids.size() (falsenode). (weights).Unable to find node One falsenode is pointing either to itself, either to another tree. (truenode).- in tree Node onnxruntime::ml::detail::TreeAggregator<float,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum<float,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum<float,float>::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorMin<float,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorAverage<float,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum<int,float>::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorClassifier<int,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum<int,float>::MergePredictiononnxruntime::ml::detail::TreeEnsembleCommon<int,float>::TreeEnsembleCommonpredictions.size() == 2X dims is empty.onnxruntime::ml::detail::TreeEnsembleCommon<__int64,float>::TreeEnsembleCommononnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float>::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier<double,float>::_set_score_binaryclasses.size() == 2 || classes.size() == 1onnxruntime::ml::detail::TreeAggregatorClassifier<int,float>::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier<double,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorClassifier<float,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum<__int64,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum<__int64,float>::ProcessTreeNodePredictionTreeEnsembleClassifierclass_nodeidsclass_idsclass_weightsclass_treeidsonnxruntime::ml::detail::TreeAggregatorClassifier<float,float>::_set_score_binaryonnxruntime::ml::SVMRegressor<float>::SVMRegressorC:\a\5\s\onnxruntime\core\providers\cpu\ml\svmregressor.ccrhoinfo.GetAttrs<float>("rho", rho_).IsOK()n_supportssupport_vectorsPOLYRBFLINEARSVMRegressoronnxruntime::ml::SVMCommon::SVMCommonC:\a\5\s\onnxruntime\core\providers\cpu\ml\svmclassifier.hkernel_paramsinfo.GetAttrs<float>("kernel_params", kernel_params).IsOK()kernel_typeUnexpected mode:num_features == feature_count_onnxruntime::ml::SVMRegressor<float>::Compute!coefficients_.empty()one_classcoefficientsinfo.GetAttrs<float>("coefficients", coefficients_).IsOK()C:\a\5\s\onnxruntime\core\providers\cpu\ml\ml_common.hprob_aprob_bSVMClassifiervectors_per_classUnexpected value for 'add_second_class' of scores.size() == static_cast<size_t>(expected_num_scores)onnxruntime::ml::batched_update_scores_inplacecoefficients_.size() > 0onnxruntime::ml::SVMClassifier::Computeinfo.GetAttrs<std::string>("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs<int64_t>("classlabels_ints", classlabels_ints_).IsOK()classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0proba_.size() == probb_.size()classlabels_intsonnxruntime::ml::SVMClassifier::SVMClassifierC:\a\5\s\onnxruntime\core\providers\cpu\ml\svmclassifier.ccUnsupported data type of C:\a\5\s\onnxruntime\core\providers\cpu\ml\scaler.cc) != (!scale_.empty()onnxruntime::ml::ScalerOp<float>::ScalerOpoffsetEmpty scale in attributesonnxruntime::ml::ScalerOp<__int64>::ScalerOponnxruntime::ml::ScalerOp<int>::ScalerOpEither both scale and offset can be of feature size (onnxruntime::ml::ScalerOp<double>::ScalerOpInvalid argument: input has empty dimensions.) or 1Scale size: (scale_.size() == offset_.size()ScalerUnknown Category and zeros = 0.OneHotEncodertmp_cats_int64s.empty() || tmp_cats_strings.empty()onnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOpcats_stringsOne and only one of the 'cats_*' attributes must be definedzeroscats_int64sonnxruntime::ml::OneHotEncoderOp<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp<float>::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp<double>::OneHotEncoderOpC:\a\5\s\onnxruntime\core\providers\cpu\ml\onehotencoder.ccnum_categories_ > 0C:\a\5\s\onnxruntime\core/providers/cpu/ml/ml_common.hUnexpected NORMALIZE value of Rank of input to Normalized must be less than 2. Got onnxruntime::ml::Normalizer::Normalizerinfo.GetAttr<std::string>("norm", &norm).IsOK()normonnxruntime::ml::MakeNormalizeInvalid normalize value of L2L1MAXNormalizerC:\a\5\s\onnxruntime\core/providers/cpu/ml/normalizer.hC:\a\5\s\onnxruntime\core\providers\cpu\ml\linearregressor.cconnxruntime::ml::LinearRegressor::LinearRegressorinfo.GetAttr<int64_t>("targets", &num_targets_).IsOK()targetsinterceptsLinearRegressorInput shape had more than 2 dimension. Dims=scores_output_data.length() >= scores_output_sizeScores output is incorrect size. Expected: Found:C:\a\5\s\onnxruntime\core\providers\cpu\ml\linearclassifier.cconnxruntime::ml::LinearClassifier::LinearClassifiermulti_classLinearClassifierUnsupported input element type of onnxruntime::ml::LinearClassifier::ComputeImplonnxruntime::ml::CastInputToFloatshape_size == out.length()Empty input dimensions.Empty value of imputed values.Expected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedreplaced_value_floatimputed_value_int64simputed_value_floatsImputerInvalid typeonnxruntime::ml::ImputerOp::Computeimputed_values_float_.empty() ^ imputed_values_int64_.empty()Must provide imputed_values_float_ or imputed_values_int64_ but not both.Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedreplaced_value_int64C:\a\5\s\onnxruntime\core\providers\cpu\ml\imputer.cconnxruntime::ml::ImputerOp::ImputerOpInvalid input type:C:\a\5\s\onnxruntime\core\providers\cpu\ml\feature_vectorizer.cconnxruntime::ml::FeatureVectorizer::Computeinput_count >= 0 && static_cast<size_t>(input_count) == input_dimensions_.size()Number of inputs () does not match number of inputdimensions values ().FeatureVectorizerC:\a\5\s\onnxruntime\core/providers/cpu/ml/feature_vectorizer.honnxruntime::ml::FeatureVectorizer::FeatureVectorizerstatus.IsOK() && !input_dimensions_.empty()inputdimensions attribute must be providedinputdimensionsonnxruntime::ml::DictVectorizerOp<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,double>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOpC:\a\5\s\onnxruntime\core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOpinfo.GetAttrs(std::is_same<AttrType, std::string>::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()int64_vocabularystring_vocabularyDictVectorizerInput of int64 must have output of string Input of string must have output of int64CategoryMappernum_entries == int_categories.size()info.GetAttrs<int64_t>("cats_int64s", int_categories).IsOK()C:\a\5\s\onnxruntime\core/providers/cpu/ml/category_mapper.honnxruntime::ml::CategoryMapper::CategoryMapperinfo.GetAttrs<std::string>("cats_strings", string_categories).IsOK()%fTO_FLOATUnexpected CAST_TO value of max_mapinfo.GetAttr<int64_t>("max_map", &max_map_).IsOK()max_map must be > 0 if map_form is SPARSEmap_form_ != PACK_MAP::SPARSE || max_map_ > 0CastMap Expected std::map<int64_t, float> or std::map<int64_t, std::string>Invalid input type of value: 0.fInvalid PACK_MAP value of onnxruntime::ml::MakePackcast_toinfo.GetAttr<std::string>("cast_to", &attr).IsOK()onnxruntime::ml::CastMap::CastMapC:\a\5\s\onnxruntime\core/providers/cpu/ml/cast_map.hmap_forminfo.GetAttr<std::string>("map_form", &attr).IsOK()TO_STRINGTO_INT64 Expected TO_FLOAT, TO_STRING or TO_INT64Invalid CAST_TO value of onnxruntime::ml::MakeCastDENSESPARSE Expected DENSE or SPARSENegative index values are not permitted. First entry in map has index value of cur_input == end_input || cur_input->first >= 0onnxruntime::ml::CastMap::ComputeImplC:\a\5\s\onnxruntime\core\providers\cpu\ml\cast_map.ccthreshold is NaNInput data with index: BinarizerArrayFeatureExtractorInvalid argument: X input has empty dimensions.Invalid Y argument: num_indices = 0) >=] (Invalid Y argument: index is out of range: Y[onnxruntime::EinsumTypedComputeProcessor<double>::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor<double>::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor<__int64>::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor<__int64>::FinalizeOutputEinsum op: The candidate output cannot be reshaped into the op's outputcandidate_output.Shape().Size() == output_shape.Size()onnxruntime::EinsumTypedComputeProcessor<float>::FinalizeOutputNot all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: candidate_output_dims[iter] == 1Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: onnxruntime::EinsumTypedComputeProcessor<int>::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor<int>::FinalizeOutputRight shape: right.Shape().Size() == right_shape_override.Size() Right shape: Ranks of pair-wise operands must be equal. left_rank == right_rankEinsum op: Input dimensions must be equal along an axis to be reduced across all inputsleft_dim == right_dimEinsum op: Input shapes do not align Left shape override: Left shape: The override dims are not compatible with given tensor's shape. left.Shape().Size() == left_shape_override.Size()onnxruntime::EinsumTypedComputeProcessor<float>::PairwiseOperandProcessC:\a\5\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_typed_compute_processor.cc Right shape override: Number of subscripts in the input equation does not match number of input tensorsFound a '.' not part of an ellipsis in input: Einsum subscripts string contains too many subscript labels when compared to the rank of the inputEllipsis must indicate a fixed number of dimensions across all inputsFound '.' not part of an ellipsis in input: The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z) in the same dimensionAnother operand has a dim value of onnxruntime::EinsumComputePreprocessor::RunC:\a\5\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_compute_preprocessor.ccRank of the input must match number of subscript labels corresponding to the inputdim_iter == rank...Inputs have ellipses in them but the provided output subscript does not contain an ellipsisFound a '.' not part of an ellipsis in the output subscript providedFound '.' not part of an ellipsis in the output subscript providedThe only subscript labels allowed in the output subscript are lowercase letters (a-z)Output subscript contains repeated lettersOutput subscript contains letters not seen in the inputs. The shape is: is incompatible in the dimension Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand Einsum subscripts string contains too many subscript labels when compared to the rank of the input Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input num_broadcasted_indices < num_of_ellipsis_dims_onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDimsThe broadcasted dimensions of the inputs are incompatibleEinsum op: The candidate output does not match the actual output's shapeoutput.SizeInBytes() == input.SizeInBytes()onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopyC:\a\5\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_auxiliary_ops.ccThe innermost dims should have the same dim value to parse the diagonal elementsinput_dims[rank - 2] == input_dims[rank - 1]input_1.DataType() == input_2.DataType()onnxruntime::EinsumOp::MatMulOnly 1 batch dimension is allowed for MatMulinput_shape_1_override.size() == 3 && input_shape_2_override.size() == 3Batch dimension should match for MatMul;input_shape_1_override[0] == input_shape_2_override[0]Incompatible matrix dimensions for matMulinput_shape_1_override[2] == input_shape_2_override[1]Data types of the inputs must match for MatMulonnxruntime::EinsumOp::IsTransposeRequiredLength of permutation must match the rank of the input to be permutatedonnxruntime::EinsumOp::TransposeEinsum op: Transpose failed: onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDimsEinsum op: Unsupported data type for Diagonal for input shape Cannot parse the diagonal elements along dims rank >= 2 && dim_1 != dim_2 && input_dims[dim_1] == input_dims[dim_2]onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalThe rank of the input must match permutation size for Transposeinput_rank == permutation.size()Einsum op: Exception during MatMul operation: onnxruntime::scan::detail::CreateFeedsFetchesManagerSubgraph must have the shape set for all outputs but onnxruntime::scan::detail::IterateSequenceonnxruntime::scan::detail::IterateSequence::<lambda_579c49645976a567928d3fb950e85f28>::operator ()iteration_num_ < sequence_len_Misuse of LoopStateVariable. Attempt to move beyond end of sequence is not compatible with onnxruntime::scan::detail::LoopStateVariable::NextNumber of entries in '' was onnxruntime::scan::detail::ReadDirectionsnum_entries < 0 || gsl::narrow_cast<int64_t>(directions.size()) == num_entriesInvalid values in ''. 0 == forward. 1 == reverse. did not.valid inputs but Scan was only given num_variadic_inputs == num_subgraph_inputsThe subgraph in 'body' requires C:\a\5\s\onnxruntime\core\providers\cpu\controlflow\scan_utils.cconnxruntime::scan::detail::Info::InfoExpected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.onnxruntime::scan::detail::OutputIterator::operator *Expected AllocateFinalOutput to have been called to before we increment the iteratoris_concrete_shape_onnxruntime::OrtValueTensorSlicer<struct OrtValue>::Iterator::operator *onnxruntime::scan::detail::OutputIterator::operator ++onnxruntime::scan::detail::OutputIterator::InitializeMismatch between expected shape and shape from first outputonnxruntime::scan::detail::OutputIterator::AllocateFinalBufferFailed to create output tensor for output #!is_concrete_shape_If shape was concrete we shouldn't be using a custom allocatorcur_iteration_ < num_iterations_onnxruntime::scan::detail::OutputIterator::AllocateFinalOutputSoftmaxCPU inputs N, D and N * D must be < Input X must have 3 dimensions only. Actual:Invalid GRU hidden gate activation function: onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameonnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByNameC:\a\5\s\onnxruntime\core\providers\cpu\rnn\rnn_helpers.cconnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCountInvalid activation function of Invalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::ActivationFuncByNameInvalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNamethresholdedreluleakyreluhardsigmoidscaledtanhsoftsigneluExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got softplusInput R must have shape {Input W must have shape {Input sequence_lens must have shape {Input B must have shape {Input initial_h must have shape {Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=reluaffineonnxruntime::contrib::Trilu::Triluinfo.GetAttr<int64_t>("upper", &temp).IsOK()TriluC:\a\5\s\onnxruntime\contrib_ops\cpu\trilu.hIsScalarOr1ElementVector(k)k should be a 1-D or 0-D tensor.C:\a\5\s\onnxruntime\contrib_ops\cpu\trilu.cconnxruntime::contrib::Trilu::ComputeupperInverseonnxruntime::utils::MLTypeCallDispatcher<struct onnxruntime::contrib::Inverse::ComputeImpl,float,double,union onnxruntime::MLFloat16>::Invokeonnxruntime::contrib::SkipLayerNorm<double>::SkipLayerNormLast dimension of bias and input does not matchinput is expected to have 3 dimensions, got epsilon_ >= 0gamma is expected to have 1 dimension, got skip is expected to have same shape as inputbeta is expected to have 1 dimension, got Last dimension of gamma and input does not matchbias is expected to have 1 dimension, got Last dimension of beta and input does not matchC:\a\5\s\onnxruntime\contrib_ops\cpu\skip_layer_norm.cconnxruntime::contrib::SkipLayerNorm<float>::SkipLayerNormonnxruntime::contrib::LayerNorm<double>::Computeop_kernel_info.GetAttr("axis", &axis_).IsOK()C:\a\5\s\onnxruntime\contrib_ops\cpu\layer_norm.cconnxruntime::contrib::LayerNorm<float>::LayerNormonnxruntime::contrib::LayerNorm<double>::LayerNormonnxruntime::contrib::LayerNorm<float>::Computeinfo.GetAttr("alpha", &alpha_).IsOK()onnxruntime::contrib::Affine<float>::Affineonnxruntime::contrib::Scale<float>::ScaleC:\a\5\s\onnxruntime\contrib_ops\cpu\element_wise_ops.hinfo.GetAttr("beta", &beta_).IsOK()Scaleinfo.GetAttr("scale", &scale_).IsOK()onnxruntime::ElementWiseKernel<struct onnxruntime::functors::ParametricSoftplus<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::ParametricSoftplus<float> >::ElementWiseKernelonnxruntime::ElementWiseKernel<struct onnxruntime::functors::ScaledTanh<float> >::Computeonnxruntime::ElementWiseKernel<struct onnxruntime::functors::ScaledTanh<float> >::ElementWiseKernel) does not match the number of channels (Bias size (ImageScalerInput is expected to have four dimensions corresponding to [N,C,H,W], got info.GetAttrs<float>("bias", bias_).IsOK()onnxruntime::contrib::ImageScaler<float>::ImageScalerC:\a\5\s\onnxruntime\contrib_ops\cpu\image_scaler.hDynamicSliceonnxruntime::contrib::Crop<float>::ComputeC:\a\5\s\onnxruntime\contrib_ops\cpu\crop.h) + scale_[1] (Crop) needs to be greater than or equal to the topBorder (Input's height ( input dimensions instead) + bottomBorder (Input's width () + scale_[0] () + rightBorder () needs to be greater than or equal to the leftBorder (borderAttribute border needs to be specified with four border elements, got onnxruntime::contrib::BiasGelu<float,1>::Computeuse_approximationC:\a\5\s\onnxruntime\contrib_ops\cpu\bert\bias_gelu.cconnxruntime::contrib::BiasGelu<float,0>::ComputeeuclideansqeuclideanC:\a\5\s\onnxruntime\contrib_ops\cpu\cdist.honnxruntime::contrib::CDist<double>::CDistonnxruntime::contrib::CDist<float>::CDistinfo.GetAttr<std::string>("metric", &metric).IsOK()metricCDistInput shape dimensions mismatch:The second input of CDist kernel has wrong shape: The first input of CDist kernel has wrong shape: C:\a\5\s\onnxruntime\contrib_ops/cpu/crop_and_resize.honnxruntime::contrib::CropAndResize<float>::CropAndResize specified. It should be either bilinear or nearestbilinearCropAndResizeNumber of dimensions for crop size should be exactly 1Null crop_size_ptrConvTransposeWithDynamicPadsInput tensor to Unique op should be 1DMaxpoolWithMaskC:\a\5\s\onnxruntime\contrib_ops\cpu\maxpool_with_mask.honnxruntime::contrib::MaxpoolWithMask::Computeonnxruntime::contrib::TransposeMatMul::ComputeC:\a\5\s\onnxruntime\contrib_ops\cpu\transpose_scale_matmul.cconnxruntime::contrib::TransposeMatMul::TransposeMatMulMurmurHash3positiveInvalid assumption of output element sizeC:\a\5\s\onnxruntime\contrib_ops\cpu\murmur_hash3.cconnxruntime::contrib::MurmurHash3::Computekeysinput_num_bytes % 4 == 0sizeof(uint32_t) == output_element_bytes Char embedding size: char_embedding_size attribute: Char embedding size does not match char_embedding_size attribute. Conv kernal size 2 : Conv filter size does not match embedding_size attribute. conv kernal size 1: conv_window_size attribute: Conv kernal size 1 does not match conv_window_size attribute . conv filter size: embedding_size attribute: embedding_sizeconv_window_sizechar_embedding_sizeChar embedding size does not match conv kernal size 2.onnxruntime::contrib::WordConvEmbedding::ComputeC:\a\5\s\onnxruntime\contrib_ops\cpu\word_conv_embedding.ccWordConvEmbeddingattribute mark is not setonnxruntime::contrib::Tokenizer::TokenizerC:\a\5\s\onnxruntime\contrib_ops\cpu\tokenizer.ccpad_valueTokenizermarkInput string contains invalid utf8 chars: Match contains invalid utf8 chars: tensor(string) expected as inputInput dimensions are either [C] or [N][C] allowedmincharnum is too big for char level tokenezation!char_tokenezation_ || mincharnum_ < 2Can not digest separators: Can not digest tokenexp: Expecting a non-empty tokenexp!tokenexp.empty()separators must not be empty!separators.empty()mincharnum_ > 0separatorstokenexpEither one of the separators OR tokenexp attributes required but none is setattribute pad_value is not setmincharnumattribute mincharnum is not setattribute mincharnum must have a positive valueC:\a\5\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.cc}, actural: Attention mechanism memory shape error! Expected: {Attention mechanism memory sequence lengths must have shape {C:\a\5\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.hstatic_cast<int>(activation_func_names.size()) == num_directions_ * 3AttnLSTMonnxruntime::contrib::DeepCpuAttnLstmOp::Computeonnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOponnxruntime::contrib::DeepCpuAttnLstmOp::ComputeImplAttention v weight shape error! Expected:{, aw_attn_size}. Got:Attention layer weight shape error! Expected: {onnxruntime::contrib::DeepCpuAttnLstmOp::ValidateInputsAttention memory layer weight shape error! Expected:{}, Got: Attention query layer weight shape error! Expected:{}. Got: found!], while Attention mechanism memory sequence lengths value must in (0, , am_attn_size}, Got:onnxruntime::contrib::FusedGemm<float>::FusedGemmC:\a\5\s\onnxruntime\contrib_ops\cpu\fused_gemm.ccGetFusedActivationAttr(info, activation_).IsOK()onnxruntime::contrib::FusedConvFloat::FusedConvFloatC:\a\5\s\onnxruntime\contrib_ops\cpu\fused_conv.ccaxis <= X_NumDims && axis >= -X_NumDimsExpandDimsonnxruntime::contrib::ExpandDims::ComputeC:\a\5\s\onnxruntime\contrib_ops\cpu\expand_dims.h Axis is Axis must be within range [An axis tensor must be a scalar tensor.axis_tensor->Shape().IsScalar()onnxruntime::contrib::EmbedLayerNorm<float>::EmbedLayerNormC:\a\5\s\onnxruntime\contrib_ops\cpu\bert\embed_layer_norm.cconnxruntime::contrib::EmbedLayerNorm<float>::Computeinput index out of rangeonnxruntime::contrib::AttentionBase::AttentionBaseinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0Input 'input' is expected to have 3 dimensions, got C:\a\5\s\onnxruntime\contrib_ops\cpu\bert\attention.cconnxruntime::contrib::AttentionCPUBase::ApplyAttentiononnxruntime::contrib::Attention<float>::ComputeC:\a\5\s\onnxruntime\contrib_ops\cpu\bert\attention_cpu_base.honnxruntime::contrib::AttentionBase::GetPresentInputs 'mask_index' with raw attention mask shall have shape batch_size x (past_sequence_length + sequence_length)Inputs 'mask_index' dimension 0 shall have length of batch_size or 2 * batch_sizeExpect to have present state output when past state input is givenInput 'mask_index' is expected to have 1 or 2 dimensions, got Inputs 'past' dimension 1 shall have same length as dimension 0 of input 0Inputs 'past' dimension 0 shall have length of 2Inputs 'past' dimension 2 shall have length of Inputs 'past' dimension 2 shall have length of num_headsInput 'bias' dimension 0 should have same length as dimension 1 of input 'weights'Input 'bias' is expected to have 1 dimension, got Input 'past' is expected to have 5 dimension, got Input 'past' is only allowed for unidirectionalInput 'weights' is expected to have 2 dimensions, got Input 0 dimension 2 should be divisiable by value of the num_heads attribute.Input 'weights' dimension 1 should be 3 times of dimension 0Input 1 dimension 0 should have same length as dimension 2 of input 0SampleOponnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommonMatMulIntegerToFloat : input B zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.MatMulIntegerToFloat : input B scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.onnxruntime::contrib::MatMulIntegerToFloat::ComputeIsScalarOr1ElementVector(a_zero_point_tensor)MatMulIntegerToFloat : input A zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(b_zero_point_tensor)DynamicQuantizeMatMul : input B zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(a_scale_tensor)MatMulIntegerToFloat : input A scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.DynamicQuantizeMatMul : input B scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.C:\a\5\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_matmul.cconnxruntime::contrib::DynamicQuantizeMatMul::ComputeIsScalarOr1ElementVector(b_scale_tensor)input zero point must be a scalar or 1D tensor of size 1.Not satisfied: IsScalarOr1ElementVector(weight_scale_tensor) +weight zero point must be a scalar or 1D tensor of size 1.Not satisfied: IsScalarOr1ElementVector(i_zp_tensor) +input scale must be a scalar or 1D tensor of size 1C:\a\5\s\onnxruntime\contrib_ops\cpu\quantization\attention_quant.ccweight must be a scalar or 1D tensor of size 1Not satisfied: IsScalarOr1ElementVector(input_scale_tensor) +onnxruntime::contrib::QAttention<float>::ComputeQAttentionNot satisfied: IsScalarOr1ElementVector(w_zp_tensor) +QLinearAddtensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point)MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if givenonnxruntime::TBroadcaster<signed char,signed char>::AdvanceBytensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point)MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_c_scale)MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1tensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point)MatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_b_scale)MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_a_scale)MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1C:\a\5\s\onnxruntime\contrib_ops\cpu\qlinear_binary_op.cconnxruntime::contrib::QLinearBroadcastTwoQLinearMulQLinearLeakyRelutensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)QLinearLeakyRelu : input Y_zero_point must be a scalar or 1D tensor of size 1tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)QLinearLeakyRelu : input X_zero_point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_y_scale)QLinearLeakyRelu : input Y_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_x_scale)QLinearLeakyRelu : input X_scale must be a scalar or 1D tensor of size 1C:\a\5\s\onnxruntime\contrib_ops\cpu\qlinear_lookup_table.cconnxruntime::contrib::QlinearBuildLookupTableQLinearSigmoidonnxruntime::contrib::MatMulInteger16<short,short,int>::ComputeC:\a\5\s\onnxruntime\contrib_ops\cpu\matmul_integer16.ccMatMulInteger16A != nullptr && B != nullptr(X_shape[1] % MlasNchwcGetBlockSize()) == 0X_rank == 4onnxruntime::contrib::ReorderInput::ComputeC:\a\5\s\onnxruntime\contrib_ops\cpu\nchwc_ops.ccscales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1X_shape.NumDimensions() == 4onnxruntime::contrib::NchwcUpsample::NchwcUpsamplescales_.size() == 4onnxruntime::contrib::NchwcPoolBase::NchwcPoolBaseinfo.GetAttrs<int64_t>("scales", scales_).IsOK()onnxruntime::contrib::NchwcConv::NchwcConvpool_attrs_.kernel_shape.size() == 2channels_ > 0info.GetAttr<int64_t>("channels_last", &channels_last_).IsOK()C:\a\5\s\onnxruntime\contrib_ops\cpu\nchwc_ops.hinvalid channel countinfo.GetAttr<int64_t>("channels", &channels_).IsOK()onnxruntime::contrib::ReorderOutput::ReorderOutputonnxruntime::contrib::NchwcUpsample::ComputeNot satisfied: Y->Shape() == sum_shape +onnxruntime::contrib::NchwcPoolBase::NchwcPoolUnsupported convolution size.output and sum shape must matchonnxruntime::contrib::NchwcConv::Compute(static_cast<size_t>(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)onnxruntime::contrib::ReorderOutput::Computechannels_ <= X_shape[1]Input 1 is expected to have 1 dimensions, got Input 1 dimension 0 should have same length as the last dimension of input 0Input 0 is expected to have 1 or more dimensions, got row[ [seqno=mem_steps <= max_memory_steps_ && mem_steps > 0onnxruntime::contrib::BahdanauAttention<float>::PrepareMemory is not in (0, Real memory steps onnxruntime::contrib::BahdanauAttention<float>::BahdanauAttentionC:\a\5\s\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.ccnot support normalize yet.!normalize_activation_params count mismatchunimplemented activation: Input 0 and 7 (mask) shall have same shapeinput_ids is expected to have 2 dimensions, got Input 0 and 1 shall have same shapegamma is expected to have size of beta is expected to have size of gamma is expected to have 1 dimensions, got word_embedding and position_embedding shall have same dimension 1beta is expected to have 1 dimensions, got segment_embedding is expected to have 2 dimensions, got word_embedding and segment_embedding shall have same dimension 1word_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got Received invalid value for allow_spinning. Valid values are 0 or 1Received null OrtThreadingOptions is not supportedTensor types should have been handled alreadyOrtValue is TensorSequence type but has no element Tensor DataType.CudaPinnedCudaOrtMemoryInfo:[name: id: OrtMemType: OrtAllocatorType:Device:[DeviceType: MemoryType: DeviceId:Specified device is not supported.C:\a\5\s\onnxruntime\core\framework\allocator.cconnxruntime::IAllocator::CalcMemSizeForArrayWithAlignment::<lambda_4f73994de27b34c85d6237a552eb7bf0>::operator ()Argument is not a tensorC:\a\5\s\onnxruntime\core\framework\tensor_type_and_shape.ccOrtApis::GetTensorTypeAndShapetype != nullptrOrtValue is not a TensorNot implementedtype_proto is not of type sequence!type_proto is not of type map!C:\a\5\s\onnxruntime\core\framework\tensor.cconnxruntime::Tensor::Tensorp_type != nullptrdtype_ != nullptrTensor is expected to contain one of the primitive data types. Got: onnxruntime::Tensor::Initonnxruntime::Tensor::SizeInBytestensor size overflowtensor failed memory size calculationshape.Size() must >=0(unknown type)(null)onnxruntime::NonTensorTypeBase::ToDataContaineronnxruntime::NonTensorTypeBase::FromDataContaineronnxruntime::NonTensorTypeBase::IsSequenceCompatibleutils::HasKeyType(thisProto->map_type())onnxruntime::NonTensorTypeBase::IsMapCompatiblethisProto->value_case() == TypeProto::ValueCase::kMapTypeutils::HasElemType(thisProto->sequence_type())onnxruntime::SequenceTensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kSequenceTypeutils::HasElemType(thisProto->sparse_tensor_type())onnxruntime::SparseTensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeutils::HasElemType(thisProto->tensor_type())onnxruntime::TensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kTensorTypeWe do not expect duplicate registration of types for: onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypeproto != nullptrOnly ONNX MLDataType can be registeredC:\a\5\s\onnxruntime\core\framework\data_types.cconnxruntime::data_types_internal::IsCompatibleonnxruntime::SequenceTensorTypeBase::GetElementTypeonnxruntime::SparseTensorTypeBase::GetElementTypeonnxruntime::TensorTypeBase::GetElementTypeonnxruntime::data_types_internal::SetSequenceType<unsigned __int64>::Setonnxruntime::data_types_internal::SetSequenceType<__int64>::Setonnxruntime::data_types_internal::SetSequenceType<unsigned int>::Setonnxruntime::data_types_internal::SetSequenceType<int>::Setonnxruntime::data_types_internal::SetSequenceType<unsigned short>::Setonnxruntime::data_types_internal::SetSequenceType<short>::Setonnxruntime::data_types_internal::SetSequenceType<unsigned char>::Setonnxruntime::data_types_internal::SetSequenceType<signed char>::Setonnxruntime::data_types_internal::SetSequenceType<double>::Setonnxruntime::data_types_internal::SetSequenceType<float>::Setelem_proto != nullptronnxruntime::data_types_internal::SetMapTypes<__int64,double>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,float>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,__int64>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::Setonnxruntime::data_types_internal::SetMapTypes<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,double>::Setonnxruntime::data_types_internal::SetMapTypes<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float>::Setonnxruntime::data_types_internal::SetMapTypes<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,__int64>::SetC:\a\5\s\include\onnxruntime\core/framework/data_types.honnxruntime::data_types_internal::SetMapTypes<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::Setvalue_proto != nullptrexpected a registered ONNX typeonnxruntime::utils::ContainerChecker::ContainerCheckerInvalid DataTypeImpl TypeProto definitionMLDataType for: is not currently registered or supportedtensor type onnxruntime::data_types_internal::SetSequenceType<class std::map<__int64,float,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,float> > > >::Setonnxruntime::data_types_internal::SetSequenceType<class std::map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,float,struct std::less<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,float> > > >::Setonnxruntime::data_types_internal::SetSequenceType<struct onnxruntime::BFloat16>::Setonnxruntime::data_types_internal::SetSequenceType<union onnxruntime::MLFloat16>::Setonnxruntime::data_types_internal::SetSequenceType<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::Setonnxruntime::data_types_internal::SetSequenceType<bool>::Set dimensions. for SizeFromDimension. Tensor has Invalid dimension of dimension <= num_dimsonnxruntime::TensorShape::SizeToDimensionC:\a\5\s\onnxruntime\core\framework\tensor_shape.cconnxruntime::TensorShape::SizeFromDimensionInvalid tensor shape slice argument.dimstart <= dimend && dimend <= size()onnxruntime::TensorShape::Slice{}Config key is empty or longer than maximum length 128Config value is longer than maximum length 1024]. It will be overwritten] already exists with value [Session Config with key [onnxruntime::SessionOptions::AddConfigEntryC:\a\5\s\onnxruntime\core\framework\session_options.ccinvalid allocatorFailed to find allocator for device allocator != nullptronnxruntime::utils::BatchOrCopyMLValueC:\a\5\s\onnxruntime\core\framework\utils.cconnxruntime::utils::FindMemoryInfoForValueexec_plan_ptronnxruntime::utils::CalculateStaticCopyInfoForFeedonnxruntime::utils::CalculateStaticCopyInfoForFeedsonnxruntime::utils::InitializeFeedFetchCopyInfofeed_locations.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFeedsfetch_alloc_info.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFetchescopy_info.size() == num_feedsonnxruntime::utils::CopyInputsAcrossDevicesonnxruntime::utils::CopyOneInputAcrossDevicesonnxruntime::utils::CopyOutputsAcrossDevicesOnly one thread was configured for parallel execution. Hence will use sequential execution.onnxruntime::utils::ExecuteGraphImplonnxruntime::utils::ExecuteGraphRknpuExecutionProviderAttibute name and type don't matchNo attribute with this name is defined.cannot find allocatoronnxruntime::OpKernelInfo::GetMemoryInfoC:\a\5\s\onnxruntime\core\framework\op_kernel_info.ccnode_offsets_index < node_offsets_size_onnxruntime::NodeIndexInfo::GetNodeOffsetC:\a\5\s\onnxruntime\core/framework/node_index_info.hExecution frame was nullframe != nullptronnxruntime::OpKernelContext::OpKernelContextC:\a\5\s\onnxruntime\core\framework\op_kernel.ccOpKernel was nullkernel != nullptronnxruntime::OpKernelContext::OutputMLValue. Num args is Invalid arg_num of arg_num < arg_counts.size()onnxruntime::OpKernelContext::NumVariadicInputsTempSpace allocator not foundonnxruntime::OpKernelContext::GetOrCreateOutputMLValueReceived invalid value of arena_extend_strategy C:\a\5\s\onnxruntime\core\framework\allocatormgr.cconnxruntime::CreateAllocatorsession.disable_prepackingCould not find OrtValue with idx 'node_index_info_SetGraphAndCreateKernels must be called prior to GetExecutionInfo.onnxruntime::SessionState::UpdateToBeExecutedNodesonnxruntime::SessionState::GetNodeIndexInfoonnxruntime::SessionState::SaveToOrtFormatonnxruntime::GetSubGraphSessionStatesOrtFormatsubgraphMain Graph instance should have populated all subgraphs when being resolved.Kernel create info is null. Invalid ORT format model.onnxruntime::SessionState::CreateSubgraphSessionStateSatisfied, but should not be: nullptr == fbs_kcis +onnxruntime::SessionState::LoadFromOrtFormatSatisfied, but should not be: nullptr == node_indices +Kernel create info node indices are null. Invalid ORT format model.Satisfied, but should not be: nullptr == kernel_def_hashes +Kernel create info hashes are null. Invalid ORT format model.Not satisfied: node_indices->size() == kernel_def_hashes->size() +Size mismatch for kernel create info node indexes and hashes. Invalid ORT format model.Can't find node with index . Invalid ORT format model.SessionState for subgraphs is null. Invalid ORT format model.Satisfied, but should not be: node == nullptr + is missing. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_sub_graph_session_states +Satisfied, but should not be: nullptr == fbs_sub_graph_ss +Subgraph SessionState entry for Subgraph SessionState for is null. Invalid ORT format model.onnxruntime::SessionState::FinalizeSessionStateSatisfied, but should not be: nullptr == fbs_sub_session_state + Attribute:onnxruntime::SessionState::FinalizeSessionStateImpl. Ignoring allocator from onnxruntime::SessionState::SetupAllocatorsAllocator already registered for SaveMLValueNameIndexMappingC:\a\5\s\onnxruntime\core\framework\session_state.cconnxruntime::SessionState::CreateGraphInfoDone saving OrtValue mappings.entry != kernel_create_info_map_.cend()onnxruntime::SessionState::PopulateKernelCreateInfo. Do you have duplicated calls to SessionState::AddInitializedTensor function?onnxruntime::SessionState::GetNodeKernelCreateInfoonnxruntime::SessionState::PrepackInitializedConstantTensorsduplicated ort_value index: is used by node ) and node Failed to find input name in the mapping: Using an input in multiple nodes on different devices is not supported currently. Input:output_names_to_nodeinfo.empty()Only one node should produce an output. Existing entry for onnxruntime::SessionState::AddOutputNameToNodeInfoMappingEntry exists in node for attribute onnxruntime::SessionState::AddSubgraphSessionStateexisting_entries.find(attribute_name) == existing_entries.cend()' OpType: Index:entry != node_to_subgraph_ss.second.cend()Missing session state for subgraph. Node:'p_op_kernelfound duplicated provider in KernelRegistryManager (node ). Failed to find kernel for The node is not placed on any Execution Provider. ) in protocorrupted protobuf data: tensor shape size() does not match the data size(data overflowUnpackTensor: the pre-allocate size does not match the size in protoC:\a\5\s\onnxruntime\core\framework\tensorprotoutils.cconnxruntime::utils::GetFileContentonnxruntime::utils::TensorProtoToMLValuestring tensor can not have raw dataThe buffer planner is not consistent with tensor buffer size, expected tensor can't contain negative dimsInitialized tensor with unexpected type: initialize preallocated buffer failed in 'Constant' node 'onnxruntime::utils::ConstantNodeProtoToTensorProtoonnxruntime::utils::SparseTensorProtoToDenseTensorProtoUnsupported attribute value type of Unsupported type: Unsupported sparse tensor data type of UnpackTensor: the pre-allocated size does not match the raw data size, expected Invalid TensorProto`anonymous-namespace'::UnpackTensorWithRawDataonnxruntime::utils::CopySparseDatarank == dims.size() && rank > 0Invalid SparseTensor indices. Should be rank 0 or 1. Got:cur_index == &*indices_data.cend()onnxruntime::utils::ReadLittleEndiansource and destination buffer size mismatchNot satisfied: source_bytes.size_bytes() == destination.size_bytes() +C:\a\5\s\onnxruntime\core/framework/endian_utils.hnullptr != type_protoC:\a\5\s\onnxruntime\core\framework\mldata_type_utils.cconnxruntime::utils::GetMLDataTypeonnxruntime::IExecutionProvider::InsertAllocatorduplicated allocatorC:\a\5\s\onnxruntime\core\framework\execution_provider.cconnxruntime::FeedsFetchesInfo::MapNamesToMLValueIdxsError mapping feeds: C:\a\5\s\onnxruntime\core\framework\feeds_fetches_manager.ccinput_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::UnknownError mapping output names: onnxruntime::FeedsFetchesManager::SetDeviceCopyChecksonnxruntime::FunctionKernel::FunctionKernelcreate_func(&context, &func_state_) == 0C:\a\5\s\onnxruntime\core/framework/func_kernel.honnxruntime::PlaceNode1 == capability->nodes.size()C:\a\5\s\onnxruntime\core\framework\graph_partitioner.cconnxruntime::GraphPartitioner::PartitionNo provider specified.node_compute_funcs.size() == nodes_need_compile.size()Provider doesn't return correct number of compiled functionsdata_transfer registered is nullptr.Tensor size mismatchonnxruntime::DataTransferManager::CopyTensorsC:\a\5\s\onnxruntime\core\framework\data_transfer_manager.cc to There's no data transfer registered for copying tensors from onnxruntime::ExLibLoader::~ExLibLoaderFailed to unload DSO: C:\a\5\s\onnxruntime\core\framework\ex_lib_loader.ccUnloading DSO A dso with name onnxruntime::ExLibLoader::LoadExternalLibCaught exception while loading custom ops with message: has already been loaded.Caught exception while destructing CustomOpsLoader with message: onnxruntime::ExLibLoader::{dtor}::<lambda_533d5fe9378a338313ead04f4e0c8c73>::operator ()offset >= 0 && static_cast<size_t>(offset) < node_values_size_onnxruntime::NodeIndexInfo::GetMLValueIndexort_value_index >= 0 && static_cast<size_t>(ort_value_index) < all_values_size_onnxruntime::IExecutionFrame::GetMLValueC:\a\5\s\onnxruntime\core/framework/execution_frame.hnode_index_info and ort_value_idx_map are out of sync and cannot be usedmlvalue.Fence() == nullptronnxruntime::AllocateSparseTensorShape mismatch attempting to re-use buffer. onnxruntime::ExecutionFrame::AllocateMLValueTensorPreAllocateBufferWe don't expect custom allocators for non-tensor types, so a shape is mandatory here.Allocation of tensor types requires a shape.ort_value_index >= 0 && static_cast<size_t>(ort_value_index) < alloc_plan.size()onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlanort_value_idx >= 0 && static_cast<size_t>(ort_value_idx) < alloc_plan.size()onnxruntime::ExecutionFrame::GetAllocationPlanInvalid allocation kind: onnxruntime::ExecutionFrame::ReleaseMLValueImplTraceAllocation for ort_value_idx=onnxruntime::ExecutionFrame::TraceAllocate failed: size=Memory pattern planner is not enabled on this execution framework.onnxruntime::ExecutionFrame::TraceFreeTraceFree for ort_value_idx=C:\a\5\s\onnxruntime\core\framework\execution_frame.cc Requested shape:node_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()onnxruntime::IExecutionFrame::IExecutionFrameonnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValueinvalid index OrtValue shape verification failed. Current shape:shape && tensor.Shape() == *shapefetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size() entries which doesn't match the number of fetches the frame was initialized with of feeds.size() == feed_mlvalue_idxs.size()onnxruntime::IExecutionFrame::InitAllocation of memory pattern buffer for onnxruntime::ExecutionFrame::{ctor}::<lambda_2e9f375968b456c4e25b5ecc03f8b9d4>::operator ()Fetches vector passed to GetOutputs contains failed. Error: should only appear once.Memory type buffers_.find(location) == buffers_.end()onnxruntime::ExecutionFrame::ExecutionFrameTrying to allocate memory for unused optional inputs/outputsTensor shape cannot contain any negative valuestatic_activation_memory_sizes_in_byte_.find(location.name) == static_activation_memory_sizes_in_byte_.end() returned nullptronnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper, fall back to default allocation behaviorort_value.Fence() == nullptrFor ort_value with index: . 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. but the actually size is: , block in memory pattern size is: model format error!model format error! Missing 'location'lengthchecksummodel format error! Need a key for the external data info failedparsing model format error! Need a value for the external data infolocation kernel start version: node_version: kernel_end_version: Op with name ( This op has been implemented only for the following types ( Version mismatch. and type ( in the supported version rangeFound kernel for Op with name ( However the types are incompatible. (node_version: onnxruntime::KernelRegistry::TryCreateKernelC:\a\5\s\onnxruntime\core\framework\kernel_registry.cc), but the node in the model has the following type ( Encountered following errors: ( kernel is not supported in No matching hash for kernel not found in : Conflicting with a registered kernel with op versions.Failed to add kernel for Kernel not foundkernel def can't be NULLlen <= op_schema.inputs().size()onnxruntime::`anonymous-namespace'::TraverseFormalParametersWithTypeProtoonnxruntime::IDataTransfer::CopyTensorsonnxruntime::CPUDataTransfer::CopyTensorC:\a\5\s\onnxruntime\core\framework\data_transfer.ccsrc.SizeInBytes() == dst.SizeInBytes()C:\a\5\s\onnxruntime\core\framework\node_index_info.cconnxruntime::NodeIndexInfo::Init::<lambda_2ec164253bfbadd7a088da77d36f23dd>::operator ()onnxruntime::NodeIndexInfo::Init::<lambda_971d8eab480f291b1f28d1347bdfd658>::operator ()Can't slice a non-tensor OrtValue. Type was ort_value.IsTensor()OrtValue has not been allocated so can't be sliced.ort_value.IsAllocated()onnxruntime::OrtValueTensorSlicer<struct OrtValue>::CreateC:\a\5\s\onnxruntime\core\framework\ort_value_tensor_slicer.ccgsl::narrow_cast<int64_t>(tensor_shape.NumDimensions()) >= slice_dimension. Dimension 0 is . Shape:Insufficient dimensions to slice on onnxruntime::OrtValueTensorSlicer<struct OrtValue>::Iterator::Iteratoronnxruntime::OrtValueTensorSlicer<struct OrtValue const >::CreateInvalid dim0_offset of dim0_offset < dim0_sizeonnxruntime::OrtValueTensorSlicer<struct OrtValue const >::Iterator::Iteratorentry != nullptronnxruntime::OpKernelContextInternal::OpKernelContextInternal does not.All implicit inputs should have OrtValue instances by now. onnxruntime::ParallelExecutor::ExecuteC:\a\5\s\onnxruntime\core\framework\parallel_executor.ccC:\a\5\s\onnxruntime\core/framework/op_kernel_context_internal.hMultiple errors were found.ParallelExecutor::ExecuteBegin executionGot nullptr from GetKernel for node: op_nameonnxruntime::ParallelExecutor::RunNodeAsyncExiting due to terminate flag being set to true.' Status Message: node. Name:'_fence_before_kernel_time_fence_afterNon-zero status code returned while running providerException running nodes starting at Unknown exception was caught by catch-all handler.'. C:\a\5\s\onnxruntime\core\framework\sequential_executor.cconnxruntime::SequentialExecutor::Executeparameter_sizeactivation_sizeexec_plan_indexgraph_indexSequentialExecutor::Executeoutput_size[Memory] ExecutionFrame dynamically allocates [Memory] ExecutionFrame statically allocates bytes for onnxruntime::ReleaseNodeMLValuesCan't use func with null ptrfunc info for node: already exist.C:\a\5\s\onnxruntime\core\framework\fuse_nodes_funcs.cconnxruntime::FuncManager::GetFuncs not found.BinForSize(bin_size) == BinFromIndex(b)Creating bins of max chunk size C:\a\5\s\onnxruntime\core\framework\bfc_arena.cch < chunks_.size()BinForSize(bin_size * 2) != BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b)onnxruntime::BFCArena::Extend::<lambda_74f9b5f133e689e9707d11ae644a63b3>::operator ()Incorrect arena extend strategy.onnxruntime::BFCArena::ChunkFromHandle bytes.Failed to allocate memory for requested buffer of size Available memory of is smaller than requested bytes of Allocated memory at Total allocated bytes: onnxruntime::BFCArena::ExtendExtended allocation by onnxruntime::BFCArena::Reservereserved_chunks_.find(ptr) == reserved_chunks_.end()tried to allocate 0 bytesh != kInvalidChunkHandleExtending BFCArena for . bin_num: rounded_bytes:onnxruntime::BFCArena::AllocateRawInternalonnxruntime::BFCArena::AllocationRegion::AllocationRegion0 == memory_size % kMinAllocationSizep_int < base_int + memory_size_onnxruntime::BFCArena::AllocationRegion::IndexForp_int >= base_intC:\a\5\s\onnxruntime\core/framework/bfc_arena.h memory limit: arena_extend_strategy onnxruntime::BFCArena::RegionManager::RegionForCould not find Region for onnxruntime::BFCArena::BFCArenaCreating BFCArena for with following configs: initial_chunk_size_bytes: max_dead_bytes_per_chunk: !chunk->in_use()Failed to find a free memory block despite calling Extend. rounded_bytes=onnxruntime::BFCArena::DeallocateRawInternalonnxruntime::BFCArena::SplitChunk!c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::FindChunkPtronnxruntime::BFCArena::InsertFreeChunkIntoBinc2->prev == h1onnxruntime::BFCArena::Merge!c1->in_use() && !c2->in_use()Could not find chunk in binonnxruntime::BFCArena::RemoveFreeChunkFromBinonnxruntime::BFCArena::RemoveFreeChunkIterFromBin!c->in_use() && (c->bin_num != kInvalidBinNum)onnxruntime::BFCArena::FreeAndMaybeCoalescec->in_use() && (c->bin_num == kInvalidBinNum)BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0. string tensor is not supported for copying between allocatorsInternal error. The preallocated buffer is too small. Requires , Got C:\a\5\s\onnxruntime\core\framework\session_state_utils.cconnxruntime::session_state_utils::DeserializeTensorProtoOrtValue indexes should have been populated.onnxruntime::session_state_utils::SaveInitializedTensorsSaving initialized tensors.Failed to copy tensor to Deserialize tensor failed.[Memory] SessionStateInitializer statically allocates ort_value_name_idx_map.MaxIdx() > -1onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::<lambda_8f66191b70ee053095843ffa8ce41187>::operator ()Done saving initialized tensors input with name is not used by any node.onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMappingonnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::<lambda_84c70c288a8071b5de68f1a019413973>::operator ()GraphSubgraphduplicated locationFailed to get allocator for location: is not foundInternal error.C:\a\5\s\onnxruntime\core\framework\tensor_allocator_with_mem_pattern.honnxruntime::TensorAllocatorWithMemPattern::FinalizePlan' failedWeight buffer for initializer '' is not foundMem pattern for initializer onnxruntime::TensorAllocatorWithMemPattern::TraceGet preallocated buffer for initializer ') : C:\a\5\s\onnxruntime\core\framework\allocation_planner.ccentry != kernel_create_info_map.cend()SessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:onnxruntime::PlannerImpl::UseCountn >= 0 && static_cast<size_t>(n) < ort_value_info_.size()onnxruntime::PlannerImpl::Indexonnxruntime::GetKernelCreateInfoid >= 0 && static_cast<size_t>(id) < ort_value_info_.size()onnxruntime::PlannerImpl::AllocPlann >= 0 && static_cast<size_t>(n) < plan_.allocation_plan.size()onnxruntime::PlannerImpl::Buffernullptr != tensor_type_baseonnxruntime::PlannerImpl::Reusereused != reused_foronnxruntime::PlannerImpl::ProcessDefp_kernel_defShould not have entry in kernel create info with nullptr for kernel_defCan not find the node onnxruntime::PlannerImpl::GetElementSizeonnxruntime::PlannerImpl::GeneratePlanForWeightsonnxruntime::PlannerImpl::GetLocationForNodeInputCan not find the execution provider onnxruntime::PlannerImpl::ComputeUseCountsonnxruntime::PlannerImpl::CreatePlanonnxruntime::PlannerImpl::ComputeReusePlanSafeIntExceptionHandler<class onnxruntime::OnnxRuntimeException>::SafeIntOnDivZeroDivide by zero', location: Failed to get allocator for initializer 'invalid ort_value_index:onnxruntime::SimpleTensorAllocator::GetPreallocatedBufferC:\a\5\s\onnxruntime\core\framework\simple_tensor_allocator.cc[ShapeInferenceError] Dimension= Target=Can't merge shape info. Both source and target dimension have values but they differ. Source=Mismatch between number of source and target dimensions. Source=NoOpNode [Serialization of fused function body is not currently supported, does not have the graph for key onnxruntime::Node::SaveToOrtFormatTensor element type mismatch. onnxruntime::NodeArg::UpdateTypeAndShapeSparseTensor element type mismatch. ] op_type [node_arg_name cannot be nullSatisfied, but should not be: nullptr == node_arg_name +], could not find NodeArg LoadNodeArgsFromOrtFormat: Node [Not satisfied: it != attr_to_subgraph_map_.cend() +fbs_node_arg_names cannot be nullonnxruntime::Node::LoadFromOrtFormat::<lambda_bdd70102f38fd25fad37544ae1e8d055>::operator ()Satisfied, but should not be: nullptr == fbs_node_arg_names +Serialization error. Graph attribute was serialized without Graph instanceNot satisfied: subgraph +Node::LoadFromOrtFormat, input_arg_counts is missingSatisfied, but should not be: nullptr == fbs_input_arg_counts +Satisfied, but should not be: nullptr == node_arg +onnxruntime::Node::LoadFromOrtFormatfbs_attr cannot be nullSatisfied, but should not be: nullptr == fbs_attr +input index: onnxruntime::Node::LoadEdgesFromOrtFormatSatisfied, but should not be: fbs_node_edges.node_index() != index_ +input edgesNode::LoadEdgesFromOrtFormat, edge is missing for onnxruntime::Node::LoadEdgesFromOrtFormat::<lambda_f08997c2d6ae1f43b70c976bf387c755>::operator ()Satisfied, but should not be: nullptr == fbs_edge + is not the same as this node's index:Constant are '0' and '1'. The environment variable contained the value: onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySetC:\a\5\s\onnxruntime\core/graph/model_load_utils.hALLOW_RELEASED_ONNX_OPSET_ONLYOutput:onnxruntime::MergeShapeInfoC:\a\5\s\onnxruntime\core\graph\graph.cc Input=Type mismatch. Current=. Falling back to lenient merge. target:' source:Error merging shape info for output. 'This is an invalid model. Error: the graph is not acyclic.Graph attribute inferencing failed: in node No Graph instance was found for attribute onnxruntime::Graph::BuildConnections Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.This is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name=This is an invalid model. Failed to find NodeArg in all parent graphs. Name=Node:Subgraph input missing type.onnxruntime::Graph::InferAndVerifySubgraphTypesUpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphs inputs. Either provide all subgraph inputs, or just the required inputs. inputs and requires inputs but subgraph has Size mismatch validating subgraph inputs. Got ) does not have type information set by parent node.) input arg (This is an invalid model. Node () is invalid.node.GetAttributeNameToMutableSubgraphMap().empty()onnxruntime::Graph::UpdateShapeInference) Op (Node ( in node () bound to different types (Type Error: Type parameter (onnxruntime::Graph::InferAndVerifyTypeMatch) in node () of operator (' of input parameter (This is an invalid model. Type Error: Type 'onnxruntime::Graph::GraphThis is an invalid model. Tensor does not have type information.or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.by either re-generating the model with latest exporter/converter output edgesThis is an invalid model. The sum of input arg count is not equal to size of input defs in node (graph_proto cannot be nullgraph_proto != nullptrGraph state to be loaded into must be empty.graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()onnxruntime::Graph::InitializeStateFromModelFileGraphProtoGraph ctor should have created NodeArg for initializer. Missing:Move it out of graph inputs if there is no need to override it, This may prevent some of the graph optimizations, like const folding. appears in graph inputs and will not be treated as constant value/weight. Initializer This is an invalid model. Error: Duplicate definition of name (onnxruntime::Graph::SetOuterScopeNodeArgsInvalid node indexes specified when adding edge.onnxruntime::Graph::AddEdgenode_arg) does not exist in the graph.This is an invalid model. Graph output (This is an invalid model. Error: two nodes with same node name (onnxruntime::Graph::RemoveEdgeInvalid source node arg slot specified when removing edge.Invalid destination node arg slot specified when removing edge.Argument mismatch when removing edge.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 removing edge.!foundonnxruntime::Graph::RemoveInitializedTensor_token_onnxruntime::Graph::SaveToOrtFormat'was added but does not exist. Outer scope node arg name 'onnxruntime::Graph::ToGraphProtoInternal'. It is not used by any node and should be removed from the model. as it still has output edges.Can't remove node node->GetOutputEdgesCount() == 0onnxruntime::Graph::RemoveNodenodes_.size() < static_cast<unsigned int>(std::numeric_limits<int>::max())onnxruntime::Graph::AllocateNodenullptr != sub_graph && nullptr != sub_graph->GetMetaDef()onnxruntime::Graph::FuseSubGraphRemoving initializer 'onnxruntime::Graph::CleanUnusedInitializers'. It is no longer used by any node. must be either specified in graph inputs or graph initializers.) of output arg (Type Error: Type () does not have type information.This is an invalid model. Model input () type inference failed) output arg () does not match expected type () of node (Type Error: Shape of initializer This is an invalid model. Error in Node:onnxruntime::Graph::VerifyNodeAndOpMatch is not a registered function/op but usage of initializer in graph expects ' has element type Type Error: Data in initializer ' does not match. onnxruntime::Graph::InitInputsInitializersOutputsonnxruntime::Graph::PerformTypeAndShapeInferencingonnxruntime::Graph::ForThisAndAllSubgraphsFatal error: ) is required but not specified.) attribute (Error: Duplicate definition-site for (AddInitializedTensor already has tensor with name existing->second == &tensoronnxruntime::Graph::AddInitializedTensorgraph_proto_ is not in sync with name_to_initial_tensor_.onnxruntime::Graph::ResolveShouldn't be possible to have NodeArgs that haven't been handled already.outer_scope_node_args_consumed.empty() but different TensorProto.onnxruntime::Graph::InlineFunctionInput to set must exist.input->Exists()onnxruntime::Graph::SetInputs's number of inputs is different from function body graph's number of input.'s number of outputs is different from function body graph's number of outputs.node.MutableOutputDefs().size() == subgraph.GetOutputs().size()onnxruntime::Graph::LoadFromOrtFormatNodeArg Name is missing. Invalid ORT format model.onnxruntime::Graph::LoadFromOrtFormat::<lambda_8e630da33c629a21b7e03b5c475b46b7>::operator ()Satisfied, but should not be: nullptr == fbs_value_info +Node is missing. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_node +NodeEdge is missing. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_node_arg_name +Initializer tensor is missing. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_tensor +NodeArg is missing. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_node_edge +index < data_.size()onnxruntime::ConstPointerContainer<class std::vector<class onnxruntime::NodeArg *,class std::allocator<class onnxruntime::NodeArg *> > >::atC:\a\5\s\include\onnxruntime\core/common/const_pointer_container.h8NDomain already set in registryonnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSetC:\a\5\s\onnxruntime\core\graph\schema_registry.cconnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSchemaInternalknown by the checker.than the operator set version , but it its version is higher is till opset is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset onnxruntime::Model::ModelC:\a\5\s\onnxruntime\core\graph\model.ccMissing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.Unknown model file format version.onnxruntime::model_load_utils::ValidateOpsetForDomain is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain ModelProto does not have a graph.Failed to load model with error: No graph was found in the protobuf.onnxruntime::Model::Load model may run depending upon legacy support of some older opset version operators.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 onnxruntime::Model::SaveProtobuf serialization failed.onnxruntime::Model::SaveToOrtFormatModel must have opset imports. Invalid ORT format model.Protobuf parsing failed.<p_fd> less than 0.<p_fd> is less than 0.Graph is null. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_graph +onnxruntime::Model::LoadFromOrtFormatSatisfied, but should not be: nullptr == fbs_op_set_ids +opset import domain is null. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_domain +Load model system error number onnxruntime::SaveModel failed. File doesn't existC should specify a shapeNegative values are not allowed in a shape specificationIncompatible dimensionsAttribute Input data type does not match the expected data type. Current data type is Scale and Zero-point must be a scalar'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Pads has incorrect number of valuesInput data type does not match the expected data typeFirst operand.A_scaleInput A zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.inputs are expected to have tensor type. Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} -{additionalDocumentation}Input A's scale. It's a scalar, which means a per-tensor/layer quantization.A_scaleInput A zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.A_zero_pointInput B's scale. It's a scalar, which means a per-tensor/layer quantization.B_scaleInput B zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.B_zero_pointC_scaleOutput zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.C_zero_pointConstrain input and output types to 8 bit signed and unsigned tensors.Inputs 0 shall be 3 dimensionsInputs 4 shall be 5 dimensionsInputs 0 shall be 2 dimensionsword_embedding should have 2 dimensions and dimension size is known.D:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\graph\contrib_ops\contrib_defs.ccNumber of attention headsWhether every token can only attend to previous tokens. Default value is 0.3D input tensor with shape (batch_size, sequence_length, hidden_size), hidden_size = num_heads * head_size2D input tensor with shape (hidden_size, 3 * hidden_size)1D input tensor with shape (3 * hidden_size)Attention mask with shape (batch_size, past_sequence_length + sequence_length), or index with shape (batch_size) or (2 * batch_size).mask_indexpast state for key and value with shape (2, batch_size, num_heads, past_sequence_length, head_size).past3D output tensor with shape (batch_size, append_length, hidden_size)present state for key and value with shape (2, batch_size, num_heads, past_sequence_length + sequence_length, head_size)presentConstrain mask index to integer typesscale of quantized input tensor. It's a scalar, which means a per-tensor/layer quantization.input_scalescale of weight scale. It's a scalar, which means a per-tensor/layer quantization.weight_scaleAttention mask index with shape (batch_size)zero point of quantized input tensor.It's a scalar, which means a per-tensor/layer quantization.input_zero_pointzero point of quantized weight tensor. It's a scalar, which means a per-tensor/layer quantization.weight_zero_point3D output tensor with shape (batch_size, sequence_length, hidden_size)Constrain input and output types to int8 tensors.2D words IDs with shape (batch_size, sequence_length)input_ids2D segment IDs with shape (batch_size, sequence_length)segment_ids2D with shape (,hidden_size)word_embedding2D with shape (, hidden_size)position_embeddingsegment_embedding1D 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 typesConstrain input and output float tensors types.input tensorbias tensoroutput tensorConstrain 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)skip1D input tensor with shape (hidden_size)1D skip tensor with shape (hidden_size1D bias tensor with shape (hidden_sizeUSaved mean used during training to speed up gradient computationSaved inverse standard variance used during training to speed up gradient computation.inv_std_varConstrain mean and inv_std_var to float tensors.input_as_shapeextra_shapeInput's shape must be 4-D'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)) + bottom_border () needs to be greater than or equal to the top_border () + right_border () needs to be greater than or equal to the left_border () + scale[0] () + scale[1] (Input axis is invalid: 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 int64first input tensor has wrong dimensionrois input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensioncrop_size shape input tensor has wrong dimensionValue of alphaValue of beta1D output tensorBias applied to each channel, same size as C.The scale to apply.Input tensor of shape [N,C,H,W]Result, has same shape and type as inputA 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.1-D tensor of ending indices (exclusive) of corresponding axis in axesThe shape of filled tensorThe filled tensorInput data to be scaledOutput data after scalingBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.drop_statesThe previous GRU hidden state.hidden_prevUnactivated gate outputs from forget, update, and output gates, pre-activation.gatesArray of sequence lengths. len(seq_lengths) should equal batch size N.seq_lengthsThe timestep for this operation.The new GRU hidden state calculated by this op.hiddenIf 1, mean and variance are computed across channels. Default is 0.If 0, normalize the mean only. Default is 1.Scaling valueThe scaled hyperbolic tangent values of the input tensor computed element-wiseConstrain input0 and output types to float tensorsRfftsignal_ndimnormalizedonesidedIrfftComplexMulinput_0input_1ComplexMulConjPadsConstrain input and output types to float tensorsactivation_gammaSpecified axis to insert a dimensionThe 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.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'.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'.Constrain 'x' and 'x_zero_point' to 8-bit integer tensors.Constrain 'y', 'x_scale' to float tensors.Strings to tokenizeTokenized stringsInput/Output is a string tensorBoolean 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 ignoredConstrain input A data types as 16-bit integer tensorConstrain input B data types as 16-bit integer tensorConstrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16).Scale of quantized input 'B'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.1D input tensor, whose dimension is same as B's last dimensionConstrain input A, b_scale and output Y data type as float tensor.Scale of quantized input 'A'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'.Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'.Constrain input a_scale, b_scale and output Y data type as float tensor.Whether A should be transposed on the last two dimensions before doing multiplicationWhether B should be transposed on the last two dimensions before doing multiplicationMatrix multiply resultsReduceSumIntegerConstrain 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).C = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointQLinearMulC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointQLinearReduceMeanInput scale. It's a scalar, which means a per-tensor/layer quantization.data_scaleInput zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.data_zero_pointreduced_scalereduced_zero_pointConstrain input types to 8 bit signed and unsigned tensors.MulIntegerConstrain output to 32 bit tensorConstrain output types to 32 bit tensors.QLinearAveragePoolConstrain input and output types to 8 bit tensors.Input X's scale. It's a scalar, which means a per-tensor/layer quantization.X_scaleInput X's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.X_zero_pointOutput Y's scale. It's a scalar, which means a per-tensor/layer quantization.Y_scaleOutput Y's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Y_zero_pointAn input tensor to hash.32-bit hash value.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.Tensor of rank q-1+r-indices[-1].Constrain indice type to int32 or int64Integer representing the embedding vector size for each word.If not provide, use the fileter size of conv weightThis 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 embeddingSequenceSpecify weights of convSpecify bias of convSpecify embedding vector of charConstrain to tensor(int32).Constrain to tensor(float).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 arrayTensor 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).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'idxA 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".2D matrix with shape (M,N)2D matrix with shape (K,N)A 2D Matrix that represents the distance between each pair of the two collections of inputs.The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.Value used for extrapolation, when applicable. Default is 0.0f. 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 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.crop_sizeRoI 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].The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).Input data tensor from the previous layer.Scale tensor.Bias tensor.Output data tensor.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 normal input data.The bias input data that is a 1D tensor.Input tensor. Every matrix in the batch must be invertible.Output tensor of the same type and shape as the input tensor.HV@B@BBB$$VBBBB`$HV@B@B0BB$$VBBBB$$HVPBPBBB$$V B BpBB$$HVBBpBpB$$VPBPB@BB$$HV0B0B`B$$VBBp@B$$HVBBBB$$VBBBB$$HVBBpB`B$$VBBBB$$HVBBBB$$VBBBB$$HVBB B$$VBB0BPB$$H V B BBB$$ VBB`B`B$$H!VBBBB$$!V`B`BB B$$H"V0B0B BB$$"VBBBB$$H#V`B`BPB B$$#VBBBB$$H$VBBBB$$$VpBpB B0B$$H%VBBBpB$$&VCCpCC$$&VCC0CC$$std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraphD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\graph\graph_utils.cc in one of the subgraphs. cannot be safely updated to Implicit input name onnxruntime::graph_utils::CanUpdateImplicitInputNameInSubgraphsNode must only have one used outputstd::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutputAttempting to get index for an input which does not exist.itr != node.InputDefs().end()onnxruntime::graph_utils::GetNodeInputIndexFromInputNameAttempting to get an input that does not exist.index >= 0 && static_cast<size_t>(index) < inputs.size()onnxruntime::graph_utils::GetNodeInputNameAttempting to get an output that does not exist.index >= 0 && static_cast<size_t>(index) < outputs.size()onnxruntime::graph_utils::GetNodeOutputNameShould be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.onnxruntime::graph_utils::RemoveNodeInitializer with same name exists. Name:!graph.GetInitializedTensor(new_initializer.name(), existing)onnxruntime::graph_utils::AddInitializer ImplicitInputs: ExplicitInputs:. Index:Invalid input index for node onnxruntime::graph_utils::ReplaceNodeInputCan only add a new input at the end of the current ones.num_explicit_inputs == static_cast<size_t>(target_input_idx)onnxruntime::graph_utils::AddNodeInputFailed since multiple edges matched:onnxruntime::graph_utils::FindPathRh'V@@ C`outA node with a function body within a subgraph within another function body is currently not supported in ORTonnxruntime::update_subgraphs_within_function_bodyD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\graph\function.ccfused_function_subgraphinput_arg->Type() != nullptronnxruntime::FunctionImpl::FunctionImplResolve subgraph failed:'VCCCPC$$Number of neurons in the hidden layer.Constrain seq_lens to integral tensors.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]` The weight tensor of the query layer in the attention mechanism. Should be of shape `[num_directions, am_query_depth(hidden_size of lstm), am_attn_size]` QWThe weight tensor of the memory layer in the attention mechanism. Should be of shape `[num_directions, memory_depth, am_attn_size]` MWThe 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]` memory_seq_lensThe 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.` AWA 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]`. tensor rank too smallinvalid scales dimensioninvalid scales valueD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\graph\contrib_ops\nchwc_schema_defs.cch(VCCCpC$$(VCC@C$$h)VCC0CC$$)VCCp`C$$h*VpCpC ^@C$$*VCCCPC$$Can not get shape initializer data!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 1.1-D Tensor of the range.h+VCCCC$$Unsupported non-raw-data data type!ExceptionReturnHrLogHrFailFast%hs(%u)\%hs!%p: %hs!%p: (caller: %p) %hs(%d) tid(%x) %08X %ws Msg:[%ws] CallContext:[%hs] [%hs(%hs)] -[%hs] - -kernelbase.dllRaiseFailFastException+VpCC%wsstd::exception: %hs,V&DCD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\platform\windows\env.ccSetThreadDescriptionkernel32.dllonnxruntime8-VCpDDDDD%D%D?DpDD &D0%D -D@ -DPCPDDD D D@DD D`DP!DED`DpD -D`:pDFatal error: 0 count processors from GetSystemInfoonnxruntime::`anonymous-namespace'::WindowsEnv::GetNumCpuCoresFatal error: 0 count processors from GetLogicalProcessorInformation fail, errcode = GetFileSizeEx Invalid fd was supplied: Received negative size from stat callonnxruntime::`anonymous-namespace'::WindowsEnv::ReadFileIntoBufferNot satisfied: file_path -Not satisfied: offset >= 0 -Not satisfied: length <= buffer.size() -open file SetFilePointerEx ReadFile fail: unexpected endMapFileIntoMemory is not implemented on Windows.\/, error code: DeleteFile() failed - path: onnxruntime::`anonymous-namespace'::WindowsEnv::DeleteFolderRemoveDirectory() failed - path: GetFinalPathNameByHandle() failed: onnxruntime::`anonymous-namespace'::WindowsEnv::GetCanonicalPathNot satisfied: result_length > 0 -UNC\Failed to load libraryFailed to unload libraryFailed to find symbol in libraryonnxruntime::`anonymous-namespace'::WindowsEnv::FormatLibraryFileNameGetSystemTimePreciseAsFileTimeSUCCESSFAILNO_SUCHFILENO_MODELENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFMODEL_LOADEDNOT_IMPLEMENTEDINVALID_GRAPHEP_FAILGENERAL ERRORcode != static_cast<int>(common::OK)onnxruntime::common::Status::StatusD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\common\status.ccOKSystemError[ONNXRuntimeError]length overflowonnxruntimeSystemISink must be provided.default_logger_id must be provided if instance_type is InstanceType::DefaultOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.Default logger already set. N@QQSessionNodeenabled_onnxruntime::profiling::Profiler::StartTimeD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\common\profiler.ccsession_logger != nullptronnxruntime::profiling::Profiler::InitializeMaximum number of events reached, could not record profile event.onnxruntime::profiling::Profiler::EndTimeAndRecordEventWriting profiler data to file onnxruntime::profiling::Profiler::EndProfiling[ -",{"cat" : ""pid" :"tid" :"dur" :"ts" :"ph" : "X","name" :""args" : {"" : "} -}, -] -.V D}   D.VPDDpDPDDDPDdegree_of_parallelism >= 1onnxruntime::concurrency::ThreadPool::ThreadPoolD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\common\threadpool.ccfn != nullptronnxruntime::concurrency::ThreadPool::Scheduleonnxruntime::concurrency::ThreadPool::RunInParalleln >= 0onnxruntime::concurrency::ThreadPool::ParallelFor/VDDD0D`$00V@D@DDD$$0VDD`DD`$01VDDЭD D4!$1V`D`DPDD$${Gz????@@@j@A02VDJJ DDD0DDVIWEF [ is not a valid dateampm is not a valid day is not a valid year -N -N\/\Failed to parse path root: onnxruntime::`anonymous-namespace'::ParsePathRootD:\a\ml-builds\ml-builds\onnxruntime\onnxruntime\core\common\path.ccNot satisfied: has_trailing_separator -onnxruntime::Path::Parseunexpected failureillegal input path:2V`     %D%D                   D    `\83VDDDDpDDDD@DDPD3VDDPD4VPR PDAQ5@.N3;A8X} <2?~>++{16?:>=4?~??++Q5@.{1N36;A8?:X} <>=2?~>4?~?A++++++++Q5@.Q5@.Q5@.Q5@.{1{1{1{1N3N3N3N36666;A8;A8;A8;A8?:?:?:?:X} <X} <X} <X} <>=>=>=>=2?~>2?~>2?~>2?~>4?~?4?~?4?~?4?~?AAAAA%~3a*7A\3JCy7 ':Y;Ӡ5֕8;Y;AAAAA33{@l?A ;Eۼ# =pu>7fȹ~;ƼX=P"?>?B;?r1mL:e9 <*=*>>??@K57f9A :mL:~;;e9 <<E<*=X=# =u>>*>p>>P"?r1?l?;?33{@@K7777mL:mL:mL:mL:~;~;~;~;;;;;e9 <e9 <e9 <e9 <*=*=*=*=X=X=X=X=# =# =# =# =u>u>u>u>>>>>*>*>*>*>>>>>P"?P"?P"?P"?l?l?l?l?33{@33{@33{@33{@fȹfȹfȹfȹA A A A ƼƼƼƼEۼEۼEۼEۼppppr1r1r1r1rB¥B@K;?r1:n/ <*=(*>>??::::n/ <n/ <n/ <n/ <*=*=*=*=(*>(*>(*>(*>rBrBrBrBµµµ0A?@@@PcEpgEuEaEdErEaEdErEЄEEEnE@EiEyEiEyE4V@E@E`EE4!$@@@@||||88888888   - packing unavailable??05V@E@E`EE`$Unknown encoding D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\re2\re2\re2.cc': Error parsing 'Error compiling 'pattern too large - compile failedError reverse compiling 'pattern too large - reverse compile failedInvalid RE2: text size: endpos: startpos: RE2: invalid startpos, endpos pair. 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Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.B_zero_pointOutput scale. It's a scalar, which means a per-tensor/layer quantization.C_scaleA_zero_pointSecond operand.B_scalePadding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.Inputs 0 shall be 3 dimensionsOutput zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.C_zero_pointConstrain input and output types to 8 bit signed and unsigned tensors.segment_idsword_embeddingConstrain input and output types to int8 tensors.The epsilon value to use to avoid division by zero.input_idsmaskposition_embeddingsegment_embeddingoutput tensorConstrain input and output types to float or half tensors.Constrain input and output integer tensors typesConstrain input and output float tensors types.input tensorUSaved mean used during training to speed up gradient computationSaved inverse standard variance used during training to speed up gradient computation.skipC:\a\5\s\onnxruntime\core\graph\contrib_ops\contrib_defs.ccNumber of attention headsWhether every token can only attend to previous tokens. Default value is 0.Inputs 4 shall be 5 dimensionsInputs 0 shall be 2 dimensionsword_embedding should have 2 dimensions and dimension size is known.MpastweightConstrain mask index to integer typesinput_scalepresentConstrain input and output types to float tensors.input_zero_pointweight_zero_pointweight_scalerois input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensioncrop_size shape input tensor has wrong dimensionGaussian Error Linear Unit. +A high-performing neural network activation function.The GELU nonlinearity is +the expected transformation of a stochastic regularizer which randomly applies +the identity or zero map to a neuron's input. The GELU nonlinearity weights +inputs by their magnitude, rather than gates inputs by their sign as in ReLUs.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 int64first input tensor has wrong dimension + Returns the upper or lower triangular part of a 2-D matrix, or batches of 2-D matrices. If the attribute "upper" is set to true, + the upper triangular matrix is retained. Lower triangular matrix is retained otherwise. Default value for upper is true. + Trilu takes one input tensor of shape [*, N, M], where * is zero or more batch dimensions. The upper triangular part consists + of the elements on and above the given diagonal (k). The lower triangular part consists of elements on and below the diagonal. + All other elements in the matrix are set to zero. + If k = 0, the triangular part on and above/below the main diagonal is retained. + If upper is set to true, a positive k retains the upper triangular matrix excluding k diagonals above + the main diagonal. A negative k value includes as many diagonals below the main diagonal. + If upper is set to false, a positive k retains the lower triangular matrix including k diagonals above + the main diagonal. A negative k value excludes as many diagonals below the main diagonal. + Value of alphaValue of beta1D input tensorBias Gelu. +It's an extension of Gelu. It takes the sum of input A and bias input B as the input of Gelu activation. Input rank must be >= 2. != mat_h:The inner-most 2 dimensions must have the same size (mat_w:Result, has same shape and type as inputA 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.1D output tensorBias applied to each channel, same size as C.The scale to apply.Input tensor of shape [N,C,H,W]data1-D tensor of starting indices of corresponding axis in `axes`1-D tensor of ending indices (exclusive) of corresponding axis in axes1-D tensor of axes that `starts` and `ends` apply to.Threshold valueInput tensorOutput tensorTensor of data to extract slices from.Scale and bias the input image. Bias values are stored in +the same ordering as the image pixel format.Crop and image to the specified spatial dimensions. If scale is given, +then optionally start the crop offset by the left/top border amounts. +If scale is not provided, crop the borders as provided. +Produces a slice of the input tensor along multiple axes. Similar to numpy: +https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html +Slices uses `axes`, `starts` and `ends` inputs to specify the start and end +dimension for each axis in the list of axes, it uses this information to +slice the input `data` tensor. If a negative value is passed for any of the +start or end indices, it represent number of elements before the end of that +dimension. If the value passed to start or end is larger than the `n` (the +number of elements in this dimension), it represents `n`. For slicing to the +end of a dimension with unknown size, it is recommended to pass in `INT_MAX`. +If `axes` are omitted, they are set to `[0, ..., ndim-1]`. +Example 1: + data = [ + [1, 2, 3, 4], + [5, 6, 7, 8], + ] + axes = [0, 1] + starts = [1, 0] + ends = [2, 3] + result = [ + [5, 6, 7], + ] +Example 2: + data = [ + [1, 2, 3, 4], + [5, 6, 7, 8], + ] + starts = [0, 1] + ends = [-1, 1000] + result = [ + [2, 3, 4], + ] +inv_std_varConstrain mean and inv_std_var to float tensors. +Affine takes one input data (Tensor<T>) and produces one output data +(Tensor<T>) where the affine function, y = alpha * x + beta, +is applied to the tensor elementwise. + +ParametricSoftplus takes one input data (Tensor<T>) and produces one output data +(Tensor<T>) where the softplus function, y = alpha * ln(exp(beta * x) + 1), is applied to +the tensor elementwise. +Input's shape must be 4-D'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)) + bottom_border (input_as_shapeextra_shape +Scale takes one input data (Tensor<float>) and produces one output data +(Tensor<float>) whose value is the input data tensor scaled element-wise. + +GRUUnit computes the activations of a standard GRU, +in a sequence-length aware fashion. +Concretely, given the (fused) inputs X (TxNxD), the previous hidden +state (NxD), and the sequence lengths (N), computes the GRU +activations, avoiding computation if the input is invalid (as in, the +value at X[t][n] >= seqLengths[n]. +) + scale[1] (First input does not have rank 2Second input does not have rank 2Input axis is invalid: ) needs to be greater than or equal to the top_border () + right_border () needs to be greater than or equal to the left_border () + scale[0] ( +Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html +Input tensors of wrong rank (0).Incompatible dimensions for matrix multiplicationaxis must be in [-rank, rank-1]. input rank was +The linear quantization operator. It consumes a full precision data, a scale, a zero point to compute the low precision / quantized tensor. +The quantization formula is y = saturate ((x / y_scale) + y_zero_point).For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8. +For (x / y_scale), it's rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. +Scale and zero point must have same shape. They must be either scalar (per tensor) or 1-D tensor (per 'axis'). +The linear dequantization operator. It consumes a quantized data, a scale, a zero point and computes the full precision data. +The dequantization formula is y = (x - x_zero_point) * x_scale. +Scale and zero point must have same shape. They must be either scalar (per tensor) or 1-D tensor (per 'axis'). + Tokenizer divides each string in X into a vector of strings along the last axis. Allowed input shapes are [C] and [N, C]. + If the maximum number of tokens found per input string is D, the output shape would be [N, C, D] when input shape is [N, C]. + Similarly, if input shape is [C] then the output should be [C, D]. Tokenizer has two different operation modes. + The first mode is selected when "tokenexp" is not set and "separators" is set. If "tokenexp" is set and "separators" is not set, + the second mode will be used. The first mode breaks each input string into tokens by matching and removing separators. + "separators" is a list of strings which are regular expressions. "tokenexp" is a single regular expression. + Let's assume "separators" is [" "] and consider an example. + If input is + ["Hello World", "I love computer science !"] whose shape is [2], + then the output would be + [["Hello", "World", padvalue, padvalue, padvalue], + ["I", "love", "computer", "science", "!"]] + whose shape is [2, 5] because you can find at most 5 tokens per input string. + Note that the input at most can have two axes, so 3-D and higher dimension are not supported. + If "separators" contains a single empty string, the Tokenizer will enter into character tokenezation mode. This means all strings + will be broken part into individual characters. + For each input string, the second mode searches matches of "tokenexp" and each match will be a token in Y. + The matching of "tokenexp" is conducted greedily (i.e., a match should be as long as possible). + This operator searches for the first match starting from the beginning of the considered string, + and then launches another search starting from the first remained character after the first matched token. + If no match found, this operator will remove the first character from the remained string and do another search. + This procedure will be repeated until reaching the end of the considered string. + Let's consider another example to illustrate the effect of setting "mark" to true. + If input is ["Hello", "World"], + then the corresponding output would be [0x02, "Hello", "World", 0x03]. + This implies that if mark is true, [C]/[N, C] - input's output shape becomes [C, D+2]/[N, C, D+2]. +If tokenizer removes the entire content of [C]-input, it will produce [[]]. +I.e. the output shape should be [C][0] or [N][C][0] if input shape was [N][C]. +If the tokenizer receives empty input of [0] then the output is [0] if empty input +of [N, 0] then [N, 0]. +inputs are expected to have tensor type and output type should not be null.input_1ComplexMulConjWPadsonesidedIrfftComplexMulinput_0Input 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.Constrain input and output types to float tensors +The fused convolution operator schema is the same as Conv besides it includes an attribute +activation. +The FusedGemm operator schema is the same as Gemm besides it includes attributes +activation and leaky_relu_alpha.Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.activation_gammaSpecified axis to insert a dimensionExpandDims echo operator.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.Whether A should be transposedWhether B should be transposedScalar multiplier for the product of input tensors A * B.Scalar multiplier for input tensor C.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'.y_zero_pointN-D quantized output tensor. It has same shape as input 'x'.Constrain 'x', 'y_scale' to float tensors.N-D full precision Input tensor to be quantized.xScale 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'.y_scaleThe shape of filled tensorThe filled tensorvaluesInput data to be scaledSliced data tensor.Constrain input and output types to all tensor types.Constrain indices to integer typesGivenTensorFillThe previous GRU hidden state.hidden_prevUnactivated gate outputs from forget, update, and output gates, pre-activation.gatesOutput data after scalingGRUUnitBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.drop_stateshiddenPerform mean variance normalization.If 1, mean and variance are computed across channels. Default is 0.If 0, normalize the mean only. Default is 1.Array of sequence lengths. len(seq_lengths) should equal batch size N.seq_lengthsThe timestep for this operation.The new GRU hidden state calculated by this op.Constrain input0 and output types to float tensorsRfftsignal_ndimnormalizedScaling valueThe scaled hyperbolic tangent values of the input tensor computed element-wise +Sample echo operator.For internal use.Constrain input a_scale, b_scale and output Y data type as float tensor.Scalar multiplier for the product of the input tensors.Whether A should be transposed on the last two dimensions before doing multiplicationWhether B should be transposed on the last two dimensions before doing multiplicationa_scaleZero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'.a_zero_pointConstrain input A data type to 8-bit integer tensor.Reduced output tensor.reducedConstrain 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).Matrix multiply resultsReduceSumInteger +Computes the sum of the low-precision input tensor's element along the provided axes. +The resulting tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, +then the resulting tensor have the reduced dimension pruned. The above behavior is similar to numpy, +with the exception that numpy default keepdims to False instead of True.An input tensor.C = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointmultiplicationQLinearReduceMean +Computes the mean of the low-precision input tensor's element along the provided axes. +The resulting tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, +then the resulting tensor have the reduced dimension pruned. The above behavior is similar to numpy, +with the exception that numpy default keepdims to False instead of True. +Input and Output scales and zero points are used to requantize the output in a new range. +This helps to improve accuracy as after ReduceMean operation the range of the output is expected to decrease. + +``` +"Output = Dequantize(Input) -> ReduceMean on fp32 data -> Quantize(output)", + +``` +A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.Keep the reduced dimension or not, default 1 mean keep reduced dimension.C = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointadditionreduced_scalereduced_zero_pointConstrain input types to 8 bit signed and unsigned tensors.MulIntegerInput scale. It's a scalar, which means a per-tensor/layer quantization.data_scaleInput zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.data_zero_pointZero 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'.x_zero_pointN-D full precision output tensor. It has same shape as input 'x'.Constrain 'x' and 'x_zero_point' to 8-bit integer tensors.Constrain 'y_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'.x_scaleBoolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).The string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers.An optional string. Token's regular expression in basic POSIX format (pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set.an optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string.Constrain 'y', 'x_scale' to float tensors.Strings to tokenizeTokenized stringsInput/Output is a string tensorMatrix multiply results from A * BConstrain input A data types as 16-bit integer tensorConstrain input B data types as 16-bit integer tensorConstrain 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).Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignored +Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. + The production MUST never overflow. The accumulation may overflow if and only if in 32 bits.N-dimensional matrix AN-dimensional matrix B1D input tensor, whose dimension is same as B's last dimensionConstrain input A, b_scale and output Y data type as float tensor.Constrain input B data type to 8-bit integer tensor.Scale of quantized input 'A'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'.Scale of quantized input 'B'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.b_scaleZero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.b_zero_point +Given `data` tensor of rank r >= 1, and `indices` tensor of rank q >= 1, gather +slices of `data` into an output tensor of rank q - 1 + r - indices[-1]. +Example 1: + data = [[0,1],[2,3]] + indices = [[0,0],[1,1]] + output = [0,3] +Example 2: + data = [[0,1],[2,3]] + indices = [[1],[0]] + output = [[2,3],[0,1]] +Example 3: + data = [[[0,1],[2,3]],[[4,5],[6,7]]] + indices = [[0,1],[1,0]] + output = [[2,3],[4,5]] +Example 4: + data = [[[0,1],[2,3]],[[4,5],[6,7]]] + indices = [[[0,1]],[[1,0]]] + output = [[[2,3]],[[4,5]]] +Integer representing the embedding vector size for each word.If not provide, use the fileter size of conv weightThis 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.indicesTensor of rank q-1+r-indices[-1].Constrain input and output types to any tensor type.Constrain indice type to int32 or int64Specify embedding vector of charConstrain to tensor(int32).Constrain to tensor(float).The WordConvEmbedding takes in a batch of sequence words and embed each word to a vector.Specify batchs of sequence words to embeddingSequenceSpecify weights of convSpecify bias of convTensor after padding. + Given `data` tensor, pads, mode, and value. + Example: + Insert 0 pads to the beginning of the second dimension. + data = [ + [1.0, 1.2], + [2.3, 3.4], + [4.5, 5.7], + ] + pads = [0, 2, 0, 0] + output = [ + [ + [0.0, 0.0, 1.0, 1.2], + [0.0, 0.0, 2.3, 3.4], + [0.0, 0.0, 4.5, 5.7], + ], + ] + 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'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 arrayInput tensor.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. + Finds all the unique values (deduped list) present in the given input tensor. + This operator returns 3 outputs. + The first output tensor 'uniques' contains all of the unique elements of the input, + sorted in the same order that they occur in the input. + The second output tensor 'idx' is the same size as the input and it contains the index + of each value of the input in 'uniques'. + The third output tensor 'counts' contains the count of each element of 'uniques' in the input. + Example: + input_x = [2, 1, 1, 3, 4, 3] + output_uniques = [2, 1, 3, 4] + output_idx = [0, 1, 1, 2, 3, 2] + output_counts = [1, 2, 2, 1] + 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".2D matrix with shape (M,N)A 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques'idxA 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x'countsQLinearAveragePoolWhether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.The size of the kernel along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.Performs element-wise binary quantized multiplication (with Numpy-style broadcasting support). +"This operator supports **multidirectional (i.e., Numpy-style) broadcasting**" +The output of this op is the int32 accumulated result of the mul operation + +``` +C (int32) = (A - A_zero_point) * (B - B_zero_point) +``` + +Constrain output to 32 bit tensorConstrain output types to 32 bit tensors. + QLinearAveragePool consumes an input tensor X and applies average pooling across + the tensor according to kernel sizes, stride sizes, and pad lengths. + average pooling consisting of computing the average on all values of a + subset of the input tensor according to the kernel size and downsampling the + data into the output tensor Y for further processing. The output spatial shape will be following: + ``` + output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1) + ``` + or + ``` + output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1) + ``` + if ceil_mode is enabled + + ``` + * pad_shape[i] is sum of pads along axis i + ``` + + `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following: + ``` + VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i]) + SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) + ``` + And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: + ``` + pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i] + ``` + +The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero). + +Input and output scales and zero points are used to convert the output to a new quantization range. +Output = Dequantize(Input) -> AveragePool on fp32 data -> Quantize(output) + +QLinearLeakyRelu takes quantized input data (Tensor), an argument alpha, and quantize parameter for output, +and produces one output data (Tensor<T>) where the function `f(x) = quantize(alpha * dequantize(x)) for dequantize(x) < 0`, +`f(x) = quantize(dequantize(x)) for dequantize(x) >= 0`, is applied to the data tensor elementwise. +Coefficient of leakage.Input X's scale. It's a scalar, which means a per-tensor/layer quantization.X_scaleWhether to use ceil or floor (default) to compute the output shape.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is usedConstrain input and output types to 8 bit tensors.Output Y's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Y_zero_point +QLinearSigmoid takes quantized input data (Tensor), and quantize parameter for output, and produces one output data +(Tensor<T>) where the function `f(x) = quantize(Sigmoid(dequantize(x)))`, is applied to the data tensor elementwise. +Wwhere the function `Sigmoid(x) = 1 / (1 + exp(-x))` The underlying implementation is MurmurHash3_x86_32 generating low latency 32bits hash suitable for implementing lookup tables, Bloom filters, count min sketch or feature hashing.Input X's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.X_zero_pointOutput Y's scale. It's a scalar, which means a per-tensor/layer quantization.Y_scaleSeed 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.Tensor of rank r >= 1.Tensor of rank q >= 1.An input tensor to hash.32-bit hash value.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.Constrain input and output types to all numeric tensors and bool tensors.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.rois2D matrix with shape (K,N)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'.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. + Extracts crops from the input image tensor and resizes them using bilinear sampling or nearest neighbor sampling + (possibly with aspect ratio change) to a common output size specified by crop_height and crop_width. + Returns a tensor with crops from the input image at positions defined at the bounding box locations in boxes. + The cropped boxes are all resized (with bilinear or nearest neighbor interpolation) to + a fixed size = [crop_height, crop_width]. The result is a 4-D tensor [num_boxes, crop_height, crop_width, depth]. + The resizing is corner aligned.1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.batch_indices1-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.crop_sizeOutput data tensor.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 input data as Tensor.The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).Input data tensor from the previous layer.Scale tensor.Bias tensor.Output tensor of the same type and shape as the input tensor.Boolean. Indicates whether upper or lower part of matrix is retained. Default is true.Input tensor of rank 2 or higher.A 0-D tensor containing a single value corresponding to the number diagonals above or the main diagonal to exclude or include.Default value is 0 if it's not specified.The output.The normal input data.The bias input data that is a 1D tensor.Input tensor. Every matrix in the batch must be invertible.̼+̼+Attempting to get an input that does not exist.index >= 0 && static_cast<size_t>(index) < inputs.size()onnxruntime::graph_utils::GetNodeInputNameAttempting to get an output that does not exist.onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutputAttempting to get index for an input which does not exist.itr != node.InputDefs().end()onnxruntime::graph_utils::GetNodeInputIndexFromInputNameInitializer with same name exists. Name:!graph.GetInitializedTensor(new_initializer.name(), existing)onnxruntime::graph_utils::AddInitializer ImplicitInputs:index >= 0 && static_cast<size_t>(index) < outputs.size()onnxruntime::graph_utils::GetNodeOutputNameShould be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.onnxruntime::graph_utils::RemoveNodeCan only add a new input at the end of the current ones.num_explicit_inputs == static_cast<size_t>(target_input_idx)onnxruntime::graph_utils::AddNodeInput ExplicitInputs:. Index:Invalid input index for node onnxruntime::graph_utils::ReplaceNodeInputFailed since multiple edges matched:onnxruntime::graph_utils::FindPathstd::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0 Implicit input name onnxruntime::graph_utils::CanUpdateImplicitInputNameInSubgraphsNode must only have one used outputstd::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraphC:\a\5\s\onnxruntime\core\graph\graph_utils.cc in one of the subgraphs. cannot be safely updated to GRAPHSSPARSE_TENSORSPARSE_TENSORStensor_typesequence_typemap_typeUNDEFINEDFLOATFLOATSINTSSTRINGSTENSORSINTSTRINGTENSORGRAPHGraph attribute value was null. Invalid ORT format model.Satisfied, but should not be: nullptr == graph +SaveAttributeOrtFormat: Unsupported attribute type: Missing dimensions for initializer. Invalid ORT format model. is missing type info.SaveValueInfoOrtFormat: value_info_proto for onnxruntime::experimental::utils::SaveInitializerOrtFormatonnxruntime::experimental::utils::SaveAttributeOrtFormatMissing raw data for initializer. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_raw_data +dim_param value with no name. Invalid ORT format model.onnxruntime::experimental::utils::LoadTensorDimensionOrtFormatonnxruntime::experimental::utils::LoadInitializerOrtFormatSatisfied, but should not be: nullptr == fbs_dims +Missing string data for initializer. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_str_data +Null value type info in fbs::SequenceType. Invalid ORT format model.onnxruntime::experimental::utils::LoadSequenceTypeOrtFormatSatisfied, but should not be: nullptr == fbs_type_info +Null value type info in fbs::MapType. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_dim_param +Null entry in dimensions. Invalid ORT format model.onnxruntime::experimental::utils::LoadTensorTypeAndShapeOrtFormatSatisfied, but should not be: nullptr == fbs_dim +Null sequence type info. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_sequence_type +Null map type info. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_map_type +onnxruntime::experimental::utils::LoadMapTypeOrtFormatNull tensor type info. Invalid ORT format model.onnxruntime::experimental::utils::LoadTypeInfoOrtFormatSatisfied, but should not be: nullptr == fbs_tensor_type +onnxruntime::experimental::utils::SaveTypeInfoOrtFormat] for nowWe do not support type [onnxruntime::experimental::utils::SaveValueInfoOrtFormatonnxruntime::experimental::utils::SaveSequenceTypeOrtFormatC:\a\5\s\onnxruntime\core\graph\graph_flatbuffers_utils.cconnxruntime::experimental::utils::SaveMapTypeOrtFormatonnxruntime::experimental::utils::SaveTensorTypeAndShapeOrtFormatSatisfied, but should not be: !value_info_proto.name().empty() +Null string attribute. Invalid ORT format model.onnxruntime::experimental::utils::LoadAttributeOrtFormatSatisfied, but should not be: nullptr == fbs_str + is not supported currentlyType:Null type info for onnxruntime::experimental::utils::LoadValueInfoOrtFormatSatisfied, but should not be: nullptr == fbs_floats +Null ints attribute. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_ints +Null strings attribute. Invalid ORT format model.Null tensor attribute. Invalid ORT format model.Null graph attribute. Invalid ORT format model.Empty graph proto from deserialization of ORT format modelNull floats attribute. Invalid ORT format model.Null tensor in tensors attribute. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_strings +Null string in strings attribute. Invalid ORT format model.Null tensors attribute. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_tensors +NNNNNNNNNNXN`NpNLNNNfused_function_subgraphinput_arg->Type() != nullptronnxruntime::FunctionImpl::FunctionImplResolve subgraph failed:outA node with a function body within a subgraph within another function body is currently not supported in ORTonnxruntime::update_subgraphs_within_function_bodyC:\a\5\s\onnxruntime\core\graph\function.ccinstartlimitCan not get shape initializer data!Constrain input and output types.Unsupported type:deltaUnsupported non-raw-data data type!invalid scales dimensiontensor rank too smallC:\a\5\s\onnxruntime\core\graph\contrib_ops\nchwc_schema_defs.ccinvalid scales valueA 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.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.Constrain seq_lens to integral tensors.Couple the input and forget gates if 1, default 0.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.Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Number of neurons in the hidden layer.initial_hinitial_cRsequence_lensMWPQWY_hmemory_seq_lensAWY_cExceptionLogHrReturnHr %hs(%d) tid(%x) %08X %wsCallContext:[%hs] Msg:[%ws] %hs(%u)\%hs!%p: FailFast(caller: %p) %hs!%p: RaiseFailFastException%ws[%hs] +[%hs(%hs)] +kernelbase.dll +SetThreadDescriptionC:\a\5\s\onnxruntime\core\platform\windows\env.cconnxruntimekernel32.dll\*C:\a\5\s\onnxruntime\core/platform/path_lib.honnxruntime::LoopDirGetFileSizeEx Received negative size from stat callInvalid fd was supplied: onnxruntime::`anonymous-namespace'::WindowsEnv::GetNumCpuCoresFatal error: 0 count processors from GetSystemInfo fail, errcode = Fatal error: 0 count processors from GetLogicalProcessorInformationopen file Not satisfied: length <= buffer.size() +ReadFile SetFilePointerEx onnxruntime::`anonymous-namespace'::WindowsEnv::ReadFileIntoBufferNot satisfied: offset >= 0 +Not satisfied: file_path +onnxruntime::`anonymous-namespace'::WindowsEnv::DeleteFolderDeleteFile() failed - path: GetFinalPathNameByHandle() failed: RemoveDirectory() failed - path: MapFileIntoMemory is not implemented on Windows. fail: unexpected end, error code: \/Failed to find symbol in library, error code: Failed to unload library, error code: onnxruntime::`anonymous-namespace'::WindowsEnv::FormatLibraryFileNameNot satisfied: result_length > 0 +onnxruntime::`anonymous-namespace'::WindowsEnv::GetCanonicalPathFailed to load library, error code: UNC\[ONNXRuntimeError]SystemErrorcode != static_cast<int>(common::OK)C:\a\5\s\onnxruntime\core\common\status.cconnxruntime::common::Status::StatusNO_SUCHFILEFAILENGINE_ERRORNO_MODELSUCCESSEP_FAILINVALID_GRAPHGENERAL ERRORINVALID_PROTOBUFRUNTIME_EXCEPTIONNOT_IMPLEMENTEDMODEL_LOADED is not a valid year is not a valid day is not a valid datepmam [C:\a\5\s\onnxruntime\core\common\logging\logging.cconnxruntime::logging::LoggingManager::LoggingManagerOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.default_logger_id must be provided if instance_type is InstanceType::DefaultonnxruntimeISink must be provided.onnxruntime::logging::LoggingManager::CreateDefaultLoggerDefault logger already set. onnxruntime::concurrency::ThreadPool::ThreadPooldegree_of_parallelism >= 1fn != nullptrC:\a\5\s\onnxruntime\core\common\threadpool.cconnxruntime::concurrency::ThreadPool::TryParallelFortotal >= 0onnxruntime::concurrency::ThreadPool::Scheduleonnxruntime::ToMBStringlength overflowonnxruntime::ToWideStringC:\a\5\s\onnxruntime\core\common\str_helper.cc"pid" :{"cat" : ""dur" :"tid" :onnxruntime::profiling::Profiler::EndProfilingWriting profiler data to file ",[ +" : ""}, +} +"ph" : "X","ts" :"args" : {"name" :"] +enabled_SessionNodeonnxruntime::profiling::Profiler::EndTimeAndRecordEventMaximum number of events reached, could not record profile event.C:\a\5\s\onnxruntime\core\common\profiler.cconnxruntime::profiling::Profiler::StartTimeonnxruntime::profiling::Profiler::Initializesession_logger != nullptr`NhNC:\a\5\s\onnxruntime\core\common\path.cconnxruntime::`anonymous-namespace'::ParsePathRootonnxruntime::Path::ParseNot satisfied: has_trailing_separator +Failed to parse path root: /\unexpected failureillegal input path:EventSetInformationadvapi32api-ms-win-eventing-provider-l1-1-0GetSystemTimePreciseAsFileTimekernel32.dllrB¥B@K;?r1:n/ <*=(*>>??33{@l?A ;Eۼ# =pu>7fȹ~;ƼX=P"?>?B;?r1mL:e9 <*=*>>??@Kbad dimensions?@@@AQ5@.N3;A8X} <2?~>++{16?:>=4?~??A%~3a*7A\3JCy7 ':Y;Ӡ5֕8;Y;packing unavailable   + ??Operator ', max=' has been deprecated since version (outputs_.size() - 1) == i!(it.GetName().empty()): failed validating the check: ONNX Schema ) in op definition.) than declared ( not in allowed output sizes.has output size not in allowed input sizes.) has output size not in range [min=) has input size Attribute ' for operator )'s output ' appeared multiple times.)'s input ) has more outputs () has more inputs ( is marked single but has an empty string in the graph' is expected to have field 'ints'' is expected to have field 'strings'' is expected to have field 'g'' is expected to have field 'floats'' is expected to have field 't'' is expected to have field 'sparse_tensor'Unrecognized attribute: Mismatched attribute type in 'Duplicate type constraint name(inputs_.size() - 1) == iRequired attribute ' has unknown expected type' is missing.' is expected to have field 'tensors'' is expected to have field 'graphs'C:\a\5\s\cmake\external\onnx\onnx\defs\traditionalml\defs.ccZThe input must be a tensor of a numeric type or string. The output will be of the same tensor type.map(int64, string)map(int64, float)The input must be an integer map to either string or float.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.Label encoder has only one output.Only one of keys_*'s can be set in label encoder.Label encoder has only one input.A value that needs replacing.Value(s) to change to.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.Value(s) to change toThe input type is a tensor of any shape.Output type is determined by the specified 'values_*' attribute.Input type is not float tensor but keys_floats is setOnly one of values_*'s can be set in label encoder.Input type is not string tensor but key_strings is setInput type is not int64 tensor but keys_int64s is setA float.A string.An integer.A list of floats.A list of strings. One and only one of 'value_*'s should be set.A list of strings. One and only one of 'keys_*'s should be set.A list of ints.A collection of weights of the model(s).A collection of intercepts.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.Input's shape should be 1D or 2DThe input must be a tensor of strings or integers, either [N,C] or [C].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.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'.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.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.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' sequenceA 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.map(string, double)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.map(int64, double)map(string, float)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.map(string, int64)The size of each input in the input listThe input type must be a tensor of a numeric type.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.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'List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.First set of probability coefficients.The number of support vectors.Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Flag indicating whether the regression is a one-class SVM or not.Support vector coefficients.The input type must be a tensor of a numeric type, either [C] or [N,C].Chosen support vectorsThresholds to do the splitting on for each node.Popularity of each node, used for performance and may be omitted.Node id for each node. Ids may restart at zero for each tree, but it not required to.Feature id for each node.The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.Tree id for each node.The index of the class list that each weight is for.The weight for the class in class_id.The id of the tree that this node is in.node id that this weight is for.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 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.The input must be a tensor of a numeric type.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'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.One of 'MAX,' 'L1,' 'L2'Weights of the intercepts, if used.The total number of regression targets, 1 if not defined.Indicates the transform to apply to the regression output vector.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Weights of the model(s).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.List of categories, ints.<br>One and only one of the 'cats_*' attributes must be defined.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'.The input must be a tensor of a numeric type, either [C] or [N,C].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'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.Child node if expression is falseFor 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.Node id for each node. Node ids must restart at zero for each tree and increase sequentially.Child node if expression is trueIndicates 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)The total number of targets.Defines how to aggregate leaf values within a target. <br>One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The index of the target that each weight is forThe weight for each targetThe id of the tree that each node is in.The node id of each weightThe 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.seq(map(string, float))seq(map(int64, float))The output will be a sequence of string or integer maps to float.The output type will be a tensor of strings or integers, and will have the same shape as the input.A list of labels.The input type must be a tensor of integers or strings, of any shape.C:\a\5\s\cmake\external\onnx\onnx\defs\traditionalml\old.ccInput element type of Output was expected to have tensor type. Got Element type of input was unknown does not match existing output type of Input type was nullInput was expected to have tensor type. Got Input was expected to have either tensor or sequence type. Got Mismatched type:Mismatched tensor element type: target= source=seq(tensor(uint8))seq(tensor(uint16))source sequence type missing element type.target sequence type missing element type.condLoop 'body' subgraph outputs should all be tensors or sequences but output Loop 'body' subgraph scan outputs should all be tensors but output If node has was Only boolC:\a\5\s\cmake\external\onnx\onnx\defs\controlflow\defs.ccGraph 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.All Tensor and Sequence typesoutputsGraph 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.tensor of int64, which should be a scalar.tensor of bool, which should be a scalar.v_final_and_scan_outputsThe 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.v_initialAn 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.final_state_and_scan_outputsThe 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.initial_state_and_scan_inputsseq(tensor(float16))seq(tensor(float))seq(tensor(int32))seq(tensor(int64))seq(tensor(int8))seq(tensor(int16))seq(tensor(uint32))seq(tensor(uint64))seq(tensor(complex128))seq(tensor(bool))seq(tensor(complex64))seq(tensor(double))seq(tensor(string))) is not equal to number of scan outputs (Number of scan output axes specified () is not equal to number of scan inputs (Number of scan input axes specified ( is invalid for a tensor of rank axis value then_branch and else_branch produce different number of outputs. but subgraphs produce was notScan 'body' subgraph outputs should all be tensors but output outputs. Expected Graph attribute inferencing returned type information for was not a tensor.Scan input All Tensor typesAn 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].Int64 tensorAn 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).Constrain 'x' to float tensor.(Optional) The axis of the dequantizing dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input)Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.X_Max_AdjustedX_RangeX_Min_AdjustedX_MaxQ_MaxX_MinConstrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.Q_MinZeropointClipped_ZeroPoint_FPRounded_ZeroPoint_FPMin_ScaledInitial_ZeroPoint_FPConstrain 'y_zero_point' and 'y' to 8-bit integer tensor.C:\a\5\s\cmake\external\onnx\onnx\defs\quantization\defs.cc(Optional) The axis of the quantization dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input)Constrain 'x' to float or int32 tensor.Constrain input types. Casting from complex is not supported.The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoInvalid Target shape product of 0Dimension could not be inferred: incompatible shapesInvalid position of 0Invalid dimension value: Target shape may not have multiple -1 dimensionsConstrain output types. Casting to complex is not supported.C:\a\5\s\cmake\external\onnx\onnx\defs\tensor\defs.ccConstrain output to int64 tensor.Input tensor can be of arbitrary type.reshapedAll inputs to Concat must have same rankWhich axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..Required attribute axis is missingaxis must be in [-rank, rank-1].Constrain output to int64 tensor, which should be a scalar though. should be of integer type and specify a type. does not specify a valid type. not specifiedValue of attribute updatesWhich axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).transposedaxis must be in [-r, r-1]Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).data tensor must have rank >= 1Input and output types can be of any tensor type.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).expandedvalues in 'axes' are beyond the bounds of the computed output shapeList 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).'axes' attribute must not contain any duplicatessqueezedInvalid value of attribute 'axis'. Rank=) and outputs ( Value=concat_resultConstrain output types to any tensor type.inputsWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).length of each output. Values should be >= 0.Mismatch between the sum of 'split' (The input is not evenly splittableMismatch between number of splits () and the split dimension of the input (Input axes has invalid data'step' cannot be 0Input axes has incorrect lengthInput steps has incorrect lengthSlice op must have either three, four or five inputs.Incorrect or missing input value for starts and endsOnly supports `int32_t` or `int64_t` inputs for starts/ends/axes/steps}, input shape = {A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.Invalid attribute perm {stepsIndices tensor must have rank >= 1sizesConstrain roi type to float or double.OneHot node must have three inputs.Constrains to boolean tensors.condition'axis' must be in [-rank(indices), rank(indices)-1](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).depth(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 'values' must be rank 1 tensor.Input 'values' must have exactly two elements.Input 'depth' must be a scalar or rank 1 tensor.Input 'depth' must have exactly one element.(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.Constrain input types to float tensors.Constrain output types to boolean tensors.Constrain to any tensor type.DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.Input tensor must be 4-dimensionalBlocks of [blocksize, blocksize] are moved.Blocksize must be positiverepeats'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.Constrain input 'X' and output 'Y' to all tensor types.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'.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)Constrain repeat's type to int64 tensors.roiFour 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.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/> + +if coordinate_transformation_mode is "half_pixel", <br/> +x_original = (x_resized + 0.5) / scale - 0.5, <br/> + +if coordinate_transformation_mode is "pytorch_half_pixel", <br/> +x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0, <br/> + +if coordinate_transformation_mode is "align_corners", <br/> +x_original = x_resized * (length_original - 1) / (length_resized - 1), <br/> + +if coordinate_transformation_mode is "asymmetric", <br/> +x_original = x_resized / scale, <br/> + +if coordinate_transformation_mode is "tf_half_pixel_for_nn", <br/> +x_original = (x_resized + 0.5) / scale, <br/> + +if coordinate_transformation_mode is "tf_crop_and_resize", <br/> +x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).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).Constrains input and output to only numeric types.constant_valueSupported modes: `constant`(default), `reflect`, `edge`'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64Constrain to boolean tensors.(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.(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.'input' must have rank >= 2'sequence_lens' must have rank of 1Constrain to all tensor types.(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).Invalid value for attribute axis(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]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` tensorinverse_indicesConstrain input and output types to all numeric tensors.The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).C:\a\5\s\cmake\external\onnx\onnx\defs\reduction\defs.ccsumConstrain input and output types to high-precision and 8 bit numeric tensors.Constrain input and output types to high-precision numeric tensors.A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).Attribute pads has incorrect sizeSecond input tensor has wrong dimensionAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedAttribute dilations has incorrect sizeAttribute strides has incorrect sizeInput tensor must have atleast 2 dimensionsDilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.The storage order of the tensor. 0 is row major, and 1 is column major.C:\a\5\s\cmake\external\onnx\onnx\defs\nn\defs.ccConstrain input and output types to float and 8 bit tensors.Attribute kernel_shape has incorrect size.Attribute kernel_shape must be specified.Attribute pads has incorrect size.Attribute strides has incorrect size.MaxUnpool op must have either two or three inputs.Input tensor X must have atleast 2 dimensions.IndicesConstrain index tensor to int64p value of the Lp norm used to pool over the input data.'output_shape' must be rank 1 tensor.'output_shape' must have same number of elements as the shape of input tensor X.This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. +Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.number of groups input channels and output channels are divided into. default is 1.The shape of the convolution kernel. If not present, should be inferred from input 'w'.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.Constrain output type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.Constrain filter type to 8-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain output y data type to 32-bit integer tensor.Constrain input x and its zero point data type to 8-bit integer tensor. +Carries out batch normalization as described in the paper +https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, +there are multiple cases for the number of outputs, which we list below: + +Output case #1: Y, mean, var, saved_mean, saved_var (training mode) +Output case #2: Y (test mode) + +For previous (depreciated) non-spatial cases, implementors are suggested +to flatten the input shape to (N x C*D1*D2 ..*Dn) before a BatchNormalization Op. +Additional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate padsvarFactor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).momentumROI pool output shape (height, width).Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.Attribute pooled_shape has incorrect lengthAttribute pooled_shape must be specifiedInput tensor must have at least 2 dimensionsRoIs tensor must have 2 dimensionsThe shape of the convolution kernel. If not present, should be inferred from input W.weight and zero_point pair is expected to have same type.input and zero_point pair is expected to have be same type.number of groups input channels and output channels are divided into.dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.w_zero_pointw_scalewThe number of channels to sum overScaling parameter.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). Constrain input and output to all tensor types.Invalid value(ngram_indexes must be non-empty with no negative valuesInput tensor must have rank 1 or 2Constrain input and output types to float tensors.The exponent.List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.Maximum n-gram length. If this value is 3, 3-grams will be used to generate the output.Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.Input is ether string UTF-8 or int32/int641-D tensor of floatsstring enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"Input shape must have either [C] or [1,C] dimensions where C > 0The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.saved_varsaved_meanThe axis on which to apply normalization, -1 mean last axis.The order of the normalization, only 1 or 2 are supported.training_moderatiotraining_mode of Dropout must be a scalar.(Optional) Seed to the random generator, if not specified we will auto generate one.Ratio of Dropout must be a scalar.) for attribute 'axis'The lambd value for the Shrink formulation. Default is 0.5.The bias value added to output. Default is 0.Constrain output 'mask' types to boolean tensors.Constrain input 'ratio' types to float tensors.EpsilonX_RMA list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation.Boolean. Whether the identification of stop words in X is case-sensitive. Default is falseList of stop words. If not set, no word would be removed from X.Processed_STDSTDX_varianceE_XsquaredVarianceEX_squaredX_squaredConstrain input and output types to signed numeric tensors.Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentThe Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.Elu_ResultX_alphaCoefficient of ELU.Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).CeluConstrain input and output types to float32 tensors.C:\a\5\s\cmake\external\onnx\onnx\defs\math\defs.ccDescribes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).'axis' must be in []. Its actual value is: KAxis has less than the requested k elements.K input must be of type int64.K input must be a one-dimensional tensor of size 1.Whether to return the elements in sorted order.Whether to return the top-K largest or smallest elements.Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).ValuesConstrain input and output types to all tensors.'shape' input must be 1D tensor of type INT64Constrain input Y types to float/int tensors.Constrain input X and output types to float/int tensors. +Pow takes input data (Tensor<T>) and exponent Tensor, and +produces one output data (Tensor<T>) where the function `f(x) = x^exponent`, +is applied to the data tensor elementwise. +data_0List of tensors for Value of beta.Value of alpha.slopeConstrain input and output types to numeric tensors.axis tensor can be int32 or int64 onlyIf set to 1 will perform the sums in reverse direction.If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.input_gather_elementignore_indexexpanded_targettargetconst_oneconst_zeroConstrain input and output types to floating-point tensors.loss_unweightedweight_gatherloss_Nddlossreductionnoneloss_N1ddloss_NCddinput_gather_element_transformconst_zero_floattransform_targetsexpanded_target_int64const_zero_target_typedconst_ignore_indexweight_gather_sumloss_sumainput and zero_point pair is expected to have same type.bConstrain output Y data type as 32-bit integer tensor.Constrain output y and its zero point data type to 8-bit integer tensor.Constrain input b and its zero point data type to 8-bit integer tensor.Constrain input a and its zero point data type to 8-bit integer tensor.labelslog_probX_LogX_DivSoftmaxCrossEntropyLossWeight rank must be 1.Input and target dimension value mismatch.Target rank must be 1 less than the input rank.weight_gather_temp_1weight_gather_tempconst_one_floatsqueeze_maskNegativeLogLikelihoodLossConstrain target to integer typesConstrain input, weight, and output types to floating-point tensors.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.Type of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.InputsEinsum expression string.Rank of input does not match the equation indices.Ellipsis represents incompatible dimensions.Number of input tensors does not match the operands in the equation.X_RSX_ExpX_SubscoresType of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.Constrain input and output types to all numerical tensor types.OutputGreaterOrEqualC:\a\5\s\cmake\external\onnx\onnx\defs\logical\defs.ccConstrains output to boolean tensor.Constrains input to boolean tensor.Constrains input/output to boolean tensors.Constrains input types to all numeric tensors.LessOrEqualDirection of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).Constrain input and output types to integer tensors.Attribute expected to have a one-dim tensorAttribute expected to have tensor typevalue_intsparse_valueConstrain output types. Strings and complex are not supported.Constrain input types. Strings and complex are not supported.(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'.(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.Input tensor must be 2-dimensionalThe mean of the normal distribution.Constrain output types to float tensors.The shape of the output tensor.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.Upper boundary of the output values.Lower boundary of the output values.(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.The standard deviation of the normal distribution.Constrain output types to integral tensors.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Number of times to sample.Input tensor must have rank 2Output type must be int32 or int64Attribute 'value_ints' expect a list of integers.Attribute 'value_int' expect an integer.One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.value_stringsvalue_stringvalue_floatsvalue_floatvalue_intsThe value for the sole element for the scalar, int64, output tensor.The value for the elements of the output tensor in sparse format.The value for the elements of the output tensor.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.Attribute 'value_strings' expect a list of strings.Attribute 'value_string' expect a string.Attribute 'value_floats' expect a list of floats.Attribute 'value_float' expect a float.C:\a\5\s\cmake\external\onnx\onnx\defs\generator\defs.ccThe values for the elements for the 1D, UTF-8 string, output tensor.The value for the sole element for the scalar, UTF-8 string, output tensor.The values for the elements for the 1D, float32, output tensor.The value for the sole element for the scalar, float32, output tensor.The values for the elements for the 1D, int64, output tensor.Constrain output types to be numerics.Constrain input types.(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 float32Invalid shape value: Shape input must be a one-dimensional tensor.Input to 'Range' op should be scalars (Tensor with only one element and shape empty)sub_result_castedsub_resultrangecurrentcond_outpreviloop_body_attributeAll inputs to 'Range' op must be of the same typevariadic_outputceil_result_relu_boolceil_result_relu_intceil_result_reluceil_resultdiv_resultdelta_castedConstrain input types to common numeric type tensors.This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).If set, defines the broadcast dimensions. See doc for details.consumed_inputslegacy optimization attribute.broadcastPass 1 to enable broadcasting +If necessary the right-hand-side argument will be broadcasted to match the +shape of left-hand-side argument. When broadcasting is specified, the second +tensor can either be of element size 1 (including a scalar tensor and any +tensor with rank equal to or smaller than the first tensor), or having its +shape as a contiguous subset of the first tensor's shape. The starting of the +mutually equal shape is specified by the argument "axis", and if it is not set, +suffix matching is assumed. 1-dim expansion doesn't work yet. + +For example, the following tensor shapes are supported (with broadcast=1): + + shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor + shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor + shape(A) = (2, 3, 4, 5), shape(B) = (5,) + shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) + shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 + shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 + +Attribute `broadcast=1` needs to be passed to enable broadcasting. +Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizeC:\a\5\s\cmake\external\onnx\onnx\defs\math\old.ccCoefficient of SELU default to 1.0507.Coefficient of SELU default to 1.6732.Coefficient of leakage default to 0.01.Value of beta default to 0.5Value of alpha default to 0.2Coefficient of ELU default to 1.0.Scalar multiplier for the product of input tensors A * B, the default value is 1.0.Whether C should be broadcastedMaximum value, above which element is replaced by maxMinimum value, under which element is replaced by minDimension on which to do the sort.Number of top elements to retrieveInvalid value for attribute kScalar multiplier for input tensor C, the default value is 1.0.One of the attributes 'value' or 'sparse_value' must be specified for a Constant node.Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.Attribute 'value' of Constant node must exist with 'Tensor' data.C:\a\5\s\cmake\external\onnx\onnx\defs\generator\old.ccThe epsilon value to use to avoid division by zero, default is 1e-5f.is_testIf set to nonzero, run spatial batch normalization in test mode, default is 0.If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.dilation value along each spatial axis of the filter.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.(float, default 0.5) the ratio of random dropoutIf true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). Constrain output mask types to boolean tensors.The ratio of random dropoutStride along each spatial axis.C:\a\5\s\cmake\external\onnx\onnx\defs\nn\old.ccp value of the Lp norm used to pool over the input data, default is 2.0.Stride along each axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute.Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.Dilation value along each spatial axis of filter.Constrain output types. Casting to strings and complex are not supported.Constrain input types. Casting from strings and complex are not supported.C:\a\5\s\cmake\external\onnx\onnx\defs\tensor\old.ccWhich axis to split onoutputs...Which axis to concat onrank must be greater than axisWhich axis to concat on. Default value is 1.Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Ending indices (exclusive) of corresponding axis in axes`Starting indices of corresponding axis in `axes`Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Attribute axes has incorrect lengthList of non-negative integers, indicate the dimensions to be insertedList of non-negative integers, indicate the dimensions to squeeze.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Invalid value of attribute 'axis'(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.(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.'axis' must be in [-rank(indices)-1, rank(indices)]One float, indicates the value to be filled.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`.Attribute pads has incorrect lengthAttribute value for pads is requiredWhich axis to split on. New shapeOne float, indicates the value to be filled, default is 0Three modes: constant(default), reflect, edgepaddingsList 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`.length of each outputThe scale along width dimension. It takes value greater than or equal to 1.Constrain tiles and axis's type to int64 tensors.tiles) is not equal to the existing rank value (Constrain output types to bool, int32, int64, float16, float, double tensors.Two interpolation modes: nearest(default), bilinearheight_scaleThe scale along height dimension. It takes value greater than or equal to 1.width_scaleIncorrect or missing attribute value for starts and endsAttribute 'scales' is required.Attribute 'scales' must have floats type.Number of elements of attribute 'scales' must be same as rank of input 'X'Ranks inferred ( +Computes the indices of the {name} elements of the input tensor's element along the +provided axis. The resulting tensor has the same rank as the input if keepdims equal 1. +If keepdims equal 0, then the resulting tensor have the reduced dimension pruned. +The type of the output tensor is integer.The axis in which to compute the arg indices.C:\a\5\s\cmake\external\onnx\onnx\defs\reduction\old.ccInteger indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.selected_indicesscore_thresholdiou_thresholdDimension mismatch in unification between expected to have rank but has rank expected to have rank >The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.default 1; Pooled output Y's width.default 1; Pooled output Y's height.Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. max_output_boxes_per_classboxesC:\a\5\s\cmake\external\onnx\onnx\defs\object_detection\defs.ccLoop 'body' subgraph outputs should all be tensors but output C:\a\5\s\cmake\external\onnx\onnx\defs\controlflow\old.ccMismatched type for output then= else=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.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.Mismatched tensor element type for output SequenceConstruct is expected to have at least 1 input.C:\a\5\s\cmake\external\onnx\onnx\defs\sequence\defs.cc(Optional) The data type of the tensors in the output sequence. The default type is 'float'.Attribute dtype should be of integer type and specify a type.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 split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].Constrain output types to all tensor types.Constrain split size to integral tensor.Constrain input types to all tensor types.Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size=Insert and concatenate on a new axis or not, default 0 means do not insert new axis.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]`. Invalid value of attribute 'axis'. Accepted range=[], Value=new_axis must be either 0 or 1Constrain input types to any tensor type.output_sequenceElement type of inputs are expected to be the same.Input type for input at index is null. Type info is expected.tensorinput_sequenceInput Sequence and Tensor are expected to have the same elem type. Sequence= Tensor=Input Sequence and Tensor are expected to have type info. Current type is null.Input type for input at index 0 is null. Type info is expected.Constrain position to integral tensor. It must be a scalar(tensor of empty shape).position sum of split values=Input 'split' can not be empty.Only supports `int32_t` or `int64_t` inputs for splitConstrain output to integral tensor. It must be a scalar(tensor of empty shape).Constrains input to integral tensors.Constrains input to float tensors.If set, defines the broadcast dimensions.Enable broadcastingC:\a\5\s\cmake\external\onnx\onnx\defs\logical\old.ccC:\a\5\s\cmake\external\onnx\onnx\defs\object_detection\old.ccC:\a\5\s\cmake\external\onnx\onnx\defs\quantization\old.ccConstrain seq_lens to integer tensor.Number of neurons in the hidden layerFirst input tensor must have rank 3A 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.C:\a\5\s\cmake\external\onnx\onnx\defs\rnn\defs.ccOne (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.Couple the input and forget gates if 1.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.C:\a\5\s\cmake\external\onnx\onnx\defs\rnn\old.ccThe sequence output for the hidden is optional if 0. Default 0.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.fowardint64uint8uint16uint32uint64complex64complex128sparse_tensorfloatfloat16doubleint16int32tensor(seq(map(sparse_tensor(seqmapopaque is required but missing.typeelem_typekey_typevalue_type): Unrecognized type value case (value_info name: data_type is required to be non-empty.value_info' of nameField 'uint64_data) is stored externally and should not have data field.Data of TensorProto ( tensor name: , but it doesn't exist or is not accessible.) should be stored in ) is stored externally but doesn't have a location.TensorProto ( tensor name: ) is 0-element but contains data!) to UNDEFINED is not allowedsetting data_type field (tensor name: float_dataint32_datastring_dataint64_dataraw_datadouble_data + +==> Context: Attribute (name: ) should refer to attribute in parent node.nodeop_type) has zero input and zero output., type: NodeProto (name: Warning: Checker does not support models with experimental ops: ) must have a dense-rank > 0) dimensions are not positive.) must have INT64 type.) must have rank 1 or 2.) has no index values.attrtype field and data field mismatch in attribute ) should not contain more than one value field. in initializer but not in graph input + is not output of any previous nodes.' of node: +Nodes in a graph must be topologically sorted, however input 'Bad node spec: ' has been used as output names multiple times.No opset import for domain ' with domain_version of No Op registered for is deprecated in domain_version of Op registered for graph' has been used as graph input names multiple times.Graph must be in single static assignment (SSA) form, however 'TensorProto (tensor name: ) should contain one and only one value field.) should not be stored in raw_data fieldSTRING data (tensor name: ' instead of '' should be stored in field 'values of data_type 'Unrecognized data_type (tensor name: ] not in sorted order.) first dimension size does not equal NNZ.) second dimension size does not match rank of tensor.] out of range.] not in lexicographic sorted order.sparse_tensor_proto) must have rank 1.Sparse tensor values ( values, but NNZ is ) has Sparse tensor indices (] out of range [0, ) index value at position [Sparse tensor (ConstantFillopaque_typesparse_tensor_typeNOT_SET) vs (Inferred elem type differs from existing elem type: (Inferred shape and existing shape differ in rank: ( is out of boundsinput output GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. does not contain a graph.Warning: Unsupported operator Shape inference error(s): were provided inputs but Graph has , node name: (op_type:: (Inferred shape and existing shape differ in dimension inferred=type case mismatch. existing=type case unsupported. existing= models with experimental operators: Warning: Shape inference does not support. No schema registered for this operator.) is not equal to the existing dim value (Dimension value inferred (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.onnx.TensorShapeProtoonnx.TypeProto.Tensoronnx.TypeProto.Sequenceonnx.TypeProto.Maponnx.TypeProto.SparseTensoronnx.TypeProto.Opaqueonnx.TypeProtoonnx.OperatorSetIdProtoonnx.ModelProtoonnx.StringStringEntryProtoonnx.TensorAnnotationonnx.GraphProtoonnx.TensorProto.Segmentonnx.TensorProtoonnx.SparseTensorProtoonnx.TensorShapeProto.Dimensiononnx.AttributeProtoonnx.ValueInfoProtoonnx.NodeProtoonnx.TrainingInfoProtoonnx.FunctionProtolist count bytemap range DFA out of memory: size SearchDFA inconsistencySearchOnePass inconsistencySearchBitState inconsistencySearchNFA inconsistencyError reverse compiling 'pattern too large - reverse compile failedInvalid RE2: text size: endpos: startpos: RE2: invalid startpos, endpos pair. [Unknown encoding C:\a\5\s\cmake\external\re2\re2\re2.cc': Error parsing 'Error compiling 'pattern too large - compile failedUnexpected op in Regexp::Equal: no errorunexpected errorinvalid escape sequenceinvalid character classinvalid character class rangemissing ]missing )Regexp not destroyed.C:\a\5\s\cmake\external\re2\re2\regexp.ccBad reference count trailing \no argument for repetition operatorinvalid repetition sizebad repetition operatorinvalid perl operatorinvalid UTF-8invalid named capture groupNumCapturesWalker::ShortVisit calledStack not empty.C:\a\5\s\cmake\external\re2\re2/walker-inl.hWalk NULLOOOOOOOOOOOOOOshould never happenC:\a\5\s\cmake\external\re2\re2\compile.ccCompiler::Copy called!No ranges in char classMissing case in Compiler: AddFoldedRange recurses too much.C:\a\5\s\cmake\external\re2\re2\parse.ccRepetitionWalker::ShortVisit calledConcat of unknown round: RE2: unexpected op: Bad hex digit AnyBad call to ParseState::ParsePerlFlagsunhandled C:\a\5\s\cmake\external\re2\re2\nfa.cc in stepUnhandled in AddToThreadq in ComputeFirstBytecontext does not contain textBad args: nsubmatch=Unexpected opcode in short circuit: unhandled opcode: C:\a\5\s\cmake\external\re2\re2\dfa.ccDeadState in RunStateOnByteNULL state in RunStateOnByte|RunStateOnByteUnlocked failed after ResetFailed to analyze start state.Unexpected special state in RunStateOnByteStateSaver failed to restore state.RunStateOnByteUnlocked failed after ResetCachejob_.size() = njob_ = GrowStack() failed: C:\a\5\s\cmake\external\re2\re2\bitstate.cc arg Unexpected opcode: Bad arg in kInstAltMatch: Bad arg in kInstCapture: Cannot use SearchOnePass for unanchored matches.C:\a\5\s\cmake\external\re2\re2\onepass.ccUnexpected opcode in IsMatch: C:\a\5\s\cmake\external\re2\re2\prog.ccCase not handled in ComputeSimple: C:\a\5\s\cmake\external\re2\re2\simplify.ccDoCoalesce failed: r1->op() is CoalesceWalker::ShortVisit calledSimplifyWalker::ShortVisit calledDoCoalesce failed: r2->op() is Malformed repeat Simplify case not handled: OOxOOO OO OOTOOTOO(OO(O OOOO@OPO0OPO`OOPOOHO\O8O\OhOOXOOOOxOOOOOOOOOOO@OO@O(O0OO0OOXOOXOOOOOpOOlOO[:^blank:][:blank:][:^cntrl:][:cntrl:][:^digit:][:digit:][:^graph:][:graph:]\W\w[:^alnum:][:alnum:][:^alpha:][:alpha:][:^ascii:][:ascii:][:^upper:][:upper:][:^word:][:word:][:^xdigit:][:xdigit:][:^lower:][:lower:][:^print:][:print:][:^punct:][:punct:][:^space:][:space:]\D\d\S\s09AZ__az ~ + !~!/:@[`{~ 09AZazAZAZaz 09AFaf09AZ__az0909azPIPXP0IP@PHPlP3P@AP#`PPP@xPxP(PPP-PP -PP~PPH~PPhPP0aP P8vPP<bP0PЄP(PȄP49N`HP-PX P 'PL PsPp PPDPl PPEP P}P xP P@P PhP P0bP PP@P P(PZ~PL PHP PP( PaP P`IP@ PIP8 P0JP!P aP P؄P0!PȈP!P?PP!PPxP@!PaP h!PXaP +X!P('PpFP P%P P0P P?P!`BP PP P8-P P[P PP 8P PH@P PFPPP8PMP0PLP XP-P0AP@PFPPP`ZPhPVPPp-PP8~PPЊPP8FP PFPPPPPPPDPPHPP8@PP-P@bPPZPPPPPrPPHIPPaPPsPP0PP iP\+P"PP6xDPPP'QPP%P +P`6PR%P%Px]Pl#PHPENWP0{PX`#P AP#P6Px#PVP#PXxP#P6P#PVP#PP"PPnPvP8"PP#P+P"PZP0#PP#P-PH#P]P@#PP#P]P MPi#P3P$PP'P$PDP $PJP$P(~P4$PrPCBP:($Ph]P#PP%`EP#PP#P`DP#P%P#PAP`P#PPP'P&#PHP#PIP!PP!PIP"PYP!PDP0"P0-P "PIPX"PIP@"PP!PhPt!P 6P!P'P!P~PtNPFP0!P~P!PȊP!P(AP!P@P"P+P"PZPH"PDP +"PXZP"PaP"P@JP "P@P *P0"P0\PKp"PFPd"P~P"P[PqLpJPsPI"PX6P"PP"P0'PPaP"PLP"P6P$P@aP$P%P$PxVP P$P@P`]P$PPzP5@P $P@4Pp`xP>$PLP$PDPP$PhPP@$PP`$P`PX$P%P$PHPp$PP$P8JP$PP@%Px'P4%PDPT%PEPL%PFPd%PIP \%Ph-Px%PXBPp%PP$PxP$PFP%PHP$PHP%PP %PVP@N[P %PP%PLP<$PP8$PpPAdlamBengaliBatakBopomofoBhaiksukiBrailleBrahmiBuhidBugineseAnatolian_HieroglyphsAhomArmenianArabicBalineseAvestanBassa_VahBamumJavaneseInscriptional_ParthianKannadaKaithiKayah_LiKatakanaKhmerKharoshthiHanunooHanifi_RohingyaHebrewHatranImperial_AramaicHiraganaInscriptional_PahlaviInheritedLisuLinear_BLmLlLtLoLycianLuKhudawadiKhojkiLaoLLepchaLatinLinear_ALimbuCommonCoCsCopticCypriotCuneiformDeseretCyrillicCarianCanadian_AboriginalCcCaucasian_AlbanianChakmaCfCherokeeChamGranthaGothicGujaratiGreekGurmukhiGunjala_GondiHangulHanDograDevanagariEgyptian_HieroglyphsDuployanEthiopicElbasanGlagoliticGeorgianOriyaOld_TurkicOsmanyaOsagePalmyrenePahawh_HmongPcPau_Cin_HauOld_HungarianOl_ChikiOld_North_ArabianOld_ItalicOld_PersianOld_PermicOld_South_ArabianOld_SogdianRejangPsalter_PahlaviSamaritanRunicScSaurashtraShavianSharadaPePdPhags_PaPfPiPhoenicianPsPoMeMcMeetei_MayekMedefaidrinMeroitic_CursiveMende_KikakuiMiaoMeroitic_HieroglyphsMahajaniLydianMalayalamMakasarManichaeanMandaicMasaram_GondiMarchenNew_Tai_LueNdNkoNewaNoNlOghamNushuModiMnMroMongolianMyanmarMultaniNabataeanNZsZpSundaneseSoyomboSyriacSyloti_NagriTagbanwaTagalogTai_ThamTai_LeSignWritingSiddhamSkSinhalaSoSmSora_SompengSogdianTirhutaTifinaghVaiUgariticYiWarang_CitiZlZanabazar_SquareTakriTai_VietTangutTamilThaanaTeluguTibetanThai JMO`j0J'  @n_n4Mh + 89;>@DFFJPl!<Up4Vn!`l,.,0,^,$$   + + ?? 88iiª۪ߪ3ux #X_y        @ H } ~     -X _ x      `~&QTRe:;Zl[kaknn`xq  ?AJnoq/M@X`j 9 = = P P X a r  +X _ x      `~&QTRe:;Zl[kaknn`xq @[`{tt~~  @@d e ??56  d f p t ~ !%!'!)!,!1!3!M!O!_!!!!&$@$J$`$')s+v++++++.N.//00000 00070<0?00000001111 2_222X33MM!09..ϩϩ[[>?0RTfhk ;@[eppooWW  ? ? 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P -X - - - - -9 ?   UYGM@Ctu8=KO[[]]AC`l<>;;?FAEpqp$t$njojjj7k;kDkDknn^_  * . ` d f o sz   ; ; > @ I L N O  +.@@r  0FF`eghj #$C%0.4DFFh8j@j^jjjk/kckwk}kkoDoPoPoppjp|!"$$'')24799;;BBGGIIKKMOQRTTWWYY[[]]__abddgjlrtwy|~~֦4@  ` r x  Ƥ      D F H J O T c f  poK_pp0J#%')-Y[ : < > O Q W b c   -> -@ - - - - - - - - -  > > @ @ G H K L W W   A D   > @ F H J L W W >?+,1188;<VWbdgm#&)+0138UUWWaacdmr55;;=ACD$+45.0/0#$''èRS/034MM{{}},,EF,.2355>?ADGHKMWWbc57@AEE02;<>> !&&,.8899WX//>>Qo~oefmroK_pp0J#%')-Y[ : : < < A H M M Q W b c  - < < -A +> B G H K M @@ -6319,67 +6363,122 @@ p q u u - + - + - + + - -  < < ? ? A D M M V V b c   > @ F H J M U V b c  ; < A D M M b c 114:GN557799q~-0279:=>XY^`qt]_24RSrs   "'(229;VVX^``bbels|446:<<BBks,367 ,,----*0-000oot} %&ĨŨ&-GQ).1256CCLL|| /vz - - - - - -8 -: -? -? - - -$ ' FP8F'+-4ss/14467>>;<@@flpt8?BDFF^^3:==?@"%'+/79: -38;>GGQVY[068=??16::<=?EGGjj0k6koogi{BD6;luu!#$&*DJ  * . ` d f o @woK_pp0J#%')-Y[ : < > O Q W b c  +  < < > D G H K M V W b c  > D F H J M U V b c  ; < > D F H J M W W b c 114:GN557799>?q+>VY^`bdgmqt]_24RSrs   +0;U^`|4Dks$7 ,,----*0/000ort} #'Ũ&-GS)6CCLM{} /#-/@w;;gi{))]]}};;==F F ~ ~ # # # #*#*#i'i'k'k'm'm'o'o'q'q's's'u'u'''''''''''''))))))))))))))))))))))))))))#.#.%.%.'.'.).). 0 0 0 0 0 000000000000000>>6688::<<>>@@BBDDHHZZ\\^^ ==]]``cc  + ( ) / / _ _ 00  + + + + + + +( +* +0 +2 +3 +5 +6 +8 +9 < < > B G H K M Q Q +Y +\ +^ +^ +f +v +(([[{{::<<    E E } } ## +# +#)#)#h'h'j'j'l'l'n'n'p'p'r'r't't'''''''''''''))))))))))))))))))))))))))))".".$.$.&.&.(.(.B.B.00 +0 +0 0 000000000000000??557799;;==??AACCGGYY[[]];;[[__bb  oK_pp0J#%')-Y[ : : < < A H M M Q W b c  + +< +< +A +B +G +H +K +M +Q +Q p q u u - + - + - - + + -  < < > D G H K M V W b c  > D F H J M U V b c  ; < > D F H J M W W b c 114:GN557799>?q+>VY^`bdgmqt]_24RSrs   +0;U^`|4Dks$7 ,,----*0/000ort} #'Ũ&-GS)6CCLM{} / 4vz +  < < ? ? 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'@[^^7?ywx + +??<k?kEkEk&)djlAEEV7:mtv+0kp;@HPQ`es GPY` >@psvzz|`m++<>||~~D D R R z | !!@!D!K!K!!!!!!!!!!!!!!!!!!!!" #!#|#|#####%%%%%%o&o&'''''')))))))*0+D+G+L+))bbdf \\^^vz      @@ -6388,11 +6487,50 @@ ? ?   $ ' FP8F'4EFss,7>>;<>DGHKMWWbcflpt5F^^0@+,: -39;>GGQ[/68?16::<=?EGGjj0k6kQo~oooeimr{BD6;luu!#$&*DJAZazptvwz}/1VYY` Jnoq/M$$((@X`j 9 = = P P X a q  +39;>GGQ[/68?16::<=?EGGjj0k6kQo~oooeimr{BD6;luu!#$&*DJ  * . ` d f o `ͩЩ٩ީߩ kEkPkYk[kakckwk}kk0S__37?&)fjzV`xTV + 9;>@DFFJPRq+0 kp;@HPQ`es GPY` >@psvzz|`m Ф֦4@  H   + / / _ _ 00 4P!#%*,/:;?@[]__{{}}~~Z_  +  jm 0>^^d e p p v +v + + + OOZ[:=JO`hmn56 +DEZ`;?~ ' 0 C E Q S ^ } ~ # #)#*#h'u''''')))))),,,,p-p-...0.N.0000000000=0=00000 ss~~twΨϨ./__ͩީߩ\_ުߪ>?0RTacchhjk +  ;=??[[]]_eoDoPo~ooo....//0000!0)080;04MNmp) ) GIlq09`if o f +o + + +f o f o f o PY )@IFOPY@IPY )Ш٨ Щ٩PY@S;ttzzYY@@$$((q q FFCCx},jxxq q   |,},o-o-/./.001050;0;00000 ppϩϩppݪݪ\_ppoKUppQ R  *0-000 -!#%'**,,./:;?@\\~~Z_  +  jm 0>^^d e p p v +v + + + OOZ[JO`hmn56 +DEZ`;?~  ' 0 8 ; > A C G Q S S U ^ ,,,,p-p-.... . .........*...0.9.<.?.A.A.C.N.00=0=000 ss~~twΨϨ./__ͩީߩ\_ުߪ00EFILPRTW_ahhjk + +  <<aade^^``uu00 ![[>>@@G`lnprsjp| ^`| xKP| (*02359<DGHKMPPWW]cflpt  6::<=?GPY-//ŨΨ٨ + + + + + + + + +5 +8 +: +? +H +P +X +46F r w x ~ X ^ p x *3i|p p t y P!_!!!`$$$$v'',,11 2)2H2O2Q2_2222205`1VY` + + ?AJnoq/M@X`j 9 = = P P X a r     @@ -6429,90 +6567,26 @@ -    ( * 0 2 3 5 9 = = \ ] _ a q q     ( * 9 = = X Z ` a     : = = N N T V _ a z  023@F@GIl*??PUZ]aaefnpuHJMPVXXZ]`Zlo  1@Q`lnp xPmpt T3EK#MOZ} EHMPWYY[[]]_}q q   !!!! -!!!!!!$!$!&!&!(!(!*!-!/!9!<!?!E!I!N!N!!!,.,0,^,`,,,,,,-%-'-'-----0-g-o-o-------------------/./.001050;0<0A000000001/111111114MNФ *+@n" +    ( * 0 2 3 5 9 = = \ ] _ a q q     ( * 9 = = X Z ` a     : = = N N T V _ a z  023@E@GIl*??PUZ]aaefnpuHJMPVXXZ]`Zlo  1@Q`lnp BDxPmpt T3EK#MOZw5!8!0-g-------------------00<0<0A000000001/111111114MNФ *+nn妏 "@s -%0F`|ϩϩ(@BDK`vzz~ªª۪ݪ  &(.0Z\epװmp(*68<>>@ACDF=Pptv!:AZf  &(:<=?MP]-@BIPu'0c6@U`g -578<<?U`v   9      - - - - - - -5 -` -| - - - - - - - 5 @ U ` r    H      # ''0E7&DDPrvv+ (*02359==PP]a4GJ/DD+ 2::PP\ -.@@r  0FF`eghj #$C%0.4DFFh8j@j^jjjk/k@kCkckwk}kk@nnoDoPoPoooooppjp|TV - 9;>@DFFJPR46NPnpC!"$$'')24799;;BBGGIIKKMOQRTTWWYY[[]]__abddgjlrtwy|~~֦4@ oop09`if o f -o - - -f o r w f o x ~ X ^ f x PY 3@Ii|FOPY@IPYp p t y P!!!!`$$$$v'',,00!0)080:011 2)2H2O2Q2_22222 )05Ш٨ Щ٩PYФ3@x #AAJJX_y        @ -H -} -~ - - - - -X _ x      0 9 `~&QTRo6?PYPY0;PlPY$n$`jijPkYk[kaknn`xPYq ^^``uu00 ![[>>@@!#%*,/:;?@[]__{{}}~~Z_  -  jm 0>^^d e p p v -v - - - OOZ[:=JO`hmn56 -DEZ`;?~ ' 0 C E Q S ^ } ~ # #)#*#h'u''''')))))),,,,p-p-...0.N.0000000000=0=00000 ss~~twΨϨ./__ͩީߩ\_ުߪ>?0RTacchhjk -  ;=??[[]]_eJPY^_ooWW  ? ? P -X - - - - -9 ?   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AdlamAhomAnatolian_HieroglyphsArabicArmenianAvestanBalineseBamumBassa_VahBatakBengaliBhaiksukiBopomofoBrahmiBrailleBugineseBuhidCanadian_AboriginalCarianCaucasian_AlbanianCcCfChakmaChamCherokeeCoCommonCopticCsCuneiformCypriotCyrillicDeseretDevanagariDograDuployanEgyptian_HieroglyphsElbasanEthiopicGeorgianGlagoliticGothicGranthaGreekGujaratiGunjala_GondiGurmukhiHanHangulHanifi_RohingyaHanunooHatranHebrewHiraganaImperial_AramaicInheritedInscriptional_PahlaviInscriptional_ParthianJavaneseKaithiKannadaKatakanaKayah_LiKharoshthiKhmerKhojkiKhudawadiLLaoLatinLepchaLimbuLinear_ALinear_BLisuLlLmLoLtLuLycianLydianMahajaniMakasarMalayalamMandaicManichaeanMarchenMasaram_GondiMcMeMedefaidrinMeetei_MayekMende_KikakuiMeroitic_CursiveMeroitic_HieroglyphsMiaoMnModiMongolianMroMultaniMyanmarNNabataeanNdNew_Tai_LueNewaNkoNlNoNushuOghamOl_ChikiOld_HungarianOld_ItalicOld_North_ArabianOld_PermicOld_PersianOld_SogdianOld_South_ArabianOld_TurkicOriyaOsageOsmanyaPahawh_HmongPalmyrenePau_Cin_HauPcPdPePfPhags_PaPhoenicianPiPoPsPsalter_PahlaviRejangRunicSamaritanSaurashtraScSharadaShavianSiddhamSignWritingSinhalaSkSmSoSogdianSora_SompengSoyomboSundaneseSyloti_NagriSyriacTagalogTagbanwaTai_LeTai_ThamTai_VietTakriTamilTangutTeluguThaanaThaiTibetanTifinaghTirhutaUgariticVaiWarang_CitiYiZanabazar_SquareZlZpZsR RR RȰR0RذR0RRܮRRܮRR8RR8R(R@R8R@RHR RXR RhR,RxR,RRHRRHRRLRRLRȱRPRرRPRR`RR`RRhRRhR(RpR8RpRHRRXRR09RخRRخRRRRRRRRR - 0909AZ__az09AZaz!~AZaz az ~!/:@[`{~ AZ09AZ__az09AFaf\d\D\s\S\w\W[: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:]Unknown exceptionbad array new lengthstring too longmap/set too longai.onnx.mlai.onnx.trainingai.onnx.preview.trainingunknown line , but it is already registered from file ) from file version: (domain: Trying to register schema with name known by the checker., but it its domain is notin onnx/defs/schema.h).forgot to update the version range in DomainToVersionRange bumped the operator version but ] (usually, this means you , in the inclusive range [, but it its version is not Schema error: ' has been deprecated since version Operator ']., max= not in range [min=) has input size Node ( not in allowed input sizes.) has output size not in allowed output sizes.has output size ) in op definition.) than declared () has more inputs ( is marked single but has an empty string in the graph)'s input ) has more outputs ()'s output ' appeared multiple times.Attribute ' for operator Unrecognized attribute: ' : 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' has unknown expected type' is missing.Required attribute 'Attribute specification type mismatch.Duplicate type constraint name(inputs_.size() - 1) == i: failed validating the check: ONNX Schema (outputs_.size() - 1) == i!(it.GetName().empty()) :vector too longinvalid string positioninvalid hash bucket countunordered_map/set too longh>VPROAV''''@'$`;V 65@JV\\[\$$KV[[[[$$JV`[`[P[@[$$HV]]]]$$0IV0[0[ [[$$@GVp]p]`\P\$$KV[[[[$$FVZZZZ$$@KV]]]]$$FV@\@\0\ \$$EV^^]]$$HV[[[p[$$MV$$hMVЄЄ$$MV00 $$PVPP@0$$OV$$NV$$(PV  $$OV``P@$$QVp$$ TV$$WV  $$ XV``P$$XV@@0$$XV$$VVp$$0SV$$pUV $$VVpp`P$$WVP@$$SV$$8VV$$hQV@@0 $$pTV00 $$xVV$$UV$$@`V$$aVPP@$$]Vpp$$[Vpp`P$$P_VЪ$$YVШ$$@YVp`$$h`VЪ$$h[V@@0 $$pZV00 $$\V$$`V``P@$$`VЩЩ$$XaVp$$ZV$$ZV$$X^V00 $$bV$$aV$$bV@@0 $$cV$$pcV$$kV`P$$oVp$$xoV$$lVpp@`$$gV$$lVPPp`$$eV $$fV00 $$iV$$PnV$$kV$$mV$$dV  $$iV$$hiV@@0 $$nV  $$iV$$nV`$$kV$$jVPP@0$$fV$$iV``P@$$lVp`$$hVPP@0$$qV`P$$HrV@@0$$PqV $$qVp$$xV$${V00 $$(~VPP@0$$h~V$$|V  $$sV@@0 $$xV$$yV$$uV$$PtV``P@$$}V $$@|V$$8}V$$rV$$xV  $$pxV$$x}VppЪ`$$uV$$vVp`$$`zV$$vVPP@0$$Vpp`P$$`sV  $$pV`$$pV$$8yV$$0V$$V$$ȀVpp`\`$$V++++$$V////$$pV++++$$V..--$$@V--,-$$8V--,,$$Vp/p/`/P/$$V//-/$$V@/@/0/ /$$V   p $$8V` ` P @ $$8V0 0 `\ $$V    $$V    $$@V    $$V    $$V   [ $$V  p ` $$hV  `\ $$V0 0   $$V  `\ $$`V    $$XV@ @ [0 $$V  P[ $$0VP P P @ $$ȈV    $$Vp p ` P $$V    $$V - - - -$$VF -F -@E -0E -$$V E - E -PD -@D -$$@VK -K -pK -`K -$$V0i -0i -h -h -$$V`i -`i -Pi -@i -$$V - - -ж -$$V0 -0 - - -$$@V - - -$$V - - - -$$8Vp p ` P $$xV  @ 0 $$V    $$V@ @ 0   $$HV      $$HV0 0   $$Vp p ` P $$V@ @   $$Vp p  Н $$ЛV` ` 0 P $$hV    $$ЙV    $$V    $$VP P @ 0 $$V@ @ 0  $$(V   p $$V    $$Vpn V P p  Є   Ї HV } 0V|  0  '  $30    0 'VР P p ` '  $3@   `  'V    ' @ $30    ` 'V     '  $3    ` 'XV@6 @O   ' : $3pJ PF  ; B 'PVv P{   '  w $3z @z  `w Py 'V`  P @ '  $3 @  p  'xV@  0  ' Q $3`U T  `R T '(V `   '  $3   0  '@VO V   ' Q $3`U T  `R T 'VP,  5 p ` ' , $33 01  p- / 'V    ' @ $3     'V` + P @ ' 0 $3p( `&   # 'PV  p ` ' P $3 p    'V`W ]   ' W $3P\ [  PX Z 'pV` 0   '  $3p   0  '8V^ v   ' a $3q j  b g 'hV    '  $3     'V ` 0  '  $3      'V    '  $3   0  'V  P @ ' p $3     'C[TypeInferenceError] expected to have tensor typeInput unknownElement type of input Output [ShapeInferenceError] Incompatible dimensionstensor(uint32)tensor(uint64)tensor(int32)tensor(int64)tensor(float16)tensor(float)tensor(double)This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizeaxisTinputoutputConstrain input and output types to float tensors.SoftmaxD:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\math\old.ccLogSoftmaxHardmax -If necessary the right-hand-side argument will be broadcasted to match the -shape of left-hand-side argument. When broadcasting is specified, the second -tensor can either be of element size 1 (including a scalar tensor and any -tensor with rank equal to or smaller than the first tensor), or having its -shape as a contiguous subset of the first tensor's shape. The starting of the -mutually equal shape is specified by the argument "axis", and if it is not set, -suffix matching is assumed. 1-dim expansion doesn't work yet. - -For example, the following tensor shapes are supported (with broadcast=1): - - shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor - shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor - shape(A) = (2, 3, 4, 5), shape(B) = (5,) - shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) - shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 - shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 - -Attribute `broadcast=1` needs to be passed to enable broadcasting. -Pass 1 to enable broadcastingbroadcastlegacy optimization attribute.consumed_inputsIf set, defines the broadcast dimensions. See doc for details.ABResult, has same dimensions and type as AConstrain input and output types to high-precision numeric tensors.additionAddSubmultiplicationMulDiv -Pow takes input data (Tensor<T>) and exponent Tensor, and -produces one output data (Tensor<T>) where the function `f(x) = x^exponent`, -is applied to the data tensor elementwise. -XYZPowInput tensorOutput tensorNegAbsReciprocalFloorCeilSqrtReluCoefficient of leakage default to 0.01.alphaLeakyReluCoefficient of SELU default to 1.6732.Coefficient of SELU default to 1.0507.gammaSeluCoefficient of ELU default to 1.0.1D input tensorEluExpLogTanhslopePReluSigmoidValue of alpha default to 0.2Value of beta default to 0.5betaHardSigmoiddata_0maxMaxminMinsumSummeanMeanMinimum value, under which element is replaced by minMaximum value, above which element is replaced by maxOutput tensor with clipped input elementsClipWhether A should be transposedtransAWhether B should be transposedtransBWhether C should be broadcastedScalar multiplier for the product of input tensors A * B, the default value is 1.0.Scalar multiplier for input tensor C, the default value is 1.0.GemmFirst input does not have rank 2Second input does not have rank 2Input 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).Scalar multiplier for the product of input tensors A * B.Scalar multiplier for input tensor C.Constrain input and output types to float/int tensors.Input tensors of wrong rank (0).Incompatible dimensions for matrix multiplicationN-dimensional matrix AN-dimensional matrix BMatrix multiply results from A * BMatMulInvalid value for attribute axiskInvalid value for attribute kValuesITensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values.IndicesConstrain index tensor to int64Number of top elements to retrieveDimension on which to do the sort.TopKK input must be a one-dimensional tensor of size 1.K input must be of type int64.Axis has less than the requested k elements..List of tensors for tensor(bool)Enable broadcastingIf set, defines the broadcast dimensions.T1Result tensor.Constrains input to boolean tensor.Constrains output to boolean tensor.AndD:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\logical\old.ccOrXorConstrains input to float tensors.GreaterLessConstrains input to integral tensors.Equaltensor(uint8)tensor(uint16)tensor(int8)tensor(int16)keepdimsaxesA list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).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.An input tensor.dataReduced output tensor.reducedReduceMaxD:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\reduction\old.ccReduceMinReduceSumReduceSumSquareReduceMeanReduceProdReduceLogSumReduceLogSumExpReduceL1ReduceL2The axis in which to compute the arg indices.Reduced output tensor with integer data type.Constrain input and output types to all numeric tensors.ArgMaxArgMin'axis' must be in [-rank(indices), rank(indices)-1] -Computes the indices of the {name} elements of the input tensor's element along the -provided axis. The resulting tensor has the same rank as the input if keepdims equal 1. -If keepdims equal 0, then the resulting tensor have the reduced dimension pruned. -The type of the output tensor is integer.{name}The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).tensor(string)tensor(complex64)tensor(complex128)Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.Input tensor must have atleast 2 dimensionsdilationsAttribute dilations has incorrect sizestridesAttribute strides has incorrect sizekernel_shapeAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedpadsAttribute pads has incorrect sizeauto_padVALIDSAME_UPPERSAME_LOWERSecond input tensor has wrong dimensionceil_modeThe size of the kernel along each axis.Stride along each spatial axis.NOTSETInput data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is usedStride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.Whether to use ceil or floor (default) to compute the output shape.AveragePoolD:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\nn\old.ccWhether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.count_include_padMaxPoolThe storage order of the tensor. 0 is row major, and 1 is column major.storage_orderDilation value along each spatial axis of filter.Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.MaxUnpool op must have either two or three inputs.Input tensor X must have atleast 2 dimensions.Attribute pads has incorrect size.Attribute strides has incorrect size.Attribute kernel_shape has incorrect size.Attribute kernel_shape must be specified.'output_shape' must be rank 1 tensor.'output_shape' must have same number of elements as the shape of input tensor X.T2Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored.output_shapeOutput data tensor that contains the result of the unpooling.MaxUnpoolPadding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute.Stride along each axis.p value of the Lp norm used to pool over the input data, default is 2.0.pInput data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimension are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.LpPoolp value of the Lp norm used to pool over the input data.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.WOutput data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.The shape of the convolution kernel. If not present, should be inferred from input W.dilation value along each spatial axis of the filter.number of groups input channels and output channels are divided into.groupConvoutput_paddingThe shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate padsThe zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.ConvTransposeGlobalLpPoolIf true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.spatialIf set to nonzero, run spatial batch normalization in test mode, default is 0.is_testThe epsilon value to use to avoid division by zero, default is 1e-5f.epsilonFactor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.momentumThe input 4-dimensional tensor of shape NCHW.The scale as a 1-dimensional tensor of size C to be applied to the output.scaleThe bias as a 1-dimensional tensor of size C to be applied to the output.The running mean (training) or the estimated mean (testing) as a 1-dimensional tensor of size C.The running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C.varThe output 4-dimensional tensor of the same shape as X.The running mean after the BatchNormalization operator. Must be in-place with the input mean. Should not be used for testing.The running variance after the BatchNormalization operator. Must be in-place with the input var. Should not be used for testing.Saved mean used during training to speed up gradient computation. Should not be used for testing.saved_meanSaved variance used during training to speed up gradient computation. Should not be used for testing.saved_varBatchNormalizationThe input 1-dimensional bias tensor of size C.The output 4-dimensional tensor of the same shape as input.InstanceNormalization(float, default 0.5) the ratio of random dropoutratio(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.The input data as Tensor.The output.The output mask. If is_test is nonzero, this output is not filled.maskDropoutThe ratio of random dropoutThe output mask.Constrain output mask types to boolean tensors.The output tensor of the same shape as X.) for attribute 'axis'Invalid value(Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). FlattenConstrain input and output to all tensor types.If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.The epsilon value to use to avoid division by zero.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).If spatial is true, the dimension of scale is (C). If spatial is false, the dimensions of scale are (C x D1 x ... x Dn)If spatial is true, the dimension of bias is (C). If spatial is false, the dimensions of bias are (C x D1 x ... x Dn)If spatial is true, the dimension of the running mean (training) or the estimated mean (testing) is (C). If spatial is false, the dimensions of the running mean (training) or the estimated mean (testing) are (C x D1 x ... x Dn).If spatial is true, the dimension of the running variance(training) or the estimated variance (testing) is (C). If spatial is false, the dimensions of the running variance(training) or the estimated variance (testing) are (C x D1 x ... x Dn).The output tensor of the same shape as XThe running mean after the BatchNormalization operator.The running variance after the BatchNormalization operator.Saved mean used during training to speed up gradient computation.Saved variance used during training to speed up gradient computation. not specifiedValue of attribute should be of integer type and specify a type.Attribute does not specify a valid type. Dimension= Target=Can't merge shape info. Both source and target dimension have values but they differ. Source=The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorPrototoConstrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported.CastD:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\tensor\old.ccWhich axis to concat on. Default value is 1.inputsConcatenated tensorconcat_resultConstrain output types to float tensors.ConcatRequired attribute axis is missingrank must be greater than axisAll inputs to Concat must have same rankWhich axis to concat onConstrain output types to any tensor type.splitoutputs...Constrain input types to float tensors.Which axis to split onlength of each outputSplitList 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`.constantThree modes: constant(default), reflect, edgemodeOne float, indicates the value to be filled, default is 0valueInput tensor.Tensor after padding.PadNew shapeshapeReshapetilesConstrain tiles and axis's type to int64 tensors.TileThe scale along width dimension. It takes value greater than or equal to 1.width_scaleThe scale along height dimension. It takes value greater than or equal to 1.height_scalenearestTwo interpolation modes: nearest(default), bilinear4-D tensor, [N,C,H,W]4-D tensor after resizing, [N,C,H,W]Constrain output types to bool, int32, int64, float16, float, double tensors.Upsamplescales).) is not equal to the existing rank value (Ranks inferred (Number of elements of attribute 'scales' must be same as rank of input 'X'Attribute 'scales' must have floats type.Attribute 'scales' is required.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'.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)N-D tensorN-D tensor after resizingConstrain input and output types to all tensor types.Constrain input 'X' and output 'Y' to all tensor types.ResizestartsendsIncorrect or missing attribute value for starts and endsAttribute axes has incorrect lengthTensor of data to extract slices from.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`Sliced data tensor.SliceOnly supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsSlice op must have either three, four or five inputs.Incorrect or missing input value for starts and endsInput axes has incorrect lengthInput steps has incorrect lengthInput axes has invalid data'axes' has duplicates'step' cannot be 0Tind1-D tensor of starting indices of corresponding axis in `axes`1-D tensor of axes that `starts` and `ends` apply to.1-D tensor of slice step of corresponding axis in `axes`. Default to 1. stepsConstrain indices to integer typesWhich axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of rank r >= 1.indicesTensor of rank r >=1 (same rank and shape as indices)updatesTensor of rank r >= 1 (same rank as input).Input and output types can be of any tensor type.ScatterblocksizeBlocksize must be positiveInput tensor must be 4-dimensionalBlocks 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].DepthToSpacedata tensor must have rank >= 1axis must be in [-r, r-1]Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of rank q + (r - 1).Constrain input and output types to any tensor type.Gather must be 1 instead of Dimension of input List of non-negative integers, indicate the dimensions to squeeze.SqueezeList of non-negative integers, indicate the dimensions to be insertedUnsqueezeOneHot node must have three inputs.Input 'depth' must be a scalar or rank 1 tensor.Input 'depth' must have exactly one element.Input 'values' must be rank 1 tensor.Input 'values' must have exactly two elements.Indices tensor must have rank >= 1'axis' must be in [-rank(indices)-1, rank(indices)](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.depthT3Rank 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. valuesTensor 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.Constrains input to only numeric types.Constrain to any tensor type.OneHot(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.conditionTensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.Constrains to boolean tensors.CompressInvalid value of attribute 'axis'outputsWhich axis to split on. Attribute value for pads is requiredAttribute pads has incorrect lengthList 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.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` tensorTensor of rank q + r - indices_shape[-1] - 1.GatherNDThe value for the elements of the output tensor.Output tensor containing the same value of the provided tensor.ConstantD:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\generator\old.ccAttribute 'value' of Constant node must exist with 'Tensor' data.sparse_valueOnly 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.The value for the elements of the output tensor in sparse format.fowardSpecify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.directionNumber of neurons in the hidden layerhidden_sizeOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.activation_alphaactivation_betaThe sequence output for the hidden is optional if 0. Default 0.output_sequenceCell 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.clipThe input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.sequence_lensOptional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.initial_hA 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.The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.Y_hConstrain seq_lens to integer tensor.A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.activationsRThe 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 0GRUD:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\rnn\old.ccforwardreversebidirectionalOptional 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.One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0.RNNWhen computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.linear_before_resetA list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.Couple the input and forget gates if 1, default 0.input_forgetThe 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 initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.initial_cThe 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.PLSTMThe last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.Y_cThis operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. -Constrain input and output types to float and 8 bit tensors.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\nn\defs.ccInput tensor must have at least 2 dimensionsRoIs tensor must have 2 dimensionspooled_shapeAttribute pooled_shape has incorrect lengthAttribute pooled_shape must be specifiedROI pool output shape (height, width).Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.spatial_scaleroisRoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).MaxRoiPooldilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.inputs are expected to have tensor type.input and zero_point pair is expected to have be same type.weight and zero_point pair is expected to have same type.xx_scalex_zero_pointwScale tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).w_scaleZero point tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).w_zero_pointScale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.y_scaleZero point tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.y_zero_pointT4Optional 1D bias to be added to the convolution, has size of M. Bias must be quantized using scale = x_scale * w_scale and zero_point = 0yConstrain input type to 8-bit integer tensor.Constrain filter type to 8-bit integer tensor.Constrain output type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.The shape of the convolution kernel. If not present, should be inferred from input 'w'.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.number of groups input channels and output channels are divided into. default is 1.QLinearConvinputs are expected to have tensor type and output type should not be null.Constrain input x and its zero point data type to 8-bit integer tensor.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain output y data type to 32-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.ConvIntegerAdditional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.GlobalAveragePoolGlobalMaxPool -Carries out batch normalization as described in the paper -https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, -there are multiple cases for the number of outputs, which we list below: - -Output case #1: Y, mean, var, saved_mean, saved_var (training mode) -Output case #2: Y (test mode) - -For previous (depreciated) non-spatial cases, implementors are suggested -to flatten the input shape to (N x C*D1*D2 ..*Dn) before a BatchNormalization Op. -Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1Scale tensor of shape (C).Bias tensor of shape (C).running (training) or estimated (testing) mean tensor of shape (C).running (training) or estimated (testing) variance tensor of shape (C).The output tensor of the same shape as input.The axis on which to apply normalization, -1 mean last axis.The order of the normalization, only 1 or 2 are supported.LpNormalizationRatio of Dropout must be a scalar.training_mode of Dropout must be a scalar.(Optional) Seed to the random generator, if not specified we will auto generate one.seedtraining_modeConstrain input 'ratio' types to float tensors.Constrain output 'mask' types to boolean tensors.The lambd value for the Shrink formulation. Default is 0.5.lambdThe bias value added to output. Default is 0.biasShrinkIndicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). The number of channels to sum oversizeScaling parameter.The exponent.Output tensor, which has the shape and type as input tensorConstrain input and output types to float tensors.LRNngram_indexesngram_indexes must be non-empty with no negative valuesInput tensor must have rank 1 or 2Input is ether string UTF-8 or int32/int641-D tensor of floatsMaximum n-gram length. If this value is 3, 3-grams will be used to generate the output.max_gram_lengthMinimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.min_gram_lengthMaximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1max_skip_countList of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.pool_stringsList of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.pool_int64sThe starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.ngram_countslist of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.weightsThe weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)TfIdfVectorizerInput shape must have either [C] or [1,C] dimensions where C > 0NONEstring enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"case_change_actionBoolean. Whether the identification of stop words in X is case-sensitive. Default is falseis_case_sensitiveList of stop words. If not set, no word would be removed from X.stopwordsEnvironment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation.localeStringNormalizerA list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.ExponentEpsilonX_RMEX_squaredX_squaredE_XsquaredVarianceSTDX_varianceProcessed_STDMeanVarianceNormalizationConstrain output types. Casting to complex is not supported.Constrain input types. Casting from complex is not supported.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\tensor\defs.ccInvalid position of 0Target shape may not have multiple -1 dimensionsInvalid Target shape product of 0Invalid dimension value: Dimension could not be inferred: incompatible shapesConstrain output to int64 tensor.Input tensor can be of arbitrary type.ShapeConstrain output to int64 tensor, which should be a scalar though.axis must be in [-rank, rank-1].SizeWhich axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..Invalid value of attribute 'axis'. Rank= Value=) and outputs ()) and the split dimension of the input (Mismatch between number of splits (The input is not evenly splittableMismatch between the sum of 'split' (length of each output. Values should be >= 0.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 axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).1-D tensor of slice step of corresponding axis in `axes`. Negative value means slicing backward. 'steps' cannot be 0. Defaults to 1.Invalid attribute perm {perm}}, input shape = {A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.TransposetransposedWhich axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).ScatterNDTensor of rank q >= 1.ScatterElementsWhich axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).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).GatherElements'axes' attribute must not contain any duplicatesList 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).values in 'axes' are beyond the bounds of the computed output shapeSpaceToDepthDCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.DCR'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.repeatsConstrain repeat's type to int64 tensors.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). -This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor. <br/> - -The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. -Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original, <br/> - -if coordinate_transformation_mode is "half_pixel", <br/> -x_original = (x_resized + 0.5) / scale - 0.5, <br/> - -if coordinate_transformation_mode is "pytorch_half_pixel", <br/> -x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0, <br/> - -if coordinate_transformation_mode is "align_corners", <br/> -x_original = x_resized * (length_original - 1) / (length_resized - 1), <br/> - -if coordinate_transformation_mode is "asymmetric", <br/> -x_original = x_resized / scale, <br/> - -if coordinate_transformation_mode is "tf_half_pixel_for_nn", <br/> -x_original = (x_resized + 0.5) / scale, <br/> - -if coordinate_transformation_mode is "tf_crop_and_resize", <br/> -x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).cubic_coeff_aThe 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".exclude_outsideIf 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.coordinate_transformation_modehalf_pixelFour 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".round_prefer_floorWhen 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.nearest_mode1-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"extrapolation_valueThe 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.roisizesThe 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.Identity(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).(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).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.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.Constrain output types to boolean tensors.IsNaNdetect_positive(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.detect_negative(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.IsInfTensor of shape equal to the broadcasted shape of condition, X, and Y.WhereConstrain to boolean tensors.Constrain to all tensor types.NonZero'sequence_lens' must have rank of 1'input' must have rank >= 2time_axis(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.batch_axis(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 of rank r >= 2.ReverseSequenceTensor 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).sortedA 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 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 the count of each element of 'Y' in input 'X'inverse_indicesInput can be of any tensor type.countsUniqueThe number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]batch_dims'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64Supported modes: `constant`(default), `reflect`, `edge`Pads has incorrect number of values(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0).Constrains input and output to only numeric types.constant_valuevector<bool> too long does not match existing output type of Element type of input was unknownOutput was expected to have tensor type. Got Input element type of Mismatch between number of source and target dimensions. Source=num_scan_inputsScan input was not a tensor. outputs. Expected body was notGraph attribute inferencing returned type information for is invalid for a tensor of rank Scan 'body' subgraph outputs should all be tensors but output scan_input_axes axis value Number of scan input axes specified () is not equal to number of scan inputs () is not equal to number of scan outputs (scan_output_axesNumber of scan output axes specified (Vinitial_state_and_scan_inputsfinal_state_and_scan_outputsAn attribute specifying the number of scan_inputs M. 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.directionsAn 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.All Tensor typesInt64 tensorD:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\controlflow\old.ccScanLoop 'body' subgraph outputs should all be tensors but output was McondFinal N loop carried dependency values then K scan_outputsv_initialThe 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.v_final_and_scan_outputstensor of bool, which should be a scalar.tensor of int64, which should be a scalar.LoopAn 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.scan_input_directionsAn 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.scan_output_directionsthen_branchAn 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. != else_branch but subgraphs produce then_branch and else_branch produce different number of outputs. else=If node has Mismatched type for output then=Mismatched tensor element type for output 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.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.IfOnly boolConstrains input types to all numeric tensors.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\logical\defs.ccConstrains input/output to boolean tensors.NotDirection of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).Constrain input and output types to integer tensors.BitShiftO2O1LessOrEqualGreaterOrEqualAttribute expected to have a one-dim tensorAttribute expected to have tensor typeNegative values are not allowed in a shape specification should specify a shapevalue_intsvalue_intvalue_floatsvalue_floatvalue_stringsvalue_stringAttribute 'value_int' expect an integer.One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.Attribute 'value_float' expect a float.Attribute 'value_ints' expect a list of integers.Attribute 'value_string' expect a string.Attribute 'value_floats' expect a list of floats.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.Attribute 'value_strings' expect a list of strings.The values for the elements for the 1D, int64, output tensor.The value for the sole element for the scalar, int64, output tensor.The values for the elements for the 1D, float32, output tensor.The value for the sole element for the scalar, float32, output tensor.The values for the elements for the 1D, UTF-8 string, output tensor.The value for the sole element for the scalar, UTF-8 string, output tensor.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\generator\defs.ccInvalid shape value: Shape input must be a one-dimensional tensor.(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 float32Constrain input types.ConstantOfShapeConstrain output types to be numerics.dtype(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.Input tensor must be 2-dimensional(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'.Constrain input types. Strings and complex are not supported.EyeLikeConstrain output types. Strings and complex are not supported.Lower boundary of the output values.Upper boundary of the output values.lowThe data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.highOutput tensor of random values drawn from uniform distributionThe shape of the output tensor.RandomUniformThe standard deviation of the normal distribution.The mean of the normal distribution.Output tensor of random values drawn from normal distributionThe data type for the elements of the output tensor. Default is TensorProto::FLOAT.RandomNormalInput 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 the data type of the input tensor.RandomUniformLikeConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.RandomNormalLikeOutput type must be int32 or int64sample_sizeInput tensor must have rank 2(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Number of times to sample.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.MultinomialConstrain output types to integral tensors.loop_body_attributeprevideltacond_outrangecurrentstartlimitsub_result_castedsub_resultdiv_resultdelta_castedceil_result_reluceil_resultceil_result_relu_boolceil_result_relu_intAll inputs to 'Range' op must be of the same typevariadic_outputRangeConstrain input types to common numeric type tensors.Input to 'Range' op should be scalars (Tensor with only one element and shape empty)Second operand.First operand.]. Its actual value is: Result, has same element type as two inputs'axis' must be in [ , D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\math\defs.ccDescribes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentfmodRemainder tensorConstrain input and output types to signed numeric tensors.ModCoefficient of leakage.ThresholdedReluThreshold valueCoefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).Coefficient of ELU.Elu_ResultX_alphaConstrain input and output types to float32 tensors.The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.Constrain input X and output types to float/int tensors.CeluValue of alpha.Constrain input Y types to float/int tensors.Constrain input and output types to numeric tensors.Value of beta.SoftsignSoftplusDimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).largestWhether to return the top-K largest or smallest elements.Whether to return the elements in sorted order.SinCosTanAsinAcosAtan'shape' input must be 1D tensor of type INT64ExpandConstrain input and output types to all tensors.SinhCoshAsinhAcoshAtanhSignErfinput and zero_point pair is expected to have same type.a_scalea_zero_pointscale of quantized input bbzero point of quantized input bb_scalescale of quantized output yb_zero_pointQuantized matrix multiply results from a * bzero point of quantized output yConstrain input b and its zero point data type to 8-bit integer tensor.Constrain input a and its zero point data type to 8-bit integer tensor.QLinearMatMulConstrain output y and its zero point data type to 8-bit integer tensor.Constrain input A data type to 8-bit integer tensor.Zero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.Constrain output Y data type as 32-bit integer tensor.Constrain input B data type to 8-bit integer tensor.MatMulIntegerexclusiveIf set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.If set to 1 will perform the sums in reverse direction.Output tensor of the same type as 'x' with cumulative sums of the x's elements(Optional) A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.CumSumaxis tensor can be int32 or int64 onlyRoundInput rank must be >= 2.The inner-most 2 dimensions must have the same size (mat_w: != mat_h:DetConstrain input and output types to floating-point tensors.const_zerotargetconst_oneignore_indexexpanded_targetloss_NCddinput_gather_elementnoneloss_N1ddlossreductionweightloss_Nddloss_unweightedweight_gatherweight_gather_sumloss_sumconst_zero_target_typedconst_ignore_indextransform_targetsexpanded_target_int64input_gather_element_transformconst_zero_floatconst_one_floatsqueeze_maskweight_gather_temp_1weight_gather_tempInput and target dimension value mismatch.Target rank must be 1 less than the input rank.Weight rank must be 1.Type of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.Constrain input, weight, and output types to floating-point tensors.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.NegativeLogLikelihoodLossConstrain target to integer types...->Ellipsis represents incompatible dimensions.Number of input tensors does not match the operands in the equation.Rank of input does not match the equation indices.equationEinsum expression string.OutputInputsEinsumConstrain input and output types to all numerical tensor types.Type of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.X_MaxscoresX_ExpX_SubX_DivX_RSlog_probX_LoglabelsA manual rescaling weight given to each class. If given, it has to be a 1D Tensor assigning weight to each of the classes. Otherwise, it is treated as if having all ones.The ground truth output tensor, with shape [batch_size], or [batch_size, D1, D2, ..., Dk], where K is the number of dimensions. Labels element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the label values should either be in the range [0, C) or have the value ignore_index.Log probability tensor. If the output of softmax is prob, its value is log(prob).Weighted loss float Tensor. If reduction is 'none', this has the shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of K-dimensional loss. Otherwise, it is a scalar.SoftmaxCrossEntropyLossFirst input tensor must have rank 3A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\rnn\defs.ccCouple the input and forget gates if 1.N-D full precision Input tensor to be quantized.N-D quantized output tensor. It has same shape as input 'x'.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.Constrain 'x' to float or int32 tensor.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\quantization\defs.ccQuantizeLinearConstrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.N-D full precision output tensor. It has same shape as input 'x'.DequantizeLinearOutput scale. It's a scalar, which means a per-tensor/layer quantization.Quantized output tensorConstrain 'x' to float tensor.Output zero point. It's a scalar, which means a per-tensor/layer quantization.Q_MinConstrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.X_MinQ_MaxX_Max_AdjustedX_Min_AdjustedScaleX_RangeInitial_ZeroPoint_FPMin_ScaledRounded_ZeroPoint_FPClipped_ZeroPoint_FPDynamicQuantizeLinearZeropointboxesmax_output_boxes_per_classiou_thresholdscore_thresholdFloat representing the threshold for deciding when to remove boxes based on score. It is a scalar.selected_indicesselected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].center_point_boxInteger indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\object_detection\old.ccNonMaxSuppression but has rank and expected to have rank expected to have rank >Dimension mismatch in unification between output_widthoutput_heightdefault 1; Pooled output Y's height.Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.default 1; Pooled output Y's width.avgsampling_ratioInput 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.The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].batch_indicesConstrain types to int tensors.Constrain types to float tensors.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\object_detection\defs.ccRoiAlignAn 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).D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\controlflow\defs.ccAn 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].axis must be in [-rank, rank-1]. input rank was Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).select_last_indexConstrain input and output types to high-precision and 8 bit numeric tensors.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\reduction\defs.ccseq(tensor(uint32))seq(tensor(uint64))seq(tensor(uint8))seq(tensor(uint16))seq(tensor(int32))seq(tensor(int64))seq(tensor(int8))seq(tensor(int16))seq(tensor(double))seq(tensor(string))seq(tensor(float16))seq(tensor(float))seq(tensor(complex128))seq(tensor(bool))seq(tensor(complex64))Attribute dtype should be of integer type and specify a type.SequenceEmpty(Optional) The data type of the tensors in the output sequence. The default type is 'float'.SSequenceConstruct is expected to have at least 1 input.Element type of inputs are expected to be the same.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\sequence\defs.ccConstrain input types to any tensor type.SequenceConstructInput Sequence and Tensor are expected to have the same elem type. Sequence= Tensor=tensorinput_sequenceConstrain position to integral tensor. It must be a scalar(tensor of empty shape).SequenceInsertpositionOutput sequence that contains the inserted tensor at given position.SequenceAtSequenceEraseConstrain output to integral tensor. It must be a scalar(tensor of empty shape).SequenceLengthlengthInput 'split' can not be empty. sum of split values=Only supports `int32_t` or `int64_t` inputs for splitConstrain input types to all tensor types.Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size=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.Constrain split size to integral tensor.Constrain output types to all tensor types.new_axisnew_axis must be either 0 or 1SplitToSequenceWhich 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.], Value=Invalid value of attribute 'axis'. Accepted range=[Sequence of tensors for concatenationConcatFromSequenceArrayFeatureExtractorD:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\traditionalml\defs.ccThe input must be a tensor of a numeric type or string. The output will be of the same tensor type.The input must be a tensor of a numeric type. The output will be of the same tensor type.BinarizerValues greater than this are mapped to 1, others to 0.thresholdTO_INT64TO_STRINGcast_toTO_FLOATThe input must be an integer map to either string or float.map(int64, string)A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.DENSEmap(int64, float)The output is a 1-D tensor of string, float, or integer.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.max_mapIndicates 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'.map_formCastMapThe strings of the map. This sequence must be the same length as the 'cats_int64s' sequencecats_stringsThe 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._UnusedA 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.The integers of the map. This sequence must be the same length as the 'cats_strings' sequence.cats_int64sdefault_int64CategoryMapperdefault_stringAn 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.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.map(string, float)map(string, double)map(string, int64)map(int64, double)string_vocabularyAn integer vocabulary array.<br>One and only one of the vocabularies must be defined.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.int64_vocabularyDictVectorizerThe size of each input in the input listinputdimensionsThe input type must be a tensor of a numeric type.FeatureVectorizerimputed_value_floatsA value that needs replacing.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.Value(s) to change toimputed_value_int64sreplaced_value_int64replaced_value_floatValue(s) to change to.Label encoder has only one input.Label encoder has only one output.Imputerkeys_floatsOnly one of keys_*'s can be set in label encoder.keys_stringskeys_int64sInput type is not float tensor but keys_floats is setvalues_stringsInput type is not string tensor but key_strings is setInput type is not int64 tensor but keys_int64s is setOnly one of values_*'s can be set in label encoder.values_int64svalues_floatsOutput type is determined by the specified 'values_*' attribute.A list of strings. One and only one of 'keys_*'s should be set.The input type is a tensor of any shape.A list of strings. One and only one of 'value_*'s should be set.A string.A list of ints.A list of floats.default_floatLabelEncoderAn integer.A float.classlabels_intsinterceptsclasslabels_stringsInput's shape should be 1D or 2DA collection of weights of the model(s).coefficientsThe 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.multi_classClass labels when using string labels. One and only one 'classlabels' attribute must be defined.A collection of intercepts.Indicates whether to do OvR or multinomial (0=OvR is the default).post_transformLinearClassifierClass 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'The input must be a tensor of a numeric type.Weights of the intercepts, if used.The total number of regression targets, 1 if not defined.Indicates the transform to apply to the regression output vector.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Weights of the model(s).targetsLinearRegressorOne of 'MAX,' 'L1,' 'L2'normMAXNormalizerIf true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.zerosList 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.OneHotEncoderSecond, 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'ScalerFirst, 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.offsetThe 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.kernel_typeList of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.LINEARThe kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.support_vectorsFirst set of probability coefficients.kernel_paramsvectors_per_classprob_brhoprob_aSecond 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.Class labels if using integer labels.<br>One and only one of the 'classlabels_*' attributes must be defined.SVMClassifierIndicates 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.The input type must be a tensor of a numeric type, either [C] or [N,C].Chosen support vectorsSupport vector coefficients.The number of support vectors.Flag indicating whether the regression is a one-class SVM or not.one_classSVMRegressorn_supportsIndicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'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.nodes_nodeidsTree id for each node.nodes_treeidsThresholds to do the splitting on for each node.nodes_valuesFeature id for each node.nodes_featureidsThe 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'nodes_modesPopularity of each node, used for performance and may be omitted.nodes_hitratesChild node if expression is false.nodes_falsenodeidsChild node if expression is true.nodes_truenodeidsThe id of the tree that this node is in.class_treeidsFor 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.nodes_missing_value_tracks_trueThe index of the class list that each weight is for.class_idsnode id that this weight is for.class_nodeidsclasslabels_int64sIndicates the transform to apply to the score. <br> One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'The weight for the class in class_id.class_weightsTreeEnsembleClassifierBase values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)base_valuesChild node if expression is trueChild node if expression is falseNode id for each node. Node ids must restart at zero for each tree and increase sequentially.target_treeidsThe node id of each weightFor 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.target_idsThe weight for each targettarget_nodeidsThe index of the target that each weight is forn_targetsSUMtarget_weightsThe total number of targets.TreeEnsembleRegressorDefines how to aggregate leaf values within a target. <br>One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'aggregate_functionThe output will be a sequence of string or integer maps to float.seq(map(string, float))The keys when using int keys.<br>One and only one of the 'classlabels_*' attributes must be defined.ZipMapseq(map(int64, float))The keys when using string keys.<br>One and only one of the 'classlabels_*' attributes must be defined.A list of labels.classes_stringsThe 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.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\onnx\onnx\defs\traditionalml\old.cc,map(tensor(seqmapopaque(sparse_tensor((floatopaquesparse_tensordoublefloat16int64uint8int16int32uint64complex64uint16uint32boolcomplex128stringbad cast - -==> Context: is required to be non-empty.value_infoField ' is required but missing.' of namekey_typevalue_typetypeelem_typedata_type) to UNDEFINED is not allowed): Unrecognized type value case (value_info name: int32_datastring_datasetting data_type field (tensor name: float_datadouble_datauint64_dataint64_dataraw_datalocation, but it doesn't exist or is not accessible.) is stored externally and should not have data field.Data of TensorProto ( tensor name: TensorProto ( tensor name: ) is 0-element but contains data!) should be stored in ) is stored externally but doesn't have a location.) should not be stored in raw_data fieldSTRING data (tensor name: TensorProto (tensor name: ) should contain one and only one value field.values of data_type 'Unrecognized data_type (tensor name: ' instead of '' should be stored in field 'Sparse tensor indices (] values, but NNZ is ) has Sparse tensor (] not in sorted order.] out of range [0, ) index value at position [] out of range.] not in lexicographic sorted order.) first dimension size does not equal NNZ.) second dimension size does not match rank of tensor.Sparse tensor values () must have a dense-rank > 0sparse_tensor_proto) must have rank 1.) must have rank 1 or 2.) has no index values.) dimensions are not positive.) must have INT64 type.) should not contain more than one value field.Attribute (name: attrtype field and data field mismatch in attribute op_type) has zero input and zero output.) should refer to attribute in parent node.nodeAffine, type: NodeProto (name: DynamicSliceGRUUnitConstantFillCropParametricSoftplusScaledTanhGivenTensorFillImageScalerexperimental ops. In the future, we may directly was a removed as possible.reject this operator. Please update your model as soon ai.onnx with domain_version of Warning: No opset import for domain 'Op registered for graphNo Op registered for is deprecated in domain_version of in initializer but not in graph input - is not output of any previous nodes.' has been used as graph input names multiple times.Graph must be in single static assignment (SSA) form, however 'Bad node spec: ' has been used as output names multiple times.' of node: -Nodes in a graph must be topologically sorted, however input '\/output GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. is out of boundsinput sequence_type does not contain a graph.tensor_typeNOT_SET) vs (opaque_typesparse_tensor_type: (Inferred shape and existing shape differ in dimension Inferred elem type differs from existing elem type: (Inferred shape and existing shape differ in rank: (type case unsupported. existing=type mismatch. existing= inferred=type case mismatch. existing=(op_type: were providedno name, name: is tensor type, but provided type is Graph input # inputs but Graph has Number of elements of input 'sizes' must be same as rank of input 'X'Input 'sizes' must have int64 element type.) is not equal to the existing dim value (Dimension value inferred (Number of elements of input 'scales' must be same as rank of input 'X'Input 'scales' must have float element type.D:\a\ml-builds\ml-builds\onnxruntime\cmake\external\protobuf\src\google/protobuf/parse_context.honnx.TrainingInfoProtoonnx.NodeProtoonnx.ValueInfoProtoonnx.AttributeProtoonnx.GraphProtoonnx.TensorAnnotationonnx.StringStringEntryProtoonnx.ModelProtoonnx.TensorShapeProto.Dimensiononnx.SparseTensorProtoonnx.TensorProtoonnx.TensorProto.Segmentonnx.TypeProto.Maponnx.TypeProto.Sequenceonnx.TypeProto.Tensoronnx.TensorShapeProtoonnx.OperatorSetIdProtoonnx.TypeProtoonnx.TypeProto.Opaqueonnx.TypeProto.SparseTensoronnx.FunctionProtoD:\a\ml-builds\ml-builds\onnxruntime\build\onnx\onnx-ml.pb.cc_p0'78 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N.?AV?$_Func_impl_no_alloc@V<lambda_4f07ad08d951f2412255fcd32991b778>@@V?$unique_ptr@VIDeviceAllocator@onnxruntime@@U?$default_delete@VIDeviceAllocator@onnxruntime@@@std@@@std@@F@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_b32e5f2bc2fafd58a4afcda5e8537c65>@@VStatus@common@onnxruntime@@AEAV?$shared_ptr@VModel@onnxruntime@@@std@@@std@@` N.?AVISink@logging@onnxruntime@@` N.?AVLoggingWrapper@@` N.?AVOStreamSink@logging@onnxruntime@@` N.?AVCLogSink@logging@onnxruntime@@` N.?AVout_of_range@std@@` N.?AVout_of_range@detail@nlohmann@@` N.?AVtype_error@detail@nlohmann@@` N.?AVinvalid_iterator@detail@nlohmann@@` N.?AVparse_error@detail@nlohmann@@` N.?AUinput_adapter_protocol@detail@nlohmann@@` N.?AVinput_buffer_adapter@detail@nlohmann@@` N.?AV?$_Ref_count_obj2@Vinput_buffer_adapter@detail@nlohmann@@@std@@` N.P6AX$$QEAVOpSchema@onnx@@@Z` N.?AV?$_Func_base@X$$QEAVOpSchema@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@P6AX$$QEAVOpSchema@onnx@@@ZX$$QEAV12@@std@@` N.?AVRemoveDuplicateCastTransformer@onnxruntime@@` N.?AV<lambda_73d151dd9f087a4786e14d26c37f9f1f>@@` N.?AV<lambda_170eb7415bead306baf0c6b0a2038992>@@` N.?AV<lambda_6cf09a4d87483efc80956d9a5ca8e5cd>@@` N.?AV?$_Func_base@VStatus@common@onnxruntime@@AEBVNodeArg@3@_K@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_6cf09a4d87483efc80956d9a5ca8e5cd>@@VStatus@common@onnxruntime@@AEBVNodeArg@4@_K@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_170eb7415bead306baf0c6b0a2038992>@@VStatus@common@onnxruntime@@AEBVNodeArg@4@_K@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_73d151dd9f087a4786e14d26c37f9f1f>@@VStatus@common@onnxruntime@@AEBVNodeArg@4@_K@std@@` N.?AVRewriteRule@onnxruntime@@` N.?AVRuleBasedGraphTransformer@onnxruntime@@` N.?AVAttentionFusion@onnxruntime@@` N.?AVBiasGeluFusion@onnxruntime@@` N.?AVCastElimination@onnxruntime@@` N.?AVConstantFolding@onnxruntime@@` N.?AVConvActivationFusion@onnxruntime@@` N.?AVConvAddFusion@onnxruntime@@` N.?AVConvBNFusion@onnxruntime@@` N.?AVConvMulFusion@onnxruntime@@` N.?AVEliminateDropout@onnxruntime@@` N.?AVDynamicQuantizeMatMulFusion@onnxruntime@@` N.?AVEmbedLayerNormFusion@onnxruntime@@` N.?AVExpandElimination@onnxruntime@@` N.?AVFastGeluFusion@onnxruntime@@` N.?AVGeluApproximation@onnxruntime@@` N.?AVGeluFusion@onnxruntime@@` N.?AVGemmActivationFusion@onnxruntime@@` N.?AVEliminateIdentity@onnxruntime@@` N.?AVLayerNormFusion@onnxruntime@@` N.?AVMatMulAddFusion@onnxruntime@@` N.?AVNchwcTransformer@onnxruntime@@` N.?AVFuseReluClip@onnxruntime@@` N.?AVReshapeFusion@onnxruntime@@` N.?AVShapeToInitializer@onnxruntime@@` N.?AVSkipLayerNormFusion@onnxruntime@@` N.?AVEliminateSlice@onnxruntime@@` N.?AVUnsqueezeElimination@onnxruntime@@` N.?AVFreeDimensionOverrideTransformer@onnxruntime@@` N.?AV<lambda_963868dc1eb2d80ff6ea67a629ea4e1f>@@` N.?AVIDataTransfer@onnxruntime@@` N.?AVCPUDataTransfer@onnxruntime@@` N.?AVOptimizerExecutionFrame@onnxruntime@@` N.?AVIExecutionFrame@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@V<lambda_963868dc1eb2d80ff6ea67a629ea4e1f>@@VStatus@common@onnxruntime@@AEBVNodeArg@4@_K@std@@` N.?AUIExecutionProviderFactory@onnxruntime@@` N.?AUCpuProviderFactory@onnxruntime@@` N.?AV?$_Ref_count_obj2@UCpuProviderFactory@onnxruntime@@@std@@` N.P6APEAVOpKernel@onnxruntime@@AEBVOpKernelInfo@1@@Z` N.?AVClip@onnxruntime@@` N.?AV?$Clip_6@M@onnxruntime@@` N.?AV?$Clip_6Base@M@clip_internal@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@P6APEAVOpKernel@onnxruntime@@AEBVOpKernelInfo@2@@ZPEAV12@AEBV32@@std@@` N.?AU?$Elu@M@functors@onnxruntime@@` N.?AU?$HardSigmoid@M@functors@onnxruntime@@` N.?AU?$LeakyRelu@M@functors@onnxruntime@@` N.?AU?$Relu@M@functors@onnxruntime@@` N.?AU?$Selu@M@functors@onnxruntime@@` N.?AU?$Sigmoid@M@functors@onnxruntime@@` N.?AU?$Softplus@M@functors@onnxruntime@@` N.?AU?$Softsign@M@functors@onnxruntime@@` N.?AU?$Tanh@M@functors@onnxruntime@@` N.?AU?$ThresholdedRelu@M@functors@onnxruntime@@` N.?AV?$_Func_base@X_J_J@std@@` N.?AU?$ElementWiseRangedTransform@M@functors@onnxruntime@@` N.?AV?$ElementWiseKernel@U?$Elu@M@functors@onnxruntime@@@onnxruntime@@` N.?AV?$ElementWiseKernel@U?$HardSigmoid@M@functors@onnxruntime@@@onnxruntime@@` N.?AV?$ElementWiseKernel@U?$LeakyRelu@M@functors@onnxruntime@@@onnxruntime@@` N.?AV?$ElementWiseKernel@U?$Relu@M@functors@onnxruntime@@@onnxruntime@@` N.?AV?$ElementWiseKernel@U?$Selu@M@functors@onnxruntime@@@onnxruntime@@` N.?AV?$ElementWiseKernel@U?$Sigmoid@M@functors@onnxruntime@@@onnxruntime@@` N.?AV?$ElementWiseKernel@U?$Softplus@M@functors@onnxruntime@@@onnxruntime@@` N.?AV?$ElementWiseKernel@U?$Softsign@M@functors@onnxruntime@@@onnxruntime@@` N.?AV?$ElementWiseKernel@U?$Tanh@M@functors@onnxruntime@@@onnxruntime@@` N.?AV?$ElementWiseKernel@U?$ThresholdedRelu@M@functors@onnxruntime@@@onnxruntime@@` N.?AU?$ParametricSoftplus@M@functors@onnxruntime@@` N.?AU?$ScaledTanh@M@functors@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@U?$ThresholdedRelu@M@functors@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$Tanh@M@functors@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$Softsign@M@functors@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$Softplus@M@functors@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$Sigmoid@M@functors@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$Selu@M@functors@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$Relu@M@functors@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$LeakyRelu@M@functors@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$HardSigmoid@M@functors@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$Elu@M@functors@onnxruntime@@X_J_J@std@@` N.?AV<lambda_c518504e4362bc6fde787252124a6cbd>@@` N.?AV<lambda_9425fa52f3259b84319bb1698be71754>@@` N.?AV<lambda_2875304110a86147c8040e03092d4772>@@` N.?AV<lambda_2f33325f11bf0b76ef60eab6c56e74fe>@@` 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N.?AV?$Mean_6@M@onnxruntime@@` N.?AV?$Mean_8@M@onnxruntime@@` N.?AV?$BitShift@E@onnxruntime@@` N.?AV?$BitShift@I@onnxruntime@@` N.?AV?$BitShift@_K@onnxruntime@@` N.?AV?$Erf@M@onnxruntime@@` N.?AV?$Sin@M@onnxruntime@@` N.?AV?$Sin@N@onnxruntime@@` N.?AV?$Cos@M@onnxruntime@@` N.?AV?$Tan@M@onnxruntime@@` N.?AV?$Asin@M@onnxruntime@@` N.?AV?$Acos@M@onnxruntime@@` N.?AV?$Atan@M@onnxruntime@@` N.?AV?$Sinh@M@onnxruntime@@` N.?AV?$Cosh@M@onnxruntime@@` N.?AV?$Asinh@M@onnxruntime@@` N.?AV?$Acosh@M@onnxruntime@@` N.?AV?$Atanh@M@onnxruntime@@` N.?AV?$PRelu@M@onnxruntime@@` N.?AV?$Expand_8@M@onnxruntime@@` N.?AV?$Expand_8@N@onnxruntime@@` N.?AV?$Expand_8@C@onnxruntime@@` N.?AV?$Expand_8@F@onnxruntime@@` N.?AV?$Expand_8@H@onnxruntime@@` N.?AV?$Expand_8@_J@onnxruntime@@` N.?AV?$Expand_8@E@onnxruntime@@` N.?AV?$Expand_8@G@onnxruntime@@` N.?AV?$Expand_8@I@onnxruntime@@` N.?AV?$Expand_8@_K@onnxruntime@@` N.?AV?$Expand_8@_N@onnxruntime@@` N.?AV?$Expand_8@TMLFloat16@onnxruntime@@@onnxruntime@@` N.?AVMod@onnxruntime@@` N.?AV?$_Func_base@XV?$span@H@gsl@@V?$span@$$CBH@2@H@std@@` N.?AV?$_Func_base@XV?$span@H@gsl@@V?$span@$$CBH@2@_J@std@@` N.?AV?$_Func_base@XV?$span@H@gsl@@V?$span@$$CBH@2@M@std@@` N.?AV?$_Func_base@XV?$span@H@gsl@@V?$span@$$CBH@2@N@std@@` N.?AV?$_Func_base@XV?$span@_J@gsl@@V?$span@$$CB_J@2@H@std@@` N.?AV?$_Func_base@XV?$span@_J@gsl@@V?$span@$$CB_J@2@_J@std@@` N.?AV?$_Func_base@XV?$span@_J@gsl@@V?$span@$$CB_J@2@M@std@@` N.?AV?$_Func_base@XV?$span@_J@gsl@@V?$span@$$CB_J@2@N@std@@` N.?AV?$_Func_base@XV?$span@M@gsl@@V?$span@$$CBM@2@H@std@@` N.?AV?$_Func_base@XV?$span@M@gsl@@V?$span@$$CBM@2@_J@std@@` N.?AV?$_Func_base@XV?$span@M@gsl@@V?$span@$$CBM@2@M@std@@` N.?AV?$_Func_base@XV?$span@M@gsl@@V?$span@$$CBM@2@N@std@@` N.?AV?$_Func_base@XV?$span@N@gsl@@V?$span@$$CBN@2@H@std@@` N.?AV?$_Func_base@XV?$span@N@gsl@@V?$span@$$CBN@2@_J@std@@` N.?AV?$_Func_base@XV?$span@N@gsl@@V?$span@$$CBN@2@M@std@@` N.?AV?$_Func_base@XV?$span@N@gsl@@V?$span@$$CBN@2@N@std@@` 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N.?AV?$_Func_impl_no_alloc@V<lambda_14e04ecf8700452366ba6b7a5f4b36a1>@@XV?$span@M@gsl@@V?$span@$$CBM@3@H@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_d5bedf6edafb95653bf000e4b6a627a2>@@XV?$span@M@gsl@@V?$span@$$CBM@3@_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_c5b731b72c4214dd75f9ec2780d8d2bd>@@XV?$span@M@gsl@@V?$span@$$CBM@3@M@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_9c8993aad3ba26bf4637045d0165a4a0>@@XV?$span@M@gsl@@V?$span@$$CBM@3@N@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_2f33325f11bf0b76ef60eab6c56e74fe>@@XV?$span@N@gsl@@V?$span@$$CBN@3@H@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_2875304110a86147c8040e03092d4772>@@XV?$span@N@gsl@@V?$span@$$CBN@3@_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_9425fa52f3259b84319bb1698be71754>@@XV?$span@N@gsl@@V?$span@$$CBN@3@M@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_c518504e4362bc6fde787252124a6cbd>@@XV?$span@N@gsl@@V?$span@$$CBN@3@N@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_76a32be7747bdcfd74c858f22834d15e>@@XV?$span@H@gsl@@V?$span@$$CBH@3@H@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_49c59d8d66dee43cfac75d491a83f2cf>@@XV?$span@H@gsl@@V?$span@$$CBH@3@H@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_b7d3c4581fa395e8eaca5300936493d6>@@XV?$span@H@gsl@@V?$span@$$CBH@3@_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_782a8ba5f61dd1d98164edd3d5ac212c>@@XV?$span@H@gsl@@V?$span@$$CBH@3@_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_ae4a0739d4f9d760480a4b4a0d5a7294>@@XV?$span@H@gsl@@V?$span@$$CBH@3@M@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_806886be8e110842151c49c04151b202>@@XV?$span@H@gsl@@V?$span@$$CBH@3@M@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_1f0919bcbcc2745d2354075b975b43cf>@@XV?$span@H@gsl@@V?$span@$$CBH@3@N@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_400515dedb1be0bdb8531f2e2b07c0ed>@@XV?$span@H@gsl@@V?$span@$$CBH@3@N@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_7905c7102f6c30892b78fe2d5ff951ef>@@XV?$span@_J@gsl@@V?$span@$$CB_J@3@H@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_0cf2cf251f88635417674206081b39a9>@@XV?$span@_J@gsl@@V?$span@$$CB_J@3@H@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_6e2d0a3bc43bbd1a2e6d6b1989fd6388>@@XV?$span@_J@gsl@@V?$span@$$CB_J@3@_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_2809b7716627b21df21f740f5d83f524>@@XV?$span@_J@gsl@@V?$span@$$CB_J@3@_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_411a6833140aeabeec5d0cf061da2685>@@XV?$span@_J@gsl@@V?$span@$$CB_J@3@M@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_132ec89e719cfc8b1e5ade03aa1bbc92>@@XV?$span@_J@gsl@@V?$span@$$CB_J@3@M@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_bdd61ed3daf65ff539661596417dd97f>@@XV?$span@_J@gsl@@V?$span@$$CB_J@3@N@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_46c9fe7c2ae0b1e99109d1cd31262f94>@@XV?$span@_J@gsl@@V?$span@$$CB_J@3@N@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_ae9131b8ee9e1d6ec26272e26a4891b3>@@XV?$span@M@gsl@@V?$span@$$CBM@3@H@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_a871848ebddae9d5656148f58c63c5cd>@@XV?$span@M@gsl@@V?$span@$$CBM@3@H@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_f884ec5caaec663e7421ffcd0c18fed9>@@XV?$span@M@gsl@@V?$span@$$CBM@3@_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_a6886c26b41e5ae51592e59b239b4e1e>@@XV?$span@M@gsl@@V?$span@$$CBM@3@_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_efb1d75bef449a94e5f1f02ac502a603>@@XV?$span@M@gsl@@V?$span@$$CBM@3@M@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_cc7db55231370c1a60527f4bc32e8391>@@XV?$span@M@gsl@@V?$span@$$CBM@3@M@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_4cd219d9b14d08911eea971fb78f69f5>@@XV?$span@M@gsl@@V?$span@$$CBM@3@N@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_b64e5906c2011b856f3a66f62277f6c8>@@XV?$span@M@gsl@@V?$span@$$CBM@3@N@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_8d6837b13fd7fd73722c56078f2d1212>@@XV?$span@N@gsl@@V?$span@$$CBN@3@H@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_c12f2c711a33157aa2200e2c5df1e1a2>@@XV?$span@N@gsl@@V?$span@$$CBN@3@H@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_36a5e1be3f027a71cd8af06667239ec8>@@XV?$span@N@gsl@@V?$span@$$CBN@3@_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_29253933f1cb3a3bac029dfcd225c708>@@XV?$span@N@gsl@@V?$span@$$CBN@3@_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_0b15b2d021099f140a1dee0060474700>@@XV?$span@N@gsl@@V?$span@$$CBN@3@M@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_6fc2fdd30101efb67f941b7653594918>@@XV?$span@N@gsl@@V?$span@$$CBN@3@M@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_680993efa09bb96385d6171c22030bbd>@@XV?$span@N@gsl@@V?$span@$$CBN@3@N@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_7a570abdfacc41152a2325e1cc71d694>@@XV?$span@N@gsl@@V?$span@$$CBN@3@N@std@@` N.?AVRandomNormal@onnxruntime@@` N.?AVRandomNormalLike@onnxruntime@@` N.?AVRandomUniform@onnxruntime@@` N.?AVRandomUniformLike@onnxruntime@@` N.?AVMultinomial@onnxruntime@@` N.?AVCallWrapper@?$Gemm@M@onnxruntime@@` N.?AV?$Gemm@M@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@VCallWrapper@?$Gemm@M@onnxruntime@@X_J_J@std@@` N.?AV?$Hardmax@M@onnxruntime@@` N.?AV?$Softmax@M@onnxruntime@@` N.?AV?$Softmax@N@onnxruntime@@` N.?AV?$MatMul@M@onnxruntime@@` N.?AV?$MatMul@N@onnxruntime@@` N.?AV?$MatMul@H@onnxruntime@@` N.?AV?$MatMul@I@onnxruntime@@` N.?AV?$MatMul@_J@onnxruntime@@` N.?AV?$MatMul@_K@onnxruntime@@` N.?AV<lambda_88f211d79a091f6a481688e49635f453>@@` N.?AV<lambda_8205a59784c4f81abae5636754370a87>@@` N.?AV<lambda_03e7406b29b220fa131a6585ce48dcd2>@@` N.?AV<lambda_2a3f9f19c48a100998729328075237dc>@@` N.?AV<lambda_8ffce33af8696a7a42fa073f6875aacb>@@` N.?AV<lambda_8f0d91faf2fd476a9a61179d9b72837d>@@` N.?AV<lambda_a1b033c1238f74f04d136673bf3141a7>@@` N.?AV<lambda_1cd5cfe6b76bb3ba1c53203d58a4eedd>@@` N.?AV<lambda_72e83488395612e0f6a2602b726a187c>@@` N.?AV<lambda_f55fed088cc6887bf534564a0809769f>@@` N.?AV<lambda_c6728963f8bb91595df67d14c61fd8fa>@@` N.?AV<lambda_b2b5b00a4ad3647a9a40b4f0dbb0745d>@@` N.?AV?$_Func_base@X_J@std@@` N.?AV?$TopK@$08M@onnxruntime@@` N.?AV?$TopK@$09M@onnxruntime@@` N.?AV?$TopK@$0L@M@onnxruntime@@` N.?AV?$TopK@$0L@_J@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@V<lambda_8f0d91faf2fd476a9a61179d9b72837d>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_8ffce33af8696a7a42fa073f6875aacb>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_2a3f9f19c48a100998729328075237dc>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_03e7406b29b220fa131a6585ce48dcd2>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_8205a59784c4f81abae5636754370a87>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_88f211d79a091f6a481688e49635f453>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_b2b5b00a4ad3647a9a40b4f0dbb0745d>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_c6728963f8bb91595df67d14c61fd8fa>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_f55fed088cc6887bf534564a0809769f>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_72e83488395612e0f6a2602b726a187c>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_1cd5cfe6b76bb3ba1c53203d58a4eedd>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_a1b033c1238f74f04d136673bf3141a7>@@X_J@std@@` N.?AV?$BatchNorm@M@onnxruntime@@` N.?AV?$BatchNorm@N@onnxruntime@@` N.?AV?$Conv@M@onnxruntime@@` N.?AV?$ConvTranspose@M@onnxruntime@@` N.?AVFlatten@onnxruntime@@` N.?AV?$InstanceNorm@M@onnxruntime@@` N.?AV?$LpNorm@M@onnxruntime@@` N.?AU?$Powx@M@functors@onnxruntime@@` N.?AV?$LRN@M@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@U?$Powx@M@functors@onnxruntime@@X_J_J@std@@` N.?AU?$Pool3DTask@MVLpPool@onnxruntime@@@onnxruntime@@` N.?AU?$Pool2DTask@MVLpPool@onnxruntime@@@onnxruntime@@` N.?AU?$Pool1DTask@MVLpPool@onnxruntime@@@onnxruntime@@` N.?AU?$MaxPool3DTask@M@onnxruntime@@` N.?AU?$MaxPool2DTask@M@onnxruntime@@` N.?AU?$MaxPool1DTask@M@onnxruntime@@` N.?AU?$MaxPool3DTask@N@onnxruntime@@` N.?AU?$MaxPool2DTask@N@onnxruntime@@` N.?AU?$MaxPool1DTask@N@onnxruntime@@` N.?AU?$MaxPool3DTask@C@onnxruntime@@` N.?AU?$MaxPool2DTask@C@onnxruntime@@` N.?AU?$MaxPool1DTask@C@onnxruntime@@` N.?AU?$MaxPool3DTask@E@onnxruntime@@` N.?AU?$MaxPool2DTask@E@onnxruntime@@` N.?AU?$MaxPool1DTask@E@onnxruntime@@` N.?AVMaxPoolV8@onnxruntime@@` N.?AVPoolBase@onnxruntime@@` N.?AV?$Pool@MV?$MaxPool@$00@onnxruntime@@@onnxruntime@@` N.?AV?$Pool@MVAveragePool@onnxruntime@@@onnxruntime@@` N.?AV?$Pool@MVLpPool@onnxruntime@@@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@U?$Pool1DTask@MVLpPool@onnxruntime@@@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$Pool2DTask@MVLpPool@onnxruntime@@@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$Pool3DTask@MVLpPool@onnxruntime@@@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$MaxPool1DTask@E@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$MaxPool2DTask@E@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$MaxPool3DTask@E@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$MaxPool1DTask@C@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$MaxPool2DTask@C@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$MaxPool3DTask@C@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$MaxPool1DTask@N@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$MaxPool2DTask@N@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$MaxPool3DTask@N@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$MaxPool1DTask@M@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$MaxPool2DTask@M@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$MaxPool3DTask@M@onnxruntime@@X_J_J@std@@` N.?AV?$RoiPool@M@onnxruntime@@` N.?AV<lambda_5ea498b66bbdc69f17a456d2560429e7>@@` N.?AV<lambda_02eeac179a98f274b71887a0afee55c5>@@` N.?AV<lambda_91fddcc4e3c00cb2729a83a05112494c>@@` N.?AV<lambda_09975b8ee1f1d0da7b0dd818c5ffae63>@@` N.?AV<lambda_bba0ccc456154951dfbaee4eec056f08>@@` N.?AV<lambda_d24aad37166f79e4f7f03887f29ae48b>@@` N.?AV?$ReduceKernel@$00@onnxruntime@@` N.?AV?$ReduceKernelBase@$00@onnxruntime@@` N.?AV?$ReduceKernel@$0A@@onnxruntime@@` N.?AV?$ReduceKernelBase@$0A@@onnxruntime@@` N.?AV?$ReduceL1@M@onnxruntime@@` N.?AV?$ReduceL1@H@onnxruntime@@` N.?AV?$ReduceL2@M@onnxruntime@@` N.?AV?$ReduceL2@H@onnxruntime@@` N.?AV?$ReduceLogSum@M@onnxruntime@@` N.?AV?$ReduceLogSum@H@onnxruntime@@` N.?AV?$ReduceLogSumExp@M@onnxruntime@@` N.?AV?$ReduceLogSumExp@H@onnxruntime@@` N.?AV?$ReduceMax@M@onnxruntime@@` N.?AV?$ReduceMax@H@onnxruntime@@` N.?AV?$ReduceMax@_J@onnxruntime@@` N.?AV?$ReduceMax@C@onnxruntime@@` N.?AV?$ReduceMax@E@onnxruntime@@` N.?AV?$ReduceMean@M@onnxruntime@@` N.?AV?$ReduceMean@H@onnxruntime@@` N.?AV?$ReduceMin@M@onnxruntime@@` N.?AV?$ReduceMin@H@onnxruntime@@` N.?AV?$ReduceMin@_J@onnxruntime@@` N.?AV?$ReduceMin@C@onnxruntime@@` N.?AV?$ReduceMin@E@onnxruntime@@` N.?AV?$ReduceProd@M@onnxruntime@@` N.?AV?$ReduceProd@H@onnxruntime@@` N.?AV?$ReduceProd@_J@onnxruntime@@` N.?AV?$ReduceSum@M@onnxruntime@@` N.?AV?$ReduceSum@H@onnxruntime@@` N.?AV?$ReduceSum@_J@onnxruntime@@` N.?AV?$ReduceSum@N@onnxruntime@@` N.?AV?$ReduceSumSquare@M@onnxruntime@@` N.?AV?$ReduceSumSquare@H@onnxruntime@@` N.?AV?$ReduceSumSquare@N@onnxruntime@@` N.?AV?$ArgMax@M@onnxruntime@@` N.?AV?$ArgMax@H@onnxruntime@@` N.?AV?$ArgMin@M@onnxruntime@@` N.?AV?$ArgMin@H@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@V<lambda_02eeac179a98f274b71887a0afee55c5>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_5ea498b66bbdc69f17a456d2560429e7>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_d24aad37166f79e4f7f03887f29ae48b>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_bba0ccc456154951dfbaee4eec056f08>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_09975b8ee1f1d0da7b0dd818c5ffae63>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_91fddcc4e3c00cb2729a83a05112494c>@@X_J@std@@` N.?AUnarrowing_error@gsl@@` N.?AV<lambda_31044cbb2065f4b8855bbdc646057e42>@@` N.?AV<lambda_84c0c09494d01cc30f16e3449edead54>@@` N.?AVDeepCpuGruOp@onnxruntime@@` N.?AV?$_Func_base@XPEAH@std@@` N.?AV?$_Func_base@XPEAM@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_31044cbb2065f4b8855bbdc646057e42>@@XPEAM@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_84c0c09494d01cc30f16e3449edead54>@@XPEAH@std@@` N.?AV<lambda_9fda960f5bd7daedc59dac77a8ad1c7e>@@` N.?AVDeepCpuLstmOp@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@V<lambda_9fda960f5bd7daedc59dac77a8ad1c7e>@@X_J_J@std@@` N.P6AMMMM@Z` N.?AV?$RNN@M@onnxruntime@@` N.?AV?$_Func_base@MMMM@std@@` N.?AV?$_Func_impl_no_alloc@P6AMMMM@ZMMMM@std@@` N.?AV?$Cast@E@onnxruntime@@` N.?AV?$Cast@G@onnxruntime@@` N.?AV?$Cast@I@onnxruntime@@` N.?AV?$Cast@_K@onnxruntime@@` N.?AV?$Cast@C@onnxruntime@@` N.?AV?$Cast@F@onnxruntime@@` N.?AV?$Cast@H@onnxruntime@@` N.?AV?$Cast@_J@onnxruntime@@` N.?AV?$Cast@_N@onnxruntime@@` N.?AV?$Cast@M@onnxruntime@@` N.?AV?$Cast@N@onnxruntime@@` N.?AV?$Cast@TMLFloat16@onnxruntime@@@onnxruntime@@` N.?AV?$Cast@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@@onnxruntime@@` N.?AVConcat@onnxruntime@@` N.?AVConcatBase@onnxruntime@@` N.?AV<lambda_9ad4dd46fb7ebf522a3e85e715143745>@@` N.?AV<lambda_a7f558f1dd0bc6dfbc34073d8010b2d2>@@` N.?AVGather@onnxruntime@@` N.?AVGatherBase@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@V<lambda_a7f558f1dd0bc6dfbc34073d8010b2d2>@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_9ad4dd46fb7ebf522a3e85e715143745>@@X_J_J@std@@` N.?AV?$IdentityOp@$00@onnxruntime@@` N.?AV?$IdentityOp@$0A@@onnxruntime@@` N.?AUPad@onnxruntime@@` N.?AVPadBase@onnxruntime@@` N.?AVReshape@onnxruntime@@` N.?AVReshape_1@onnxruntime@@` N.?AVShape@onnxruntime@@` N.?AVSize@onnxruntime@@` N.?AUSlice1@onnxruntime@@` N.?AVSliceBase@onnxruntime@@` N.?AUSlice10@onnxruntime@@` N.?AVSpaceDepthBase@onnxruntime@@` N.?AV?$SpaceToDepth@M@onnxruntime@@` N.?AV?$DepthToSpace@M@onnxruntime@@` N.?AVSplit@onnxruntime@@` N.?AVSplitBase@onnxruntime@@` N.?AVSqueeze@onnxruntime@@` N.?AVSqueezeBase@onnxruntime@@` N.?AUTile@onnxruntime@@` N.?AVTranspose@onnxruntime@@` N.?AVTransposeBase@onnxruntime@@` N.?AVUnsqueeze@onnxruntime@@` N.?AVUnsqueezeBase@onnxruntime@@` N.?AV<lambda_09052ceb1bf3b577c728dec9fe3eab9b>@@` N.?AV<lambda_5868813267026bde29d771b4b1b80b4f>@@` N.?AV<lambda_ffee7b280847cd780e3c0aef3f3c198c>@@` N.?AV<lambda_7c9d354511f23b5ab35f0d23acf59e69>@@` N.?AV<lambda_55ea1c7ed7f7f7268714c360c9a1eb07>@@` N.?AV<lambda_70a5014ee65d8eb269c12c537da5a5f1>@@` N.?AV<lambda_30fd1fff2c31f5dad21f99700592798a>@@` N.?AV<lambda_b38c51459887501772a6d011e08a4556>@@` N.?AV<lambda_1b259c1c37867f7117af2630fb7a6d3a>@@` N.?AV<lambda_e3b884cd04a55a9aff856d7ab36b0422>@@` N.?AV<lambda_7365299d20f0c0cc81db24afaeca7624>@@` N.?AV?$_Func_base@MMMMMMM@std@@` N.?AV?$_Func_base@_JM_N@std@@` N.?AV?$Upsample@M@onnxruntime@@` N.?AVUpsampleBase@onnxruntime@@` N.?AV?$Upsample@H@onnxruntime@@` N.?AV?$Upsample@E@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@V<lambda_7365299d20f0c0cc81db24afaeca7624>@@MMMMMMM@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_e3b884cd04a55a9aff856d7ab36b0422>@@MMMMMMM@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_1b259c1c37867f7117af2630fb7a6d3a>@@MMMMMMM@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_b38c51459887501772a6d011e08a4556>@@MMMMMMM@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_30fd1fff2c31f5dad21f99700592798a>@@MMMMMMM@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_70a5014ee65d8eb269c12c537da5a5f1>@@MMMMMMM@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_55ea1c7ed7f7f7268714c360c9a1eb07>@@_JM_N@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_7c9d354511f23b5ab35f0d23acf59e69>@@_JM_N@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_ffee7b280847cd780e3c0aef3f3c198c>@@_JM_N@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_5868813267026bde29d771b4b1b80b4f>@@_JM_N@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_09052ceb1bf3b577c728dec9fe3eab9b>@@_JM_N@std@@` N.P6A?AV?$OrtValueTensorSlicer@UOrtValue@@@onnxruntime@@AEAUOrtValue@@_J1@Z` N.P6A?AV?$OrtValueTensorSlicer@$$CBUOrtValue@@@onnxruntime@@AEBUOrtValue@@_J1@Z` N.?AV<lambda_ed53b4c0957e241f93b3be9c259f7b0d>@@` N.?AV<lambda_489ff9e685d897b37de349c79fb051ee>@@` N.?AVIControlFlowKernel@controlflow@onnxruntime@@` N.?AV?$_Func_base@VStatus@common@onnxruntime@@PEAX_K@std@@` N.?AV?$_Func_base@VStatus@common@onnxruntime@@AEBV?$vector@_KV?$allocator@_K@std@@@std@@AEBVTensor@3@AEAV63@@std@@` N.?AV?$_Func_base@V?$OrtValueTensorSlicer@$$CBUOrtValue@@@onnxruntime@@AEBUOrtValue@@_J_J@std@@` N.?AV?$_Func_base@V?$OrtValueTensorSlicer@UOrtValue@@@onnxruntime@@AEAUOrtValue@@_J_J@std@@` N.?AVIterator@?$OrtValueTensorSlicer@$$CBUOrtValue@@@onnxruntime@@` N.?AV?$Scan@$07@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@P6A?AV?$OrtValueTensorSlicer@$$CBUOrtValue@@@onnxruntime@@AEBUOrtValue@@_J1@ZV12@AEBU3@_J_J@std@@` N.?AV?$_Func_impl_no_alloc@P6A?AV?$OrtValueTensorSlicer@UOrtValue@@@onnxruntime@@AEAUOrtValue@@_J1@ZV12@AEAU3@_J_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_489ff9e685d897b37de349c79fb051ee>@@VStatus@common@onnxruntime@@AEBV?$vector@_KV?$allocator@_K@std@@@std@@AEBVTensor@4@AEAV74@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_ed53b4c0957e241f93b3be9c259f7b0d>@@VStatus@common@onnxruntime@@PEAX_K@std@@` N.?AV<lambda_91a43e75998e3a49d1905d29f4a7bae4>@@` N.?AVIf@onnxruntime@@` N.?AV?$_Func_base@VStatus@common@onnxruntime@@AEBVTensorShape@3@AEBUOrtMemoryInfo@@AEAUOrtValue@@AEA_N@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_91a43e75998e3a49d1905d29f4a7bae4>@@VStatus@common@onnxruntime@@AEBVTensorShape@4@AEBUOrtMemoryInfo@@AEAUOrtValue@@AEA_N@std@@` N.P6A?AVStatus@common@onnxruntime@@AEAV?$vector@UOrtValue@@V?$allocator@UOrtValue@@@std@@@std@@PEAX_K@Z` N.?AVLoop@onnxruntime@@` N.?AV?$_Func_base@VStatus@common@onnxruntime@@AEAV?$vector@UOrtValue@@V?$allocator@UOrtValue@@@std@@@std@@PEAX_K@std@@` N.?AV?$_Func_impl_no_alloc@P6A?AVStatus@common@onnxruntime@@AEAV?$vector@UOrtValue@@V?$allocator@UOrtValue@@@std@@@std@@PEAX_K@ZV123@AEAV45@PEAX_K@std@@` N.?AVCompress@onnxruntime@@` N.?AVConstantOfShape@onnxruntime@@` N.?AVConstantOfShapeBase@onnxruntime@@` N.?AV?$MeanVarianceNormalization_1@M@onnxruntime@@` N.?AV?$MeanVarianceNormalization_0@M@onnxruntime@@` N.?AVEyeLike@onnxruntime@@` N.?AV?$IsNaN@M@onnxruntime@@` N.?AV?$IsNaN@TMLFloat16@onnxruntime@@@onnxruntime@@` N.?AVSign@onnxruntime@@` N.?AVShrink@onnxruntime@@` N.?AV?$OneHotOp@_J_J_J@onnxruntime@@` N.?AV?$OneHotOp@M_J_J@onnxruntime@@` N.?AV?$OneHotOp@_JV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@_J@onnxruntime@@` N.?AV?$OneHotOp@MV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@_J@onnxruntime@@` N.?AV?$OneHotOp@_JM_J@onnxruntime@@` N.?AV?$OneHotOp@HMH@onnxruntime@@` N.?AV?$OneHotOp@HMM@onnxruntime@@` N.?AV?$OneHotOp@MMM@onnxruntime@@` N.?AV?$OneHotOp@_JHM@onnxruntime@@` N.?AV?$OneHotOp@_JMM@onnxruntime@@` N.?AV?$OneHotOp@_JMH@onnxruntime@@` N.?AVMaxUnpool@onnxruntime@@` N.?AV<lambda_f87191d92810657cd25a2973b1734a59>@@` N.?AV<lambda_563fdc041becc3012b0755fecfa41a3e>@@` N.?AV?$Scan@$08@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@V<lambda_563fdc041becc3012b0755fecfa41a3e>@@VStatus@common@onnxruntime@@AEBV?$vector@_KV?$allocator@_K@std@@@std@@AEBVTensor@4@AEAV74@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_f87191d92810657cd25a2973b1734a59>@@VStatus@common@onnxruntime@@PEAX_K@std@@` N.?AVScatter@onnxruntime@@` N.?AV<lambda_05c8e068d08f49052da87d86b6e09131>@@` N.?AV<lambda_e1f234778fb80305bf70a017789747a5>@@` N.?AVTfIdfVectorizer@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@V<lambda_05c8e068d08f49052da87d86b6e09131>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_e1f234778fb80305bf70a017789747a5>@@X_J@std@@` N.?AV?$NonZero@_N@onnxruntime@@` N.?AV?$NonZero@E@onnxruntime@@` N.?AV?$NonZero@H@onnxruntime@@` N.?AV?$NonZero@_J@onnxruntime@@` N.?AV?$NonZero@M@onnxruntime@@` N.?AV?$Where@E@onnxruntime@@` N.?AV?$Where@H@onnxruntime@@` N.?AV?$Where@_J@onnxruntime@@` N.?AV?$Where@M@onnxruntime@@` N.?AV?$Where@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@@onnxruntime@@` N.?AVrange_error@std@@` N.?AVcodecvt_base@std@@` N.?AVfacet@locale@std@@` N.?AV_Facet_base@std@@` N.?AU_Crt_new_delete@std@@` N.?AV?$codecvt@_WDU_Mbstatet@@@std@@` N.?AVStringNormalizer@onnxruntime@@` N.?AV?$codecvt_utf8@_W$0BAPPPP@$0A@@std@@` N.?AV?$wstring_convert@V?$codecvt_utf8@_W$0BAPPPP@$0A@@std@@_WV?$allocator@_W@2@V?$allocator@D@2@@std@@` N.?AV?$Resize@M@onnxruntime@@` N.?AV?$Resize@H@onnxruntime@@` N.?AV?$Resize@E@onnxruntime@@` N.?AV?$DequantizeLinear@E@onnxruntime@@` N.?AV?$DequantizeLinear@C@onnxruntime@@` N.?AV?$QuantizeLinear@E@onnxruntime@@` N.?AV?$QuantizeLinear@C@onnxruntime@@` N.?AVQLinearMatMul@onnxruntime@@` N.?AVMatMulInteger@onnxruntime@@` N.?AVConvInteger@onnxruntime@@` N.?AVQLinearConv@onnxruntime@@` N.?AVNonMaxSuppression@onnxruntime@@` N.?AVNonMaxSuppressionBase@onnxruntime@@` N.?AVIsInf@onnxruntime@@` N.?AV<lambda_947afc8739f501c3bcb478fbfe4b5cad>@@` N.?AV<lambda_14e8748bb7b6daa778fde6ec19e06b72>@@` N.?AV?$RoiAlign@M@onnxruntime@@` N.?AV?$RoiAlignBase@M@onnxruntime@@` N.?AV?$RoiAlign@N@onnxruntime@@` N.?AV?$RoiAlignBase@N@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@V<lambda_14e8748bb7b6daa778fde6ec19e06b72>@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_947afc8739f501c3bcb478fbfe4b5cad>@@X_J_J@std@@` N.?AVReverseSequenceOp@onnxruntime@@` N.?AV?$CumSum@M@onnxruntime@@` N.?AV?$CumSum@N@onnxruntime@@` N.?AV?$CumSum@H@onnxruntime@@` N.?AV?$CumSum@_J@onnxruntime@@` N.?AV?$Round@TMLFloat16@onnxruntime@@@onnxruntime@@` N.?AV?$Round@M@onnxruntime@@` N.?AV?$Round@N@onnxruntime@@` N.?AV?$DynamicQuantizeLinear@E@onnxruntime@@` N.?AV?$Det@M@onnxruntime@@` N.?AVSequenceLength@onnxruntime@@` N.?AVSequenceAt@onnxruntime@@` N.?AVSequenceEmpty@onnxruntime@@` N.?AVSequenceInsert@onnxruntime@@` N.?AVSequenceErase@onnxruntime@@` N.?AVSequenceConstruct@onnxruntime@@` N.?AVSplitToSequence@onnxruntime@@` N.?AVConcatFromSequence@onnxruntime@@` N.?AV<lambda_29a27f66e5d65fb36ed8965e3fa92ded>@@` N.?AV<lambda_626d338c2f6a43327586cf0d89586aa1>@@` N.?AVScatterND@onnxruntime@@` N.?AVScatterNDBase@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@V<lambda_626d338c2f6a43327586cf0d89586aa1>@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_29a27f66e5d65fb36ed8965e3fa92ded>@@X_J_J@std@@` N.?AVGatherElements@onnxruntime@@` N.?AV<lambda_f4c664674d3291639f6f1c524accc00e>@@` N.?AV<lambda_74a481eac2e8d64c2bfb5722b5b1cb89>@@` N.?AV<lambda_4888e52cfaab8cdad25ecea017b54d57>@@` N.?AV<lambda_399e4ac9a8be74e8d14d0d5e67b02a32>@@` N.?AVGatherND@onnxruntime@@` N.?AVGatherNDBase@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@V<lambda_74a481eac2e8d64c2bfb5722b5b1cb89>@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_f4c664674d3291639f6f1c524accc00e>@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_399e4ac9a8be74e8d14d0d5e67b02a32>@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_4888e52cfaab8cdad25ecea017b54d57>@@X_J_J@std@@` N.?AVRange@onnxruntime@@` N.?AVUnique@onnxruntime@@` N.P6A?AVStatus@common@onnxruntime@@PEB_J0PEA_J_K222222PEAVThreadPool@concurrency@2@PEAX@Z` N.P6A?AVStatus@common@onnxruntime@@PEBN0PEAN_K222222PEAVThreadPool@concurrency@2@PEAX@Z` N.P6A?AVStatus@common@onnxruntime@@PEBH0PEAH_K222222PEAVThreadPool@concurrency@2@PEAX@Z` N.P6A?AVStatus@common@onnxruntime@@AEBVTensor@2@AEAV32@@Z` N.P6A?AVTensor@onnxruntime@@AEBV01@AEBV?$vector@_JV?$allocator@_J@std@@@std@@_NV?$shared_ptr@VIAllocator@onnxruntime@@@3@PEBVTensorShape@1@PEAVThreadPool@concurrency@1@PEAX@Z` N.P6A?AVStatus@common@onnxruntime@@PEBM0PEAM_K222222PEAVThreadPool@concurrency@2@PEAX@Z` N.P6A?AVStatus@common@onnxruntime@@AEBV?$vector@_KV?$allocator@_K@std@@@std@@AEBVTensor@2@AEAV52@PEBVTensorShape@2@PEAX@Z` N.P6A?AV?$unique_ptr@VTensor@onnxruntime@@U?$default_delete@VTensor@onnxruntime@@@std@@@std@@AEBVTensor@onnxruntime@@_J1V?$shared_ptr@VIAllocator@onnxruntime@@@1@@Z` N.?AV?$_Func_base@V?$unique_ptr@VTensor@onnxruntime@@U?$default_delete@VTensor@onnxruntime@@@std@@@std@@AEBVTensor@onnxruntime@@_J_JV?$shared_ptr@VIAllocator@onnxruntime@@@2@@std@@` N.?AV?$_Func_base@VStatus@common@onnxruntime@@AEBV?$vector@_KV?$allocator@_K@std@@@std@@AEBVTensor@3@AEAV63@PEBVTensorShape@3@PEAX@std@@` N.?AV?$_Func_base@VTensor@onnxruntime@@AEBV12@AEBV?$vector@_JV?$allocator@_J@std@@@std@@_NV?$shared_ptr@VIAllocator@onnxruntime@@@4@PEBVTensorShape@2@PEAVThreadPool@concurrency@2@PEAX@std@@` N.?AV?$_Func_base@VStatus@common@onnxruntime@@AEBVTensor@3@AEAV43@@std@@` N.?AVEinsum@onnxruntime@@` N.?AV?$_Func_base@VStatus@common@onnxruntime@@PEBMPEBMPEAM_K_K_K_K_K_K_KPEAVThreadPool@concurrency@3@PEAX@std@@` N.?AV?$_Func_base@VStatus@common@onnxruntime@@PEBHPEBHPEAH_K_K_K_K_K_K_KPEAVThreadPool@concurrency@3@PEAX@std@@` N.?AV?$_Func_base@VStatus@common@onnxruntime@@PEBNPEBNPEAN_K_K_K_K_K_K_KPEAVThreadPool@concurrency@3@PEAX@std@@` N.?AV?$_Func_base@VStatus@common@onnxruntime@@PEB_JPEB_JPEA_J_K_K_K_K_K_K_KPEAVThreadPool@concurrency@3@PEAX@std@@` N.?AV?$_Func_impl_no_alloc@P6A?AV?$unique_ptr@VTensor@onnxruntime@@U?$default_delete@VTensor@onnxruntime@@@std@@@std@@AEBVTensor@onnxruntime@@_J1V?$shared_ptr@VIAllocator@onnxruntime@@@2@@ZV12@AEBV34@_J_JV52@@std@@` N.?AV?$_Func_impl_no_alloc@P6A?AVStatus@common@onnxruntime@@AEBV?$vector@_KV?$allocator@_K@std@@@std@@AEBVTensor@3@AEAV63@PEBVTensorShape@3@PEAX@ZV123@AEBV45@AEBV63@AEAV63@PEBV73@PEAX@std@@` N.?AV?$_Func_impl_no_alloc@P6A?AVStatus@common@onnxruntime@@PEBM0PEAM_K222222PEAVThreadPool@concurrency@3@PEAX@ZV123@PEBMPEBMPEAM_K_K_K_K_K_K_KPEAV453@PEAX@std@@` N.?AV?$_Func_impl_no_alloc@P6A?AVTensor@onnxruntime@@AEBV12@AEBV?$vector@_JV?$allocator@_J@std@@@std@@_NV?$shared_ptr@VIAllocator@onnxruntime@@@4@PEBVTensorShape@2@PEAVThreadPool@concurrency@2@PEAX@ZV12@AEBV12@AEBV34@_NV54@PEBV62@PEAV782@PEAX@std@@` N.?AV?$_Func_impl_no_alloc@P6A?AVStatus@common@onnxruntime@@AEBVTensor@3@AEAV43@@ZV123@AEBV43@AEAV43@@std@@` N.?AV?$_Func_impl_no_alloc@P6A?AVStatus@common@onnxruntime@@PEBH0PEAH_K222222PEAVThreadPool@concurrency@3@PEAX@ZV123@PEBHPEBHPEAH_K_K_K_K_K_K_KPEAV453@PEAX@std@@` N.?AV?$_Func_impl_no_alloc@P6A?AVStatus@common@onnxruntime@@PEBN0PEAN_K222222PEAVThreadPool@concurrency@3@PEAX@ZV123@PEBNPEBNPEAN_K_K_K_K_K_K_KPEAV453@PEAX@std@@` N.?AV?$_Func_impl_no_alloc@P6A?AVStatus@common@onnxruntime@@PEB_J0PEA_J_K222222PEAVThreadPool@concurrency@3@PEAX@ZV123@PEB_JPEB_JPEA_J_K_K_K_K_K_K_KPEAV453@PEAX@std@@` N.?AV?$Dropout@MM$0A@@onnxruntime@@` N.?AV?$Dropout@MN$0A@@onnxruntime@@` N.?AV?$Dropout@NM$0A@@onnxruntime@@` N.?AV?$Dropout@NN$0A@@onnxruntime@@` N.?AV?$ArrayFeatureExtractorOp@M@ml@onnxruntime@@` N.?AV?$ArrayFeatureExtractorOp@N@ml@onnxruntime@@` N.?AV?$ArrayFeatureExtractorOp@H@ml@onnxruntime@@` N.?AV?$ArrayFeatureExtractorOp@_J@ml@onnxruntime@@` N.?AV?$ArrayFeatureExtractorOp@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@@ml@onnxruntime@@` N.?AV?$BinarizerOp@M@ml@onnxruntime@@` N.?AVCastMap@ml@onnxruntime@@` N.?AVCategoryMapper@ml@onnxruntime@@` N.?AV?$DictVectorizerOp@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@_J@ml@onnxruntime@@` N.?AV?$DictVectorizerOp@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@M@ml@onnxruntime@@` N.?AV?$DictVectorizerOp@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@N@ml@onnxruntime@@` N.?AV?$DictVectorizerOp@_JV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@@ml@onnxruntime@@` N.?AV?$DictVectorizerOp@_JM@ml@onnxruntime@@` N.?AV?$DictVectorizerOp@_JN@ml@onnxruntime@@` N.?AVFeatureVectorizer@ml@onnxruntime@@` N.?AVImputerOp@ml@onnxruntime@@` N.?AVLabelEncoder@ml@onnxruntime@@` N.?AV?$LabelEncoder_2@MV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@@ml@onnxruntime@@` N.?AV?$LabelEncoder_2@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@M@ml@onnxruntime@@` N.?AV?$LabelEncoder_2@_JM@ml@onnxruntime@@` N.?AV?$LabelEncoder_2@M_J@ml@onnxruntime@@` N.?AV?$LabelEncoder_2@_JV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@@ml@onnxruntime@@` N.?AV?$LabelEncoder_2@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@_J@ml@onnxruntime@@` N.?AV?$LabelEncoder_2@_J_J@ml@onnxruntime@@` N.?AV<lambda_cf0d99f02d80c9fcb9d4fc5032f5b198>@@` N.?AV<lambda_a47ee9eece330f845238b622e01ff20a>@@` N.?AV<lambda_99b6e836d69b69305b62d8ff2093a102>@@` N.?AVLinearClassifier@ml@onnxruntime@@` N.?AV?$_Func_base@XMPEAM@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_99b6e836d69b69305b62d8ff2093a102>@@XMPEAM@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_a47ee9eece330f845238b622e01ff20a>@@XMPEAM@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_cf0d99f02d80c9fcb9d4fc5032f5b198>@@XMPEAM@std@@` N.?AVLinearRegressor@ml@onnxruntime@@` N.?AVNormalizer@ml@onnxruntime@@` N.?AV?$OneHotEncoderOp@_J@ml@onnxruntime@@` N.?AV?$OneHotEncoderOp@M@ml@onnxruntime@@` N.?AV?$OneHotEncoderOp@N@ml@onnxruntime@@` N.?AV?$OneHotEncoderOp@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@@ml@onnxruntime@@` N.?AV?$ScalerOp@M@ml@onnxruntime@@` N.?AV?$ScalerOp@N@ml@onnxruntime@@` N.?AV?$ScalerOp@_J@ml@onnxruntime@@` N.?AV?$ScalerOp@H@ml@onnxruntime@@` N.?AV<lambda_ac55c4ccc442f0bccdc9fc717158ade1>@@` N.?AVSVMClassifier@ml@onnxruntime@@` N.?AVSVMCommon@ml@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@V<lambda_ac55c4ccc442f0bccdc9fc717158ade1>@@X_J@std@@` N.?AV?$SVMRegressor@M@ml@onnxruntime@@` N.?AV<lambda_959b8361497e19898ca852a4058b29b4>@@` N.?AV<lambda_403fb7d10cd00daaebaad121232bdf53>@@` N.?AV<lambda_df8f09957380fda37bd75205e74ab017>@@` N.?AV<lambda_f81e85f88fe571ed4186d6b65c29e725>@@` N.?AV<lambda_8052aa8f31d91e8553d059cc8a579374>@@` N.?AV<lambda_16193311b56803fab9af9f1ee32b1423>@@` N.?AV<lambda_745ebee95180d242f8918f6ee05b1702>@@` N.?AV<lambda_75be6c47ae903da69d9cbf60e877b3d4>@@` N.?AV<lambda_f56fca5ecfd99d917a534486128ac494>@@` N.?AV<lambda_f6162a7407be4a24b214de890e5ae00b>@@` N.?AV<lambda_8cdf471af827d0e9951dd9e8d76fe9d5>@@` N.?AV<lambda_14977f956c5ec645efaba7edcd49cda2>@@` N.?AV<lambda_586b05829726dc013f7e278aed4f91a7>@@` N.?AV<lambda_64fd6a6a8e2e63faddce56c110f7a86a>@@` N.?AV<lambda_9f741f7813d44fec4c7118fa834bba6c>@@` N.?AV<lambda_63d73b571532fd90b3ff4e661852894f>@@` N.?AV?$TreeEnsembleClassifier@M@ml@onnxruntime@@` N.?AV?$TreeEnsembleClassifier@N@ml@onnxruntime@@` N.?AV?$TreeEnsembleClassifier@_J@ml@onnxruntime@@` N.?AV?$TreeEnsembleClassifier@H@ml@onnxruntime@@` N.?AV?$_Ref_count_obj2@VCPUAllocator@onnxruntime@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_75be6c47ae903da69d9cbf60e877b3d4>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_745ebee95180d242f8918f6ee05b1702>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_16193311b56803fab9af9f1ee32b1423>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_8052aa8f31d91e8553d059cc8a579374>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_f81e85f88fe571ed4186d6b65c29e725>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_df8f09957380fda37bd75205e74ab017>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_403fb7d10cd00daaebaad121232bdf53>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_959b8361497e19898ca852a4058b29b4>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_63d73b571532fd90b3ff4e661852894f>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_9f741f7813d44fec4c7118fa834bba6c>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_64fd6a6a8e2e63faddce56c110f7a86a>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_586b05829726dc013f7e278aed4f91a7>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_14977f956c5ec645efaba7edcd49cda2>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_8cdf471af827d0e9951dd9e8d76fe9d5>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_f6162a7407be4a24b214de890e5ae00b>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_f56fca5ecfd99d917a534486128ac494>@@X_J@std@@` N.?AV<lambda_adbb0ed82c3e353a2ef8b51f4773ed3d>@@` N.?AV<lambda_dc1d45b7832ae8b4e8193a30205c17a1>@@` N.?AV<lambda_c327e8b0febe30d927a0cd8dc39dba92>@@` N.?AV<lambda_754485b4f4a6e247d8fd46f499ecbff1>@@` N.?AV<lambda_3928e725003c7a62c76f41f34cee3234>@@` N.?AV<lambda_247636e7ce7e67357cbf46607c82bed0>@@` N.?AV<lambda_7ff2aca09375d1b200a4b303c393fea9>@@` N.?AV<lambda_61b0a8f1bffa9925035250aed8800a73>@@` N.?AV<lambda_825b24178740a83899599d9a15262ad0>@@` N.?AV<lambda_bb17f1c9fdefab41fa9f3f1d7058625d>@@` N.?AV<lambda_1038cf6b5b3a06f7836c03470cc73cfe>@@` N.?AV<lambda_1942fa4df5477ceb10f450a8beea8e3d>@@` N.?AV<lambda_8d89ab19f4dfa0ecffb29771863af791>@@` N.?AV<lambda_738b33a611f9a1f6d03758979f5cc533>@@` N.?AV<lambda_41ebeab5da8beef13ef5472118318ec8>@@` N.?AV<lambda_541cc282145ec8343140168eb3a104b8>@@` N.?AV<lambda_48a019694fb1712147887137d62e08bb>@@` N.?AV<lambda_cdf9a96be7f44ca64e9ca975d3a2e639>@@` N.?AV<lambda_f77d5c754e562d83991d8811ad6b8d9d>@@` N.?AV<lambda_8c0299b0bf89e6a3d14cac26e860593d>@@` N.?AV<lambda_b580fcda3a3024eff26df3c7424a3711>@@` N.?AV<lambda_2f7e6c9c388961921f339c558c64c7e0>@@` N.?AV<lambda_1be97077013d896b2585b5a1da2676d4>@@` N.?AV<lambda_e7006400d91f58ec3eac037bf49aac05>@@` N.?AV<lambda_84022bcc91846e2503f37be11723786f>@@` N.?AV<lambda_a6aba0176aba85267e01b03dae46193a>@@` N.?AV<lambda_f280485dd28a5a60c44583447c6712da>@@` N.?AV<lambda_0f2d7c63521fa66d6e19b6e7ea518845>@@` N.?AV<lambda_c856ea038f3ff85cdf434cbf89a84d09>@@` N.?AV<lambda_b4f55abaf552b988f9f372a9e0a05c48>@@` N.?AV<lambda_a5f5a9f171e58e65be69dae6ef698567>@@` N.?AV<lambda_5dc989b5395b9c0e6dc2b203955f6f53>@@` N.?AV?$TreeEnsembleRegressor@M@ml@onnxruntime@@` N.?AV?$TreeEnsembleRegressor@N@ml@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@V<lambda_541cc282145ec8343140168eb3a104b8>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_41ebeab5da8beef13ef5472118318ec8>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_738b33a611f9a1f6d03758979f5cc533>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_8d89ab19f4dfa0ecffb29771863af791>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_1942fa4df5477ceb10f450a8beea8e3d>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_1038cf6b5b3a06f7836c03470cc73cfe>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_bb17f1c9fdefab41fa9f3f1d7058625d>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_825b24178740a83899599d9a15262ad0>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_61b0a8f1bffa9925035250aed8800a73>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_7ff2aca09375d1b200a4b303c393fea9>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_247636e7ce7e67357cbf46607c82bed0>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_3928e725003c7a62c76f41f34cee3234>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_754485b4f4a6e247d8fd46f499ecbff1>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_c327e8b0febe30d927a0cd8dc39dba92>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_dc1d45b7832ae8b4e8193a30205c17a1>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_adbb0ed82c3e353a2ef8b51f4773ed3d>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_5dc989b5395b9c0e6dc2b203955f6f53>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_a5f5a9f171e58e65be69dae6ef698567>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_b4f55abaf552b988f9f372a9e0a05c48>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_c856ea038f3ff85cdf434cbf89a84d09>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_0f2d7c63521fa66d6e19b6e7ea518845>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_f280485dd28a5a60c44583447c6712da>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_a6aba0176aba85267e01b03dae46193a>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_84022bcc91846e2503f37be11723786f>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_e7006400d91f58ec3eac037bf49aac05>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_1be97077013d896b2585b5a1da2676d4>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_2f7e6c9c388961921f339c558c64c7e0>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_b580fcda3a3024eff26df3c7424a3711>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_8c0299b0bf89e6a3d14cac26e860593d>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_f77d5c754e562d83991d8811ad6b8d9d>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_cdf9a96be7f44ca64e9ca975d3a2e639>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_48a019694fb1712147887137d62e08bb>@@X_J@std@@` N.?AVZipMapOp@ml@onnxruntime@@` N.?AVReorderInput@contrib@onnxruntime@@` N.?AVReorderOutput@contrib@onnxruntime@@` N.?AVNchwcConv@contrib@onnxruntime@@` N.?AVNchwcMaxPool@contrib@onnxruntime@@` N.?AVNchwcPoolBase@contrib@onnxruntime@@` N.?AVNchwcAveragePool@contrib@onnxruntime@@` N.?AVNchwcUpsample@contrib@onnxruntime@@` N.?AV?$MatMulInteger16@FFH@contrib@onnxruntime@@` N.?AV?$QLinearLeakyRelu@C@contrib@onnxruntime@@` N.?AV?$QLinearLeakyRelu@E@contrib@onnxruntime@@` N.?AV<lambda_aa8adc05924e6be486c4fb6cc0747f9d>@@` N.?AV<lambda_cbcddd3d150686e1dc6a8ac44b0832dc>@@` N.?AV<lambda_78c322de656eddf9c1d065383df6072b>@@` N.?AV<lambda_5808f761dfc930ed79ea63df457669e1>@@` N.?AV<lambda_6646fce5ec2cef2dee2935b016776919>@@` N.?AV<lambda_1d3bd4f732a1f44a4c08227035b444b6>@@` N.?AV<lambda_c8f03fa2699f5ed252631702f9438724>@@` N.?AV<lambda_7d612d0b1b7b7c7243e3079c5a49ac80>@@` N.?AV?$QLinearAdd@C@contrib@onnxruntime@@` N.?AV?$QLinearAdd@E@contrib@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@V<lambda_cbcddd3d150686e1dc6a8ac44b0832dc>@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_aa8adc05924e6be486c4fb6cc0747f9d>@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_7d612d0b1b7b7c7243e3079c5a49ac80>@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_c8f03fa2699f5ed252631702f9438724>@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_1d3bd4f732a1f44a4c08227035b444b6>@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_6646fce5ec2cef2dee2935b016776919>@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_5808f761dfc930ed79ea63df457669e1>@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_78c322de656eddf9c1d065383df6072b>@@X_J_J@std@@` N.?AV<lambda_5d5370497b20b66134af9b7e2b337fcd>@@` N.?AV<lambda_25395042ec336ff6c2d2bf12e6e33481>@@` N.?AV<lambda_4a3dbd1b89fc0bebb34de978cf1b5369>@@` N.?AV?$QAttention@M@contrib@onnxruntime@@` N.?AVAttentionCPUBase@contrib@onnxruntime@@` N.?AVAttentionBase@contrib@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@V<lambda_5d5370497b20b66134af9b7e2b337fcd>@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_4a3dbd1b89fc0bebb34de978cf1b5369>@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_25395042ec336ff6c2d2bf12e6e33481>@@X_J_J@std@@` 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N.?AU?$MaxpoolWithMask1DTask@M@contrib@onnxruntime@@` N.?AVMaxpoolWithMask@contrib@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@U?$MaxpoolWithMask1DTask@M@contrib@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$MaxpoolWithMask2DTask@M@contrib@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$MaxpoolWithMask3DTask@M@contrib@onnxruntime@@X_J_J@std@@` N.?AV?$Unique@M@contrib@onnxruntime@@` N.?AV?$ConvTransposeWithDynamicPads@M@contrib@onnxruntime@@` N.?AV<lambda_d2a63565f05cd53c8bbd857da251fdac>@@` N.?AV?$CropAndResize@M@contrib@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@V<lambda_d2a63565f05cd53c8bbd857da251fdac>@@X_J@std@@` N.?AV?$CDist@M@contrib@onnxruntime@@` N.?AV?$CDist@N@contrib@onnxruntime@@` N.?AV<lambda_d44234032f9da3a8e659111375d4b0b2>@@` N.?AV<lambda_7e973787028859cebe35750559b8a424>@@` N.?AV<lambda_4092000fa2c851eb2ddefc1b6efbf1fc>@@` N.?AV?$BiasGelu@M$0A@@contrib@onnxruntime@@` N.?AV?$BiasGelu@M$00@contrib@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@V<lambda_4092000fa2c851eb2ddefc1b6efbf1fc>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_7e973787028859cebe35750559b8a424>@@X_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_d44234032f9da3a8e659111375d4b0b2>@@X_J@std@@` N.?AV<lambda_95c74ffd32a4d994a62a191daf0fff27>@@` N.?AV?$ElementWiseKernel@U?$ParametricSoftplus@M@functors@onnxruntime@@@onnxruntime@@` N.?AV?$ElementWiseKernel@U?$ScaledTanh@M@functors@onnxruntime@@@onnxruntime@@` N.?AV?$Gelu@M@contrib@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@U?$ScaledTanh@M@functors@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@U?$ParametricSoftplus@M@functors@onnxruntime@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_95c74ffd32a4d994a62a191daf0fff27>@@X_J@std@@` N.?AV?$Affine@M@contrib@onnxruntime@@` N.?AV?$Scale@M@contrib@onnxruntime@@` N.?AV?$Crop@M@contrib@onnxruntime@@` N.?AVCropBase@contrib@onnxruntime@@` N.?AV?$ImageScaler@M@contrib@onnxruntime@@` N.?AV<lambda_df2a0964730ff5c7d7d1a1cf96b7d7a0>@@` 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N.?AV?$_Func_impl_no_alloc@V<lambda_8fb056035962b4b1c96ec8d77794334f>@@VStatus@common@onnxruntime@@AEBVTensorShape@4@AEBUOrtMemoryInfo@@AEAUOrtValue@@AEA_N@std@@` N.?AV?$BahdanauAttention@M@contrib@onnxruntime@@` N.?AV?$IAttentionMechanism@M@contrib@onnxruntime@@` N.?AV?$AttentionWrapper@M@contrib@onnxruntime@@` N._J` N.M` N.N` N.C` N.E` N.F` N.G` N.H` N.I` N._K` N._N` N.?ATMLFloat16@onnxruntime@@` N.?AUBFloat16@onnxruntime@@` N.?AV?$map@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@MU?$less@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@@2@V?$allocator@U?$pair@$$CBV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@M@std@@@2@@std@@` N.?AV?$map@_JMU?$less@_J@std@@V?$allocator@U?$pair@$$CB_JM@std@@@2@@std@@` N.?AV<lambda_d8a9ac16dd775f4bb1f670ae469711dc>@@` N.?AVDataTypeImpl@onnxruntime@@` N.?AVTensorTypeBase@onnxruntime@@` N.?AVSparseTensorTypeBase@onnxruntime@@` N.?AVSequenceTensorTypeBase@onnxruntime@@` N.?AVNonTensorTypeBase@onnxruntime@@` 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N.?AV?$_Func_impl_no_alloc@V<lambda_e15d10c4385b9adab7c3b2967076a5a0>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_423c7f4514cb0f580d17c1969d94ab4d>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_6a32611970906250c6b47d8292ad00e7>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_1c17b461588ff1b22a6f9497bff5b28b>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_26de9b5e95466f00708e3ae83a84c608>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_01985760e667ba58bff711ef31d8e638>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_a30982bdf1dc90ee13caf4d420ec5e7f>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_98f67966096a245ba60cb535f4355420>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_2d30aee68f6d7fc70a227ffb90481678>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_29de7925891b86f5f645868375930fe4>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_6dbfc8ac2ef01e9edaeb93285970ba53>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_4232a0dfb9482a2299101a5b477c0d1d>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_835d62b1924b8ffc54c829b13cf11321>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_aec4de7949c02bcbac8b3e442b961471>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_535e3d990370b194f36a71a48f5370e9>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_d49bbfb4942fd85adb6441f849e70bf8>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_9d8771d050eb3774da949f76ff7b2f7a>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_e74921842af68f126d6a963647d9b662>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_29460ba72c9a9f61937f695b212c2385>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_9a36e00d72a5d737ec77f3dedc2d9072>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_591099244d1a9304b34abc9d814aa7e1>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_076f399d54601fd1059236c6a3813828>@@XAEAUInferenceContext@onnx@@@std@@` N.?AUNodeCompare@onnxruntime@@` N.?AV<lambda_3749a2cd3b1b084d4974335060b45681>@@` N.?AV?$_Func_base@_NPEBVNode@onnxruntime@@PEBV12@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_3749a2cd3b1b084d4974335060b45681>@@XPEBVNode@onnxruntime@@@std@@` N.?AV?$_Func_impl_no_alloc@UNodeCompare@onnxruntime@@_NPEBVNode@2@PEBV32@@std@@` N.?AV<lambda_11ec20585bb2fe859a74ddb859a4fc34>@@` N.?AVFunction@onnxruntime@@` N.?AVFunctionImpl@onnxruntime@@` N.?AV?$_Func_impl_no_alloc@V<lambda_11ec20585bb2fe859a74ddb859a4fc34>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_354dc85dad3805ff6f2fbfb7380e3b0e>@@` N.P6AXAEAVOpSchema@onnx@@@Z` N.?AV<lambda_581e172f6e6bf6d53a792d4f2adbb4c0>@@` N.?AV<lambda_d6594629af7ec171cce8376fcf8dcd4e>@@` N.?AV<lambda_c2fef1cfbd08e9e21bcb74b985df87e6>@@` N.?AV<lambda_a7a0686f23e7f9337eaa10f069ee321b>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_a7a0686f23e7f9337eaa10f069ee321b>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_c2fef1cfbd08e9e21bcb74b985df87e6>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_d6594629af7ec171cce8376fcf8dcd4e>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_581e172f6e6bf6d53a792d4f2adbb4c0>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@P6AXAEAVOpSchema@onnx@@@ZXAEAV12@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_354dc85dad3805ff6f2fbfb7380e3b0e>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_9289ae269610356b68d5c3f46e89bf66>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_9289ae269610356b68d5c3f46e89bf66>@@XAEAUInferenceContext@onnx@@@std@@` N.?AVResultException@wil@@` N.?AVEnvThread@onnxruntime@@` N.?AVWindowsThread@?A0x60113b0b@onnxruntime@@` N.?AVWindowsEnv@?A0x60113b0b@onnxruntime@@` N.?AVEnv@onnxruntime@@` N.?AV<lambda_bf1e17f352952e1691663e19b2a4e39f>@@` N.?AV<lambda_7bd5699a6d14768c6970f4f46517e650>@@` N.?AV<lambda_fbf846fcdb216fc0eda391b10677ea03>@@` N.?AV<lambda_bced8396cef9d7b290e4a73834f6ea86>@@` N.?AV<lambda_c95605adc80afd47f38e7169b5ea1e30>@@` N.?AVThreadPoolInterface@Eigen@@` N.?AVExtendedThreadPoolInterface@concurrency@onnxruntime@@` N.?AV?$ThreadPoolTempl@VEnv@onnxruntime@@@onnxruntime@@` N.?AV?$_Func_base@_N$$V@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_fbf846fcdb216fc0eda391b10677ea03>@@X$$V@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_7bd5699a6d14768c6970f4f46517e650>@@X_J_J@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_bf1e17f352952e1691663e19b2a4e39f>@@X$$V@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_c95605adc80afd47f38e7169b5ea1e30>@@_N$$V@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_bced8396cef9d7b290e4a73834f6ea86>@@X$$V@std@@` N.?AV?$numpunct@D@std@@` N.?AVTelemetry@onnxruntime@@` N.?AVWindowsTelemetry@onnxruntime@@` N.?AVWindowsEnvTime@?A0xbc212d12@onnxruntime@@` N.?AVEnvTime@onnxruntime@@` N.?AV<lambda_9a122db8c0c6f400c116ad5d2cdaf0bc>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_9a122db8c0c6f400c116ad5d2cdaf0bc>@@X_J@std@@` N.?AV<lambda_1009fe77501925cd48ec95dce1652db1>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_1009fe77501925cd48ec95dce1652db1>@@X_J@std@@` N.?AVNumCapturesWalker@re2@@` N.?AV?$Walker@H@Regexp@re2@@` N.?AVRepetitionWalker@re2@@` N.?AVCompiler@re2@@` N.?AV?$Walker@UFrag@re2@@@Regexp@re2@@` N.?AVToStringWalker@re2@@` N.?AVCoalesceWalker@re2@@` N.?AV?$Walker@PEAVRegexp@re2@@@Regexp@re2@@` N.?AVSimplifyWalker@re2@@` N.?AV?$basic_stringstream@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@` N.?AVruntime_error@std@@` N.?AVbad_alloc@std@@` N.?AV<lambda_a4fe3bd39dcd0107f90455cb45c21429>@@` N.?AV?$basic_stringbuf@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@` N.?AVios_base@std@@` N.?AV?$basic_iostream@DU?$char_traits@D@std@@@std@@` N.?AV?$_Func_base@_NH@std@@` N.?AV?$_Iosb@H@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_cda3c60f0bca96d0a80bc61bce840378>@@_NH@std@@` N.?AV?$basic_streambuf@DU?$char_traits@D@std@@@std@@` N.?AVlogic_error@std@@` N.?AV<lambda_cda3c60f0bca96d0a80bc61bce840378>@@` N.?AVValidationError@checker@onnx@@` N.?AVISchemaRegistry@onnx@@` N.?AV?$_Func_impl_no_alloc@V<lambda_ca87e505bf8a7e1f26579083c3683d94>@@_NH@std@@` N.?AVOpSchemaRegistry@onnx@@` N.?AV?$basic_istream@DU?$char_traits@D@std@@@std@@` N.?AVSchemaError@onnx@@` N.?AV?$basic_ios@DU?$char_traits@D@std@@@std@@` N.?AV<lambda_ca87e505bf8a7e1f26579083c3683d94>@@` N.?AV?$basic_ostream@DU?$char_traits@D@std@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_a4fe3bd39dcd0107f90455cb45c21429>@@_NH@std@@` N.?AVexception@std@@` N.?AVbad_array_new_length@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_33763db33c7430e0074fd25b65b4479f>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_8e283faf1927c37e7b785e03970f6e42>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_2e20e62e8d812957e92321838accac54>@@XAEAVOpSchema@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_0af4c846dd5af6632beb77ffbd6469c9>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_a4d231a5b40cc526083f2482aa6952a1>@@` N.?AV<lambda_1249aa664b6dd8c7cf40d23fbaec080e>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_5c5ac1f6c71d812ad45802a5c8ea5757>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_5fe773ac1f878f81aadf16bc155901d0>@@XAEAVOpSchema@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_a4d231a5b40cc526083f2482aa6952a1>@@XAEAUInferenceContext@onnx@@@std@@` N.?AVInferenceError@onnx@@` N.?AV<lambda_abb9581c367ec240a8c0e5afd8181e99>@@` N.?AV<lambda_22aeb0e20ec618f43aaa785665edaa1d>@@` N.?AV<lambda_5c5ac1f6c71d812ad45802a5c8ea5757>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_fd266a9166d0b10d35a0d8f14994daa2>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@P6AXAEAUInferenceContext@onnx@@@ZXAEAU12@@std@@` N.?AV?$_Func_base@XAEAVOpSchema@onnx@@@std@@` N.?AV<lambda_33763db33c7430e0074fd25b65b4479f>@@` N.?AV<lambda_0af4c846dd5af6632beb77ffbd6469c9>@@` N.?AV?$_Func_base@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_abb9581c367ec240a8c0e5afd8181e99>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_22aeb0e20ec618f43aaa785665edaa1d>@@XAEAVOpSchema@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_aec81523952d967a50c07470b5814993>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_8e283faf1927c37e7b785e03970f6e42>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_5fe773ac1f878f81aadf16bc155901d0>@@` N.?AV<lambda_aec81523952d967a50c07470b5814993>@@` N.?AV<lambda_fd266a9166d0b10d35a0d8f14994daa2>@@` N.?AV<lambda_2e20e62e8d812957e92321838accac54>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_1249aa664b6dd8c7cf40d23fbaec080e>@@XAEAVOpSchema@onnx@@@std@@` N.P6AXAEAUInferenceContext@onnx@@@Z` N.?AV?$_Func_impl_no_alloc@V<lambda_51967558c7fd075e660195f4e6702ce2>@@XAEAVOpSchema@onnx@@@std@@` N.?AV<lambda_51967558c7fd075e660195f4e6702ce2>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_7c61692eadeb4ccd34c8bb00529bfb83>@@XAEAVOpSchema@onnx@@@std@@` N.?AV<lambda_8dc767ccba8e089e41f89b04cb108c10>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_8dc767ccba8e089e41f89b04cb108c10>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_7c61692eadeb4ccd34c8bb00529bfb83>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_6b0122b627329aed747a7281e017781a>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_fdb0134f4d01b5f723238dd45cc4059f>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_aa88e462824a3ef64606c92b2b205d10>@@XAEAVOpSchema@onnx@@@std@@` 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N.?AV<lambda_a26d879b56b4c4dd33ee8f6ff5f1da50>@@` N.?AV<lambda_d2d693a490e9887da5a024c703dc8e3e>@@` N.?AV<lambda_90811df5034c6b064f00ff028e410b34>@@` N.?AV<lambda_4bfaa77b47cc3b72c0199e4158dcfdd3>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_747f5a5054cea5042105b3988bb8e54a>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_90811df5034c6b064f00ff028e410b34>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_a26d879b56b4c4dd33ee8f6ff5f1da50>@@XAEAVOpSchema@onnx@@@std@@` N.?AV<lambda_6aeba50936d3f6c7b11f3c24b58165e0>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_6aeba50936d3f6c7b11f3c24b58165e0>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_af999ce2e03a2039dbf31c1ff13e0675>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_006f042491572090b778a6887556c541>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_bc30b190ccdc11003b68eaf48bc55331>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_242966bc823a6ea57016299e4846b19a>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_af999ce2e03a2039dbf31c1ff13e0675>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_3e82b80a46eb8b8da77a09b479ed55aa>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_048b899a3305cb2c0c070dd18e5b4d17>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_845fb0d365668e61a65e09bae6280c09>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_cc9e076beb57119667e5b07bb3c48707>@@` N.?AV<lambda_acdb2664af6229da1e4c43d330c314d8>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_048b899a3305cb2c0c070dd18e5b4d17>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_3e82b80a46eb8b8da77a09b479ed55aa>@@` N.?AV<lambda_d125101b1e77d289ab0937ad814f16d4>@@` N.?AV<lambda_af2c39467b4484bc2a2f611cf533daa7>@@` N.?AV<lambda_5fe1569db2b0273ffb3c5cf2a14ad27f>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_36945aef9b69b8bc246158e60ded74ff>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_af2c39467b4484bc2a2f611cf533daa7>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_dec8978adb68f88cd37c14b215792fcb>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_cc9e076beb57119667e5b07bb3c48707>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_845fb0d365668e61a65e09bae6280c09>@@` N.?AV<lambda_b5a1569a4859bf7d5992d0c8667fc3a1>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_6665c408830d0dff611752f43635ad2f>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_9f66adbf60979efdb31380fc6e67cfc6>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_5fe1569db2b0273ffb3c5cf2a14ad27f>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_b5a1569a4859bf7d5992d0c8667fc3a1>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_dec8978adb68f88cd37c14b215792fcb>@@` N.?AV<lambda_0f569c3741dec4cbd25547f5cfa47a1f>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_d125101b1e77d289ab0937ad814f16d4>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_a4fb1c1b5f905e04c31cbbe6c165878a>@@` N.?AV<lambda_36945aef9b69b8bc246158e60ded74ff>@@` N.?AV<lambda_5151cdb5e2d0e967b0ff9b516f4dde5e>@@` N.?AV<lambda_6665c408830d0dff611752f43635ad2f>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_0f569c3741dec4cbd25547f5cfa47a1f>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_9f66adbf60979efdb31380fc6e67cfc6>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_acdb2664af6229da1e4c43d330c314d8>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_a4fb1c1b5f905e04c31cbbe6c165878a>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_5151cdb5e2d0e967b0ff9b516f4dde5e>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_179736a688e3684f33e9d2d771388e57>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_179736a688e3684f33e9d2d771388e57>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_50bf1563c4f2a658d5a1f8997cf7d841>@@` N.?AV<lambda_4ae79009ffed4baaf3b13205e614d6ec>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_266a697d285ed599f30d31357bce4a03>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_266a697d285ed599f30d31357bce4a03>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_50bf1563c4f2a658d5a1f8997cf7d841>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_4ae79009ffed4baaf3b13205e614d6ec>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_b79d623619c8b488913a18719fb07ba7>@@XAEAVOpSchema@onnx@@@std@@` N.?AV<lambda_b79d623619c8b488913a18719fb07ba7>@@` N.?AV<lambda_1d7aed3977eb6b0e8500a44cbf6dc587>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_1d7aed3977eb6b0e8500a44cbf6dc587>@@XAEAVOpSchema@onnx@@@std@@` N.?AV<lambda_b9b9d9750b6f3b659b7284e2910babff>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_53687b87e8fc26669694bb154ed06213>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_509060774d0b6c6cfc7caff077e88a71>@@` N.?AV<lambda_f78d8769dd40946ef61ad5956cbf10fe>@@` N.?AV<lambda_5a12b29a96640e1d50d0ef1b8bb7a69d>@@` N.?AV<lambda_b86aa8ef1de0593a5547f08d792c1565>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_47f0845d7b66643be4c58515f34249ea>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_712b3a1726a5ab1ce39d1b3e7f50d979>@@` N.?AV<lambda_2ddd0d8cd869fa8dc4fac2f9f4975331>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_f78d8769dd40946ef61ad5956cbf10fe>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_6b832a451ffb27575469b881ed2ae55f>@@` N.?AV<lambda_3354f944db7877754471d985be3fe29b>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_d46115169117df503aa51a5d168fb77b>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_9fe0fc2569126954352ddf4163bf4b0e>@@XAEAVOpSchema@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_d4401871bdc5ffca65a3961365881175>@@XAEAVOpSchema@onnx@@@std@@` N.?AV<lambda_8521399b4c6db3300201719397cab6ec>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_bd7fa35736f1900749b076a153cb575b>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_e6c74cfaedca483820814aad39162c18>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_991d63fb8a809dc84e4e523068ce486a>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_8521399b4c6db3300201719397cab6ec>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_250623d3ed4737c34dbd29a2414d2af8>@@XAEAVOpSchema@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_6b832a451ffb27575469b881ed2ae55f>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_c5c3eebc37781735abaa6644caf20441>@@` N.?AV<lambda_be44d9505d2a14fdbc5db43bf81e0320>@@` N.?AV<lambda_53687b87e8fc26669694bb154ed06213>@@` N.?AV<lambda_bd25e880f204cf3766f62930b3453f41>@@` N.?AV<lambda_47f0845d7b66643be4c58515f34249ea>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_bd25e880f204cf3766f62930b3453f41>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_991d63fb8a809dc84e4e523068ce486a>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_be44d9505d2a14fdbc5db43bf81e0320>@@XAEAVOpSchema@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_509060774d0b6c6cfc7caff077e88a71>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_253a3836eec6c1ddbcb7d28211e21bd6>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_253a3836eec6c1ddbcb7d28211e21bd6>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_b86aa8ef1de0593a5547f08d792c1565>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_bd7fa35736f1900749b076a153cb575b>@@` N.?AV<lambda_e6c74cfaedca483820814aad39162c18>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_c5c3eebc37781735abaa6644caf20441>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_712b3a1726a5ab1ce39d1b3e7f50d979>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_5a12b29a96640e1d50d0ef1b8bb7a69d>@@XAEAVOpSchema@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_6df9981d4852f552acd570612ebd2ffb>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_250623d3ed4737c34dbd29a2414d2af8>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_b9b9d9750b6f3b659b7284e2910babff>@@XAEAVOpSchema@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_3354f944db7877754471d985be3fe29b>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_9fe0fc2569126954352ddf4163bf4b0e>@@` N.?AV<lambda_d4401871bdc5ffca65a3961365881175>@@` N.?AV<lambda_d46115169117df503aa51a5d168fb77b>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_2ddd0d8cd869fa8dc4fac2f9f4975331>@@XAEAVOpSchema@onnx@@@std@@` N.?AV<lambda_6df9981d4852f552acd570612ebd2ffb>@@` N.?AV<lambda_2cf5a4a82673463f54ffac9172886ca4>@@` N.?AV<lambda_0b84cd883df2cf8f5da7751da99be58c>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_6dc2963702e16df290c0b974e000f741>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_26f8b100d862b421c66c54de1bbfa771>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_0b70c8b67f3139e48932fe25265ba3f6>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_221457049dd6e599b1e592be3898266a>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_221457049dd6e599b1e592be3898266a>@@` N.?AV<lambda_21bbe55a8317350b271af4fea78406d7>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_a6d62ed7a3aadce21a3a08f0b111f7e9>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_c5d939c151a5b0150dd301b9cecd34a4>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_1cdda74d195f4dc7bbdbaed3ee174bf7>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_c8d5656d7d62065e8f8ae45430481a0c>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_5a03cad439a535c73c9479bab198ff8a>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_5ac88b4415b3e2499965cefbd12b6078>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_8e59199324395720fba09bee90960ebd>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_2cf5a4a82673463f54ffac9172886ca4>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_4a689e51a08fa28fb0cc4e712ffec807>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_c482cce9a8fa14226cb54b28108e6505>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_2c336c69e37df1f232f1301ffdf6cbc8>@@` N.?AV?$basic_ostringstream@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_4b00d74e2c612e5c8818898b970e1549>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_5ac88b4415b3e2499965cefbd12b6078>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_1460d732f05545e9f33432100af6ef9e>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_2a39988023ade309aa40582a95355447>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_0b70c8b67f3139e48932fe25265ba3f6>@@` N.?AV<lambda_c677074de8527bbe1c1cd81da13161de>@@` N.?AV<lambda_c8d5656d7d62065e8f8ae45430481a0c>@@` N.?AV<lambda_4b00d74e2c612e5c8818898b970e1549>@@` N.?AV<lambda_1082961df5fe1bd5c097e00a9fdaa794>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_4d1d7b87337da46946e4654aecea589b>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_2a39988023ade309aa40582a95355447>@@` N.?AV<lambda_ce473b479310095c7321cd7e93391654>@@` N.?AV<lambda_5a03cad439a535c73c9479bab198ff8a>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_88b1496ecc213c07fd24b30bb5c856e7>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_ce473b479310095c7321cd7e93391654>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_4a689e51a08fa28fb0cc4e712ffec807>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_2c336c69e37df1f232f1301ffdf6cbc8>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_1cdda74d195f4dc7bbdbaed3ee174bf7>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_21bbe55a8317350b271af4fea78406d7>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_da4f627e80a11bdea4a1accacd5b827d>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_3e2860b55958cf532cc6672e843fd5e5>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_1460d732f05545e9f33432100af6ef9e>@@` N.?AV<lambda_26f8b100d862b421c66c54de1bbfa771>@@` N.?AV<lambda_da4f627e80a11bdea4a1accacd5b827d>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_c677074de8527bbe1c1cd81da13161de>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_ba8d0238de6b36721be5610f549806d6>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_2056df6b8dc225b88032605745f7ccf8>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_6dc2963702e16df290c0b974e000f741>@@` N.?AV<lambda_7301a0db9d31d6acd442bda8894c6274>@@` N.?AV<lambda_3e2860b55958cf532cc6672e843fd5e5>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_8e59199324395720fba09bee90960ebd>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_7301a0db9d31d6acd442bda8894c6274>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_ba8d0238de6b36721be5610f549806d6>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_0b84cd883df2cf8f5da7751da99be58c>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_c482cce9a8fa14226cb54b28108e6505>@@` N.?AV<lambda_c5d939c151a5b0150dd301b9cecd34a4>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_1082961df5fe1bd5c097e00a9fdaa794>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_4d1d7b87337da46946e4654aecea589b>@@` N.?AV<lambda_a6d62ed7a3aadce21a3a08f0b111f7e9>@@` N.?AV<lambda_88b1496ecc213c07fd24b30bb5c856e7>@@` N.?AV<lambda_2056df6b8dc225b88032605745f7ccf8>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_62f1ce9bc208e37c7e5739c8f36388ed>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_8008e4ccf60b7b3461762c21fcc34459>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_ba3833c4d2d1066f3c4df2520bacec42>@@XAEAVOpSchema@onnx@@@std@@` N.?AV<lambda_62f1ce9bc208e37c7e5739c8f36388ed>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_8008e4ccf60b7b3461762c21fcc34459>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_ba3833c4d2d1066f3c4df2520bacec42>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_65ea036252223d30a37862dfd4f7bc69>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_1bcb070a4c4ebd1f4b8f93445027461d>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_16aeacf7ad4b53ba550defbc9c03abde>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_1f77acb4e34034b00a5a2150384c9d77>@@` N.?AV<lambda_65ea036252223d30a37862dfd4f7bc69>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_c5865cad64516847135ebdc45b1a26fd>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_5f7fb9364373cc8ddaf36bbb9f71e01f>@@` N.?AV<lambda_5e0f6565dc7aa7f6ebc79fe2fd37d8ea>@@` N.?AV<lambda_9e6ceadf9f52df63851bbc9de7ecfdef>@@` N.?AV<lambda_16aeacf7ad4b53ba550defbc9c03abde>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_5e0f6565dc7aa7f6ebc79fe2fd37d8ea>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_42f0b87bcc5dd8c3610944e6f35bd1b4>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_9e6ceadf9f52df63851bbc9de7ecfdef>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_5f7fb9364373cc8ddaf36bbb9f71e01f>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_1f77acb4e34034b00a5a2150384c9d77>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_42f0b87bcc5dd8c3610944e6f35bd1b4>@@` N.?AV<lambda_c5865cad64516847135ebdc45b1a26fd>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_1bcb070a4c4ebd1f4b8f93445027461d>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_af9e3a5498c32e3a850424bc29815ee4>@@` N.?AV<lambda_3ce4407e5879111e6c1a6435d36077e4>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_2a39ec2312de9d32eb03cad38e15c652>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_348952d4e8cb4d6ca40b91f67d9fe4cc>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_fc7b35ab3608d976b44d1ea7b5ca9a27>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_0994f7bd42ae4e9229e92c4d5377c341>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_af9e3a5498c32e3a850424bc29815ee4>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_5dbb8c8b529c925f8e2dcff99b214d3d>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_fa747d3dc1360106b7536ffe2f5b13bc>@@` N.?AV<lambda_52aa0d6f333d1cf3e838c9b5d46f6179>@@` N.?AV<lambda_43cffa19aa633384f3f2bcef719191ba>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_3ce4407e5879111e6c1a6435d36077e4>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_2a39ec2312de9d32eb03cad38e15c652>@@` N.?AV<lambda_b881ee6a3336741e3e3697374c374936>@@` N.?AV<lambda_770c0f136e5e8ad37ffc1c73d3de5ab3>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_8a3f03cf93ba764f7eebf7fd98d3a893>@@XAEAUInferenceContext@onnx@@@std@@` N.P6A_NAEBUFunctionBodyBuildContext@onnx@@AEBVOpSchema@1@AEAVFunctionProto@1@@Z` N.?AV?$_Func_impl_no_alloc@V<lambda_2190ceb29dc0a951ce0549179f6c8731>@@XAEAVOpSchema@onnx@@@std@@` N.?AV<lambda_8a3f03cf93ba764f7eebf7fd98d3a893>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_52aa0d6f333d1cf3e838c9b5d46f6179>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_34ccb8e58adb0e2109c03b2e2ce030da>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_5dbb8c8b529c925f8e2dcff99b214d3d>@@` N.?AV<lambda_895eb2e0f48a5ea5d33fa51877100e88>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_fa747d3dc1360106b7536ffe2f5b13bc>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_770c0f136e5e8ad37ffc1c73d3de5ab3>@@XAEAVOpSchema@onnx@@@std@@` N.?AV<lambda_2190ceb29dc0a951ce0549179f6c8731>@@` N.?AV<lambda_348952d4e8cb4d6ca40b91f67d9fe4cc>@@` N.?AV<lambda_a4f8d0b63d2d14454dd574c398496bb4>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_43cffa19aa633384f3f2bcef719191ba>@@XAEAVOpSchema@onnx@@@std@@` N.?AV<lambda_fc7b35ab3608d976b44d1ea7b5ca9a27>@@` N.?AV<lambda_34ccb8e58adb0e2109c03b2e2ce030da>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_b881ee6a3336741e3e3697374c374936>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_027f0b72d20b824e4e30fa2ccfde3681>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_a4f8d0b63d2d14454dd574c398496bb4>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_0994f7bd42ae4e9229e92c4d5377c341>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_895eb2e0f48a5ea5d33fa51877100e88>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_base@_NAEBUFunctionBodyBuildContext@onnx@@AEBVOpSchema@2@AEAVFunctionProto@2@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_027f0b72d20b824e4e30fa2ccfde3681>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@P6A_NAEBUFunctionBodyBuildContext@onnx@@AEBVOpSchema@2@AEAVFunctionProto@2@@Z_NAEBU12@AEBV32@AEAV42@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_f627104a03bd1a7183756e2f736a43e4>@@XAEAVOpSchema@onnx@@@std@@` N.?AV<lambda_f627104a03bd1a7183756e2f736a43e4>@@` N.?AV<lambda_404084d3edacc8294adecb096c00b5ef>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_a4a8f47c88f81824ff45fecdeb8c2272>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_a4a8f47c88f81824ff45fecdeb8c2272>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_404084d3edacc8294adecb096c00b5ef>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_64826d400df5e2683a863a4dbb954602>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_64826d400df5e2683a863a4dbb954602>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_2106de50a90423f7f3e101a627b3ccb6>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_cd453f5abbb4020fb3775475830cd8d1>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_cd453f5abbb4020fb3775475830cd8d1>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_2106de50a90423f7f3e101a627b3ccb6>@@` N.?AV<lambda_8cfc19b159bc2ad02b511fa28adaf6ba>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_3439ab6883c391ffb4d0d8b3d13a7231>@@XAEAVOpSchema@onnx@@@std@@` N.?AV<lambda_1c41652b6052bb757260928fa258e407>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_8cfc19b159bc2ad02b511fa28adaf6ba>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_16efe5fe9404c7d1fc0ba9a0b9c1a8da>@@XAEAVOpSchema@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_1c41652b6052bb757260928fa258e407>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_16efe5fe9404c7d1fc0ba9a0b9c1a8da>@@` N.?AV<lambda_3439ab6883c391ffb4d0d8b3d13a7231>@@` N.?AV<lambda_37fc46271b5a9d577e78557058b76819>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_fd8e5ea2295ada6c690d45751a324e49>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_b1f9e5e36b949ed88a11b0aef456627c>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_6be06c8ff959093bab4d8aab0886de78>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_6be06c8ff959093bab4d8aab0886de78>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_498152c69cf01a4f569fe16eb60f370c>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_e9693890915c6d7ba62baf31a8b3446b>@@` N.?AV<lambda_c4dc4b2967ca7204e8ba49b0607e0250>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_b5eebe635e03bae7b4df2758c52bcfd7>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_fd8e5ea2295ada6c690d45751a324e49>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_37fc46271b5a9d577e78557058b76819>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_b1f9e5e36b949ed88a11b0aef456627c>@@` N.?AV<lambda_b5eebe635e03bae7b4df2758c52bcfd7>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_c4dc4b2967ca7204e8ba49b0607e0250>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_498152c69cf01a4f569fe16eb60f370c>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_e9693890915c6d7ba62baf31a8b3446b>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_b735437b7aef22111ecd0d5feb1ba583>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_53958a524045125d538038a834c170f9>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_53958a524045125d538038a834c170f9>@@` N.?AV<lambda_73119717ae86d28cb548ac4543eb73ed>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_9f22cccb787c8b0be66f7b40706128ae>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_0aa1cbb10b7e27c8eaa9e1c4d936a012>@@` N.?AV<lambda_9f22cccb787c8b0be66f7b40706128ae>@@` N.?AV<lambda_1476157c6a284e4f379191854345eea5>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_d7d65f11678621cb189879800cf53faf>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_b735437b7aef22111ecd0d5feb1ba583>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_d7d65f11678621cb189879800cf53faf>@@` N.?AV<lambda_62526b7eac31a4fea5091c2f348a5e23>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_62526b7eac31a4fea5091c2f348a5e23>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_1476157c6a284e4f379191854345eea5>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_0aa1cbb10b7e27c8eaa9e1c4d936a012>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$_Func_impl_no_alloc@V<lambda_73119717ae86d28cb548ac4543eb73ed>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV<lambda_2997ea06f466f3cb7af7580139e0e9b1>@@` N.?AV?$_Func_impl_no_alloc@V<lambda_2997ea06f466f3cb7af7580139e0e9b1>@@XAEAUInferenceContext@onnx@@@std@@` N.?AV?$basic_filebuf@DU?$char_traits@D@std@@@std@@` N.?AV?$basic_ifstream@DU?$char_traits@D@std@@@std@@` N.?AVZeroCopyInputStream@io@protobuf@google@@` N.?AVbad_cast@std@@` N.?AUInferenceContext@onnx@@` N.?AUInferenceContextImpl@shape_inference@onnx@@` N.?AVGraphInferencer@onnx@@` N.?AVGraphInferencerImpl@shape_inference@onnx@@` N.?AVTensorProto@onnx@@` N.?AVTensorProto_Segment@onnx@@` N.?AVModelProto@onnx@@` N.?AVTensorShapeProto_Dimension@onnx@@` N.?AVTensorAnnotation@onnx@@` N.?AVTypeProto_Map@onnx@@` N.?AVOperatorSetIdProto@onnx@@` N.?AVValueInfoProto@onnx@@` N.?AVTypeProto_Tensor@onnx@@` N.?AVGraphProto@onnx@@` N.?AVStringStringEntryProto@onnx@@` N.?AVTypeProto_Sequence@onnx@@` N.?AVSparseTensorProto@onnx@@` N.?AVTypeProto_SparseTensor@onnx@@` N.?AVTypeProto_Opaque@onnx@@` N.?AVTrainingInfoProto@onnx@@` 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