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E.B.0@F@I.@I.C.0I.0@PFX.X.X.X.0@FX.X.X.X.0@ FP(/P(/0)/@(/0@F(/(/*/(/0@F'/'/*/'/0@F(/(/0)/(/0@F(/(/p*/(/0@F'/'/+'/0@F@'/@'/+/0'/0@0F'/'/*/'/0@PF'/'/+/&/0@F '/ '/p+'/0@xF(/(/`*/(/0@0F`'/`'/*/P'/0@Fp(/p(/*/`(/ >@pF0(/0(/0)/ (/0@F'/'/*/p'/0@F//`//0@0F@/@//0/0@XF / /P//0@F////0@F`/`/0/P/0@F////0@F////0@F////0@0F////0@(F////0@F////0@F`/`//P/0@0F////0@F////0@F//0/p/0@xF////0@XF////0@`F`/`//P/0@F//0//0@F//p/p/0@@F / ///0@hF@/@//0/0@F// /p/0@F// //0@F / ///0@XF////0@F@/@/P/0/0@F`/`/@/P/0@F////0@F///p/0@FP0P0`0@00@F00000@xF00000@F00000@F00P000@Fp0p00`00@F0000p0 00@F00000@XF00000@FP0P0+@00@F0000/ 00@8Fк0к0P000@Fp0p00`00@ F00/00@F000p00@F00@+00@`F00000@F00000@ Fм0м0о000@pFp0p00`00@FP0P0@0@0 >@ F@0@00000@F00 000@xF`0`00P00@F00000 >@F00/00@Fл0л0 +00@F 0 0000@F0000>@hF00 +p00@F00000@F 0 0000@xF00000@Fp0p00`00@F00000@F@0@00000@0F@11@11110110@F0101@41p010@F`11`11p11P110@8F010121010@F111111010@F 11 1111110@xF010111010@F`01`01P41P010@F010121010@`F@[1@[1 +0[10@F [1 [1+[10@Fm1m1m1m10@@Fm1m1o1m10@Fm1m10)/pm10@`Fq1q1q1pq10@(G11/10@@G`1`1/P10@G11/p10@F11/10@F@1@101010@G11P1p10@F11 110@@G11+10@G11@110@F`1`1`1P10@F11110@F@1@1p1010@F11/10@F11`/10@F 1 1110@xG 1 1/10@0G!2!2"2!20@XG@!2@!200!20@ G0 20 2/ 20@ G 2 20p 20@0G22$220@G 2 2#2 20@G 2 2#2 20@G 2 2#2 20@HG!2!2"2!20]@8 G`!2`!2`#2P!2>@G!2!2"2!20@pG 2 2$2 20@HGP 2P 2#2@ 20@ G !2 !2p#2!20@G22/20@XGЌ2Ќ2+20@ GP2P2*/@20@G22+20@ Gp2p22`2 >@ G@2@22020@ G22220@ G22220@ G22Ѝ220@X Gp2p22`20@G22`220@`G0202@2 20@G22 220@G 2 2220@Gp2p22`20@GP2P2m1@20@G22220@`G22 220@(G 3 3 3 30@G 3 3 3 30@G 3 3@ 3p 30@G 3 30 3 30@PG 3 GА3@3`3333Д33xGp3PBG@t4|433043v44z4w40z4y45p3 504PGЕ44330434444445p3 504GI4O4P3@3043J44`N4 K4M40M45p3 504Gp4*43304344(440&4p$45p3 504G@l4s433043`n44pr4n4q4p45p3 504 G404p3`3043P-4404-4@04/45p3 504PG`44P3@3043@44Ї440445p3 504G+42433043P-4404-4@04/45p3 504(G 4433043444@44p45p3 504 Gp4433043044444045p3 504G24 8433043p344@7434p64545p3 504G443304344 44445p3 504G`4403 3043444`4`445p3 504G3:5:043::4::: 55: 504(G4 43304344P 44p 4 45p3 504G`}4Ђ433043~44 4~44Ѐ45p3 5048G4433043@44Ї440445p3 504`GP4k4p3`3043U44 e4V4a4[45p3 504@G 94 I403 30430<44PE4<4B4@45p3 504G3 4`3P3043343`3335p3 504xG4p433043444P4p445p3 5040!G`4 4!G444`40Q5["G440440Q5["G@44p440Q5[!G044444$G5P555`5#G 5555:$G5 5@5P55$G5p550:%G 5P5p55P5z6z6z6z6{60{6P{6p{6X&G6P6p6p660&G6666:&G666p66'G566504Unknown exception/\ : was false. Stacktrace: CpuVIWEFai.onnx.ml!=C:\agent\_work\30\s\include\onnxruntime\core/framework/tensor.h)Trying to get a Tensor, but got: IsTensor()OrtValue::GetC:\agent\_work\30\s\include\onnxruntime\core/framework/ml_value.hOrtValue::GetMutableTrying to get a TensorSeq, but got: IsTensorSequence()ai.onnxCPUExecutionProviderDmlExecutionProviderTensor sequence must contain only primitive typeselem_type_ != nullptronnxruntime::TensorSeq::SetTypeC:\agent\_work\30\s\onnxruntime\core/framework/TensorSeq.hi < tensors_.size()onnxruntime::TensorSeq::Getenv_ptr == p_instance_OrtEnv::ReleaseC:\agent\_work\30\s\onnxruntime\core\session\onnxruntime_c_api.ccsize 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()OrtCreateValueImplSeqHelperSequences 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::GetOpaqueValue1.1.0vector too longinvalid string positionT Tensor type mismatch. utils::IsPrimitiveDataType(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::allocator > > >::checkC:\agent\_work\30\s\include\onnxruntime\core/framework/data_types_internal.honnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,struct std::less<__int64>,class std::allocator,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,double,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,double> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,__int64,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,__int64> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,class std::basic_string,class std::allocator >,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,class std::basic_string,class std::allocator > > > > >::checkCheck for duplicate types in MLTypeCallDispatcherRetcalled_ < 2onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper::GetSequence is missing type entry for its elementonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > >,class std::allocator,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > >,class std::allocator,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > > > >::checkstring too longinvalid vector subscript != onnxruntime::DataTypeImpl::GetType() == type_map/set too longEncountered nullptr.not implementedexecution_mode is not validgraph_optimization_level is not valid is not implementedunknownonnxruntime::OpKernel::ComputeAsync' in custom op 'Result buffer is not large enoughDescriptionUnsupported version 'onnxruntime::CreateCustomRegistryAC:\agent\_work\30\s\onnxruntime\core\session\custom_ops.cclist too longcom.microsoft.nchwccom.microsoftcom.microsoft.automl[TypeInferenceError] expected to have tensor type unknownInput Output Element type of input tensor(uint32)tensor(int32)tensor(uint64)tensor(float16)tensor(int64)tensor(double)tensor(float)tensor(uint16)tensor(uint8)tensor(int16)tensor(int8)tensor(string)tensor(complex64)tensor(bool)tensor(complex128) line ) from file , but it is already registered from file (domain: version: known by the checker.Trying to register schema with name in onnx/defs/schema.h)., but it its domain is notbumped the operator version but forgot to update the version range in DomainToVersionRange , ] (usually, this means you , but it its version is not in the inclusive range [Schema error: MemcpyFromHostC:\agent\_work\30\s\onnxruntime\core\session\environment.ccinputToutputXConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.YMemcpyToHostException caught: bad castAttempt to use DefaultLogger but none has been registered.]CUDAExecutionProviderProvider has already been registered.C:\agent\_work\30\s\onnxruntime\core/framework/execution_providers.honnxruntime::ExecutionProviders::AddTrying to get a SparseTensor, but got: allocator already registered.IsSparseTensor()onnxruntime::FeedsFetchesInfo::FeedsFetchesInfoC:\agent\_work\30\s\onnxruntime\core/framework/feeds_fetches_manager.hCreate_State_Compute_Release_State_onnxruntime::DummyArena::Maxonnxruntime::DummyArena::Used] MemcpyTransformer[json.exception..ort_configother_errorORT_LOAD_CONFIG_FROM_MODELsession_options%Y-%m-%d_%H-%M-%S are '0' and '1'. The environment variable contained the value: Reading the provided model for the ORT configThe only supported values for the environment variable C:\agent\_work\30\s\onnxruntime\core\session\inference_session.cconnxruntime::FinalizeSessionOptionsmodel_protoModel needs to be provided to check for ORT config within itEnvironment::IsInitialized()Environment must be initialized before creating an InferenceSession.Could not finalize session options while constructing the inference session. Error Message: onnxruntime::InferenceSession::ConstructorCommonintra_op_thread_poolstatus.IsOK()CastFloat16Transformerinter_op_thread_poolonnxruntime::InferenceSession::InferenceSessionGiven model could not be parsed while creating inference session. Error message: resultCould not parse model successfully while constructing the inference sessiononnxruntime::InferenceSession::~InferenceSessionError during EndProfiling(): Received nullptr for exec providerUnknown error during EndProfiling()Sequential execution must be enabled before registering DMLExecutionProviderMemory pattern must be disabled before registering DMLExecutionProviderReceived nullptr for custom registryonnxruntime::InferenceSession::AddCustomOpDomainsonnxruntime::InferenceSession::LoadThis session already contains a loaded model.Unknown exception in Load()Exception during loading: Encountered unknown exception in Load()model_loading_from_saved_protoModelProto 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.Could not find an implementation for the node onnxruntime::InferenceSession::TransformGraph ((].Node placements: [All nodes have been placed on [Main Graph instance should have populated all subgraphs when being resolved. Provider: [onnxruntime::InferenceSession::CreateSubgraphSessionStatesubgraphsubgraph_session_stateCreateSubgraphSessionState should have created an entry earlier.p_op_kernelonnxruntime::InferenceSession::InitializeSubgraphSessionsonnxruntime::InferenceSession::InitializeInitializing session.Model was not loaded.Model was not loadedAdding default CPU execution provider.Session has already been initialized.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.Parallel execution is currently not supported for the registered CUDA Execution Provider.Exception during initialization: Session successfully initialized.session_initializationEncountered unknown exception in Initialize() Expected: Please fix either the inputs or the model.Invalid rank for input: Got: Got invalid dimensions for input: for the following indices ) , expected: ( index: elements.Unexpected input data type. Actual: (Size mismatch: feed_names has elements, but feeds has is not expected to be of type tensor.Invalid Feed Input Name:onnxruntime::InferenceSession::ValidateInputsInput with name: is not expected to be of type tensor sequence. is not expected to be of type sparse tensor.At least one output should be requested.Output vector pointer is NULLOutput vector incorrectly sized: output_names.size(): p_fetches->size(): Session was not initializedInvalid Output Name:Session not initialized.onnxruntime::InferenceSession::RunEncountered unknown exception in Run()Running with tag: model_runonnxruntime::InferenceSession::GetOverridableInitializersonnxruntime::InferenceSession::GetModelInputsonnxruntime::InferenceSession::GetModelOutputsonnxruntime::InferenceSession::EndProfilingProfiler is disabled.Could not write a profile because no model was loaded.run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::CreateLoggerForRunInvalid session log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::InitLoggersession_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)graph_optimization_level <= TransformerLevel::MaxLevelExceeded max transformer level. Current level is set to onnxruntime::InferenceSession::AddPredefinedTransformersinvalid hash bucket count961c151d2e87f2686a955a9be24d316f1362bf21 3.7.1.json_localtime_s(&local_tm, &in_time_t) == 0onnxruntime::`anonymous-namespace'::GetCurrentTimeStringI=p_providertruefalse: value', column parse errorparse_errortype_errorout_of_range at line invalid_iteratorC:\agent\_work\30\s\onnxruntime\core\session\inference_session_utils.ccSetting intra_op_num_threads to Unsupported value for intra_op_num_threads: onnxruntime::SetIntraOpNumThreadsSetting inter_op_num_threads to Unsupported execution_mode value in ORT config: Unsupported value for inter_op_num_threads: onnxruntime::SetInterOpNumThreadsParallel modeSetting execution_mode to onnxruntime::SetExecutionModeSequential modeSetting graph_optimization_level to ORT_ENABLE_BASICSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_DISABLE_ALLonnxruntime::SetGraphOptimizationLevelUnsupported value for enable_profiling option: onnxruntime::SetEnableProfilingSetting graph_optimization_level to ORT_ENABLE_ALLUnsupported graph_optimization_level value in ORT config: Found session/run/environment configuration in the model file to be used while running the modelonnxruntime::InferenceSessionUtils::ParseOrtConfigJsonInModelProtoSetting enable_profiling to The Model Proto has already been checked for the ORT config json.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 modelORT config json from the model: Json stored in the `ort_config` key cannot be parsed. Error message: intra_op_num_threads option in the model file must be an integerinter_op_num_threadsonnxruntime::InferenceSessionUtils::ParseSessionOptionsFromModelProtointra_op_num_threadsexecution_mode option in the model file must be an integergraph_optimization_levelinter_op_num_threads option in the model file must be an integerexecution_modeenable_profiling option in the model file must be an integerIgnoring unsupported session option in ORT config: graph_optimization_level option in the model file must be an integerenable_profilingkey 'cannot use at() with ' not foundinvalid map keycannot use key() for non-object iterators0objectnullbooleandiscardedarraystringcannot compare iterators of different containersnumbercannot get valuesyntax error while parsing unexpected ; expected - ; last read: 'true literalinvalid BOM; must be 0xEF 0xBB 0xBF if giveninvalid literalstring literalnumber literalfalse literalnull literal']''}''[''{'end of input':'','invalid number; expected digit after '-'invalid number; expected digit after '.''[', '{', or a literalunknown tokeninvalid string: missing closing quoteinvalid string: '\u' must be followed by 4 hex digitsinvalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after exponent signinvalid string: forbidden character after backslashinvalid string: control character U+0000 (NUL) must be escaped to \u0000invalid 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: 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+0001 (SOH) must be escaped to \u0001invalid string: control character U+0002 (STX) must be escaped to \u0002invalid 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+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid 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+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+000F (SI) must be escaped to \u000Finvalid string: control character U+0010 (DLE) must be escaped to \u0010invalid 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+0013 (DC3) must be escaped to \u0013invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid 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+0017 (ETB) must be escaped to \u0017invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid 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+001B (ESC) must be escaped to \u001Binvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid 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+001F (US) must be escaped to \u001Finvalid string: ill-formed UTF-8 byteinvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001E (RS) must be escaped to \u001Evector too longnumber overflow parsing 'object keyobject separatorcannot use erase() with iterator does not fit current valueiterator out of rangetype must be number, but is C:\agent\_work\30\s\include\onnxruntime\core/graph/graph.h Max:onnxruntime::Graph::NodeAtIndexImplValidating no unexpected access using an invalid node_index. Got:node_index < nodes_.size()C:\agent\_work\30\s\include\onnxruntime\core/optimizer/graph_transformer.hCastDef_%donnxruntime::GraphTransformer::RecursetoRemoveDuplicateCastTransformercast node to cast from float16 to float32 on cpuCastonnxruntime::InsertCastTransformer::ApplyImplonnxruntime::RemoveDuplicateCastTransformer::ApplyImplC:\agent\_work\30\s\onnxruntime\core\optimizer\insert_cast_transformer.ccEliminateIdentityIdentityConvMulFusionConvEliminateSliceSliceConvBNFusionConvAddFusionRandomNormalRandomUniformLikeConstantFoldingRandomUniformUnsqueezeEliminationUnsqueezeRandomNormalLikeMultinomialMatMulAddFusionEliminateDropoutConvActivationFusionGemmActivationFusionReluShapeToInitializerDropoutFuseReluClipBiasGeluGeluFusionShapeNchwcTransformerSkipLayerNormFusionEmbedLayerNormFusionGeluApproximationLayerNormFusionReshapeFusionAttentionFusionUnsupported levelonnxruntime::optimizer_utils::GenerateRewriteRules_RuleBasedTransformerLevelonnxruntime::optimizer_utils::GenerateTransformersC:\agent\_work\30\s\onnxruntime\core\optimizer\graph_transformer_utils.ccUnsupported level DnnlExecutionProviderNupharExecutionProviderNGRAPHExecutionProviderOpenVINOExecutionProviderACLExecutionProvideronnxruntime::Node::ForEachWithIndexTensorrtExecutionProvideronnxruntime::TransformerMemcpyImpl::ProcessDefs' doesn't support memcpy onnxruntime::MemcpyTransformer::ApplyImplC:\agent\_work\30\s\onnxruntime\core\optimizer\transformer_memcpy.ccCopy from/to host memorydup_replacements.find(&arg) == dup_replacements.end()Execution type 'Memcpyonnxruntime::TransformerMemcpyImpl::ProcessInitializers::::operator ()onnxruntime::TransformerMemcpyImpl::ProcessInitializersonnxruntime::GraphTransformer::ApplyC:\agent\_work\30\s\onnxruntime\core\optimizer\graph_transformer.cconnxruntime::GraphTransformerManager::ApplyTransformersC:\agent\_work\30\s\onnxruntime\core\optimizer\graph_transformer_mgr.ccThis transformer is already registered Requested size is too large to fit into size_t.GeluAddC:\agent\_work\30\s\onnxruntime\core\optimizer\bias_gelu_fusion.cconnxruntime::BiasGelu::ApplyImplfused Add and GeluCould not find OrtValue with name 'fetches.size() == node->OutputDefs().size()C:\agent\_work\30\s\onnxruntime\core\optimizer\constant_folding.cconnxruntime::ConstantFolding::ApplyImplort_value.IsTensor()Unsupported output type of . Can't constant fold node 'size_ == sizesize is differentC:\agent\_work\30\s\onnxruntime\core/optimizer/initializer.honnxruntime::Initializer::Initializerqkv_biasqkv_weightsdata type is not supportedMaskIndex_Int32MaskCastCast mask from int64 to int32Mask_Int32axesMaskIndexReduceSumCount number of wordsC:\agent\_work\30\s\onnxruntime\core\optimizer\attention_fusion.cconnxruntime::ProcessMaskMask shape is unknown or not 2D, or data type unknownkeepdimsshape of layer norm bias tensor not expectedLayerNormalizationonnxruntime::AttentionFusion::ApplyImplMask data type is not int32 or int64ReshapeTotal fused Attention node count: Attention subgraph expects 1 Add and 3 MatMul as children of LayerNormalization.MatMulOutput edge count not expected for nodes in path vonnxruntime::AttentionFusion::FuseSubGraphFaild to find path vTransposev_reshape initializer value is not expectedFailed in match v_transpose attribute perm. Expected: 0, 2, 1, 3Failed in match Transpose attribute perm. Expected: 0, 2, 1, 3permMulSoftmaxFailed in match v_matmul and v_add input shapereshape initializer value is not expectedaxisOutput edge count not expected for mask nodesFailed to find path for maskSubmask_sub const input not matchedmask_unsqueeze_2 axes not matched. Expect: 2mask_unsqueeze_1 axes not matched. Expect: 1Softmax attribute axis is expected to be 3q root should be layer normalizationFailed to find path for qDivmask_mul const input not matchedq_matmul and q_add shape not matchedq_transpose perm attribute not matchedqk_div const not matched.q_reshape const not matchedk_matmul and k_add shape not matchedk_transpose perm attribute not matchedk root is not layer normFailed to find path for kFailed to create mask indexFailed to load Q, K and V bias tensors, or data type is not float or float16.Failed to load Q, K and V weights, or data type is not float or float16.k_reshape const not matchedFused an attention node.num_headsAttentionFused Attention subgraphs onnxruntime::ConvBNFusion::Applybn_scale_tensor_protoepsilononnxruntime::Initializer::ToProtobn_var_tensor_protobn_mean_tensor_protobn_B_tensor_protoC:\agent\_work\30\s\onnxruntime\core\optimizer\conv_bn_fusion.ccConvBnFusion_BN_B_ConvBnFusion_W_conv_B_tensor_protoconv_W_tensor_protoBatchNormalizationC:\agent\_work\30\s\onnxruntime\core\optimizer\conv_activation_fusion.cconnxruntime::`anonymous-namespace'::GetClipConstantMinMax::::operator ()Unexpected data type for Clip input of onnxruntime::ConvActivationFusion::ApplyImplmaxminClipalphaLeakyReluTanhSigmoidfused FusedConvfused Conv with activation activation_paramsactivationUnsqueezeElimination_UnsqueezeElimination cannot remove node onnxruntime::UnsqueezeElimination::ApplyC:\agent\_work\30\s\onnxruntime\core\optimizer\unsqueeze_elimination.ccdata type Unexpected data type for Clip 'min' input of onnxruntime::FuseReluClip::ApplyC:\agent\_work\30\s\onnxruntime\core\optimizer\relu_clip_fusion.ccis not supported._min_zero_constantFuseReluClip_onnxruntime::GeluApproximation::ApplyImplC:\agent\_work\30\s\onnxruntime\core\optimizer\gelu_approximation.ccGelu approximationFastGeluTotal Gelu Approximation (FastGelu) node count: ConcatGatherCannot replace concat node with initializer:onnxruntime::ReshapeFusion::Fuse_Subgraph1onnxruntime::ReshapeFusion::ApplyImplC:\agent\_work\30\s\onnxruntime\core\optimizer\reshape_fusion.ccFused reshape node: Total fused reshape node count: _CastInput shape is unknown or not 2D, or data type unknownonnxruntime::CheckInputC:\agent\_work\30\s\onnxruntime\core\optimizer\embed_layer_norm_fusion.cc_Int32Cast Input from int64 to int32Second 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.Input data type is not int32 or int64Failed to find path 1 of position shape.onnxruntime::MatchPositionSubgraphOutput edge count not expected for nodes in path 1 of position shape.NonZeroConstantOfShapeOutput edge count not expected for nodes in path1.onnxruntime::MatchPositionEmbeddingSubgraph1Second 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.ExpandSqueezeRangeFailed to find path 1.onnxruntime::MatchPositionEmbeddingSubgraph2Output edge count not expected for nodes in path 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.Failed to match position subgraph.Word embedding shape not expected.Input is expected to have dim value in all dimensions.Failed to get initializer tensor.Position embedding shape not matched.The first input of Range should be a constant with value 0.The third input of Range should be a constant with value 1.position_embeddingsonnxruntime::EmbedLayerNormFusion::ApplyImplInput id is not valid. Segment id is not valid. Mask is not valid. Input_ids and segment id should have the same shape. 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.EmbedLayerNormalizationInput_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 startsendsFreeDimensionOverrideTransformerThe model has input 'onnxruntime::FreeDimensionOverrideTransformer::ApplyImplC:\agent\_work\30\s\onnxruntime\core\optimizer\free_dim_override_transformer.ccInvalid free dimension override.does not equal the specified override ofbut the size of this dimension ' with a fixed dimension denotation 'gemmGemmonnxruntime::MatMulAddFusion::ApplyImplfused Matmul and Add C:\agent\_work\30\s\onnxruntime\core\optimizer\matmul_add_fusion.cconnxruntime::GemmActivationFusion::ApplyImplfused Gemm C:\agent\_work\30\s\onnxruntime\core\optimizer\gemm_activation_fusion.ccleaky_relu_FusedGemmmul_B_tensor_protoC:\agent\_work\30\s\onnxruntime\core\optimizer\conv_mul_fusion.ccConvMulFusion_Mul_B_ConvMulFusion_W_onnxruntime::ConvMulFusion::Applyonnxruntime::SkipLayerNormFusion::ApplyImplfused SkipLayerNorm subgraphs C:\agent\_work\30\s\onnxruntime\core\optimizer\skip_layer_norm_fusion.ccSkipLayerNormalizationpadsReorderInputdilationsstridesreorderVALIDSAME_LOWER_nchwcgroupauto_padkernel_shapeSAME_UPPERNOTSETReorderOutputSumonnxruntime::NchwcTransformer::ApplyImplchannelsAveragePoolMaxPoolGlobalAveragePoolGlobalMaxPoolC:\agent\_work\30\s\onnxruntime\core\optimizer\nchwc_transformer.ccdeque too longC:\agent\_work\30\s\onnxruntime\core\optimizer\layer_norm_fusion.cconnxruntime::LayerNormFusion::ApplyImplSqrtReduceMeanfused LayerNorm subgraphs Powonnxruntime::RuleBasedGraphTransformer::ApplyRulesOnNodeonnxruntime::RuleBasedGraphTransformer::ApplyImplC:\agent\_work\30\s\onnxruntime\core\optimizer\rule_based_graph_transformer.ccErffused Gelu subgraphs onnxruntime::GeluFusion::ApplyImplC:\agent\_work\30\s\onnxruntime\core\optimizer\gelu_fusion.ccConvAddFusion_B_ConvAddFusion_Add_B_C:\agent\_work\30\s\onnxruntime\core\optimizer\conv_add_fusion.ccadd_B_tensor_protoonnxruntime::ConvAddFusion::Applyallocator_ptr_ != nullptronnxruntime::OptimizerExecutionFrame::Info::InfoC:\agent\_work\30\s\onnxruntime\core\optimizer\optimizer_execution_frame.ccFailed to get allocator for optimizeronnxruntime::OptimizerExecutionFrame::Info::{ctor}::::operator ()Tried to allocate without valid type information, ort_value index=invalid unordered_map keyonnxruntime::RegisterCPUKernelsonnxruntime::CPUExecutionProvider::GetKernelRegistryC:\agent\_work\30\s\onnxruntime\core\providers\cpu\cpu_execution_provider.cconnxruntime::ml::RegisterOnnxMLOperatorKernelsonnxruntime::RegisterOnnxOperatorKernelsinfo.GetAttr("mode", &mode).IsOK()onnxruntime::UpsampleBase::UpsampleBasecubicmodenearestlinearasymmetricround_prefer_floorhalf_pixelcoordinate_transformation_modeinfo.GetAttrs("scales", scales_).IsOK()extrapolation_valueC:\agent\_work\30\s\onnxruntime\core/providers/cpu/tensor/upsample.hscales. It can only be mode attribute is or (default) or exclude_outsideexclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to nearest_modecubic_coeff_acoordinate_transform_mode:[onnxruntime::UpsampleBase::StringToCoordinateTransformationModetf_crop_and_resize] is not supportted!tf_half_pixel_for_nnalign_cornersonnxruntime::UpsampleBase::StringToUpsampleModepytorch_half_pixelscale >= 1onnxruntime::UpsampleBase::ScalesValidationonnxruntime::UpsampleBase::StringToNearstModeScale value should be greater than or equal to 1.ceilnearst_mode:[round_prefer_ceilfloorscales size should be greater than 0.scales_size > 0'Linear' mode and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1)Resize operatorUpsample operatorScale value should be greater than 0.scale > 0onnxruntime::UpsampleBase::ParseScalesDataResizebad allocationC:\agent\_work\30\s\include\onnxruntime\core/framework/op_kernel.hInvalid argument for depth; it's not a scalar.OneHotT3T2T1Invalid argument for values; either it's rank is more than 1 or it has more than 2 elementsDepth is negative.C:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\onehot.cconnxruntime::OneHotOp<__int64,__int64,__int64>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,float,__int64>::Computeonnxruntime::OneHotOp,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp<__int64,class std::basic_string,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp::Compute'axis' attribute must have a value in the range [Missing Input: onnxruntime::OneHotOp<__int64,float,int>::Computeonnxruntime::OneHotOp<__int64,float,float>::Computeonnxruntime::OneHotOp<__int64,int,float>::Computeonnxruntime::OpKernelContext::InputC:\agent\_work\30\s\onnxruntime\core/providers/common.honnxruntime::HandleNegativeAxisaxis >= -tensor_rank && axis <= tensor_rank - 1axis is not in valid range [-C:\agent\_work\30\s\onnxruntime\core\providers\cpu\math\top_k.cconnxruntime::TopK<9,float>::TopKop_kernel_info.GetAttr("k", &k_temp).IsOK()koutput count mismatch, expected 2 outputs to be present for TopK operatork argument [] should not be greater than specified axis dim value [onnxruntime::TopK<11,float>::TopKvalue of k must not be negativek tensor should be a 1D tensor of size 1input count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valueonnxruntime::TopK<10,float>::TopKinput count mismatch, expected 1 input - the tensor to be processedop_kernel_info.GetAttr("axis", &axis_temp).IsOK()k_temp > 0TopKIop_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()sortedop_kernel_info.GetAttr("largest", &largest_temp).IsOK()largestUnsupported tensor type of Uniqueonnxruntime::Tensor::MutableDataAsSpanonnxruntime::Tensor::DataAsSpaninput count mismatchstart in Range operator should be scalar like tensor, yet got shape:onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator ()Unsupported data type: onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper >::Getdelta in Range operator can not be zero!delta in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:C:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\gather_nd.cconnxruntime::GatherND::ComputeGatherNDTindinvalid index found, index = last dimension of indices must not be larger than rank of input tensorindices tensor must has rank larger than 0onnxruntime::GatherNDBase::PrepareForComputeinput_tensor != nullptr && indices_tensor != nullptrGatherND op: Input count mismatchInvalid 'pads' attribute valueC:\agent\_work\30\s\onnxruntime\core/providers/cpu/tensor/pad.honnxruntime::PadBase::PadBaseInvalid 'mode' attribute valueedgereflectconstantPadC:\agent\_work\30\s\onnxruntime\core/providers/cpu/tensor/utils.honnxruntime::SliceSkips::SliceSkipsdims.size() == extents.size() && dims.size() >= steps.size()Unexpected mode of Cannot use 'reflect' mode to pad dimension with a value of 0. Input shape:pads_tensor.IsDataType()Pads tensor should be an INT64 tensorvalue_tensor->IsDataType() && value_tensor->Shape().Size() == 1Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensorpads_size == 2 * data_rankPads tensor size should be equal to twice the input dimension count pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Pads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]C:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\pad.cconnxruntime::Pad::ComputeInput tensor has no dimensionsonnxruntime::Pad<__int64>::Computeonnxruntime::Pad::Computeonnxruntime::Pad::Computeonnxruntime::SliceIterator::Initonnxruntime::SliceIterator::Initonnxruntime::SliceIterator<__int64>::Initdims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()data_rank * 2 == pads.size()'pads' has wrong number of valuesonnxruntime::PadCpuImpldata_rank > 0onnxruntime::PadInputWithDimValueOfZeroonnxruntime::SliceIterator::Initonnxruntime::OpKernelContext::Outputp_ml_valuePlease fetch output tensor with specified shape.C:\agent\_work\30\s\onnxruntime\core/providers/cpu/math/element_wise_ops.honnxruntime::BroadcastIterator::Initaxis == 1 || axis == largestAttempting to broadcast an axis by a dimension other than 1. by FloorNegAbsonnxruntime::Broadcaster::Broadcasterlargest <= 1Can broadcast 0 by 0 or 1. is invalid.onnxruntime::BroadcastIterator::AppendGreaterLessMaxMinLogExpReciprocalCeilMust have 1 or more inputsXorOrAndNotBitShiftMeanEqualonnxruntime::Min_6::ComputeinputCount >= 1data_n.Shape() == shapedata_1.Shape() == shapeAll inputs must have the same shapeC:\agent\_work\30\s\onnxruntime\core\providers\cpu\math\element_wise_ops.cconnxruntime::Sum_6::Computeinput_count >= 1AtanAcosAsinTanCosSinonnxruntime::Mean_6::Computeonnxruntime::Max_6::Computeonnxruntime::Erf::ComputeX_ptr != nullptrPReluAtanhAcoshAsinhCoshSinh vs onnxruntime::mod_internal::BroadCastMFloat16FModfailed to get first output!Modonnxruntime::Mod::Mod(fmod == 0) || (fmod == 1)fmod must have value either 0 or 1fmodonnxruntime::Mod::Computefmod_fmod attribute must be true for float, float16 and double typesX and Y input types do not match: onnxruntime::BitShift::Compute::::operator ()cur1 == end1Invalid direction value of ''. Valid values are 'LEFT' or 'RIGHT'.RIGHTLEFTonnxruntime::BitShift::BitShiftdirectiontensor_shape.Shape().GetDims().size() == 1Shape must be 1 dimensional as it's tensor data is a shapeonnxruntime::BroadcastVariadiconnxruntime::BitShift::Compute::::operator ()onnxruntime::BitShift::BitShiftonnxruntime::BitShift::Compute::::operator ()onnxruntime::BitShift::BitShiftcur_out == end_outonnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::Expand_8<__int64>::Computeonnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::utils::MLTypeCallDispatcher::Invokehelper.called_ < 2Check for duplicate types in MLTypeCallDispatcheronnxruntime::mod_internal::BroadCastFModonnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::mod_internal::BroadCastModonnxruntime::TensorAllocator::TensorAllocatoronnxruntime::TensorAllocator::TensorAllocatorhelper.called_ == 1((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOonnxruntime::GatherElements::GatherElementsc:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\gather_elements.hGatherElementsGatherElements op: Data type for 'indices' tensor must be 'int32_t' and 'int64_t'onnxruntime::parse_and_validate_indices_tensorC:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\gather_elements.cc]. Actual value is , Missing/Invalid 'axis' attribute valueinfo.GetAttr("axis", &axis_).IsOK()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 outputleft.NumDimensions() == 2 || left.NumDimensions() == 1onnxruntime::GemmHelper::GemmHelperc:\agent\_work\30\s\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_ >= 0C:\agent\_work\30\s\onnxruntime\core/providers/cpu/math/gemm.htransBinfo.GetAttr("transB", &temp).IsOK()info.GetAttr("alpha", &alpha_).IsOK()betainfo.GetAttr("beta", &beta_).IsOK()transAinfo.GetAttr("transA", &temp).IsOK()onnxruntime::Gemm::GemmNot implemented fused activation: ScatterNDonnxruntime::ScatterND::ComputeC:\agent\_work\30\s\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. , data shape: updates shape: updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. invalid indice found, indice = ) != new size (Tensor size (shape_.Size() == new_shape.Size()onnxruntime::Tensor::ReshapeY != nullptrSequenceAtonnxruntime::GetSeqIdxonnxruntime::SequenceAt::ComputeGot nullptr input for index tensorI != nullptr) specified for sequence of size (Invalid sequence index (SSequenceLengthGot nullptr for sequence input.X != nullptronnxruntime::SequenceLength::ComputeC:\agent\_work\30\s\onnxruntime\core\providers\cpu\sequence\sequence_ops.ccSequenceLength: Got nullptr for output tensorS != 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 sequenceSequenceEraseonnxruntime::SequenceErase::ComputeSequenceAt: Got nullptr for output tensorSequenceEmptydtypeSequenceEmpty: Got nullptr for output sequenceonnxruntime::SequenceEmpty::ComputeUnsupported 'dtype' value: SequenceInsertonnxruntime::CreateCopyAndAppendCpuTensorSplitToSequence operator does not support onnxruntime::SplitToSequence::Compute) != split_dim_size (split_size_sum (Invalid data type for split tensor onnxruntime::GetScalarSplitInputonnxruntime::GetSplitSizesInputSequenceErase: Got nullptr for output sequenceSequenceConstructnum_inputs >= 1onnxruntime::SequenceConstruct::ComputeSequenceConstruct: Got nullptr for output sequenceViolation of the requirment that all input tensors must have the same data type.SplitToSequence yetSplit 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; })Must have valid 'axis' attributeonnxruntime::ConcatBase::ConcatBaseC:\agent\_work\30\s\onnxruntime\core/providers/cpu//tensor/concat.hnew_axisC:\agent\_work\30\s\onnxruntime\core\providers\cpu\sequence\concat_from_sequence.ccConcatFromSequenceonnxruntime::ConcatFromSequence::Computethen_branchC:\agent\_work\30\s\onnxruntime\core\providers\cpu\controlflow\if.cconnxruntime::If::Ifinfo.GetAttr("then_branch", &proto).IsOK()info.GetAttr("else_branch", &proto).IsOK()else_branchinfo == nullptrSetupSubgraphExecutionInfo should only be called once for each subgraph.VB outputs.If'If' node has outputs which doesn't match the subgraph's onnxruntime::If::Info::Infonum_subgraph_outputs == static_cast(num_outputs)Subgraph must have the shape set for all outputs but did not.Failed to create output tensor for If output Failed to create output tensor for onnxruntime::IfImpl::ExecuteCreateFeedsFetchesManager must be called prior to execution of graph.onnxruntime::If::SetupSubgraphExecutionInfoonnxruntime::If::Computethen_feeds_fetches_manager_ && else_feeds_fetches_manager_Subgraph SessionState was not found for '' attribute.onnxruntime::IfImpl::Initializesession_stateC:\agent\_work\30\s\onnxruntime\core/providers/cpu/nn/autopad_type.honnxruntime::StringToAutoPadType kernel_shape: W: kernel_shape is not compatible with W shape.kernel_shape num_dims is not compatible with W num_dims.X num_dims does not match W num_dims. X: Unknown AutoPadType StringOnly 1D and 2D ConvTranspose is supported.group count is <= 0 filter_number: num_input_channels: Input channels is not divisible by group.filter number not equal to input channel number.C:\agent\_work\30\s\onnxruntime\core/providers/cpu/nn/conv_transpose_attributes.honnxruntime::ConvTransposeAttributes::PrepareForCompute group: output_shapeoutput_paddingdim_size > 0Invalid input shape: *out_size >= 0onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShapepad type not supportedonnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShapeConvTransposeC:\agent\_work\30\s\onnxruntime\core\providers\cpu\nn\conv_transpose.cconnxruntime::ConvTranspose::DoConvTranspose C: kernel channels: M: Input channels C is not equal to kernel channels * group.Output channels M is not divisible by group.C:\agent\_work\30\s\onnxruntime\core\providers\cpu\nn\conv.cconnxruntime::Conv::Computeonnxruntime::math::Im2colNd::operator ()d_iter[d_i] < d_maxC:\agent\_work\30\s\onnxruntime\core/util/math.hOut of bound access to arrayC:\agent\_work\30\s\onnxruntime\core/providers/cpu/nn/conv_attributes.honnxruntime::ConvAttributes::InferOutputShapedilation == 1Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.pad type not supported.onnxruntime::ComputePadAndOutputShapeNo kernel shape is set.GlobalLpPoolPad should be smaller than kernel.kernel_shape[dim] > 0strides.size() == kernel_shape.size()pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]dilations.size() == kernel_shape.size()Dilations dimensions should match kernel shapeinput_shape.Size() > 0 || input_shape[0] == 0Invalid input shape. Only N can be zero. Got:onnxruntime::PoolAttributes::PoolAttributesinfo.GetAttrs("kernel_shape", kernel_shape).IsOK()info.GetAttr("auto_pad", &auto_padding).IsOK()C:\agent\_work\30\s\onnxruntime\core/providers/cpu/nn/pool_attributes.hcount_include_padceil_modestorage_orderinfo.GetAttr("count_include_pad", &temp).IsOK()Input dimension cannot be less than 3.C:\agent\_work\30\s\onnxruntime\core/providers/cpu/nn/pool_base.hC:\agent\_work\30\s\onnxruntime\core\providers\cpu\nn\pool.cconnxruntime::PoolBase::ComputeUnsupported pooling size.Not satsified: input_dims >= 3 Not satsified: pooling_dims == pool_attrs_.kernel_shape.size() kernel_shape num_dims is not compatible with X num_dims.input_dims.size() >= 2onnxruntime::PoolAttributes::SetOutputSizeUnsupported AutoPad Type.onnxruntime::PoolAttributes::InferOutputSizeponnxruntime::PoolAttributes::ComputeSizePadDilationsonnxruntime::PoolProcessContext::initinfo.GetAttr("p", &p_).IsOK()Not satsified: x_shape.NumDimensions() >= 3 onnxruntime::Pool >::ComputeLpPoolUnsupported pooling size : onnxruntime::Pool::Computeinfo.GetAttrs("kernel_shape", kernel_shape_).IsOK()C:\agent\_work\30\s\onnxruntime\core/providers/cpu/nn/unpool.honnxruntime::MaxUnpool::MaxUnpoolpads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]kernel_shape_[dim] > 0strides_.size() == kernel_shape_.size()Not satsified: X_shape.NumDimensions() >= 3 C:\agent\_work\30\s\onnxruntime\core\providers\cpu\nn\Unpool.ccNot satsified: I_shape == X_shape Index tensor shape should be same as that of the input data tensor to unpool.Not satsified: 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:onnxruntime::MaxUnpool::ComputeMaxUnpoolNot satsified: given_shape.Size() >= shape.Size() c:\agent\_work\30\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.honnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBaseboxes_tensorNonMaxSuppressionC:\agent\_work\30\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.cconnxruntime::NonMaxSuppressionBase::PrepareComputeboxes must be a 3D tensor.scores_tensorcenter_point_box0 == center_point_box_ || 1 == center_point_box_center_point_box only support 0 or 1iou_threshold must be in range [0, 1].Not satsified: boxes_dims[2] == 4 Not satsified: (iou_threshold >= 0 && iou_threshold <= 1.f) onnxruntime::NonMaxSuppressionBase::GetThresholdsFromInputsNot satsified: ret.IsOK() onnxruntime::NonMaxSuppression::Computeoutput != nullptrscores must be a 3D tensor.Not satsified: boxes_shape.NumDimensions() == 3 boxes and scores should have same num_batches.Not satsified: scores_shape.NumDimensions() == 3 boxes and scores should have same spatial_dimension.Not satsified: boxes_dims[0] == scores_dims[0] The most inner dimension in boxes must have 4 data.Not satsified: boxes_dims[1] == scores_dims[2] . Input rank=Indices vs updates dimensions differs at position= is greater than input dim=Indices must have the same rank as Input. Indices rank=Indices dim= at pos=onnxruntime::Scatter::ComputeUnknown tensor type of C:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\scatter.cconnxruntime::Scatter::ScatterScatterElementsScatterIndices and updates must have the same rankdata type is different from updates typeExpecting indices to be either int32_t or int64_t must be within the inclusive range [indices element out of data bounds, idx=onnxruntime::Det::ComputeC:\agent\_work\30\s\onnxruntime\core\providers\cpu\math\det.ccDetInput tensor should have a rank of at least 2Matrix dimensions are not equal. Square matrix is expectedMissing/Invalid 'axes' attribute valueC:\agent\_work\30\s\onnxruntime\core/providers/cpu/tensor/unsqueeze.hinfo.GetAttrs("axes", axes_).IsOK()onnxruntime::UnsqueezeBase::UnsqueezeBaseonnxruntime::UnsqueezeBase::PrepareComputeC:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\unsqueeze.cconnxruntime::Unsqueeze::Compute'axes' has an out of range axis'axes' has a duplicate axisDimension of input input_shape[i] == 1. shape= must be 1 instead of onnxruntime::SqueezeBase::ComputeOutputShapeC:\agent\_work\30\s\onnxruntime\core/providers/cpu/tensor/squeeze.hC:\agent\_work\30\s\onnxruntime\core/providers/cpu/tensor/split.hstd::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value > 0; })onnxruntime::SplitBase::SplitBasesplitInvalid value in 'split' attribute. All values must be > 0 Input shape=Cannot split using values in 'split' attribute. Axis= Sum of sizes in 'split' (must equal size of selected axis) was Num entries in 'split' (must equal number of outputs) was C:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\split.ccSplit operator does not support onnxruntime::Split::Compute Axis=Input cannot be split evenly on selected axis. Input shape=Split NumOutputs=onnxruntime::Split::ComputeImplonnxruntime::SliceBase::SliceBaseC:\agent\_work\30\s\onnxruntime\core/providers/cpu/tensor/slice.hMissing or invalid starts and ends attributehas_starts && has_ends && attr_starts_.size() == attr_ends_.size()Invalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributes!has_axes || attr_axes_.size() == attr_starts_.size()'step' value cannot be 0Starts must be a 1-D array'axes' has an axis outside of the tensor dimension count'axes' has duplicatesC:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\slice.ccEnds must be a 1-D arraynullptr != start_tensor && start_tensor->Shape().NumDimensions() == 1onnxruntime::SliceBase::FillVectorsFromInputstart_tensor->Shape() == ends_tensor->Shape()Starts and axes shape mismatchnullptr != ends_tensor && ends_tensor->Shape().NumDimensions() == 1Starts and ends 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 nullptr == axes_tensor || start_tensor->Shape() == axes_tensor->Shape()Starts and steps shape mismatchonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice<__int64,0>::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice::ComputeCannot slice scalarsMissing input tensor to be processedinput_tensor_ptr != nullptronnxruntime::Slice::Computeonnxruntime::Slice<__int64,1>::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice,class std::allocator >,0>::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::Slice,class std::allocator >,1>::Computeonnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()output == output_endonnxruntime::SliceIterator::Initonnxruntime::SliceIterator::Initonnxruntime::SliceIterator::Initonnxruntime::SliceIterator::Initonnxruntime::SliceImpl::::operator ()onnxruntime::SliceIterator,class std::allocator > >::Initonnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceIterator::Initonnxruntime::SliceIterator::Initonnxruntime::SliceIterator::Initonnxruntime::SliceIterator::Initonnxruntime::Gather::ComputeC:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\gather.cconnxruntime::GatherBase::GatherBaseC:\agent\_work\30\s\onnxruntime\core/providers/cpu/tensor/gather.hType for Tind not supported yet in Gather. and Cannot concatenate scalarsNot satsified: inputs_0_rank > 0 C:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\concat.ccNot satsified: input_count >= 1 inputs_n_rank == inputs_0_rank has mismatched dimensions of got: Ranks of input data are different, cannot concatenate them. expected rank: onnxruntime::ConcatBase::PrepareForComputeNot satsified: data_n.DataType() == p.output_tensor->DataType() Non concat axis dimensions must match: Axis Not satsified: dim_value == inputs_0_dims[axis_index] CompressC:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\compress.cconnxruntime::Compress::Computeaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()Flattenonnxruntime::Flatten::Computegsl::narrow_cast(X_shape.NumDimensions()) >= axisThe rank of input tensor must be >= axisC:\agent\_work\30\s\onnxruntime\core/providers/cpu/nn/flatten.honnxruntime::Flatten::FlattenC:\agent\_work\30\s\onnxruntime\core/providers/cpu/controlflow/scan_utils.honnxruntime::scan::detail::OutputIterator::GetOutputfinal_output_mlvalue_Attempt to retrieve final output before it was set.bodyscan_output_axesgsl::narrow_cast(input_axes_.size()) == num_scan_inputs_Number of entries in 'scan_input_axes' was but expected onnxruntime::Scan<9>::SetupSubgraphExecutionInfoinfo_ == nullptrgsl::narrow_cast(output_axes_.size()) == num_scan_outputsNumber of entries in 'scan_output_axes' was num_scan_inputsC:\agent\_work\30\s\onnxruntime\core\providers\cpu\controlflow\scan_9.cconnxruntime::Scan<9>::Scaninfo.GetAttr("body", &proto).IsOK()scan_input_axesscan_output_directionsscan_input_directionsinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK(). Input tensor rank was Scan inputs have inconsistent sequence lengths. Previous value was but input '' dimension onnxruntime::ScanImpl::SetupInputsonnxruntime::ScanImpl::ValidateInputInvalid value in scan_input_axes for input of onnxruntime::ScanImpl::InitializeSubgraph SessionState was not found for 'body' attribute.onnxruntime::Scan<9>::Computefeeds_fetches_manager_ && info_ 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 onnxruntime::ScanImpl::CreateLoopStateVariablesonnxruntime::ScanImpl::AllocateOutputTensorsSubgraph in 'body' produces outputs but Scan expects . Output tensor rank was onnxruntime::ScanImpl::Executeoutput_mlvalueOutput OrtValue has not been created for loop state variable output blocksizeC:\agent\_work\30\s\onnxruntime\core/providers/cpu/tensor/space_depth_ops.honnxruntime::SpaceDepthBase::SpaceDepthBaseinfo.GetAttr("blocksize", &blocksize_).IsOK()Attribute blocksize is not set.CRDinput_depth % (blocksize_ * blocksize_) == 0onnxruntime::DepthToSpace::Computeinput_width % this->blocksize_ == 0onnxruntime::DepthToSpace::DepthToSpaceDepthToSpace op: only 'DCR' and 'CRD' modes are supportedDCRDepthToSpaceSpaceToDepthinput_height % this->blocksize_ == 0C:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\space_depth_ops.cconnxruntime::SpaceToDepth::Computeinput.Shape().NumDimensions() == 4 inputs. Found:Looponnxruntime::Loop::LoopInconsistent shape in loop output for output. Expected: Got:'Loop' input 'M' should be a scalar tensor. Got shape of feeds_fetches_manager_onnxruntime::Loop::Computeonnxruntime::Loop::SetupSubgraphExecutionInfoC:\agent\_work\30\s\onnxruntime\core\providers\cpu\controlflow\loop.cconnxruntime::Loop::Info::Infostatic_cast(num_subgraph_inputs) == subgraph_inputs.size()Graph in 'body' attribute of Loop should have num_subgraph_outputs - 1 == num_outputs'Loop' node has outputs so the subgraph requires but has 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 had zero iterations and the shape of subgraph output LogSoftmaxHardmax inputs N, D and N * D must be < Hardmax, D=. N=ReduceLogSumExpReduceMaxReduceMinReduceProdReduceL1ReduceL2ReduceLogSumC:\agent\_work\30\s\onnxruntime\core/providers/cpu/reduction/reduction_ops.honnxruntime::ReduceKernelBase<1>::ReduceKernelBaseinfo.GetAttr("keepdims", &keepdims).IsOK()onnxruntime::ReduceKernelBase<0>::ReduceKernelBaseReduceSumSquareArgMaxArgMinC:\agent\_work\30\s\onnxruntime\core\providers\cpu\reduction\reduction_ops.ccCan't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:in_dim != 0onnxruntime::PrepareForReduceDynamicQuantizeLinearonnxruntime::DynamicQuantizeLinear::ComputeC:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\dynamicquantizelinear.ccx_ptr != nullptrRoundCumSumAxis should be in the range [Axis tensor should be 0D or 1D) but got: reverseexclusiveonnxruntime::WritableSliceIterator::Initonnxruntime::WritableSliceIterator::Initonnxruntime::WritableSliceIterator::Initextents.size()=dims.size() == extents_.size()steps.size()=dims.size() == steps.size()starts.size()=dims.size()=dims.size() == starts.size()onnxruntime::WritableSliceIterator<__int64>::Initinfo.GetAttr("min", &min_).IsOK()max should be a scalar.max->Shape().NumDimensions() == 0info.GetAttr("max", &max_).IsOK()onnxruntime::Clip_6::Clip_6min should be a scalar.min->Shape().NumDimensions() == 0onnxruntime::Clip::ComputeC:\agent\_work\30\s\onnxruntime\core/providers/cpu/math/clip.hinfo.GetAttr("batch_axis", &batch_axis).IsOK()onnxruntime::ReverseSequenceOp::ReverseSequenceOpc:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.htime_axisbatch_axisInvalid time_axis of time_axis < 2time_axis and batch_axis must have different values but both are batch_axis != time_axisinfo.GetAttr("time_axis", &time_axis).IsOK(). Must be 0 or 1Invalid batch_axis of batch_axis < 2ReverseSequence. batch_size=sequence_lens shape must be {batch_size}. Got:onnxruntime::ReverseSequenceOp::ComputeC:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.ccNull rois_ptrNull input X ptrNumber of dimensions for batch indices should be exactly 1Null batch_indices_ptrRoiAlignoutput_heightc:\agent\_work\30\s\onnxruntime\core\providers\cpu\object_detection\roialign.hsampling_ratiooutput_width specified. It should be either avg or maxavgonnxruntime::RoiAlignBase::RoiAlignBaseInvalid mode of value Second dimension for rois should be exactly Number of dimensions for rois should be exactly First dimension (num_rois) of batch_indices and rois don't matchsampling_ratio_ >= 0Sampling ratio should be >=0, but it was onnxruntime::RoiAlignBase::RoiAlignBasespatial_scaleonnxruntime::IsInf::Computedetect_negativeC:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\isinf.ccData type X must be float or double, but instead got Failed to obtain detect_negativedetect_positiveIsInfonnxruntime::IsInf::IsInfFailed to obtain detect_positiveTensor type mismatch.onnxruntime::Tensor::MutableDataRawtype == dtype_C:\agent\_work\30\s\onnxruntime\core/providers/cpu/tensor/identity_op.honnxruntime::IdentityOp<0>::Computeonnxruntime::IdentityOp<1>::Computeonnxruntime::Tensor::DataRawQLinearConvT4IsScalarOr1ElementVector(input_offset)QLinearConv : input zero point must be a scalar or 1D tensor of size 1onnxruntime::math::Im2colNd::operator ()IsScalarOr1ElementVector(filter_scale)QLinearConv : filter scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(result_scale)QLinearConv : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(result_offset)QLinearConv : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(input_scale)QLinearConv : input scale must be a scalar or 1D tensor of size 1C:\agent\_work\30\s\onnxruntime\core\providers\cpu\nn\qlinearconv.cconnxruntime::QLinearConv::ComputeIsScalarOr1ElementVector(filter_offset)QLinearConv : filter zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(W_Zero_Point)Non per-tensor quantization is not supported now.IsScalarOr1ElementVector(X_Zero_Point)Must be a scalar or 1D tensor or size 1.C:\agent\_work\30\s\onnxruntime\core\providers\cpu\nn\conv_integer.cconnxruntime::ConvInteger::ComputeConvIntegerNot satsified: left_padded_dims_[idx_dim] == 1 left operand cannot broadcast on dim Not satsified: right_padded_dims_[idx_dim] == 1 right operand cannot broadcast on dim Right:Not satsified: left_num_dims >= 1 && right_num_dims >= 1 Not satsified: K_ == right_shape[right_num_dims - 2] MatMul dimension mismatch, Left:C:\agent\_work\30\s\onnxruntime\core/providers/cpu/math/matmul_helper.honnxruntime::MatMulComputeHelper::ComputeIsScalarOr1ElementVector(b_zero_point)MatmulInteger : input2 zero point must be a scalar or 1D tensor of size 1t->Shape().NumDimensions() <= 1 && t->Shape().Size() == 1Currently only scalar zero_point is supported. TODO: add per channel zero point support.C:\agent\_work\30\s\onnxruntime\core\providers\cpu\math\matmul_integer.cconnxruntime::MatMulInteger::ComputeIsScalarOr1ElementVector(a_zero_point)MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1Not satsified: K_ == right_shape[0] Not satsified: num_dims_with_pad - 2 == num_output_dims a != nullptr && b != nullptrMatMulIntegerNot satsified: num_dims_with_pad == num_output_dims MatMul dimension mismatchNot satsified: num_dims_with_pad - 1 == num_output_dims Not satsified: M_ == 1 && N_ == 1 t->IsDataType() || t->IsDataType()onnxruntime::MatMulInteger::Compute::::operator ()MatMulInteger: Unsupported input types with zero pointonnxruntime::MatMulInteger::ComputeQLinearMatmul : 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)onnxruntime::QLinearMatMul::ComputeQLinearMatMulQLinearMatmul : input zero point must be a scalar or 1D tensor of size 1C:\agent\_work\30\s\onnxruntime\core\providers\cpu\math\quantize_linear_matmul.ccQLinearMatmul : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_scale)IsScalarOr1ElementVector(y_scale)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)x_scalexyx_zero_pointx_scale must be 1D tensor with size y_zero_pointDequantizeLinearQuantizeLineary_scale.Shape().NumDimensions() == 1 && y_scale.Shape().Size() == broadcastDimIsScalarOr1ElementVector(&y_zero_point)onnxruntime::QuantizeLinear::Computey_zero_point.Shape().NumDimensions() == 1 && y_zero_point.Shape().Size() == broadcastDimonnxruntime::DequantizeLinear::ComputeIsScalarOr1ElementVector(&x_zero_point)onnxruntime::QuantizeLinear::ComputeIsScalarOr1ElementVector(&y_scale)x_scale must be a scalar or 1D tensor or size 1.x_zero_point.Shape().NumDimensions() == 1 && x_zero_point.Shape().Size() == broadcastDimx_zero_point must be a scalar or 1D tensor or size 1.IsScalarOr1ElementVector(&x_scale)onnxruntime::DequantizeLinear::Computex_scale.Shape().NumDimensions() == 1 && x_scale.Shape().Size() == broadcastDimx_zero_point must be 1D tensor with size C:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\quantize_linear.ccThresholdedRelugammaSoftplusSoftsignHardSigmoidSeluElu*:Please, install necessary language-pack-XX and configure localesFailed to construct locale with name:Conversion ErrorConversion ErrorStringNormalizerEmpty stopwords not allowed!sw.empty()localestopwordsNONEattribute case_change_action has invalid valueLOWERUPPERcase_change_actionattribute case_change_action is not setattribute is_case_sensitive is not setonnxruntime::StringNormalizer::StringNormalizeren-USis_case_sensitiveonnxruntime::string_normalizer::Locale::LocaleC:\agent\_work\30\s\onnxruntime\core\providers\cpu\nn\string_normalizer.ccInput contains invalid utf8 chars at: Single dimension value must be greater than 0Input dimensions are either[C > 0] or [1][C > 0] allowedStopword contains invalid utf8 charswstr != wconv_errorDuplicate stopwords not allowedp.secondbad conversiononnxruntime::MatMul<__int64>::Computeonnxruntime::MatMul::Computeonnxruntime::MatMul::Computeonnxruntime::MatMul::ComputeC:\agent\_work\30\s\onnxruntime\core\providers\cpu\math\matmul.cconnxruntime::MatMul::Computeonnxruntime::MatMul::ComputeInvalid input B: Invalid input mean: NumDimensions() != Invalid input B: NumDimensions() != Invalid input B: 0th dimension != dimension != Invalid input scale: Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != Invalid input X: The rank of input X must be atleast 2. Got rank: C:\agent\_work\30\s\onnxruntime\core/providers/cpu/nn/batch_norm.hspatialst.IsOK()onnxruntime::BatchNorm::BatchNormonnxruntime::BatchNorm::ComputeC:\agent\_work\30\s\onnxruntime\core\providers\cpu\nn\batch_norm.ccmeanvarInvalid input var: scaleInvalid input var: NumDimensions() != Invalid input var: 0th dimension != Invalid input mean: 0th dimension != Invalid input mean: condition, X, and Y inputs are required!condition && X && YWhereonnxruntime::TensorAllocator<__int64>::TensorAllocatoronnxruntime::TensorAllocator::TensorAllocatoronnxruntime::Where,class std::allocator > >::Computeonnxruntime::TensorAllocator,class std::allocator > >::TensorAllocatoronnxruntime::Where<__int64>::Computeonnxruntime::Where::Computeonnxruntime::Where::ComputeC:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\where_op.ccC:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\nonzero_op.cconnxruntime::NonZero::ComputeX input is required!onnxruntime::NonZero::Computeonnxruntime::NonZero::Computeonnxruntime::NonZero::Computeonnxruntime::NonZero<__int64>::Computeimpl_->min_gram_length_ > 0max_gram_lengthmin_gram_length is requiredRequired min_gram_length must be positive: impl_->weighting_criteria_ != kNonemin_gram_length is unrecognized, acceptable values are TF,IDF,TFIDFmode: IDFTFIDFC:\agent\_work\30\s\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.ccTFmode is requiredonnxruntime::TfIdfVectorizer::TfIdfVectorizerTfIdfVectorizersize_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()ngram_indexessize_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: Non-empty ngram_counts is requiredstatus.IsOK() && !impl_->ngram_counts_.empty()impl_->max_skip_count_ >= 0ngram_countsmax_skip_count is requiredmax_skip_count must be non-negative: impl_->max_gram_length_ >= impl_->min_gram_length_max_skip_count >= min_gram_length >= max_gram_length required: status.IsOK() && !pool_int64s.empty()-gramspool_int64snon-empty pool_int64s is required if pool_strings not providedpool_strings must not be empty if specified!impl_->pool_strings_.empty()impl_->weights_.size() == impl_->ngram_indexes_.size()pool_strings but ngram_indexes size: Got weights of size: weights 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; })Non-empty ngram_indexes is requiredstatus.IsOK() && !impl_->ngram_indexes_.empty()(before_insert + ngrams) == impl_->str_set_.size()Invalid type of the input argument(before_insert + ngrams) == impl_->int64_set_.size()poll_strings duplicate -grams detectedpool_int64s duplicate Number of items must compose whole (items % ngram_size == 0)n-gram counts out of bounds for end_idx >= start_idx && end_idx <= total_itemsInput shape must have either [C] or [B,C] dimensions with B > 0.op_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()lambdC:\agent\_work\30\s\onnxruntime\core/providers/cpu/nn/shrink.honnxruntime::Shrink::Shrinkop_kernel_info.GetAttr("bias", &bias_temp).IsOK()biasShrinkSignonnxruntime::utils::MLTypeCallDispatcher::InvokeNull input ptrIsNaNstol argument out of rangeinvalid stol argumentstoi argument out of rangeinvalid stoi argumentstod argument out of rangeinvalid stod argumentstof argument out of rangeinvalid stof argumentstoull argument out of rangeinvalid stoull argumentstoll argument out of rangeinvalid stoll argumentstoul argument out of rangeinvalid stoul argumentonnxruntime::Cast::Computeonnxruntime::Cast::Computeonnxruntime::Cast::Computeonnxruntime::Cast::ComputeUnexpected 'to' argument value: C:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\cast_op.cconnxruntime::Cast::ComputeCast op must have 'to' argument of type DataTypeonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast,class std::allocator > >::CastAttribute to is not set.onnxruntime::Cast,class std::allocator > >::ComputeInput is missing. The operator Cast expects one and only one inputonnxruntime::Cast::ComputeCasting from 'float16' to 'string' is not supported yet.onnxruntime::Cast::Computeonnxruntime::Cast::Computeonnxruntime::Cast::Computeonnxruntime::Cast<__int64>::Computeonnxruntime::Cast::Computeonnxruntime::Cast::Computeonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast<__int64>::Castonnxruntime::Cast::CastFloat16Dataonnxruntime::Cast::CastFloat16Dataonnxruntime::Cast::CastFloat16Dataonnxruntime::Cast::CastFloat16Dataonnxruntime::Cast::CastFloat16Dataonnxruntime::Cast::CastFloat16Dataonnxruntime::Cast<__int64>::CastFloat16Dataonnxruntime::Cast::CastFloat16Dataonnxruntime::Cast::CastFloat16Dataonnxruntime::Cast::CastFloat16Dataonnxruntime::Cast::CastFloat16Dataonnxruntime::CastFromStringDataINF-INFNaNonnxruntime::CastToStringDatabufferlen > 0onnxruntime::CastFloat16Dataallocator != nullptrC:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\eye_like.cconnxruntime::EyeLike::ComputeT1 != nullptrEyeLikeEyeLike : Input tensor dimension is not 2info.GetAttr("normalize_variance", &normalize_variance_).IsOK()normalize_varianceC:\agent\_work\30\s\onnxruntime\core/providers/cpu/tensor/mean_variance_normalization.honnxruntime::MeanVarianceNormalization_0::MeanVarianceNormalization_0info.GetAttr("across_channels", &across_channels_).IsOK()across_channelsInput is expected to have four dimensions corresponding to [N,C,H,W]MeanVarianceNormalizationUnsupported value attribute datatype with sizeof=: onnxruntime::ConstantOfShapeBase::SetValueC:\agent\_work\30\s\onnxruntime\core/providers/cpu/generator/constant_of_shape.hutils::HasDataType(t_proto)onnxruntime::ConstantOfShapeBase::SetValueFromTensorProtoC:\agent\_work\30\s\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::ComputeScan<8> spec does not support transpose of output. This should never be called.onnxruntime::Scan<8>::ScanC:\agent\_work\30\s\onnxruntime\core\providers\cpu\controlflow\scan_8.ccdirectionsonnxruntime::Scan<8>::SetupSubgraphExecutionInfoonnxruntime::Scan<8>::Computeonnxruntime::Scan8Impl::Initialize has batch size of but Scan inputs have inconsistent batch size. 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 onnxruntime::Scan8Impl::AllocateOutputTensorsp_mlvalueonnxruntime::Scan8Impl::CreateLoopStateVariablesonnxruntime::Scan8Impl::Executeposition_ >= 0 && position_ < sequence_length_onnxruntime::OrtValueTensorSlicer::Iterator::operator *C:\agent\_work\30\s\onnxruntime\core/framework/ort_value_tensor_slicer.hUpsampleonnxruntime::Upsample::Computeonnxruntime::Upsample::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::BaseComputeonnxruntime::Upsample::BaseComputeonnxruntime::Upsample::ComputeC:\agent\_work\30\s\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::ComputeResize: 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.Attribute perm of Transpose has an invalid value. Value is repeated.[ does not align with rank of input data: perm: v >= 0 && static_cast(v) <= std::numeric_limits::max()onnxruntime::TransposeBase::TransposeBaseC:\agent\_work\30\s\onnxruntime\core/providers/cpu/tensor/transpose.h is outside range.Mismatched data types between input and output Tensors. onnxruntime::Transpose::ComputeC:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\transpose.ccTileInput 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::ComputeC:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\tile.ccSizeA dimension cannot be less than -1.onnxruntime::ReshapeHelper::ReshapeHelperrequested_shape[i] >= -1At most one dimension can be -1.c:\agent\_work\30\s\onnxruntime\core\providers\cpu\tensor\reshape_helper.hThe dimension with value zero exceeds the dimension size of the input tensor.unknown_dim == -1A shape tensor must be a vector tensor.onnxruntime::Reshape::ComputeshapeTensor->Shape().NumDimensions() == 1shapeC:\agent\_work\30\s\onnxruntime\core/providers/cpu/tensor/reshape.h, requested shape:i < input_shape.NumDimensions()(input_shape.Size() % size) == 0The input tensor cannot be reshaped to the requested shape. Input shape:gsl::narrow_cast(input_shape.Size()) == sizeonnxruntime::Reshape_1::Reshape_1Attribute shape is not set.Reshape_1bidirectionalforwardonnxruntime::RNN::ComputeRNNAffineC:\agent\_work\30\s\onnxruntime\core\providers\cpu\rnn\rnn.ccclipScaledTanhonnxruntime::RNN::RNNinfo.GetAttr("direction", &direction_).IsOK()allowed_directions.find(direction_) != allowed_directions.end()C:\agent\_work\30\s\onnxruntime\core/providers/cpu/rnn/rnn.hactivation_betaactivation_alphainfo.GetAttrs("activations", activations_).IsOK()activationsinfo.GetAttr("hidden_size", &hidden_size_).IsOK()hidden_sizeallowed_activations.find(activations_[direction]) != allowed_activations.end()activations_.size() == static_cast(num_directions)onnxruntime::DeepCpuLstmOp::DeepCpuLstmOpinfo.GetAttr("direction", &direction).IsOK()info.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0C:\agent\_work\30\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_lstm.hinput_forgetclip_ > 0.ftanhsigmoidLSTMactivation_func_names.size() == static_cast(num_directions_) * 3Invalid data type for LSTM operator of LSTM operator does not support double yetC:\agent\_work\30\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_lstm.cconnxruntime::DeepCpuLstmOp::Compute}. Actual:onnxruntime::DeepCpuLstmOp::ValidateInputsfuture already retrievedbroken promiseno statepromise already satisfied'. Must be one of 'forward', 'reverse', or 'bidirectional'.futureonnxruntime::rnn::detail::MakeDirectionInvalid 'direction' argument of 'DEEPCPU_LSTMC:\agent\_work\30\s\onnxruntime\core/providers/cpu/rnn/rnn_helpers.hProcessing batch rows[..onnxruntime::rnn::detail::ComputeGemmlda >= K && ldb >= K && ldc >= NB + (N * ldb - (ldb - K)) <= B_endA + (M * lda - (lda - K)) <= A_endcur + size <= endC + (M * ldc - (ldc - N)) <= C_endoffset + size <= size_t(span.size())onnxruntime::rnn::detail::SafeRawPointerInput P must have shape {Input initial_c must have shape {onnxruntime::DeepCpuLstmOp::ComputeImplonnxruntime::rnn::detail::SafeRawConstPointeractivation_func_names.size() == static_cast(num_directions_) * 2info.GetAttr("linear_before_reset", &int64_value).IsOK()GRU operator does not support double yetGRUonnxruntime::DeepCpuGruOp::ComputeInvalid data type for GRU operator of C:\agent\_work\30\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.cconnxruntime::DeepCpuGruOp::DeepCpuGruOplinear_before_resetC:\agent\_work\30\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.honnxruntime::DeepCpuGruOp::ComputeImplMaxRoiPoolonnxruntime::RoiPool::ComputeR->Shape()[1] == 5roi_batch_id >= 0C:\agent\_work\30\s\onnxruntime\core\providers\cpu\nn\roi_pool.ccpooled_shaperoi_batch_id < batch_sizeonnxruntime::RoiPool::RoiPoolinfo.GetAttrs("pooled_shape", pooled_shape).IsOK()pooled_shape.size() == 2C:\agent\_work\30\s\onnxruntime\core/providers/cpu/nn/roi_pool.hpooled_width_ > 0pooled_height_ > 0spatial_scale_ > 0info.GetAttr("spatial_scale", &spatial_scale_).IsOK()X->Shape().NumDimensions() == 4C:\agent\_work\30\s\onnxruntime\core\providers\cpu\nn\lrn.cconnxruntime::LRN::ComputesizeLRNonnxruntime::LRN::LRNinfo.GetAttr("size", &size).IsOK()size_ > 0C:\agent\_work\30\s\onnxruntime\core/providers/cpu/nn/lrn.halpha_ > 0.0fsize_ % 2 == 1beta_ > 0.0fLpNormalizationonnxruntime::LpNorm::LpNormop_kernel_info.GetAttr("axis", &axis_).IsOK()op_kernel_info.GetAttr("p", &p_).IsOK()C:\agent\_work\30\s\onnxruntime\core/providers/cpu/nn/lp_norm.hp_ == 1 || p_ == 2onnxruntime::InstanceNorm::ComputeC:\agent\_work\30\s\onnxruntime\core\providers\cpu\nn\instance_norm.ccInstanceNormalizationC:\agent\_work\30\s\onnxruntime\core/providers/cpu/nn/instance_norm.hop_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()onnxruntime::InstanceNorm::InstanceNorm vs. Mismatch between input data and scale: size of scale != input channel count Invalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count onnxruntime::RandomUniformLike::RandomUniformLikesample_sizelowinfo.GetAttr("low", &low_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDinfo.GetAttr("sample_size", &num_samples_).IsOK()onnxruntime::Multinomial::Multinomialinfo.GetAttr("mean", &mean_).IsOK()info.GetAttr("scale", &scale_).IsOK()seedonnxruntime::RandomNormal::RandomNormalC:\agent\_work\30\s\onnxruntime\core/providers/cpu/generator/random.hONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDinfo.GetAttrs("shape", shape).IsOK()info.GetAttr("dtype", &dtype).IsOK()Invalid dtype of info.GetAttr("high", &high_).IsOK()onnxruntime::RandomUniform::RandomUniformonnxruntime::RandomNormalLike::RandomNormalLikehighonnxruntime::RandomUniformComputeonnxruntime::MultinomialComputeC:\agent\_work\30\s\onnxruntime\core\providers\cpu\generator\random.ccCould not infer data type from input tensor with data type onnxruntime::RandomNormalLike::Computebatch_size is < 1num_classes is < 1onnxruntime::RandomUniformLike::ComputeEmpty dimensions for input tensorFLOAT16 is not supportedonnxruntime::RandomNormalComputenum_samples is < 1Invalid data type of onnxruntime::contrib::RegisterCpuContribKernelsonnxruntime::contrib::RegisterNchwcKernelsC:\agent\_work\30\s\onnxruntime\contrib_ops\cpu_contrib_kernels.ccclasses_stringsinfo.GetAttrs(_key_field_name, keys).IsOK()onnxruntime::ml::LabelEncoder_2,class std::allocator >,__int64>::LabelEncoder_2keys_int64svalues_int64s and the number of However, the number of key is info.GetAttrs(_value_field_name, values).IsOK()values is attribtues in LabelEncoder The ) must have the same length. (name: onnxruntime::ml::LabelEncoder_2::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,float>::LabelEncoder_2num_keys == num_valuesonnxruntime::ml::LabelEncoder_2<__int64,class std::basic_string,class std::allocator > >::LabelEncoder_2C:\agent\_work\30\s\onnxruntime\core/providers/cpu/ml/label_encoder.hdefault_stringinfo.GetAttrs("classes_strings", string_classes).IsOK()onnxruntime::ml::LabelEncoder::LabelEncoderinfo.GetAttr("default_int64", &default_int_).IsOK()LabelEncoderinfo.GetAttr("default_string", &default_string_).IsOK()default_int64keys_floatsvalues_stringsInput of tensor(string) must have output of tensor(int64)Input of tensor(int64) must have output of tensor(string)values_floatsdefault_float_Unusedkeys_stringsonnxruntime::ml::LabelEncoder_2,class std::allocator >,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator > >::LabelEncoder_2ZipMapclasslabels_int64sclasslabels_strings_.empty() ^ classlabels_int64s_.empty()onnxruntime::ml::ZipMapOp::ZipMapOpclasslabels_stringsMust provide classlabels_strings or classlabels_int64s but not both.Zipmap only supports 1D or 2D input tensors] != number of classlabels[C:\agent\_work\30\s\onnxruntime\core\providers\cpu\ml\zipmap.ccZipmap does not support empty dim countInput features_per_batch[BRANCH_LEQLEAFc:\agent\_work\30\s\onnxruntime\core\providers\cpu\ml\ml_common.hTreeEnsembleRegressorBRANCH_GTBRANCH_EQBRANCH_LTBRANCH_GTESOFTMAX_ZEROAVERAGELOGISTICSOFTMAXSUMMINnodes_missing_value_tracks_truetarget_nodeidsnodes_truenodeidsnodes_falsenodeidstarget_weightsbase_valuestarget_treeidstarget_idsn_targetsinfo.GetAttr("n_targets", &n_targets_).IsOK()post_transformaggregate_function!nodes_treeids_.empty()nodes_modesonnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressorC:\agent\_work\30\s\onnxruntime\core\providers\cpu\ml\treeregressor.ccnodes_treeidsnodes_valuesnodes_hitratesnodes_nodeidsnodes_featureidstreeindex evaluated to a negative value, which should not happen.length overflowonnxruntime::ml::write_scoresnodes_id_size == nodes_featureids_.size()nodes_id_size == nodes_values_.size()target_nodeids_.size() == target_ids_.size()target_nodeids_.size() == target_weights_.size()nodes_id_size == nodes_falsenodeids_.size()(nodes_id_size == nodes_hitrates_.size()) || (nodes_hitrates_.empty())nodes_id_size == nodes_modes_.size()nodes_id_size == nodes_truenodeids_.size()base_values_.empty() || base_values_.size() == static_cast(n_targets_)Input shape needs to be at least a single dimension.it != parents.end()it != indices.end()onnxruntime::ml::TreeEnsembleRegressor::Computeonnxruntime::ml::TreeEnsembleRegressor::Computeonnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressorTreeEnsembleClassifiernodes_nodeids_.size() == nodes_truenodeids_.size()nodes_nodeids_.size() == nodes_falsenodeids_.size()nodes_nodeids_.size() == nodes_modes_names_.size()nodes_nodeids_.size() == nodes_values_.size()X dims is empty.onnxruntime::ml::TreeEnsembleClassifier::Compute(nodes_nodeids_.size() == nodes_hitrates_.size()) || (nodes_hitrates_.empty())std::all_of( std::begin(missing_tracks_true_), std::end(missing_tracks_true_), [](int64_t elem) { return elem >= 0; })onnxruntime::ml::TreeEnsembleClassifier::Computeonnxruntime::ml::TreeEnsembleClassifier<__int64>::TreeEnsembleClassifier1onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier::Computeonnxruntime::ml::TreeEnsembleClassifier<__int64>::Computeonnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifierclass_nodeidsclass_weightsonnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifierclass_treeidsclass_idsclass_nodeids_.size() == class_weights_.size()nodes_nodeids_.size() == nodes_featureids_.size()C:\agent\_work\30\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_classifier.ccclass_nodeids_.size() == class_ids_.size()onnxruntime::ml::TreeEnsembleClassifier::Initializebase_values_.empty() || base_values_.size() == static_cast(class_count_) || base_values_.size() == weights_classes_.size()onnxruntime::ml::TreeEnsembleClassifier::ProcessTreeNodeonnxruntime::ml::TreeEnsembleClassifier::InitializeInvalid mode of value: treeindex >= 0onnxruntime::ml::TreeEnsembleClassifier<__int64>::ProcessTreeNodeonnxruntime::ml::TreeEnsembleClassifier::Initializeonnxruntime::ml::TreeEnsembleClassifier::ProcessTreeNodeonnxruntime::ml::TreeEnsembleClassifier<__int64>::Initializeonnxruntime::ml::TreeEnsembleClassifier::ProcessTreeNodeLINEARRBFPOLYSVMRegressorinfo.GetAttrs("coefficients", coefficients_).IsOK()coefficientsC:\agent\_work\30\s\onnxruntime\core\providers\cpu\ml\svmregressor.cconnxruntime::ml::SVMRegressor::SVMRegressorkernel_paramskernel_typeone_class!coefficients_.empty()c:\agent\_work\30\s\onnxruntime\core\providers\cpu\ml\svmclassifier.honnxruntime::ml::SVMCommon::SVMCommoninfo.GetAttrs("kernel_params", kernel_params).IsOK()info.GetAttrs("rho", rho_).IsOK()rhosupport_vectorsn_supportsSVMClassifieronnxruntime::ml::SVMClassifier::SVMClassifieronnxruntime::ml::SVMClassifier<__int64>::SVMClassifieronnxruntime::ml::SVMClassifier::SVMClassifierNo support vectors.onnxruntime::ml::SVMCommon::SVMCommononnxruntime::ml::SVMCommon<__int64>::SVMCommonC:\agent\_work\30\s\onnxruntime\core/providers/cpu/ml/svmclassifier.honnxruntime::ml::SVMCommon::SVMCommonprob_avectors_per_classproba_.size() == probb_.size()C:\agent\_work\30\s\onnxruntime\core\providers\cpu\ml\svmclassifier.cconnxruntime::ml::SVMClassifier::SVMClassifierprob_bcoefficients_.size() > 0classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0info.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_intsScalerscale_.size() == offset_.size()Scale size: () != (C:\agent\_work\30\s\onnxruntime\core\providers\cpu\ml\scaler.cconnxruntime::ml::ScalerOp::ScalerOpEither both scale and offset can be of feature size () or 1Invalid argument: input has empty dimensions.onnxruntime::ml::ScalerOp::ScalerOponnxruntime::ml::ScalerOp<__int64>::ScalerOponnxruntime::ml::ScalerOp::ScalerOp!scale_.empty()Empty scale in attributesoffsetOneHotEncoderUnknown Category and zeros = 0.cats_stringscats_int64szerosC:\agent\_work\30\s\onnxruntime\core\providers\cpu\ml\onehotencoder.cconnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOptmp_cats_int64s.empty() || tmp_cats_strings.empty()One and only one of the 'cats_*' attributes must be definedonnxruntime::ml::OneHotEncoderOp,class std::allocator > >::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOpnum_categories_ > 0C:\agent\_work\30\s\onnxruntime\core/providers/cpu/ml/ml_common.hMAXonnxruntime::ml::MakeNormalizeInvalid normalize value of L2L1onnxruntime::utils::MLTypeCallDispatcher::Invokeonnxruntime::ml::Normalizer::NormalizeUnexpected NORMALIZE value of C:\agent\_work\30\s\onnxruntime\core/providers/cpu/ml/normalizer.honnxruntime::ml::Normalizer::Normalizerinfo.GetAttr("norm", &norm).IsOK()normC:\agent\_work\30\s\onnxruntime\core\providers\cpu\ml\normalizer.cconnxruntime::ml::Normalizer::ComputeNormalizerC:\agent\_work\30\s\onnxruntime\core\providers\cpu\ml\linearregressor.cconnxruntime::ml::LinearRegressor::LinearRegressorinfo.GetAttr("targets", &targets_).IsOK()targetsLinearRegressorinterceptsLinearClassifieronnxruntime::ml::LinearClassifier::LinearClassifiermulti_classonnxruntime::ml::LinearClassifier<__int64>::LinearClassifieronnxruntime::ml::LinearClassifier::LinearClassifierC:\agent\_work\30\s\onnxruntime\core\providers\cpu\ml\linearclassifier.cconnxruntime::ml::LinearClassifier::LinearClassifierimputed_value_floatsImputeronnxruntime::ml::ImputerOp::ImputerOpExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedreplaced_value_floatimputed_value_int64sMust provide imputed_values_float_ or imputed_values_int64_ but not both.Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedreplaced_value_int64C:\agent\_work\30\s\onnxruntime\core\providers\cpu\ml\imputer.ccInvalid typeonnxruntime::ml::ImputerOp::Computeimputed_values_float_.empty() ^ imputed_values_int64_.empty()Empty value of imputed values.Empty input dimensions.status.IsOK() && !input_dimensions_.empty()onnxruntime::ml::FeatureVectorizer::FeatureVectorizerC:\agent\_work\30\s\onnxruntime\core/providers/cpu/ml/feature_vectorizer.hFeatureVectorizerinputdimensionsinputdimensions attribute must be providedonnxruntime::ml::FeatureVectorizer::ComputeC:\agent\_work\30\s\onnxruntime\core\providers\cpu\ml\feature_vectorizer.ccInvalid input type:).) does not match number of inputdimensions values (Number of inputs (input_count >= 0 && static_cast(input_count) == input_dimensions_.size()int64_vocabularyinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()onnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOpC:\agent\_work\30\s\onnxruntime\core/providers/cpu/ml/dictvectorizer.hDictVectorizerstring_vocabularyonnxruntime::ml::DictVectorizerOp,class std::allocator >,__int64>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string,class std::allocator > >::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,double>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,float>::DictVectorizerOpinfo.GetAttrs("cats_strings", string_categories).IsOK()onnxruntime::ml::CategoryMapper::CategoryMapperC:\agent\_work\30\s\onnxruntime\core/providers/cpu/ml/category_mapper.hinfo.GetAttrs("cats_int64s", int_categories).IsOK()num_entries == int_categories.size()CategoryMapperInput of string must have output of int64Input of int64 must have output of string %fTO_FLOATTO_STRINGTO_INT64SPARSE Expected DENSE or SPARSEInvalid PACK_MAP value of onnxruntime::ml::MakePack Expected TO_FLOAT, TO_STRING or TO_INT64Invalid CAST_TO value of onnxruntime::ml::MakeCastDENSEcur_input == end_input || cur_input->first >= 0onnxruntime::ml::CastMap::ComputeImplC:\agent\_work\30\s\onnxruntime\core\providers\cpu\ml\cast_map.ccNegative index values are not permitted. First entry in map has index value of map_forminfo.GetAttr("map_form", &attr).IsOK()max_mapinfo.GetAttr("max_map", &max_map_).IsOK()cast_toinfo.GetAttr("cast_to", &attr).IsOK()onnxruntime::ml::CastMap::CastMapC:\agent\_work\30\s\onnxruntime\core/providers/cpu/ml/cast_map.hInvalid input type of value: 0.fUnexpected CAST_TO value of max_map must be > 0 if map_form is SPARSEmap_form_ != PACK_MAP::SPARSE || max_map_ > 0CastMap Expected std::map or std::mapBinarizerInput data with index: threshold is NaNInvalid Y argument: num_indices = 0) >=] (Invalid Y argument: index is out of range: Y[ArrayFeatureExtractorInvalid argument: X input has empty dimensions.iteration_num_ < sequence_len_onnxruntime::scan::detail::LoopStateVariable::Next is not compatible with Mismatch between expected shape and shape from first outputonnxruntime::scan::detail::CreateFeedsFetchesManageronnxruntime::scan::detail::IterateSequence::::operator ()onnxruntime::scan::detail::IterateSequenceMisuse of LoopStateVariable. Attempt to move beyond end of sequence!is_concrete_shape_onnxruntime::scan::detail::OutputIterator::AllocateFinalOutputcur_iteration_ < num_iterations_onnxruntime::scan::detail::OutputIterator::operator *onnxruntime::scan::detail::OutputIterator::InitializeFailed to create output tensor for output #onnxruntime::scan::detail::OutputIterator::AllocateFinalBufferIf shape was concrete we shouldn't be using a custom allocator inputs but Scan was only given The subgraph in 'body' requires num_variadic_inputs == num_subgraph_inputsonnxruntime::scan::detail::Info::Infoonnxruntime::scan::detail::ReadDirections'. 0 == forward. 1 == reverse.Invalid values in 'validC:\agent\_work\30\s\onnxruntime\core\providers\cpu\controlflow\scan_utils.cc' was Number of entries in 'num_entries < 0 || gsl::narrow_cast(directions.size()) == num_entriesonnxruntime::OrtValueTensorSlicer::Iterator::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 ++Invalid activation function of Invalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::ActivationFuncByNameExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got softplusC:\agent\_work\30\s\onnxruntime\core\providers\cpu\rnn\rnn_helpers.cconnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCountonnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByNameInvalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNameInvalid GRU hidden gate activation function: onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameInput 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=Input X must have 3 dimensions only. Actual:Input R must have shape {Input W must have shape {hardsigmoidscaledtanhsoftsignelureluaffinethresholdedreluleakyreluskip is expected to have same shape as inputinput is expected to have 3 dimensions, got Last dimension of gamma and input does not matchgamma is expected to have 1 dimension, got Last dimension of beta and input does not matchbeta is expected to have 1 dimension, got Last dimension of bias and input does not matchbias is expected to have 1 dimension, got onnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::Computeop_kernel_info.GetAttr("axis", &axis_).IsOK()C:\agent\_work\30\s\onnxruntime\contrib_ops\cpu\layer_norm.cconnxruntime::contrib::LayerNorm::LayerNormScaleonnxruntime::contrib::Scale::Scaleinfo.GetAttr("scale", &scale_).IsOK()info.GetAttr("alpha", &alpha_).IsOK()c:\agent\_work\30\s\onnxruntime\contrib_ops\cpu\element_wise_ops.hinfo.GetAttr("beta", &beta_).IsOK()onnxruntime::contrib::Affine::AffineParametricSoftplusonnxruntime::contrib::ImageScaler::ImageScalerBias size (info.GetAttrs("bias", bias_).IsOK()c:\agent\_work\30\s\onnxruntime\contrib_ops\cpu\image_scaler.hImageScaler) does not match the number of channels (Input is expected to have four dimensions corresponding to [N,C,H,W], got DynamicSlice) needs to be greater than or equal to the topBorder (Input's height ( input dimensions instead) + bottomBorder (borderAttribute border needs to be specified with four border elements, got onnxruntime::contrib::Crop::Computec:\agent\_work\30\s\onnxruntime\contrib_ops\cpu\crop.h) + scale_[1] (CropInput's width () + scale_[0] () + rightBorder () needs to be greater than or equal to the leftBorder (onnxruntime::contrib::BiasGelu::ComputeC:\agent\_work\30\s\onnxruntime\contrib_ops\cpu\bert\bias_gelu_fusion.ccInput 1 is expected to have 1 dimension, got dimension 0 of Input 1 should have same length as the last dimension of input 0Shape of Input 0 is expected to have at least 1 dimension, got euclideanonnxruntime::contrib::CDist::CDistc:\agent\_work\30\s\onnxruntime\contrib_ops\cpu\cdist.hsqeuclideaninfo.GetAttr("metric", &metric).IsOK()onnxruntime::contrib::CDist::CDistInput shape dimensions mismatch:metricThe first input of CDist kernel has wrong shape: The second input of CDist kernel has wrong shape: CDisty_scaleCropAndResizebilinearC:\agent\_work\30\s\onnxruntime\contrib_ops/cpu/crop_and_resize.h specified. It should be either bilinear or nearestonnxruntime::contrib::CropAndResize::CropAndResizeNull crop_size_ptrNumber of dimensions for crop size should be exactly 1ConvTransposeWithDynamicPadsInput tensor to Unique op should be 1Dc:\agent\_work\30\s\onnxruntime\contrib_ops\cpu\maxpool_with_mask.honnxruntime::contrib::MaxpoolWithMask::ComputeMaxpoolWithMaskC:\agent\_work\30\s\onnxruntime\contrib_ops\cpu\matmul_integer16.cconnxruntime::contrib::MatMulInteger16::ComputeA != nullptr && B != nullptrMatMulInteger16sizeof(uint32_t) == output_element_bytesInvalid assumption of output element sizeC:\agent\_work\30\s\onnxruntime\contrib_ops\cpu\murmur_hash3.cconnxruntime::contrib::MurmurHash3::ComputekeysMurmurHash3positiveConv filter size does not match embedding_size attribute. embedding_size attribute: conv filter size: char_embedding_sizeconv_window_sizeembedding_sizeChar embedding size does not match conv kernal size 2. Conv kernal size 2 : Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Char embedding size: Conv kernal size 1 does not match conv_window_size attribute . conv_window_size attribute: conv kernal size 1: WordConvEmbeddingC:\agent\_work\30\s\onnxruntime\contrib_ops\cpu\word_conv_embedding.cconnxruntime::contrib::WordConvEmbedding::Computemincharnum_ > 0attribute mincharnum must have a positive valueattribute mincharnum is not setmincharnumattribute pad_value is not setpad_valueC:\agent\_work\30\s\onnxruntime\contrib_ops\cpu\tokenizer.cconnxruntime::contrib::Tokenizer::Tokenizermincharnum is too big for char level tokenezation!separators.empty()separators must not be empty!tokenexp.empty()Expecting a non-empty tokenexpEither one of the separators OR tokenexp attributes required but none is settokenexpseparatorsattribute mark is not setmarkTokenizerInput dimensions are either [C] or [N][C] allowedtensor(string) expected as inputMatch contains invalid utf8 chars: Input string contains invalid utf8 chars: Can not digest tokenexp: Can not digest separators: !char_tokenezation_ || mincharnum_ < 2static_cast(activation_func_names.size()) == num_directions_ * 3c:\agent\_work\30\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.honnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOpDEEPCPU_ATTN_LSTMAttention mechanism memory sequence lengths value must in (0, ], while found!Attention mechanism memory sequence lengths must have shape {Attention mechanism memory shape error! Expected: {}, actural: C:\agent\_work\30\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.cconnxruntime::contrib::DeepCpuAttnLstmOp::ComputeAttention layer weight shape error! Expected: {, aw_attn_size}. Got:Attention v weight shape error! Expected:{}. Got: Attention query layer weight shape error! Expected:{}, Got: Attention memory layer weight shape error! Expected:{, am_attn_size}, Got:AttnLSTMonnxruntime::contrib::DeepCpuAttnLstmOp::ValidateInputsonnxruntime::contrib::DeepCpuAttnLstmOp::ComputeImplleaky_relu_alphaGetFusedActivationAttr(info, activation_).IsOK()onnxruntime::contrib::FusedConvFloat::FusedConvFloatC:\agent\_work\30\s\onnxruntime\contrib_ops\cpu\fused_conv.ccAn axis tensor must be a scalar tensor.axis_tensor->Shape().IsScalar()onnxruntime::contrib::ExpandDims::Computec:\agent\_work\30\s\onnxruntime\contrib_ops\cpu\expand_dims.h Axis is Axis must be within range [axis <= X_NumDims && axis >= -X_NumDimsExpandDimsonnxruntime::contrib::EmbedLayerNorm::ComputeC:\agent\_work\30\s\onnxruntime\contrib_ops\cpu\bert\embed_layer_norm.ccinput index out of rangeinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0onnxruntime::contrib::AttentionBase::AttentionBaseonnxruntime::contrib::Attention::ComputeInput 3 is expected to have 1 dimension, got Inputs 3 and 0 shall have same length at dimension 0C:\agent\_work\30\s\onnxruntime\contrib_ops\cpu\bert\attention.ccInput 0 is expected to have 3 dimensions, got Input 0 dimension 2 should be divisiable by value of the num_heads attribute.Input 1 is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0Input 1 dimension 1 should be 3 times of dimension 0Input 2 is expected to have 1 dimension, got Input 2 dimension 0 should have same length as dimension 1 of input 1SampleOpoutput and sum shape must matchNot satsified: Y->Shape() == sum_shape onnxruntime::contrib::NchwcPoolBase::NchwcPool(X_shape[1] % MlasNchwcGetBlockSize()) == 0info.GetAttr("channels", &channels_).IsOK()onnxruntime::contrib::ReorderOutput::ReorderOutputinvalid channel countchannels_ > 0c:\agent\_work\30\s\onnxruntime\contrib_ops\cpu\nchwc_ops.hpool_attrs_.kernel_shape.size() == 2onnxruntime::contrib::NchwcPoolBase::NchwcPoolBaseonnxruntime::contrib::NchwcConv::ComputeC:\agent\_work\30\s\onnxruntime\contrib_ops\cpu\nchwc_ops.ccX_shape.NumDimensions() == 4(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.onnxruntime::contrib::NchwcConv::NchwcConvonnxruntime::contrib::ReorderInput::Computeonnxruntime::contrib::ReorderOutput::Computechannels_ <= Y_shape[1] [seqno= row[onnxruntime::contrib::BahdanauAttention::PrepareMemorymem_steps <= max_memory_steps_ && mem_steps > 0!normalize_not support normalize yet.C:\agent\_work\30\s\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.cconnxruntime::contrib::BahdanauAttention::BahdanauAttentionReal memory steps is not in (0, activation_params count mismatchunimplemented activation: beta is expected to have 1 dimensions, got word_embedding and segment_embedding shall have same dimension 1gamma is expected to have 1 dimensions, got beta is expected to have size of gamma is expected to have size of Input 0 and 1 shall have same shapeinput_ids is expected to have 2 dimensions, got Input 0 and 7 (mask) shall have same shapeposition_embedding is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got word_embedding and position_embedding shall have same dimension 1segment_embedding is expected to have 2 dimensions, got C:\agent\_work\30\s\onnxruntime\core\util\protobuf_parsing_utils.ccCHECK failed: !is_closed_: close() failed: gemmlowp error: %s WidthMajorallocation failurereference(Lhs: %d cells %dx%d %s, Rhs: %d cells %dx%d %s) is not supportedTensor types should have been handled alreadyCudaCudaPinned type: name:OrtMemoryInfo: [ mem_type: id:Specified device is not supported.OrtValue is not a Tensortype != nullptrNot implementedArgument is not a tensorOrtApis::GetTensorTypeAndShapeC:\agent\_work\30\s\onnxruntime\core\framework\tensor_type_and_shape.cconnxruntime::Tensor::InitTensor is expected to contain one of the primitive data types. Got: tensor size overflowonnxruntime::Tensor::SizeInBytesdtype_ != nullptrp_type != nullptrshape.Size() must >=0tensor failed memory size calculationonnxruntime::Tensor::TensorC:\agent\_work\30\s\onnxruntime\core\framework\tensor.cconnxruntime::SequenceTensorTypeBase::GetElementTypeonnxruntime::TensorTypeBase::GetElementTypeonnxruntime::SparseTensorTypeBase::GetElementTypeutils::HasElemType(thisProto->tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypethisProto->value_case() == TypeProto::ValueCase::kTensorTypeonnxruntime::TensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kSequenceTypeonnxruntime::SequenceTensorTypeBase::IsCompatibleonnxruntime::SparseTensorTypeBase::IsCompatibleutils::HasElemType(thisProto->sparse_tensor_type())onnxruntime::data_types_internal::IsCompatibleC:\agent\_work\30\s\onnxruntime\core\framework\data_types.cconnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypeWe do not expect duplicate registration of types for: Only ONNX MLDataType can be registeredproto != nullptrC:\agent\_work\30\s\include\onnxruntime\core/framework/data_types.honnxruntime::data_types_internal::SetMapTypes,class std::allocator >,__int64>::Setvalue_proto != nullptronnxruntime::data_types_internal::SetMapTypes,class std::allocator >,class std::basic_string,class std::allocator > >::Setonnxruntime::data_types_internal::SetMapTypes<__int64,class std::basic_string,class std::allocator > >::Setonnxruntime::data_types_internal::SetMapTypes<__int64,__int64>::Setonnxruntime::data_types_internal::SetMapTypes,class std::allocator >,float>::Setonnxruntime::data_types_internal::SetMapTypes,class std::allocator >,double>::Setonnxruntime::utils::ContainerChecker::ContainerChecker expected to be a registered ONNX typetensor(bfloat16)tensor type onnxruntime::NonTensorTypeBase::ToDataContainer(null)MLDataType for: Invalid DataTypeImpl TypeProto definition is not currently registered or supportedonnxruntime::NonTensorTypeBase::IsMapCompatibleutils::HasKeyType(thisProto->map_type())utils::HasElemType(thisProto->sequence_type())thisProto->value_case() == TypeProto::ValueCase::kMapTypeonnxruntime::NonTensorTypeBase::FromDataContaineronnxruntime::NonTensorTypeBase::IsSequenceCompatibleonnxruntime::data_types_internal::SetSequenceType,class std::allocator > > >::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > >::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType<__int64>::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType,class std::allocator > >::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setelem_proto != nullptronnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetMapTypes<__int64,float>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,double>::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::SetInvalid tensor shape slice argument.dimstart <= dimend && dimend <= size()C:\agent\_work\30\s\onnxruntime\core\framework\tensor_shape.cconnxruntime::TensorShape::SizeFromDimension}onnxruntime::TensorShape::Slice{ dimensions.dimension <= num_dimsonnxruntime::TensorShape::SizeToDimension for SizeFromDimension. Tensor has Invalid dimension of NnapiExecutionProviderfetch_alloc_info.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFetchesfeed_locations.size() == copy_info.size()onnxruntime::utils::FinalizeCopyInfoForFeedsonnxruntime::utils::CopyOutputsAcrossDevicescopy_info.size() == num_feedsonnxruntime::utils::CopyInputsAcrossDevicesC:\agent\_work\30\s\onnxruntime\core\framework\utils.cconnxruntime::utils::FindMemoryInfoForValueinvalid allocatoronnxruntime::utils::CopyMLValueonnxruntime::utils::CalculateStaticCopyInfoForFeedsonnxruntime::utils::InitializeFeedFetchCopyInfoexec_plan_ptronnxruntime::utils::CalculateStaticCopyInfoForFeedonnxruntime::utils::ExecuteGraphOnly one thread was configured for parallel execution. Hence will use sequential execution.onnxruntime::utils::ExecuteGraphImplNo attribute with name:'Attibute name and type don't match'is defined.No attribute with this name is defined.C:\agent\_work\30\s\onnxruntime\core\framework\op_kernel_info.cconnxruntime::OpKernelInfo::GetMemoryInfocannot find allocatorC:\agent\_work\30\s\onnxruntime\core/framework/node_index_info.honnxruntime::NodeIndexInfo::GetNodeOffsetnode_offsets_index < node_offsets_size_OpKernel was nullC:\agent\_work\30\s\onnxruntime\core\framework\op_kernel.cconnxruntime::OpKernelContext::OpKernelContextframe != nullptrInvalid arg_num of . Num args is onnxruntime::OpKernelContext::OutputMLValuekernel != nullptrExecution frame was nullonnxruntime::OpKernelContext::GetOrCreateOutputMLValueTempSpace allocator not foundonnxruntime::OpKernelContext::NumVariadicInputsarg_num < arg_counts.size()Device: [ memory_type: device_id:existing_entries.find(attribute_name) == existing_entries.cend()Entry exists in node for attribute onnxruntime::SessionState::GetNodeIndexInfonode_index_info_SetGraphAndCreateKernels must be called prior to GetExecutionInfo.onnxruntime::SessionState::AddSubgraphSessionStateUsing an input in multiple nodes on different devices is not supported currently. Input: is used by node ) and node duplicated ort_value index:onnxruntime::SessionState::AddOutputNameToNodeInfoMappingoutput_names_to_nodeinfo.empty()Only one node should produce an output. Existing entry for Failed to find input name in the mapping: as there's no execution provider allocated.Done saving OrtValue mappings.. Do you have duplicated calls to SessionState::AddInitializedTensor function?Kernel creation failed for node: with error: Could not create kernel for node: C:\agent\_work\30\s\onnxruntime\core\framework\session_state.cconnxruntime::SessionState::SetGraphSaveMLValueNameIndexMappingC:\agent\_work\30\s\onnxruntime\core\framework\session_state_initializer.cconnxruntime::SessionStateInitializer::CreatePlanSaving initialized tensors. failed.ort_value_name_idx_map.MaxIdx() > 0OrtValue indexes should have been populated.onnxruntime::SaveInitializedTensors input with name is not used by any node.onnxruntime::SaveInputOutputNamesToNodeMappingonnxruntime::SaveInputOutputNamesToNodeMapping::::operator ()GraphSubgraphonnxruntime::DeserializeTensorProtostring tensor is not supported for copying between allocatorsInvalid allocation info. Provider name = Execution plan was not found in SessionState. CreatePlan must be called first.onnxruntime::SaveInputOutputNamesToNodeMapping::::operator ()Failed to copy tensor to execution provider: Internal error. The preallocated buffer is too small. Requires , Got Done saving initialized tensorsDeserialize tensor The node is not placed on any Execution Providerfound duplicated provider in KernelRegistryManagerFailed to find kernel for The node is not placed on any Execution Provider, therefore, can't find a suitable kernel for (node Initialized tensor with unexpected type: string tensor can not have raw dataC:\agent\_work\30\s\onnxruntime\core\framework\tensorprotoutils.cconnxruntime::utils::GetFileContentdata overflowinitialize preallocated buffer failedThe buffer planner is not consistent with tensor buffer size, expected tensor can't contain negative dimsonnxruntime::utils::TensorProtoToMLValueUnpackTensor: the pre-allocate size does not match the size in protocorrupted protobuf data: tensor shape size() does not match the data size() in protoNot satsified: source_bytes.size_bytes() == destination.size_bytes() C:\agent\_work\30\s\onnxruntime\core/framework/endian_utils.honnxruntime::utils::ReadLittleEndianInvalid TensorProto`anonymous-namespace'::UnpackTensorWithRawDataUnpackTensor: the pre-allocated size does not match the raw data size, expected source and destination buffer size mismatchnullptr != type_protoonnxruntime::utils::GetMLDataTypeC:\agent\_work\30\s\onnxruntime\core\framework\mldata_type_utils.ccC:\agent\_work\30\s\onnxruntime\core\framework\execution_provider.ccduplicated allocatoronnxruntime::IExecutionProvider::InsertAllocatorinput_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknownonnxruntime::FeedsFetchesManager::SetDeviceCopyChecksonnxruntime::FeedsFetchesInfo::MapNamesToMLValueIdxsC:\agent\_work\30\s\onnxruntime\core\framework\feeds_fetches_manager.ccError mapping feeds: Error mapping output names: Provider doesn't return correct number of compiled functionsnode_compute_funcs.size() == nodes_need_compile.size()C:\agent\_work\30\s\onnxruntime\core\framework\graph_partitioner.ccNo provider specified.onnxruntime::GraphPartitioner::PartitionC:\agent\_work\30\s\onnxruntime\core/framework/func_kernel.hcreate_func(&context, &func_state_) == 01 == capability->nodes.size()onnxruntime::PlaceNodeonnxruntime::FunctionKernel::FunctionKernel to There's no data transfer registered for copying tensors from data_transfer registered is nullptr.Tensor size mismatch has already been loaded.A dso with name onnxruntime::ExLibLoader::LoadExternalLibCaught exception while loading custom ops with message: onnxruntime::ExLibLoader::~ExLibLoaderC:\agent\_work\30\s\onnxruntime\core\framework\ex_lib_loader.ccFailed to unload DSO: Caught exception while destructing CustomOpsLoader with message: Unloading DSO ort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_onnxruntime::IExecutionFrame::GetMLValueC:\agent\_work\30\s\onnxruntime\core/framework/execution_frame.hoffset >= 0 && static_cast(offset) < node_values_size_onnxruntime::NodeIndexInfo::GetMLValueIndex. 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() == nullptr but the actually size is: , block in memory pattern size is: For ort_value with index: , block not found in target location. fall back to default allocation behaviorTensor shape cannot contain any negative valueort_value.Fence() == nullptronnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper, fall back to default allocation behaviorFetches vector passed to GetOutputs contains buffers_.find(mem_patterns_->locations[i]) == buffers_.end()onnxruntime::ExecutionFrame::ExecutionFrameTrying to allocate memory for unused optional inputs/outputsshape && tensor.Shape() == *shapeonnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValueinvalid index entries which doesn't match the number of fetches the frame was initialized with of node_index_info and ort_value_idx_map are out of sync and cannot be usednode_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx() Requested shape:OrtValue shape verification failed. Current shape:feeds.size() == feed_mlvalue_idxs.size()onnxruntime::IExecutionFrame::IExecutionFrameC:\agent\_work\30\s\onnxruntime\core\framework\execution_frame.ccfetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size()onnxruntime::ExecutionFrame::TraceAllocateonnxruntime::ExecutionFrame::TraceFreeTraceFree for ort_value_idx=Memory pattern planner is not enabled on this execution framework.onnxruntime::ExecutionFrame::GetAllocationPlan failed: size=TraceAllocation for ort_value_idx=Allocation of tensor types requires a shape.Invalid allocation kind: onnxruntime::ExecutionFrame::ReleaseMLValueImplort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()onnxruntime::AllocateSparseTensorort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlanWe don't expect custom allocators for non-tensor types, so a shape is mandatory here. kernel start version: node_version: Version mismatch.kernel def can't be NULL Incompatible types.Op: kernel_end_version: len <= op_schema.inputs().size()onnxruntime::`anonymous-namespace'::TraverseFormalParametersWithTypeProtoonnxruntime::KernelRegistry::TryFindKernelC:\agent\_work\30\s\onnxruntime\core\framework\kernel_registry.cc Encountered following errors: ( kernel is not supported in : Conflicting with a registered kernel with op versions.Failed to add kernel for since it was ill-formed during registrationFailed to create kernel for op: src.SizeInBytes() == dst.SizeInBytes()C:\agent\_work\30\s\onnxruntime\core\framework\data_transfer.cconnxruntime::CPUDataTransfer::CopyTensoronnxruntime::NodeIndexInfo::Init::::operator ()onnxruntime::NodeIndexInfo::Init::::operator ()C:\agent\_work\30\s\onnxruntime\core\framework\node_index_info.cconnxruntime::OrtValueTensorSlicer::Create. Dimension 0 is gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensiondim0_offset < dim0_sizeInvalid dim0_offset of ort_value.IsAllocated()OrtValue has not been allocated so can't be sliced.Insufficient dimensions to slice on . Shape:Can't slice a non-tensor OrtValue. Type was C:\agent\_work\30\s\onnxruntime\core\framework\ort_value_tensor_slicer.cconnxruntime::OrtValueTensorSlicer::Create'. Unknown exception was caught by catch-all handler.Exception running nodes starting at providerNon-zero status code returned while running _fence_after_kernel_time_fence_before node. Name:'' Status Message: Exiting due to terminate flag being set to true.onnxruntime::ParallelExecutor::RunNodeAsyncop_nameGot nullptr from GetKernel for node: Begin executionParallelExecutor::ExecuteMultiple errors were found.C:\agent\_work\30\s\onnxruntime\core/framework/op_kernel_context_internal.hC:\agent\_work\30\s\onnxruntime\core\framework\parallel_executor.cconnxruntime::ParallelExecutor::ExecuteAll implicit inputs should have OrtValue instances by now. does not.onnxruntime::OpKernelContextInternal::OpKernelContextInternalentry != nullptronnxruntime::ReleaseNodeMLValuesSequentialExecutor::Executeonnxruntime::SequentialExecutor::ExecuteC:\agent\_work\30\s\onnxruntime\core\framework\sequential_executor.cc not found.Can't use func with null ptrC:\agent\_work\30\s\onnxruntime\core\framework\fuse_nodes_funcs.cconnxruntime::FuncManager::GetFuncsfunc info for node: already exist.onnxruntime::TensorAllocatorWithMemPattern::FinalizePlanduplicated locationInternal error.c:\agent\_work\30\s\onnxruntime\core\framework\tensor_allocator_with_mem_pattern.hFailed to get allocator for location: Get preallocated buffer for initializer '' failedonnxruntime::TensorAllocatorWithMemPattern::TraceMem pattern for initializer is not foundWeight buffer for initializer '' is not foundnullptr != tensor_type_baseonnxruntime::PlannerImpl::ReuseCan not find the node onnxruntime::PlannerImpl::GetElementSizeid >= 0 && static_cast(id) < ort_value_info_.size()onnxruntime::PlannerImpl::AllocPlanreused != reused_foronnxruntime::PlannerImpl::ProcessDefonnxruntime::PlannerImpl::UseCountn >= 0 && static_cast(n) < ort_value_info_.size()n >= 0 && static_cast(n) < plan_.allocation_plan.size()onnxruntime::PlannerImpl::Buffer[onnxruntime::PlannerImpl::IndexC:\agent\_work\30\s\onnxruntime\core\framework\allocation_planner.cc) : onnxruntime::PlannerImpl::GeneratePlanForWeightskernel_create_info != nullptr && kernel_create_info->kernel_def != nullptronnxruntime::PlannerImpl::CreatePlanonnxruntime::PlannerImpl::ComputeReusePlanNo suitable kernel definition found for op onnxruntime::PlannerImpl::ComputeUseCountsonnxruntime::PlannerImpl::GetLocationForNodeInputCan not find the execution provider . illegal input path:unexpected failure.location failedmodel format error! Need a key for the external data infomodel format error! Need a value for the external data infomodel format error! Missing 'location'checksummodel format error!parsing lengthAllocated memory at onnxruntime::BFCArena::ExtendTotal allocated bytes: bytes.Extended allocation by h < chunks_.size()onnxruntime::BFCArena::ChunkFromHandleBinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size * 2) != BinFromIndex(b)BinForSize(bin_size) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b)onnxruntime::BFCArena::BFCArenaC:\agent\_work\30\s\onnxruntime\core\framework\bfc_arena.cconnxruntime::BFCArena::RegionManager::RegionForCreating bin of max chunk size p_int < base_int + memory_size_Could not find Region for p_int >= base_intonnxruntime::BFCArena::AllocationRegion::IndexForonnxruntime::BFCArena::AllocationRegion::AllocationRegionC:\agent\_work\30\s\onnxruntime\core/framework/bfc_arena.h0 == memory_size % kMinAllocationSizeonnxruntime::BFCArena::FreeAndMaybeCoalesceBinFromIndex(c->bin_num)->free_chunks.erase(h) > 0c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::RemoveFreeChunkFromBinCould not find chunk in bin!c->in_use() && (c->bin_num != kInvalidBinNum)onnxruntime::BFCArena::RemoveFreeChunkIterFromBinc2->prev == h1onnxruntime::BFCArena::InsertFreeChunkIntoBin!c1->in_use() && !c2->in_use()onnxruntime::BFCArena::Mergeonnxruntime::BFCArena::SplitChunkonnxruntime::BFCArena::DeallocateRawInternalonnxruntime::BFCArena::FindChunkPtr!c->in_use() && (c->bin_num == kInvalidBinNum)!chunk->in_use()tried to allocate 0 bytesonnxruntime::BFCArena::AllocateRawInternalonnxruntime::BFCArena::Reserveh != kInvalidChunkHandlereserved_chunks_.find(ptr) == reserved_chunks_.end() was ', location: Failed to get allocator for initializer 'onnxruntime::SimpleTensorAllocator::GetPreallocatedBufferC:\agent\_work\30\s\onnxruntime\core\framework\simple_tensor_allocator.ccinvalid ort_value_index:Mismatch between number of source and target dimensions. Source= Target=Can't merge shape info. Both source and target dimension have values but they differ. Source= Dimension=[ShapeInferenceError] ConstantNoOpThis is an invalid model. Error: two nodes with same node name (This is an invalid model. Error: Duplicate definition of name (Only 'value' attribute is supported within a 'Constant' node in ORTconstant_attribute.has_t()graph_proto != nullptronnxruntime::Graph::GraphThis is an invalid model. The sum of input arg count is not equal to size of input defs in node (graph_proto cannot be nullonnxruntime::NodeArg::UpdateTypeAndShapeSparseTensor element type mismatch. Type mismatch. Current=Tensor element type mismatch. Output: Input=onnxruntime::MergeShapeInfoC:\agent\_work\30\s\onnxruntime\core\graph\graph.cc' source:Error merging shape info for output. '. Falling back to lenient merge. target:) in node () of operator (This is an invalid model. Node () is invalid.) does not have type information set by parent node.) input arg (Subgraph input missing type.onnxruntime::Graph::InferAndVerifySubgraphTypesSize mismatch validating subgraph inputs. Got Node: inputs and requires inputs but subgraph has No Graph instance was found for attribute inputs. Either provide all subgraph inputs, or just the required inputs.Graph attribute inferencing failed: in node 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. 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=Argument mismatch when removing edge.onnxruntime::Graph::BuildConnectionsInvalid source node arg slot specified when removing edge.Invalid destination node arg slot specified when removing edge.Invalid node indexes specified when removing edge.onnxruntime::Graph::RemoveEdgeInvalid destination node arg slot specified when adding edge.Argument type mismatch when adding edge.onnxruntime::Graph::AddEdgeInvalid source node arg slot specified when adding edge.onnxruntime::Graph::SetOuterScopeNodeArgsInvalid node indexes specified when adding edge.Shouldn't be possible to have NodeArgs that haven't been handled already.outer_scope_node_args_consumed.empty()onnxruntime::Graph::ForThisAndAllSubgraphsonnxruntime::Graph::Resolveonnxruntime::Graph::InitInputsInitializersOutputsonnxruntime::Graph::PerformTypeAndShapeInferencing) attribute (Error: Duplicate definition-site for (onnxruntime::Graph::VerifyNodeAndOpMatch) is required but not specified. is not a registered function/opFatal error: Type Error: Shape of initializer This is an invalid model. Error in Node:Type Error: Value of initializer does not match. This is an invalid model. Model input ( does not match its type.Type Error: Type () does not have type information.) of node () of output arg () output arg () does not match expected type (Node () type inference failedType Error: Type parameter () Op ( in node () bound to different types (' of input parameter (This is an invalid model. Type Error: Type ') does not exist in the graph.This is an invalid model. Graph output (Graph ctor should have created NodeArg for initializer.onnxruntime::Graph::SetGraphInputsOutputsonnxruntime::Graph::CleanUnusedInitializers'. It is no longer used by any node.'. It is not used by any node and should be removed from the model.Removing initializer 'node_argonnxruntime::Graph::ToGraphProtoInternal'was added but does not exist. Outer scope node arg name 'node->GetOutputEdgesCount() == 0onnxruntime::Graph::RemoveNode as it still has output edges.Can't remove node onnxruntime::Graph::RemoveInitializedTensorgraph_proto_ is not in sync with name_to_initial_tensor_.!foundexisting->second == &tensoronnxruntime::Graph::AddInitializedTensor but different TensorProto.AddInitializedTensor already has tensor with name C:\agent\_work\30\s\include\onnxruntime\core/common/const_pointer_container.hindex < data_.size()onnxruntime::ConstPointerContainer > >::atonnxruntime::Graph::SetInputsonnxruntime::Graph::SetOutputsonnxruntime::Graph::InlineFunctionThis API is not supported when model is loaded from proto file right now.onnxruntime::Graph::FuseSubGraphonnxruntime::Graph::AllocateNodenullptr != sub_graph && nullptr != sub_graph->GetMetaDef() must be either specified in graph inputs or graph initializers.nodes_.size() < static_cast(std::numeric_limits::max()), but it its version is higherthan the operator set version known by the checker.C:\agent\_work\30\s\onnxruntime\core\graph\schema_registry.cconnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSetDomain already set in registryCNegative values are not allowed in a shape specification should specify a shapeAttribute Constrain input and output types to float tensorsFor internal use.2D attention mask with shape (batch_size, sequence_length)1D beta tensor for layer normalization with shape (hidden_size)1D gamma tensor for layer normalization with shape (hidden_size)segment_embeddingposition_embedding2D with shape (, hidden_size)word_embedding2D with shape (,hidden_size)segment_ids2D segment IDs with shape (batch_size, sequence_length)input_ids2D words IDs with shape (batch_size, sequence_length)Constrain mask index to integer typesConstrain input and output types to float tensors.3D output tensor with shape (batch_size, sequence_length, hidden_size)mask_indexAttention mask index with shape (batch_size)M1D input tensor with shape (3 * hidden_size)weight2D input tensor with shape (hidden_size, 3 * hidden_size)3D input tensor with shape (batch_size, sequence_length, hidden_size), hidden_size = num_heads * head_sizeNumber of attention headsword_embedding should have 2 dimensions and dimension size is known.Inputs 0 shall be 2 dimensionsWConstrain input and output types to float/quantized tensorsC:\agent\_work\30\s\onnxruntime\core\graph\contrib_ops\contrib_defs.cctensor rank too small) needs to be greater than or equal to the left_border () + right_border () needs to be greater than or equal to the top_border () + bottom_border ('Scale' must contain exactly 2 values - (height, width)'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)Input's shape must be 4-D 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 takes one input data (Tensor) and produces one output data (Tensor) whose value is the input data tensor scaled element-wise. extra_shapeinput_as_shape 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], ] 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.Scale and bias the input image. Bias values are stored in the same ordering as the image pixel format. ParametricSoftplus takes one input data (Tensor) and produces one output data (Tensor) where the softplus function, y = alpha * ln(exp(beta * x) + 1), is applied to the tensor elementwise. Affine takes one input data (Tensor) and produces one output data (Tensor) where the affine function, y = alpha * x + beta, is applied to the tensor elementwise. 1D bias tensor with shape (hidden_size1D skip tensor with shape (hidden_size1D input tensor with shape (hidden_size)skip3D skip tensor with shape (batch_size, sequence_length, hidden_size)3D input tensor with shape (batch_size, sequence_length, hidden_size)Constrain input and output types to float or half tensors.output tensorbias tensorinput tensorConstrain input and output float tensors types.Constrain input and output integer tensors types1D mask_index tensor with shape (batch_size)maskOutput tensorInput tensorThreshold valueResult, has same type as input, with H and W dimensions reduced.A 1-D values of (height, width).A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).Result, has same shape and type as inputInput tensor of shape [N,C,H,W]The scale to apply.Bias applied to each channel, same size as C.1D output tensor1D input tensorValue of betaValue of alphaBias Gelu. It's an extension of Gelu. It takes the sum of input A and bias input B as the input of Gelu activation. Gaussian 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.crop_size shape input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensionrois input tensor has wrong dimensionfirst input tensor has wrong dimension'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64last dimension of indices must not be larger and rank of data tensorboth data and indices tensor need to have rank larger than zero.inputs are expected to have tensor type and output type should not be null. 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]. 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'). The linear quantization operator. It consumes a full precision data, a scale, a zero point and computes the quantized data. The quantization formula is y = (x / y_scale) + y_zero_point. For (x / y_scale), it computes the nearest integer value to arg (in floating-point format), rounding halfway cases away from zero. Scale and zero point must have same shape. They must be either scalar (per tensor) or 1-D tensor (per 'axis').Input axis is invalid: Second input does not have rank 2First input does not have rank 2) + scale[1] () + scale[0] (The scaled hyperbolic tangent values of the input tensor computed element-wiseScaling valueIf 0, normalize the mean only. Default is 1.If 1, mean and variance are computed across channels. Default is 0.Perform mean variance normalization.hiddenThe new GRU hidden state calculated by this op.tThe timestep for this operation.seq_lengthsArray of sequence lengths. len(seq_lengths) should equal batch size N.gatesUnactivated gate outputs from forget, update, and output gates, pre-activation.hidden_prevThe previous GRU hidden state.drop_statesBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.GRUUnitOutput data after scalingInput data to be scaledvaluesThe filled tensorThe shape of filled tensorGivenTensorFillConstrain indices to integer typesConstrain input and output types to all tensor types.Sliced data tensor.1-D tensor of axes that `starts` and `ends` apply to.1-D tensor of ending indices (exclusive) of corresponding axis in axes1-D tensor of starting indices of corresponding axis in `axes`dataTensor of data to extract slices from.Input/Output is a string tensorTokenized stringsStrings to tokenizeConstrain 'x_zero_point' and 'x' to 8-bit integer tensors.Constrain 'y', 'x_scale' to float tensors.N-D full precision output tensor. It has same shape as 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'.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'.N-D quantized Input tensor to be de-quantized.Constrain 'y_zero_point' and 'y' to 8-bit integer tensors.Constrain 'x', 'y_scale' to float tensors.N-D quantized output tensor. It has same shape as 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'.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'.N-D full precision Input tensor to be quantized.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.ExpandDims echo operator.Specified axis to insert a dimensionScalar multiplier for input tensor C.Scalar multiplier for the product of input tensors A * B.Whether B should be transposedWhether A should be transposedConstrain input and output types to float/int tensors.Output tensor of shape (M, N).Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).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 A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero. The FusedGemm operator schema is the same as Gemm besides it includes attributes activation and leaky_relu_alpha. The fused convolution operator schema is the same as Conv besides it includes an attribute activation.PadsConstrain input0 and output types to float tensors Sample echo operator.Tensor of rank q-1+r-indices[-1].indicesTensor of rank q >= 1.Tensor of rank r >= 1.If value is 1, output type is uint32_t, else int32_t. Default value is 1.Seed for the hashing algorithm, unsigned 32-bit integer, default to 0.Constrain output type to unsigned and signed 32-bit integer tensor.Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.32-bit hash value.An input tensor to hash.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.Keep the reduced dimension or not, default 1 mean keep reduced dimension.A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input 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).Constrain input type to 8-bit integer tensor.reducedReduced output tensor.An input tensor. 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.ReduceSumIntegerConstrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16).Constrain input B data types as 16-bit integer tensorConstrain input A data types as 16-bit integer tensorMatrix multiply results from A * BN-dimensional matrix BN-dimensional matrix A 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.Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredan 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.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.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.Boolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).A 2D Matrix that represents the distance between each pair of the two collections of inputs.2D matrix with shape (K,N)2D matrix with shape (M,N)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". 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] Input can be of any tensor type.countsA 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x'idxA 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques'A 1-D 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 input tensor that is to be processed. 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], ], ] Tensor after padding.(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).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`.Input tensor.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 arrayThe WordConvEmbedding takes in a batch of sequence words and embed each word to a vector.Constrain to tensor(float).Constrain to tensor(int32).Specify embedding vector of charSpecify bias of convSpecify weights of convSequenceSpecify batchs of sequence words to embeddingInteger representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.This operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernal shape.Integer representing the embedding vector size for each word.If not provide, use the fileter size of conv weight 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]]] Constrain indice type to int32 or int64Constrain input and output types to any tensor type.The bias input data that is a 1D tensor.The normal input data.The output.The input data as Tensor.Constrain mean and inv_std_var to be float tensors.Constrain input and output types (except mean and inv_std_var) to float tensors.inv_std_varSaved inverse standard variance used during training to speed up gradient computation.Saved mean used during training to speed up gradient computationUOutput data tensor.Bias tensor.Scale tensor.Input data tensor from the previous layer.The epsilon value to use to avoid division by zero.The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs). 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.Constrain types to int tensors.Constrain types to float tensors.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].crop_size1-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.batch_indices1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.roisRoIs (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.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.Value used for extrapolation, when applicable. Default is 0.0f. The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.Constrains input to only numeric types.Protobuf serialization failed.onnxruntime::Model::Save is less than 0. less than 0.Protobuf parsing failed.onnxruntime::Model::LoadFailed to load model with error: No graph was found in the protobuf.C:\agent\_work\30\s\onnxruntime\core\graph\model.cconnxruntime::Model::ModelONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset model may run depending upon legacy support of some older opset version operators.Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.ModelProto does not have a graph.ModelProto was null.system error number Load model failed. File doesn't existonnxruntime::SaveModelonnxruntime::graph_utils::AddInitializer!graph.GetInitializedTensor(new_initializer.name(), existing)Initializer with same name exists. Name:onnxruntime::graph_utils::RemoveNodeShould be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.onnxruntime::graph_utils::GetNodeOutputNameindex >= 0 && static_cast(index) < outputs.size()Attempting to get an output that does not exist.onnxruntime::graph_utils::GetNodeInputNameindex >= 0 && static_cast(index) < inputs.size()Attempting to get an input that does not exist.onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutputstd::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })Node must only have one used outputonnxruntime::graph_utils::CanUpdateImplicitInputNameInSubgraphs Implicit input name cannot be safely updated to in one of the subgraphs.C:\agent\_work\30\s\onnxruntime\core\graph\graph_utils.cconnxruntime::graph_utils::UpdateImplicitInputNameInSubgraphstd::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0onnxruntime::graph_utils::AddNodeInputnum_explicit_inputs == static_cast(target_input_idx)Can only add a new input at the end of the current ones.onnxruntime::graph_utils::ReplaceNodeInputInvalid input index for node . Index: ExplicitInputs: ImplicitInputs:onnxruntime::update_subgraphs_within_function_bodyA node with a function body within a subgraph within another function body is currently not supported in ORToutinResolve subgraph failed:onnxruntime::FunctionImpl::FunctionImplinput_arg->Type() != nullptrfused_function_subgraphC:\agent\_work\30\s\onnxruntime\core\graph\function.ccH=Unsupported non-raw-data data type!1-D Tensor of the range.deltaTensor(scalar, or dims=[1]). Number that increments start. Defaults to 1.limitTensor(scalar, or dims=[1]). Upper limit of sequence, exclusive.startTensor(scalar, or dims=[1]). First entry in the range.Constrain input and output types.Unsupported type:Can not get shape initializer data!The attention_v tensor in the attention mechanism. Should be of shape `[num_directions, am_attn_size]` MWThe weight tensor of the memory layer in the attention mechanism. Should be of shape `[num_directions, memory_depth, am_attn_size]` QWThe 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]` PThe 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.initial_cOptional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.initial_hOptional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.sequence_lensOptional 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 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.RThe 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 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 input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`Constrain seq_lens to integral tensors.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.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.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.A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.Y_cThe last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.Y_hThe last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`. A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`AWThe 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.` memory_seq_lensThe sequence length of the input memory for the attention mechanism. Should be of `[batch_size]` The sequence of the memory (input) for attention mechanism. Should be of `[batch_size, max_memory_step, memory_depth]` [ is not a valid year is not a valid day is not a valid dateDefault logger already set. Only 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::DefaultISink must be provided.onnxruntime - error code: Failed to close file handle onnxruntime::`anonymous-namespace'::FileHandleTraits::CleanUpC:\agent\_work\30\s\onnxruntime\core\platform\windows\env.ccFatal error: 0 count processors from GetSystemInfoonnxruntime::`anonymous-namespace'::WindowsEnv::GetNumCpuCoresFatal error: 0 count processors from GetLogicalProcessorInformation fail, errcode = GetFileSizeEx onnxruntime::`anonymous-namespace'::WindowsEnv::ReadFileIntoBufferNot satsified: file_path Not satsified: offset >= 0 Not satsified: length <= buffer.size() open file SetFilePointerEx ReadFile fail: unexpected endMapFileIntoMemory is not implemented on Windows.Failed to unload libraryFailed to find symbol in libraryonnxruntime::`anonymous-namespace'::WindowsEnv::FormatLibraryFileNamekernel32.dllGetSystemTimePreciseAsFileTimeSUCCESSFAILINVALID_ARGUMENTNO_SUCHFILENO_MODELENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFMODEL_LOADEDNOT_IMPLEMENTEDINVALID_GRAPHEP_FAILGENERAL ERRORcode != static_cast(common::OK)onnxruntime::common::Status::StatusC:\agent\_work\30\s\onnxruntime\core\common\status.ccOKSystemError[ONNXRuntimeError]SessionNodeenabled_onnxruntime::profiling::Profiler::StartTimeC:\agent\_work\30\s\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" : {"" : "} }, ] @@1.033{@l?A ;Eۼ# =pu>7fȹ~;ƼX=P"?>?B;?r1mL:e9 <*=*>>??@K?@@@AQ5@.N3;A8X} <2?~>++{16?:>=4?~??A%~3a*7A\3JCy7 ':Y;Ӡ5֕8;Y;    ' has been deprecated since version , max=Operator ') has input size not in range [min=) has output size not in allowed input sizes.has output size not in allowed output sizes.) than declared () in op definition. is marked single but has an empty string in the graph) has more inputs () has more outputs ()'s input ' appeared multiple times.)'s output for operator Attribute 'Mismatched attribute type in 'Unrecognized attribute: ' is expected to have field 'sparse_tensor'' is expected to have field 't'' is expected to have field 'floats'' is expected to have field 'g'' is expected to have field 'strings'' is expected to have field 'ints'' is expected to have field 'graphs'' is expected to have field 'tensors'' is missing. has unknown expected typeRequired attribute '(inputs_.size() - 1) == iDuplicate type constraint nameONNX Schema : failed validating the check: !(it.GetName().empty())(outputs_.size() - 1) == iThe indices, based on 0 as the first index of any dimension.Data to be selectedZSelected output data as an arrayC:\agent\_work\30\s\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.Data to be binarizedThe input must be a tensor of a numeric type. The output will be of the same tensor type.Binarized output dataValues greater than this are mapped to 1, others to 0.The size of each input in the input listThe input type must be a tensor of a numeric type.Data to be processed.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.Imputed output dataA value that needs replacing.Value(s) to change toValue(s) to change to.Label encoder has only one output.Label encoder has only one input.Input type is not string tensor but key_strings is setOnly one of keys_*'s can be set in label encoder.Input type is not float tensor but keys_floats is setInput type is not int64 tensor but keys_int64s is setInput data. It can be either tensor or scalar.Only one of values_*'s can be set in label encoder.The input type is a tensor of any shape.Output data.A list of strings. One and only one of 'keys_*'s should be set.Output type is determined by the specified 'values_*' attribute.A list of floats.A list of ints.A string.A list of strings. One and only one of 'value_*'s should be set.A float.An integer.Input's shape should be 1D or 2DClassification outputs (one class per example).Data to be classified.A tensor representing the same data as the input map, ordered by their keysThe input map that is to be cast to a tensormap(int64, string)The input must be an integer map to either string or float.The output is a 1-D tensor of string, float, or integer.map(int64, float)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).
One of 'DENSE', 'SPARSE'.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.Output data. If strings are input, the output values are integers, and vice versa.Input dataThe output is a tensor of strings or integers. Its shape will be the same as the input shape.The input must be a tensor of strings or integers, either [N,C] or [C].The integers of the map. This sequence must be the same length as the 'cats_strings' sequence.The strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceAn integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined.A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.A dictionary.The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.A 1-D tensor holding values from the input dictionary.map(int64, double)map(string, int64)map(string, double)map(string, float)A string vocabulary array.
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.An integer vocabulary array.
One and only one of the vocabularies must be defined.The output array, elements ordered as the inputs.An ordered collection of tensors, all with the same element type.Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores.The 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 input must be a tensor of a numeric type, either [C] or [N,C].List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.Second set of probability coefficients. This array must be same size as prob_a.
If these are provided then output Z are probability estimates, otherwise they are raw scores.First set of probability coefficients.Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Class labels if using integer labels.
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].Regression outputs (one score per target per example).Flag indicating whether the regression is a one-class SVM or not.Chosen support vectorsThe number of support vectors.Support vector coefficients.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'N, Top class for each pointInput of shape [N,F]The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.The class score for each class, for each point, a tensor of shape [N,E].Node id for each node. Ids may restart at zero for each tree, but it not required to.Tree id for each node.Thresholds to do the splitting on for each node.Feature id for each node.The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'Popularity of each node, used for performance and may be omitted.Child node if expression is false.Child node if expression is true.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]Classification scores ([N,E] - one score for each class and exampleA collection of weights of the model(s).The output will be a tensor of strings or integers.Indicates whether to do OvR or multinomial (0=OvR is the default).A collection of intercepts.Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Class labels when using string labels. One and only one 'classlabels' attribute must be defined.Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Regression outputs (one per target, per example).Data to be regressed.Indicates the transform to apply to the regression output vector.
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.Weights of the model(s).The total number of regression targets, 1 if not defined.Encoded output dataData to be encoded, a tensor of shape [N,C] or [C]One of 'MAX,' 'L1,' 'L2'Encoded output data, having one more dimension than X.Data to be encoded.List of categories, strings.
One and only one of the 'cats_*' attributes must be defined.List of categories, ints.
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.Scaled output data.Data to be scaled.Second, multiply by this.
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.
Must be same length as 'offset'First, offset by this.
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.The id of the tree that this node is in.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.
This attribute may be left undefined, and the defalt value is false (0) for all nodes.The index of the class list that each weight is for.node id that this weight is for.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'The weight for the class in class_id.Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)Node id for each node. Node ids must restart at zero for each tree and increase sequentially.N classesChild node if expression is falseChild node if expression is trueThe id of the tree that each node is in.For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the defalt value is false (0) for all nodes.The index of the target that each weight is forThe node id of each weightThe total number of targets.The weight for each targetDefines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The output mapThe input valuesseq(map(string, float))The output will be a sequence of string or integer maps to float.The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.seq(map(int64, float))The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.Input data.The output type will be a tensor of strings or integers, and will have the same shape as the input.The input type must be a tensor of integers or strings, of any shape.C:\agent\_work\30\s\cmake\external\onnx\onnx\defs\traditionalml\old.ccA list of labels.Dimension mismatch in unification between expected to have rank expected to have rank >Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. default 1; Pooled output Y's width.default 1; Pooled output Y's height.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.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].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. but has rank C:\agent\_work\30\s\cmake\external\onnx\onnx\defs\object_detection\defs.ccboxesAn input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.scoresAn input tensor with shape [num_batches, num_classes, spatial_dimension]max_output_boxes_per_classInteger representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.iou_thresholdFloat representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.score_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].Integer indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.Input was expected to have tensor type. Got Input type was null does not match existing output type of Element type of input was unknownOutput was expected to have tensor type. Got Input element type of 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.outputsAll Tensor typesGraph 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.C:\agent\_work\30\s\cmake\external\onnx\onnx\defs\controlflow\defs.ccOnly boolA maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.The initial values of any loop-carried dependencies (values that change across loop iterations)A boolean termination condition. Optional. Pass empty string to skip.Final 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.Initial values of the loop's N state variables followed by M scan_inputsFinal values of the loop's N state variables followed by K scan_outputsinitial_state_and_scan_inputsThe 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.final_state_and_scan_outputsAn optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.An attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).An optional list of K flags, 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.Int64 tensorAn 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 value is invalid for a tensor of rank Number of scan input axes specified () is not equal to number of scan inputs (Number of scan output axes specified () is not equal to number of scan outputs (Scan input was not a tensor.Graph attribute inferencing returned type information for outputs. Expected Scan 'body' subgraph outputs should all be tensors but output was not but subgraphs produce then_branch and else_branch produce different number of outputs. else=If node has Mismatched type for output then=Loop 'body' subgraph outputs should all be tensors but output Mismatched tensor element type for output Condition for the ifValues that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.condIncompatible dimensionsConstrain input and output types to all numeric tensors.Coefficient of leakage.Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).Coefficient of ELU.The exponential of the input tensor computed element-wiseThe natural log of the input tensor computed element-wise Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. The hyperbolic tangent values of the input tensor computed element-wiseSecond operand, power of the exponent.First operand, base of the exponent. PRelu takes input data (Tensor) and slope tensor as input, and produces one output data (Tensor) where the function `f(x) = slope * x for x < 0`, `f(x) = x for x >= 0`., is applied to the data tensor elementwise. Output tensor (same size as X)tensor slopeinput tensor XslopeSlope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to XThis 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. This operator supports **unidirectional broadcasting** (This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). should be unidirectional broadcastable to ); for more details please check [the doc](Broadcasting.md).{name} Performs element-wise binary {name} (with Numpy-style broadcasting support). {broadcast_doc} First operand.{broadcast_doc}Result, has same element type as two inputsSecond operand.]. Its actual value is: Constrain input and output types to high-precision numeric tensors. The operator computes the {name} ({description}) values for each layer in the batch of the given input. The input does not need to explicitly be a 2D vector; rather, it will be coerced into one. For an arbitrary n-dimensional tensor input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is the axis provided, then input will be coerced into a 2-dimensional tensor with dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default case where axis=1, this means the input tensor will be coerced into a 2D tensor of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size. In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D. Each of these dimensions must be matched correctly, or else the operator will throw errors. The output tensor has the same shape and contains the {name} values of the corresponding input. 'axis' must be in [Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).{description}The output values with the same shape as input tensor (the original size without coercion).The input tensor that's coerced into a 2D matrix of size (NxD) as described above.C:\agent\_work\30\s\cmake\external\onnx\onnx\defs\math\defs.ccadditionsubtractionDividend tensorWhether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentRemainder tensorDivisor tensordivisionmultiplicationConstrain input and output types to signed numeric tensors.Tensor of shape [a_1, a_2, ..., a_n, r]Axis has less than the requested k elements.KA 1-D tensor containing a single positive value corresponding to the number of top elements to retrieveValuesTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensorIndicesTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values.Constrain index tensor to int64Constrain input and output types to numeric tensors.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).Whether to return the elements in sorted order.The sine of the input tensor computed element-wiseThe cosine of the input tensor computed element-wiseThe tangent of the input tensor computed element-wiseThe arcsine of the input tensor computed element-wiseThe arccosine of the input tensor computed element-wiseThe arctangent of the input tensor computed element-wiseA 1-D tensor indicates the shape you want to expand to, following the broadcast rule'shape' input must be 1D tensor of type INT64Constrain input and output types to all tensors.The hyperbolic sine values of the input tensor computed element-wiseValue of beta.Value of alpha.List of tensors for Element-wise {name} of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. {broadcast_doc} Output tensor.data_0sumMinimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).Input tensor whose elements to be clippedOutput tensor with clipped input elementsMaximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).softmaxnormalized exponentiallogsoftmaxlog of softmaxhardmax1 for the first maximum value, and 0 for all othersThe softsign (x/(1+|x|)) values of the input tensor computed element-wiseGeneral Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3 A' = transpose(A) if transA else A B' = transpose(B) if transB else B Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M), input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), and output tensor Y has shape (M, N). A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB. tensor Ctensor A * BInput tensors of wrong rank (0).Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N).Incompatible dimensions for matrix multiplicationInvalid value for attribute axisK input must be of type int64.K input must be a one-dimensional tensor of size 1.Constrain output y and its zero point data type to 8-bit integer tensor.Scale 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'.Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-row quantization. If it's a 1-D tensor, its number of elements should be equal to the number of rows of input 'A'.Constrain input B data type to 8-bit integer tensor.Constrain input A data type to 8-bit integer tensor.Constrain output Y data type as 32-bit integer tensor.If 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.(Optional) A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.An input tensor that is to be processed.axis tensor can be int32 or int64 onlyOutput tensor of the same type as 'x' with cumulative sums of the x's elements != mat_h:Input rank must be >= 2.Constrain input and output types to floating-point tensors.The inner-most 2 dimensions must have the same size (mat_w:The hyperbolic cosine values of the input tensor computed element-wiseThe hyperbolic arcsine values of the input tensor computed element-wiseThe hyperbolic arccosine values of the input tensor computed element-wiseThe hyperbolic arctangent values of the input tensor computed element-wiseThe sign of the input tensor computed element-wise. It has the same shape and type of the input.The error function of the input tensor computed element-wise. It has the same shape and type of the input.input and zero_point pair is expected to have be same type.inputs are expected to have tensor type.N-dimensional quantized matrix ainput and zero_point pair is expected to have same type.scale of quantized input aazero point of quantized input aa_scaleN-dimensional quantized matrix ba_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.Input tensor to be cast.The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoConstrain input types. Casting from complex is not supported.Output tensor with the same shape as input with type specified by the 'to' argumentC:\agent\_work\30\s\cmake\external\onnx\onnx\defs\tensor\defs.ccConstrain output types. Casting to complex is not supported.Target shape may not have multiple -1 dimensionsInvalid dimension value: Invalid position of 0Dimension could not be inferred: incompatible shapesInvalid Target shape product of 0Reshaped data.Specified shape for output.reshapedInput tensor can be of arbitrary type.Shape of the input tensor not specified should be of integer type and specify a type.Value of attribute does not specify a valid type.steps}, input shape = {Invalid attribute perm {Transposed output.A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.transposedTensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).updatesTensor of rank r >=1 (same rank and shape as indices)Input and output types can be of any tensor type.Tensor of rank r >= 1 (same rank as input).Tensor of rank q + r - indices_shape[-1] - 1.axis must be in [-r, r-1]data tensor must have rank >= 1Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of rank q + (r - 1).Tensor of the same shape as indices.Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Reshaped tensor with same data as input.Tensors with at least max(dims) dimensions.squeezedConstrain output to int64 tensor.Constrain output to int64 tensor, which should be a scalar though.Total number of elements of the input tensoraxis must be in [-rank, rank-1].Required attribute axis is missingWhich axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..All inputs to Concat must have same rankList of tensors for concatenationConcatenated tensorinputsConstrain output types to any tensor type.concat_result Value=The tensor to splitInvalid value of attribute 'axis'. Rank=Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).One or more outputs forming list of tensors after splittinglength of each outputSlice op must have either three, four or five inputs.Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsInput axes has incorrect lengthIncorrect or missing input value for starts and endsInput axes has invalid dataInput steps has incorrect length1-D tensor of ending indices (exclusive) of corresponding axis in `axes`'step' cannot be 01-D tensor of slice step of corresponding axis in `axes`. Default to 1. 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).Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".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 0.1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"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 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.Tensor to copy input into.'axis' must be in [-rank(indices), rank(indices)-1]Indices tensor must have rank >= 1Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.(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).Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.conditionConstrains to boolean tensors.Input 'depth' must be a scalar or rank 1 tensor.OneHot node must have three inputs.Input 'values' must be rank 1 tensor.Input 'depth' must have exactly one element.(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 have exactly two elements.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.Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor. depthConstrain to any tensor type.Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used.values in 'axes' are beyond the bounds of the computed output shape'axes' attribute must not contain any duplicatesOriginal tensorList 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).expandedInput tensor must be 4-dimensionalBlocksize must be positiveInput 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.Blocks of [blocksize, blocksize] are moved.Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.'Repeats' input must be 1D tensor of type int64Input tensor of any shape.'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.repeats1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.Constrain repeat's type to int64 tensors.Output tensor of the same dimension and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.N-D tensorConstrain input 'X' and output 'Y' to all tensor types.N-D tensor after resizing This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
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,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_half_pixel_for_nn",
x_original = (x_resized + 0.5) / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).The 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".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).Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0.Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensor'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type 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).Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`.Constrains input and output to only numeric types.constant_valueConstrain input types to float tensors.Constrain output types 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) 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.When True (nonzero), yield X, otherwise yield Yvalues selected at indices where condition is Falsevalues selected at indices where condition is TrueConstrain to boolean tensors.Tensor of shape equal to the broadcasted shape of condition, X, and Y.output (always 2D tensor)Constrain to all tensor types.'sequence_lens' must have rank of 1'input' must have rank >= 2(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.Tensor of rank r >= 2.Tensor with same shape of input.(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).(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X'A N-D input tensor that is to be processed.A 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'. A 1-D INT64 tensor containing 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_indicesseq(tensor(uint8))seq(tensor(uint32))seq(tensor(uint16))seq(tensor(int8))seq(tensor(uint64))seq(tensor(int32))seq(tensor(int16))seq(tensor(float16))seq(tensor(int64))seq(tensor(double))seq(tensor(float))seq(tensor(bool))seq(tensor(string))seq(tensor(complex128))seq(tensor(complex64))Attribute dtype should be of integer type and specify a type.Empty sequence.(Optional) The data type of the tensors in the output sequence. The default type is 'float'.One or more outputs forming a sequence of tensors after splittingLength of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be positive. Constrain split size to integral tensor.Constrain input types to all tensor types.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.Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.], Value=new_axis must be either 0 or 1Which 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=[Sequence of tensors for concatenationInsert and concatenate on a new axis or not, default 0 means do not insert new axis.C:\agent\_work\30\s\cmake\external\onnx\onnx\defs\sequence\defs.ccElement type of inputs are expected to be the same.SequenceConstruct is expected to have at least 1 input.Sequence enclosing the input tensors.Tensors.Constrain input types to any tensor type.output_sequence Tensor=Input sequence.Input Sequence and Tensor are expected to have the same elem type. Sequence=Input tensor to be inserted into the input sequence.input_sequencePosition in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).tensorOutput sequence that contains the inserted tensor at given position.positionConstrain position to integral tensor. It must be a scalar(tensor of empty shape).Output tensor at the specified position in the input sequence.Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Output sequence that has the tensor at the specified position removed.Length of input sequence. It must be a scalar(tensor of empty shape).Constrain output to integral tensor. It must be a scalar(tensor of empty shape).Input 'split' can not be empty.Only supports `int32_t` or `int64_t` inputs for splitSum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size= sum of split values=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.Attribute strides has incorrect sizeAttribute kernel_shape has incorrect sizeInput tensor must have atleast 2 dimensionsAttribute dilations has incorrect sizeSecond input tensor has wrong dimension {name} consumes an input tensor X and applies {opName} pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. {opName} pooling consisting of computing the {opName} 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] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / 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] - {kernelSpatialShape} + 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] + {kernelSpatialShape} - input_spatial_shape[i] ``` {additionalDescription} Attribute kernel_shape must be specifiedAttribute pads has incorrect size((kernel_spatial_shape[i] - 1) * dilations[i] + 1)kernel_spatial_shape[i]{opName}{additionalDescription}Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.Wether to use ceil or floor (default) to compute the output shape.{kernelSpatialShape}The size of the kernel along each axis.The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero).averageInput 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 usedThe output of each pooling window is maximum number of elements exclude pad.The storage order of the tensor. 0 is row major, and 1 is column major.Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.C:\agent\_work\30\s\cmake\external\onnx\onnx\defs\nn\defs.ccOutput 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.The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. Optional 1D bias to be added to the convolution, has size of M.number of groups input channels and output channels are divided into.a filterdilation 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.Scale tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.weight and zero_point pair is expected to have same type.w_scalew_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).Constrain output type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.Constrain filter type to 8-bit integer tensor.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.Scale tensor for input 'w'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M)Constrain input x and its zero point data type to 8-bit integer tensor.Zero point tensor for input 'x'. It's optional and default value is 0. It's a scalar, which means a per-tensor/layer quantization.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.MaxUnpool op must have either two or three inputs.Input tensor X must have atleast 2 dimensions.Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).Attribute kernel_shape has incorrect size.Attribute kernel_shape must be specified.Attribute pads has incorrect size.Attribute strides has incorrect size.Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].'output_shape' must be rank 1 tensor.'output_shape' must have same number of elements as the shape of input tensor X.Output data tensor that contains the result of the unpooling. {name} consumes an input tensor X and applies Lp pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. Lp pooling consisting of computing the Lp norm 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.Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored.Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.Input tensor must have at least 2 dimensionsp 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.Attribute pooled_shape must be specified ROI {name} pool consumes an input tensor X and region of interests (RoIs) to apply {name} pooling across each RoI, to produce output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).RoIs tensor must have 2 dimensionsAttribute pooled_shape has incorrect lengthInput data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...].ROI pool output shape (height, width).Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.{filter_desc}Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]). The convolution operator consumes an input tensor and {filter_desc}, and computes the output.The order of the normalization, only 1 or 2 are supported. Dropout takes one input floating tensor and produces two tensor outputs, output (floating tensor) and mask (`Tensor`). Depending on whether it is in test mode or not, the output Y will either be a random dropout, or a simple copy of the input. Note that our implementation of Dropout does scaling in the training phase, so during testing nothing needs to be done. Matrix after normalizationThe axis on which to apply normalization, -1 mean last axis.The output mask.Constrain output mask types to boolean tensors.The ratio of random dropoutratioThe bias value added to output. Default is 0.The lambd value for the Shrink formulation. Default is 0.5.A tensor of rank >= axis.A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.) for attribute 'axis'Invalid value(The number of channels to sum overConstrain input and output to all tensor types.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). Output tensor, which has the shape and type as input tensorConstrain input and output types to float tensors.Scaling parameter.The exponent.Input tensor must have rank 1 or 2Input for n-gram extractionngram_indexes must be non-empty with no negative values1-D tensor of floatsMaximum n-gram length. If this value is 3, 3-grams will be used to generate the output.Ngram resultsInput is ether string UTF-8 or int32/int64The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the weight shape will be (C x M/group x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, pad lengths and group count. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array) The convolution transpose operator consumes an input tensor and {filter_desc}, and computes the output. If the pads parameter is provided the shape of the output is calculated via the following equation: output_shape[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - pads[start_i] - pads[end_i] output_shape can also be explicitly specified in which case pads values are auto generated using these equations: total_padding[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - output_shape[i] If (auto_pads != SAME_UPPER): pads[start_i] = total_padding[i]/2; pads[end_i] = total_padding[i] - (total_padding[i]/2) Else: pads[start_i] = total_padding[i] - (total_padding[i]/2); pads[end_i] = (total_padding[i]/2). Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn) Global{op_type} consumes an input tensor X and applies {op} pooling across the values in the same channel. This is equivalent to {op_type} with kernel size equal to the spatial dimension of input tensor.{op_type}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 padsAdditional 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.lp pool 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. {op}Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.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).Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).momentumrunning (training) or estimated (testing) variance tensor of shape (C).The output tensor of the same shape as XBias tensor of shape (C).running (training) or estimated (testing) mean tensor of shape (C).Saved mean used during training to speed up gradient computation.saved_meanThe running mean after the BatchNormalization operator.The running variance after the BatchNormalization operator.The input 1-dimensional scale tensor of size C.Saved variance used during training to speed up gradient computation.saved_varInput matrixThe input 1-dimensional bias tensor of size C.The output tensor of the same shape as input.List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.UTF-8 strings to normalizeUTF-8 Normalized stringsInput shape must have either [C] or [1,C] dimensions where C > 0List of stop words. If not set, no word would be removed from X.Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation.string enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"Boolean. Whether the identification of stop words in X is case-sensitive. Default is falseExponentEpsilonA 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.X_squaredE_XsquaredX_RMEX_squaredX_varianceProcessed_STDVarianceSTDThe axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).Reduced output tensor with integer data type.L2 norm Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulted tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. The type of the output tensor is integer.axis must be in [-rank, rank-1]. input rank was C:\agent\_work\30\s\cmake\external\onnx\onnx\defs\reduction\defs.ccsum square Computes the {name} of the input tensor's element along the provided axes. The resulted tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulted 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.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).log sum exponentL1 normproductlog sum(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.2D input tensor to copy shape, and optionally, type information from.Input tensor must be 2-dimensional(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.Constrain output types. Strings and complex are not supported.Output tensor, same shape as input tensor T1.Constrain input types. Strings and complex are not supported.(Optional) Seed to the random generator, if not specified we will auto generate one.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.Lower boundary of the output values.Upper boundary of the output values.Constrain output types to float tensors.The shape of the output tensor.Output tensor of random values drawn from uniform distributionThe data type for the elements of the output tensor. Default is TensorProto::FLOAT.Output tensor of random values drawn from normal distributionThe mean of the normal distribution.The standard deviation of the normal distribution.Input tensor to copy shape and optionally type information from.(Optional) The data type for the elements of the output tensor, if not specified, we will usethe data type of the input tensor.Input tensor must have rank 2Number of times to sample.Output type must be int32 or int64Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.Constrain output types to integral tensors.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.Attribute expected to have a one-dim tensorAttribute expected to have tensor typeOne of the attributes 'value' or 'sparse_value' must be specified for a Constant node.The value for the elements of the output tensor.sparse_valueOnly one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.C:\agent\_work\30\s\cmake\external\onnx\onnx\defs\generator\defs.ccThe value for the elements of the output tensor in sparse format.Output tensor containing the same value of the provided 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 float321D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar.Shape input must be a one-dimensional tensor.Invalid shape value: Constrain output types to be numerics.Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.Constrain input types.Input to 'Range' op should be scalars (Tensor with only one element and shape empty)iprevloop_body_attributerangesub_resultcond_outcurrentdiv_resultceil_resultsub_result_casteddelta_castedceil_result_relu_boolvariadic_outputceil_result_reluceil_result_relu_intScalar. Exclusive upper limit for the range of output values.Scalar. Value to step by.All inputs to 'Range' op must be of the same typeScalar. First entry for the range of output values.A 1-D tensor with same type as the inputs containing generated range of values.Constrain input types to common numeric type tensors.Quantized output tensorOutput scale. It's a scalar, which means a per-tensor/layer quantization.Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.Q_MinOutput zero point. It's a scalar, which means a per-tensor/layer quantization.Constrain 'x' to float tensor.X_Min_AdjustedX_MaxQ_MaxX_MinMin_ScaledInitial_ZeroPoint_FPX_Max_AdjustedX_RangeZeropointClipped_ZeroPoint_FPRounded_ZeroPoint_FPScale for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.C:\agent\_work\30\s\cmake\external\onnx\onnx\defs\quantization\defs.ccZero point for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization. Default value is uint8 typed 0 if it's not specified.Constrain 'x' to float or int32 tensor.Scale for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Zero point for input 'x'. It's a scalar, which means a per-tensor/layer quantization. It's optional. 0 is the default value when it's not specified.N-D quantized input tensor to be de-quantized.First input operand for the logical operator.Second input operand for the logical operator. Returns the tensor resulted from performing the `{name}` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). {broadcast_doc} Constrains input to boolean tensor.Constrains output to boolean tensor.Result tensor.andxorgreaterC:\agent\_work\30\s\cmake\external\onnx\onnx\defs\logical\defs.ccorequalConstrains input types to all numeric tensors.lessFirst operand, input to be shifted.Second operand, amounts of shift.Constrains input/output to boolean tensors. Bitwise shift operator performs element-wise operation. For each input element, if the attribute "direction" is "RIGHT", this operator moves its binary representation toward the right side so that the input value is effectively decreased. If the attribute "direction" is "LEFT", bits of binary representation moves toward the left side, which results the increase of its actual value. The input X is the tensor to be shifted and another input Y specifies the amounts of shifting. For example, if "direction" is "Right", X is [1, 4], and S is [1, 1], the corresponding output Z would be [0, 2]. If "direction" is "LEFT" with X=[1, 2] and S=[1, 2], the corresponding output Y would be [2, 8]. Because this operator supports Numpy-style broadcasting, X's and Y's shapes are not necessarily identical. Constrain input and output types to integer tensors.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).C:\agent\_work\30\s\cmake\external\onnx\onnx\defs\object_detection\old.ccConstrain input types. Casting from strings and complex are not supported.Which axis to concat on. Default value is 1.Constrain output types. Casting to strings and complex are not supported.C:\agent\_work\30\s\cmake\external\onnx\onnx\defs\tensor\old.ccAttribute 'scales' is required.Number of elements of attribute 'scales' must be same as rank of input 'X'Attribute 'scales' must have floats type.Incorrect or missing attribute value for starts and endsAttribute axes has incorrect lengthThe 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'.Ending indices (exclusive) 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].Starting indices of corresponding axis in `axes`Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of r >= 1 (same rank as input).List 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]Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. It is an error if any of the index values are out of bounds.'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.List of non-negative integers, indicate the dimensions to be inserted(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length alone the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.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.Attribute value for pads is requiredInvalid value of attribute 'axis'Which axis to split on. Optional list of output lengths (see also arg 'split')rank must be greater than axisWhich axis to concat onList 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`.outputs...Which axis to split onOne float, indicates the value to be filled, default is 0paddingsThree modes: constant(default), reflect, edgeconsumed_inputsNew shapelegacy optimization attribute.tilesAxis along which to repeat.Number of repeated copies to make of the input tensor.The scale along width dimension. It takes value greater than or equal to 1.width_scaleOutput tensor of same shape and type as input.Constrain tiles and axis's type to int64 tensors.Two interpolation modes: nearest(default), bilinear4-D tensor, [N,C,H,W]The scale along height dimension. It takes value greater than or equal to 1.height_scale) is not equal to the existing rank value (Ranks inferred (4-D tensor after resizing, [N,C,H,W]Constrain output types to bool, int32, int64, float16, float, double tensors.One float, indicates the value to be filled.Attribute 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`.Stride along each axis.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.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.dilation value along each spatial axis of the filter.p value of the Lp norm used to pool over the input data, default is 2.0.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimension are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.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.Output data tensor from pooling across the input tensor. Dimensions will be N x C x 1 x 1The epsilon value to use to avoid division by zero, default is 1e-5f.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.If set to nonzero, run spatial batch normalization in test mode, default is 0.is_testThe 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 input 4-dimensional tensor of shape NCHW.The scale as a 1-dimensional tensor of size C to be applied to the output.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.The running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C.The output 4-dimensional tensor of the same shape as X.The output 4-dimensional tensor of the same shape as input. Dropout takes one input data (Tensor) and produces two Tensor outputs, output (Tensor) and mask (Tensor). Depending on whether it is in test mode or not, the output Y will either be a random dropout, or a simple copy of the input. Note that our implementation of Dropout does scaling in the training phase, so during testing nothing needs to be done. Saved mean used during training to speed up gradient computation. Should not be used for testing.Saved variance used during training to speed up gradient computation. Should not be used for testing.The output mask. If is_test is nonzero, this output is not filled.(float, default 0.5) the ratio of random dropout(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X. {name} consumes an input tensor X and applies {opName} pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. {opName} pooling consisting of computing the {opName} 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) * 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] ``` {additionalDescription} C:\agent\_work\30\s\cmake\external\onnx\onnx\defs\nn\old.ccDilation value along each spatial axis of filter.Stride along each spatial axis.The output of each pooling window is divided by the number of elements exclude pad. 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) 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 output tensor of the same shape as X.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). If 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).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)Minimum value, under which element is replaced by minMaximum value, above which element is replaced by maxList of tensors for Mean.Input tensor BInput tensor C, can be inplace.Input tensor AScalar multiplier for input tensor C, the default value is 1.0.Input tensor CWhether C should be broadcastedScalar multiplier for the product of input tensors A * B, the default value is 1.0.Number of top elements to retrieveDimension on which to do the sort.Invalid value for attribute k The operator computes the {name} ({description}) values for each layer in the batch of the given input. The input is a 2-D tensor (Tensor) of size (batch_size x input_feature_dimensions). The output tensor has the same shape and contains the {name} values of the corresponding input. Input does not need to explicitly be a 2D vector; rather, it will be coerced into one. For an arbitrary n-dimensional tensor input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is the axis provided, then input will be coerced into a 2-dimensional tensor with dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default case where axis=1, this means the input tensor will be coerced into a 2D tensor of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size. In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D. Each of these dimensions must be matched correctly, or else the operator will throw errors. Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size Performs element-wise binary {name} (with limited broadcast support). {broadcast_doc}Pass 1 to enable broadcastingC:\agent\_work\30\s\cmake\external\onnx\onnx\defs\math\old.cc 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. First operand, should share the type with the second operand.Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.broadcastIf set, defines the broadcast dimensions. See doc for details.Input tensor of any shape broadcastable to X shape, the exponent component.Coefficient of leakage default to 0.01.Result, has same dimensions and type as AInput tensor of any shape, base of the exponent.Coefficient of ELU default to 1.0.1-D input tensorCoefficient of SELU default to 1.6732.Coefficient of SELU default to 1.0507.Value of alpha default to 0.2Value of beta default to 0.5Slope tensor. If `Slope` is of size 1, the value is sharedacross different channelsOutput tensor. Same dimension as inputs.List of tensors for Max.List of tensors for Sum.List of tensors for MinAn 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.Optional tensor specifying lengths of the sequences in a batch. If this input is not specified, all sequences are assumed to be of the maximum sequence length (the dimension of the sequence axis of the scan_input tensors).C:\agent\_work\30\s\cmake\external\onnx\onnx\defs\controlflow\old.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.Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same shape and same data type.C:\agent\_work\30\s\cmake\external\onnx\onnx\defs\generator\old.ccAttribute 'value' of Constant node must exist with 'Tensor' data.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 input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.Number of neurons in the hidden layerFirst input tensor must have rank 3 Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `t` - time step (t-1 means previous time step) `Wi` - W parameter weight matrix for input gate `Ri` - R recurrence weight matrix for input gate `Wbi` - W parameter bias vector for input gate `Rbi` - R parameter bias vector for input gate `WBi` - W parameter weight matrix for backward input gate `RBi` - R recurrence weight matrix for backward input gate `WBbi` - WR bias vectors for backward input gate `RBbi` - RR bias vectors for backward input gate `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Tanh): - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) Constrain seq_lens to integer tensor.The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. The 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.The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.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.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.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. Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `z` - update gate `r` - reset gate `h` - hidden gate `t` - time step (t-1 means previous time step) `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates `Wb[zrh]` - W bias vectors for update, reset, and hidden gates `Rb[zrh]` - R bias vectors for update, reset, and hidden gates `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh): - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz) - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr) - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0 - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0 - Ht = (1 - zt) (.) ht + zt (.) Ht-1 C:\agent\_work\30\s\cmake\external\onnx\onnx\defs\rnn\defs.cc Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `o` - output gate `f` - forget gate `c` - cell gate `t` - time step (t-1 means previous time step) `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates `P[iof]` - P peephole weight vector for input, output, and forget gates `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates `PB[iof]` - P peephole weight vector for backward input, output, and forget gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) - Ct = ft (.) Ct-1 + it (.) ct - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) - Ht = ot (.) h(Ct) The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.Couple the input and forget gates if 1.Enable broadcasting Returns the tensor resulted from performing the `{name}` logical operation elementwise on the input tensors `A` and `B`. If broadcasting is enabled, the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. See the doc of `Add` for a detailed description of the broadcasting rules. C:\agent\_work\30\s\cmake\external\onnx\onnx\defs\logical\old.ccRight input tensor for the logical operator.Left input tensor for the logical operator.If set, defines the broadcast dimensions.Constrains input to integral tensors.Constrains input to float tensors.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.fowardC:\agent\_work\30\s\cmake\external\onnx\onnx\defs\rnn\old.ccA 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 sequence output for the hidden is optional if 0. Default 0. Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulted tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. The type of the output tensor is integer.C:\agent\_work\30\s\cmake\external\onnx\onnx\defs\reduction\old.ccThe axis in which to compute the arg indices.tensor(seqsparse_tensor(map(seq(floatsparse_tensoropaquemapint8doublebfloat16float16uint8int64int32int16complex64uint64uint32uint16boolcomplex128value_info is required to be non-empty. ==> Context: is required but missing.Field 'name' of value_typekey_typeelem_typetype) second dimension size does not match rank of tensor.) first dimension size does not equal NNZ.] not in sorted order.Sparse tensor () must have rank 1.sparse_tensor_proto] not in lexicographic sorted order.] out of range.) must have INT64 type.) dimensions are not positive.) must have a dense-rank > 0Sparse tensor values (type field and data field mismatch in attribute attr) has no index values.) must have rank 1 or 2.node) should refer to attribute in parent node.Attribute (name: ) should not contain more than one value field.NodeProto (name: , type: ) has zero input and zero output.op_typereject this operator. Please update your model as soon as possible.ConstantFillATenNo opset import for domain 'Warning: was a removed experimental ops. In the future, we may directly ) to UNDEFINED is not alloweddata_typeUnrecognized type value case (value_info name: ): string_dataint32_datafloat_datasetting data_type field (tensor name: uint64_datadouble_dataraw_dataint64_data) should be stored in , but it doesn't exist or is not accessible.Data of TensorProto ( tensor name: ) is stored externally and should not have data field.TensorProto (tensor name: ) is 0-element but contains data!TensorProto ( tensor name: ) is stored externally but doesn't have a location.' instead of 'STRING data (tensor name: ) should not be stored in raw_data field) should contain one and only one value field. values, but NNZ is Unrecognized data_type (tensor name: values of data_type '' should be stored in field ') index value at position [] out of range [0, Sparse tensor indices () has Op registered for is deprecated in domain_version of No Op registered for with domain_version of in initializer but not in graph inputGraph must be in single static assignment (SSA) form, however '' has been used as graph input names multiple times.graphBad node spec: Nodes in a graph must be topologically sorted, however input '' of node: is not output of any previous nodes.' has been used as output names multiple times.Graph input # is tensor type, but provided type is Graph has output input is out of boundsInferred elem type differs from existing elem type: () vs ( does not contain a graph.GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. inferred=Inferred shape and existing shape differ in dimension : (Inferred shape and existing shape differ in rank: (no nametype mismatch. existing=type case unsupported. existing=type case mismatch. existing= inputs but were provided(op_type:, name:Input 'scales' must have float element type.Number of elements of input 'scales' must be same as rank of input 'X'Input 'sizes' must have int64 element type.Number of elements of input 'sizes' must be same as rank of input 'X'Dimension value inferred () is not equal to the existing dim value (onnx.ValueInfoProtoonnx.AttributeProtoonnx.StringStringEntryProtoonnx.ModelProtoonnx.NodeProtoonnx.TensorProto.Segmentonnx.GraphProtoonnx.TensorAnnotationonnx.SparseTensorProtoonnx.TensorProtoonnx.TypeProto.Tensoronnx.TensorShapeProtoonnx.TensorShapeProto.Dimensiononnx.TypeProto.Maponnx.TypeProto.Sequenceonnx.OperatorSetIdProtoonnx.TypeProtoonnx.TypeProto.Opaqueonnx.TypeProto.SparseTensoronnx.FunctionProtoERRORFATAL[libprotobuf %s %s:%d] %s INFOWARNING%I64u%d%uTA`A$A,ACHECK failed: (last_returned_size_) > (0): BackUp() can only be called after a successful Next().CHECK failed: (count) <= (last_returned_size_): CHECK failed: (count) >= (0): C:\agent\_work\30\s\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream_impl_lite.ccCHECK 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().CHECK failed: target_ != NULL: Cannot allocate buffer larger than kint32max for StringOutputStream.CHECK failed: (count) <= (target_->size()): Parameter to BackUp() can't be negative.CHECK failed: (backup_bytes_) == (0): CHECK failed: (buffer_used_) == (buffer_size_): CHECK failed: (bytes_produced_by_serialization) == (byte_size_before_serialization): Byte size calculation and serialization were inconsistent. This may indicate a bug in protocol buffers or it may be caused by concurrent modification of This shouldn't be called if all the sizes are equal.Can't (cannot determine missing fields for lite message)C:\agent\_work\30\s\cmake\external\protobuf\src\google\protobuf\message_lite.ccCHECK failed: (byte_size_before_serialization) == (byte_size_after_serialization): was modified concurrently during serialization.Exceeded maximum protobuf size of 2GB: CHECK failed: !coded_out.HadError(): message of type "" because it is missing required fields: parse with type Not implemented field number CHECK failed: (scc->visit_status.load(std::memory_order_relaxed)) == (SCCInfoBase::kRunning): C:\agent\_work\30\s\cmake\external\protobuf\src\google\protobuf\generated_message_util.ccC:\agent\_work\30\s\cmake\external\protobuf\src\google\protobuf\arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - kBlockHeaderSize): C:\agent\_work\30\s\cmake\external\protobuf\src\google\protobuf\repeated_field.ccCHECK failed: (new_size) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(old_rep->elements[0])): This ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug.C:\agent\_work\30\s\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream.ccC:\agent\_work\30\s\cmake\external\protobuf\src\google\protobuf\io\coded_stream.ccA protocol message was rejected because it was too big (more than bytes). To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h.CHECK failed: (buffer_size) >= (0): CHECK failed: (value.size()) <= (kint32max): C:\agent\_work\30\s\cmake\external\protobuf\src\google\protobuf\wire_format_lite.ccUnknown encoding C:\agent\_work\30\s\cmake\external\re2\re2\re2.cc': Error parsing '...Invalid RE2: text size: endpos: Error compiling 'pattern too large - compile failedError reverse compiling 'pattern too large - reverse compile failedDFA out of memory: size SearchDFA inconsistencySearchOnePass inconsistencySearchBitState inconsistencystartpos: RE2: invalid startpos, endpos pair. [list count bytemap range SearchNFA inconsistencyinvalid named capture groupNumCapturesWalker::ShortVisit calledWalk NULLStack not empty.C:\agent\_work\30\s\cmake\external\re2\re2/walker-inl.hRegexp not destroyed.unexpected errorinvalid escape sequenceinvalid character classinvalid character class rangeC:\agent\_work\30\s\cmake\external\re2\re2\regexp.ccBad reference count Unexpected op in Regexp::Equal: no errorinvalid repetition sizebad repetition operatorinvalid perl operatorinvalid UTF-8missing ]missing )trailing \no argument for repetition operatorAA0AHA`A`ApAAAA A8APAXAMissing case in Compiler: should never happenC:\agent\_work\30\s\cmake\external\re2\re2\compile.ccCompiler::Copy called!No ranges in char classAddFoldedRange recurses too much.C:\agent\_work\30\s\cmake\external\re2\re2\parse.ccRepetitionWalker::ShortVisit calledConcat of AnyBad call to ParseState::ParsePerlFlagsunknown round: RE2: unexpected op: Bad hex digit Bad args: nsubmatch=context does not contain textUnhandled in ComputeFirstByteUnexpected opcode in short circuit: C:\agent\_work\30\s\cmake\external\re2\re2\nfa.ccunhandled in step in AddToThreadqC:\agent\_work\30\s\cmake\external\re2\re2\dfa.ccunhandled opcode: StateSaver failed to restore state.DeadState in RunStateOnByteUnexpected special state in RunStateOnByteNULL state in RunStateOnByteFailed to analyze start state.RunStateOnByteUnlocked failed after ResetCacheRunStateOnByteUnlocked failed after Reset|njob_ = job_.size() = Bad arg in kInstCapture: Bad arg in kInstAltMatch: C:\agent\_work\30\s\cmake\external\re2\re2\bitstate.ccGrowStack() failed: Unexpected opcode: arg C:\agent\_work\30\s\cmake\external\re2\re2\onepass.ccCannot use SearchOnePass for unanchored matches.Unexpected opcode in IsMatch: C:\agent\_work\30\s\cmake\external\re2\re2\prog.ccSimplifyWalker::ShortVisit calledDoCoalesce failed: r2->op() is Malformed repeat Simplify case not handled: Case not handled in ComputeSimple: C:\agent\_work\30\s\cmake\external\re2\re2\simplify.ccDoCoalesce failed: r1->op() is CoalesceWalker::ShortVisit calledXAxAxAxAhAPAAPAAAAAAXAAXAAAAAAAhAAXAAAAxAA(AAAHAHAHA8AAAAApAApAALAALAA(AA(AA`AA`APAAAAAAAAA8AdA8A\d[:alnum:]\W[:alpha:][:^alnum:]\s\D\w\S[:cntrl:][:^blank:][:digit:][:^cntrl:][:ascii:][:^alpha:][:blank:][:^ascii:][:print:][:^lower:][:punct:][:^print:][:graph:][:^digit:][:lower:][:^graph:][:word:][:^upper:][:xdigit:][:^word:][:space:][:^punct:][:upper:][:^space:][:^xdigit:]09AZ__az09AZ__az ~AZAZaz 09AFaf 09AZaz09 !~!/:@[`{~09azBp-BB8YBB8ZBB>B:B#BPBBmBBP\BBH^BEBBEBBIBpBRB`B BBiBxBmB8BB(B@RBTBQB?UB@?B@B;B|BqBhB BB0WBBOB 0mBHBgB@BfBXB@BPBQBBBP7BZPIBLBZBBQBBpiBBYBBYBB.BBPiB$BQBB\B8BB0BBBphB BhB B:BUBBB BPBR;B%BhiBBXZBX?3BKBXBH#BB?BB7BB(mBBBB6BBhZBB``BnoB8,BbB BHB@B6B0BP`BBHBBlBB@UBBiB`)BiB?BxBZB=BB( BB8-BXBNBPB@[BC` B:hBlB`BpTB%`VBBQBB"BB=BB"BoBBgB`9B&B ZBB@ZBB4UBB YBBUBB@#BBEBB.BxBZBBgBBOB4BB,B?BPBNB<@`RBPWB0@BKBB0gBB #B(B(#B$B9BxB 2BHtBB B/BBlB\B-B XBP^B =B0pBp.BK`BYBB8IBB .B=/B`mBIBBB8hBB8BHiBB,BBBBXhBhB>B`B 7B 8[BxB]BlBtBNB5P#B @BGBpOB>8B-BPB8WBHB#BB8UBB`iBB=BBZBBfB|Bh-BBX\BB;BBYBBXB(BVB BPYB BEBB#BB(iBB0bB`BUBPB@IBtB0ZBhBHRBDB6B@/B0Bp\BLB`-BHBXRBBIBChamChakmaCoCherokeeCaucasian_AlbanianCarianCfCcCyrillicCypriotDevanagariDeseretCopticCommonCuneiformCsGeorgianEthiopicGothicGlagoliticDuployanDograElbasanEgyptian_HieroglyphsHanGurmukhiHanifi_RohingyaHangulGreekGranthaGunjala_GondiGujaratiAhomAdlamBamumBalineseBatakBassa_VahArabicAnatolian_HieroglyphsAvestanArmenianBugineseBrailleCanadian_AboriginalBuhidBhaiksukiBengaliBrahmiBopomofoMarchenManichaeanMcMasaram_GondiMakasarMahajaniMandaicMalayalamMeroitic_HieroglyphsMeroitic_CursiveMnMiaoMedefaidrinMeMende_KikakuiMeetei_MayekNMyanmarNdNabataeanMongolianModiMultaniMroNushuNoOl_ChikiOghamNewaNew_Tai_LueNlNkoInheritedImperial_AramaicInscriptional_ParthianInscriptional_PahlaviHatranHanunooHiraganaHebrewKharoshthiKayah_LiKhojkiKhmerKaithiJavaneseKatakanaKannadaLimbuLepchaLinear_BLinear_ALKhudawadiLatinLaoLuLtLydianLycianLlLisuLoLmSogdianSoSoyomboSora_SompengSinhalaSignWritingSmSkTai_LeTagbanwaTai_VietTai_ThamSyloti_NagriSundaneseTagalogSyriacThaiThaanaTifinaghTibetanTamilTakriTeluguTangutZanabazar_SquareYiZpZlUgariticTirhutaWarang_CitiVaiOld_SogdianOld_PersianOld_TurkicOld_South_ArabianOld_ItalicOld_HungarianOld_PermicOld_North_ArabianPau_Cin_HauPalmyrenePdPcOsageOriyaPahawh_HmongOsmanyaPoPiPsalter_PahlaviPsPfPePhoenicianPhags_PaSharadaScSiddhamShavianRunicRejangSaurashtraSamaritanZs0.4      ( * 0 2 3 5 9 < D G H K M V W \ ] _ c f w AZ      ""$$&&((**,,..0022446699;;==??AACCEEGGJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjllnnpprrttvvxy{{}}      ""$$&&((**,,..0022:;=>AACFHHJJLLNNpprrvv/``bbddffhhjjllnnpprrttvvxxzz||~~      ""$$&&((**,,..1V      ""$$&&((**,,..0022446688::<<>>@@BBDDFFHHJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjllnnpprrttvvxxzz||~~(/8?HMYY[[]]__ho!!!! 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