vendor/libonnxruntime.so in onnxruntime-0.2.2 vs vendor/libonnxruntime.so in onnxruntime-0.2.3

- old
+ new

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 + :m 1];?mY " I|" X9vK Cd  CDe* _ Q* gEiDX i,  vw RH 0 Wi 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L⍢HK7HHHD$Ll$MtBI}Ht1IEHI](HhH9vfDH;H贔H9wI}覔L螔H8L[]A\A]HI<$Ht$H[HӚIHEHLHD$f.1f.ATH7UHSHHLcMtMI<$Ht.ID$HI\$(HhH9vH;HH9wI<$擢[]LA\ړf.[]A\f.AUIATUSHH7HHpMeMtAI<$Ht0ID$HI\$(HhH9vH;HlH9wI<$^LVHL[]A\A]D@U1H& .SHHxGGGGGG G G HfG -Ht$0H|$HH?HHH)HT$ɚHt|T$8D$DVUUUL$<ST$4lD$@KɈST$0ST$HCS T$LfS -ЃȈS $I)ʈS Hx[]fDH|$x[H\$ ።HT$H5=HHZHHHD$ HxH;=7uKHv7H57HH\$ 膍HT$H5HHHH蔘H=27HWtHHt$HHD$ Ht$HxgH_HחHfPJHf.DH[HAHAMcIH1L9t0HIHLGLH/L1HH1HI9uڃt91HcLf.8HHHyH1H[HHH[H/H1HHH/H1fHFH7HGHFH8H~fHHWHHPÐH1҅IHfSHwH~2HHS㥛 H?HHH)[EIf[f.@AUA ATUSH(Hl$tHHT$H5PHI吢HHꌢHD$Ht$HxwH7HHH57DkLcHH{HH@H踕HHD$Ht$Hx"ؿHҊH;7H57HHHHf.@H1H1;H$Hiʚ;HD$HfDUHSHH H7+HX(HHH H;1vZ@HHH H91rQHHH;+M\$D$3 Yf.s5H,HEHH[]HH@H)HYHxHwf\H,HEHH1EH) 0ÉD$D$\$D$詆 yXf.sH,HEH[]\H,HEHH1E&XDSHHH H;WHHT$l$Ht$Hl$ Ht$re \$D$͒X% WHt$f.sjH,HHH9HC H H¸[D\$D$u UWf.swH,HCfDH 11[fD\HH,H1WHt$Hl$Ht$V!tf.\H,HCHH1Cxf.@H5 -H7H!H7SHH H[ċ@H7HHf.SHH[鎋f.@H7HHHG騒SHH[Nf.@H7SHH8HHGԑH{HtHHC[f.fSHH[f.@=q8tHX8H=Y8HXtKH=58H*8Hv7H='8H8裇Hx6H58H=~鉢H8Hf.H8HwHPH|$ HQHD$ HxH;=7uH8H=7HWt# -ʅHt$HD$ֈHD$ʋPJH@USHHH7HH~Nt)udH53HH[]fH5HH[]DuH5ꊢHH[]H5 H̊HH[]fH5诊HH[]H;Ht$HHᅢHYfHHfHI7HHHG阃H7HHÐH7HHÐH -H7HH}f.SHH[^HH1.1.0out of indexinternal error/\basic_string::substrnot enough space: expected , got RegisterCustomOps was false.Stacktrace: -opaque(ml_type != nullptrnon_tensor_base != nullptrenv_ptr == p_instance_IsTensorSequence()elem_type_ != nullptrinput name cannot be emptyCPUExecutionProvideroutput name cannot be empty!=T Tensor type mismatch. input array is too shortspace is not enoughshape is invalid++index < c.size()vector::reservevector::_M_default_appendDmlExecutionProvideronnxruntime_profile_i < tensors_.size()vector::_M_range_checkEncountered nullptr.value_type != nullptrin[idx]->IsTensor()Unsupported input typeRegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in librarySpecified domain and type names combination does not refer to a registered opaque type/onnxruntime_src/onnxruntime/core/session/onnxruntime_c_api.ccOpaque type is not a non_tensor type!!!Trying to get a Tensor, but got: /onnxruntime_src/include/onnxruntime/core/framework/ml_value.hTrying to get a TensorSeq, but got: is not supported in this functionTensor sequence must contain only primitive types/onnxruntime_src/onnxruntime/core/framework/TensorSeq.h/onnxruntime_src/include/onnxruntime/core/framework/tensor.hutils::IsPrimitiveDataType<T>(dtype_)Sequence is missing type entry for its element/onnxruntime_src/include/onnxruntime/core/framework/data_types_internal.hMap is missing type entry for its valueMem pattern should be disabled when using DML execution provider.Sequential execution should be enabled when using DML execution provider.onnxruntime::DataTypeImpl::GetType<T>() == type_Input is not of one of the supported sequence types.Input is not of type sequence or map.Check for duplicate types in MLTypeCallDispatcherRetUnsupported tensor element type in the input: Invalid index requested for map type.Input is not of one of the supported map types.Number of values should be at least 1.For map type num_values MUST be 2Key and value tensors have unequal number of elements.Tensor must always contain primitive types. Found: Value type is not supported yet: Key type is not supported yet.Each element of the sequence should be either tensor or map.At least one element in the sequence is of a type different from others.Expecting all elements to be tensors. Got: Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.Either the key tensor or the value tensor has NumDimensions > 1@P`p DL\l|  static void OrtEnv::Release(OrtEnv*)OrtStatus* OrtApis::GetOpaqueValue(const char*, const char*, const OrtValue*, void*, size_t)OrtStatus* OrtApis::CreateOpaqueValue(const char*, const char*, const void*, size_t, OrtValue**)void onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType)T* onnxruntime::Tensor::MutableData() [with T = long unsigned int]T* onnxruntime::Tensor::MutableData() [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = unsigned int]T* onnxruntime::Tensor::MutableData() [with T = int]T* onnxruntime::Tensor::MutableData() [with T = short unsigned int]T* onnxruntime::Tensor::MutableData() [with T = short int]T* onnxruntime::Tensor::MutableData() [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = signed char]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::BFloat16]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16]T* onnxruntime::Tensor::MutableData() [with T = double]T* onnxruntime::Tensor::MutableData() [with T = float]T* onnxruntime::Tensor::MutableData() [with T = bool]OrtStatus* OrtCreateValueImplSeqHelper(const OrtValue* const*, size_t, OrtValue**)OrtStatus* OrtCreateValueImplMapHelper(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; OrtStatus = OrtStatus; OrtValue = OrtValue]OrtStatus* OrtCreateValueImplMapHelper(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string<char>; OrtStatus = OrtStatus; OrtValue = OrtValue]const T& OrtValue::Get() const [with T = std::map<long int, double>]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = double; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int]const T& OrtValue::Get() const [with T = std::map<long int, float>]const T& OrtValue::Get() const [with T = std::map<long int, long int>]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = long int; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int]const T& OrtValue::Get() const [with T = std::map<long int, std::basic_string<char> >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = std::basic_string<char>; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int]const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, double>]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = std::basic_string<char>; V = double; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int]const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, float>]const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, long int>]const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, std::basic_string<char> >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = std::basic_string<char>; V = std::basic_string<char>; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int]Ret onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper<Ret, UnsupportedPolicy>::Get() [with Ret = OrtStatus*; UnsupportedPolicy = c_api_internal::UnsupportedReturnFailStatus]const T* onnxruntime::Tensor::Data() const [with T = long unsigned int]const T* onnxruntime::Tensor::Data() const [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = unsigned int]const T* onnxruntime::Tensor::Data() const [with T = int]const T* onnxruntime::Tensor::Data() const [with T = short unsigned int]const T* onnxruntime::Tensor::Data() const [with T = short int]const T* onnxruntime::Tensor::Data() const [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = signed char]const T* onnxruntime::Tensor::Data() const [with T = bool]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::BFloat16]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16]const T* onnxruntime::Tensor::Data() const [with T = double]const T* onnxruntime::Tensor::Data() const [with T = float]const onnxruntime::Tensor& onnxruntime::TensorSeq::Get(size_t) constconst T& OrtValue::Get() const [with T = std::vector<std::map<long int, float> >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::vector<_RealType> >::check(const Cont&, size_t) [with T = std::map<long int, float>; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int]const T& OrtValue::Get() const [with T = std::vector<std::map<std::basic_string<char>, float> >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = std::basic_string<char>; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::vector<_RealType> >::check(const Cont&, size_t) [with T = std::map<std::basic_string<char>, float>; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int]const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq]const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>]T* OrtValue::GetMutable() [with T = onnxruntime::Tensor]St11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EEPFvPvESt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime23NotImplementedExceptionEN11onnxruntime20OnnxRuntimeExceptionEN11onnxruntime10IAllocatorEN11onnxruntime16AllocatorWrapperEN11onnxruntime7logging5ISinkEN11onnxruntime7logging8CLogSinkE14LoggingWrapperSt19_Sp_counted_deleterIPvPFvS0_ESaIiELN9__gnu_cxx12_Lock_policyE2EESt19_Sp_make_shared_tagSt23_Sp_counted_ptr_inplaceIN11onnxruntime16AllocatorWrapperESaIS1_ELN9__gnu_cxx12_Lock_policyE2EEexecution_mode is not validgraph_optimization_level is not validResult buffer is not large enoughbasic_string::_S_construct null not valid/onnxruntime_src/onnxruntime/core/session/custom_ops.cc%uUnsupported version '' in custom op ' is not implementedDescriptionCreateCustomRegistryComputeAsync*ZN11onnxruntime20CreateCustomRegistryERKSt6vectorIP17OrtCustomOpDomainSaIS2_EERSt10shared_ptrINS_14CustomRegistryEEEUlRKNS_12OpKernelInfoEE_N4onnx15ISchemaRegistryEN11onnxruntime30IOnnxRuntimeOpSchemaCollectionEN11onnxruntime8OpKernelEN11onnxruntime14CustomOpKernelEN4onnx8OpSchema19num_inputs_allowed_MUliE_EN4onnx8OpSchema20num_outputs_allowed_MUliE_ESt23_Sp_counted_ptr_inplaceIN11onnxruntime14KernelRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN11onnxruntime27OnnxRuntimeOpSchemaRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN11onnxruntime14CustomRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE12OrtAllocatorN3Ort9ExceptionE16OrtAllocatorImpl19OrtDefaultAllocatortensor(uint8)tensor(uint16)tensor(uint32)tensor(uint64)tensor(int8)tensor(int16)tensor(int32)tensor(int64)tensor(float16)tensor(float)tensor(double)tensor(string)tensor(bool)tensor(complex64)tensor(complex128) unknownElement type of input [TypeInferenceError] expected to have tensor typeInput Output com.microsoftcom.microsoft.nchwccom.microsoft.automl (domain: ) from file , but it its domain is not known by the checker., but it its version is not in the inclusive range [] (usually, this means you in onnx/defs/schema.h).Schema error: MemcpyFromHostMemcpyToHostException caught: Trying to register schema with name , but it is already registered from file bumped the operator version but forgot to update the version range in DomainToVersionRange Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type./onnxruntime_src/onnxruntime/core/session/environment.ccFvON4onnx8OpSchemaEEFvRN4onnx16InferenceContextEEN4onnx14InferenceErrorEN4onnx11SchemaErrorEPFvRN4onnx16InferenceContextEEPFvON4onnx8OpSchemaEECpuModel was not loadedModel was not loaded.Profiler is disabled.CUDAExecutionProvidersubgraph_session_statep_op_kernel) , expected: (other_error[json.exception.%Y-%m-%d_%H-%M-%S.jsonSession was not initializedSession not initialized.Compute_Create_State_Release_State_Error during EndProfiling(): Invalid rank for input: Expected: for the following indices -Output vector pointer is NULLp_fetches->size(): Invalid Output Name:Provider has already been registered.elements, but feeds has elements.Invalid Feed Input Name:Input with name: IsSparseTensor()Running with tag: model_runException during loading: Unknown exception in Load()model_loading_uriLoad model from model_loading_protomodel_loading_istreammodel_loading_arrayNode placements]. Provider: [MemcpyTransformerInitializing session.session_initializationEnvironment::IsInitialized() are '0' and '1'. model_protointra_op_thread_poolinter_op_thread_poolCastFloat16Transformerort_configsession_optionsORT_LOAD_CONFIG_FROM_MODELAttempt to use DefaultLogger but none has been registered.Received nullptr for graph transformer/onnxruntime_src/onnxruntime/core/session/inference_session.ccCould not write a profile because no model was loaded.Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast<int>(logging::Severity::kFATAL)Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast<int>(logging::Severity::kFATAL)CreateSubgraphSessionState should have created an entry earlier./onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.hUnexpected input data type. Actual: (Exceeded max transformer level. Current level is set to graph_optimization_level <= TransformerLevel::MaxLeveltimeout_in_ms >0 is not supportedMain Graph instance should have populated all subgraphs when being resolved.Unknown error during EndProfiling()Received nullptr for custom registry Please fix either the inputs or the model.Got invalid dimensions for input: At least one output should be requested.Output vector incorrectly sized: output_names.size(): /onnxruntime_src/onnxruntime/core/framework/execution_providers.h allocator already registered.Received nullptr for exec providerMemory pattern must be disabled before registering DMLExecutionProviderSequential execution must be enabled before registering DMLExecutionProviderSize mismatch: feed_names has is not expected to be of type tensor. is not expected to be of type sparse tensor.Trying to get a SparseTensor, but got: is not expected to be of type tensor sequence.Encountered unknown exception in Run()This session already contains a loaded model.Encountered unknown exception in Load()ModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().ModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.model_loading_from_saved_protoCould not find an implementation for the node All nodes have been placed on [Session has already been initialized.Adding default CPU execution provider.Parallel execution is currently not supported for the registered CUDA Execution Provider.Serializing Optimized ONNX model with Graph Optimization level greater than ORT_ENABLE_EXTENDED. The generated model may contain hardware and execution provider specific optimizations, and should only be used in the same environment the model was optimized for.Session successfully initialized.Exception during initialization: Encountered unknown exception in Initialize()Environment must be initialized before creating an InferenceSession.The only supported values for the environment variable The environment variable contained the value: Reading the provided model for the ORT configModel needs to be provided to check for ORT config within itCould not finalize session options while constructing the inference session. Error Message: Given model could not be parsed while creating inference session. Error message: Could not parse model successfully while constructing the inference sessionFailed to load model because protobuf parsing failed.void onnxruntime::InferenceSession::AddPredefinedTransformers(onnxruntime::GraphTransformerManager&, onnxruntime::TransformerLevel, const std::vector<std::basic_string<char> >&)void onnxruntime::InferenceSession::InitLogger(onnxruntime::logging::LoggingManager*)const onnxruntime::logging::Logger& onnxruntime::InferenceSession::CreateLoggerForRun(const RunOptions&, std::unique_ptr<onnxruntime::logging::Logger>&)EndProfilingGetModelOutputsGetOverridableInitializersGetModelInputsonnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(const std::vector<std::basic_string<char> >&, const std::vector<std::basic_string<char> >&, const onnxruntime::OrtValueNameIdxMap&)FeedsFetchesInfoRunconst T& OrtValue::Get() const [with T = onnxruntime::TensorSeq]const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor]ValidateInputsInitializeInitializeSubgraphSessionsonnxruntime::common::Status onnxruntime::InferenceSession::InitializeSubgraphSessions(onnxruntime::Graph&, onnxruntime::SessionState&)CreateSubgraphSessionStateonnxruntime::common::Status onnxruntime::InferenceSession::CreateSubgraphSessionState(onnxruntime::Graph&, onnxruntime::SessionState&)TransformGraphLoadAddCustomOpDomainsAdd~InferenceSessiononnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const void*, int, onnxruntime::logging::LoggingManager*)onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const string&, onnxruntime::logging::LoggingManager*)onnxruntime::common::Status onnxruntime::FinalizeSessionOptions(const onnxruntime::SessionOptions&, const onnx::ModelProto*, onnxruntime::SessionOptions&)FinalizeSessionOptionsvoid onnxruntime::InferenceSession::ConstructorCommon(const onnxruntime::SessionOptions&, onnxruntime::logging::LoggingManager*)MaxUsedSt14default_deleteIN11onnxruntime10DummyArenaEE*ZN11onnxruntime16InferenceSession4LoadEvEUlRSt10shared_ptrINS_5ModelEEE_N11onnxruntime16IDeviceAllocatorEN6google8protobuf2io19ZeroCopyInputStreamEN5Eigen19ThreadPoolInterfaceEN5Eigen15ThreadPoolTemplINS_20StlThreadEnvironmentEEEN11onnxruntime16GraphTransformerEN11onnxruntime16InferenceSessionEN11onnxruntime15IArenaAllocatorEN11onnxruntime10DummyArenaEN11onnxruntime11controlflow18IControlFlowKernelEN8nlohmann6detail9exceptionEN8nlohmann6detail11other_errorEZN11onnxruntime20CPUExecutionProviderC1ERKNS_24CPUExecutionProviderInfoEEUliE_St19_Sp_counted_deleterIPN11onnxruntime10DummyArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceISt13unordered_mapISsN11onnxruntime11FuncManager8FuncInfoESt4hashISsESt8equal_toISsESaISt4pairIKSsS3_EEESaISC_ELN9__gnu_cxx12_Lock_policyE2EE__C%lu at line , column parse_errorparse errorinvalid_iteratortype_errorout_of_rangeobjectbooleandiscardednumbercannot use at() with key '' not foundmap::at<U+%.4X><uninitialized>true literalfalse literalnull literalstring literalnumber literal'[''{'']''}'':'','unknown tokenend of input'[', '{', or a literal<parse error>syntax error while parsing - ; last read: 'unexpected ; expected cannot get valuetype must be number, but is Sequential modeParallel modefalsetrueintra_op_num_threadsinter_op_num_threadsexecution_modeSetting execution_mode to graph_optimization_levelenable_profilingSetting enable_profiling to vector::_M_emplace_back_auxvector<bool>::_M_insert_auxiterator out of rangecannot use erase() with invalid literalobject keyobject separatornumber overflow parsing 'Parsing RunOptions from ModelProto is not supported yet961c151d2e87f2686a955a9be24d316f1362bf21 3.7.1The 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 modelcannot compare iterators of different containersintra_op_num_threads option in the model file must be an integerUnsupported value for intra_op_num_threads: Setting intra_op_num_threads to /onnxruntime_src/onnxruntime/core/session/inference_session_utils.ccinter_op_num_threads option in the model file must be an integerUnsupported value for inter_op_num_threads: Setting inter_op_num_threads to execution_mode option in the model file must be an integerUnsupported execution_mode value in ORT config: graph_optimization_level option in the model file must be an integerSetting graph_optimization_level to ORT_DISABLE_ALLSetting graph_optimization_level to ORT_ENABLE_BASICSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_ALLUnsupported graph_optimization_level value in ORT config: enable_profiling option in the model file must be an integerUnsupported value for enable_profiling option: Ignoring unsupported session option in ORT config: invalid string: ill-formed UTF-8 byteinvalid string: missing closing quoteinvalid string: '\u' must be followed by 4 hex digitsinvalid string: surrogate U+DC00..U+DFFF must be followed by U+DC00..U+DFFFinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: forbidden character after backslashinvalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001F (US) must be escaped to \u001Fiterator does not fit current valueinvalid number; expected digit after '-'invalid number; expected digit after '.'invalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after exponent signinvalid BOM; must be 0xEF 0xBB 0xBF if givenThe Model Proto has already been checked for the ORT config json.Found session/run/environment configuration in the model file to be used while running the modelORT config json from the model: Json stored in the `ort_config` key cannot be parsed. Error message: ۦۦۦhۦ\ۦۦۦۦڦܦܦݦ4ݦܦܦܦܦTtDDD4$,d(8HHHXhxd,TT$|DtdTpX@(hP8 x`H0pXXHXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXo     ((*********************************D)*********:)*)***)*)*)*)*)*)*)*)*)*) )********************************)*T)********^)*******)*****)******)* )QVUUUT`T SRPQQQQO"hth igi4h4hThrvwdvvvvt tqrrrrpSetEnableProfilingSetGraphOptimizationLevelSetExecutionModeSetInterOpNumThreadsSetIntraOpNumThreadsParseSessionOptionsFromModelProtoParseOrtConfigJsonInModelProto N8nlohmann6detail11parse_errorEN8nlohmann6detail16invalid_iteratorEN8nlohmann6detail10type_errorEN8nlohmann6detail12out_of_rangeEN8nlohmann6detail22input_adapter_protocolEN8nlohmann6detail20input_buffer_adapterESt23_Sp_counted_ptr_inplaceIN8nlohmann6detail20input_buffer_adapterESaIS2_ELN9__gnu_cxx12_Lock_policyE2EE/onnxruntime_src/onnxruntime/core/optimizer/graph_transformer.ccApply/onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_mgr.ccThis transformer is already registered ApplyTransformersLevel_RuleBasedTransformerNchwcTransformerEliminateIdentityEliminateSliceUnsqueezeEliminationEliminateDropoutFuseReluClipShapeToInitializerConvAddFusionConvMulFusionConvBNFusionUnsupported levelConstantFoldingRandomUniformRandomNormalRandomUniformLikeRandomNormalLikeMultinomialMatMulAddFusionReshapeFusionGemmActivationFusionConvActivationFusionGeluFusionAttentionFusionEmbedLayerNormFusionBiasGeluSkipLayerNormFusionGeluApproximationUnsupported level /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_utils.ccstd::vector<std::unique_ptr<onnxruntime::GraphTransformer> > onnxruntime::optimizer_utils::GenerateTransformers(onnxruntime::TransformerLevel, gsl::span<const onnxruntime::FreeDimensionOverride>, const std::vector<std::basic_string<char> >&)std::vector<std::unique_ptr<onnxruntime::RewriteRule> > onnxruntime::optimizer_utils::GenerateRewriteRules(onnxruntime::TransformerLevel, const std::vector<std::basic_string<char> >&)N11onnxruntime11RewriteRuleEN11onnxruntime17EliminateIdentityE Max:node_index < nodes_.size()CastDef_%dValidating no unexpected access using an invalid node_index. Got:/onnxruntime_src/include/onnxruntime/core/graph/graph.h/onnxruntime_src/include/onnxruntime/core/optimizer/graph_transformer.h/onnxruntime_src/onnxruntime/core/optimizer/insert_cast_transformer.cccast node to cast from float16 to float32 on cpuRemoveDuplicateCastTransformerApplyImplRecurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constApplyImplN11onnxruntime21InsertCastTransformerEN11onnxruntime30RemoveDuplicateCastTransformerEsize is differentsize_ == sizedata type is not supportedai.onnxReduceMeanSubDivSqrtAddPowfused LayerNorm subgraphs epsilon/onnxruntime_src/onnxruntime/core/optimizer/initializer.h/onnxruntime_src/onnxruntime/core/optimizer/layer_norm_fusion.cc;;;;;;499;;-::hhhhhhxhghhhYhKhInitializeronnxruntime::Initializer::Initializer(const onnx::TensorProto&)onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constRecurseApplyImpl  N11onnxruntime15LayerNormFusionEfused Matmul and Add gemm/onnxruntime_src/onnxruntime/core/optimizer/matmul_add_fusion.ccRecurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constApplyImpl N11onnxruntime15MatMulAddFusionEpadsstridesdilationskernel_shapeauto_padSAME_UPPERSAME_LOWERNOTSETVALIDReorderOutputreorderReorderInputactivation_nchwcFusedConvGlobalMaxPoolGlobalAveragePoolConcat/onnxruntime_src/onnxruntime/core/optimizer/nchwc_transformer.cctdTRecurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constApplyImpl  +Hl$0D$0H}`MHVF8Ƅ$p1HDŽ$hƄ$qHDŽ$xHDŽ$HHDŽ$HDŽ$H$H%F8LpIFLt$8H|H F8LxL?L%F8H}HHD$HHD$PHD$XHD$`HD$hID$HD$pHD$8ID$@H$HE8HHD$@HF8L-eE8HuH}`DŽ$HHD$@IEH$H}H53H}H5nH}d`H5fHLH}H5)#7H}H$#|$0ID$HD$8ID$@H$HE8HHD$@H$HxL9HD8H}HHHD$@IFLt$8H}`L|8H:D8HH$1fH}H5{HD$hIUHT$HLt$XH\$`L9I9HI)HT$1L]IIHHHAGM9H\$ H|$HL$ HHD$ HxL9H|$HD8HWH6HD$D$0HxL9SHhC8LpLxH9HH)HT$1HIIIHHuzAGM9H\$ H|$HL$ H-CHHaHHGP1HLHHwHLHHHHZHuXH|$pMSH=۞H\$ HD$HHxݢH=H=|C8HWtuHt$ H=PC8HW3Ht$ $H}HtH 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+8Hf.H8HwHPH|$ HQHD$ HxH;=w7uH8H=7HWt# +ʅHt$HD$FqHD$ʋPJH@USHHH 7HH~Nt)udH5P sHH[]fH5n sHH[]DuH5 ZsHH[]H5B  H<sHH[]fH5sHH[]H;Ht$HHQnHwfHHfH7HHHGlHa7HHÐHQ7HHÐHj H7HHwf.SHH[pHH1.1.1out of indexinternal error/\basic_string::substrnot enough space: expected , got RegisterCustomOps was false.Stacktrace: +opaque(ml_type != nullptrnon_tensor_base != nullptrenv_ptr == p_instance_IsTensorSequence()elem_type_ != nullptrinput name cannot be emptyCPUExecutionProvideroutput name cannot be empty!=T Tensor type mismatch. input array is too shortspace is not enoughshape is invalid++index < c.size()vector::reservevector::_M_default_appendDmlExecutionProvideronnxruntime_profile_i < tensors_.size()vector::_M_range_checkEncountered nullptr.value_type != nullptrin[idx]->IsTensor()Unsupported input typeRegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in librarySpecified domain and type names combination does not refer to a registered opaque type/onnxruntime_src/onnxruntime/core/session/onnxruntime_c_api.ccOpaque type is not a non_tensor type!!!Trying to get a Tensor, but got: /onnxruntime_src/include/onnxruntime/core/framework/ml_value.hTrying to get a TensorSeq, but got: is not supported in this functionTensor sequence must contain only primitive types/onnxruntime_src/onnxruntime/core/framework/TensorSeq.h/onnxruntime_src/include/onnxruntime/core/framework/tensor.hutils::IsPrimitiveDataType<T>(dtype_)Sequence is missing type entry for its element/onnxruntime_src/include/onnxruntime/core/framework/data_types_internal.hMap is missing type entry for its valueMem pattern should be disabled when using DML execution provider.Sequential execution should be enabled when using DML execution provider.onnxruntime::DataTypeImpl::GetType<T>() == type_Input is not of one of the supported sequence types.Input is not of type sequence or map.Check for duplicate types in MLTypeCallDispatcherRetUnsupported tensor element type in the input: Invalid index requested for map type.Input is not of one of the supported map types.Number of values should be at least 1.For map type num_values MUST be 2Key and value tensors have unequal number of elements.Tensor must always contain primitive types. Found: Value type is not supported yet: Key type is not supported yet.Each element of the sequence should be either tensor or map.At least one element in the sequence is of a type different from others.Expecting all elements to be tensors. Got: Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.Either the key tensor or the value tensor has NumDimensions > 10`p 00,dl| ,,static void OrtEnv::Release(OrtEnv*)OrtStatus* OrtApis::GetOpaqueValue(const char*, const char*, const OrtValue*, void*, size_t)OrtStatus* OrtApis::CreateOpaqueValue(const char*, const char*, const void*, size_t, OrtValue**)void onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType)T* onnxruntime::Tensor::MutableData() [with T = long unsigned int]T* onnxruntime::Tensor::MutableData() [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = unsigned int]T* onnxruntime::Tensor::MutableData() [with T = int]T* onnxruntime::Tensor::MutableData() [with T = short unsigned int]T* onnxruntime::Tensor::MutableData() [with T = short int]T* onnxruntime::Tensor::MutableData() [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = signed char]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::BFloat16]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16]T* onnxruntime::Tensor::MutableData() [with T = double]T* onnxruntime::Tensor::MutableData() [with T = float]T* onnxruntime::Tensor::MutableData() [with T = bool]OrtStatus* OrtCreateValueImplSeqHelper(const OrtValue* const*, size_t, OrtValue**)OrtStatus* OrtCreateValueImplMapHelper(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; OrtStatus = OrtStatus; OrtValue = OrtValue]OrtStatus* OrtCreateValueImplMapHelper(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string<char>; OrtStatus = OrtStatus; OrtValue = OrtValue]const T& OrtValue::Get() const [with T = std::map<long int, double>]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = double; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int]const T& OrtValue::Get() const [with T = std::map<long int, float>]const T& OrtValue::Get() const [with T = std::map<long int, long int>]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = long int; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int]const T& OrtValue::Get() const [with T = std::map<long int, std::basic_string<char> >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = std::basic_string<char>; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int]const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, double>]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = std::basic_string<char>; V = double; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int]const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, float>]const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, long int>]const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, std::basic_string<char> >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = std::basic_string<char>; V = std::basic_string<char>; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int]Ret onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper<Ret, UnsupportedPolicy>::Get() [with Ret = OrtStatus*; UnsupportedPolicy = c_api_internal::UnsupportedReturnFailStatus]const T* onnxruntime::Tensor::Data() const [with T = long unsigned int]const T* onnxruntime::Tensor::Data() const [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = unsigned int]const T* onnxruntime::Tensor::Data() const [with T = int]const T* onnxruntime::Tensor::Data() const [with T = short unsigned int]const T* onnxruntime::Tensor::Data() const [with T = short int]const T* onnxruntime::Tensor::Data() const [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = signed char]const T* onnxruntime::Tensor::Data() const [with T = bool]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::BFloat16]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16]const T* onnxruntime::Tensor::Data() const [with T = double]const T* onnxruntime::Tensor::Data() const [with T = float]const onnxruntime::Tensor& onnxruntime::TensorSeq::Get(size_t) constconst T& OrtValue::Get() const [with T = std::vector<std::map<long int, float> >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::vector<_RealType> >::check(const Cont&, size_t) [with T = std::map<long int, float>; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int]const T& OrtValue::Get() const [with T = std::vector<std::map<std::basic_string<char>, float> >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = std::basic_string<char>; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::vector<_RealType> >::check(const Cont&, size_t) [with T = std::map<std::basic_string<char>, float>; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int]const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq]const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>]T* OrtValue::GetMutable() [with T = onnxruntime::Tensor]St11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EEPFvPvESt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime23NotImplementedExceptionEN11onnxruntime20OnnxRuntimeExceptionEN11onnxruntime10IAllocatorEN11onnxruntime16AllocatorWrapperEN11onnxruntime7logging5ISinkEN11onnxruntime7logging8CLogSinkE14LoggingWrapperSt19_Sp_counted_deleterIPvPFvS0_ESaIiELN9__gnu_cxx12_Lock_policyE2EESt19_Sp_make_shared_tagSt23_Sp_counted_ptr_inplaceIN11onnxruntime16AllocatorWrapperESaIS1_ELN9__gnu_cxx12_Lock_policyE2EEexecution_mode is not validgraph_optimization_level is not validResult buffer is not large enoughbasic_string::_S_construct null not valid/onnxruntime_src/onnxruntime/core/session/custom_ops.cc%uUnsupported version '' in custom op ' is not implementedDescriptionCreateCustomRegistryComputeAsync*ZN11onnxruntime20CreateCustomRegistryERKSt6vectorIP17OrtCustomOpDomainSaIS2_EERSt10shared_ptrINS_14CustomRegistryEEEUlRKNS_12OpKernelInfoEE_N4onnx15ISchemaRegistryEN11onnxruntime30IOnnxRuntimeOpSchemaCollectionEN11onnxruntime8OpKernelEN11onnxruntime14CustomOpKernelEN4onnx8OpSchema19num_inputs_allowed_MUliE_EN4onnx8OpSchema20num_outputs_allowed_MUliE_ESt23_Sp_counted_ptr_inplaceIN11onnxruntime14KernelRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN11onnxruntime27OnnxRuntimeOpSchemaRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN11onnxruntime14CustomRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE12OrtAllocatorN3Ort9ExceptionE16OrtAllocatorImpl19OrtDefaultAllocatortensor(uint8)tensor(uint16)tensor(uint32)tensor(uint64)tensor(int8)tensor(int16)tensor(int32)tensor(int64)tensor(float16)tensor(float)tensor(double)tensor(string)tensor(bool)tensor(complex64)tensor(complex128) unknownElement type of input [TypeInferenceError] expected to have tensor typeInput Output com.microsoftcom.microsoft.nchwccom.microsoft.automl (domain: ) from file , but it its domain is not known by the checker., but it its version is not in the inclusive range [] (usually, this means you in onnx/defs/schema.h).Schema error: MemcpyFromHostMemcpyToHostException caught: Trying to register schema with name , but it is already registered from file bumped the operator version but forgot to update the version range in DomainToVersionRange Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type./onnxruntime_src/onnxruntime/core/session/environment.ccFvON4onnx8OpSchemaEEFvRN4onnx16InferenceContextEEN4onnx14InferenceErrorEN4onnx11SchemaErrorEPFvRN4onnx16InferenceContextEEPFvON4onnx8OpSchemaEECpuModel was not loadedModel was not loaded.Profiler is disabled.CUDAExecutionProvidersubgraph_session_statep_op_kernel) , expected: (other_error[json.exception.%Y-%m-%d_%H-%M-%S.jsonSession was not initializedSession not initialized.Compute_Create_State_Release_State_Error during EndProfiling(): Invalid rank for input: Expected: for the following indices +Output vector pointer is NULLp_fetches->size(): Invalid Output Name:Provider has already been registered.elements, but feeds has elements.Invalid Feed Input Name:Input with name: IsSparseTensor()Running with tag: model_runException during loading: Unknown exception in Load()model_loading_uriLoad model from model_loading_protomodel_loading_istreammodel_loading_arrayNode placements]. Provider: [MemcpyTransformerInitializing session.session_initializationEnvironment::IsInitialized() are '0' and '1'. model_protointra_op_thread_poolinter_op_thread_poolCastFloat16Transformerort_configsession_optionsORT_LOAD_CONFIG_FROM_MODELAttempt to use DefaultLogger but none has been registered.Received nullptr for graph transformer/onnxruntime_src/onnxruntime/core/session/inference_session.ccCould not write a profile because no model was loaded.Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast<int>(logging::Severity::kFATAL)Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast<int>(logging::Severity::kFATAL)CreateSubgraphSessionState should have created an entry earlier./onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.hUnexpected input data type. Actual: (Exceeded max transformer level. Current level is set to graph_optimization_level <= TransformerLevel::MaxLeveltimeout_in_ms >0 is not supportedMain Graph instance should have populated all subgraphs when being resolved.Unknown error during EndProfiling()Received nullptr for custom registry Please fix either the inputs or the model.Got invalid dimensions for input: At least one output should be requested.Output vector incorrectly sized: output_names.size(): /onnxruntime_src/onnxruntime/core/framework/execution_providers.h allocator already registered.Received nullptr for exec providerMemory pattern must be disabled before registering DMLExecutionProviderSequential execution must be enabled before registering DMLExecutionProviderSize mismatch: feed_names has is not expected to be of type tensor. is not expected to be of type sparse tensor.Trying to get a SparseTensor, but got: is not expected to be of type tensor sequence.Encountered unknown exception in Run()This session already contains a loaded model.Encountered unknown exception in Load()ModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().ModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.model_loading_from_saved_protoCould not find an implementation for the node All nodes have been placed on [Session has already been initialized.Adding default CPU execution provider.Parallel execution is currently not supported for the registered CUDA Execution Provider.Serializing Optimized ONNX model with Graph Optimization level greater than ORT_ENABLE_EXTENDED. The generated model may contain hardware and execution provider specific optimizations, and should only be used in the same environment the model was optimized for.Session successfully initialized.Exception during initialization: Encountered unknown exception in Initialize()Environment must be initialized before creating an InferenceSession.The only supported values for the environment variable The environment variable contained the value: Reading the provided model for the ORT configModel needs to be provided to check for ORT config within itCould not finalize session options while constructing the inference session. Error Message: Given model could not be parsed while creating inference session. Error message: Could not parse model successfully while constructing the inference sessionFailed to load model because protobuf parsing failed.void onnxruntime::InferenceSession::AddPredefinedTransformers(onnxruntime::GraphTransformerManager&, onnxruntime::TransformerLevel, const std::vector<std::basic_string<char> >&)void onnxruntime::InferenceSession::InitLogger(onnxruntime::logging::LoggingManager*)const onnxruntime::logging::Logger& onnxruntime::InferenceSession::CreateLoggerForRun(const RunOptions&, std::unique_ptr<onnxruntime::logging::Logger>&)EndProfilingGetModelOutputsGetOverridableInitializersGetModelInputsonnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(const std::vector<std::basic_string<char> >&, const std::vector<std::basic_string<char> >&, const onnxruntime::OrtValueNameIdxMap&)FeedsFetchesInfoRunconst T& OrtValue::Get() const [with T = onnxruntime::TensorSeq]const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor]ValidateInputsInitializeInitializeSubgraphSessionsonnxruntime::common::Status onnxruntime::InferenceSession::InitializeSubgraphSessions(onnxruntime::Graph&, onnxruntime::SessionState&)CreateSubgraphSessionStateonnxruntime::common::Status onnxruntime::InferenceSession::CreateSubgraphSessionState(onnxruntime::Graph&, onnxruntime::SessionState&)TransformGraphLoadAddCustomOpDomainsAdd~InferenceSessiononnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const void*, int, onnxruntime::logging::LoggingManager*)onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const string&, onnxruntime::logging::LoggingManager*)onnxruntime::common::Status onnxruntime::FinalizeSessionOptions(const onnxruntime::SessionOptions&, const onnx::ModelProto*, onnxruntime::SessionOptions&)FinalizeSessionOptionsvoid onnxruntime::InferenceSession::ConstructorCommon(const onnxruntime::SessionOptions&, onnxruntime::logging::LoggingManager*)MaxUsedSt14default_deleteIN11onnxruntime10DummyArenaEE*ZN11onnxruntime16InferenceSession4LoadEvEUlRSt10shared_ptrINS_5ModelEEE_N11onnxruntime16IDeviceAllocatorEN6google8protobuf2io19ZeroCopyInputStreamEN5Eigen19ThreadPoolInterfaceEN5Eigen15ThreadPoolTemplINS_20StlThreadEnvironmentEEEN11onnxruntime16GraphTransformerEN11onnxruntime16InferenceSessionEN11onnxruntime15IArenaAllocatorEN11onnxruntime10DummyArenaEN11onnxruntime11controlflow18IControlFlowKernelEN8nlohmann6detail9exceptionEN8nlohmann6detail11other_errorEZN11onnxruntime20CPUExecutionProviderC1ERKNS_24CPUExecutionProviderInfoEEUliE_St19_Sp_counted_deleterIPN11onnxruntime10DummyArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceISt13unordered_mapISsN11onnxruntime11FuncManager8FuncInfoESt4hashISsESt8equal_toISsESaISt4pairIKSsS3_EEESaISC_ELN9__gnu_cxx12_Lock_policyE2EE__C%lu at line , column parse_errorparse errorinvalid_iteratortype_errorout_of_rangeobjectbooleandiscardednumbercannot use at() with key '' not foundmap::at<U+%.4X><uninitialized>true literalfalse literalnull literalstring literalnumber literal'[''{'']''}'':'','unknown tokenend of input'[', '{', or a literal<parse error>syntax error while parsing - ; last read: 'unexpected ; expected cannot get valuetype must be number, but is Sequential modeParallel modefalsetrueintra_op_num_threadsinter_op_num_threadsexecution_modeSetting execution_mode to graph_optimization_levelenable_profilingSetting enable_profiling to vector::_M_emplace_back_auxvector<bool>::_M_insert_auxiterator out of rangecannot use erase() with invalid literalobject keyobject separatornumber overflow parsing 'Parsing RunOptions from ModelProto is not supported yet961c151d2e87f2686a955a9be24d316f1362bf21 3.7.1The 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 modelcannot compare iterators of different containersintra_op_num_threads option in the model file must be an integerUnsupported value for intra_op_num_threads: Setting intra_op_num_threads to /onnxruntime_src/onnxruntime/core/session/inference_session_utils.ccinter_op_num_threads option in the model file must be an integerUnsupported value for inter_op_num_threads: Setting inter_op_num_threads to execution_mode option in the model file must be an integerUnsupported execution_mode value in ORT config: graph_optimization_level option in the model file must be an integerSetting graph_optimization_level to ORT_DISABLE_ALLSetting graph_optimization_level to ORT_ENABLE_BASICSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_ALLUnsupported graph_optimization_level value in ORT config: enable_profiling option in the model file must be an integerUnsupported value for enable_profiling option: Ignoring unsupported session option in ORT config: invalid string: ill-formed UTF-8 byteinvalid string: missing closing quoteinvalid string: '\u' must be followed by 4 hex digitsinvalid string: surrogate U+DC00..U+DFFF must be followed by U+DC00..U+DFFFinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: forbidden character after backslashinvalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001F (US) must be escaped to \u001Fiterator does not fit current valueinvalid number; expected digit after '-'invalid number; expected digit after '.'invalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after exponent signinvalid BOM; must be 0xEF 0xBB 0xBF if givenThe Model Proto has already been checked for the ORT config json.Found session/run/environment configuration in the model file to be used while running the modelORT config json from the model: Json stored in the `ort_config` key cannot be parsed. Error message: ææææ|ææææ¦ĦĦ$ŦTŦĦĦĦĦtʦɦʦʦʦdʦdʦdʦTʦDʦʦʦʦʦL̦ʦ4ʦ̦8˦ʦH˦X˦h˦h˦h˦x˦˦˦˦˦˦˦˦˦ϦͦLΦΦϦtͦҦѦtѦѦDҦЦd֦զ֦֦֦֦֦֦t֦x`H0pX@(hP8 xxhxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx( @ 000'''''''''''''''''''''''''''''''''d'''''''''ZJ''JJJJJJJJJJ@'''''''''''''''''''''''''''''''''t''''''''~''''''''''''''''''6',9(>>= =<<@;:899997BPP,QPQTPTPtP[_4_^^^^],\Y[[[[XSetEnableProfilingSetGraphOptimizationLevelSetExecutionModeSetInterOpNumThreadsSetIntraOpNumThreadsParseSessionOptionsFromModelProtoParseOrtConfigJsonInModelProto N8nlohmann6detail11parse_errorEN8nlohmann6detail16invalid_iteratorEN8nlohmann6detail10type_errorEN8nlohmann6detail12out_of_rangeEN8nlohmann6detail22input_adapter_protocolEN8nlohmann6detail20input_buffer_adapterESt23_Sp_counted_ptr_inplaceIN8nlohmann6detail20input_buffer_adapterESaIS2_ELN9__gnu_cxx12_Lock_policyE2EE/onnxruntime_src/onnxruntime/core/optimizer/graph_transformer.ccApply/onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_mgr.ccThis transformer is already registered ApplyTransformersLevel_RuleBasedTransformerNchwcTransformerEliminateIdentityEliminateSliceUnsqueezeEliminationEliminateDropoutFuseReluClipShapeToInitializerConvAddFusionConvMulFusionConvBNFusionUnsupported levelConstantFoldingRandomUniformRandomNormalRandomUniformLikeRandomNormalLikeMultinomialMatMulAddFusionReshapeFusionGemmActivationFusionConvActivationFusionGeluFusionAttentionFusionEmbedLayerNormFusionBiasGeluSkipLayerNormFusionGeluApproximationUnsupported level /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_utils.ccstd::vector<std::unique_ptr<onnxruntime::GraphTransformer> > onnxruntime::optimizer_utils::GenerateTransformers(onnxruntime::TransformerLevel, gsl::span<const onnxruntime::FreeDimensionOverride>, const std::vector<std::basic_string<char> >&)std::vector<std::unique_ptr<onnxruntime::RewriteRule> > onnxruntime::optimizer_utils::GenerateRewriteRules(onnxruntime::TransformerLevel, const std::vector<std::basic_string<char> >&)N11onnxruntime11RewriteRuleEN11onnxruntime17EliminateIdentityE Max:node_index < nodes_.size()CastDef_%dValidating no unexpected access using an invalid node_index. Got:/onnxruntime_src/include/onnxruntime/core/graph/graph.h/onnxruntime_src/include/onnxruntime/core/optimizer/graph_transformer.h/onnxruntime_src/onnxruntime/core/optimizer/insert_cast_transformer.cccast node to cast from float16 to float32 on cpuRemoveDuplicateCastTransformerApplyImplRecurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constApplyImplN11onnxruntime21InsertCastTransformerEN11onnxruntime30RemoveDuplicateCastTransformerEsize is differentsize_ == sizedata type is not supportedai.onnxReduceMeanSubDivSqrtAddPowfused LayerNorm subgraphs epsilon/onnxruntime_src/onnxruntime/core/optimizer/initializer.h/onnxruntime_src/onnxruntime/core/optimizer/layer_norm_fusion.cc#3#####T!!##M""PPPPPPPPPPyPkPInitializeronnxruntime::Initializer::Initializer(const onnx::TensorProto&)onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constRecurseApplyImpl  N11onnxruntime15LayerNormFusionEfused Matmul and Add gemm/onnxruntime_src/onnxruntime/core/optimizer/matmul_add_fusion.ccRecurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constApplyImpl N11onnxruntime15MatMulAddFusionEpadsstridesdilationskernel_shapeauto_padSAME_UPPERSAME_LOWERNOTSETVALIDReorderOutputreorderReorderInputactivation_nchwcFusedConvGlobalMaxPoolGlobalAveragePoolConcat/onnxruntime_src/onnxruntime/core/optimizer/nchwc_transformer.ccĂĂĂĂĂĂĂtRecurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constApplyImpl   -  N11onnxruntime16NchwcTransformerEdata type is not supported.minFuseReluClip__min_zero_constantUnexpected data type for Clip 'min' input of /onnxruntime_src/onnxruntime/core/optimizer/relu_clip_fusion.ccX8u1uuuuuuP8XXXXXXXonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constToProtoonnxruntime::Initializer::Initializer(onnx::TensorProto_DataType, const string&, const std::vector<long int>&)virtual onnxruntime::common::Status onnxruntime::FuseReluClip::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const N11onnxruntime12FuseReluClipEGatherUnsqueezeReshapeFused reshape node: Cannot replace concat node with initializer:/onnxruntime_src/onnxruntime/core/optimizer/reshape_fusion.ccTotal fused reshape node count: Fuse_Subgraph1Recurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constApplyImpl     N11onnxruntime13ReshapeFusionE/onnxruntime_src/onnxruntime/core/optimizer/rule_based_graph_transformer.ccRecurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constApplyImplApplyRulesOnNodeN11onnxruntime25RuleBasedGraphTransformerEN11onnxruntime18ShapeToInitializerESkipLayerNormalization/onnxruntime_src/onnxruntime/core/optimizer/skip_layer_norm_fusion.ccfused SkipLayerNorm subgraphs Recurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constApplyImplN11onnxruntime19SkipLayerNormFusionEstartsends1$ -kݜݜݜݜ^QݜݜD7q====뗩ޗ==їė ⚩ КÚgttttZMtt@3 +  N11onnxruntime16NchwcTransformerEdata type is not supported.minFuseReluClip__min_zero_constantUnexpected data type for Clip 'min' input of /onnxruntime_src/onnxruntime/core/optimizer/relu_clip_fusion.ccߨxXߨQpXxxxxxxxonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constToProtoonnxruntime::Initializer::Initializer(onnx::TensorProto_DataType, const string&, const std::vector<long int>&)virtual onnxruntime::common::Status onnxruntime::FuseReluClip::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const N11onnxruntime12FuseReluClipEGatherUnsqueezeReshapeFused reshape node: Cannot replace concat node with initializer:/onnxruntime_src/onnxruntime/core/optimizer/reshape_fusion.ccTotal fused reshape node count: Fuse_Subgraph1Recurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constApplyImpl     N11onnxruntime13ReshapeFusionE/onnxruntime_src/onnxruntime/core/optimizer/rule_based_graph_transformer.ccRecurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constApplyImplApplyRulesOnNodeN11onnxruntime25RuleBasedGraphTransformerEN11onnxruntime18ShapeToInitializerESkipLayerNormalization/onnxruntime_src/onnxruntime/core/optimizer/skip_layer_norm_fusion.ccfused SkipLayerNorm subgraphs Recurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constApplyImplN11onnxruntime19SkipLayerNormFusionEstartsendsaTG:-  tg))))))(mmmmmm,999999}pc  - N11onnxruntime14EliminateSliceE/onnxruntime_src/onnxruntime/core/optimizer/transformer_memcpy.ccdup_replacements.find(&arg) == dup_replacements.end()TensorrtExecutionProviderNGRAPHExecutionProvider' doesn't support memcpy Execution type 'MemcpyCopy from/to host memoryDnnlExecutionProviderNupharExecutionProviderOpenVINOExecutionProviderACLExecutionProvideronnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&)::__lambda8bool onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&)ForEachWithIndexvoid onnxruntime::TransformerMemcpyImpl::ProcessDefs(onnxruntime::Node&, const onnxruntime::KernelRegistryManager&, onnxruntime::InitializedTensorSet&)RecurseApplyImpl*ZN11onnxruntime21TransformerMemcpyImpl11ProcessDefsERNS_4NodeERKNS_21KernelRegistryManagerERSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSA_EEEEUlRKNS_7NodeArgEmE_*ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE_*ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE0_N11onnxruntime17MemcpyTransformerEUnsqueezeElimination_UnsqueezeElimination cannot remove node /onnxruntime_src/onnxruntime/core/optimizer/unsqueeze_elimination.ccApplyN11onnxruntime20UnsqueezeEliminationEvector::_M_range_insertzk[Km}}qdWxh_      -  - - 4<Output edge count not expected for nodes in path vFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3v_reshape initializer value is not expectedreshape initializer value is not expectedFailed in match v_matmul and v_add input shapeOutput edge count not expected for mask nodesSoftmax attribute axis is expected to be 3mask_unsqueeze_1 axes not matched. Expect: 1mask_unsqueeze_2 axes not matched. Expect: 2mask_sub const input not matchedmask_mul const input not matchedq root should be layer normalizationq_transpose perm attribute not matchedq_matmul and q_add shape not matchedk_transpose perm attribute not matchedk_matmul and k_add shape not matchedFailed to load Q, K and V weights, or data type is not float or float16.Failed to load Q, K and V bias tensors, or data type is not float or float16.Mask shape is unknown or not 2D, or data type unknownMask data type is not int32 or int64/onnxruntime_src/onnxruntime/core/optimizer/attention_fusion.ccshape of layer norm bias tensor not expectedAttention subgraph expects 1 Add and 3 MatMul as children of LayerNormalization.Total fused Attention node count: qkv_weightsqkv_biasFaild to find path vFailed to find path for maskFailed to find path for qq_reshape const not matchedqk_div const not matched.permFailed to find path for kk root is not layer normk_reshape const not matchedMask_Int32Cast mask from int64 to int32MaskCastMaskIndex_Int32Count number of wordsReduceSumMaskIndexkeepdimsFailed to create mask indexFused Attention subgraphs Attentionnum_headsFused an attention node.X -H -X -X -X -X -8 -( -X -X - - -ProcessMaskFuseSubGraphRecurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constApplyImpl         N11onnxruntime15AttentionFusionE@?fused Add and Gelu/onnxruntime_src/onnxruntime/core/optimizer/bias_gelu_fusion.ccRecurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constApplyImplN11onnxruntime8BiasGeluEUnsupported output type of . Can't constant fold node 'ort_value.IsTensor()/onnxruntime_src/onnxruntime/core/optimizer/constant_folding.ccCould not find OrtValue with name 'fetches.size() == node->OutputDefs().size()const T& OrtValue::Get() const [with T = onnxruntime::Tensor]Recurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constvirtual onnxruntime::common::Status onnxruntime::ConstantFolding::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) constApplyImplN11onnxruntime15OpKernelContextEN11onnxruntime15ConstantFoldingELeakyRelufused Conv with activation fused activation_params/onnxruntime_src/onnxruntime/core/optimizer/conv_activation_fusion.ccUnexpected data type for Clip input of ΪΪΪΪΪΪΪΪΪΪqͪdͪ|ϪoϪ|Ϫ|Ϫ|Ϫ|Ϫ]ϪPϪ|Ϫ|Ϫ>Ϫ,ϪѪvѪѪѪѪѪiѪ\ѪѪѪOѪBѪܪܪܪܪܪܪ۪۪ܪܪ۪۪onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constonnxruntime::{anonymous}::GetClipConstantMinMax(const onnxruntime::Graph&, const onnxruntime::Node&, float&, float&)::__lambda15RecurseApplyImpl  N11onnxruntime20ConvActivationFusionEconv_W_tensor_protoadd_B_tensor_protoconv_B_tensor_protoConvAddFusion_B_ConvAddFusion_Add_B_@maXOX -xxxxx xxxxxxxxx&/onnxruntime_src/onnxruntime/core/optimizer/conv_add_fusion.cchhhhhhhhToProtoonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constvirtual onnxruntime::common::Status onnxruntime::ConvAddFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const N11onnxruntime13ConvAddFusionEBatchNormalizationbn_scale_tensor_protobn_B_tensor_protobn_mean_tensor_protobn_var_tensor_protoConvBnFusion_W_ConvBnFusion_BN_B_hH ( -(((( - -(( - -~ -r -f -] -T -d -dddd - -dd  -x -l -c -Z - -{ -o -f -] - x        X 0 babbbbUIbb@7 - - - - - - -zq" " " " " " "    hhhhhThhTTTTTTTw>2&xl`WN(/onnxruntime_src/onnxruntime/core/optimizer/conv_bn_fusion.cc('((((']'(('&^.b-^.^.^.^.M-A-^.^.Y-,.-.....-..---D,----;,2,--,,+------%-----,-r+----+}+--i+;'onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constvirtual onnxruntime::common::Status onnxruntime::ConvBNFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const  N11onnxruntime12ConvBNFusionEmul_B_tensor_protoConvMulFusion_W_ConvMulFusion_Mul_B_/onnxruntime_src/onnxruntime/core/optimizer/conv_mul_fusion.ccFEFFFF'EDFFkDDEKJEKEKEKEKIIEKEKH%H#KI#K#K#K#KIdI#K#KwIHKIKKKKHoHKKHGJ~JJJJJJJJJpJIJWGJJJJPH?HJJaHDMMMMMMML LMMrKJToProtoonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constvirtual onnxruntime::common::Status onnxruntime::ConvMulFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const N11onnxruntime13ConvMulFusionE -N11onnxruntime16EliminateDropoutEInput shape is unknown or not 2D, or data type unknownInput data type is not int32 or int64/onnxruntime_src/onnxruntime/core/optimizer/embed_layer_norm_fusion.ccFailed to find path 1 of position shape.Output edge count not expected for nodes in path 1 of position shape.Second input of Gather in path 1 of position shape should be a constant with value 0.Failed to find path 2 of position shape.Output edge count not expected for nodes in path 2 of position shape.Gather node in path 2 is not linked to another subgraph.Second input of Gather in path 2 of position shape should be a constant with value 1.The parent of two shape nodes are expected to be input_ids.Cast Input from int64 to int32Word embedding shape not expected.Input is expected to have dim value in all dimensions.Failed to get initializer tensor.Position embedding shape not matched.Position embedding data type shall be float or float16.Output edge count not expected for nodes in path1.Second input of Gather should be a constant with value 1. The parent of shape nodes are expected to be input_ids.Failed to match position subgraph.Output edge count not expected for nodes in path 1.The first input of Range should be a constant with value 0.The third input of Range should be a constant with value 1.Failed to match position embedding subgraph.Failed to get position embedding weights.Position embedding shape is not expected.Input_ids and segment id should have the same shape. Input_ids and mask should have the same shape. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). fused EmbedLayerNorm subgraphs _Castposition_embeddingsExpandSqueezeNonZeroConstantOfShapeFailed to match Shape node. Failed to find path 1.Input id is not valid. Segment id is not valid. Mask is not valid. EmbedLayerNormalization唫{onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constCheckInputMatchPositionEmbeddingSubgraph2MatchPositionSubgraphMatchPositionEmbeddingSubgraph1RecurseApplyImpl             N11onnxruntime20EmbedLayerNormFusionEFreeDimensionOverrideTransformer/onnxruntime_src/onnxruntime/core/optimizer/free_dim_override_transformer.ccwith a fixed dimension denotation 'but the size of this dimension does not equal the specified override ofInvalid free dimension override.The model has input '' ApplyImplN11onnxruntime32FreeDimensionOverrideTransformerEGelu approximationFastGelu/onnxruntime_src/onnxruntime/core/optimizer/gelu_approximation.ccTotal Gelu Approximation (FastGelu) node count: Recurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constApplyImpl N11onnxruntime17GeluApproximationEErffused Gelu subgraphs /onnxruntime_src/onnxruntime/core/optimizer/gelu_fusion.cconnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constRecurseApplyImpl N11onnxruntime10GeluFusionE??fused Gemm FusedGemmleaky_relu_/onnxruntime_src/onnxruntime/core/optimizer/gemm_activation_fusion.cconnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constRecurseApplyImpl N11onnxruntime20GemmActivationFusionE_Map_base::atallocator_ptr_ != nullptrTried to allocate without valid type information, ort_value index=Failed to get allocator for optimizer/onnxruntime_src/onnxruntime/core/optimizer/optimizer_execution_frame.ccvoid onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType)ForEachWithIndexoperator()Infoonnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector<const onnxruntime::Node*>&, const InitializedTensorSet&)*ZN11onnxruntime23OptimizerExecutionFrame4InfoC1ERKSt6vectorIPKNS_4NodeESaIS5_EERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSE_EEEEUlRKNS_7NodeArgEmE_N11onnxruntime23OptimizerExecutionFrameE/onnxruntime_src/onnxruntime/core/providers/cpu/cpu_execution_provider.ccvirtual std::shared_ptr<onnxruntime::KernelRegistry> onnxruntime::CPUExecutionProvider::GetKernelRegistry() constGetKernelRegistryRegisterCPUKernelsRegisterOnnxMLOperatorKernelsRegisterOnnxOperatorKernelsN11onnxruntime20CPUExecutionProviderEN11onnxruntime25IExecutionProviderFactoryEN11onnxruntime18CpuProviderFactoryESt23_Sp_counted_ptr_inplaceIN11onnxruntime18CpuProviderFactoryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EEutils::HasDataType(t_proto)unpack_status.IsOK()T1T2Missing Input: Must have a single dimensiont_proto.dims_size() == 1t_proto.dims()[0] == 1Unsupported value attribute datatype with sizeof=: /onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape.h/onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape.ccTensorProto::DataType_IsValid(t_proto.data_type())Value attribute unpacking failed:Unsupported value attribute datatype: /onnxruntime_src/include/onnxruntime/core/framework/op_kernel.hMust have a valid input shape.input_shape.NumDimensions() > 0Must have a single dimension of 1[ dc|c4cbb\b[ba|a<a`virtual onnxruntime::common::Status onnxruntime::ConstantOfShape::Compute(onnxruntime::OpKernelContext*) constComputeconst T* onnxruntime::Tensor::Data() const [with T = long int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::ConstantOfShapeBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::Tensor**) constonnxruntime::ConstantOfShapeBase::ConstantOfShapeBase(const onnxruntime::OpKernelInfo&)void onnxruntime::ConstantOfShapeBase::SetValue(size_t, void*)void onnxruntime::ConstantOfShapeBase::SetValueFromTensorProto(const onnx::TensorProto&)N11onnxruntime19ConstantOfShapeBaseEFPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEEN11onnxruntime15ConstantOfShapeEPFPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEEseeddtypeInvalid dtype of highsample_sizeFLOAT16 is not supportedInvalid data type of input count mismatchbatch_size is < 1num_classes is < 1num_samples is < 1/onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.hinfo.GetAttr<float>("mean", &mean_).IsOK()info.GetAttr<float>("scale", &scale_).IsOK()info.GetAttr<int64_t>("dtype", &dtype).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDinfo.GetAttrs<int64_t>("shape", shape).IsOK()info.GetAttr<float>("high", &high_).IsOK()info.GetAttr<float>("low", &low_).IsOK()info.GetAttr<int64_t>("sample_size", &num_samples_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINED/onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.ccCould not infer data type from input tensor with data type Empty dimensions for input tensorT* onnxruntime::Tensor::MutableData() [with T = long int]MultinomialComputeT* onnxruntime::Tensor::MutableData() [with T = int]MultinomialComputeconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::RandomUniformCompute(float, float, std::default_random_engine&, onnx::TensorProto::DataType, onnxruntime::Tensor&)T* onnxruntime::Tensor::MutableData() [with T = float]onnxruntime::common::Status onnxruntime::RandomNormalCompute(float, float, std::default_random_engine&, onnx::TensorProto::DataType, onnxruntime::Tensor&)onnxruntime::Multinomial::Multinomial(const onnxruntime::OpKernelInfo&)onnxruntime::RandomUniformLike::RandomUniformLike(const onnxruntime::OpKernelInfo&)onnxruntime::RandomNormalLike::RandomNormalLike(const onnxruntime::OpKernelInfo&)onnxruntime::RandomUniform::RandomUniform(const onnxruntime::OpKernelInfo&)onnxruntime::RandomNormal::RandomNormal(const onnxruntime::OpKernelInfo&)N11onnxruntime12RandomNormalEN11onnxruntime16RandomNormalLikeEN11onnxruntime13RandomUniformEN11onnxruntime17RandomUniformLikeEN11onnxruntime11MultinomialE0AC?"~+@"~+@W6}+W6}++eG?+eG?????@.?@.?ʫy>ʫy>K͉ ?K͉ ?~, ?~, ?_6.>_6.>9d?9d?t ?t ?@@Unsupported data type: delta in Range operator can not be zero!start in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:delta in Range operator should be scalar like tensor, yet got shape:Ret onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy<Ret>::operator()(int32_t) const [with Ret = onnxruntime::common::Status; int32_t = int]T* onnxruntime::Tensor::MutableData() [with T = short int]const T* onnxruntime::Tensor::Data() const [with T = short int]T* onnxruntime::Tensor::MutableData() [with T = double]const T* onnxruntime::Tensor::Data() const [with T = double]T* onnxruntime::Tensor::MutableData() [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = int]const T* onnxruntime::Tensor::Data() const [with T = int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime5RangeEmin should be a scalar.max should be a scalar./onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.hinfo.GetAttr<T>("max", &max_).IsOK()info.GetAttr<T>("min", &min_).IsOK()min->Shape().NumDimensions() == 0max->Shape().NumDimensions() == 0T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::Clip<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::Clip_6<T>::Clip_6(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime6Clip_6IfEEN11onnxruntime4ClipIfEEexclusivereverseCumSumstarts.size()=dims.size()=dims.size() == starts.size()extents.size()=steps.size()=dims.size() == steps.size()) but got: Axis should be in the range [/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/utils.hdims.size() == extents.size() && dims.size() >= steps.size()dims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()dims.size() == extents_.size()Axis tensor should be 0D or 1Dvoid onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double]const T* onnxruntime::Tensor::Data() const [with T = double]void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double]T* onnxruntime::Tensor::MutableData() [with T = double]void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = int]void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = int]T* onnxruntime::Tensor::MutableData() [with T = int]void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = long int]void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int]onnxruntime::SliceSkips::SliceSkips(const onnxruntime::TensorShape&, gsl::span<const long int>, gsl::span<const long int>)T* onnxruntime::Tensor::MutableData() [with T = long int]N11onnxruntime6CumSumIfEEN11onnxruntime6CumSumIdEEN11onnxruntime6CumSumIiEEN11onnxruntime6CumSumIlEE$$$d4$4D4DTdt4Dlt|$$T4DTD4DTTdtDetX != nullptr/onnxruntime_src/onnxruntime/core/providers/cpu/math/det.ccInput tensor should have a rank of at least 2Matrix dimensions are not equal. Square matrix is expectedconst T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::Det<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]N11onnxruntime3DetIfEEj@fmod(fmod == 0) || (fmod == 1)AbsNegFloorCeilReciprocalLogLessGreaterEqualBitShiftNotAndOrXorSinCosTanAsinAcosAtanSinhCoshAsinhAcoshAtanhPReluModaxis == 1 || axis == largestdirectionLEFTRIGHTInvalid direction value of 'X_ptr != nullptrvector::_M_fill_insertCan broadcast 0 by 0 or 1. largest <= 1failed to get first output!Must have 1 or more inputsinputCount >= 1data_n.Shape() == shapeinput_count >= 1data_1.Shape() == shapep_ml_valuehelper.called_ < 2helper.called_ == 1 vs fmod_cur1 == end1cur_out == end_outfmod must have value either 0 or 1/onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.ccAttempting to broadcast an axis by a dimension other than 1. /onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.h'. Valid values are 'LEFT' or 'RIGHT'.All inputs must have the same shapePlease fetch output tensor with specified shape.Shape must be 1 dimensional as it's tensor data is a shapetensor_shape.Shape().GetDims().size() == 1Check for duplicate types in MLTypeCallDispatcherX and Y input types do not match: fmod attribute must be true for float, float16 and double typesonnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda100; Input1Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda101; General = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda102]onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = double]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = double; TOutput = double; Input0Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda100; Input1Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda101; General = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda102]onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda126onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda126onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda126onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short int]onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int]onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool]onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = long int]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long int]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long unsigned int]T* onnxruntime::Tensor::MutableData() [with T = long unsigned int]const T* onnxruntime::Tensor::Data() const [with T = long unsigned int]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long unsigned int]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = int]T* onnxruntime::Tensor::MutableData() [with T = int]const T* onnxruntime::Tensor::Data() const [with T = int]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = int]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned int]T* onnxruntime::Tensor::MutableData() [with T = unsigned int]const T* onnxruntime::Tensor::Data() const [with T = unsigned int]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned int]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short int]T* onnxruntime::Tensor::MutableData() [with T = short int]const T* onnxruntime::Tensor::Data() const [with T = short int]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short int]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short unsigned int]T* onnxruntime::Tensor::MutableData() [with T = short unsigned int]const T* onnxruntime::Tensor::Data() const [with T = short unsigned int]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short unsigned int]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = signed char]T* onnxruntime::Tensor::MutableData() [with T = signed char]const T* onnxruntime::Tensor::Data() const [with T = signed char]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = signed char]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = unsigned char]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned char]void onnxruntime::utils::MLTypeCallDispatcher<Fn, Types>::Invoke(Args&& ...) const [with Args = {const bool&, const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*&}; Fn = onnxruntime::mod_internal::CallModImpl; Types = {unsigned char, signed char, short unsigned int, short int, unsigned int, int, long unsigned int, long int}]T* onnxruntime::Tensor::MutableData() [with T = double]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = double]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = float]virtual onnxruntime::common::Status onnxruntime::Mod::Compute(onnxruntime::OpKernelContext*) constT* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16]void onnxruntime::mod_internal::BroadCastMFloat16FMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*)onnxruntime::Mod::Mod(const onnxruntime::OpKernelInfo&)onnxruntime::common::Status onnxruntime::Erf<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]T* OrtValue::GetMutable() [with T = onnxruntime::Tensor]T* onnxruntime::OpKernelContext::Output(int) [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda121; Input1Scalar = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda122; General = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda123]onnxruntime::common::Status onnxruntime::Mean_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = bool]const T* onnxruntime::Tensor::Data() const [with T = bool]onnxruntime::common::Status onnxruntime::Max_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Min_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda97; Input1Scalar = onnxruntime::Min_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda98; General = onnxruntime::Min_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda99]onnxruntime::common::Status onnxruntime::Min_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]void onnxruntime::BroadcastIterator::Append(int64_t, int64_t)void onnxruntime::BroadcastIterator::Init(int64_t, int64_t)onnxruntime::Broadcaster::Broadcaster(const std::vector<long int>&, const std::vector<long int>&)onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = float]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda94; Input1Scalar = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda95; General = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda96]onnxruntime::common::Status onnxruntime::Sum_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = long unsigned int]onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned int]onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned char]((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOZNK11onnxruntime3PowIdE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIdLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEdE_ZNK11onnxruntime3PowIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEfE_ZNK11onnxruntime3PowIdE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIdLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEdE0_ZNK11onnxruntime3PowIdE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIdLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEdE1_ZNK11onnxruntime3PowIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEfE0_ZNK11onnxruntime3PowIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEfE1_N11onnxruntime3NotEN11onnxruntime3AndEN11onnxruntime2OrEN11onnxruntime3XorEN11onnxruntime3AddIfEEN11onnxruntime3AddIdEEN11onnxruntime3AddIiEEN11onnxruntime3AddIlEEN11onnxruntime3SubIfEEN11onnxruntime3SubIdEEN11onnxruntime3SubIiEEN11onnxruntime3SubIlEEN11onnxruntime3MulIfEEN11onnxruntime3MulIdEEN11onnxruntime3MulIiEEN11onnxruntime3MulIlEEN11onnxruntime3DivIfEEN11onnxruntime3DivIdEEN11onnxruntime3DivIiEEN11onnxruntime3DivIlEEN11onnxruntime3AbsIfEEN11onnxruntime3AbsIdEEN11onnxruntime3AbsIaEEN11onnxruntime3AbsIsEEN11onnxruntime3AbsIiEEN11onnxruntime3AbsIlEEN11onnxruntime3AbsIhEEN11onnxruntime3AbsItEEN11onnxruntime3AbsIjEEN11onnxruntime3AbsImEEN11onnxruntime3NegIfEEN11onnxruntime3NegIdEEN11onnxruntime3NegIaEEN11onnxruntime3NegIiEEN11onnxruntime5FloorIfEEN11onnxruntime4CeilIfEEN11onnxruntime10ReciprocalIfEEN11onnxruntime4SqrtIfEEN11onnxruntime4SqrtIdEEN11onnxruntime3PowIfEEN11onnxruntime3PowIdEEN11onnxruntime3ExpIfEEN11onnxruntime3ExpIdEEN11onnxruntime3LogIfEEN11onnxruntime5Sum_6IfEEN11onnxruntime5Sum_8IfEEN11onnxruntime5Min_6IfEEN11onnxruntime5Min_8IfEEN11onnxruntime5Max_6IfEEN11onnxruntime5Max_8IfEEN11onnxruntime5Max_8IdEEN11onnxruntime4LessIfEEN11onnxruntime4LessIdEEN11onnxruntime4LessIiEEN11onnxruntime4LessIlEEN11onnxruntime7GreaterIfEEN11onnxruntime7GreaterIiEEN11onnxruntime7GreaterIlEEN11onnxruntime5EqualIbEEN11onnxruntime5EqualIiEEN11onnxruntime5EqualIlEEN11onnxruntime5EqualIfEEN11onnxruntime6Mean_6IfEEN11onnxruntime6Mean_8IfEEN11onnxruntime8BitShiftIhEEN11onnxruntime8BitShiftIjEEN11onnxruntime8BitShiftImEEN11onnxruntime3ErfIfEEN11onnxruntime3SinIfEEN11onnxruntime3SinIdEEN11onnxruntime3CosIfEEN11onnxruntime3TanIfEEN11onnxruntime4AsinIfEEN11onnxruntime4AcosIfEEN11onnxruntime4AtanIfEEN11onnxruntime4SinhIfEEN11onnxruntime4CoshIfEEN11onnxruntime5AsinhIfEEN11onnxruntime5AcoshIfEEN11onnxruntime5AtanhIfEEN11onnxruntime5PReluIfEEN11onnxruntime8Expand_8IfEEN11onnxruntime8Expand_8IdEEN11onnxruntime8Expand_8IaEEN11onnxruntime8Expand_8IsEEN11onnxruntime8Expand_8IiEEN11onnxruntime8Expand_8IlEEN11onnxruntime8Expand_8IhEEN11onnxruntime8Expand_8ItEEN11onnxruntime8Expand_8IjEEN11onnxruntime8Expand_8ImEEN11onnxruntime8Expand_8IbEEN11onnxruntime8Expand_8INS_9MLFloat16EEEN11onnxruntime3ModE,FF@@@????BBBB????5?5?5?5?!=!=!=!=뽸뽸뽸O]O]O]O]>>>>L>L>L>L>%=%=%=%=P*P*P*P*>>>>^^^^1?1?1?1?BBBB¥¥¥;?;?;?;?giP9giP9giP9giP9C:C:C:C:<<<<*=*=*=*=*>*>*>*>BBBB????"?"?"?"?@K@K@K@Kɿɿɿɿ.4.4.4.4t.t.t.t.,F,F,F,F s7 s7 s7 s7nnnn*=*=*=*=pMpMpMpMӅ<Ӆ<Ӆ<Ӆ<****FFFF-DT!<@@right.NumDimensions() == 2%ld N: K: GEMM: Dimension mismatch, W: M_ >= 0 && K_ > 0 && N_ >= 0transAtransB/onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm_helper.hleft.NumDimensions() == 2 || left.NumDimensions() == 1Gemm: Invalid bias shape for broadcast/onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm.hinfo.GetAttr<int64_t>("transA", &temp).IsOK()info.GetAttr<int64_t>("transB", &temp).IsOK()info.GetAttr<float>("alpha", &alpha_).IsOK()info.GetAttr<float>("beta", &beta_).IsOK()Not implemented fused activation: onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::Gemm<T>::Gemm(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime4GemmIfEEA%~3a*7.A\3JCy7 ':Y;Ӡ5֕8;Y;AAAA%%%%~3a*~3a*~3a*~3a*7777A\3A\3A\3A\3JCy7JCy7JCy7JCy7 ': ': ': ':Y;Y;Y;Y;Ӡ5Ӡ5Ӡ5Ӡ5֕8֕8֕8֕8;;;;Y;Y;Y;Y;Hardmax is not in valid range [-axis . N=, D=/onnxruntime_src/onnxruntime/core/providers/common.haxis >= -tensor_rank && axis <= tensor_rank - 1Hardmax inputs N, D and N * D must be < T* onnxruntime::Tensor::MutableData() [with T = float]int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime7HardmaxIfEE Right:MatMul dimension mismatch/onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_helper.hNot satsified: left_num_dims >= 1 && right_num_dims >= 1 + N11onnxruntime14EliminateSliceE/onnxruntime_src/onnxruntime/core/optimizer/transformer_memcpy.ccdup_replacements.find(&arg) == dup_replacements.end()TensorrtExecutionProviderNGRAPHExecutionProvider' doesn't support memcpy Execution type 'MemcpyCopy from/to host memoryDnnlExecutionProviderNupharExecutionProviderOpenVINOExecutionProviderACLExecutionProvideronnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&)::__lambda8bool onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&)ForEachWithIndexvoid onnxruntime::TransformerMemcpyImpl::ProcessDefs(onnxruntime::Node&, const onnxruntime::KernelRegistryManager&, onnxruntime::InitializedTensorSet&)RecurseApplyImpl*ZN11onnxruntime21TransformerMemcpyImpl11ProcessDefsERNS_4NodeERKNS_21KernelRegistryManagerERSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSA_EEEEUlRKNS_7NodeArgEmE_*ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE_*ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE0_N11onnxruntime17MemcpyTransformerEUnsqueezeElimination_UnsqueezeElimination cannot remove node /onnxruntime_src/onnxruntime/core/optimizer/unsqueeze_elimination.ccApplyN11onnxruntime20UnsqueezeEliminationEvector::_M_range_insert{ + + + + + + +C=CCCC4+CC% + + + + + + +4<Output edge count not expected for nodes in path vFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3v_reshape initializer value is not expectedreshape initializer value is not expectedFailed in match v_matmul and v_add input shapeOutput edge count not expected for mask nodesSoftmax attribute axis is expected to be 3mask_unsqueeze_1 axes not matched. Expect: 1mask_unsqueeze_2 axes not matched. Expect: 2mask_sub const input not matchedmask_mul const input not matchedq root should be layer normalizationq_transpose perm attribute not matchedq_matmul and q_add shape not matchedk_transpose perm attribute not matchedk_matmul and k_add shape not matchedFailed to load Q, K and V weights, or data type is not float or float16.Failed to load Q, K and V bias tensors, or data type is not float or float16.Mask shape is unknown or not 2D, or data type unknownMask data type is not int32 or int64/onnxruntime_src/onnxruntime/core/optimizer/attention_fusion.ccshape of layer norm bias tensor not expectedAttention subgraph expects 1 Add and 3 MatMul as children of LayerNormalization.Total fused Attention node count: qkv_weightsqkv_biasFaild to find path vFailed to find path for maskFailed to find path for qq_reshape const not matchedqk_div const not matched.permFailed to find path for kk root is not layer normk_reshape const not matchedMask_Int32Cast mask from int64 to int32MaskCastMaskIndex_Int32Count number of wordsReduceSumMaskIndexkeepdimsFailed to create mask indexFused Attention subgraphs Attentionnum_headsFused an attention node.xhXH8ProcessMaskFuseSubGraphRecurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constApplyImpl         N11onnxruntime15AttentionFusionE@?fused Add and Gelu/onnxruntime_src/onnxruntime/core/optimizer/bias_gelu_fusion.ccRecurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constApplyImplN11onnxruntime8BiasGeluEUnsupported output type of . Can't constant fold node 'ort_value.IsTensor()/onnxruntime_src/onnxruntime/core/optimizer/constant_folding.ccCould not find OrtValue with name 'fetches.size() == node->OutputDefs().size()const T& OrtValue::Get() const [with T = onnxruntime::Tensor]Recurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constvirtual onnxruntime::common::Status onnxruntime::ConstantFolding::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) constApplyImplN11onnxruntime15OpKernelContextEN11onnxruntime15ConstantFoldingELeakyRelufused Conv with activation fused activation_params/onnxruntime_src/onnxruntime/core/optimizer/conv_activation_fusion.ccUnexpected data type for Clip input of  +n\rBĪ5ĪBĪBĪBĪBĪ ĪĪBĪBĪĪêonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constonnxruntime::{anonymous}::GetClipConstantMinMax(const onnxruntime::Graph&, const onnxruntime::Node&, float&, float&)::__lambda15RecurseApplyImpl  N11onnxruntime20ConvActivationFusionEconv_W_tensor_protoadd_B_tensor_protoconv_B_tensor_protoConvAddFusion_B_ConvAddFusion_Add_B_̪(̪̪̪̪ͪp̪̪ͪͪΪ̪(ҪѪ(Ҫ(Ҫ(Ҫ(ҪΪѪ(Ҫ(ҪѪѪѪFѪѪѪѪѪ˪:ѪѪѪ1Ѫ(ѪѪЪѪѪѪѪ<ϪЪѪѪЪЪѪ4ѪѪѪѪѪͪ(ѪѪѪѪѪ8Ѫ+Ѫ8Ѫ8Ѫ8Ѫ8ѪVͪѪ8Ѫ8ѪѪ Ѫ/onnxruntime_src/onnxruntime/core/optimizer/conv_add_fusion.ccժHӪժժժժӪ(ԪժժԪժToProtoonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constvirtual onnxruntime::common::Status onnxruntime::ConvAddFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const N11onnxruntime13ConvAddFusionEBatchNormalizationbn_scale_tensor_protobn_B_tensor_protobn_mean_tensor_protobn_var_tensor_protoConvBnFusion_W_ConvBnFusion_BN_B_88888(88xPXXXXXXXK?3*!IIIIIIIHHHHH8HH`ypg." R4RRRR(RR +7+ AnbVMD@ @@@@@@~XG6(/onnxruntime_src/onnxruntime/core/optimizer/conv_bn_fusion.cc(((((((I}qA)AAAA8/AA#'t''''kb''\%FUL@ konnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constvirtual onnxruntime::common::Status onnxruntime::ConvBNFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const  N11onnxruntime12ConvBNFusionEmul_B_tensor_protoConvMulFusion_W_ConvMulFusion_Mul_B_/onnxruntime_src/onnxruntime/core/optimizer/conv_mul_fusion.cc.-....W-,..,B,u3@2u3u3u3u3/22u3u30U0S31S3S3S3S311S3S31/11311313131300131304/2222222222212/22220o0220-#6}5#6#6#6#64;4#6#632ToProtoonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constvirtual onnxruntime::common::Status onnxruntime::ConvMulFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const N11onnxruntime13ConvMulFusionE +N11onnxruntime16EliminateDropoutEInput shape is unknown or not 2D, or data type unknownInput data type is not int32 or int64/onnxruntime_src/onnxruntime/core/optimizer/embed_layer_norm_fusion.ccFailed to find path 1 of position shape.Output edge count not expected for nodes in path 1 of position shape.Second input of Gather in path 1 of position shape should be a constant with value 0.Failed to find path 2 of position shape.Output edge count not expected for nodes in path 2 of position shape.Gather node in path 2 is not linked to another subgraph.Second input of Gather in path 2 of position shape should be a constant with value 1.The parent of two shape nodes are expected to be input_ids.Cast Input from int64 to int32Word embedding shape not expected.Input is expected to have dim value in all dimensions.Failed to get initializer tensor.Position embedding shape not matched.Position embedding data type shall be float or float16.Output edge count not expected for nodes in path1.Second input of Gather should be a constant with value 1. The parent of shape nodes are expected to be input_ids.Failed to match position subgraph.Output edge count not expected for nodes in path 1.The first input of Range should be a constant with value 0.The third input of Range should be a constant with value 1.Failed to match position embedding subgraph.Failed to get position embedding weights.Position embedding shape is not expected.Input_ids and segment id should have the same shape. Input_ids and mask should have the same shape. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). fused EmbedLayerNorm subgraphs _Castposition_embeddingsExpandSqueezeNonZeroConstantOfShapeFailed to match Shape node. Failed to find path 1.Input id is not valid. Segment id is not valid. Mask is not valid. EmbedLayerNormalization6}-}6}6}6}6}!}}6}6}||||||||||||||onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constCheckInputMatchPositionEmbeddingSubgraph2MatchPositionSubgraphMatchPositionEmbeddingSubgraph1RecurseApplyImpl             N11onnxruntime20EmbedLayerNormFusionEFreeDimensionOverrideTransformer/onnxruntime_src/onnxruntime/core/optimizer/free_dim_override_transformer.ccwith a fixed dimension denotation 'but the size of this dimension does not equal the specified override ofInvalid free dimension override.The model has input '' ApplyImplN11onnxruntime32FreeDimensionOverrideTransformerEGelu approximationFastGelu/onnxruntime_src/onnxruntime/core/optimizer/gelu_approximation.ccTotal Gelu Approximation (FastGelu) node count: Recurseonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constApplyImpl N11onnxruntime17GeluApproximationEErffused Gelu subgraphs /onnxruntime_src/onnxruntime/core/optimizer/gelu_fusion.cconnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constRecurseApplyImpl N11onnxruntime10GeluFusionE??fused Gemm FusedGemmleaky_relu_/onnxruntime_src/onnxruntime/core/optimizer/gemm_activation_fusion.cconnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constRecurseApplyImpl N11onnxruntime20GemmActivationFusionE_Map_base::atallocator_ptr_ != nullptrTried to allocate without valid type information, ort_value index=Failed to get allocator for optimizer/onnxruntime_src/onnxruntime/core/optimizer/optimizer_execution_frame.ccvoid onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType)ForEachWithIndexoperator()Infoonnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector<const onnxruntime::Node*>&, const InitializedTensorSet&)*ZN11onnxruntime23OptimizerExecutionFrame4InfoC1ERKSt6vectorIPKNS_4NodeESaIS5_EERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSE_EEEEUlRKNS_7NodeArgEmE_N11onnxruntime23OptimizerExecutionFrameE/onnxruntime_src/onnxruntime/core/providers/cpu/cpu_execution_provider.ccvirtual std::shared_ptr<onnxruntime::KernelRegistry> onnxruntime::CPUExecutionProvider::GetKernelRegistry() constGetKernelRegistryRegisterCPUKernelsRegisterOnnxMLOperatorKernelsRegisterOnnxOperatorKernelsN11onnxruntime20CPUExecutionProviderEN11onnxruntime25IExecutionProviderFactoryEN11onnxruntime18CpuProviderFactoryESt23_Sp_counted_ptr_inplaceIN11onnxruntime18CpuProviderFactoryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EEutils::HasDataType(t_proto)unpack_status.IsOK()T1T2Missing Input: Must have a single dimensiont_proto.dims_size() == 1t_proto.dims()[0] == 1Unsupported value attribute datatype with sizeof=: /onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape.h/onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape.ccTensorProto::DataType_IsValid(t_proto.data_type())Value attribute unpacking failed:Unsupported value attribute datatype: /onnxruntime_src/include/onnxruntime/core/framework/op_kernel.hMust have a valid input shape.input_shape.NumDimensions() > 0Must have a single dimension of 1DLLlL$LKKLKDKJlJ,JIvirtual onnxruntime::common::Status onnxruntime::ConstantOfShape::Compute(onnxruntime::OpKernelContext*) constComputeconst T* onnxruntime::Tensor::Data() const [with T = long int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::ConstantOfShapeBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::Tensor**) constonnxruntime::ConstantOfShapeBase::ConstantOfShapeBase(const onnxruntime::OpKernelInfo&)void onnxruntime::ConstantOfShapeBase::SetValue(size_t, void*)void onnxruntime::ConstantOfShapeBase::SetValueFromTensorProto(const onnx::TensorProto&)N11onnxruntime19ConstantOfShapeBaseEFPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEEN11onnxruntime15ConstantOfShapeEPFPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEEseeddtypeInvalid dtype of highsample_sizeFLOAT16 is not supportedInvalid data type of input count mismatchbatch_size is < 1num_classes is < 1num_samples is < 1/onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.hinfo.GetAttr<float>("mean", &mean_).IsOK()info.GetAttr<float>("scale", &scale_).IsOK()info.GetAttr<int64_t>("dtype", &dtype).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDinfo.GetAttrs<int64_t>("shape", shape).IsOK()info.GetAttr<float>("high", &high_).IsOK()info.GetAttr<float>("low", &low_).IsOK()info.GetAttr<int64_t>("sample_size", &num_samples_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINED/onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.ccCould not infer data type from input tensor with data type Empty dimensions for input tensorT* onnxruntime::Tensor::MutableData() [with T = long int]MultinomialComputeT* onnxruntime::Tensor::MutableData() [with T = int]MultinomialComputeconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::RandomUniformCompute(float, float, std::default_random_engine&, onnx::TensorProto::DataType, onnxruntime::Tensor&)T* onnxruntime::Tensor::MutableData() [with T = float]onnxruntime::common::Status onnxruntime::RandomNormalCompute(float, float, std::default_random_engine&, onnx::TensorProto::DataType, onnxruntime::Tensor&)onnxruntime::Multinomial::Multinomial(const onnxruntime::OpKernelInfo&)onnxruntime::RandomUniformLike::RandomUniformLike(const onnxruntime::OpKernelInfo&)onnxruntime::RandomNormalLike::RandomNormalLike(const onnxruntime::OpKernelInfo&)onnxruntime::RandomUniform::RandomUniform(const onnxruntime::OpKernelInfo&)onnxruntime::RandomNormal::RandomNormal(const onnxruntime::OpKernelInfo&)N11onnxruntime12RandomNormalEN11onnxruntime16RandomNormalLikeEN11onnxruntime13RandomUniformEN11onnxruntime17RandomUniformLikeEN11onnxruntime11MultinomialE0AC?"~+@"~+@W6}+W6}++eG?+eG?????@.?@.?ʫy>ʫy>K͉ ?K͉ ?~, ?~, ?_6.>_6.>9d?9d?t ?t ?@@Unsupported data type: delta in Range operator can not be zero!start in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:delta in Range operator should be scalar like tensor, yet got shape:Ret onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy<Ret>::operator()(int32_t) const [with Ret = onnxruntime::common::Status; int32_t = int]T* onnxruntime::Tensor::MutableData() [with T = short int]const T* onnxruntime::Tensor::Data() const [with T = short int]T* onnxruntime::Tensor::MutableData() [with T = double]const T* onnxruntime::Tensor::Data() const [with T = double]T* onnxruntime::Tensor::MutableData() [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = int]const T* onnxruntime::Tensor::Data() const [with T = int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime5RangeEmin should be a scalar.max should be a scalar./onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.hinfo.GetAttr<T>("max", &max_).IsOK()info.GetAttr<T>("min", &min_).IsOK()min->Shape().NumDimensions() == 0max->Shape().NumDimensions() == 0T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::Clip<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::Clip_6<T>::Clip_6(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime6Clip_6IfEEN11onnxruntime4ClipIfEEexclusivereverseCumSumstarts.size()=dims.size()=dims.size() == starts.size()extents.size()=steps.size()=dims.size() == steps.size()) but got: Axis should be in the range [/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/utils.hdims.size() == extents.size() && dims.size() >= steps.size()dims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()dims.size() == extents_.size()Axis tensor should be 0D or 1Dvoid onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double]const T* onnxruntime::Tensor::Data() const [with T = double]void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double]T* onnxruntime::Tensor::MutableData() [with T = double]void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = int]void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = int]T* onnxruntime::Tensor::MutableData() [with T = int]void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = long int]void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int]onnxruntime::SliceSkips::SliceSkips(const onnxruntime::TensorShape&, gsl::span<const long int>, gsl::span<const long int>)T* onnxruntime::Tensor::MutableData() [with T = long int]N11onnxruntime6CumSumIfEEN11onnxruntime6CumSumIdEEN11onnxruntime6CumSumIiEEN11onnxruntime6CumSumIlEETtt$$4$4DTd$4\dl|tD$4D4$4DDTdDetX != nullptr/onnxruntime_src/onnxruntime/core/providers/cpu/math/det.ccInput tensor should have a rank of at least 2Matrix dimensions are not equal. Square matrix is expectedconst T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::Det<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]N11onnxruntime3DetIfEEj@fmod(fmod == 0) || (fmod == 1)AbsNegFloorCeilReciprocalLogLessGreaterEqualBitShiftNotAndOrXorSinCosTanAsinAcosAtanSinhCoshAsinhAcoshAtanhPReluModaxis == 1 || axis == largestdirectionLEFTRIGHTInvalid direction value of 'X_ptr != nullptrvector::_M_fill_insertCan broadcast 0 by 0 or 1. largest <= 1failed to get first output!Must have 1 or more inputsinputCount >= 1data_n.Shape() == shapeinput_count >= 1data_1.Shape() == shapep_ml_valuehelper.called_ < 2helper.called_ == 1 vs fmod_cur1 == end1cur_out == end_outfmod must have value either 0 or 1/onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.ccAttempting to broadcast an axis by a dimension other than 1. /onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.h'. Valid values are 'LEFT' or 'RIGHT'.All inputs must have the same shapePlease fetch output tensor with specified shape.Shape must be 1 dimensional as it's tensor data is a shapetensor_shape.Shape().GetDims().size() == 1Check for duplicate types in MLTypeCallDispatcherX and Y input types do not match: fmod attribute must be true for float, float16 and double typesonnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda100; Input1Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda101; General = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda102]onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = double]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = double; TOutput = double; Input0Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda100; Input1Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda101; General = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda102]onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda126onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda126onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda126onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short int]onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int]onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool]onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = long int]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long int]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long unsigned int]T* onnxruntime::Tensor::MutableData() [with T = long unsigned int]const T* onnxruntime::Tensor::Data() const [with T = long unsigned int]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long unsigned int]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = int]T* onnxruntime::Tensor::MutableData() [with T = int]const T* onnxruntime::Tensor::Data() const [with T = int]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = int]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned int]T* onnxruntime::Tensor::MutableData() [with T = unsigned int]const T* onnxruntime::Tensor::Data() const [with T = unsigned int]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned int]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short int]T* onnxruntime::Tensor::MutableData() [with T = short int]const T* onnxruntime::Tensor::Data() const [with T = short int]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short int]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short unsigned int]T* onnxruntime::Tensor::MutableData() [with T = short unsigned int]const T* onnxruntime::Tensor::Data() const [with T = short unsigned int]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short unsigned int]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = signed char]T* onnxruntime::Tensor::MutableData() [with T = signed char]const T* onnxruntime::Tensor::Data() const [with T = signed char]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = signed char]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = unsigned char]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned char]void onnxruntime::utils::MLTypeCallDispatcher<Fn, Types>::Invoke(Args&& ...) const [with Args = {const bool&, const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*&}; Fn = onnxruntime::mod_internal::CallModImpl; Types = {unsigned char, signed char, short unsigned int, short int, unsigned int, int, long unsigned int, long int}]T* onnxruntime::Tensor::MutableData() [with T = double]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = double]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = float]virtual onnxruntime::common::Status onnxruntime::Mod::Compute(onnxruntime::OpKernelContext*) constT* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16]void onnxruntime::mod_internal::BroadCastMFloat16FMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*)onnxruntime::Mod::Mod(const onnxruntime::OpKernelInfo&)onnxruntime::common::Status onnxruntime::Erf<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]T* OrtValue::GetMutable() [with T = onnxruntime::Tensor]T* onnxruntime::OpKernelContext::Output(int) [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda121; Input1Scalar = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda122; General = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda123]onnxruntime::common::Status onnxruntime::Mean_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = bool]const T* onnxruntime::Tensor::Data() const [with T = bool]onnxruntime::common::Status onnxruntime::Max_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Min_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda97; Input1Scalar = onnxruntime::Min_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda98; General = onnxruntime::Min_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda99]onnxruntime::common::Status onnxruntime::Min_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]void onnxruntime::BroadcastIterator::Append(int64_t, int64_t)void onnxruntime::BroadcastIterator::Init(int64_t, int64_t)onnxruntime::Broadcaster::Broadcaster(const std::vector<long int>&, const std::vector<long int>&)onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = float]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda94; Input1Scalar = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda95; General = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda96]onnxruntime::common::Status onnxruntime::Sum_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = long unsigned int]onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned int]onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned char]((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOZNK11onnxruntime3PowIdE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIdLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEdE_ZNK11onnxruntime3PowIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEfE_ZNK11onnxruntime3PowIdE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIdLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEdE0_ZNK11onnxruntime3PowIdE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIdLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEdE1_ZNK11onnxruntime3PowIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEfE0_ZNK11onnxruntime3PowIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEfE1_N11onnxruntime3NotEN11onnxruntime3AndEN11onnxruntime2OrEN11onnxruntime3XorEN11onnxruntime3AddIfEEN11onnxruntime3AddIdEEN11onnxruntime3AddIiEEN11onnxruntime3AddIlEEN11onnxruntime3SubIfEEN11onnxruntime3SubIdEEN11onnxruntime3SubIiEEN11onnxruntime3SubIlEEN11onnxruntime3MulIfEEN11onnxruntime3MulIdEEN11onnxruntime3MulIiEEN11onnxruntime3MulIlEEN11onnxruntime3DivIfEEN11onnxruntime3DivIdEEN11onnxruntime3DivIiEEN11onnxruntime3DivIlEEN11onnxruntime3AbsIfEEN11onnxruntime3AbsIdEEN11onnxruntime3AbsIaEEN11onnxruntime3AbsIsEEN11onnxruntime3AbsIiEEN11onnxruntime3AbsIlEEN11onnxruntime3AbsIhEEN11onnxruntime3AbsItEEN11onnxruntime3AbsIjEEN11onnxruntime3AbsImEEN11onnxruntime3NegIfEEN11onnxruntime3NegIdEEN11onnxruntime3NegIaEEN11onnxruntime3NegIiEEN11onnxruntime5FloorIfEEN11onnxruntime4CeilIfEEN11onnxruntime10ReciprocalIfEEN11onnxruntime4SqrtIfEEN11onnxruntime4SqrtIdEEN11onnxruntime3PowIfEEN11onnxruntime3PowIdEEN11onnxruntime3ExpIfEEN11onnxruntime3ExpIdEEN11onnxruntime3LogIfEEN11onnxruntime5Sum_6IfEEN11onnxruntime5Sum_8IfEEN11onnxruntime5Min_6IfEEN11onnxruntime5Min_8IfEEN11onnxruntime5Max_6IfEEN11onnxruntime5Max_8IfEEN11onnxruntime5Max_8IdEEN11onnxruntime4LessIfEEN11onnxruntime4LessIdEEN11onnxruntime4LessIiEEN11onnxruntime4LessIlEEN11onnxruntime7GreaterIfEEN11onnxruntime7GreaterIiEEN11onnxruntime7GreaterIlEEN11onnxruntime5EqualIbEEN11onnxruntime5EqualIiEEN11onnxruntime5EqualIlEEN11onnxruntime5EqualIfEEN11onnxruntime6Mean_6IfEEN11onnxruntime6Mean_8IfEEN11onnxruntime8BitShiftIhEEN11onnxruntime8BitShiftIjEEN11onnxruntime8BitShiftImEEN11onnxruntime3ErfIfEEN11onnxruntime3SinIfEEN11onnxruntime3SinIdEEN11onnxruntime3CosIfEEN11onnxruntime3TanIfEEN11onnxruntime4AsinIfEEN11onnxruntime4AcosIfEEN11onnxruntime4AtanIfEEN11onnxruntime4SinhIfEEN11onnxruntime4CoshIfEEN11onnxruntime5AsinhIfEEN11onnxruntime5AcoshIfEEN11onnxruntime5AtanhIfEEN11onnxruntime5PReluIfEEN11onnxruntime8Expand_8IfEEN11onnxruntime8Expand_8IdEEN11onnxruntime8Expand_8IaEEN11onnxruntime8Expand_8IsEEN11onnxruntime8Expand_8IiEEN11onnxruntime8Expand_8IlEEN11onnxruntime8Expand_8IhEEN11onnxruntime8Expand_8ItEEN11onnxruntime8Expand_8IjEEN11onnxruntime8Expand_8ImEEN11onnxruntime8Expand_8IbEEN11onnxruntime8Expand_8INS_9MLFloat16EEEN11onnxruntime3ModE,FF@@@????BBBB????5?5?5?5?!=!=!=!=뽸뽸뽸O]O]O]O]>>>>L>L>L>L>%=%=%=%=P*P*P*P*>>>>^^^^1?1?1?1?BBBB¥¥¥;?;?;?;?giP9giP9giP9giP9C:C:C:C:<<<<*=*=*=*=*>*>*>*>BBBB????"?"?"?"?@K@K@K@Kɿɿɿɿ.4.4.4.4t.t.t.t.,F,F,F,F s7 s7 s7 s7nnnn*=*=*=*=pMpMpMpMӅ<Ӆ<Ӆ<Ӆ<****FFFF-DT!<@@right.NumDimensions() == 2%ld N: K: GEMM: Dimension mismatch, W: M_ >= 0 && K_ > 0 && N_ >= 0transAtransB/onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm_helper.hleft.NumDimensions() == 2 || left.NumDimensions() == 1Gemm: Invalid bias shape for broadcast/onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm.hinfo.GetAttr<int64_t>("transA", &temp).IsOK()info.GetAttr<int64_t>("transB", &temp).IsOK()info.GetAttr<float>("alpha", &alpha_).IsOK()info.GetAttr<float>("beta", &beta_).IsOK()Not implemented fused activation: onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::Gemm<T>::Gemm(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime4GemmIfEEA%~3a*7.A\3JCy7 ':Y;Ӡ5֕8;Y;AAAA%%%%~3a*~3a*~3a*~3a*7777A\3A\3A\3A\3JCy7JCy7JCy7JCy7 ': ': ': ':Y;Y;Y;Y;Ӡ5Ӡ5Ӡ5Ӡ5֕8֕8֕8֕8;;;;Y;Y;Y;Y;Hardmax is not in valid range [-axis . N=, D=/onnxruntime_src/onnxruntime/core/providers/common.haxis >= -tensor_rank && axis <= tensor_rank - 1Hardmax inputs N, D and N * D must be < T* onnxruntime::Tensor::MutableData() [with T = float]int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime7HardmaxIfEE Right:MatMul dimension mismatch/onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_helper.hNot satsified: left_num_dims >= 1 && right_num_dims >= 1 MatMul dimension mismatch, Left:Not satsified: K_ == right_shape[right_num_dims - 2] left operand cannot broadcast on dim Not satsified: left_padded_dims_[idx_dim] == 1 right operand cannot broadcast on dim Not satsified: right_padded_dims_[idx_dim] == 1 Not satsified: num_dims_with_pad == num_output_dims Not satsified: M_ == 1 && N_ == 1 Not satsified: num_dims_with_pad - 1 == num_output_dims Not satsified: num_dims_with_pad - 2 == num_output_dims Not satsified: K_ == right_shape[0] /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul.ccconst T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]Computeconst T* onnxruntime::Tensor::Data() const [with T = double]T* onnxruntime::Tensor::MutableData() [with T = double]Computeconst T* onnxruntime::Tensor::Data() const [with T = int]T* onnxruntime::Tensor::MutableData() [with T = int]Computeconst T* onnxruntime::Tensor::Data() const [with T = unsigned int]T* onnxruntime::Tensor::MutableData() [with T = unsigned int]Computeconst T* onnxruntime::Tensor::Data() const [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = long int]Computeconst T* onnxruntime::Tensor::Data() const [with T = long unsigned int]T* onnxruntime::Tensor::MutableData() [with T = long unsigned int]ComputeComputeN11onnxruntime6MatMulIfEEN11onnxruntime6MatMulIdEEN11onnxruntime6MatMulIiEEN11onnxruntime6MatMulIjEEN11onnxruntime6MatMulIlEEN11onnxruntime6MatMulImEET3MatMulIntegera != nullptr && b != nullptr/onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_integer.cct->Shape().NumDimensions() <= 1 && t->Shape().Size() == 1t->IsDataType<int8_t>() || t->IsDataType<uint8_t>()Currently only scalar zero_point is supported. TODO: add per channel zero point support.IsScalarOr1ElementVector(a_zero_point)IsScalarOr1ElementVector(b_zero_point)MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1MatmulInteger : input2 zero point must be a scalar or 1D tensor of size 1MatMulInteger: Unsupported input types with zero pointconst T* onnxruntime::Tensor::Data() const [with T = signed char]onnxruntime::MatMulInteger<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = signed char]::__lambda9Computeonnxruntime::common::Status onnxruntime::MatMulInteger<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = signed char]const T* onnxruntime::Tensor::Data() const [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = int]Computeonnxruntime::common::Status onnxruntime::MatMulInteger<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = unsigned char]N11onnxruntime13MatMulIntegerIhhEEN11onnxruntime13MatMulIntegerIhaEEQLinearMatMul/onnxruntime_src/onnxruntime/core/providers/cpu/math/quantize_linear_matmul.ccIsScalarOr1ElementVector(a_offset)IsScalarOr1ElementVector(b_offset)IsScalarOr1ElementVector(y_offset)IsScalarOr1ElementVector(a_scale)IsScalarOr1ElementVector(b_scale)IsScalarOr1ElementVector(y_scale)QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1QLinearMatmul : weight zero point must be a scalar or 1D tensor of size 1QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1QLinearMatmul : input scale must be a scalar or 1D tensor of size 1QLinearMatmul : weight scale must be a scalar or 1D tensor of size 1QLinearMatmul : result scale must be a scalar or 1D tensor of size 1const T* onnxruntime::Tensor::Data() const [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = float]Computeonnxruntime::common::Status onnxruntime::QLinearMatMul<T1, T2, T3>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = unsigned char; T3 = unsigned char]N11onnxruntime13QLinearMatMulIhhhEEORoundT* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = double]const T* onnxruntime::Tensor::Data() const [with T = double]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime5RoundINS_9MLFloat16EEEN11onnxruntime5RoundIfEEN11onnxruntime5RoundIdEESignT* onnxruntime::Tensor::MutableData() [with T = long unsigned int]const T* onnxruntime::Tensor::Data() const [with T = long unsigned int]T* onnxruntime::Tensor::MutableData() [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = unsigned int]const T* onnxruntime::Tensor::Data() const [with T = unsigned int]T* onnxruntime::Tensor::MutableData() [with T = int]const T* onnxruntime::Tensor::Data() const [with T = int]T* onnxruntime::Tensor::MutableData() [with T = short unsigned int]const T* onnxruntime::Tensor::Data() const [with T = short unsigned int]T* onnxruntime::Tensor::MutableData() [with T = short int]const T* onnxruntime::Tensor::Data() const [with T = short int]T* onnxruntime::Tensor::MutableData() [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = signed char]const T* onnxruntime::Tensor::Data() const [with T = signed char]T* onnxruntime::Tensor::MutableData() [with T = double]const T* onnxruntime::Tensor::Data() const [with T = double]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]void onnxruntime::utils::MLTypeCallDispatcher<Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor*&, onnxruntime::Tensor*&}; Fn = onnxruntime::sign_internal::CallSignImpl; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int}]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::BFloat16]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::BFloat16]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16]N11onnxruntime4SignELogSoftmaxint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)N11onnxruntime7SoftmaxIfLb0EEEN11onnxruntime7SoftmaxIfLb1EEEk_temp > 0sortedITopKk argument [/onnxruntime_src/onnxruntime/core/providers/cpu/math/top_k.ccop_kernel_info.GetAttr<int64_t>("k", &k_temp).IsOK()op_kernel_info.GetAttr<int64_t>("axis", &axis_temp).IsOK()op_kernel_info.GetAttr<int64_t>("largest", &largest_temp).IsOK()op_kernel_info.GetAttr<int64_t>("sorted", &sorted_temp).IsOK()] should not be greater than specified axis dim value [output count mismatch, expected 2 outputs to be present for TopK operatorinput count mismatch, expected 1 input - the tensor to be processedinput count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valuevalue of k must not be negativek tensor should be a 1D tensor of size 1onnxruntime::TopK<OpSet, T>::TopK(const onnxruntime::OpKernelInfo&) [with int OpSet = 11; T = float]const T* onnxruntime::Tensor::Data() const [with T = long int]onnxruntime::TopK<OpSet, T>::TopK(const onnxruntime::OpKernelInfo&) [with int OpSet = 10; T = float]int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::TopK<OpSet, T>::TopK(const onnxruntime::OpKernelInfo&) [with int OpSet = 9; T = float]N11onnxruntime4TopKILi9EfEEN11onnxruntime4TopKILi10EfEEN11onnxruntime4TopKILi11EfEEArrayFeatureExtractorai.onnx.ml) >=Invalid argument: X input has empty dimensions.Invalid Y argument: num_indices = 0Invalid Y argument: index is out of range: Y[T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = double]const T* onnxruntime::Tensor::Data() const [with T = double]T* onnxruntime::Tensor::MutableData() [with T = int]const T* onnxruntime::Tensor::Data() const [with T = int]T* onnxruntime::Tensor::MutableData() [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>]const T* onnxruntime::Tensor::Data() const [with T = long int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>]N11onnxruntime2ml23ArrayFeatureExtractorOpIfEEN11onnxruntime2ml23ArrayFeatureExtractorOpIdEEN11onnxruntime2ml23ArrayFeatureExtractorOpIiEEN11onnxruntime2ml23ArrayFeatureExtractorOpIlEEN11onnxruntime2ml23ArrayFeatureExtractorOpISsEEBinarizerInput data with index: is NaNT* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime2ml11BinarizerOpIfEE/onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.hinfo.GetAttr<std::string>("cast_to", &attr).IsOK() Expected TO_FLOAT, TO_STRING or TO_INT64/onnxruntime_src/onnxruntime/core/providers/cpu/ml/ml_common.hinfo.GetAttr<std::string>("map_form", &attr).IsOK()info.GetAttr<int64_t>("max_map", &max_map_).IsOK()max_map must be > 0 if map_form is SPARSEmap_form_ != PACK_MAP::SPARSE || max_map_ > 0/onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.cccur_input == end_input || cur_input->first >= 0Negative index values are not permitted. First entry in map has index value of Expected std::map<int64_t, float> or std::map<int64_t, std::string>CastMapcast_toTO_FLOATTO_STRINGTO_INT64Invalid CAST_TO value of map_formDENSE Expected DENSE or SPARSEInvalid PACK_MAP value of max_map%fstofstollInvalid input type of value: Unexpected CAST_TO value of onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = std::basic_string<char>]T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = std::basic_string<char>]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = long int]T* onnxruntime::Tensor::MutableData() [with T = long int]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = long int]const T& OrtValue::Get() const [with T = std::map<long int, std::basic_string<char> >]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, std::basic_string<char> >]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = float]T* onnxruntime::Tensor::MutableData() [with T = float]const T& OrtValue::Get() const [with T = std::map<long int, float>]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, float>]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = float]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int]onnxruntime::ml::PACK_MAP onnxruntime::ml::MakePack(const string&)onnxruntime::ml::CAST_TO onnxruntime::ml::MakeCast(const string&)onnxruntime::ml::CastMap::CastMap(const onnxruntime::OpKernelInfo&)N11onnxruntime2ml7CastMapECategoryMappercats_stringscats_int64sdefault_stringdefault_int64Input of string must have output of int64Input of int64 must have output of string /onnxruntime_src/onnxruntime/core/providers/cpu/ml/category_mapper.hinfo.GetAttrs<std::string>("cats_strings", string_categories).IsOK()info.GetAttrs<int64_t>("cats_int64s", int_categories).IsOK()info.GetAttr<std::string>("default_string", &default_string_).IsOK()info.GetAttr<int64_t>("default_int64", &default_int_).IsOK()num_entries == int_categories.size()T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>]const T* onnxruntime::Tensor::Data() const [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::ml::CategoryMapper::CategoryMapper(const onnxruntime::OpKernelInfo&)N11onnxruntime2ml14CategoryMapperEstring_vocabularyint64_vocabularyDictVectorizer/onnxruntime_src/onnxruntime/core/providers/cpu/ml/dictvectorizer.hinfo.GetAttrs(std::is_same<AttrType, std::string>::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()T* onnxruntime::Tensor::MutableData() [with T = long int]const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, long int>]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, long int>]const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, float>]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, float>]const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, double>]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, double>]T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>]const T& OrtValue::Get() const [with T = std::map<long int, std::basic_string<char> >]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, std::basic_string<char> >]T* onnxruntime::Tensor::MutableData() [with T = float]const T& OrtValue::Get() const [with T = std::map<long int, float>]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, float>]T* onnxruntime::Tensor::MutableData() [with T = double]const T& OrtValue::Get() const [with T = std::map<long int, double>]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, double>]onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = double]onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = float]onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = std::basic_string<char>]onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = double]onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = float]onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = long int]N11onnxruntime2ml16DictVectorizerOpISslEEN11onnxruntime2ml16DictVectorizerOpISsfEEN11onnxruntime2ml16DictVectorizerOpISsdEEN11onnxruntime2ml16DictVectorizerOpIlSsEEN11onnxruntime2ml16DictVectorizerOpIlfEEN11onnxruntime2ml16DictVectorizerOpIldEEinputdimensionsFeatureVectorizerinput_tensor_ptr != nullptrInvalid input type:Number of inputs (/onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.hstatus.IsOK() && !input_dimensions_.empty()inputdimensions attribute must be provided/onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.ccinput_count >= 0 && static_cast<size_t>(input_count) == input_dimensions_.size()) does not match number of inputdimensions values (const T* onnxruntime::Tensor::Data() const [with T = double]const T* onnxruntime::Tensor::Data() const [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = int]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]T* onnxruntime::Tensor::MutableData() [with T = float]virtual onnxruntime::common::Status onnxruntime::ml::FeatureVectorizer::Compute(onnxruntime::OpKernelContext*) constonnxruntime::ml::FeatureVectorizer::FeatureVectorizer(const onnxruntime::OpKernelInfo&)N11onnxruntime2ml17FeatureVectorizerEimputed_value_floatsimputed_value_int64sreplaced_value_floatreplaced_value_int64ImputerEmpty input dimensions.Invalid typeExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specified/onnxruntime_src/onnxruntime/core/providers/cpu/ml/imputer.ccExpected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedMust provide imputed_values_float_ or imputed_values_int64_ but not both.imputed_values_float_.empty() ^ imputed_values_int64_.empty()Empty value of imputed values.T* onnxruntime::Tensor::MutableData() [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::ml::ImputerOp::Compute(onnxruntime::OpKernelContext*) constonnxruntime::ml::ImputerOp::ImputerOp(const onnxruntime::OpKernelInfo&)N11onnxruntime2ml9ImputerOpEkeys_floatsvalues_strings_Unusedkeys_stringsvalues_floatsdefault_floatkeys_int64svalues_int64sLabelEncoder attribtues in LabelEncoder and The values is and the number of ) must have the same length. num_keys == num_valuesclasses_strings/onnxruntime_src/onnxruntime/core/providers/cpu/ml/label_encoder.hinfo.GetAttrs<TKey>(_key_field_name, keys).IsOK()info.GetAttrs<TValue>(_value_field_name, values).IsOK()However, the number of key is Input of tensor(string) must have output of tensor(int64)Input of tensor(int64) must have output of tensor(string)info.GetAttrs<std::string>("classes_strings", string_classes).IsOK()gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = std::basic_string<char>]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long int]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = std::basic_string<char>]onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string<char>; TValue = long int]onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = std::basic_string<char>]onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = long int]onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = float]onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string<char>; TValue = float]onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = std::basic_string<char>]T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>]const T* onnxruntime::Tensor::Data() const [with T = long int]onnxruntime::ml::LabelEncoder::LabelEncoder(const onnxruntime::OpKernelInfo&)N11onnxruntime2ml12LabelEncoderEN11onnxruntime2ml14LabelEncoder_2IfSsEEN11onnxruntime2ml14LabelEncoder_2ISsfEEN11onnxruntime2ml14LabelEncoder_2IlfEEN11onnxruntime2ml14LabelEncoder_2IflEEN11onnxruntime2ml14LabelEncoder_2IlSsEEN11onnxruntime2ml14LabelEncoder_2ISslEESOFTMAX_ZEROLinearClassifiermulti_classNONEpost_transformLOGISTICSOFTMAXinterceptsclasslabels_stringsclasslabels_intscoefficients!coefficients_.empty()length overflow/onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearclassifier.ccInput shape needs to be at least a single dimension.const T* onnxruntime::Tensor::Data() const [with T = float]const T* onnxruntime::Tensor::Data() const [with T = double]const T* onnxruntime::Tensor::Data() const [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = float]void onnxruntime::ml::write_scores(std::vector<_RealType>&, onnxruntime::ml::POST_EVAL_TRANSFORM, int64_t, onnxruntime::Tensor*, int) [with T = float; int64_t = long int]T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>]const T* onnxruntime::Tensor::Data() const [with T = int]onnxruntime::ml::LinearClassifier<T>::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = int]onnxruntime::ml::LinearClassifier<T>::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = long int]onnxruntime::ml::LinearClassifier<T>::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::ml::LinearClassifier<T>::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime2ml16LinearClassifierIfEEN11onnxruntime2ml16LinearClassifierIdEEN11onnxruntime2ml16LinearClassifierIlEEN11onnxruntime2ml16LinearClassifierIiEE@@3ֳLinearRegressor/onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearregressor.ccinfo.GetAttr<int64_t>("targets", &targets_).IsOK()info.GetAttrs<float>("coefficients", coefficients_).IsOK()T* onnxruntime::Tensor::MutableData() [with T = float]void onnxruntime::ml::write_scores(std::vector<_RealType>&, onnxruntime::ml::POST_EVAL_TRANSFORM, int64_t, onnxruntime::Tensor*, int) [with T = float; int64_t = long int]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::ml::LinearRegressor<T>::LinearRegressor(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime2ml15LinearRegressorIfEEInvalid normalize value of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/normalizer.hinfo.GetAttr<std::string>("norm", &norm).IsOK()Unexpected NORMALIZE value of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/normalizer.ccconst T* onnxruntime::Tensor::Data() const [with T = int]void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = int]const T* onnxruntime::Tensor::Data() const [with T = long int]void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = double]void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = double]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = float]void onnxruntime::utils::MLTypeCallDispatcher<Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::ml::Normalizer* const, onnxruntime::OpKernelContext*&}; Fn = onnxruntime::ml::Normalizer::CallNormalizerImpl; Types = {float, double, long int, int}]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::ml::Normalizer::Compute(onnxruntime::OpKernelContext*) constonnxruntime::ml::NORMALIZE onnxruntime::ml::MakeNormalize(const string&)onnxruntime::ml::Normalizer::Normalizer(const onnxruntime::OpKernelInfo&)N11onnxruntime2ml10NormalizerEUnknown Category and zeros = 0./onnxruntime_src/onnxruntime/core/providers/cpu/ml/onehotencoder.cctmp_cats_int64s.empty() || tmp_cats_strings.empty()One and only one of the 'cats_*' attributes must be definedOneHotEncoderzerosnum_categories_ > 0const T* onnxruntime::Tensor::Data() const [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = float]const T* onnxruntime::Tensor::Data() const [with T = double]const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>]onnxruntime::ml::OneHotEncoderOp<T>::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = std::basic_string<char>]onnxruntime::ml::OneHotEncoderOp<T>::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::ml::OneHotEncoderOp<T>::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = float]onnxruntime::ml::OneHotEncoderOp<T>::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = long int]N11onnxruntime2ml15OneHotEncoderOpIlEEN11onnxruntime2ml15OneHotEncoderOpIfEEN11onnxruntime2ml15OneHotEncoderOpIdEEN11onnxruntime2ml15OneHotEncoderOpISsEEoffsetEmpty scale in attributes!scale_.empty()Scale size: () != () or 1/onnxruntime_src/onnxruntime/core/providers/cpu/ml/scaler.ccscale_.size() == offset_.size()Invalid argument: input has empty dimensions.Either both scale and offset can be of feature size (const T* onnxruntime::Tensor::Data() const [with T = float]const T* onnxruntime::Tensor::Data() const [with T = double]const T* onnxruntime::Tensor::Data() const [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = int]onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = long int]onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime2ml8ScalerOpIfEEN11onnxruntime2ml8ScalerOpIdEEN11onnxruntime2ml8ScalerOpIlEEN11onnxruntime2ml8ScalerOpIiEESVMClassifierLINEARkernel_typePOLYRBFkernel_paramsvectors_per_classprob_aprob_bsupport_vectorsrhocoefficients_.size() > 0No support vectors./onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.hinfo.GetAttrs<float>("kernel_params", kernel_params).IsOK()/onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.ccinfo.GetAttrs<float>("rho", rho_).IsOK()proba_.size() == probb_.size()info.GetAttrs<std::string>("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs<int64_t>("classlabels_ints", classlabels_ints_).IsOK()classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0T* onnxruntime::Tensor::MutableData() [with T = float]void onnxruntime::ml::write_scores(std::vector<_RealType>&, onnxruntime::ml::POST_EVAL_TRANSFORM, int64_t, onnxruntime::Tensor*, int) [with T = float; int64_t = long int]T* onnxruntime::Tensor::MutableData() [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>]onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = int]onnxruntime::ml::SVMClassifier<T>::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = int]onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = long int]onnxruntime::ml::SVMClassifier<T>::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = long int]onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::ml::SVMClassifier<T>::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = float]onnxruntime::ml::SVMClassifier<T>::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime2ml9SVMCommonIfEEN11onnxruntime2ml9SVMCommonIdEEN11onnxruntime2ml9SVMCommonIlEEN11onnxruntime2ml9SVMCommonIiEEN11onnxruntime2ml13SVMClassifierIfEEN11onnxruntime2ml13SVMClassifierIdEEN11onnxruntime2ml13SVMClassifierIlEEN11onnxruntime2ml13SVMClassifierIiEE -ף;?n_supportsone_classSVMRegressor/onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmregressor.ccT* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = float]onnxruntime::ml::SVMRegressor<T>::SVMRegressor(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime2ml12SVMRegressorIfEEBRANCH_EQTreeEnsembleClassifierBRANCH_LEQLEAFBRANCH_LTBRANCH_GTEBRANCH_GTit != parents.end()nodes_treeidsnodes_nodeidsnodes_featureidsnodes_valuesnodes_hitratesnodes_modesnodes_truenodeidsnodes_falsenodeidsclass_nodeidsclass_treeidsclass_idsclass_weightsbase_valuesclasslabels_int64s!nodes_treeids_.empty()Invalid mode of value: treeindex >= 0X dims is empty./onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_classifier.ccbase_values_.empty() || base_values_.size() == static_cast<size_t>(class_count_) || base_values_.size() == weights_classes_.size()nodes_missing_value_tracks_trueclass_nodeids_.size() == class_ids_.size()class_nodeids_.size() == class_weights_.size()nodes_nodeids_.size() == nodes_featureids_.size()nodes_nodeids_.size() == nodes_modes_names_.size()nodes_nodeids_.size() == nodes_values_.size()nodes_nodeids_.size() == nodes_truenodeids_.size()nodes_nodeids_.size() == nodes_falsenodeids_.size()(nodes_nodeids_.size() == nodes_hitrates_.size()) || (nodes_hitrates_.empty())Must provide classlabels_strings or classlabels_int64s but not both.classlabels_strings_.empty() ^ classlabels_int64s_.empty()std::all_of( std::begin(missing_tracks_true_), std::end(missing_tracks_true_), [](int64_t elem) { return elem >= 0; })@(P(X0@onnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = float; int64_t = long int]const T* onnxruntime::Tensor::Data() const [with T = float]Computeonnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = double; int64_t = long int]const T* onnxruntime::Tensor::Data() const [with T = double]Computeonnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = long int; int64_t = long int]const T* onnxruntime::Tensor::Data() const [with T = long int]Computeonnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = int; int64_t = long int]const T* onnxruntime::Tensor::Data() const [with T = int]Computevoid onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = int]onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = int]void onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = long int]onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = long int]void onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = double]onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = double]void onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = float]onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime2ml22TreeEnsembleClassifierIfEEN11onnxruntime2ml22TreeEnsembleClassifierIdEEN11onnxruntime2ml22TreeEnsembleClassifierIlEEN11onnxruntime2ml22TreeEnsembleClassifierIiEETreeEnsembleRegressortarget_nodeidstarget_treeidstarget_idstarget_weightsSUMaggregate_functionAVERAGEMINn_targetsit != indices.end()/onnxruntime_src/onnxruntime/core/providers/cpu/ml/treeregressor.ccinfo.GetAttr<int64_t>("n_targets", &n_targets_).IsOK()target_nodeids_.size() == target_ids_.size()target_nodeids_.size() == target_weights_.size()nodes_id_size == nodes_featureids_.size()nodes_id_size == nodes_values_.size()nodes_id_size == nodes_modes_.size()nodes_id_size == nodes_truenodeids_.size()nodes_id_size == nodes_falsenodeids_.size()(nodes_id_size == nodes_hitrates_.size()) || (nodes_hitrates_.empty())base_values_.empty() || base_values_.size() == static_cast<size_t>(n_targets_)treeindex evaluated to a negative value, which should not happen.ComputeComputeonnxruntime::ml::TreeEnsembleRegressor<T>::TreeEnsembleRegressor(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::ml::TreeEnsembleRegressor<T>::TreeEnsembleRegressor(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime2ml21TreeEnsembleRegressorIfEEN11onnxruntime2ml21TreeEnsembleRegressorIdEEZipMapInput features_per_batch[] != number of classlabels[/onnxruntime_src/onnxruntime/core/providers/cpu/ml/zipmap.ccZipmap does not support empty dim countZipmap only supports 1D or 2D input tensorsT* OrtValue::GetMutable() [with T = std::vector<std::map<long int, float> >]T* OrtValue::GetMutable() [with T = std::vector<std::map<std::basic_string<char>, float> >]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::ml::ZipMapOp::ZipMapOp(const onnxruntime::OpKernelInfo&)N11onnxruntime2ml8ZipMapOpEMaxUnpoolUnsupported pooling size. inferred output shape:No kernel shape is set.kernel_shape_[dim] > 0Input dimension cannot be less than 3./onnxruntime_src/onnxruntime/core/providers/cpu/nn/Unpool.ccNot satsified: X_shape.NumDimensions() >= 3 +ף;?n_supportsone_classSVMRegressor/onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmregressor.ccT* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = float]onnxruntime::ml::SVMRegressor<T>::SVMRegressor(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime2ml12SVMRegressorIfEEBRANCH_EQTreeEnsembleClassifierBRANCH_LEQLEAFBRANCH_LTBRANCH_GTEBRANCH_GTit != parents.end()nodes_treeidsnodes_nodeidsnodes_featureidsnodes_valuesnodes_hitratesnodes_modesnodes_truenodeidsnodes_falsenodeidsclass_nodeidsclass_treeidsclass_idsclass_weightsbase_valuesclasslabels_int64s!nodes_treeids_.empty()Invalid mode of value: treeindex >= 0X dims is empty./onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_classifier.ccbase_values_.empty() || base_values_.size() == static_cast<size_t>(class_count_) || base_values_.size() == weights_classes_.size()nodes_missing_value_tracks_trueclass_nodeids_.size() == class_ids_.size()class_nodeids_.size() == class_weights_.size()nodes_nodeids_.size() == nodes_featureids_.size()nodes_nodeids_.size() == nodes_modes_names_.size()nodes_nodeids_.size() == nodes_values_.size()nodes_nodeids_.size() == nodes_truenodeids_.size()nodes_nodeids_.size() == nodes_falsenodeids_.size()(nodes_nodeids_.size() == nodes_hitrates_.size()) || (nodes_hitrates_.empty())Must provide classlabels_strings or classlabels_int64s but not both.classlabels_strings_.empty() ^ classlabels_int64s_.empty()std::all_of( std::begin(missing_tracks_true_), std::end(missing_tracks_true_), [](int64_t elem) { return elem >= 0; })ععع׹׹ع0޹޹޹@ݹݹH޹x 0onnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = float; int64_t = long int]const T* onnxruntime::Tensor::Data() const [with T = float]Computeonnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = double; int64_t = long int]const T* onnxruntime::Tensor::Data() const [with T = double]Computeonnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = long int; int64_t = long int]const T* onnxruntime::Tensor::Data() const [with T = long int]Computeonnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = int; int64_t = long int]const T* onnxruntime::Tensor::Data() const [with T = int]Computevoid onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = int]onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = int]void onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = long int]onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = long int]void onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = double]onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = double]void onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = float]onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime2ml22TreeEnsembleClassifierIfEEN11onnxruntime2ml22TreeEnsembleClassifierIdEEN11onnxruntime2ml22TreeEnsembleClassifierIlEEN11onnxruntime2ml22TreeEnsembleClassifierIiEETreeEnsembleRegressortarget_nodeidstarget_treeidstarget_idstarget_weightsSUMaggregate_functionAVERAGEMINn_targetsit != indices.end()/onnxruntime_src/onnxruntime/core/providers/cpu/ml/treeregressor.ccinfo.GetAttr<int64_t>("n_targets", &n_targets_).IsOK()target_nodeids_.size() == target_ids_.size()target_nodeids_.size() == target_weights_.size()nodes_id_size == nodes_featureids_.size()nodes_id_size == nodes_values_.size()nodes_id_size == nodes_modes_.size()nodes_id_size == nodes_truenodeids_.size()nodes_id_size == nodes_falsenodeids_.size()(nodes_id_size == nodes_hitrates_.size()) || (nodes_hitrates_.empty())base_values_.empty() || base_values_.size() == static_cast<size_t>(n_targets_)treeindex evaluated to a negative value, which should not happen.ComputeComputeonnxruntime::ml::TreeEnsembleRegressor<T>::TreeEnsembleRegressor(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::ml::TreeEnsembleRegressor<T>::TreeEnsembleRegressor(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime2ml21TreeEnsembleRegressorIfEEN11onnxruntime2ml21TreeEnsembleRegressorIdEEZipMapInput features_per_batch[] != number of classlabels[/onnxruntime_src/onnxruntime/core/providers/cpu/ml/zipmap.ccZipmap does not support empty dim countZipmap only supports 1D or 2D input tensorsT* OrtValue::GetMutable() [with T = std::vector<std::map<long int, float> >]T* OrtValue::GetMutable() [with T = std::vector<std::map<std::basic_string<char>, float> >]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::ml::ZipMapOp::ZipMapOp(const onnxruntime::OpKernelInfo&)N11onnxruntime2ml8ZipMapOpEMaxUnpoolUnsupported pooling size. inferred output shape:No kernel shape is set.kernel_shape_[dim] > 0Input dimension cannot be less than 3./onnxruntime_src/onnxruntime/core/providers/cpu/nn/Unpool.ccNot satsified: X_shape.NumDimensions() >= 3 Not satsified: I_shape == X_shape Shape must be 1 dimensional as it's tensor data of a shapeNot satsified: tensor_shape->Shape().GetDims().size() == 1 output_shape is smaller than minimum required. output_shape:Not satsified: given_shape.Size() >= shape.Size() Index tensor shape should be same as that of the input data tensor to unpool./onnxruntime_src/onnxruntime/core/providers/cpu/nn/unpool.hinfo.GetAttrs<int64_t>("kernel_shape", kernel_shape_).IsOK()pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]strides_.size() == kernel_shape_.size()Pad should be smaller than kernel.T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = long int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]const T* onnxruntime::Tensor::Data() const [with T = float]Computeonnxruntime::MaxUnpool::MaxUnpool(const onnxruntime::OpKernelInfo&)N11onnxruntime9MaxUnpoolEst.IsOK()spatialInvalid input scale: dimension != Invalid input B: Invalid input mean: Invalid input var: /onnxruntime_src/onnxruntime/core/providers/cpu/nn/batch_norm.hInvalid input X: The rank of input X must be atleast 2. Got rank: Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input var: NumDimensions() != Invalid input var: 0th dimension != /onnxruntime_src/onnxruntime/core/providers/cpu/nn/batch_norm.ccT* onnxruntime::Tensor::MutableData() [with T = float]Computeonnxruntime::BatchNorm<T>::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime9BatchNormIfEEkernel_shape num_dims is not compatible with W num_dims.kernel_shape is not compatible with W shape.X num_dims does not match W num_dims.Input channels C is not equal to kernel channels * group.Output channels M is not divisible by group./onnxruntime_src/onnxruntime/core/providers/cpu/nn/autopad_type.hDilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER./onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_attributes.h/onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv.cc/onnxruntime_src/onnxruntime/core/util/math.h kernel_shape: X: C: kernel channels: group: M: Unknown AutoPadType Stringdilation == 1pad type not supported.Invalid input shape: Out of bound access to arrayd_iter[d_i] < d_maxonnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const string&)void onnxruntime::math::Im2colNd<T, 2>::operator()(const T*, const int64_t*, const int64_t*, int64_t, int64_t, const int64_t*, const int64_t*, const int64_t*, const int64_t*, int64_t, T*, bool, T) [with T = float; int64_t = long int]onnxruntime::common::Status onnxruntime::ComputePadAndOutputShape(int64_t, int64_t, int64_t, int64_t, onnxruntime::AutoPadType, int64_t*, int64_t*, int64_t*) [with bool ForceSymmetricAutoPadding = false; int64_t = long int]InferOutputShapeComputeN11onnxruntime4ConvIfEE/onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_integer.ccIsScalarOr1ElementVector(X_Zero_Point)IsScalarOr1ElementVector(W_Zero_Point)Must be a scalar or 1D tensor or size 1.Non per-tensor quantization is not supported now.ConvIntegervoid onnxruntime::math::Im2colNd<T, 2>::operator()(const T*, const int64_t*, const int64_t*, int64_t, int64_t, const int64_t*, const int64_t*, const int64_t*, const int64_t*, int64_t, T*, bool, T) [with T = unsigned char; int64_t = long int]Computevirtual onnxruntime::common::Status onnxruntime::ConvInteger::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime11ConvIntegerEConvTranspose*out_size >= 0pad type not supporteddim_size > 0output_paddingoutput_shapegroup count is <= 0 filter_number: num_input_channels: /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose_attributes.hOnly 1D and 2D ConvTranspose is supported.filter number not equal to input channel number.Input channels is not divisible by group./onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose.ccvoid onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape(int64_t, int64_t, int64_t, int64_t, int64_t, onnxruntime::AutoPadType, int64_t*, int64_t*, int64_t*) constvoid onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape(onnxruntime::TensorShape, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, int64_t, std::vector<long int>*, std::vector<long int>*) constconst T* onnxruntime::Tensor::Data() const [with T = long int]PrepareForComputeDoConvTransposeN11onnxruntime13ConvTransposeIfEEFlatten/onnxruntime_src/onnxruntime/core/providers/cpu/nn/flatten.hinfo.GetAttr<int64_t>("axis", &axis_).IsOK()The rank of input tensor must be >= axisgsl::narrow_cast<int64_t>(X_shape.NumDimensions()) >= axisonnxruntime::Flatten::Flatten(const onnxruntime::OpKernelInfo&)int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::Flatten::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime7FlattenEInvalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: Mismatch between input data and scale: size of scale != input channel count Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count /onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.hop_kernel_info.GetAttr<float>("epsilon", &epsilon_).IsOK()/onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.cc vs. InstanceNormalizationconst T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]Computeonnxruntime::InstanceNorm<T>::InstanceNorm(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime12InstanceNormIfEEp_ == 1 || p_ == 2LpNormalization/onnxruntime_src/onnxruntime/core/providers/cpu/nn/lp_norm.hop_kernel_info.GetAttr<int64_t>("axis", &axis_).IsOK()op_kernel_info.GetAttr<int64_t>("p", &p_).IsOK()const T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::LpNorm<T>::LpNorm(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime6LpNormIfEEsize_ > 0size_ % 2 == 1alpha_ > 0.0fbeta_ > 0.0fLRN/onnxruntime_src/onnxruntime/core/providers/cpu/nn/lrn.hinfo.GetAttr<int64_t>("size", &size).IsOK()/onnxruntime_src/onnxruntime/core/providers/cpu/nn/lrn.ccX->Shape().NumDimensions() == 4onnxruntime::LRN<T>::LRN(const onnxruntime::OpKernelInfo&) [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]Computeonnxruntime::common::Status onnxruntime::LRN<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]N11onnxruntime3LRNIfEEGlobalLpPoolceil_modecount_include_padstorage_orderkernel_shape[dim] > 0input_dims.size() >= 2Unsupported AutoPad Type.Unsupported pooling size : /onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool_base.hinfo.GetAttr<int64_t>("p", &p_).IsOK()/onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool_attributes.hinfo.GetAttrs<int64_t>("kernel_shape", kernel_shape).IsOK()info.GetAttr<std::string>("auto_pad", &auto_padding).IsOK()info.GetAttr<int64_t>("count_include_pad", &temp).IsOK()pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]strides.size() == kernel_shape.size()Dilations dimensions should match kernel shapedilations.size() == kernel_shape.size()Invalid input shape. Only N can be zero. Got:input_shape.Size() > 0 || input_shape[0] == 0/onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool.ccNot satsified: input_dims >= 3 kernel_shape num_dims is not compatible with X num_dims.Not satsified: pooling_dims == pool_attrs_.kernel_shape.size() @@ -5769,42 +5721,44 @@ center_point_box only support 0 or 1/onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.h0 == center_point_box_ || 1 == center_point_box_Not satsified: boxes_shape.NumDimensions() == 3 Not satsified: scores_shape.NumDimensions() == 3 boxes and scores should have same num_batches.Not satsified: boxes_dims[0] == scores_dims[0] boxes and scores should have same spatial_dimension.Not satsified: boxes_dims[1] == scores_dims[2] The most inner dimension in boxes must have 4 data.Not satsified: boxes_dims[2] == 4 -virtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) constComputeGetThresholdsFromInputsconst T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]const T* onnxruntime::Tensor::Data() const [with T = long int]PrepareComputestatic onnxruntime::common::Status onnxruntime::NonMaxSuppressionBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::PrepareContext&)onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase(const onnxruntime::OpKernelInfo&)N11onnxruntime21NonMaxSuppressionBaseEN11onnxruntime17NonMaxSuppressionENull input X ptrNull rois_ptrNull batch_indices_ptrRoiAlignavgoutput_heightoutput_widthsampling_ratiosampling_ratio_ >= 0Invalid mode of value Number of dimensions for batch indices should be exactly 1Number of dimensions for rois should be exactly Second dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't match/onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/roialign.hSampling ratio should be >=0, but it was specified. It should be either avg or maxconst T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = double]T* onnxruntime::Tensor::MutableData() [with T = double]const T* onnxruntime::Tensor::Data() const [with T = long int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::RoiAlignBase<T>::RoiAlignBase(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::RoiAlignBase<T>::RoiAlignBase(const onnxruntime::OpKernelInfo&) [with T = float]*ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIdEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUliE_*ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIfEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUliE_N11onnxruntime12RoiAlignBaseIfEEN11onnxruntime12RoiAlignBaseIdEEN11onnxruntime8RoiAlignIfEEN11onnxruntime8RoiAlignIdEEReduceL1ReduceL2ReduceLogSumReduceLogSumExpReduceMaxReduceMinReduceProdReduceSumSquareArgMaxArgMinin_dim != 0/onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.hinfo.GetAttr("keepdims", &keepdims).IsOK()/onnxruntime_src/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:bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = long int; int64_t = long int]bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = double; int64_t = long int]bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = float; int64_t = long int]bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = int; int64_t = long int]onnxruntime::ReduceKernelBase<allow_multi_axes>::ReduceKernelBase(const onnxruntime::OpKernelInfo&) [with bool allow_multi_axes = false]onnxruntime::ReduceKernelBase<allow_multi_axes>::ReduceKernelBase(const onnxruntime::OpKernelInfo&) [with bool allow_multi_axes = true]N11onnxruntime16ReduceKernelBaseILb1EEEN11onnxruntime16ReduceKernelBaseILb0EEEN11onnxruntime12ReduceKernelILb1EEEN11onnxruntime12ReduceKernelILb0EEEN11onnxruntime8ReduceL1IfEEN11onnxruntime8ReduceL1IiEEN11onnxruntime8ReduceL2IfEEN11onnxruntime8ReduceL2IiEEN11onnxruntime12ReduceLogSumIfEEN11onnxruntime12ReduceLogSumIiEEN11onnxruntime15ReduceLogSumExpIfEEN11onnxruntime15ReduceLogSumExpIiEEN11onnxruntime9ReduceMaxIfEEN11onnxruntime9ReduceMaxIiEEN11onnxruntime9ReduceMaxIlEEN11onnxruntime10ReduceMeanIfEEN11onnxruntime10ReduceMeanIiEEN11onnxruntime9ReduceMinIfEEN11onnxruntime9ReduceMinIiEEN11onnxruntime9ReduceMinIlEEN11onnxruntime10ReduceProdIfEEN11onnxruntime10ReduceProdIiEEN11onnxruntime9ReduceSumIfEEN11onnxruntime9ReduceSumIiEEN11onnxruntime9ReduceSumIlEEN11onnxruntime9ReduceSumIdEEN11onnxruntime15ReduceSumSquareIfEEN11onnxruntime15ReduceSumSquareIiEEN11onnxruntime15ReduceSumSquareIdEEN11onnxruntime6ArgMaxIfEEN11onnxruntime6ArgMaxIiEEN11onnxruntime6ArgMinIfEEN11onnxruntime6ArgMinIiEEGRUhidden_sizeactivationsactivation_alphaactivation_betaclipclip_ > 0.fforwardbidirectionallinear_before_resetcur + size <= end/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.hinfo.GetAttr("direction", &direction).IsOK()info.GetAttr("linear_before_reset", &int64_value).IsOK()info.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0Invalid 'direction' argument of ''. Must be one of 'forward', 'reverse', or 'bidirectional'./onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.hactivation_func_names.size() == static_cast<size_t>(num_directions_) * 2lda >= K && ldb >= K && ldc >= NA + (M * lda - (lda - K)) <= A_endB + (N * ldb - (ldb - K)) <= B_endC + (M * ldc - (ldc - N)) <= C_endoffset + size <= size_t(span.size())/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.ccGRU operator does not support double yetInvalid data type for GRU operator of T* onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span<T>::iterator, typename gsl::span<T>::iterator, size_t) [with T = float; typename gsl::span<T>::iterator = float*; size_t = long unsigned int]const T* onnxruntime::rnn::detail::SafeRawConstPointer(typename gsl::span<T>::const_iterator, typename gsl::span<T>::const_iterator, size_t) [with T = float; typename gsl::span<T>::const_iterator = const float*; size_t = long unsigned int]T* onnxruntime::rnn::detail::SafeRawPointer(gsl::span<T>, size_t, size_t) [with T = float; size_t = long unsigned int]void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = float*; TSpanBIter = const float*; TSpanCIter = float*]void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = const float*; TSpanBIter = const float*; TSpanCIter = float*]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int]ComputeImplvirtual onnxruntime::common::Status onnxruntime::DeepCpuGruOp::Compute(onnxruntime::OpKernelContext*) constonnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&)onnxruntime::DeepCpuGruOp::DeepCpuGruOp(const onnxruntime::OpKernelInfo&)N3gsl15narrowing_errorEN11onnxruntime12DeepCpuGruOpEZN11onnxruntime10IAllocator13MakeUniquePtrIfEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPfE_ZN11onnxruntime10IAllocator13MakeUniquePtrIiEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPiE_}. Actual:Input P must have shape {std::future_errorDEEPCPU_LSTMinput_forget rows[Processing batchInput initial_c must have shape {/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.cc/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.hactivation_func_names.size() == static_cast<size_t>(num_directions_) * 3LSTM operator does not support double yetInvalid data type for LSTM operator of ValidateInputsconst T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span<T>, size_t, size_t) [with T = float; size_t = long unsigned int]const T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span<T>, size_t, size_t) [with T = const float; size_t = long unsigned int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int]ComputeImplvirtual onnxruntime::common::Status onnxruntime::DeepCpuLstmOp::Compute(onnxruntime::OpKernelContext*) constonnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&)onnxruntime::DeepCpuLstmOp::DeepCpuLstmOp(const onnxruntime::OpKernelInfo&)NSt13__future_base7_ResultIvEEN11onnxruntime13DeepCpuLstmOpENSt13__future_base11_State_base7_SetterIvNS0_19__exception_ptr_tagEEENSt13__future_base11_State_base7_SetterIvvEESt23_Sp_counted_ptr_inplaceINSt13__future_base11_State_baseESaIS1_ELN9__gnu_cxx12_Lock_policyE2EEZN11onnxruntime3rnn6detail23ExecuteLambdaInParallelIZNS_6detail18UniDirectionalLstmIfE7ComputeERKN3gsl4spanIKfEERKNS7_IKiEEiSB_SB_RNS7_IfEESH_SH_EUliE_EEvRKSsT_iiRNS_11concurrency10ThreadPoolERKNS_7logging6LoggerEEUlvE_St23_Sp_counted_ptr_inplaceISt7promiseIvESaIS1_ELN9__gnu_cxx12_Lock_policyE2EERNNAffineThresholdedReluScaledTanhHardSigmoidEluSoftsign/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.cc/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.hinfo.GetAttr("direction", &direction_).IsOK()allowed_directions.find(direction_) != allowed_directions.end()info.GetAttrs("activations", activations_).IsOK()info.GetAttr("hidden_size", &hidden_size_).IsOK()activations_.size() == static_cast<size_t>(num_directions)allowed_activations.find(activations_[direction]) != allowed_activations.end()const T* onnxruntime::Tensor::Data() const [with T = int]T* onnxruntime::Tensor::MutableData() [with T = float]Computeonnxruntime::RNN<T>::RNN(const onnxruntime::OpKernelInfo&) [with T = float]FffffEN11onnxruntime3RNNIfEEPFffffEDump matrix: affineleakyreluthresholdedreluscaledtanhhardsigmoidsoftsignsoftplus*Input W must have shape {Input R must have shape {Input B must have shape {Expecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.ccInvalid activation function of Invalid LSTM merge activation function of Invalid GRU reset gate activation function: Invalid GRU hidden gate activation function: Input X must have 3 dimensions only. Actual:Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input sequence_lens must have shape {Input initial_h must have shape {void (* onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByName(const string&))(float*, const float*, const float*, float*, int, float, float)void (* onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByName(const string&))(const float*, float*, float*, int, float, float)void (* onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByName(const string&))(const float*, float*, const float*, float*, int, float, float)void (* onnxruntime::rnn::detail::deepcpu::ActivationFuncByName(const string&))(float*, int, float, float)std::string onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCount(const string&, std::vector<float>::const_iterator&, const const_iterator&, std::vector<float>::const_iterator&, const const_iterator&, float&, float&)gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int]7.7.7.7.A Anew_axisConcatFromSequenceMust have valid 'axis' attribute/onnxruntime_src/onnxruntime/core/providers/cpu//tensor/concat.hGot nullptr for sequence input./onnxruntime_src/onnxruntime/core/providers/cpu/sequence/concat_from_sequence.ccconst T& OrtValue::Get() const [with T = onnxruntime::TensorSeq]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::TensorSeq]virtual onnxruntime::common::Status onnxruntime::ConcatFromSequence::Compute(onnxruntime::OpKernelContext*) constonnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo&, bool)N11onnxruntime10ConcatBaseEN11onnxruntime18ConcatFromSequenceETensor size () != new size (Y != nullptrUnsupported 'dtype' value: SequenceEmptySequenceLengthSequenceConstructSequenceEraseSequenceInsertSequenceAtSplitToSequenceI != nullptrInvalid sequence index (split_size_sum () != split_dim_size (S != nullptrGot nullptr for input tensor.), input tensor data type (num_inputs >= 1Split should be > 0split_scalar > 0 yetshape_.Size() == new_shape.Size()SequenceEmpty: Got nullptr for output sequence/onnxruntime_src/onnxruntime/core/providers/cpu/sequence/sequence_ops.ccSequenceLength: Got nullptr for output tensorGot nullptr input for index tensor) specified for sequence of size (SequenceAt: Got nullptr for output tensorInvalid data type for split tensor Data type of the input tensor MUST be same as that of the input sequence. Sequence data type (SequenceInsert: Got nullptr for output sequenceSequenceErase: Got nullptr for output sequenceSequenceConstruct: Got nullptr for output sequenceViolation of the requirment that all input tensors must have the same data type.Invalid value in 'split' input. All values must be > 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value > 0; })SplitToSequence operator does not support 4L$LLLKKKKKKKKtKdK4L4L,Kint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>]ComputeImplonnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = std::basic_string<char>]T* onnxruntime::Tensor::MutableData() [with T = int]ComputeImplonnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = int]T* onnxruntime::Tensor::MutableData() [with T = double]ComputeImplonnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = double]void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&)T* onnxruntime::Tensor::MutableData() [with T = float]void onnxruntime::GetSplitSizesInput(const onnxruntime::Tensor&, std::vector<long int>&)int64_t onnxruntime::GetScalarSplitInput(const onnxruntime::Tensor&)ComputeImplonnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = float]virtual onnxruntime::common::Status onnxruntime::SplitToSequence::Compute(onnxruntime::OpKernelContext*) constvirtual onnxruntime::common::Status onnxruntime::SequenceConstruct::Compute(onnxruntime::OpKernelContext*) constvirtual onnxruntime::common::Status onnxruntime::SequenceErase::Compute(onnxruntime::OpKernelContext*) constvirtual onnxruntime::common::Status onnxruntime::SequenceInsert::Compute(onnxruntime::OpKernelContext*) constCreateCopyAndAppendCpuTensorvoid onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType)T* OrtValue::GetMutable() [with T = onnxruntime::TensorSeq]virtual onnxruntime::common::Status onnxruntime::SequenceEmpty::Compute(onnxruntime::OpKernelContext*) constconst onnxruntime::Tensor& onnxruntime::TensorSeq::Get(size_t) constint64_t onnxruntime::GetSeqIdx(const onnxruntime::Tensor&)virtual onnxruntime::common::Status onnxruntime::SequenceAt::Compute(onnxruntime::OpKernelContext*) constT* onnxruntime::Tensor::MutableData() [with T = long int]const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::TensorSeq]virtual onnxruntime::common::Status onnxruntime::SequenceLength::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime14SequenceLengthEN11onnxruntime10SequenceAtEN11onnxruntime13SequenceEmptyEN11onnxruntime14SequenceInsertEN11onnxruntime13SequenceEraseEN11onnxruntime17SequenceConstructEN11onnxruntime15SplitToSequenceEAttribute to is not set.Tensor type mismatch.type == dtype_allocator != nullptrlen > 0bufferstolstoulstoi-INFstoullstod/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/cast_op.ccCasting from 'float16' to 'string' is not supported yet.Cast op must have 'to' argument of type DataTypeUnexpected 'to' argument value: Input is missing. The operator Cast expects one and only one input,YYZZ[[[d\\]]l^XXtyz|d~moqsu,$vvikT ԲLľܾL\\T<$$T$DL|l,\|tTL\ 4t#$&l(<4,L4* |=D>?A678:d;tC; <445T|ULVlXNOP|RLSZ|SSLMThhDikdeftghtmmmLnddduvTv4wxLyyy,zDzzD{tu| TD,<Td<l ̘|$$$t void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = signed char]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = double]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = float]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = long unsigned int]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = unsigned int]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = short unsigned int]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = unsigned char]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = long int]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = int]const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = short int]onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = std::basic_string<char>]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = signed char; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Datavoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = double; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Datavoid* onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType)const void* onnxruntime::Tensor::DataRaw(onnxruntime::MLDataType) constvoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = long unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Datavoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Datavoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = short unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Datavoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = unsigned char; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Datavoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = long int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Datavoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Datavoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = short int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataconst T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = bool; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = onnxruntime::MLFloat16]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = double]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = double]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = double; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = double]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = float]onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = float]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = bool]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = bool]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = bool; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = bool]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long int]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = long int]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = long int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = long int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = int]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short int]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = short int]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = short int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short int]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = short int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = signed char]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = signed char; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = signed char]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long unsigned int]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = long unsigned int]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = long unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = long unsigned int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned int]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = unsigned int]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = unsigned int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short unsigned int]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = short unsigned int]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = short unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = short unsigned int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned char]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = unsigned char; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = unsigned char]N11onnxruntime4CastIhEEN11onnxruntime4CastItEEN11onnxruntime4CastIjEEN11onnxruntime4CastImEEN11onnxruntime4CastIaEEN11onnxruntime4CastIsEEN11onnxruntime4CastIiEEN11onnxruntime4CastIlEEN11onnxruntime4CastIbEEN11onnxruntime4CastIfEEN11onnxruntime4CastIdEEN11onnxruntime4CastINS_9MLFloat16EEEN11onnxruntime4CastISsEE8OOOOAAAACompress/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/compress.ccaxes_right_stride >= 0 && static_cast<uint64_t>(axes_right_stride) < std::numeric_limits<size_t>::max()const T* onnxruntime::Tensor::Data() const [with T = bool]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)virtual onnxruntime::common::Status onnxruntime::Compress::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime8CompressECannot concatenate scalars/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.ccNot satsified: input_count >= 1 +virtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) constComputeGetThresholdsFromInputsconst T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]const T* onnxruntime::Tensor::Data() const [with T = long int]PrepareComputestatic onnxruntime::common::Status onnxruntime::NonMaxSuppressionBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::PrepareContext&)onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase(const onnxruntime::OpKernelInfo&)N11onnxruntime21NonMaxSuppressionBaseEN11onnxruntime17NonMaxSuppressionENull input X ptrNull rois_ptrNull batch_indices_ptrRoiAlignavgoutput_heightoutput_widthsampling_ratiosampling_ratio_ >= 0Invalid mode of value Number of dimensions for batch indices should be exactly 1Number of dimensions for rois should be exactly Second dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't match/onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/roialign.hSampling ratio should be >=0, but it was specified. It should be either avg or maxconst T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = double]T* onnxruntime::Tensor::MutableData() [with T = double]const T* onnxruntime::Tensor::Data() const [with T = long int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::RoiAlignBase<T>::RoiAlignBase(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::RoiAlignBase<T>::RoiAlignBase(const onnxruntime::OpKernelInfo&) [with T = float]*ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIdEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUliE_*ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIfEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUliE_N11onnxruntime12RoiAlignBaseIfEEN11onnxruntime12RoiAlignBaseIdEEN11onnxruntime8RoiAlignIfEEN11onnxruntime8RoiAlignIdEEReduceL1ReduceL2ReduceLogSumReduceLogSumExpReduceMaxReduceMinReduceProdReduceSumSquareArgMaxArgMinin_dim != 0/onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.hinfo.GetAttr("keepdims", &keepdims).IsOK()/onnxruntime_src/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:bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = long int; int64_t = long int]bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = double; int64_t = long int]bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = float; int64_t = long int]bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = int; int64_t = long int]onnxruntime::ReduceKernelBase<allow_multi_axes>::ReduceKernelBase(const onnxruntime::OpKernelInfo&) [with bool allow_multi_axes = false]onnxruntime::ReduceKernelBase<allow_multi_axes>::ReduceKernelBase(const onnxruntime::OpKernelInfo&) [with bool allow_multi_axes = true]N11onnxruntime16ReduceKernelBaseILb1EEEN11onnxruntime16ReduceKernelBaseILb0EEEN11onnxruntime12ReduceKernelILb1EEEN11onnxruntime12ReduceKernelILb0EEEN11onnxruntime8ReduceL1IfEEN11onnxruntime8ReduceL1IiEEN11onnxruntime8ReduceL2IfEEN11onnxruntime8ReduceL2IiEEN11onnxruntime12ReduceLogSumIfEEN11onnxruntime12ReduceLogSumIiEEN11onnxruntime15ReduceLogSumExpIfEEN11onnxruntime15ReduceLogSumExpIiEEN11onnxruntime9ReduceMaxIfEEN11onnxruntime9ReduceMaxIiEEN11onnxruntime9ReduceMaxIlEEN11onnxruntime10ReduceMeanIfEEN11onnxruntime10ReduceMeanIiEEN11onnxruntime9ReduceMinIfEEN11onnxruntime9ReduceMinIiEEN11onnxruntime9ReduceMinIlEEN11onnxruntime10ReduceProdIfEEN11onnxruntime10ReduceProdIiEEN11onnxruntime9ReduceSumIfEEN11onnxruntime9ReduceSumIiEEN11onnxruntime9ReduceSumIlEEN11onnxruntime9ReduceSumIdEEN11onnxruntime15ReduceSumSquareIfEEN11onnxruntime15ReduceSumSquareIiEEN11onnxruntime15ReduceSumSquareIdEEN11onnxruntime6ArgMaxIfEEN11onnxruntime6ArgMaxIiEEN11onnxruntime6ArgMinIfEEN11onnxruntime6ArgMinIiEEGRUhidden_sizeactivationsactivation_alphaactivation_betaclipclip_ > 0.fforwardbidirectionallinear_before_resetcur + size <= end/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.hinfo.GetAttr("direction", &direction).IsOK()info.GetAttr("linear_before_reset", &int64_value).IsOK()info.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0Invalid 'direction' argument of ''. Must be one of 'forward', 'reverse', or 'bidirectional'./onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.hactivation_func_names.size() == static_cast<size_t>(num_directions_) * 2lda >= K && ldb >= K && ldc >= NA + (M * lda - (lda - K)) <= A_endB + (N * ldb - (ldb - K)) <= B_endC + (M * ldc - (ldc - N)) <= C_endoffset + size <= size_t(span.size())/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.ccGRU operator does not support double yetInvalid data type for GRU operator of T* onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span<T>::iterator, typename gsl::span<T>::iterator, size_t) [with T = float; typename gsl::span<T>::iterator = float*; size_t = long unsigned int]const T* onnxruntime::rnn::detail::SafeRawConstPointer(typename gsl::span<T>::const_iterator, typename gsl::span<T>::const_iterator, size_t) [with T = float; typename gsl::span<T>::const_iterator = const float*; size_t = long unsigned int]T* onnxruntime::rnn::detail::SafeRawPointer(gsl::span<T>, size_t, size_t) [with T = float; size_t = long unsigned int]void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = float*; TSpanBIter = const float*; TSpanCIter = float*]void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = const float*; TSpanBIter = const float*; TSpanCIter = float*]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int]ComputeImplvirtual onnxruntime::common::Status onnxruntime::DeepCpuGruOp::Compute(onnxruntime::OpKernelContext*) constonnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&)onnxruntime::DeepCpuGruOp::DeepCpuGruOp(const onnxruntime::OpKernelInfo&)N3gsl15narrowing_errorEN11onnxruntime12DeepCpuGruOpEZN11onnxruntime10IAllocator13MakeUniquePtrIfEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPfE_ZN11onnxruntime10IAllocator13MakeUniquePtrIiEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPiE_}. Actual:Input P must have shape {std::future_errorDEEPCPU_LSTMinput_forget rows[Processing batchInput initial_c must have shape {/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.cc/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.hactivation_func_names.size() == static_cast<size_t>(num_directions_) * 3LSTM operator does not support double yetInvalid data type for LSTM operator of ValidateInputsconst T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span<T>, size_t, size_t) [with T = float; size_t = long unsigned int]const T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span<T>, size_t, size_t) [with T = const float; size_t = long unsigned int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int]ComputeImplvirtual onnxruntime::common::Status onnxruntime::DeepCpuLstmOp::Compute(onnxruntime::OpKernelContext*) constonnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&)onnxruntime::DeepCpuLstmOp::DeepCpuLstmOp(const onnxruntime::OpKernelInfo&)NSt13__future_base7_ResultIvEEN11onnxruntime13DeepCpuLstmOpENSt13__future_base11_State_base7_SetterIvNS0_19__exception_ptr_tagEEENSt13__future_base11_State_base7_SetterIvvEESt23_Sp_counted_ptr_inplaceINSt13__future_base11_State_baseESaIS1_ELN9__gnu_cxx12_Lock_policyE2EEZN11onnxruntime3rnn6detail23ExecuteLambdaInParallelIZNS_6detail18UniDirectionalLstmIfE7ComputeERKN3gsl4spanIKfEERKNS7_IKiEEiSB_SB_RNS7_IfEESH_SH_EUliE_EEvRKSsT_iiRNS_11concurrency10ThreadPoolERKNS_7logging6LoggerEEUlvE_St23_Sp_counted_ptr_inplaceISt7promiseIvESaIS1_ELN9__gnu_cxx12_Lock_policyE2EERNNAffineThresholdedReluScaledTanhHardSigmoidEluSoftsign/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.cc/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.hinfo.GetAttr("direction", &direction_).IsOK()allowed_directions.find(direction_) != allowed_directions.end()info.GetAttrs("activations", activations_).IsOK()info.GetAttr("hidden_size", &hidden_size_).IsOK()activations_.size() == static_cast<size_t>(num_directions)allowed_activations.find(activations_[direction]) != allowed_activations.end()const T* onnxruntime::Tensor::Data() const [with T = int]T* onnxruntime::Tensor::MutableData() [with T = float]Computeonnxruntime::RNN<T>::RNN(const onnxruntime::OpKernelInfo&) [with T = float]FffffEN11onnxruntime3RNNIfEEPFffffEDump matrix: affineleakyreluthresholdedreluscaledtanhhardsigmoidsoftsignsoftplus*Input W must have shape {Input R must have shape {Input B must have shape {Expecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.ccInvalid activation function of Invalid LSTM merge activation function of Invalid GRU reset gate activation function: Invalid GRU hidden gate activation function: Input X must have 3 dimensions only. Actual:Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input sequence_lens must have shape {Input initial_h must have shape {void (* onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByName(const string&))(float*, const float*, const float*, float*, int, float, float)void (* onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByName(const string&))(const float*, float*, float*, int, float, float)void (* onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByName(const string&))(const float*, float*, const float*, float*, int, float, float)void (* onnxruntime::rnn::detail::deepcpu::ActivationFuncByName(const string&))(float*, int, float, float)std::string onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCount(const string&, std::vector<float>::const_iterator&, const const_iterator&, std::vector<float>::const_iterator&, const const_iterator&, float&, float&)gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int]7.7.7.7.A Anew_axisConcatFromSequenceMust have valid 'axis' attribute/onnxruntime_src/onnxruntime/core/providers/cpu//tensor/concat.hGot nullptr for sequence input./onnxruntime_src/onnxruntime/core/providers/cpu/sequence/concat_from_sequence.ccconst T& OrtValue::Get() const [with T = onnxruntime::TensorSeq]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::TensorSeq]virtual onnxruntime::common::Status onnxruntime::ConcatFromSequence::Compute(onnxruntime::OpKernelContext*) constonnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo&, bool)N11onnxruntime10ConcatBaseEN11onnxruntime18ConcatFromSequenceETensor size () != new size (Y != nullptrUnsupported 'dtype' value: SequenceEmptySequenceLengthSequenceConstructSequenceEraseSequenceInsertSequenceAtSplitToSequenceI != nullptrInvalid sequence index (split_size_sum () != split_dim_size (S != nullptrGot nullptr for input tensor.), input tensor data type (num_inputs >= 1Split should be > 0split_scalar > 0 yetshape_.Size() == new_shape.Size()SequenceEmpty: Got nullptr for output sequence/onnxruntime_src/onnxruntime/core/providers/cpu/sequence/sequence_ops.ccSequenceLength: Got nullptr for output tensorGot nullptr input for index tensor) specified for sequence of size (SequenceAt: Got nullptr for output tensorInvalid data type for split tensor Data type of the input tensor MUST be same as that of the input sequence. Sequence data type (SequenceInsert: Got nullptr for output sequenceSequenceErase: Got nullptr for output sequenceSequenceConstruct: Got nullptr for output sequenceViolation of the requirment that all input tensors must have the same data type.Invalid value in 'split' input. All values must be > 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value > 0; })SplitToSequence operator does not support $55544444444t4d4T4$5$54int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>]ComputeImplonnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = std::basic_string<char>]T* onnxruntime::Tensor::MutableData() [with T = int]ComputeImplonnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = int]T* onnxruntime::Tensor::MutableData() [with T = double]ComputeImplonnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = double]void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&)T* onnxruntime::Tensor::MutableData() [with T = float]void onnxruntime::GetSplitSizesInput(const onnxruntime::Tensor&, std::vector<long int>&)int64_t onnxruntime::GetScalarSplitInput(const onnxruntime::Tensor&)ComputeImplonnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = float]virtual onnxruntime::common::Status onnxruntime::SplitToSequence::Compute(onnxruntime::OpKernelContext*) constvirtual onnxruntime::common::Status onnxruntime::SequenceConstruct::Compute(onnxruntime::OpKernelContext*) constvirtual onnxruntime::common::Status onnxruntime::SequenceErase::Compute(onnxruntime::OpKernelContext*) constvirtual onnxruntime::common::Status onnxruntime::SequenceInsert::Compute(onnxruntime::OpKernelContext*) constCreateCopyAndAppendCpuTensorvoid onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType)T* OrtValue::GetMutable() [with T = onnxruntime::TensorSeq]virtual onnxruntime::common::Status onnxruntime::SequenceEmpty::Compute(onnxruntime::OpKernelContext*) constconst onnxruntime::Tensor& onnxruntime::TensorSeq::Get(size_t) constint64_t onnxruntime::GetSeqIdx(const onnxruntime::Tensor&)virtual onnxruntime::common::Status onnxruntime::SequenceAt::Compute(onnxruntime::OpKernelContext*) constT* onnxruntime::Tensor::MutableData() [with T = long int]const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::TensorSeq]virtual onnxruntime::common::Status onnxruntime::SequenceLength::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime14SequenceLengthEN11onnxruntime10SequenceAtEN11onnxruntime13SequenceEmptyEN11onnxruntime14SequenceInsertEN11onnxruntime13SequenceEraseEN11onnxruntime17SequenceConstructEN11onnxruntime15SplitToSequenceEAttribute to is not set.Tensor type mismatch.type == dtype_allocator != nullptrlen > 0bufferstolstoulstoi-INFstoullstod/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/cast_op.ccCasting from 'float16' to 'string' is not supported yet.Cast op must have 'to' argument of type DataTypeUnexpected 'to' argument value: Input is missing. The operator Cast expects one and only one inputBBBtCCtDDTEE|FF\G AAdbceTgVXZ\^i__tRtTܟ|Dě<̧̪<LLD,ԾD4||<l\LldtD<L|$d  \,$<$t l&4'(|* !"T$d,$$$=l><?\A|789l;<<|Cl<<56DQQ4RTTNOdPPdV|VV<WLMT^^D_$`a<bbbc4cc4d] ^lp qqtlDn4op,wDwzT{j,l\l|dvoid onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = signed char]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = double]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = float]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = long unsigned int]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = unsigned int]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = short unsigned int]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = unsigned char]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = long int]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = int]const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>]void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = short int]onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = std::basic_string<char>]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = signed char; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Datavoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = double; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Datavoid* onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType)const void* onnxruntime::Tensor::DataRaw(onnxruntime::MLDataType) constvoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = long unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Datavoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Datavoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = short unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Datavoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = unsigned char; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Datavoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = long int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Datavoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Datavoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = short int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataconst T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = bool; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = onnxruntime::MLFloat16]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = double]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = double]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = double; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = double]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = float]onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = float]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = bool]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = bool]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = bool; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = bool]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long int]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = long int]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = long int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = long int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = int]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short int]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = short int]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = short int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short int]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = short int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = signed char]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = signed char; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = signed char]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long unsigned int]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = long unsigned int]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = long unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = long unsigned int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned int]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = unsigned int]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = unsigned int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short unsigned int]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = short unsigned int]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = short unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = short unsigned int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned char]void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = unsigned char; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]CastFloat16Dataonnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = unsigned char]N11onnxruntime4CastIhEEN11onnxruntime4CastItEEN11onnxruntime4CastIjEEN11onnxruntime4CastImEEN11onnxruntime4CastIaEEN11onnxruntime4CastIsEEN11onnxruntime4CastIiEEN11onnxruntime4CastIlEEN11onnxruntime4CastIbEEN11onnxruntime4CastIfEEN11onnxruntime4CastIdEEN11onnxruntime4CastINS_9MLFloat16EEEN11onnxruntime4CastISsEE8OOOOAAAACompress/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/compress.ccaxes_right_stride >= 0 && static_cast<uint64_t>(axes_right_stride) < std::numeric_limits<size_t>::max()const T* onnxruntime::Tensor::Data() const [with T = bool]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)virtual onnxruntime::common::Status onnxruntime::Compress::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime8CompressECannot concatenate scalars/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.ccNot satsified: input_count >= 1 Not satsified: inputs_0_rank > 0 Ranks of input data are different, cannot concatenate them. expected rank: inputs_n_rank == inputs_0_rankNon concat axis dimensions must match: Axis has mismatched dimensions of Not satsified: dim_value == inputs_0_dims[axis_index] Not satsified: data_n.DataType() == p.output_tensor->DataType() -/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.hconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)onnxruntime::common::Status onnxruntime::ConcatBase::PrepareForCompute(onnxruntime::OpKernelContext*, const std::vector<const onnxruntime::Tensor*>&, onnxruntime::Prepare&) constPrepareForComputeonnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo&, bool)N11onnxruntime6ConcatEDynamicQuantizeLinearx_ptr != nullptr/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/dynamicquantizelinear.ccT* onnxruntime::Tensor::MutableData() [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::DynamicQuantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]N11onnxruntime21DynamicQuantizeLinearIhEECEyeLikeT1 != nullptrEyeLike : Input tensor dimension is not 2/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/eye_like.ccT* onnxruntime::Tensor::MutableData() [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = long unsigned int]T* onnxruntime::Tensor::MutableData() [with T = int]T* onnxruntime::Tensor::MutableData() [with T = double]T* onnxruntime::Tensor::MutableData() [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::EyeLike::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime7EyeLikeETindMissing/Invalid 'axis' attribute value/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather.hindices element out of data bounds, idx= must be within the inclusive range [/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather.ccType for Tind not supported yet in Gather.const T* onnxruntime::Tensor::Data() const [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = int]Computeint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::GatherBase::GatherBase(const onnxruntime::OpKernelInfo&)N11onnxruntime10GatherBaseEN11onnxruntime6GatherEGatherElements]. Actual value is , /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.hGatherElements 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 for 'indices' tensor must be 'int32_t' and 'int64_t'/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.ccGatherElements op: Value in indices must be within bounds [GatherElements op: Data type of input 'data' should match the data type of the outputstd::vector<long int> onnxruntime::parse_and_validate_indices_tensor(const onnxruntime::Tensor*, int64_t, const onnxruntime::TensorShape&)T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>]int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::GatherElements::GatherElements(const onnxruntime::OpKernelInfo&)N11onnxruntime14GatherElementsEGatherNDinvalid index found, index = GatherND op: Input count mismatch/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_nd.ccinput_tensor != nullptr && indices_tensor != nullptrindices tensor must has rank larger than 0last dimension of indices must not be larger than rank of input tensorconst T* onnxruntime::Tensor::Data() const [with T = long int]onnxruntime::common::Status onnxruntime::GatherNDBase::PrepareForCompute(onnxruntime::OpKernelContext*, onnxruntime::GatherNDBase::Prepare&) const [with Tind = long int]const T* onnxruntime::Tensor::Data() const [with T = int]onnxruntime::common::Status onnxruntime::GatherNDBase::PrepareForCompute(onnxruntime::OpKernelContext*, onnxruntime::GatherNDBase::Prepare&) const [with Tind = int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]ComputeN11onnxruntime12GatherNDBaseEN11onnxruntime8GatherNDE/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/identity_op.honnxruntime::common::Status onnxruntime::IdentityOp<is_dropout>::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = true]void* onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::IdentityOp<is_dropout>::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = false]N11onnxruntime10IdentityOpILb1EEEN11onnxruntime10IdentityOpILb0EEEdetect_positivedetect_negativeIsInfFailed to obtain detect_positive/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/isinf.ccFailed to obtain detect_negativeData type X must be float or double, but instead got const T* onnxruntime::Tensor::Data() const [with T = double]const T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = bool]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::IsInf::Compute(onnxruntime::OpKernelContext*) constonnxruntime::IsInf::IsInf(const onnxruntime::OpKernelInfo&)N11onnxruntime5IsInfEIsNaNNull input ptrconst T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16]T* onnxruntime::Tensor::MutableData() [with T = bool]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime5IsNaNIfEEN11onnxruntime5IsNaNINS_9MLFloat16EEE||||MeanVarianceNormalizationInput is expected to have four dimensions corresponding to [N,C,H,W]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime27MeanVarianceNormalization_0IfEEN11onnxruntime27MeanVarianceNormalization_1IfEEX input is required!/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/nonzero_op.ccconst T* onnxruntime::Tensor::Data() const [with T = bool]onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool]const T* onnxruntime::Tensor::Data() const [with T = unsigned char]onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = int]onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]const T* onnxruntime::Tensor::Data() const [with T = long int]onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = float]onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]N11onnxruntime7NonZeroIbEEN11onnxruntime7NonZeroIhEEN11onnxruntime7NonZeroIiEEN11onnxruntime7NonZeroIlEEN11onnxruntime7NonZeroIfEEOneHotDepth is negative.Invalid argument for values; either it's rank is more than 1 or it has more than 2 elementsInvalid argument for depth; it's not a scalar./onnxruntime_src/onnxruntime/core/providers/cpu/tensor/onehot.cc'axis' attribute must have a value in the range [ComputeComputeComputeComputeComputeComputeComputeComputeComputeComputeComputeN11onnxruntime8OneHotOpIlllEEN11onnxruntime8OneHotOpIfllEEN11onnxruntime8OneHotOpIlSslEEN11onnxruntime8OneHotOpIfSslEEN11onnxruntime8OneHotOpIlflEEN11onnxruntime8OneHotOpIifiEEN11onnxruntime8OneHotOpIiffEEN11onnxruntime8OneHotOpIfffEEN11onnxruntime8OneHotOpIlifEEN11onnxruntime8OneHotOpIlffEEN11onnxruntime8OneHotOpIlfiEE>Unexpected mode of Padreflectedgedata_rank > 0data_rank * 2 == pads.size()pads_size == 2 * data_rankCannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Invalid 'mode' attribute value/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/pad.hInvalid 'pads' attribute valueInput tensor has no dimensions/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/pad.cc'pads' has wrong number of valuesPads tensor should be an INT64 tensorpads_tensor.IsDataType<int64_t>()pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Pads tensor size should be equal to twice the input dimension count value_tensor->IsDataType<T>() && value_tensor->Shape().Size() == 1Pads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensorvoid onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float]PadInputWithDimValueOfZeroonnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = float]onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double]PadInputWithDimValueOfZeroonnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = double]onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = int]PadInputWithDimValueOfZeroonnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = int]onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int]PadInputWithDimValueOfZeroonnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = long int]onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]onnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo&)N11onnxruntime7PadBaseEN11onnxruntime3PadIfEEN11onnxruntime3PadIdEEN11onnxruntime3PadIiEEN11onnxruntime3PadIlEEy_zero_pointx_scalex_zero_pointDequantizeLinear/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/quantize_linear.ccIsScalarOr1ElementVector(&y_scale)IsScalarOr1ElementVector(&y_zero_point)y_scale.Shape().NumDimensions() == 1 && y_scale.Shape().Size() == broadcastDimy_zero_point.Shape().NumDimensions() == 1 && y_zero_point.Shape().Size() == broadcastDimx_scale must be a scalar or 1D tensor or size 1.x_zero_point must be a scalar or 1D tensor or size 1.x_scale must be 1D tensor with size x_zero_point must be 1D tensor with size x_scale.Shape().NumDimensions() == 1 && x_scale.Shape().Size() == broadcastDimx_zero_point.Shape().NumDimensions() == 1 && x_zero_point.Shape().Size() == broadcastDimIsScalarOr1ElementVector(&x_scale)IsScalarOr1ElementVector(&x_zero_point)onnxruntime::common::Status onnxruntime::DequantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = float]onnxruntime::common::Status onnxruntime::DequantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]const T* onnxruntime::Tensor::Data() const [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = unsigned char]onnxruntime::common::Status onnxruntime::QuantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = signed char]T* onnxruntime::Tensor::MutableData() [with T = signed char]onnxruntime::common::Status onnxruntime::QuantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]N11onnxruntime16DequantizeLinearIhEEN11onnxruntime16DequantizeLinearIaEEN11onnxruntime14QuantizeLinearIhEEN11onnxruntime14QuantizeLinearIaEEBAttribute shape is not set.requested_shape[i] >= -1unknown_dim == -1, requested shape:Reshape_1/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape.hA dimension cannot be less than -1./onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape_helper.hAt most one dimension can be -1.The dimension with value zero exceeds the dimension size of the input tensor.i < input_shape.NumDimensions()The input tensor cannot be reshaped to the requested shape. Input shape:(input_shape.Size() % size) == 0gsl::narrow_cast<int64_t>(input_shape.Size()) == sizeA shape tensor must be a vector tensor.shapeTensor->Shape().NumDimensions() == 1onnxruntime::Reshape_1::Reshape_1(const onnxruntime::OpKernelInfo&)onnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape&, std::vector<long int>&)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]const T* onnxruntime::Tensor::Data() const [with T = long int]virtual onnxruntime::common::Status onnxruntime::Reshape::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime7ReshapeEN11onnxruntime9Reshape_1EResize operatorUpsample operatorscale >= 1scale > 0nearestcubicmode attribute is . It can only be (default) or scalesextrapolation_valueasymmetricpytorch_half_pixeltf_half_pixel_for_nnalign_cornerstf_crop_and_resizecoordinate_transform_mode:[] is not supportted!round_prefer_floornearest_moderound_prefer_ceilnearst_mode:[cubic_coeff_aexclude_outsidescales_size > 0Scale value should be greater than or equal to 1./onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsample.hScale value should be greater than 0.'Linear' mode and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1)info.GetAttr<std::string>("mode", &mode).IsOK()info.GetAttrs<float>("scales", scales_).IsOK()coordinate_transformation_modeexclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to scales size should be greater than 0.const T* onnxruntime::Tensor::Data() const [with T = float]void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor*, std::vector<float>&) constonnxruntime::ResizeNearestMode onnxruntime::UpsampleBase::StringToNearstMode(const string&)onnxruntime::ResizeCoordinateTransformationMode onnxruntime::UpsampleBase::StringToCoordinateTransformationMode(const string&)void onnxruntime::UpsampleBase::ScalesValidation(const std::vector<float>&, onnxruntime::UpsampleMode) constonnxruntime::UpsampleMode onnxruntime::UpsampleBase::StringToUpsampleMode(const string&)onnxruntime::UpsampleBase::UpsampleBase(onnxruntime::OpKernelInfo)N11onnxruntime12UpsampleBaseEN11onnxruntime8UpsampleIfEEN11onnxruntime6ResizeIfEEN11onnxruntime8UpsampleIiEEN11onnxruntime6ResizeIiEEN11onnxruntime8UpsampleIhEEN11onnxruntime6ResizeIhEEZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE_ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE0_ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE1_ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE2_ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE3_ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE4_ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE_ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE0_ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE1_ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE2_ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE3_@?batch_axisInvalid batch_axis of . Must be 0 or 1batch_axis < 2Invalid time_axis of time_axis < 2batch_axis != time_axisReverseSequenceUnknown tensor type of . batch_size=/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.hinfo.GetAttr<int64_t>("batch_axis", &batch_axis).IsOK()info.GetAttr<int64_t>("time_axis", &time_axis).IsOK()time_axis and batch_axis must have different values but both are /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.ccsequence_lens shape must be {batch_size}. Got:p:_831/G-7+)&$o"9 p:p:'6gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::BFloat16]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::BFloat16]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::MLFloat16]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::MLFloat16]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long unsigned int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long unsigned int]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long int]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = unsigned int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned int]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = short unsigned int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short unsigned int]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = short int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short int]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = signed char]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = std::basic_string<char>]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = std::basic_string<char>]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = double]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = double]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = bool]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = bool]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::ReverseSequenceOp::Compute(onnxruntime::OpKernelContext*) constonnxruntime::ReverseSequenceOp::ReverseSequenceOp(const onnxruntime::OpKernelInfo&)N11onnxruntime17ReverseSequenceOpE/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter.ccdata type is different from updates typeIndices and updates must have the same rankIndices vs updates dimensions differs at position=Indices must have the same rank as Input. Indices rank=Expecting indices to be either int32_t or int64_tScatterElementsScatter. Input rank=Indices dim= at pos= is greater than input dim=_:\7Bd?aBB<int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)virtual onnxruntime::common::Status onnxruntime::Scatter::Compute(onnxruntime::OpKernelContext*) constonnxruntime::Scatter::Scatter(const onnxruntime::OpKernelInfo&)N11onnxruntime7ScatterEScatterNDinput_tensor != nullptrindice_tensor != nullptrupdate_tensor != nullptr, indices shape: updates shape: , data shape: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter_nd.ccinput tensor and indices tensor must has rank larger than 0. updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. invalid indice found, indice = T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::ScatterNDBase::PrepareForCompute(onnxruntime::OpKernelContext*, onnxruntime::ScatterNDBase::Prepare&) const [with Tind = long int]ComputeN11onnxruntime13ScatterNDBaseEN11onnxruntime9ScatterNDET* onnxruntime::Tensor::MutableData() [with T = long int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime5ShapeESizeT* onnxruntime::Tensor::MutableData() [with T = long int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime4SizeEStarts must be a 1-D arrayEnds must be a 1-D array'axes' has duplicates'step' value cannot be 0output == output_endCannot slice scalars/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.hhas_starts && has_ends && attr_starts_.size() == attr_ends_.size()!has_axes || attr_axes_.size() == attr_starts_.size()Missing or invalid starts and ends attributeInvalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributes/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.ccnullptr != start_tensor && start_tensor->Shape().NumDimensions() == 1nullptr != ends_tensor && ends_tensor->Shape().NumDimensions() == 1start_tensor->Shape() == ends_tensor->Shape()nullptr == axes_tensor || start_tensor->Shape() == axes_tensor->Shape()nullptr == steps_tensor || start_tensor->Shape() == steps_tensor->Shape()Data type for starts and ends inputs' need to be int32_t or int64_t, but instead got Starts and ends shape mismatchStarts and axes shape mismatchStarts and steps shape mismatch'axes' has an axis outside of the tensor dimension countMissing input tensor to be processedComputeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short int; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = int; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long int; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = float; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = double; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = bool; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>; bool dynamic = false]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = unsigned char]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = unsigned char]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = short unsigned int]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = short unsigned int]T* onnxruntime::Tensor::MutableData() [with T = short unsigned int]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = unsigned int]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = unsigned int]T* onnxruntime::Tensor::MutableData() [with T = unsigned int]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = long unsigned int]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long unsigned int]T* onnxruntime::Tensor::MutableData() [with T = long unsigned int]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = signed char]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = signed char]T* onnxruntime::Tensor::MutableData() [with T = signed char]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = short int]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = short int]T* onnxruntime::Tensor::MutableData() [with T = short int]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short int; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = int]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = int]T* onnxruntime::Tensor::MutableData() [with T = int]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = int; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = long int]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = long int]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long int; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = float]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = float; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = double]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double]T* onnxruntime::Tensor::MutableData() [with T = double]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = double; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = onnxruntime::MLFloat16]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = onnxruntime::MLFloat16]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = bool]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = bool]T* onnxruntime::Tensor::MutableData() [with T = bool]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = bool; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = std::basic_string<char>]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = std::basic_string<char>]T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>; bool dynamic = true]void onnxruntime::SliceBase::FillVectorsFromInput(const onnxruntime::OpKernelContext*, std::vector<long int>&, std::vector<long int>&, std::vector<long int>&, std::vector<long int>&) constint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)onnxruntime::SliceBase::SliceBase(const onnxruntime::OpKernelInfo&, bool)N11onnxruntime9SliceBaseEN11onnxruntime5SliceIhLb0EEEN11onnxruntime5SliceItLb0EEEN11onnxruntime5SliceIjLb0EEEN11onnxruntime5SliceImLb0EEEN11onnxruntime5SliceIaLb0EEEN11onnxruntime5SliceIsLb0EEEN11onnxruntime5SliceIiLb0EEEN11onnxruntime5SliceIlLb0EEEN11onnxruntime5SliceIfLb0EEEN11onnxruntime5SliceIdLb0EEEN11onnxruntime5SliceINS_9MLFloat16ELb0EEEN11onnxruntime5SliceIbLb0EEEN11onnxruntime5SliceISsLb0EEEN11onnxruntime5SliceIhLb1EEEN11onnxruntime5SliceItLb1EEEN11onnxruntime5SliceIjLb1EEEN11onnxruntime5SliceImLb1EEEN11onnxruntime5SliceIaLb1EEEN11onnxruntime5SliceIsLb1EEEN11onnxruntime5SliceIiLb1EEEN11onnxruntime5SliceIlLb1EEEN11onnxruntime5SliceIfLb1EEEN11onnxruntime5SliceIdLb1EEEN11onnxruntime5SliceINS_9MLFloat16ELb1EEEN11onnxruntime5SliceIbLb1EEEN11onnxruntime5SliceISsLb1EEEblocksizeCRDDCRDepthToSpaceSpaceToDepthAttribute blocksize is not set./onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.hinfo.GetAttr("blocksize", &blocksize_).IsOK()DepthToSpace op: only 'DCR' and 'CRD' modes are supported/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.ccinput.Shape().NumDimensions() == 4input_depth % (blocksize_ * blocksize_) == 0input_height % this->blocksize_ == 0input_width % this->blocksize_ == 0onnxruntime::common::Status onnxruntime::DepthToSpace<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::SpaceToDepth<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::DepthToSpace<T>::DepthToSpace(const onnxruntime::OpKernelInfo&) [with T = float]onnxruntime::SpaceDepthBase::SpaceDepthBase(const onnxruntime::OpKernelInfo&)N11onnxruntime14SpaceDepthBaseEN11onnxruntime12SpaceToDepthIfEEN11onnxruntime12DepthToSpaceIfEE*> Axis= NumOutputs= Input shape=SplitInvalid value in 'split' attribute. All values must be > 0/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.hstd::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value > 0; })Input cannot be split evenly on selected axis. Input shape=Cannot split using values in 'split' attribute. Axis= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.ccSplit operator does not support T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>]const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>]ComputeImplT* onnxruntime::Tensor::MutableData() [with T = int]const T* onnxruntime::Tensor::Data() const [with T = int]ComputeImplT* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]ComputeImplconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::Split::Compute(onnxruntime::OpKernelContext*) constint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)onnxruntime::SplitBase::SplitBase(const onnxruntime::OpKernelInfo&)N11onnxruntime9SplitBaseEN11onnxruntime5SplitE. shape= must be 1 instead of Dimension of input input_shape[i] == 1/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/squeeze.hint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)static std::vector<long int> onnxruntime::SqueezeBase::ComputeOutputShape(const onnxruntime::TensorShape&, const onnxruntime::TensorShape&)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime11SqueezeBaseEN11onnxruntime7SqueezeETileInput 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_src/onnxruntime/core/providers/cpu/tensor/tile.ccconst T* onnxruntime::Tensor::Data() const [with T = long int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::Tile::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime4TileEMismatched data types between input and output Tensors. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.hv >= 0 && static_cast<uint64_t>(v) <= std::numeric_limits<size_t>::max()Attribute perm of Transpose has an invalid value. Value /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.cc does not align with rank of input data: is outside range. is repeated.[ perm: onnxruntime::TransposeBase::TransposeBase(const onnxruntime::OpKernelInfo&)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::Transpose::Compute(onnxruntime::OpKernelContext*) constT* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>]const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>]N11onnxruntime13TransposeBaseEN11onnxruntime9TransposeEUniqueUnsupported tensor type of gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = signed char]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char]N11onnxruntime6UniqueE'axes' has a duplicate axisMissing/Invalid 'axes' attribute value/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.hinfo.GetAttrs("axes", axes_).IsOK()/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.cc'axes' has an out of range axisComputeint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::UnsqueezeBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::UnsqueezeBase::Prepare&) constonnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo&)N11onnxruntime13UnsqueezeBaseEN11onnxruntime9UnsqueezeEUpsampleResize: unexpected modeUpsample: unexpected modeInvalid roi input index.roi_input_idx_ > 0scales != nullptrsizes == nullptrResize: 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.Resize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales./onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsample.ccRank of input and output tensor should be same.output_dims.size() == dims.size()Resize: size of roi array should be 2 * N where N is the rank of input tensor X.'Linear' mode only support 2-D inputs ('Bilinear') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the Only one of scales or sizes must be provided as input.sizes != nullptr && sizes->Shape().Size() != 0Resize: input tensor's rank does not match the output tensor's rank.X->Shape().GetDims().size() == output_dims.size()Either scales or sizes MUST be provided as input.BaseComputeonnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = float]onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]BaseComputeonnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = int]onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]BaseComputeonnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = unsigned char]onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = float]void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor*, std::vector<float>&) constvoid onnxruntime::UpsampleBase::ScalesValidation(const std::vector<float>&, onnxruntime::UpsampleMode) const@A@Wherecondition && X && Ycondition, X, and Y inputs are required!/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/where_op.ccT* onnxruntime::Tensor::MutableData() [with T = int]onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = int]onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]T* onnxruntime::Tensor::MutableData() [with T = long int]onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = long int]onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = float]onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = float]onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>]onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = std::basic_string<char>]onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>]N11onnxruntime5WhereIiEEN11onnxruntime5WhereIlEEN11onnxruntime5WhereIfEEN11onnxruntime5WhereISsEE/onnxruntime_src/onnxruntime/contrib_ops/cpu_contrib_kernels.ccRegisterCpuContribKernelsRegisterNchwcKernels/onnxruntime_src/onnxruntime/automl_ops/cpu_automl_kernels.ccRegisterCpuAutoMLKernelsgammaSeluconst T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime7SigmoidIfEEN11onnxruntime4TanhIfEEN11onnxruntime3EluIfEEN11onnxruntime11HardSigmoidIfEEN11onnxruntime9LeakyReluIfEEN11onnxruntime4ReluIfEEN11onnxruntime4SeluIfEEN11onnxruntime18ParametricSoftplusIfEEN11onnxruntime8SoftsignIfEEN11onnxruntime15ThresholdedReluIfEE +/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.hconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)onnxruntime::common::Status onnxruntime::ConcatBase::PrepareForCompute(onnxruntime::OpKernelContext*, const std::vector<const onnxruntime::Tensor*>&, onnxruntime::Prepare&) constPrepareForComputeonnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo&, bool)N11onnxruntime6ConcatEDynamicQuantizeLinearx_ptr != nullptr/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/dynamicquantizelinear.ccT* onnxruntime::Tensor::MutableData() [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::DynamicQuantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]N11onnxruntime21DynamicQuantizeLinearIhEECEyeLikeT1 != nullptrEyeLike : Input tensor dimension is not 2/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/eye_like.ccT* onnxruntime::Tensor::MutableData() [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = long unsigned int]T* onnxruntime::Tensor::MutableData() [with T = int]T* onnxruntime::Tensor::MutableData() [with T = double]T* onnxruntime::Tensor::MutableData() [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::EyeLike::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime7EyeLikeETindMissing/Invalid 'axis' attribute value/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather.hindices element out of data bounds, idx= must be within the inclusive range [/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather.ccType for Tind not supported yet in Gather.const T* onnxruntime::Tensor::Data() const [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = int]Computeint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::GatherBase::GatherBase(const onnxruntime::OpKernelInfo&)N11onnxruntime10GatherBaseEN11onnxruntime6GatherEGatherElements]. Actual value is , /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.hGatherElements 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 for 'indices' tensor must be 'int32_t' and 'int64_t'/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.ccGatherElements op: Value in indices must be within bounds [GatherElements op: Data type of input 'data' should match the data type of the outputstd::vector<long int> onnxruntime::parse_and_validate_indices_tensor(const onnxruntime::Tensor*, int64_t, const onnxruntime::TensorShape&)T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>]int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::GatherElements::GatherElements(const onnxruntime::OpKernelInfo&)N11onnxruntime14GatherElementsEGatherNDinvalid index found, index = GatherND op: Input count mismatch/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_nd.ccinput_tensor != nullptr && indices_tensor != nullptrindices tensor must has rank larger than 0last dimension of indices must not be larger than rank of input tensorconst T* onnxruntime::Tensor::Data() const [with T = long int]onnxruntime::common::Status onnxruntime::GatherNDBase::PrepareForCompute(onnxruntime::OpKernelContext*, onnxruntime::GatherNDBase::Prepare&) const [with Tind = long int]const T* onnxruntime::Tensor::Data() const [with T = int]onnxruntime::common::Status onnxruntime::GatherNDBase::PrepareForCompute(onnxruntime::OpKernelContext*, onnxruntime::GatherNDBase::Prepare&) const [with Tind = int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]ComputeN11onnxruntime12GatherNDBaseEN11onnxruntime8GatherNDE/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/identity_op.honnxruntime::common::Status onnxruntime::IdentityOp<is_dropout>::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = true]void* onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::IdentityOp<is_dropout>::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = false]N11onnxruntime10IdentityOpILb1EEEN11onnxruntime10IdentityOpILb0EEEdetect_positivedetect_negativeIsInfFailed to obtain detect_positive/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/isinf.ccFailed to obtain detect_negativeData type X must be float or double, but instead got const T* onnxruntime::Tensor::Data() const [with T = double]const T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = bool]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::IsInf::Compute(onnxruntime::OpKernelContext*) constonnxruntime::IsInf::IsInf(const onnxruntime::OpKernelInfo&)N11onnxruntime5IsInfEIsNaNNull input ptrconst T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16]T* onnxruntime::Tensor::MutableData() [with T = bool]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime5IsNaNIfEEN11onnxruntime5IsNaNINS_9MLFloat16EEE||||MeanVarianceNormalizationInput is expected to have four dimensions corresponding to [N,C,H,W]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime27MeanVarianceNormalization_0IfEEN11onnxruntime27MeanVarianceNormalization_1IfEEX input is required!/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/nonzero_op.ccconst T* onnxruntime::Tensor::Data() const [with T = bool]onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool]const T* onnxruntime::Tensor::Data() const [with T = unsigned char]onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = int]onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]const T* onnxruntime::Tensor::Data() const [with T = long int]onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = float]onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]N11onnxruntime7NonZeroIbEEN11onnxruntime7NonZeroIhEEN11onnxruntime7NonZeroIiEEN11onnxruntime7NonZeroIlEEN11onnxruntime7NonZeroIfEEOneHotDepth is negative.Invalid argument for values; either it's rank is more than 1 or it has more than 2 elementsInvalid argument for depth; it's not a scalar./onnxruntime_src/onnxruntime/core/providers/cpu/tensor/onehot.cc'axis' attribute must have a value in the range [ComputeComputeComputeComputeComputeComputeComputeComputeComputeComputeComputeN11onnxruntime8OneHotOpIlllEEN11onnxruntime8OneHotOpIfllEEN11onnxruntime8OneHotOpIlSslEEN11onnxruntime8OneHotOpIfSslEEN11onnxruntime8OneHotOpIlflEEN11onnxruntime8OneHotOpIifiEEN11onnxruntime8OneHotOpIiffEEN11onnxruntime8OneHotOpIfffEEN11onnxruntime8OneHotOpIlifEEN11onnxruntime8OneHotOpIlffEEN11onnxruntime8OneHotOpIlfiEE>Unexpected mode of Padreflectedgedata_rank > 0data_rank * 2 == pads.size()pads_size == 2 * data_rankCannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Invalid 'mode' attribute value/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/pad.hInvalid 'pads' attribute valueInput tensor has no dimensions/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/pad.cc'pads' has wrong number of valuesPads tensor should be an INT64 tensorpads_tensor.IsDataType<int64_t>()pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Pads tensor size should be equal to twice the input dimension count value_tensor->IsDataType<T>() && value_tensor->Shape().Size() == 1Pads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensorvoid onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float]PadInputWithDimValueOfZeroonnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = float]onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double]PadInputWithDimValueOfZeroonnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = double]onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = int]PadInputWithDimValueOfZeroonnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = int]onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int]PadInputWithDimValueOfZeroonnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = long int]onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]onnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo&)N11onnxruntime7PadBaseEN11onnxruntime3PadIfEEN11onnxruntime3PadIdEEN11onnxruntime3PadIiEEN11onnxruntime3PadIlEEy_zero_pointx_scalex_zero_pointDequantizeLinear/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/quantize_linear.ccIsScalarOr1ElementVector(&y_scale)IsScalarOr1ElementVector(&y_zero_point)y_scale.Shape().NumDimensions() == 1 && y_scale.Shape().Size() == broadcastDimy_zero_point.Shape().NumDimensions() == 1 && y_zero_point.Shape().Size() == broadcastDimx_scale must be a scalar or 1D tensor or size 1.x_zero_point must be a scalar or 1D tensor or size 1.x_scale must be 1D tensor with size x_zero_point must be 1D tensor with size x_scale.Shape().NumDimensions() == 1 && x_scale.Shape().Size() == broadcastDimx_zero_point.Shape().NumDimensions() == 1 && x_zero_point.Shape().Size() == broadcastDimIsScalarOr1ElementVector(&x_scale)IsScalarOr1ElementVector(&x_zero_point)onnxruntime::common::Status onnxruntime::DequantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = float]onnxruntime::common::Status onnxruntime::DequantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]const T* onnxruntime::Tensor::Data() const [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = unsigned char]onnxruntime::common::Status onnxruntime::QuantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = signed char]T* onnxruntime::Tensor::MutableData() [with T = signed char]onnxruntime::common::Status onnxruntime::QuantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]N11onnxruntime16DequantizeLinearIhEEN11onnxruntime16DequantizeLinearIaEEN11onnxruntime14QuantizeLinearIhEEN11onnxruntime14QuantizeLinearIaEEBAttribute shape is not set.requested_shape[i] >= -1unknown_dim == -1, requested shape:Reshape_1/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape.hA dimension cannot be less than -1./onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape_helper.hAt most one dimension can be -1.The dimension with value zero exceeds the dimension size of the input tensor.i < input_shape.NumDimensions()The input tensor cannot be reshaped to the requested shape. Input shape:(input_shape.Size() % size) == 0gsl::narrow_cast<int64_t>(input_shape.Size()) == sizeA shape tensor must be a vector tensor.shapeTensor->Shape().NumDimensions() == 1onnxruntime::Reshape_1::Reshape_1(const onnxruntime::OpKernelInfo&)onnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape&, std::vector<long int>&)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]const T* onnxruntime::Tensor::Data() const [with T = long int]virtual onnxruntime::common::Status onnxruntime::Reshape::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime7ReshapeEN11onnxruntime9Reshape_1EResize operatorUpsample operatorscale >= 1scale > 0nearestcubicmode attribute is . It can only be (default) or scalesextrapolation_valueasymmetricpytorch_half_pixeltf_half_pixel_for_nnalign_cornerstf_crop_and_resizecoordinate_transform_mode:[] is not supportted!round_prefer_floornearest_moderound_prefer_ceilnearst_mode:[cubic_coeff_aexclude_outsidescales_size > 0Scale value should be greater than or equal to 1./onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsample.hScale value should be greater than 0.'Linear' mode and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1)info.GetAttr<std::string>("mode", &mode).IsOK()info.GetAttrs<float>("scales", scales_).IsOK()coordinate_transformation_modeexclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to scales size should be greater than 0.const T* onnxruntime::Tensor::Data() const [with T = float]void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor*, std::vector<float>&) constonnxruntime::ResizeNearestMode onnxruntime::UpsampleBase::StringToNearstMode(const string&)onnxruntime::ResizeCoordinateTransformationMode onnxruntime::UpsampleBase::StringToCoordinateTransformationMode(const string&)void onnxruntime::UpsampleBase::ScalesValidation(const std::vector<float>&, onnxruntime::UpsampleMode) constonnxruntime::UpsampleMode onnxruntime::UpsampleBase::StringToUpsampleMode(const string&)onnxruntime::UpsampleBase::UpsampleBase(onnxruntime::OpKernelInfo)N11onnxruntime12UpsampleBaseEN11onnxruntime8UpsampleIfEEN11onnxruntime6ResizeIfEEN11onnxruntime8UpsampleIiEEN11onnxruntime6ResizeIiEEN11onnxruntime8UpsampleIhEEN11onnxruntime6ResizeIhEEZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE_ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE0_ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE1_ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE2_ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE3_ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE4_ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE_ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE0_ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE1_ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE2_ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE3_@?batch_axisInvalid batch_axis of . Must be 0 or 1batch_axis < 2Invalid time_axis of time_axis < 2batch_axis != time_axisReverseSequenceUnknown tensor type of . batch_size=/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.hinfo.GetAttr<int64_t>("batch_axis", &batch_axis).IsOK()info.GetAttr<int64_t>("time_axis", &time_axis).IsOK()time_axis and batch_axis must have different values but both are /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.ccsequence_lens shape must be {batch_size}. Got:`#O!o7' _ ) `#`#gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::BFloat16]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::BFloat16]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::MLFloat16]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::MLFloat16]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long unsigned int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long unsigned int]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long int]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = unsigned int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned int]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = short unsigned int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short unsigned int]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = short int]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short int]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = signed char]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = std::basic_string<char>]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = std::basic_string<char>]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = double]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = double]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = bool]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = bool]gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::ReverseSequenceOp::Compute(onnxruntime::OpKernelContext*) constonnxruntime::ReverseSequenceOp::ReverseSequenceOp(const onnxruntime::OpKernelInfo&)N11onnxruntime17ReverseSequenceOpE/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter.ccdata type is different from updates typeIndices and updates must have the same rankIndices vs updates dimensions differs at position=Indices must have the same rank as Input. Indices rank=Expecting indices to be either int32_t or int64_tScatterElementsScatter. Input rank=Indices dim= at pos= is greater than input dim=tO*qL'2 yT/ +vQ22,int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)virtual onnxruntime::common::Status onnxruntime::Scatter::Compute(onnxruntime::OpKernelContext*) constonnxruntime::Scatter::Scatter(const onnxruntime::OpKernelInfo&)N11onnxruntime7ScatterEScatterNDinput_tensor != nullptrindice_tensor != nullptrupdate_tensor != nullptr, indices shape: updates shape: , data shape: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter_nd.ccinput tensor and indices tensor must has rank larger than 0. updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. invalid indice found, indice = T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::ScatterNDBase::PrepareForCompute(onnxruntime::OpKernelContext*, onnxruntime::ScatterNDBase::Prepare&) const [with Tind = long int]ComputeN11onnxruntime13ScatterNDBaseEN11onnxruntime9ScatterNDET* onnxruntime::Tensor::MutableData() [with T = long int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime5ShapeESizeT* onnxruntime::Tensor::MutableData() [with T = long int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime4SizeEStarts must be a 1-D arrayEnds must be a 1-D array'axes' has duplicates'step' value cannot be 0output == output_endCannot slice scalars/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.hhas_starts && has_ends && attr_starts_.size() == attr_ends_.size()!has_axes || attr_axes_.size() == attr_starts_.size()Missing or invalid starts and ends attributeInvalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributes/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.ccnullptr != start_tensor && start_tensor->Shape().NumDimensions() == 1nullptr != ends_tensor && ends_tensor->Shape().NumDimensions() == 1start_tensor->Shape() == ends_tensor->Shape()nullptr == axes_tensor || start_tensor->Shape() == axes_tensor->Shape()nullptr == steps_tensor || start_tensor->Shape() == steps_tensor->Shape()Data type for starts and ends inputs' need to be int32_t or int64_t, but instead got Starts and ends shape mismatchStarts and axes shape mismatchStarts and steps shape mismatch'axes' has an axis outside of the tensor dimension countMissing input tensor to be processedComputeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short int; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = int; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long int; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = float; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = double; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = bool; bool dynamic = false]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>; bool dynamic = false]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = unsigned char]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = unsigned char]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = short unsigned int]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = short unsigned int]T* onnxruntime::Tensor::MutableData() [with T = short unsigned int]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = unsigned int]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = unsigned int]T* onnxruntime::Tensor::MutableData() [with T = unsigned int]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = long unsigned int]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long unsigned int]T* onnxruntime::Tensor::MutableData() [with T = long unsigned int]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = signed char]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = signed char]T* onnxruntime::Tensor::MutableData() [with T = signed char]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = short int]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = short int]T* onnxruntime::Tensor::MutableData() [with T = short int]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short int; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = int]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = int]T* onnxruntime::Tensor::MutableData() [with T = int]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = int; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = long int]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = long int]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long int; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = float]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = float; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = double]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double]T* onnxruntime::Tensor::MutableData() [with T = double]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = double; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = onnxruntime::MLFloat16]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = onnxruntime::MLFloat16]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = bool]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = bool]T* onnxruntime::Tensor::MutableData() [with T = bool]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = bool; bool dynamic = true]onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = std::basic_string<char>]::__lambda46void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = std::basic_string<char>]T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>]Computeonnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>; bool dynamic = true]void onnxruntime::SliceBase::FillVectorsFromInput(const onnxruntime::OpKernelContext*, std::vector<long int>&, std::vector<long int>&, std::vector<long int>&, std::vector<long int>&) constint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)onnxruntime::SliceBase::SliceBase(const onnxruntime::OpKernelInfo&, bool)N11onnxruntime9SliceBaseEN11onnxruntime5SliceIhLb0EEEN11onnxruntime5SliceItLb0EEEN11onnxruntime5SliceIjLb0EEEN11onnxruntime5SliceImLb0EEEN11onnxruntime5SliceIaLb0EEEN11onnxruntime5SliceIsLb0EEEN11onnxruntime5SliceIiLb0EEEN11onnxruntime5SliceIlLb0EEEN11onnxruntime5SliceIfLb0EEEN11onnxruntime5SliceIdLb0EEEN11onnxruntime5SliceINS_9MLFloat16ELb0EEEN11onnxruntime5SliceIbLb0EEEN11onnxruntime5SliceISsLb0EEEN11onnxruntime5SliceIhLb1EEEN11onnxruntime5SliceItLb1EEEN11onnxruntime5SliceIjLb1EEEN11onnxruntime5SliceImLb1EEEN11onnxruntime5SliceIaLb1EEEN11onnxruntime5SliceIsLb1EEEN11onnxruntime5SliceIiLb1EEEN11onnxruntime5SliceIlLb1EEEN11onnxruntime5SliceIfLb1EEEN11onnxruntime5SliceIdLb1EEEN11onnxruntime5SliceINS_9MLFloat16ELb1EEEN11onnxruntime5SliceIbLb1EEEN11onnxruntime5SliceISsLb1EEEblocksizeCRDDCRDepthToSpaceSpaceToDepthAttribute blocksize is not set./onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.hinfo.GetAttr("blocksize", &blocksize_).IsOK()DepthToSpace op: only 'DCR' and 'CRD' modes are supported/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.ccinput.Shape().NumDimensions() == 4input_depth % (blocksize_ * blocksize_) == 0input_height % this->blocksize_ == 0input_width % this->blocksize_ == 0onnxruntime::common::Status onnxruntime::DepthToSpace<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::SpaceToDepth<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::DepthToSpace<T>::DepthToSpace(const onnxruntime::OpKernelInfo&) [with T = float]onnxruntime::SpaceDepthBase::SpaceDepthBase(const onnxruntime::OpKernelInfo&)N11onnxruntime14SpaceDepthBaseEN11onnxruntime12SpaceToDepthIfEEN11onnxruntime12DepthToSpaceIfEE*> Axis= NumOutputs= Input shape=SplitInvalid value in 'split' attribute. All values must be > 0/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.hstd::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value > 0; })Input cannot be split evenly on selected axis. Input shape=Cannot split using values in 'split' attribute. Axis= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.ccSplit operator does not support T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>]const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>]ComputeImplT* onnxruntime::Tensor::MutableData() [with T = int]const T* onnxruntime::Tensor::Data() const [with T = int]ComputeImplT* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]ComputeImplconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::Split::Compute(onnxruntime::OpKernelContext*) constint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)onnxruntime::SplitBase::SplitBase(const onnxruntime::OpKernelInfo&)N11onnxruntime9SplitBaseEN11onnxruntime5SplitE. shape= must be 1 instead of Dimension of input input_shape[i] == 1/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/squeeze.hint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)static std::vector<long int> onnxruntime::SqueezeBase::ComputeOutputShape(const onnxruntime::TensorShape&, const onnxruntime::TensorShape&)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime11SqueezeBaseEN11onnxruntime7SqueezeETileInput 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_src/onnxruntime/core/providers/cpu/tensor/tile.ccconst T* onnxruntime::Tensor::Data() const [with T = long int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::Tile::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime4TileEMismatched data types between input and output Tensors. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.hv >= 0 && static_cast<uint64_t>(v) <= std::numeric_limits<size_t>::max()Attribute perm of Transpose has an invalid value. Value /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.cc does not align with rank of input data: is outside range. is repeated.[ perm: onnxruntime::TransposeBase::TransposeBase(const onnxruntime::OpKernelInfo&)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::Transpose::Compute(onnxruntime::OpKernelContext*) constT* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>]const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>]N11onnxruntime13TransposeBaseEN11onnxruntime9TransposeEUniqueUnsupported tensor type of gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = signed char]gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char]N11onnxruntime6UniqueE'axes' has a duplicate axisMissing/Invalid 'axes' attribute value/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.hinfo.GetAttrs("axes", axes_).IsOK()/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.cc'axes' has an out of range axisComputeint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::UnsqueezeBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::UnsqueezeBase::Prepare&) constonnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo&)N11onnxruntime13UnsqueezeBaseEN11onnxruntime9UnsqueezeEUpsampleResize: unexpected modeUpsample: unexpected modeInvalid roi input index.roi_input_idx_ > 0scales != nullptrsizes == nullptrResize: 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.Resize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales./onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsample.ccRank of input and output tensor should be same.output_dims.size() == dims.size()Resize: size of roi array should be 2 * N where N is the rank of input tensor X.'Linear' mode only support 2-D inputs ('Bilinear') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the Only one of scales or sizes must be provided as input.sizes != nullptr && sizes->Shape().Size() != 0Resize: input tensor's rank does not match the output tensor's rank.X->Shape().GetDims().size() == output_dims.size()Either scales or sizes MUST be provided as input.BaseComputeonnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = float]onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]BaseComputeonnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = int]onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]BaseComputeonnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = unsigned char]onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = float]void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor*, std::vector<float>&) constvoid onnxruntime::UpsampleBase::ScalesValidation(const std::vector<float>&, onnxruntime::UpsampleMode) const@A@Wherecondition && X && Ycondition, X, and Y inputs are required!/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/where_op.ccT* onnxruntime::Tensor::MutableData() [with T = unsigned char]onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = unsigned char]onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = int]onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = int]onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]T* onnxruntime::Tensor::MutableData() [with T = long int]onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = long int]onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = float]onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = float]onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>]onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = std::basic_string<char>]onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>]N11onnxruntime5WhereIhEEN11onnxruntime5WhereIiEEN11onnxruntime5WhereIlEEN11onnxruntime5WhereIfEEN11onnxruntime5WhereISsEE/onnxruntime_src/onnxruntime/contrib_ops/cpu_contrib_kernels.ccRegisterCpuContribKernelsRegisterNchwcKernels/onnxruntime_src/onnxruntime/automl_ops/cpu_automl_kernels.ccRegisterCpuAutoMLKernelsgammaSeluconst T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime7SigmoidIfEEN11onnxruntime4TanhIfEEN11onnxruntime3EluIfEEN11onnxruntime11HardSigmoidIfEEN11onnxruntime9LeakyReluIfEEN11onnxruntime4ReluIfEEN11onnxruntime4SeluIfEEN11onnxruntime18ParametricSoftplusIfEEN11onnxruntime8SoftsignIfEEN11onnxruntime15ThresholdedReluIfEE #<L>}-?_}?VIf'If' node has outputs.then_branchinfo == nullptr did not.else_branch' attribute.Failed to create output tensor for If output outputs which doesn't match the subgraph's /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/if.ccnum_subgraph_outputs == static_cast<size_t>(num_outputs)SetupSubgraphExecutionInfo should only be called once for each subgraph.Subgraph must have the shape set for all outputs but Failed to create output tensor for then_feeds_fetches_manager_ && else_feeds_fetches_manager_Subgraph SessionState was not found for 'CreateFeedsFetchesManager must be called prior to execution of graph.info.GetAttr<ONNX_NAMESPACE::GraphProto>("then_branch", &proto).IsOK()info.GetAttr<ONNX_NAMESPACE::GraphProto>("else_branch", &proto).IsOK()ExecuteInitializeconst T* onnxruntime::Tensor::Data() const [with T = bool]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]Computevirtual onnxruntime::common::Status onnxruntime::If::Compute(onnxruntime::OpKernelContext*) constonnxruntime::If::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&)SetupSubgraphExecutionInfovirtual onnxruntime::common::Status onnxruntime::If::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&)onnxruntime::If::If(const onnxruntime::OpKernelInfo&)*ZN11onnxruntime6IfImpl7ExecuteERKNS_19FeedsFetchesManagerEEUlRKNS_11TensorShapeERK13OrtMemoryInfoR8OrtValueRbE_N11onnxruntime2IfE Expected:Loop inputs. Found:'Loop' node has but has info_ == nullptrbodyfeeds_fetches_manager_Inconsistent shape in loop output for output. Graph in 'body' attribute of Loop should have /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/loop.ccstatic_cast<size_t>(num_subgraph_inputs) == subgraph_inputs.size() outputs so the subgraph requires num_subgraph_outputs - 1 == num_outputs'Loop' input 'M' should be a scalar tensor. Got shape of 'Loop' input 'cond' should be a scalar tensor. Got shape of Loop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}.Subgraph SessionState was not found for 'body' attribute.info.GetAttr<ONNX_NAMESPACE::GraphProto>("body", &proto).IsOK()ExecuteConcatenateLoopOutputconst T* onnxruntime::Tensor::Data() const [with T = bool]const T* onnxruntime::Tensor::Data() const [with T = long int]Computevirtual onnxruntime::common::Status onnxruntime::Loop::Compute(onnxruntime::OpKernelContext*) constonnxruntime::Loop::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&)SetupSubgraphExecutionInfovirtual onnxruntime::common::Status onnxruntime::Loop::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&)onnxruntime::Loop::Loop(const onnxruntime::OpKernelInfo&)FN11onnxruntime6common6StatusERSt6vectorI8OrtValueSaIS3_EEPvmEN11onnxruntime4LoopEPFN11onnxruntime6common6StatusERSt6vectorI8OrtValueSaIS3_EEPvmEScan<8> spec does not support transpose of output. This should never be called./onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_8.cc dimensions or more but input had shape of Scan inputs have inconsistent batch size. Previous value was Scan inputs have inconsistent sequence lengths. Previous value was Invalid entries in sequence_lens. Max sequence length was Output OrtValue has not been created for loop state variable output /onnxruntime_src/onnxruntime/core/framework/ort_value_tensor_slicer.hposition_ >= 0 && position_ < sequence_length_info.GetAttr<int64_t>("num_scan_inputs", &num_scan_inputs_).IsOK()feeds_fetches_manager_ && info_ScanInvalid scan input: has batch size of has length of sequence_lens length of did not match batch size of p_mlvaluenum_scan_inputsSubgraph in 'body' produces outputs but Scan expects T* OrtValue::GetMutable() [with T = onnxruntime::Tensor]Executetypename std::enable_if<(! std::is_const<T&>::value), T&>::type onnxruntime::OrtValueTensorSlicer<T>::Iterator::operator*() [with T = const OrtValue; typename std::enable_if<(! std::is_const<T&>::value), T&>::type = const OrtValue&]CreateLoopStateVariablesonnxruntime::common::Status onnxruntime::Scan8Impl::CreateLoopStateVariables(std::vector<std::vector<onnxruntime::scan::detail::LoopStateVariable> >&)AllocateOutputTensorsgsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long int]ValidateInputInitializeconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]Computeonnxruntime::common::Status onnxruntime::Scan<OpSet>::Compute(onnxruntime::OpKernelContext*) const [with int OpSet = 8]onnxruntime::common::Status onnxruntime::Scan<OpSet>::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) [with int OpSet = 8; std::string = std::basic_string<char>]onnxruntime::Scan<OpSet>::Scan(const onnxruntime::OpKernelInfo&) [with int OpSet = 8]FN11onnxruntime20OrtValueTensorSlicerIK8OrtValueEERS2_llEFN11onnxruntime20OrtValueTensorSlicerI8OrtValueEERS1_llE*ZN11onnxruntime4ScanILi8EEC1ERKNS_12OpKernelInfoEEUlRKSt6vectorImSaImEERKNS_6TensorERSA_E_*ZN11onnxruntime4ScanILi8EEC1ERKNS_12OpKernelInfoEEUlPvmE0_N11onnxruntime4ScanILi8EEEN11onnxruntime20OrtValueTensorSlicerIK8OrtValueE8IteratorEPFN11onnxruntime20OrtValueTensorSlicerIK8OrtValueEERS2_llEPFN11onnxruntime20OrtValueTensorSlicerI8OrtValueEERS1_llEfinal_output_mlvalue_. Output tensor rank was but input '' dimension . Input tensor rank was output_mlvaluescan_input_directionsscan_output_directionsscan_input_axes but expected scan_output_axes/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_9.ccAttempt to retrieve final output before it was set./onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_utils.hInvalid value in scan_output_axes for output Outputs from Scan are not optional and should never be null.Invalid value in scan_input_axes for input Number of entries in 'scan_input_axes' was gsl::narrow_cast<int64_t>(input_axes_.size()) == num_scan_inputs_Number of entries in 'scan_output_axes' was gsl::narrow_cast<int64_t>(output_axes_.size()) == num_scan_outputsconst OrtValue& onnxruntime::scan::detail::OutputIterator::GetOutput() constTransposeOutputonnxruntime::common::Status onnxruntime::ScanImpl::TransposeOutput()Executeonnxruntime::common::Status onnxruntime::ScanImpl::CreateLoopStateVariables(std::vector<onnxruntime::scan::detail::LoopStateVariable>&)CreateLoopStateVariablesAllocateOutputTensorsT* OrtValue::GetMutable() [with T = onnxruntime::Tensor]SetupInputsValidateInputInitializeComputeonnxruntime::common::Status onnxruntime::Scan<OpSet>::Compute(onnxruntime::OpKernelContext*) const [with int OpSet = 9]onnxruntime::common::Status onnxruntime::Scan<OpSet>::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) [with int OpSet = 9; std::string = std::basic_string<char>]onnxruntime::Scan<OpSet>::Scan(const onnxruntime::OpKernelInfo&) [with int OpSet = 9]FN11onnxruntime6common6StatusERKSt6vectorImSaImEERKNS_6TensorERS7_E*ZN11onnxruntime4ScanILi9EEC1ERKNS_12OpKernelInfoEEUlPvmE_N11onnxruntime4ScanILi9EEEPFN11onnxruntime6common6StatusERKSt6vectorImSaImEERKNS_6TensorERS7_ENumber of entries in '' was Invalid values in ' is not compatible with !is_concrete_shape_Misuse of LoopStateVariable. Attempt to move beyond end of sequence/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_utils.cciteration_num_ < sequence_len_Expected AllocateFinalOutput to have been called to before we increment the iteratornum_entries < 0 || gsl::narrow_cast<int64_t>(directions.size()) == num_entries'. 0 == forward. 1 == reverse.cur_iteration_ < num_iterations_Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.Mismatch between expected shape and shape from first outputThe subgraph in 'body' requires inputs but Scan was only given num_variadic_inputs == num_subgraph_inputsFailed to create output tensor for output #If shape was concrete we shouldn't be using a custom allocatoronnxruntime::scan::detail::OutputIterator& onnxruntime::scan::detail::OutputIterator::operator++()typename std::enable_if<(! std::is_const<T&>::value), T&>::type onnxruntime::OrtValueTensorSlicer<T>::Iterator::operator*() [with T = OrtValue; typename std::enable_if<(! std::is_const<T&>::value), T&>::type = OrtValue&]OrtValue& onnxruntime::scan::detail::OutputIterator::operator*()AllocateFinalOutputonnxruntime::common::Status onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput(const onnxruntime::TensorShape&)AllocateFinalBufferconst T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]Initializevoid onnxruntime::scan::detail::LoopStateVariable::Next()const T& OrtValue::Get() const [with T = onnxruntime::Tensor]operator()typename std::enable_if<(! std::is_const<T&>::value), T&>::type onnxruntime::OrtValueTensorSlicer<T>::Iterator::operator*() [with T = const OrtValue; typename std::enable_if<(! std::is_const<T&>::value), T&>::type = const OrtValue&]IterateSequenceCreateFeedsFetchesManagervoid onnxruntime::scan::detail::ReadDirections(const onnxruntime::OpKernelInfo&, const string&, std::vector<long int>&, int64_t)onnxruntime::scan::detail::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&, int, bool)*ZN11onnxruntime4scan6detail15IterateSequenceERNS_23OpKernelContextInternalERKNS_12SessionStateERSt6vectorINS1_17LoopStateVariableESaIS8_EERS7_INS_20OrtValueTensorSlicerIK8OrtValueE8IteratorESaISG_EEliiiRKS7_IPSE_SaISK_EERS7_ISt10unique_ptrINS1_14OutputIteratorESt14default_deleteISQ_EESaIST_EERKNS_19FeedsFetchesManagerEEUlRKNS_11TensorShapeERK13OrtMemoryInfoRSD_RbE_N11onnxruntime20OrtValueTensorSlicerI8OrtValueE8IteratorEParametricSoftplusT* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]ZNK11onnxruntime7contrib4GeluIfE7ComputeEPNS_15OpKernelContextEEUliE_N11onnxruntime7contrib10ScaledTanhIfEEN11onnxruntime7contrib4GeluIfEE5?AttnLSTMDEEPCPU_ATTN_LSTM}, actural: ], while found!, am_attn_size}, Got:}, Got: }. Got: , aw_attn_size}. Got:/onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.hstatic_cast<int>(activation_func_names.size()) == num_directions_ * 3Attention mechanism memory shape error! Expected: {Attention mechanism memory sequence lengths must have shape {Attention mechanism memory sequence lengths value must in (0, Attention memory layer weight shape error! Expected:{Attention query layer weight shape error! Expected:{Attention v weight shape error! Expected:{Attention layer weight shape error! Expected: {/onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.ccValidateInputsComputeImplvirtual onnxruntime::common::Status onnxruntime::contrib::DeepCpuAttnLstmOp::Compute(onnxruntime::OpKernelContext*) constonnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&)onnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOp(const onnxruntime::OpKernelInfo&)N11onnxruntime7contrib17DeepCpuAttnLstmOpEN11onnxruntime7contrib16AttentionWrapperIfEE row[ [seqno=/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention.ccinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0Input 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 1Input 3 is expected to have 1 dimension, got Inputs 3 and 0 shall have same length at dimension 0T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = int]Computeconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::contrib::AttentionBase::AttentionBase(const onnxruntime::OpKernelInfo&)N11onnxruntime7contrib13AttentionBaseEZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUliE_ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUliE0_ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUliE1_ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUliE2_N11onnxruntime7contrib9AttentionIfEE@@@@Shape of Input 0 is expected to have at least 1 dimension, got Input 1 is expected to have 1 dimension, got dimension 0 of Input 1 should have same length as the last dimension of input 0/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/bias_gelu_fusion.ccT* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]ComputeZNK11onnxruntime7contrib8BiasGeluIfE7ComputeEPNS_15OpKernelContextEEUliE_N11onnxruntime7contrib8BiasGeluIfEEinput index out of range/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/embed_layer_norm.ccT* onnxruntime::Tensor::MutableData() [with T = int]const T* onnxruntime::Tensor::Data() const [with T = int]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]ComputeZNK11onnxruntime7contrib14EmbedLayerNormIfE7ComputeEPNS_15OpKernelContextEEUliE_N11onnxruntime7contrib14EmbedLayerNormIfEE.)input_ids is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got segment_embedding is expected to have 2 dimensions, got word_embedding and position_embedding shall have same dimension 1word_embedding and segment_embedding shall have same dimension 1beta is expected to have 1 dimensions, got beta is expected to have size of gamma is expected to have 1 dimensions, got gamma is expected to have size of Input 0 and 1 shall have same shapeInput 0 and 7 (mask) shall have same shapeconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]/onnxruntime_src/onnxruntime/contrib_ops/cpu/cdist.hinfo.GetAttr<std::string>("metric", &metric).IsOK()The second input of CDist kernel has wrong shape: Input shape dimensions mismatch:The first input of CDist kernel has wrong shape: CDistsqeuclideanT* onnxruntime::Tensor::MutableData() [with T = float]T* onnxruntime::Tensor::MutableData() [with T = double]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::contrib::CDist<T>::CDist(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::contrib::CDist<T>::CDist(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime7contrib5CDistIfEEN11onnxruntime7contrib5CDistIdEEConvTransposeWithDynamicPadsN11onnxruntime7contrib28ConvTransposeWithDynamicPadsIfEEborder input dimensions insteadInput's height () + bottomBorder (Input's width () + rightBorder () + scale_[0] () + scale_[1] (CropAttribute border needs to be specified with four border elements, got Input is expected to have four dimensions corresponding to [N,C,H,W], got ) needs to be greater than or equal to the topBorder () needs to be greater than or equal to the leftBorder (/onnxruntime_src/onnxruntime/contrib_ops/cpu/crop.hT* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]ComputeN11onnxruntime7contrib8CropBaseEN11onnxruntime7contrib4CropIfEEbilinearCropAndResizeNull crop_size_ptr/onnxruntime_src/onnxruntime/contrib_ops/cpu/crop_and_resize.h specified. It should be either bilinear or nearestNumber of dimensions for crop size should be exactly 1const T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::contrib::CropAndResize<T>::CropAndResize(const onnxruntime::OpKernelInfo&) [with T = float]ZN11onnxruntime7contrib20CropAndResizeForwardIfEEvRKNS_11TensorShapeEPKT_fllS7_lPS5_RKSsPKiPNS_11concurrency10ThreadPoolEEUliE_N11onnxruntime7contrib13CropAndResizeIfEEDynamicSliceScale/onnxruntime_src/onnxruntime/contrib_ops/cpu/element_wise_ops.hinfo.GetAttr("scale", &scale_).IsOK()info.GetAttr("alpha", &alpha_).IsOK()info.GetAttr("beta", &beta_).IsOK()onnxruntime::contrib::Scale<T>::Scale(const onnxruntime::OpKernelInfo&) [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::contrib::Affine<T>::Affine(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime7contrib6AffineIfEEN11onnxruntime7contrib5ScaleIfEEExpandDimsAxis must be within range [ Axis is /onnxruntime_src/onnxruntime/contrib_ops/cpu/expand_dims.haxis_tensor->Shape().IsScalar()axis <= X_NumDims && axis >= -X_NumDimsAn axis tensor must be a scalar tensor.const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]const T* onnxruntime::Tensor::Data() const [with T = int]virtual onnxruntime::common::Status onnxruntime::contrib::ExpandDims::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime7contrib10ExpandDimsE/onnxruntime_src/onnxruntime/contrib_ops/cpu/fused_conv.ccGetFusedActivationAttr(info, activation_).IsOK()onnxruntime::contrib::FusedConvFloat::FusedConvFloat(const onnxruntime::OpKernelInfo&)N11onnxruntime7contrib14FusedConvFloatEleaky_relu_alphaonnxruntime::Gemm<T>::Gemm(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime7contrib9FusedGemmIfEEImageScalerBias size (/onnxruntime_src/onnxruntime/contrib_ops/cpu/image_scaler.hinfo.GetAttrs<float>("bias", bias_).IsOK()) does not match the number of channels (T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::contrib::ImageScaler<T>::ImageScaler(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime7contrib11ImageScalerIfEE/onnxruntime_src/onnxruntime/contrib_ops/cpu/layer_norm.ccop_kernel_info.GetAttr("axis", &axis_).IsOK()T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]ComputeT* onnxruntime::Tensor::MutableData() [with T = double]int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)const T* onnxruntime::Tensor::Data() const [with T = double]Computeonnxruntime::contrib::LayerNorm<T>::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::contrib::LayerNorm<T>::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = float]ZNK11onnxruntime7contrib9LayerNormIdE7ComputeEPNS_15OpKernelContextEEUliE_ZNK11onnxruntime7contrib9LayerNormIfE7ComputeEPNS_15OpKernelContextEEUliE_N11onnxruntime7contrib9LayerNormIfEEN11onnxruntime7contrib9LayerNormIdEEMatMulInteger16A != nullptr && B != nullptr/onnxruntime_src/onnxruntime/contrib_ops/cpu/matmul_integer16.ccconst T* onnxruntime::Tensor::Data() const [with T = short int]T* onnxruntime::Tensor::MutableData() [with T = int]Computeonnxruntime::common::Status onnxruntime::contrib::MatMulInteger16<T1, T2, T3>::Compute(onnxruntime::OpKernelContext*) const [with T1 = short int; T2 = short int; T3 = int]N11onnxruntime7contrib15MatMulInteger16IssiEEMaxpoolWithMask/onnxruntime_src/onnxruntime/contrib_ops/cpu/maxpool_with_mask.hT* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = int]const T* onnxruntime::Tensor::Data() const [with T = float]void onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) constvoid onnxruntime::PoolAttributes::InferOutputSize(const std::vector<long int>&, std::vector<long int>*, std::vector<long int>*) conststd::vector<long int> onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape&, int64_t, std::vector<long int>*) constconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]ComputeN11onnxruntime7contrib15MaxpoolWithMaskEacross_channelsnormalize_variance/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/mean_variance_normalization.hinfo.GetAttr<int64_t>("across_channels", &across_channels_).IsOK()info.GetAttr<int64_t>("normalize_variance", &normalize_variance_).IsOK()onnxruntime::MeanVarianceNormalization_0<T>::MeanVarianceNormalization_0(const onnxruntime::OpKernelInfo&, bool) [with T = float]MurmurHash3keys/onnxruntime_src/onnxruntime/contrib_ops/cpu/murmur_hash3.ccInvalid assumption of output element sizesizeof(uint32_t) == output_element_bytesconst T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]virtual onnxruntime::common::Status onnxruntime::contrib::MurmurHash3::Compute(onnxruntime::OpKernelContext*) constN11onnxruntime7contrib11MurmurHash3Einvalid channel countchannels_ > 0X_shape.NumDimensions() == 4channels_ <= Y_shape[1]Unsupported convolution size./onnxruntime_src/onnxruntime/contrib_ops/cpu/nchwc_ops.hinfo.GetAttr<int64_t>("channels", &channels_).IsOK()pool_attrs_.kernel_shape.size() == 2/onnxruntime_src/onnxruntime/contrib_ops/cpu/nchwc_ops.cc(X_shape[1] % MlasNchwcGetBlockSize()) == 0(static_cast<size_t>(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Not satsified: Y->Shape() == sum_shape output and sum shape must matchonnxruntime::common::Status onnxruntime::contrib::ReorderInput<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::common::Status onnxruntime::contrib::ReorderOutput<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]void onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) constvoid onnxruntime::PoolAttributes::InferOutputSize(const std::vector<long int>&, std::vector<long int>*, std::vector<long int>*) conststd::vector<long int> onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape&, int64_t, std::vector<long int>*) constonnxruntime::common::Status onnxruntime::contrib::NchwcPoolBase::NchwcPool(onnxruntime::OpKernelContext*, MLAS_POOLING_KIND) constvirtual onnxruntime::common::Status onnxruntime::contrib::NchwcConv::Compute(onnxruntime::OpKernelContext*) constComputeonnxruntime::contrib::NchwcPoolBase::NchwcPoolBase(const onnxruntime::OpKernelInfo&)onnxruntime::contrib::NchwcConv::NchwcConv(const onnxruntime::OpKernelInfo&)onnxruntime::contrib::ReorderOutput<T>::ReorderOutput(const onnxruntime::OpKernelInfo&) [with T = float]N11onnxruntime7contrib13NchwcPoolBaseEN11onnxruntime7contrib9NchwcConvEN11onnxruntime7contrib12NchwcMaxPoolEN11onnxruntime7contrib16NchwcAveragePoolEN11onnxruntime7contrib12ReorderInputIfEEN11onnxruntime7contrib13ReorderOutputIfEEy_scaleSampleOpT* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime7contrib8SampleOpIfEEinput is expected to have 3 dimensions, got gamma is expected to have 1 dimension, got Last dimension of gamma and input does not matchbeta is expected to have 1 dimension, got Last dimension of beta and input does not matchbias is expected to have 1 dimension, got Last dimension of bias and input does not matchskip is expected to have same shape as inputconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]ZNK11onnxruntime7contrib13SkipLayerNormIdE7ComputeEPNS_15OpKernelContextEEUliE_ZNK11onnxruntime7contrib13SkipLayerNormIfE7ComputeEPNS_15OpKernelContextEEUliE_N11onnxruntime7contrib13SkipLayerNormIfEEN11onnxruntime7contrib13SkipLayerNormIdEEq=̼+Tokenizermarkattribute mark is not setpad_valuemincharnummincharnum_ > 0separatorstokenexp!tokenexp.empty()separators must not be empty!separators.empty()Can not digest separators: Can not digest tokenexp: /onnxruntime_src/onnxruntime/contrib_ops/cpu/tokenizer.ccattribute pad_value is not setattribute mincharnum is not setattribute mincharnum must have a positive valueEither one of the separators OR tokenexp attributes required but none is setExpecting a non-empty tokenexpmincharnum is too big for char level tokenezation!char_tokenezation_ || mincharnum_ < 2Input string contains invalid utf8 chars: Match contains invalid utf8 chars: tensor(string) expected as inputInput dimensions are either [C] or [N][C] allowedconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]onnxruntime::contrib::Tokenizer::Tokenizer(const onnxruntime::OpKernelInfo&)N11onnxruntime7contrib9TokenizerEInput tensor to Unique op should be 1DT* onnxruntime::Tensor::MutableData() [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = float]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime7contrib6UniqueIfEEconv_window_sizechar_embedding_size embedding_size attribute: conv filter size: conv_window_size attribute: conv kernal size 1: Char embedding size: Conv kernal size 2 : WordConvEmbeddingConv filter size does not match embedding_size attribute.Conv kernal size 1 does not match conv_window_size attribute .Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Char embedding size does not match conv kernal size 2./onnxruntime_src/onnxruntime/contrib_ops/cpu/word_conv_embedding.ccconst T* onnxruntime::Tensor::Data() const [with T = float]T* onnxruntime::Tensor::MutableData() [with T = float]const T* onnxruntime::Tensor::Data() const [with T = int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]ComputeN11onnxruntime7contrib17WordConvEmbeddingEhӥhӥhӥhDateTimeTransformerconst T* onnxruntime::Tensor::Data() const [with T = long int]T* OrtValue::GetMutable() [with T = Microsoft::Featurizer::DateTimeFeaturizer::TimePoint]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]N11onnxruntime6automl19DateTimeTransformerEReal memory steps is not in (0, not support normalize yet.!normalize_/onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/bahdanau_attention.ccmem_steps <= max_memory_steps_ && mem_steps > 0void onnxruntime::contrib::BahdanauAttention<T>::PrepareMemory(const gsl::span<const T>&, const gsl::span<const int>&) [with T = float]onnxruntime::contrib::BahdanauAttention<T>::BahdanauAttention(onnxruntime::AllocatorPtr, const onnxruntime::logging::Logger&, int, int, int, int, int, bool, onnxruntime::concurrency::ThreadPool*) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>]N11onnxruntime7contrib19IAttentionMechanismIfEEN11onnxruntime7contrib17BahdanauAttentionIfEEunimplemented activation: activation_params count mismatchExpecting an instance of ExternalTimePoint/onnxruntime_src/onnxruntime/automl_ops/automl_types.ccsizeof(DateTimeFeaturizerTimePointData) == data_sizeconst T& OrtValue::Get() const [with T = Microsoft::Featurizer::DateTimeFeaturizer::TimePoint]static void onnxruntime::NonTensorTypeConverter<Microsoft::Featurizer::DateTimeFeaturizer::TimePoint>::ToContainer(const OrtValue&, size_t, void*)static void onnxruntime::NonTensorTypeConverter<Microsoft::Featurizer::DateTimeFeaturizer::TimePoint>::FromContainer(onnxruntime::MLDataType, const void*, size_t, OrtValue&)N11onnxruntime13NonTensorTypeIN9Microsoft10Featurizer18DateTimeFeaturizer9TimePointEEEN11onnxruntime10OpaqueTypeIN9Microsoft10Featurizer18DateTimeFeaturizer9TimePointEXadL_ZNS_15kMsAutoMLDomainEEEXadL_ZNS_14kTimepointNameEEEEEDateTimeFeaturizer_TimePointcom.microsoft.automlSSE, 12x4, depth 2allocation failuregemmlowp error: %s N8gemmlowp10KernelBaseEN8gemmlowp24SSE4_64_Kernel12x4Depth2EN8gemmlowp17DefaultKernelImplILb0ELb0EEEN8gemmlowp4TaskEN8gemmlowp13DefaultKernelINS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEES3_EEEEN8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE1ELSD_1ELSD_1ENS_9VectorDupIKiLNS_11VectorShapeE0EEENSE_ISF_LSG_1EEESt5tupleIINS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEEN8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE1ELSD_1ELSD_1ENS_9VectorDupIKiLNS_11VectorShapeE0EEENSE_ISF_LSG_1EEESt5tupleIINS_23OutputStageBiasAdditionINS_9VectorMapISF_LSG_0EEEEENS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEEN8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE0ELSD_0ELSD_0ENS_9VectorDupIKiLNS_11VectorShapeE1EEENSE_ISF_LSG_0EEESt5tupleIINS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEEN8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE0ELSD_0ELSD_0ENS_9VectorDupIKiLNS_11VectorShapeE1EEENSE_ISF_LSG_0EEESt5tupleIINS_23OutputStageBiasAdditionINS_9VectorMapISF_LSG_1EEEEENS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEE@@Gemv float found an unexpected CBLAS_TRANSPOSE input of/onnxruntime_src/onnxruntime/core/util/math_cpu.ccvoid onnxruntime::math::Im2colNd<T, 2>::operator()(const T*, const int64_t*, const int64_t*, int64_t, int64_t, const int64_t*, const int64_t*, const int64_t*, const int64_t*, int64_t, T*, bool, T) [with T = float; int64_t = long int]/onnxruntime_src/onnxruntime/core/util/protobuf_parsing_utils.ccCan't BackUp() after failed Next().CHECK failed: !is_closed_: close() failed: N6google8protobuf2io19CopyingOutputStreamEN6google8protobuf2io15FileInputStream22CopyingFileInputStreamEN6google8protobuf2io15FileInputStreamEN6google8protobuf2io16FileOutputStream23CopyingFileOutputStreamEN6google8protobuf2io16FileOutputStreamEOrtMemoryInfo: [ id: mem_type:CudaCudaPinnedSpecified device is not supported.N11onnxruntime12CPUAllocatorESt14default_deleteIN11onnxruntime16IDeviceAllocatorEESt14default_deleteIN11onnxruntime8BFCArenaEESt19_Sp_counted_deleterIPN11onnxruntime8BFCArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt19_Sp_counted_deleterIPN11onnxruntime16IDeviceAllocatorESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE/onnxruntime_src/onnxruntime/core/framework/bfc_arena.h/onnxruntime_src/onnxruntime/core/framework/bfc_arena.cc!c->in_use() && (c->bin_num != kInvalidBinNum)!c->in_use() && (c->bin_num == kInvalidBinNum)p_int < base_int + memory_size_!c1->in_use() && !c2->in_use()bin->free_chunks.count(h) == 1Creating bin of max chunk size BinForSize(bin_size) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size * 2) != BinFromIndex(b)0 == memory_size % kMinAllocationSizeBinFromIndex(c->bin_num)->free_chunks.erase(h) > 0c->in_use() && (c->bin_num == kInvalidBinNum)b->free_chunks.size() == bin_info.total_chunks_in_bin - bin_info.total_chunks_in_use client-requested in use in bin. Summary of in-use Chunks by size: BFC Arena ran out of memory trying . Current allocation summary follows.reserved_chunks_.find(ptr) == reserved_chunks_.end()Could not find Region for h < chunks_.size() Size: | Requested Size: | in_use: p_int >= base_intc2->prev == h1h != kInvalidChunkHandlec->bin_num == bin_num!chunk->in_use()Extended allocation by bytes.Total allocated bytes: Allocated memory at Could not find chunk in binChunkFree Bin (): Total Chunks: , Chunks in use: allocated for chunks. in use in bin. Bin for , Chunk State: , prev: , next: Chunks of size totalling Sum Total of in-use chunks: Limit: InUse: TotalAllocated: MaxInUse: NumAllocs: MaxAllocSize: Stats: -tried to allocate 0 bytesto allocate DumpMemoryLogvoid onnxruntime::BFCArena::DumpMemoryLog(size_t)std::array<onnxruntime::BFCArena::BinDebugInfo, 21ul> onnxruntime::BFCArena::get_bin_debug_info()void onnxruntime::BFCArena::FreeAndMaybeCoalesce(onnxruntime::BFCArena::ChunkHandle)void onnxruntime::BFCArena::RemoveFreeChunkFromBin(onnxruntime::BFCArena::ChunkHandle)void onnxruntime::BFCArena::RemoveFreeChunkIterFromBin(onnxruntime::BFCArena::Bin::FreeChunkSet*, const iterator&)void onnxruntime::BFCArena::InsertFreeChunkIntoBin(onnxruntime::BFCArena::ChunkHandle)void onnxruntime::BFCArena::Merge(onnxruntime::BFCArena::ChunkHandle, onnxruntime::BFCArena::ChunkHandle)void onnxruntime::BFCArena::DeallocateRawInternal(void*)void onnxruntime::BFCArena::SplitChunk(onnxruntime::BFCArena::ChunkHandle, size_t)void* onnxruntime::BFCArena::FindChunkPtr(onnxruntime::BFCArena::BinNum, size_t, size_t)AllocateRawInternalvirtual void* onnxruntime::BFCArena::Reserve(size_t)int onnxruntime::BFCArena::AllocationRegion::IndexFor(const void*) constRegionForonnxruntime::BFCArena::AllocationRegion::AllocationRegion(void*, size_t)Extendonnxruntime::BFCArena::Chunk* onnxruntime::BFCArena::ChunkFromHandle(onnxruntime::BFCArena::ChunkHandle)onnxruntime::BFCArena::BFCArena(std::unique_ptr<onnxruntime::IDeviceAllocator>, size_t)BFCArenaN11onnxruntime8BFCArenaEfff?/onnxruntime_src/onnxruntime/core/framework/data_transfer.ccsrc.SizeInBytes() == dst.SizeInBytes()virtual onnxruntime::common::Status onnxruntime::CPUDataTransfer::CopyTensor(const onnxruntime::Tensor&, onnxruntime::Tensor&, int) constN11onnxruntime13IDataTransferEN11onnxruntime15CPUDataTransferEDevice: [ memory_type: device_id:Tensor size mismatchThere's no data transfer registered for copying tensors from data_transfer registered is nullptr.(null)tensor(bfloat16)Not implementedsparse tensor type elem_proto != nullptrvalue_proto != nullptrMLDataType for: hXH8(ءx/onnxruntime_src/onnxruntime/core/framework/data_types.ccthisProto->value_case() == TypeProto::ValueCase::kTensorTypeutils::HasElemType(thisProto->tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeutils::HasElemType(thisProto->sparse_tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSequenceTypeutils::HasElemType(thisProto->sequence_type())thisProto->value_case() == TypeProto::ValueCase::kMapTypeutils::HasKeyType(thisProto->map_type())thisProto->value_case() == TypeProto::ValueCase::kOpaqueTypeInvalid DataTypeImpl TypeProto definitionOnly ONNX MLDataType can be registeredWe do not expect duplicate registration of types for: expected to be a registered ONNX type/onnxruntime_src/include/onnxruntime/core/framework/data_types.h is not currently registered or supportedl$ܯp0$$4DTdt $$onnxruntime::utils::ContainerChecker::ContainerChecker(onnxruntime::MLDataType)void onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataType(onnxruntime::MLDataType)static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = std::map<long int, float>]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = std::map<std::basic_string<char>, float>]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = onnxruntime::BFloat16]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = onnxruntime::MLFloat16]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = std::basic_string<char>]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = bool]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = long unsigned int]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = long int]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = unsigned int]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = int]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = short unsigned int]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = short int]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = unsigned char]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = signed char]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = double]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = float]static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = double]static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = float]static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = long int]static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = std::basic_string<char>]static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = double]static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = float]static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = long int]static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = std::basic_string<char>]virtual void onnxruntime::NonTensorTypeBase::ToDataContainer(const OrtValue&, size_t, void*) constvirtual void onnxruntime::NonTensorTypeBase::FromDataContainer(const void*, size_t, OrtValue&) constbool onnxruntime::NonTensorTypeBase::IsOpaqueCompatible(const onnx::TypeProto&) constbool onnxruntime::NonTensorTypeBase::IsSequenceCompatible(const onnx::TypeProto&) constbool onnxruntime::NonTensorTypeBase::IsMapCompatible(const onnx::TypeProto&) constvirtual bool onnxruntime::SequenceTensorTypeBase::IsCompatible(const onnx::TypeProto&) constvirtual bool onnxruntime::SparseTensorTypeBase::IsCompatible(const onnx::TypeProto&) constvirtual bool onnxruntime::TensorTypeBase::IsCompatible(const onnx::TypeProto&) constbool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Sequence&, const onnx::TypeProto_Sequence&)bool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Map&, const onnx::TypeProto_Map&)GetElementTypeGetElementTypeGetElementTypeN11onnxruntime12DataTypeImplEN11onnxruntime14TensorTypeBaseEN11onnxruntime20SparseTensorTypeBaseEN11onnxruntime17NonTensorTypeBaseEN11onnxruntime22SequenceTensorTypeBaseEN11onnxruntime21PrimitiveDataTypeBaseEN11onnxruntime10TensorTypeIiEEN11onnxruntime10TensorTypeIfEEN11onnxruntime10TensorTypeIbEEN11onnxruntime10TensorTypeISsEEN11onnxruntime10TensorTypeIaEEN11onnxruntime10TensorTypeIhEEN11onnxruntime10TensorTypeItEEN11onnxruntime10TensorTypeIsEEN11onnxruntime10TensorTypeIlEEN11onnxruntime10TensorTypeIdEEN11onnxruntime10TensorTypeIjEEN11onnxruntime10TensorTypeImEEN11onnxruntime10TensorTypeINS_9MLFloat16EEEN11onnxruntime10TensorTypeINS_8BFloat16EEEN11onnxruntime16SparseTensorTypeIiEEN11onnxruntime16SparseTensorTypeIfEEN11onnxruntime16SparseTensorTypeIbEEN11onnxruntime16SparseTensorTypeIaEEN11onnxruntime16SparseTensorTypeIhEEN11onnxruntime16SparseTensorTypeItEEN11onnxruntime16SparseTensorTypeIsEEN11onnxruntime16SparseTensorTypeIlEEN11onnxruntime16SparseTensorTypeIdEEN11onnxruntime16SparseTensorTypeIjEEN11onnxruntime16SparseTensorTypeImEEN11onnxruntime16SparseTensorTypeINS_9MLFloat16EEEN11onnxruntime16SparseTensorTypeINS_8BFloat16EEEN11onnxruntime13NonTensorTypeISt3mapISsSsSt4lessISsESaISt4pairIKSsSsEEEEEN11onnxruntime7MapTypeISt3mapISsSsSt4lessISsESaISt4pairIKSsSsEEEEEN11onnxruntime13NonTensorTypeISt3mapISslSt4lessISsESaISt4pairIKSslEEEEEN11onnxruntime7MapTypeISt3mapISslSt4lessISsESaISt4pairIKSslEEEEEN11onnxruntime13NonTensorTypeISt3mapISsfSt4lessISsESaISt4pairIKSsfEEEEEN11onnxruntime7MapTypeISt3mapISsfSt4lessISsESaISt4pairIKSsfEEEEEN11onnxruntime13NonTensorTypeISt3mapISsdSt4lessISsESaISt4pairIKSsdEEEEEN11onnxruntime7MapTypeISt3mapISsdSt4lessISsESaISt4pairIKSsdEEEEEN11onnxruntime13NonTensorTypeISt3mapIlSsSt4lessIlESaISt4pairIKlSsEEEEEN11onnxruntime7MapTypeISt3mapIlSsSt4lessIlESaISt4pairIKlSsEEEEEN11onnxruntime13NonTensorTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEEN11onnxruntime7MapTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEEN11onnxruntime13NonTensorTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEEN11onnxruntime7MapTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEEN11onnxruntime13NonTensorTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEEN11onnxruntime7MapTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEEN11onnxruntime18SequenceTensorTypeIfEEN11onnxruntime18SequenceTensorTypeIdEEN11onnxruntime18SequenceTensorTypeIaEEN11onnxruntime18SequenceTensorTypeIhEEN11onnxruntime18SequenceTensorTypeIsEEN11onnxruntime18SequenceTensorTypeItEEN11onnxruntime18SequenceTensorTypeIiEEN11onnxruntime18SequenceTensorTypeIjEEN11onnxruntime18SequenceTensorTypeIlEEN11onnxruntime18SequenceTensorTypeImEEN11onnxruntime18SequenceTensorTypeIbEEN11onnxruntime18SequenceTensorTypeISsEEN11onnxruntime18SequenceTensorTypeINS_9MLFloat16EEEN11onnxruntime18SequenceTensorTypeINS_8BFloat16EEEN11onnxruntime13NonTensorTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEEN11onnxruntime12SequenceTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEEN11onnxruntime13NonTensorTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEEN11onnxruntime12SequenceTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEEN11onnxruntime17PrimitiveDataTypeIiEEN11onnxruntime17PrimitiveDataTypeIfEEN11onnxruntime17PrimitiveDataTypeIbEEN11onnxruntime17PrimitiveDataTypeISsEEN11onnxruntime17PrimitiveDataTypeIaEEN11onnxruntime17PrimitiveDataTypeIhEEN11onnxruntime17PrimitiveDataTypeItEEN11onnxruntime17PrimitiveDataTypeIsEEN11onnxruntime17PrimitiveDataTypeIlEEN11onnxruntime17PrimitiveDataTypeIdEEN11onnxruntime17PrimitiveDataTypeIjEEN11onnxruntime17PrimitiveDataTypeImEEN11onnxruntime17PrimitiveDataTypeINS_9MLFloat16EEEN11onnxruntime17PrimitiveDataTypeINS_8BFloat16EEEZN11onnxruntime19data_types_internal16DataTypeRegistryC1EvEUlPKNS_12DataTypeImplEE_ZN11onnxruntime19data_types_internal16DataTypeRegistryC1EvEUlPKNS_12DataTypeImplEE0_SsN11onnxruntime9MLFloat16EN11onnxruntime8BFloat16ESt3mapISsfSt4lessISsESaISt4pairIKSsfEEESt3mapIlfSt4lessIlESaISt4pairIKlfEEECaught exception while destructing CustomOpsLoader with message: /onnxruntime_src/onnxruntime/core/framework/ex_lib_loader.ccCaught exception while loading custom ops with message: Unloading DSO Failed to unload DSO: A dso with name has already been loaded.LoadExternalLib~ExLibLoaderN11onnxruntime11ExLibLoaderEMemory pattern planner is not enabled on this execution framework./onnxruntime_src/onnxruntime/core/framework/execution_frame.hort_value_index >= 0 && static_cast<size_t>(ort_value_index) < all_values_size_/onnxruntime_src/onnxruntime/core/framework/node_index_info.hoffset >= 0 && static_cast<size_t>(offset) < node_values_size_/onnxruntime_src/onnxruntime/core/framework/execution_frame.ccort_value_idx >= 0 && static_cast<size_t>(ort_value_idx) < alloc_plan.size()TraceAllocation for ort_value_idx=OrtValue shape verification failed. Current shape:shape && tensor.Shape() == *shapeShape mismatch attempting to re-use buffer. . Validate usage of dim_value (values should be > 0) and dim_param (all values with the same string should equate to the same size) in shapes in the model. entries which doesn't match the number of fetches the frame was initialized with of Fetches vector passed to GetOutputs contains feeds.size() == feed_mlvalue_idxs.size()fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size()node_index_info and ort_value_idx_map are out of sync and cannot be usednode_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()Trying to allocate memory for unused optional inputs/outputsTensor shape cannot contain any negative value, block in memory pattern size is: , fall back to default allocation behavior, block not found in target location. fall back to default allocation behaviorort_value_index >= 0 && static_cast<size_t>(ort_value_index) < alloc_plan.size()We don't expect custom allocators for non-tensor types, so a shape is mandatory here.Allocation of tensor types requires a shape.buffers_.find(mem_patterns_->locations[i]) == buffers_.end() size=TraceFree for ort_value_idx= Requested shape:invalid index ort_value.Fence() == nullptrFor ort_value with index: but the actually size is: Invalid allocation kind: mlvalue.Fence() == nullptrTraceFreevoid onnxruntime::ExecutionFrame::TraceFree(int)TraceAllocateconst onnxruntime::AllocPlanPerValue& onnxruntime::ExecutionFrame::GetAllocationPlan(int)ReleaseMLValueImplonnxruntime::common::Status onnxruntime::AllocateSparseTensor(OrtValue&, const onnxruntime::DataTypeImpl&, onnxruntime::AllocatorPtr, const onnxruntime::TensorShape&, size_t, bool, const onnxruntime::SessionState&)AllocateAsPerAllocationPlanonnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlan(OrtValue&, int, const onnxruntime::TensorShape*, size_t)T* OrtValue::GetMutable() [with T = onnxruntime::Tensor]AllocateMLValueTensorPreAllocateBufferAllocateMLValueTensorSelfOwnBufferHelperonnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper(OrtValue&, int, onnxruntime::MLDataType, const OrtMemoryInfo&, const onnxruntime::TensorShape&, bool)onnxruntime::ExecutionFrame::ExecutionFrame(const std::vector<int>&, const std::vector<OrtValue>&, const std::vector<int>&, const std::vector<OrtValue>&, const std::unordered_map<long unsigned int, std::function<onnxruntime::common::Status(const onnxruntime::TensorShape&, const OrtMemoryInfo&, OrtValue&, bool&)> >&, const onnxruntime::SessionState&)int onnxruntime::NodeIndexInfo::GetMLValueIndex(int) constconst OrtValue& onnxruntime::IExecutionFrame::GetMLValue(int) constconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(int, const onnxruntime::TensorShape*, OrtValue*&, size_t)onnxruntime::IExecutionFrame::IExecutionFrame(const std::vector<int>&, const std::vector<OrtValue>&, const std::unordered_map<int, OrtValue>&, const std::vector<int>&, const std::vector<OrtValue>&, const OrtValueNameIdxMap&, const onnxruntime::NodeIndexInfo&)N11onnxruntime15IExecutionFrameEN11onnxruntime14ExecutionFrameEduplicated allocator/onnxruntime_src/onnxruntime/core/framework/execution_provider.ccvoid onnxruntime::IExecutionProvider::InsertAllocator(onnxruntime::AllocatorPtr)N11onnxruntime18IExecutionProviderEError mapping feeds: Error mapping output names: /onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.ccinput_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknownvoid onnxruntime::FeedsFetchesManager::SetDeviceCopyChecks(onnxruntime::DeviceCopyCheck, onnxruntime::DeviceCopyCheck)onnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(const std::vector<std::basic_string<char> >&, const std::vector<std::basic_string<char> >&, const onnxruntime::OrtValueNameIdxMap&)FeedsFetchesInfoMapNamesToMLValueIdxs/onnxruntime_src/onnxruntime/core/framework/func_kernel.hcreate_func(&context, &func_state_) == 0/onnxruntime_src/onnxruntime/core/framework/graph_partitioner.ccProvider doesn't return correct number of compiled functionsnode_compute_funcs.size() == nodes_need_compile.size()No provider specified.1 == capability->nodes.size()onnxruntime::FunctionKernel::FunctionKernel(const onnxruntime::OpKernelInfo&)onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constonnxruntime::Node* onnxruntime::PlaceNode(onnxruntime::Graph&, std::unique_ptr<onnxruntime::IndexedSubGraph>, const onnxruntime::KernelRegistryManager&, const string&, int&)onnxruntime::common::Status onnxruntime::GraphPartitioner::Partition(onnxruntime::Graph&, bool, onnxruntime::FuncManager&) constPartitionN11onnxruntime14FunctionKernelEOp: Incompatible types.kernel def can't be NULLFailed to add kernel for Version mismatch. node_version: kernel start version: kernel_end_version: kernel is not supported in Failed to find kernel for : Conflicting with a registered kernel with op versions./onnxruntime_src/onnxruntime/core/framework/kernel_registry.cclen <= op_schema.inputs().size()Failed to create kernel for op: since it was ill-formed during registration Encountered following errors: (TryFindKernelvoid onnxruntime::{anonymous}::TraverseFormalParametersWithTypeProto(const onnxruntime::Node&, ParamFilterFn, TraverseFn) [with ParamFilterFn = onnxruntime::{anonymous}::TypeBindingResolver::Resolve(const string&) const::__lambda9; TraverseFn = onnxruntime::{anonymous}::TypeBindingResolver::Resolve(const string&) const::__lambda10]void onnxruntime::{anonymous}::TraverseFormalParametersWithTypeProto(const onnxruntime::Node&, ParamFilterFn, TraverseFn) [with ParamFilterFn = onnxruntime::{anonymous}::TypeBindingResolver::TypeBindingResolver(const onnxruntime::Node&, bool)::__lambda7; TraverseFn = onnxruntime::{anonymous}::TypeBindingResolver::TypeBindingResolver(const onnxruntime::Node&, bool)::__lambda8]The node is not placed on any Execution Provider, therefore, can't find a suitable kernel for The node is not placed on any Execution Provider (node found duplicated provider in KernelRegistryManagernullptr != type_proto/onnxruntime_src/onnxruntime/core/framework/mldata_type_utils.ccconst onnxruntime::DataTypeImpl* onnxruntime::utils::GetMLDataType(const onnxruntime::NodeArg&)/onnxruntime_src/onnxruntime/core/framework/node_index_info.cconnxruntime::NodeIndexInfo::Init(const TValidNodes&, onnxruntime::NodeIndex, const OrtValueNameIdxMap&) [with TValidNodes = onnxruntime::ValidNodes<const std::vector<const onnxruntime::Node*> >; onnxruntime::NodeIndex = long unsigned int; onnxruntime::OrtValueNameIdxMap = onnxruntime::OrtValueNameIdxMap]::__lambda8onnxruntime::NodeIndexInfo::Init(const TValidNodes&, onnxruntime::NodeIndex, const OrtValueNameIdxMap&) [with TValidNodes = onnxruntime::GraphNodes; onnxruntime::NodeIndex = long unsigned int; onnxruntime::OrtValueNameIdxMap = onnxruntime::OrtValueNameIdxMap]::__lambda8ZN11onnxruntime13NodeIndexInfo4InitINS_10GraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ZN11onnxruntime13NodeIndexInfo4InitINS_10GraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_:tensor type Tensor types should have been handled already|0||||{||0|LlLconst T& OrtValue::Get() const [with T = onnxruntime::SparseTensor]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]TempSpace allocator not foundExecution frame was nullframe != nullptrOpKernel was nullkernel != nullptr. Num args is Invalid arg_num of arg_num < arg_counts.size()/onnxruntime_src/onnxruntime/core/framework/op_kernel.ccnode_offsets_index < node_offsets_size_OrtValue* onnxruntime::OpKernelContext::GetOrCreateOutputMLValue(int)int onnxruntime::OpKernelContext::NumVariadicInputs(size_t) constOrtValue* onnxruntime::OpKernelContext::OutputMLValue(int, const onnxruntime::TensorShape&, size_t)T* OrtValue::GetMutable() [with T = onnxruntime::Tensor]int onnxruntime::NodeIndexInfo::GetNodeOffset(onnxruntime::NodeIndex) constonnxruntime::OpKernelContext::OpKernelContext(onnxruntime::IExecutionFrame*, const onnxruntime::OpKernel*, onnxruntime::concurrency::ThreadPool*, const onnxruntime::logging::Logger&)cannot find allocator/onnxruntime_src/onnxruntime/core/framework/op_kernel_info.ccconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]const OrtMemoryInfo& onnxruntime::OpKernelInfo::GetMemoryInfo(int, OrtMemType) const'is defined.No attribute with name:'attr != nullptrNo attribute with this name is defined./onnxruntime_src/onnxruntime/core/framework/op_node_proto_helper.ccvalues.size() == static_cast<size_t>(attr->floats_size())values.size() == static_cast<size_t>(attr->ints_size())values.size() == static_cast<size_t>(attr->strings_size())Attibute name and type don't match/onnxruntime_src/include/onnxruntime/core/framework/op_node_proto_helper.hvalues.size() == static_cast<size_t>(attr->tensors_size())values.size() == static_cast<size_t>(attr->graphs_size())ort_value.IsAllocated(). Shape:. Dimension 0 is Invalid dim0_offset of dim0_offset < dim0_size/onnxruntime_src/onnxruntime/core/framework/ort_value_tensor_slicer.ccInsufficient dimensions to slice on gsl::narrow_cast<int64_t>(tensor_shape.NumDimensions()) >= slice_dimensionCan't slice a non-tensor OrtValue. Type was OrtValue has not been allocated so can't be sliced.static onnxruntime::OrtValueTensorSlicer<T> onnxruntime::OrtValueTensorSlicer<T>::Create(T&, int64_t, int64_t) [with T = const OrtValue; int64_t = long int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]static onnxruntime::OrtValueTensorSlicer<T> onnxruntime::OrtValueTensorSlicer<T>::Create(T&, int64_t, int64_t) [with T = OrtValue; int64_t = long int]node_index_info_ with error: duplicated ort_value index:SaveMLValueNameIndexMapping is used by node ) and node Entry exists in node for attribute SetGraphAndCreateKernels must be called prior to GetExecutionInfo./onnxruntime_src/onnxruntime/core/framework/session_state.ccFailed to find input name in the mapping: Failed to find output name in the mapping: Could not create kernel for node: as there's no execution provider allocated.Kernel creation failed for node: . Do you have duplicated calls to SessionState::AddInitializedTensor function?Done saving OrtValue mappings.Using an input in multiple nodes on different devices is not supported currently. Input:Only one node should produce an output. Existing entry for output_names_to_nodeinfo.empty()existing_entries.find(attribute_name) == existing_entries.cend()const onnxruntime::NodeIndexInfo& onnxruntime::SessionState::GetNodeIndexInfo() constvoid onnxruntime::SessionState::AddSubgraphSessionState(onnxruntime::NodeIndex, const string&, std::unique_ptr<onnxruntime::SessionState>)void onnxruntime::SessionState::AddOutputNameToNodeInfoMapping(const string&, const onnxruntime::SessionState::NodeInfo&)SetGraph/onnxruntime_src/onnxruntime/core/framework/session_state_initializer.ccOrtValue indexes should have been populated.ort_value_name_idx_map.MaxIdx() > 0Invalid allocation info. Provider name = string tensor is not supported for copying between allocatorsInternal error. The preallocated buffer is too small. Requires Failed to copy tensor to execution provider: Done saving initialized tensorsExecution plan was not found in SessionState. CreatePlan must be called first.Saving initialized tensors., Got Deserialize tensor SubgraphGraphexec_plan_ptr input with name is not used by any node.ForEachWithIndexoperator()operator()SaveInputOutputNamesToNodeMappingDeserializeTensorProtoonnxruntime::common::Status onnxruntime::SaveInitializedTensors(const onnxruntime::Env&, const std::basic_string<char>&, const onnxruntime::Graph&, const onnxruntime::ExecutionProviders&, const OrtValueNameIdxMap&, onnxruntime::ITensorAllocator*, const T&, const onnxruntime::logging::Logger&, const onnxruntime::DataTransferManager&) [with T = onnxruntime::SessionStateInitializer::CreatePlan(const onnxruntime::Node*, const onnxruntime::ConstPointerContainer<std::vector<onnxruntime::NodeArg*> >*, ExecutionMode)::__lambda17; onnxruntime::OrtValueNameIdxMap = onnxruntime::OrtValueNameIdxMap]SaveInitializedTensorsonnxruntime::common::Status onnxruntime::SessionStateInitializer::CreatePlan(const onnxruntime::Node*, const onnxruntime::ConstPointerContainer<std::vector<onnxruntime::NodeArg*> >*, ExecutionMode)CreatePlan*ZN11onnxruntimeL33SaveInputOutputNamesToNodeMappingERKNS_5GraphERKNS_21KernelRegistryManagerERNS_12SessionStateEPKNS_21ConstPointerContainerISt6vectorIPNS_7NodeArgESaISB_EEEEEUlRKSA_mE_*ZN11onnxruntimeL33SaveInputOutputNamesToNodeMappingERKNS_5GraphERKNS_21KernelRegistryManagerERNS_12SessionStateEPKNS_21ConstPointerContainerISt6vectorIPNS_7NodeArgESaISB_EEEEEUlRKSA_mE0_N11onnxruntime25ISequentialPlannerContextEN11onnxruntime24SequentialPlannerContextEtensor size overflowshape.Size() must >=0dtype_ != nullptrp_type != nullptr/onnxruntime_src/onnxruntime/core/framework/tensor.ccTensor is expected to contain one of the primitive data types. Got: tensor failed memory size calculationvoid onnxruntime::Tensor::Init(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, onnxruntime::AllocatorPtr, int64_t)size_t onnxruntime::Tensor::SizeInBytes() constonnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, std::shared_ptr<onnxruntime::IAllocator>, int64_t)onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, const OrtMemoryInfo&, int64_t)Internal error.Mem pattern for initializer ' is not found' failedduplicated location/onnxruntime_src/onnxruntime/core/framework/tensor_allocator_with_mem_pattern.hWeight buffer for initializer 'Get preallocated buffer for initializer 'Failed to get allocator for location: TraceFinalizePlanN11onnxruntime16ITensorAllocatorEN11onnxruntime29TensorAllocatorWithMemPatternEInvalid dimension of dimension <= num_dims/onnxruntime_src/onnxruntime/core/framework/tensor_shape.ccdimstart <= dimend && dimend <= size()Invalid tensor shape slice argument. for SizeFromDimension. Tensor has onnxruntime::TensorShape onnxruntime::TensorShape::Slice(size_t, size_t) constint64_t onnxruntime::TensorShape::SizeFromDimension(size_t) constint64_t onnxruntime::TensorShape::SizeToDimension(size_t) constOrtValue is not a TensorArgument is not a tensor/onnxruntime_src/onnxruntime/core/framework/tensor_type_and_shape.cc +tried to allocate 0 bytesto allocate DumpMemoryLogvoid onnxruntime::BFCArena::DumpMemoryLog(size_t)std::array<onnxruntime::BFCArena::BinDebugInfo, 21ul> onnxruntime::BFCArena::get_bin_debug_info()void onnxruntime::BFCArena::FreeAndMaybeCoalesce(onnxruntime::BFCArena::ChunkHandle)void onnxruntime::BFCArena::RemoveFreeChunkFromBin(onnxruntime::BFCArena::ChunkHandle)void onnxruntime::BFCArena::RemoveFreeChunkIterFromBin(onnxruntime::BFCArena::Bin::FreeChunkSet*, const iterator&)void onnxruntime::BFCArena::InsertFreeChunkIntoBin(onnxruntime::BFCArena::ChunkHandle)void onnxruntime::BFCArena::Merge(onnxruntime::BFCArena::ChunkHandle, onnxruntime::BFCArena::ChunkHandle)void onnxruntime::BFCArena::DeallocateRawInternal(void*)void onnxruntime::BFCArena::SplitChunk(onnxruntime::BFCArena::ChunkHandle, size_t)void* onnxruntime::BFCArena::FindChunkPtr(onnxruntime::BFCArena::BinNum, size_t, size_t)AllocateRawInternalvirtual void* onnxruntime::BFCArena::Reserve(size_t)int onnxruntime::BFCArena::AllocationRegion::IndexFor(const void*) constRegionForonnxruntime::BFCArena::AllocationRegion::AllocationRegion(void*, size_t)Extendonnxruntime::BFCArena::Chunk* onnxruntime::BFCArena::ChunkFromHandle(onnxruntime::BFCArena::ChunkHandle)onnxruntime::BFCArena::BFCArena(std::unique_ptr<onnxruntime::IDeviceAllocator>, size_t)BFCArenaN11onnxruntime8BFCArenaEfff?/onnxruntime_src/onnxruntime/core/framework/data_transfer.ccsrc.SizeInBytes() == dst.SizeInBytes()virtual onnxruntime::common::Status onnxruntime::CPUDataTransfer::CopyTensor(const onnxruntime::Tensor&, onnxruntime::Tensor&, int) constN11onnxruntime13IDataTransferEN11onnxruntime15CPUDataTransferEDevice: [ memory_type: device_id:Tensor size mismatchThere's no data transfer registered for copying tensors from data_transfer registered is nullptr.(null)tensor(bfloat16)Not implementedsparse tensor type elem_proto != nullptrvalue_proto != nullptrMLDataType for: DؠȠhx8(DD/onnxruntime_src/onnxruntime/core/framework/data_types.ccthisProto->value_case() == TypeProto::ValueCase::kTensorTypeutils::HasElemType(thisProto->tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeutils::HasElemType(thisProto->sparse_tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSequenceTypeutils::HasElemType(thisProto->sequence_type())thisProto->value_case() == TypeProto::ValueCase::kMapTypeutils::HasKeyType(thisProto->map_type())thisProto->value_case() == TypeProto::ValueCase::kOpaqueTypeInvalid DataTypeImpl TypeProto definitionOnly ONNX MLDataType can be registeredWe do not expect duplicate registration of types for: expected to be a registered ONNX type/onnxruntime_src/include/onnxruntime/core/framework/data_types.h is not currently registered or supportedDl$@x0$<Tlonnxruntime::utils::ContainerChecker::ContainerChecker(onnxruntime::MLDataType)void onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataType(onnxruntime::MLDataType)static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = std::map<long int, float>]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = std::map<std::basic_string<char>, float>]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = onnxruntime::BFloat16]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = onnxruntime::MLFloat16]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = std::basic_string<char>]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = bool]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = long unsigned int]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = long int]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = unsigned int]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = int]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = short unsigned int]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = short int]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = unsigned char]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = signed char]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = double]static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = float]static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = double]static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = float]static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = long int]static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = std::basic_string<char>]static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = double]static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = float]static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = long int]static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = std::basic_string<char>]virtual void onnxruntime::NonTensorTypeBase::ToDataContainer(const OrtValue&, size_t, void*) constvirtual void onnxruntime::NonTensorTypeBase::FromDataContainer(const void*, size_t, OrtValue&) constbool onnxruntime::NonTensorTypeBase::IsOpaqueCompatible(const onnx::TypeProto&) constbool onnxruntime::NonTensorTypeBase::IsSequenceCompatible(const onnx::TypeProto&) constbool onnxruntime::NonTensorTypeBase::IsMapCompatible(const onnx::TypeProto&) constvirtual bool onnxruntime::SequenceTensorTypeBase::IsCompatible(const onnx::TypeProto&) constvirtual bool onnxruntime::SparseTensorTypeBase::IsCompatible(const onnx::TypeProto&) constvirtual bool onnxruntime::TensorTypeBase::IsCompatible(const onnx::TypeProto&) constbool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Sequence&, const onnx::TypeProto_Sequence&)bool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Map&, const onnx::TypeProto_Map&)GetElementTypeGetElementTypeGetElementTypeN11onnxruntime12DataTypeImplEN11onnxruntime14TensorTypeBaseEN11onnxruntime20SparseTensorTypeBaseEN11onnxruntime17NonTensorTypeBaseEN11onnxruntime22SequenceTensorTypeBaseEN11onnxruntime21PrimitiveDataTypeBaseEN11onnxruntime10TensorTypeIiEEN11onnxruntime10TensorTypeIfEEN11onnxruntime10TensorTypeIbEEN11onnxruntime10TensorTypeISsEEN11onnxruntime10TensorTypeIaEEN11onnxruntime10TensorTypeIhEEN11onnxruntime10TensorTypeItEEN11onnxruntime10TensorTypeIsEEN11onnxruntime10TensorTypeIlEEN11onnxruntime10TensorTypeIdEEN11onnxruntime10TensorTypeIjEEN11onnxruntime10TensorTypeImEEN11onnxruntime10TensorTypeINS_9MLFloat16EEEN11onnxruntime10TensorTypeINS_8BFloat16EEEN11onnxruntime16SparseTensorTypeIiEEN11onnxruntime16SparseTensorTypeIfEEN11onnxruntime16SparseTensorTypeIbEEN11onnxruntime16SparseTensorTypeIaEEN11onnxruntime16SparseTensorTypeIhEEN11onnxruntime16SparseTensorTypeItEEN11onnxruntime16SparseTensorTypeIsEEN11onnxruntime16SparseTensorTypeIlEEN11onnxruntime16SparseTensorTypeIdEEN11onnxruntime16SparseTensorTypeIjEEN11onnxruntime16SparseTensorTypeImEEN11onnxruntime16SparseTensorTypeINS_9MLFloat16EEEN11onnxruntime16SparseTensorTypeINS_8BFloat16EEEN11onnxruntime13NonTensorTypeISt3mapISsSsSt4lessISsESaISt4pairIKSsSsEEEEEN11onnxruntime7MapTypeISt3mapISsSsSt4lessISsESaISt4pairIKSsSsEEEEEN11onnxruntime13NonTensorTypeISt3mapISslSt4lessISsESaISt4pairIKSslEEEEEN11onnxruntime7MapTypeISt3mapISslSt4lessISsESaISt4pairIKSslEEEEEN11onnxruntime13NonTensorTypeISt3mapISsfSt4lessISsESaISt4pairIKSsfEEEEEN11onnxruntime7MapTypeISt3mapISsfSt4lessISsESaISt4pairIKSsfEEEEEN11onnxruntime13NonTensorTypeISt3mapISsdSt4lessISsESaISt4pairIKSsdEEEEEN11onnxruntime7MapTypeISt3mapISsdSt4lessISsESaISt4pairIKSsdEEEEEN11onnxruntime13NonTensorTypeISt3mapIlSsSt4lessIlESaISt4pairIKlSsEEEEEN11onnxruntime7MapTypeISt3mapIlSsSt4lessIlESaISt4pairIKlSsEEEEEN11onnxruntime13NonTensorTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEEN11onnxruntime7MapTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEEN11onnxruntime13NonTensorTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEEN11onnxruntime7MapTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEEN11onnxruntime13NonTensorTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEEN11onnxruntime7MapTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEEN11onnxruntime18SequenceTensorTypeIfEEN11onnxruntime18SequenceTensorTypeIdEEN11onnxruntime18SequenceTensorTypeIaEEN11onnxruntime18SequenceTensorTypeIhEEN11onnxruntime18SequenceTensorTypeIsEEN11onnxruntime18SequenceTensorTypeItEEN11onnxruntime18SequenceTensorTypeIiEEN11onnxruntime18SequenceTensorTypeIjEEN11onnxruntime18SequenceTensorTypeIlEEN11onnxruntime18SequenceTensorTypeImEEN11onnxruntime18SequenceTensorTypeIbEEN11onnxruntime18SequenceTensorTypeISsEEN11onnxruntime18SequenceTensorTypeINS_9MLFloat16EEEN11onnxruntime18SequenceTensorTypeINS_8BFloat16EEEN11onnxruntime13NonTensorTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEEN11onnxruntime12SequenceTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEEN11onnxruntime13NonTensorTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEEN11onnxruntime12SequenceTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEEN11onnxruntime17PrimitiveDataTypeIiEEN11onnxruntime17PrimitiveDataTypeIfEEN11onnxruntime17PrimitiveDataTypeIbEEN11onnxruntime17PrimitiveDataTypeISsEEN11onnxruntime17PrimitiveDataTypeIaEEN11onnxruntime17PrimitiveDataTypeIhEEN11onnxruntime17PrimitiveDataTypeItEEN11onnxruntime17PrimitiveDataTypeIsEEN11onnxruntime17PrimitiveDataTypeIlEEN11onnxruntime17PrimitiveDataTypeIdEEN11onnxruntime17PrimitiveDataTypeIjEEN11onnxruntime17PrimitiveDataTypeImEEN11onnxruntime17PrimitiveDataTypeINS_9MLFloat16EEEN11onnxruntime17PrimitiveDataTypeINS_8BFloat16EEEZN11onnxruntime19data_types_internal16DataTypeRegistryC1EvEUlPKNS_12DataTypeImplEE_ZN11onnxruntime19data_types_internal16DataTypeRegistryC1EvEUlPKNS_12DataTypeImplEE0_SsN11onnxruntime9MLFloat16EN11onnxruntime8BFloat16ESt3mapISsfSt4lessISsESaISt4pairIKSsfEEESt3mapIlfSt4lessIlESaISt4pairIKlfEEECaught exception while destructing CustomOpsLoader with message: /onnxruntime_src/onnxruntime/core/framework/ex_lib_loader.ccCaught exception while loading custom ops with message: Unloading DSO Failed to unload DSO: A dso with name has already been loaded.LoadExternalLib~ExLibLoaderN11onnxruntime11ExLibLoaderEMemory pattern planner is not enabled on this execution framework./onnxruntime_src/onnxruntime/core/framework/execution_frame.hort_value_index >= 0 && static_cast<size_t>(ort_value_index) < all_values_size_/onnxruntime_src/onnxruntime/core/framework/node_index_info.hoffset >= 0 && static_cast<size_t>(offset) < node_values_size_/onnxruntime_src/onnxruntime/core/framework/execution_frame.ccort_value_idx >= 0 && static_cast<size_t>(ort_value_idx) < alloc_plan.size()TraceAllocation for ort_value_idx=OrtValue shape verification failed. Current shape:shape && tensor.Shape() == *shapeShape mismatch attempting to re-use buffer. . Validate usage of dim_value (values should be > 0) and dim_param (all values with the same string should equate to the same size) in shapes in the model. entries which doesn't match the number of fetches the frame was initialized with of Fetches vector passed to GetOutputs contains feeds.size() == feed_mlvalue_idxs.size()fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size()node_index_info and ort_value_idx_map are out of sync and cannot be usednode_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()Trying to allocate memory for unused optional inputs/outputsTensor shape cannot contain any negative value, block in memory pattern size is: , fall back to default allocation behavior, block not found in target location. fall back to default allocation behaviorort_value_index >= 0 && static_cast<size_t>(ort_value_index) < alloc_plan.size()We don't expect custom allocators for non-tensor types, so a shape is mandatory here.Allocation of tensor types requires a shape.buffers_.find(mem_patterns_->locations[i]) == buffers_.end() size=TraceFree for ort_value_idx= Requested shape:invalid index ort_value.Fence() == nullptrFor ort_value with index: but the actually size is: Invalid allocation kind: mlvalue.Fence() == nullptrTraceFreevoid onnxruntime::ExecutionFrame::TraceFree(int)TraceAllocateconst onnxruntime::AllocPlanPerValue& onnxruntime::ExecutionFrame::GetAllocationPlan(int)ReleaseMLValueImplonnxruntime::common::Status onnxruntime::AllocateSparseTensor(OrtValue&, const onnxruntime::DataTypeImpl&, onnxruntime::AllocatorPtr, const onnxruntime::TensorShape&, size_t, bool, const onnxruntime::SessionState&)AllocateAsPerAllocationPlanonnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlan(OrtValue&, int, const onnxruntime::TensorShape*, size_t)T* OrtValue::GetMutable() [with T = onnxruntime::Tensor]AllocateMLValueTensorPreAllocateBufferAllocateMLValueTensorSelfOwnBufferHelperonnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper(OrtValue&, int, onnxruntime::MLDataType, const OrtMemoryInfo&, const onnxruntime::TensorShape&, bool)onnxruntime::ExecutionFrame::ExecutionFrame(const std::vector<int>&, const std::vector<OrtValue>&, const std::vector<int>&, const std::vector<OrtValue>&, const std::unordered_map<long unsigned int, std::function<onnxruntime::common::Status(const onnxruntime::TensorShape&, const OrtMemoryInfo&, OrtValue&, bool&)> >&, const onnxruntime::SessionState&)int onnxruntime::NodeIndexInfo::GetMLValueIndex(int) constconst OrtValue& onnxruntime::IExecutionFrame::GetMLValue(int) constconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]onnxruntime::common::Status onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(int, const onnxruntime::TensorShape*, OrtValue*&, size_t)onnxruntime::IExecutionFrame::IExecutionFrame(const std::vector<int>&, const std::vector<OrtValue>&, const std::unordered_map<int, OrtValue>&, const std::vector<int>&, const std::vector<OrtValue>&, const OrtValueNameIdxMap&, const onnxruntime::NodeIndexInfo&)N11onnxruntime15IExecutionFrameEN11onnxruntime14ExecutionFrameEduplicated allocator/onnxruntime_src/onnxruntime/core/framework/execution_provider.ccvoid onnxruntime::IExecutionProvider::InsertAllocator(onnxruntime::AllocatorPtr)N11onnxruntime18IExecutionProviderEError mapping feeds: Error mapping output names: /onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.ccinput_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknownvoid onnxruntime::FeedsFetchesManager::SetDeviceCopyChecks(onnxruntime::DeviceCopyCheck, onnxruntime::DeviceCopyCheck)onnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(const std::vector<std::basic_string<char> >&, const std::vector<std::basic_string<char> >&, const onnxruntime::OrtValueNameIdxMap&)FeedsFetchesInfoMapNamesToMLValueIdxs/onnxruntime_src/onnxruntime/core/framework/func_kernel.hcreate_func(&context, &func_state_) == 0/onnxruntime_src/onnxruntime/core/framework/graph_partitioner.ccProvider doesn't return correct number of compiled functionsnode_compute_funcs.size() == nodes_need_compile.size()No provider specified.1 == capability->nodes.size()onnxruntime::FunctionKernel::FunctionKernel(const onnxruntime::OpKernelInfo&)onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constonnxruntime::Node* onnxruntime::PlaceNode(onnxruntime::Graph&, std::unique_ptr<onnxruntime::IndexedSubGraph>, const onnxruntime::KernelRegistryManager&, const string&, int&)onnxruntime::common::Status onnxruntime::GraphPartitioner::Partition(onnxruntime::Graph&, bool, onnxruntime::FuncManager&) constPartitionN11onnxruntime14FunctionKernelEOp: Incompatible types.kernel def can't be NULLFailed to add kernel for Version mismatch. node_version: kernel start version: kernel_end_version: kernel is not supported in Failed to find kernel for : Conflicting with a registered kernel with op versions./onnxruntime_src/onnxruntime/core/framework/kernel_registry.cclen <= op_schema.inputs().size()Failed to create kernel for op: since it was ill-formed during registration Encountered following errors: (TryFindKernelvoid onnxruntime::{anonymous}::TraverseFormalParametersWithTypeProto(const onnxruntime::Node&, ParamFilterFn, TraverseFn) [with ParamFilterFn = onnxruntime::{anonymous}::TypeBindingResolver::Resolve(const string&) const::__lambda9; TraverseFn = onnxruntime::{anonymous}::TypeBindingResolver::Resolve(const string&) const::__lambda10]void onnxruntime::{anonymous}::TraverseFormalParametersWithTypeProto(const onnxruntime::Node&, ParamFilterFn, TraverseFn) [with ParamFilterFn = onnxruntime::{anonymous}::TypeBindingResolver::TypeBindingResolver(const onnxruntime::Node&, bool)::__lambda7; TraverseFn = onnxruntime::{anonymous}::TypeBindingResolver::TypeBindingResolver(const onnxruntime::Node&, bool)::__lambda8]The node is not placed on any Execution Provider, therefore, can't find a suitable kernel for The node is not placed on any Execution Provider (node found duplicated provider in KernelRegistryManagernullptr != type_proto/onnxruntime_src/onnxruntime/core/framework/mldata_type_utils.ccconst onnxruntime::DataTypeImpl* onnxruntime::utils::GetMLDataType(const onnxruntime::NodeArg&)/onnxruntime_src/onnxruntime/core/framework/node_index_info.cconnxruntime::NodeIndexInfo::Init(const TValidNodes&, onnxruntime::NodeIndex, const OrtValueNameIdxMap&) [with TValidNodes = onnxruntime::ValidNodes<const std::vector<const onnxruntime::Node*> >; onnxruntime::NodeIndex = long unsigned int; onnxruntime::OrtValueNameIdxMap = onnxruntime::OrtValueNameIdxMap]::__lambda8onnxruntime::NodeIndexInfo::Init(const TValidNodes&, onnxruntime::NodeIndex, const OrtValueNameIdxMap&) [with TValidNodes = onnxruntime::GraphNodes; onnxruntime::NodeIndex = long unsigned int; onnxruntime::OrtValueNameIdxMap = onnxruntime::OrtValueNameIdxMap]::__lambda8ZN11onnxruntime13NodeIndexInfo4InitINS_10GraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ZN11onnxruntime13NodeIndexInfo4InitINS_10GraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_:tensor type Tensor types should have been handled already@{z@{@{zXz@{`{z,܇,,,܇const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]TempSpace allocator not foundExecution frame was nullframe != nullptrOpKernel was nullkernel != nullptr. Num args is Invalid arg_num of arg_num < arg_counts.size()/onnxruntime_src/onnxruntime/core/framework/op_kernel.ccnode_offsets_index < node_offsets_size_OrtValue* onnxruntime::OpKernelContext::GetOrCreateOutputMLValue(int)int onnxruntime::OpKernelContext::NumVariadicInputs(size_t) constOrtValue* onnxruntime::OpKernelContext::OutputMLValue(int, const onnxruntime::TensorShape&, size_t)T* OrtValue::GetMutable() [with T = onnxruntime::Tensor]int onnxruntime::NodeIndexInfo::GetNodeOffset(onnxruntime::NodeIndex) constonnxruntime::OpKernelContext::OpKernelContext(onnxruntime::IExecutionFrame*, const onnxruntime::OpKernel*, onnxruntime::concurrency::ThreadPool*, const onnxruntime::logging::Logger&)cannot find allocator/onnxruntime_src/onnxruntime/core/framework/op_kernel_info.ccconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]const OrtMemoryInfo& onnxruntime::OpKernelInfo::GetMemoryInfo(int, OrtMemType) const'is defined.No attribute with name:'attr != nullptrNo attribute with this name is defined./onnxruntime_src/onnxruntime/core/framework/op_node_proto_helper.ccvalues.size() == static_cast<size_t>(attr->floats_size())values.size() == static_cast<size_t>(attr->ints_size())values.size() == static_cast<size_t>(attr->strings_size())Attibute name and type don't match/onnxruntime_src/include/onnxruntime/core/framework/op_node_proto_helper.hvalues.size() == static_cast<size_t>(attr->tensors_size())values.size() == static_cast<size_t>(attr->graphs_size())ort_value.IsAllocated(). Shape:. Dimension 0 is Invalid dim0_offset of dim0_offset < dim0_size/onnxruntime_src/onnxruntime/core/framework/ort_value_tensor_slicer.ccInsufficient dimensions to slice on gsl::narrow_cast<int64_t>(tensor_shape.NumDimensions()) >= slice_dimensionCan't slice a non-tensor OrtValue. Type was OrtValue has not been allocated so can't be sliced.static onnxruntime::OrtValueTensorSlicer<T> onnxruntime::OrtValueTensorSlicer<T>::Create(T&, int64_t, int64_t) [with T = const OrtValue; int64_t = long int]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]static onnxruntime::OrtValueTensorSlicer<T> onnxruntime::OrtValueTensorSlicer<T>::Create(T&, int64_t, int64_t) [with T = OrtValue; int64_t = long int]node_index_info_ with error: duplicated ort_value index:SaveMLValueNameIndexMapping is used by node ) and node Entry exists in node for attribute SetGraphAndCreateKernels must be called prior to GetExecutionInfo./onnxruntime_src/onnxruntime/core/framework/session_state.ccFailed to find input name in the mapping: Failed to find output name in the mapping: Could not create kernel for node: as there's no execution provider allocated.Kernel creation failed for node: . Do you have duplicated calls to SessionState::AddInitializedTensor function?Done saving OrtValue mappings.Using an input in multiple nodes on different devices is not supported currently. Input:Only one node should produce an output. Existing entry for output_names_to_nodeinfo.empty()existing_entries.find(attribute_name) == existing_entries.cend()const onnxruntime::NodeIndexInfo& onnxruntime::SessionState::GetNodeIndexInfo() constvoid onnxruntime::SessionState::AddSubgraphSessionState(onnxruntime::NodeIndex, const string&, std::unique_ptr<onnxruntime::SessionState>)void onnxruntime::SessionState::AddOutputNameToNodeInfoMapping(const string&, const onnxruntime::SessionState::NodeInfo&)SetGraph/onnxruntime_src/onnxruntime/core/framework/session_state_initializer.ccOrtValue indexes should have been populated.ort_value_name_idx_map.MaxIdx() > 0Invalid allocation info. Provider name = string tensor is not supported for copying between allocatorsInternal error. The preallocated buffer is too small. Requires Failed to copy tensor to execution provider: Done saving initialized tensorsExecution plan was not found in SessionState. CreatePlan must be called first.Saving initialized tensors., Got Deserialize tensor SubgraphGraphexec_plan_ptr input with name is not used by any node.ForEachWithIndexoperator()operator()SaveInputOutputNamesToNodeMappingDeserializeTensorProtoonnxruntime::common::Status onnxruntime::SaveInitializedTensors(const onnxruntime::Env&, const std::basic_string<char>&, const onnxruntime::Graph&, const onnxruntime::ExecutionProviders&, const OrtValueNameIdxMap&, onnxruntime::ITensorAllocator*, const T&, const onnxruntime::logging::Logger&, const onnxruntime::DataTransferManager&) [with T = onnxruntime::SessionStateInitializer::CreatePlan(const onnxruntime::Node*, const onnxruntime::ConstPointerContainer<std::vector<onnxruntime::NodeArg*> >*, ExecutionMode)::__lambda17; onnxruntime::OrtValueNameIdxMap = onnxruntime::OrtValueNameIdxMap]SaveInitializedTensorsonnxruntime::common::Status onnxruntime::SessionStateInitializer::CreatePlan(const onnxruntime::Node*, const onnxruntime::ConstPointerContainer<std::vector<onnxruntime::NodeArg*> >*, ExecutionMode)CreatePlan*ZN11onnxruntimeL33SaveInputOutputNamesToNodeMappingERKNS_5GraphERKNS_21KernelRegistryManagerERNS_12SessionStateEPKNS_21ConstPointerContainerISt6vectorIPNS_7NodeArgESaISB_EEEEEUlRKSA_mE_*ZN11onnxruntimeL33SaveInputOutputNamesToNodeMappingERKNS_5GraphERKNS_21KernelRegistryManagerERNS_12SessionStateEPKNS_21ConstPointerContainerISt6vectorIPNS_7NodeArgESaISB_EEEEEUlRKSA_mE0_N11onnxruntime25ISequentialPlannerContextEN11onnxruntime24SequentialPlannerContextEtensor size overflowshape.Size() must >=0dtype_ != nullptrp_type != nullptr/onnxruntime_src/onnxruntime/core/framework/tensor.ccTensor is expected to contain one of the primitive data types. Got: tensor failed memory size calculationvoid onnxruntime::Tensor::Init(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, onnxruntime::AllocatorPtr, int64_t)size_t onnxruntime::Tensor::SizeInBytes() constonnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, std::shared_ptr<onnxruntime::IAllocator>, int64_t)onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, const OrtMemoryInfo&, int64_t)Internal error.Mem pattern for initializer ' is not found' failedduplicated location/onnxruntime_src/onnxruntime/core/framework/tensor_allocator_with_mem_pattern.hWeight buffer for initializer 'Get preallocated buffer for initializer 'Failed to get allocator for location: TraceFinalizePlanN11onnxruntime16ITensorAllocatorEN11onnxruntime29TensorAllocatorWithMemPatternEInvalid dimension of dimension <= num_dims/onnxruntime_src/onnxruntime/core/framework/tensor_shape.ccdimstart <= dimend && dimend <= size()Invalid tensor shape slice argument. for SizeFromDimension. Tensor has onnxruntime::TensorShape onnxruntime::TensorShape::Slice(size_t, size_t) constint64_t onnxruntime::TensorShape::SizeFromDimension(size_t) constint64_t onnxruntime::TensorShape::SizeToDimension(size_t) constOrtValue is not a TensorArgument is not a tensor/onnxruntime_src/onnxruntime/core/framework/tensor_type_and_shape.cc const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor]const T& OrtValue::Get() const [with T = onnxruntime::Tensor]OrtStatus* OrtApis::GetTensorTypeAndShape(const OrtValue*, OrtTensorTypeAndShapeInfo**)Invalid TensorProto) in protodata overflowUnpackTensor: the pre-allocate size does not match the size in protosource and destination buffer size mismatch/onnxruntime_src/onnxruntime/core/framework/endian_utils.hNot satsified: source_bytes.size_bytes() == destination.size_bytes() -UnpackTensor: the pre-allocated size does not match the raw data size, expected /onnxruntime_src/onnxruntime/core/framework/tensorprotoutils.cc) does not match the data size(corrupted protobuf data: tensor shape size(string tensor can not have raw datatensor can't contain negative dimsThe buffer planner is not consistent with tensor buffer size, expected initialize preallocated buffer failedInitialized tensor with unexpected type:   ^]4\\ZK]z[\Z[[Y^YcZ^^X +UnpackTensor: the pre-allocated size does not match the raw data size, expected /onnxruntime_src/onnxruntime/core/framework/tensorprotoutils.cc) does not match the data size(corrupted protobuf data: tensor shape size(string tensor can not have raw datatensor can't contain negative dimsThe buffer planner is not consistent with tensor buffer size, expected initialize preallocated buffer failedInitialized tensor with unexpected type: CC$$XXC$XX$~~ee@@~e@@e\8\Z~[PY[ +Z![XgZYHXWX\\W GetFileContentTensorProtoToMLValueReadLittleEndianUnpackTensorWithRawDataReadLittleEndianUnpackTensorWithRawDataReadLittleEndianUnpackTensorWithRawDataReadLittleEndianUnpackTensorWithRawDataReadLittleEndianUnpackTensorWithRawDataReadLittleEndianUnpackTensorWithRawDataReadLittleEndianUnpackTensorWithRawDataReadLittleEndianUnpackTensorWithRawDataReadLittleEndianUnpackTensorWithRawDataReadLittleEndianUnpackTensorWithRawDataReadLittleEndianUnpackTensorWithRawDataReadLittleEndianUnpackTensorWithRawDataReadLittleEndianUnpackTensorWithRawDataNnapiExecutionProviderinvalid allocatorcopy_info.size() == num_feeds/onnxruntime_src/onnxruntime/core/framework/utils.ccfeed_locations.size() == copy_info.size()fetch_alloc_info.size() == copy_info.size()Only one thread was configured for parallel execution. Hence will use sequential execution.CopyOutputsAcrossDevicesCopyInputsAcrossDevicesonnxruntime::common::Status onnxruntime::utils::CopyInputsAcrossDevices(const std::vector<OrtValue>&, std::vector<OrtValue>&, const std::vector<onnxruntime::MLValueCopyInfo>&, const onnxruntime::DataTransferManager&)ExecuteGraphImplExecuteGraphT* OrtValue::GetMutable() [with T = onnxruntime::Tensor]CopyMLValueconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]bool onnxruntime::utils::FinalizeCopyInfoForFetches(const onnxruntime::SessionState&, const std::vector<const OrtMemoryInfo*>&, std::vector<onnxruntime::MLValueCopyInfo>&)bool onnxruntime::utils::FinalizeCopyInfoForFeeds(const std::vector<OrtDevice>&, std::vector<onnxruntime::MLValueCopyInfo>&)CalculateStaticCopyInfoForFeedCalculateStaticCopyInfoForFeedsInitializeFeedFetchCopyInfoconst OrtMemoryInfo& onnxruntime::utils::FindMemoryInfoForValue(const OrtValueNameIdxMap&, const onnxruntime::SequentialExecutionPlan&, const string&)FindMemoryInfoForValueconst OrtMemoryInfo& onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::SessionState&, const string&)N11onnxruntime9IExecutorE/onnxruntime_src/onnxruntime/core/framework/allocation_planner.ccid >= 0 && static_cast<size_t>(id) < ort_value_info_.size()n >= 0 && static_cast<size_t>(n) < ort_value_info_.size()n >= 0 && static_cast<size_t>(n) < plan_.allocation_plan.size()kernel_create_info != nullptr && kernel_create_info->kernel_def != nullptr(ort_value_idx) output_name : <allocation plan> No suitable kernel definition found for op Can not find the execution provider AllocateAllocateStaticallyPreExistingReuseAllocateOutputSharenullptr != tensor_type_basereused != reused_forCan not find the node p_providerAllocation Plan: Execution Plan: ) , use fence when asyncIndex out-of-range!nullptr != nodeFree ml-values: INVALID INDEXvector<bool>::_M_fill_insertonnxruntime::OrtValueIndex& onnxruntime::PlannerImpl::Buffer(onnxruntime::OrtValueIndex)void onnxruntime::PlannerImpl::Reuse(onnxruntime::OrtValueIndex, onnxruntime::OrtValueIndex, onnxruntime::AllocKind)static size_t onnxruntime::PlannerImpl::GetElementSize(const string* const&)OrtMemoryInfo onnxruntime::PlannerImpl::GetLocationForNodeInput(size_t, const onnxruntime::Node&)GeneratePlanForWeightsComputeReusePlanonnxruntime::AllocPlanPerValue& onnxruntime::PlannerImpl::AllocPlan(onnxruntime::OrtValueIndex)int& onnxruntime::PlannerImpl::UseCount(onnxruntime::OrtValueIndex)void onnxruntime::PlannerImpl::ProcessDef(onnxruntime::OrtValueIndex, const onnxruntime::NodeArg*)onnxruntime::OrtValueIndex onnxruntime::PlannerImpl::Index(const OrtValueName&)ForEachWithIndexComputeUseCountsCreatePlanN11onnxruntime17ExecutionPlanBaseEN11onnxruntime23SequentialExecutionPlanEZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEmE_ZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEbE0_ZN11onnxruntime11PlannerImpl22GeneratePlanForWeightsEvEUlRKNS_7NodeArgEmE_func info for node: already exist.Can't use func with null ptr/onnxruntime_src/onnxruntime/core/framework/fuse_nodes_funcs.ccGetFuncs*ZNK11onnxruntime11FuncManager8GetFuncsERKSsPSt8functionIFNS_6common6StatusEPvPK6OrtApiP16OrtKernelContextEEPS3_IFiPNS_14ComputeContextEPS6_EEPS3_IFvS6_EEEUlS6_S9_SB_E_*ZNK11onnxruntime11FuncManager8GetFuncsERKSsPSt8functionIFNS_6common6StatusEPvPK6OrtApiP16OrtKernelContextEEPS3_IFiPNS_14ComputeContextEPS6_EEPS3_IFvS6_EEEUlSG_SH_E0_*ZNK11onnxruntime11FuncManager8GetFuncsERKSsPSt8functionIFNS_6common6StatusEPvPK6OrtApiP16OrtKernelContextEEPS3_IFiPNS_14ComputeContextEPS6_EEPS3_IFvS6_EEEUlS6_E1_Begin execution does not.entry != nullptrop_name_fence_before node. Name:'' Status Message: _kernel_time_fence_afterMultiple errors were found.ParallelExecutor::ExecuteUnknown exception was caught by catch-all handler.Exception running nodes starting at Exiting due to terminate flag being set to true./onnxruntime_src/onnxruntime/core/framework/parallel_executor.ccGot nullptr from GetKernel for node: All implicit inputs should have OrtValue instances by now. /onnxruntime_src/onnxruntime/core/framework/op_kernel_context_internal.hNon-zero status code returned while running onnxruntime::OpKernelContextInternal::OpKernelContextInternal(const onnxruntime::SessionState&, onnxruntime::IExecutionFrame&, const onnxruntime::OpKernel&, const onnxruntime::logging::Logger&, const bool&)onnxruntime::common::Status onnxruntime::ParallelExecutor::RunNodeAsync(size_t, const onnxruntime::SessionState&, const onnxruntime::logging::Logger&)RunNodeAsyncconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]Execute*ZN11onnxruntime16ParallelExecutor11EnqueueNodeEmRKNS_12SessionStateERKNS_7logging6LoggerEEUlvE_N11onnxruntime16ParallelExecutorEN11onnxruntime23OpKernelContextInternalESequentialExecutor::Execute/onnxruntime_src/onnxruntime/core/framework/sequential_executor.ccconst T& OrtValue::Get() const [with T = onnxruntime::Tensor]ReleaseNodeMLValuesonnxruntime::OpKernelContextInternal::OpKernelContextInternal(const onnxruntime::SessionState&, onnxruntime::IExecutionFrame&, const onnxruntime::OpKernel&, const onnxruntime::logging::Logger&, const bool&)ExecuteN11onnxruntime18SequentialExecutorEinvalid ort_value_index:', location: /onnxruntime_src/onnxruntime/core/framework/simple_tensor_allocator.ccFailed to get allocator for initializer 'GetPreallocatedBufferN11onnxruntime21SimpleTensorAllocatorEmodel 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!/onnxruntime_src/onnxruntime/core/graph/automl_ops/automl_defs.ccThe input represents a number of seconds passed since the epoch, suitable to properly constructan instance of std::chrono::system_clock::time_pointThe output which is a Microsoft::DateTimeFeaturizer::TimePoint structureConstrain input type to int64 scalar tensor.Constrain output type to an AutoML specific Microsoft::Featurizers::TimePoint typecurrently not part of ONNX standard. When it becomes a part of the standard we will adjust thiskernel definition and move it to ONNX repoopaque(com.microsoft.automl,DateTimeFeaturizer_TimePoint)[ShapeInferenceError] Input's shape must be 4-D) + bottom_border () + right_border () + scale[0] () + scale[1] (tensor rank too smallNumber of attention headsmask_indexinput_idssegment_ids2D with shape (,hidden_size)word_embedding2D with shape (, hidden_size)position_embeddingsegment_embeddingbias tensorskipValue of alphaValue of beta1D input tensor1D output tensorThe scale to apply.Threshold valueInput tensorOutput tensorSliced data tensor.GivenTensorFillThe shape of filled tensorThe filled tensorinput_as_shapeextra_shapeInput data to be scaledOutput data after scalingGRUUnitdrop_stateshidden_prevgatesseq_lengthshiddenScaling valueStrings to tokenizeTokenized stringsN-dimensional matrix AN-dimensional matrix BReduceSumIntegerAn input tensor.Reduced output tensor.reducedAn input tensor to hash.32-bit hash value.Tensor of rank r >= 1.Tensor of rank q >= 1.Specify weights of convSpecify bias of convConstrain to tensor(int32).Constrain to tensor(float).Input tensor.Tensor after padding.idx2D matrix with shape (M,N)2D matrix with shape (K,N)roisbatch_indicescrop_sizeScale tensor.Bias tensor.Output data tensor.inv_std_varThe input data as Tensor.The output.The normal input data.Attribute should specify a shapeInput axis is invalid: Constrain input and output types to float tensorsfirst input tensor has wrong dimensionrois input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensioncrop_size shape input tensor has wrong dimension'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)'Scale' must contain exactly 2 values - (height, width)) needs to be greater than or equal to the top_border () needs to be greater than or equal to the left_border (First input does not have rank 2Second input does not have rank 2inputs are expected to have tensor type and output type should not be null.both data and indices tensor need to have rank larger than zero.last dimension of indices must not be larger and rank of data tensor'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64/onnxruntime_src/onnxruntime/core/graph/contrib_ops/contrib_defs.ccConstrain input and output types to float/quantized tensors3D input tensor with shape (batch_size, sequence_length, hidden_size), hidden_size = num_heads * head_size2D input tensor with shape (hidden_size, 3 * hidden_size)1D input tensor with shape (3 * hidden_size)Attention mask index with shape (batch_size)3D output tensor with shape (batch_size, sequence_length, hidden_size)Constrain input and output types to float tensors.Constrain mask index to integer types2D words IDs with shape (batch_size, sequence_length)2D segment IDs with shape (batch_size, sequence_length)1D gamma tensor for layer normalization with shape (hidden_size)1D beta tensor for layer normalization with shape (hidden_size)2D attention mask with shape (batch_size, sequence_length)1D mask_index tensor with shape (batch_size)Constrain input and output integer tensors typesConstrain input and output float tensors types.Constrain input and output types to float or half tensors.3D input tensor with shape (batch_size, sequence_length, hidden_size)3D skip tensor with shape (batch_size, sequence_length, hidden_size)1D input tensor with shape (hidden_size)1D skip tensor with shape (hidden_size1D bias tensor with shape (hidden_sizeBias applied to each channel, same size as C.Input tensor of shape [N,C,H,W]Result, has same shape and type as inputA 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).A 1-D values of (height, width).Result, has same type as input, with H and W dimensions reduced.Constrain input and output types to all tensor types.Tensor of data to extract slices from.1-D tensor of starting indices of corresponding axis in `axes`1-D tensor of ending indices (exclusive) of corresponding axis in axes1-D tensor of axes that `starts` and `ends` apply to.Constrain indices to integer typesBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.The previous GRU hidden state.Unactivated gate outputs from forget, update, and output gates, pre-activation.Array of sequence lengths. len(seq_lengths) should equal batch size N.The timestep for this operation.The new GRU hidden state calculated by this op.If 1, mean and variance are computed across channels. Default is 0.If 0, normalize the mean only. Default is 1.The scaled hyperbolic tangent values of the input tensor computed element-wiseConstrain input0 and output types to float tensorsInput tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).Output tensor of shape (M, N).Constrain input and output types to float/int tensors.Whether A should be transposedWhether B should be transposedScalar multiplier for the product of input tensors A * B.Scalar multiplier for input tensor C.Specified axis to insert a dimensionThe axis along which same quantization parameters are applied. It's optional.If it's not specified, it means per-tensor quantization and input 'x_scale' and 'x_zero_point' must be scalars.If it's specified, it means per 'axis' quantization and input 'x_scale' and 'x_zero_point' must be 1-D tensors.N-D full precision Input tensor to be quantized.Scale for doing quantization to get 'y'. It could be a scalar or a 1-D tensor,which means a per-tensor or per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.Zero point for doing quantization to get 'y'. It could be a scalar or a 1-D tensor, which means a per-tensoror per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.N-D quantized output tensor. It has same shape as input 'x'.Constrain 'x', 'y_scale' to float tensors.Constrain 'y_zero_point' and 'y' to 8-bit integer tensors.N-D quantized Input tensor to be de-quantized.Scale for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.Zero point for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.N-D full precision output tensor. It has same shape as input 'x'.Constrain 'y', 'x_scale' to float tensors.Constrain 'x_zero_point' and 'x' to 8-bit integer tensors.Input/Output is a string tensorBoolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).The string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers.An optional string. Token's regular expression in basic POSIX format (http://pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set.an optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string.Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredMatrix multiply results from A * BConstrain input A data types as 16-bit integer tensorConstrain input B data types as 16-bit integer tensorConstrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16).Constrain input type to 8-bit integer tensor.Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8).A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.Keep the reduced dimension or not, default 1 mean keep reduced dimension.Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.Constrain output type to unsigned and signed 32-bit integer tensor.Seed for the hashing algorithm, unsigned 32-bit integer, default to 0.If value is 1, output type is uint32_t, else int32_t. Default value is 1.Constrain input and output types to any tensor type.Tensor of rank q-1+r-indices[-1].Constrain indice type to int32 or int64Integer representing the embedding vector size for each word.If not provide, use the fileter size of conv weightThis operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernal shape.Integer representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.Specify batchs of sequence words to embeddingSpecify embedding vector of charThree modes: `constant`(default) - pads with a given constant value, `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis, `edge` - pads with the edge values of arrayTensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]. `pads` format (1D example) should be as follow [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.(Optional) A scalar or rank 1 tensor containing a single value to be filled if the mode chosen is `constant` (by default it is 0.0).Input can be of any tensor type.A 1-D input tensor that is to be processed.A 1-D tensor of the same type as 'x' containing all the unique values in 'x' sorted in the same order that they occur in the input 'x'A 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques'A 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x'The distance metric to use. If a string, the distance function can be "braycurtis", "canberra", "chebyshev", "cityblock", "correlation", "cosine", "dice", "euclidean", "hamming", "jaccard", "jensenshannon", "kulsinski", "mahalanobis", "matching", "minkowski", "rogerstanimoto", "russellrao", "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "wminkowski", "yule".A 2D Matrix that represents the distance between each pair of the two collections of inputs.Constrains input to only numeric types.The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.Value used for extrapolation, when applicable. Default is 0.0f. Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[y1, x1, y2, x2], ...]. The RoIs' coordinates are normalized in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.1-D tensor of 2 elements: [crop_height, crop_width]. All cropped image patches are resized to this size. Both crop_height and crop_width need to be positive.RoI pooled output, 4-D tensor of shape (num_rois, C, crop_height, crop_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].Constrain types to float tensors.Constrain types to int tensors.The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).The epsilon value to use to avoid division by zero.Input data tensor from the previous layer.Saved mean used during training to speed up gradient computationSaved inverse standard variance used during training to speed up gradient computation.Constrain input and output types (except mean and inv_std_var) to float tensors.Constrain mean and inv_std_var to be float tensors.The bias input data that is a 1D tensor.Inputs 0 shall be 2 dimensionsword_embedding should have 2 dimensions and dimension size is known.Negative values are not allowed in a shape specificationFvRN4onnx8OpSchemaEE*ZN11onnxruntime7contrib26NchwcPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_*ZN11onnxruntime7contrib32NchwcGlobalPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_*ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE_*ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE0_*ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE0_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE8_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE9_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE10_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE11_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE12_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE13_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE14_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE15_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE16_PFvRN4onnx8OpSchemaEEstartlimitdelta1-D Tensor of the range.Unsupported type:Constrain input and output types.Tensor(scalar, or dims=[1]). First entry in the range.Tensor(scalar, or dims=[1]). Upper limit of sequence, exclusive.Tensor(scalar, or dims=[1]). Number that increments start. Defaults to 1.Can not get shape initializer data!Unsupported non-raw-data data type!*ZN11onnxruntime7contrib21RegisterRangeOpSchemaEON4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_index < data_.size()Not satsified: dims_eq() -Constant!foundnode_argRemoving initializer 'Can't remove node Target= Dimension=' source: target:Output:Type mismatch. Current= Input= but different TensorProto.existing->second == &tensorgraph_proto cannot be nullgraph_proto != nullptrconstant_attribute.has_t()NoOp) input arg (' of input parameter () of operator () in node () is invalid.Type Error: Type parameter () bound to different types (Node () output arg () type inference failedType Error: Type () of output arg () of node (Node:) Op ( does not match its type. does not match. Outer scope node arg name 'Fatal error: ) attribute ( inputs but subgraph has inputs and requires Subgraph input missing type.LLL,L(((`(/onnxruntime_src/onnxruntime/core/graph/graph.ccThis API is not supported when model is loaded from proto file right now./onnxruntime_src/include/onnxruntime/core/common/const_pointer_container.hGraph attribute inferencing failed: Invalid source node arg slot specified when adding edge.Invalid destination node arg slot specified when adding edge.Argument type mismatch when adding edge.Invalid node indexes specified when adding edge.This is an invalid model. The sum of input arg count is not equal to size of input defs in node (Invalid source node arg slot specified when removing edge.Invalid destination node arg slot specified when removing edge.Argument mismatch when removing edge.Invalid node indexes specified when removing edge.No Graph instance was found for attribute Error: Duplicate definition-site for (Failed to find existing initializer with name Not satsified: name_to_initializer_it != name_to_initial_tensor_.end() +Constant!foundnode_argRemoving initializer 'Can't remove node Target= Dimension=' source: target:Output:Type mismatch. Current= Input= but different TensorProto.existing->second == &tensorgraph_proto cannot be nullgraph_proto != nullptrconstant_attribute.has_t()NoOp) input arg (' of input parameter () of operator () in node () is invalid.Type Error: Type parameter () bound to different types (Node () output arg () type inference failedType Error: Type () of output arg () of node (Node:) Op ( does not match its type. does not match. Outer scope node arg name 'Fatal error: ) attribute ( inputs but subgraph has inputs and requires Subgraph input missing type.ldDH@ /onnxruntime_src/onnxruntime/core/graph/graph.ccThis API is not supported when model is loaded from proto file right now./onnxruntime_src/include/onnxruntime/core/common/const_pointer_container.hGraph attribute inferencing failed: Invalid source node arg slot specified when adding edge.Invalid destination node arg slot specified when adding edge.Argument type mismatch when adding edge.Invalid node indexes specified when adding edge.This is an invalid model. The sum of input arg count is not equal to size of input defs in node (Invalid source node arg slot specified when removing edge.Invalid destination node arg slot specified when removing edge.Argument mismatch when removing edge.Invalid node indexes specified when removing edge.No Graph instance was found for attribute Error: Duplicate definition-site for (Failed to find existing initializer with name Not satsified: name_to_initializer_it != name_to_initial_tensor_.end() Replacement tensor's data type does not match.Not satsified: old_initializer.data_type() == new_initializer.data_type() graph_proto_ is not in sync with name_to_initial_tensor_existing_entry != mutable_initializers.pointer_end()Replacement tensor's dimensions do not match.This is an invalid model. Error: two nodes with same node name (This is an invalid model. Error: Duplicate definition of name (This is an invalid model. Error: the graph is not acyclic.graph_proto_ is not in sync with name_to_initial_tensor_.Graph ctor should have created NodeArg for initializer.This is an invalid model. Graph output () does not exist in the graph. must be either specified in graph inputs or graph initializers.'. It is not used by any node and should be removed from the model.'. It is no longer used by any node. as it still has output edges.node->GetOutputEdgesCount() == 0This is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name= Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.This is an invalid model. Failed to find NodeArg in all parent graphs. Name=nodes_.size() < static_cast<unsigned int>(std::numeric_limits<int>::max())Mismatch between number of source and target dimensions. Source=Can't merge shape info. Both source and target dimension have values but they differ. Source=Error merging shape info for output. '. Falling back to lenient merge.Tensor element type mismatch. SparseTensor element type mismatch. AddInitializedTensor already has tensor with name Only 'value' attribute is supported within a 'Constant' node in ORTnullptr != sub_graph && nullptr != sub_graph->GetMetaDef()This is an invalid model. Node () does not have type information set by parent node.This is an invalid model. Type Error: Type ') does not match expected type (This is an invalid model. Model input () does not have type information.Type Error: Value of initializer Type Error: Shape of initializer 'was added but does not exist. is not a registered function/op) is required but not specified.This is an invalid model. Error in Node:Size mismatch validating subgraph inputs. Got inputs. Either provide all subgraph inputs, or just the required inputs.Shouldn't be possible to have NodeArgs that haven't been handled already.outer_scope_node_args_consumed.empty()void onnxruntime::Graph::SetOutputs(const std::vector<const onnxruntime::NodeArg*>&)void onnxruntime::Graph::SetInputs(const std::vector<const onnxruntime::NodeArg*>&)InlineFunctiononnxruntime::Node& onnxruntime::Graph::FuseSubGraph(std::unique_ptr<onnxruntime::IndexedSubGraph>, const string&)gsl::not_null<onnxruntime::Node*> onnxruntime::Graph::AllocateNode()onnxruntime::common::Status onnxruntime::Graph::SetGraphInputsOutputs()CleanUnusedInitializersvoid onnxruntime::Graph::ToGraphProtoInternal(onnx::GraphProto&) constbool onnxruntime::Graph::RemoveNode(onnxruntime::NodeIndex)void onnxruntime::Graph::RemoveInitializedTensor(const string&)void onnxruntime::Graph::AddInitializedTensor(const onnx::TensorProto&)onnxruntime::common::Status onnxruntime::Graph::Resolve(bool)ResolveForThisAndAllSubgraphsPerformTypeAndShapeInferencingInitInputsInitializersOutputsVerifyNodeAndOpMatchInferAndVerifySubgraphTypesconst T* onnxruntime::ConstPointerContainer<Container>::at(size_t) const [with Container = std::vector<onnxruntime::NodeArg*>; onnxruntime::ConstPointerContainer<Container>::T = onnxruntime::NodeArg; size_t = long unsigned int]onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constBuildConnectionsvoid onnxruntime::Graph::RemoveEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int)void onnxruntime::Graph::AddEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int)SetOuterScopeNodeArgsGraphonnxruntime::Graph::Graph(onnx::GraphProto*, const std::unordered_map<std::basic_string<char>, int>&, onnxruntime::Version, onnxruntime::IOnnxRuntimeOpSchemaCollectionPtr, onnxruntime::Graph*, const onnxruntime::Node*, const onnxruntime::logging::Logger&, const std::unordered_map<std::basic_string<char>, const onnx::FunctionProto*>&)MergeShapeInfoUpdateTypeAndShapeFN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_E*ZN11onnxruntime5Graph7ResolveEbEUlRS0_E0_*ZN11onnxruntime5Graph7ResolveEbEUlRS0_E1_*ZN11onnxruntime5Graph7ResolveEbEUlRS0_E2_N4onnx15GraphInferencerEN4onnx16InferenceContextEN11onnxruntime5GraphEN11onnxruntime19GraphInferencerImplEN11onnxruntime20InferenceContextImplEPFN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_E ExplicitInputs:. Index:Invalid input index for node ImplicitInputs: Implicit input name cannot be safely updated to in one of the subgraphs.->/onnxruntime_src/onnxruntime/core/graph/graph_utils.ccindex >= 0 && static_cast<size_t>(index) < inputs.size()Attempting to get an input that does not exist.index >= 0 && static_cast<size_t>(index) < outputs.size()Attempting to get an output that does not exist.Failed since multiple edges matched:Can only add a new input at the end of the current ones.num_explicit_inputs == static_cast<size_t>(target_input_idx)std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0Node must only have one used outputstd::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.Initializer with same name exists. Name:!graph.GetInitializedTensor(new_initializer.name(), existing)FindPathvoid onnxruntime::graph_utils::AddNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&)void onnxruntime::graph_utils::ReplaceNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&)onnxruntime::NodeArg& onnxruntime::graph_utils::AddInitializer(onnxruntime::Graph&, const onnx::TensorProto&)void onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph(onnxruntime::Node&, const string&, const string&)bool onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutput(onnxruntime::Graph&, onnxruntime::Node&)bool onnxruntime::graph_utils::RemoveNode(onnxruntime::Graph&, onnxruntime::Node&)onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constCanUpdateImplicitInputNameInSubgraphsconst string& onnxruntime::graph_utils::GetNodeOutputName(const onnxruntime::Node&, int)const string& onnxruntime::graph_utils::GetNodeInputName(const onnxruntime::Node&, int)onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const*ZN11onnxruntime11GraphViewerC1ERKNS_5GraphEEUlPKNS_4NodeEE_N11onnxruntime11NodeCompareEProtobuf parsing failed.ModelProto was null.<p_fd> is less than 0.Invalid istream object.Null model_proto ptr.<p_fd> less than 0. failed. File doesn't existLoad model system error number ModelProto does not have a graph.Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset model may run depending upon legacy support of some older opset version operators./onnxruntime_src/onnxruntime/core/graph/model.ccNo graph was found in the protobuf.Failed to load model with error: Protobuf serialization failed.SaveSaveModelLoadModelSt15_Sp_counted_ptrIPN11onnxruntime5ModelELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN11onnxruntime21SchemaRegistryManagerESaIS1_ELN9__gnu_cxx12_Lock_policyE2EEDomain already set in registry, but it its version is higherthan the operator set version /onnxruntime_src/onnxruntime/core/graph/schema_registry.ccRegisterOpSetN11onnxruntime27OnnxRuntimeOpSchemaRegistryEN11onnxruntime21SchemaRegistryManagerEsequence_lensinitial_hinitial_cQWMWmemory_seq_lensAWY_hY_cA list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.Couple the input and forget gates if 1, default 0.Number of neurons in the hidden layer.Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Constrain seq_lens to integral tensors.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]` Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.Optional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.The weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.The weight tensor of the query layer in the attention mechanism. Should be of shape `[num_directions, am_query_depth(hidden_size of lstm), am_attn_size]` The weight tensor of the memory layer in the attention mechanism. Should be of shape `[num_directions, memory_depth, am_attn_size]` The attention_v tensor in the attention mechanism. Should be of shape `[num_directions, am_attn_size]` The sequence of the memory (input) for attention mechanism. Should be of `[batch_size, max_memory_step, memory_depth]` The sequence length of the input memory for the attention mechanism. Should be of `[batch_size]` The weights of attention layer in the attention wrapper. If exists, should be of shape `[num_directions, memory_depth+hidden_size, aw_attn_size]. Please note that attention mechanism context depth is also memory_depth in the attention mechanism.` A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`. The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.fused_function_subgraphinput_arg->Type() != nullptrResolve subgraph failed:A node with a function body within a subgraph within another function body is currently not supported in ORT/onnxruntime_src/onnxruntime/core/graph/function.ccvoid onnxruntime::update_subgraphs_within_function_body(onnx::GraphProto&, const onnxruntime::Graph&, const onnx::NodeProto&, const std::unordered_map<std::basic_string<char>, int>&, const std::unordered_map<std::basic_string<char>, int>&)onnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph&, const NodeIndex&, const onnx::FunctionProto&, const onnxruntime::logging::Logger&)onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constonnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph&, std::unique_ptr<onnxruntime::IndexedSubGraph>, const onnxruntime::logging::Logger&)*ZN11onnxruntime12FunctionImplC1ERKNS_5GraphERKmRKN4onnx13FunctionProtoERKNS_7logging6LoggerEEUlRNS6_16InferenceContextEE_N11onnxruntime8FunctionEN11onnxruntime12FunctionImplEDefault logger already set. ISink must be provided.Systemonnxruntimedefault_logger_id must be provided if instance_type is InstanceType::DefaultOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.N@VIWEF is not a valid year is not a valid day is not a valid dateN11onnxruntime7logging11OStreamSinkEWriting profiler data to file /onnxruntime_src/onnxruntime/core/common/profiler.ccMaximum number of events reached, could not record profile event.[ ] {"cat" : ""pid" :"tid" :"dur" :"ts" :"ph" : "X","name" :""args" : {} }, -enabled_session_logger != nullptrcustom_logger != nullptrSessionNodeEndProfilingEndTimeAndRecordEventvoid onnxruntime::profiling::Profiler::Initialize(const onnxruntime::logging::Logger*)onnxruntime::TimePoint onnxruntime::profiling::Profiler::StartTime() constSUCCESSINVALID_ARGUMENTNO_SUCHFILENO_MODELENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFMODEL_LOADEDNOT_IMPLEMENTEDINVALID_GRAPHEP_FAILGENERAL ERROROKSystemError[ONNXRuntimeError]E}oaS/onnxruntime_src/onnxruntime/core/common/status.cccode != static_cast<int>(common::OK)onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const char*)onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const string&)*ZN11onnxruntime11concurrency10ThreadPool11ParallelForEiSt8functionIFviEEEUliE_*ZN11onnxruntime11concurrency10ThreadPool11ParallelForEiSt8functionIFviEEEUlvE0_*ZN11onnxruntime11concurrency10ThreadPool16BatchParallelForEiSt8functionIFviEEiEUliE_NSt6thread10_Impl_baseENSt6thread5_ImplISt12_Bind_simpleIFSt8functionIFvvEEvEEEEZN5Eigen15ThreadPoolTemplINS_20StlThreadEnvironmentEEC1EibS1_EUlvE_St23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFSt8functionIFvvEEvEEEESaIS8_ELN9__gnu_cxx12_Lock_policyE2EElib.soFailed to get symbol Got null library handle file "" failed: closeopenFailed to load library munmap failed. error code: - error code: fstatNot satsified: file_path +enabled_session_logger != nullptrcustom_logger != nullptrSessionNodeEndProfilingEndTimeAndRecordEventvoid onnxruntime::profiling::Profiler::Initialize(const onnxruntime::logging::Logger*)onnxruntime::TimePoint onnxruntime::profiling::Profiler::StartTime() constSUCCESSINVALID_ARGUMENTNO_SUCHFILENO_MODELENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFMODEL_LOADEDNOT_IMPLEMENTEDINVALID_GRAPHEP_FAILGENERAL ERROROKSystemError[ONNXRuntimeError]p_N=, /onnxruntime_src/onnxruntime/core/common/status.cccode != static_cast<int>(common::OK)onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const char*)onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const string&)*ZN11onnxruntime11concurrency10ThreadPool11ParallelForEiSt8functionIFviEEEUliE_*ZN11onnxruntime11concurrency10ThreadPool11ParallelForEiSt8functionIFviEEEUlvE0_*ZN11onnxruntime11concurrency10ThreadPool16BatchParallelForEiSt8functionIFviEEiEUliE_NSt6thread10_Impl_baseENSt6thread5_ImplISt12_Bind_simpleIFSt8functionIFvvEEvEEEEZN5Eigen15ThreadPoolTemplINS_20StlThreadEnvironmentEEC1EibS1_EUlvE_St23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFSt8functionIFvvEEvEEEESaIS8_ELN9__gnu_cxx12_Lock_policyE2EElib.soFailed to get symbol Got null library handle file "" failed: closeopenFailed to load library munmap failed. error code: - error code: fstatNot satsified: file_path Not satsified: offset >= 0 mmaplseekreadFile: , offset: , length: Failed to unload library with error: /onnxruntime_src/onnxruntime/core/platform/posix/env.ccFailed to close file descriptor GetFileLength: input is not a regular fileNot satsified: length <= buffer.size() ReadFileIntoBuffer - unexpected end of file. ReadFileIntoBufferCleanUpUnmapFileMapFileIntoMemory*N11onnxruntime12_GLOBAL__N_18PosixEnvEN11onnxruntime3EnvEmutex lock failedOrtCondVar wait failed: mutex not lockedOrtCondVar wait failed, error code=OrtCondVar timed_wait failed, error code=<stacktrace not implemented>N11onnxruntime9TelemetryE*N11onnxruntime12_GLOBAL__N_112PosixEnvTimeEN11onnxruntime7EnvTimeE0A>   - ?@@@@@@@hAQ5@.N3;A8X} <2?~>++{16?:>=4?~??AQ5@.N3;A8X} <2?~>++{16?:>=4?~?AAAAQ5@.Q5@.Q5@.Q5@.N3N3N3N3;A8;A8;A8;A8X} <X} <X} <X} <2?~>2?~>2?~>2?~>++++++++{1{1{1{16666?:?:?:?:>=>=>=>=4?~?4?~?4?~?4?~?A%~3a*7A\3JCy7 ':Y;Ӡ5֕8;Y;33{@l?A ;Eۼ# =pu>7fȹ~;ƼX=P"?>?B;?r1mL:e9 <*=*>>??@KUB5?l?33{@7f9~;<X=P"?>;?@Kr1mL:e9 <*=*>>A ;E<# =p>u>33{@33{@33{@33{@A A A A ;;;;EۼEۼEۼEۼ# =# =# =# =ppppu>u>u>u>l?l?l?l?7777fȹfȹfȹfȹ~;~;~;~;ƼƼƼƼX=X=X=X=P"?P"?P"?P"?>>>>r1r1r1r1mL:mL:mL:mL:e9 <e9 <e9 <e9 <*=*=*=*=*>*>*>*>>>>>*Z19MlasExecuteThreadedPFvPviES_iPN11onnxruntime11concurrency10ThreadPoolEEUliE_ is required to be non-empty.value_info' of Field ' is required but missing.elem_typekey_typevalue_typedata_type) to UNDEFINED is not allowedfloat_dataint32_datastring_dataraw_datadouble_datauint64_dataTensorProto ( tensor name: ) should be stored in TensorProto (tensor name: STRING data (tensor name: ' instead of '' should be stored in field 'values of data_type 'Sparse tensor indices () has values, but NNZ is ] not in sorted order.) index value at position [Sparse tensor (] out of range [0, ] out of range.sparse_tensor_proto) must have rank 1.Sparse tensor values () must have a dense-rank > 0) must have INT64 type.) must have rank 1 or 2.) has no index values.op_typeNodeProto (name: , type: ATenConstantFillNo opset import for domain 'No Op registered for with domain_version of Warning: was a removed as possible.' of node: + ?@@@@@@@p0AQ5@.N3;A8X} <2?~>++{16?:>=4?~??AQ5@.N3;A8X} <2?~>++{16?:>=4?~?AAAAQ5@.Q5@.Q5@.Q5@.N3N3N3N3;A8;A8;A8;A8X} <X} <X} <X} <2?~>2?~>2?~>2?~>++++++++{1{1{1{16666?:?:?:?:>=>=>=>=4?~?4?~?4?~?4?~?A%~3a*7A\3JCy7 ':Y;Ӡ5֕8;Y;33{@l?A ;Eۼ# =pu>7fȹ~;ƼX=P"?>?B;?r1mL:e9 <*=*>>??@KUB5?l?33{@7f9~;<X=P"?>;?@Kr1mL:e9 <*=*>>A ;E<# =p>u>33{@33{@33{@33{@A A A A ;;;;EۼEۼEۼEۼ# =# =# =# =ppppu>u>u>u>l?l?l?l?7777fȹfȹfȹfȹ~;~;~;~;ƼƼƼƼX=X=X=X=P"?P"?P"?P"?>>>>r1r1r1r1mL:mL:mL:mL:e9 <e9 <e9 <e9 <*=*=*=*=*>*>*>*>>>>>*Z19MlasExecuteThreadedPFvPviES_iPN11onnxruntime11concurrency10ThreadPoolEEUliE_ is required to be non-empty.value_info' of Field ' is required but missing.elem_typekey_typevalue_typedata_type) to UNDEFINED is not allowedfloat_dataint32_datastring_dataraw_datadouble_datauint64_dataTensorProto ( tensor name: ) should be stored in TensorProto (tensor name: STRING data (tensor name: ' instead of '' should be stored in field 'values of data_type 'Sparse tensor indices () has values, but NNZ is ] not in sorted order.) index value at position [Sparse tensor (] out of range [0, ] out of range.sparse_tensor_proto) must have rank 1.Sparse tensor values () must have a dense-rank > 0) must have INT64 type.) must have rank 1 or 2.) has no index values.op_typeNodeProto (name: , type: ATenConstantFillNo opset import for domain 'No Op registered for with domain_version of Warning: was a removed as possible.' of node: Bad node spec: ==> Context: attrAttribute (name: since_versionfunction (Unable to open model file:\/Unrecognized type value case (value_info name: setting data_type field (tensor name: ) is stored externally and should not have data field.Data of TensorProto ( tensor name: ) is stored externally but doesn't have a location., but it doesn't exist or is not accessible.) is 0-element but contains data!) should contain one and only one value field.) should not be stored in raw_data fieldUnrecognized data_type (tensor name: ) first dimension size does not equal NNZ.) second dimension size does not match rank of tensor.] not in lexicographic sorted order.) dimensions are not positive.) has zero input and zero output. is deprecated in domain_version of experimental ops. In the future, we may directly reject this operator. Please update your model as soon ' has been used as graph input names multiple times.Graph must be in single static assignment (SSA) form, however ' in initializer but not in graph inputNodes in a graph must be topologically sorted, however input ' is not output of any previous nodes.' has been used as output names multiple times.) should not contain more than one value field.type field and data field mismatch in attribute ) should refer to attribute in parent node.The model does not have an ir_version set properly.Your model ir_version is higher than the checker's.Your model has duplicate keys in metadata_props.model with IR version >= 3 must specify opset_import for ONNXmodel with IR version < 3 cannot have opset_import specified' has been used multiple times.) should not have duplicate outputs specified.) should not have duplicate attributes specified.Nodes in a function must be topologically sorted, however input ' - is neither output of any previous nodes nor input of the function.Function must be in single static assignment (SSA) form, however '. Please check if it is a valid file.. Please check if it is a valid protobuf file of model.Unable to parse model from file:|tD`````H``(ZZ(`N4onnx7checker15ValidationErrorEInput type was nullInput element type of axis value All Tensor typesCondition for the ifOnly boolv_initialv_final_and_scan_outputsinitial_state_and_scan_inputsfinal_state_and_scan_outputsInt64 tensorScan input was not a tensor. outputs. Expected was notIf node has but subgraphs produce Mismatched type for output then= else=Input was expected to have tensor type. Got Element type of input was unknown does not match existing output type of Output was expected to have tensor type. Got is invalid for a tensor of rank Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch./onnxruntime_src/cmake/external/onnx/onnx/defs/controlflow/defs.ccA maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.A boolean termination condition. Optional. Pass empty string to skip.The initial values of any loop-carried dependencies (values that change across loop iterations)Final N loop carried dependency values then K scan_outputsThe graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.tensor of int64, which should be a scalar.tensor of bool, which should be a scalar.Initial values of the loop's N state variables followed by M scan_inputsFinal values of the loop's N state variables followed by K scan_outputsThe graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.An attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].Number of scan input axes specified () is not equal to number of scan inputs (Number of scan output axes specified () is not equal to number of scan outputs (Graph attribute inferencing returned type information for Scan 'body' subgraph outputs should all be tensors but output then_branch and else_branch produce different number of outputs. Mismatched tensor element type for output Loop 'body' subgraph outputs should all be tensors but output Optional tensor specifying lengths of the sequences in a batch. If this input is not specified, all sequences are assumed to be of the maximum sequence length (the dimension of the sequence axis of the scan_input tensors)./onnxruntime_src/cmake/external/onnx/onnx/defs/controlflow/old.ccAn optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output.Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same shape and same data type.bfloat16doubleuint8uint16uint32uint64complex64complex128seq(map(sparse_tensor(seqopaquesparse_tensor\PK\P\PN<M\PO,Ksparse_value not specifiedValue of attribute Invalid shape value: Constrain input types.Number of times to sample.Input tensor must have rank 2loop_body_attributecond_outcurrentsub_resultsub_result_casteddelta_casteddiv_resultceil_resultceil_result_reluceil_result_relu_intceil_result_relu_boolvariadic_outputScalar. Value to step by.Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.One of the attributes 'value' or 'sparse_value' must be specified for a Constant node.Attribute expected to have tensor typeAttribute expected to have a one-dim tensor should be of integer type and specify a type. does not specify a valid type.Input tensor must be 2-dimensionalInput to 'Range' op should be scalars (Tensor with only one element and shape empty)All inputs to 'Range' op must be of the same typeShape input must be a one-dimensional tensor.The value for the elements of the output tensor.The value for the elements of the output tensor in sparse format.Output tensor containing the same value of the provided tensor./onnxruntime_src/cmake/external/onnx/onnx/defs/generator/defs.cc(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float321D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar.Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.Constrain output types to be numerics.(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.2D input tensor to copy shape, and optionally, type information from.Output tensor, same shape as input tensor T1.Constrain input types. Strings and complex are not supported.Constrain output types. Strings and complex are not supported.Lower boundary of the output values.Upper boundary of the output values.(Optional) Seed to the random generator, if not specified we will auto generate one.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.The shape of the output tensor.Output tensor of random values drawn from uniform distributionConstrain output types to float tensors.The mean of the normal distribution.The standard deviation of the normal distribution.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.Output tensor of random values drawn from normal distribution(Optional) The data type for the elements of the output tensor, if not specified, we will usethe data type of the input tensor.Input tensor to copy shape and optionally type information from.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.Constrain input types to float tensors.Constrain output types to integral tensors.Output type must be int32 or int64Scalar. First entry for the range of output values.Scalar. Exclusive upper limit for the range of output values.A 1-D tensor with same type as the inputs containing generated range of values.Constrain input types to common numeric type tensors.*ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25ConstantOfShape_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17EyeLike_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23RandomUniform_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22RandomNormal_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27RandomUniformLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26RandomNormalLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21Multinomial_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Range_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Attribute 'value' of Constant node must exist with 'Tensor' data./onnxruntime_src/cmake/external/onnx/onnx/defs/generator/old.cc*ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ + is neither output of any previous nodes nor input of the function.Function must be in single static assignment (SSA) form, however '. Please check if it is a valid file.. Please check if it is a valid protobuf file of model.Unable to parse model from file: `H`N4onnx7checker15ValidationErrorEInput type was nullInput element type of axis value All Tensor typesCondition for the ifOnly boolv_initialv_final_and_scan_outputsinitial_state_and_scan_inputsfinal_state_and_scan_outputsInt64 tensorScan input was not a tensor. outputs. Expected was notIf node has but subgraphs produce Mismatched type for output then= else=Input was expected to have tensor type. Got Element type of input was unknown does not match existing output type of Output was expected to have tensor type. Got is invalid for a tensor of rank Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch./onnxruntime_src/cmake/external/onnx/onnx/defs/controlflow/defs.ccA maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.A boolean termination condition. Optional. Pass empty string to skip.The initial values of any loop-carried dependencies (values that change across loop iterations)Final N loop carried dependency values then K scan_outputsThe graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.tensor of int64, which should be a scalar.tensor of bool, which should be a scalar.Initial values of the loop's N state variables followed by M scan_inputsFinal values of the loop's N state variables followed by K scan_outputsThe graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.An attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].Number of scan input axes specified () is not equal to number of scan inputs (Number of scan output axes specified () is not equal to number of scan outputs (Graph attribute inferencing returned type information for Scan 'body' subgraph outputs should all be tensors but output then_branch and else_branch produce different number of outputs. Mismatched tensor element type for output Loop 'body' subgraph outputs should all be tensors but output Optional tensor specifying lengths of the sequences in a batch. If this input is not specified, all sequences are assumed to be of the maximum sequence length (the dimension of the sequence axis of the scan_input tensors)./onnxruntime_src/cmake/external/onnx/onnx/defs/controlflow/old.ccAn optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output.Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same shape and same data type.bfloat16doubleuint8uint16uint32uint64complex64complex128seq(map(sparse_tensor(seqopaquesparse_tensorNJNN4MKN$NIsparse_value not specifiedValue of attribute Invalid shape value: Constrain input types.Number of times to sample.Input tensor must have rank 2loop_body_attributecond_outcurrentsub_resultsub_result_casteddelta_casteddiv_resultceil_resultceil_result_reluceil_result_relu_intceil_result_relu_boolvariadic_outputScalar. Value to step by.Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.One of the attributes 'value' or 'sparse_value' must be specified for a Constant node.Attribute expected to have tensor typeAttribute expected to have a one-dim tensor should be of integer type and specify a type. does not specify a valid type.Input tensor must be 2-dimensionalInput to 'Range' op should be scalars (Tensor with only one element and shape empty)All inputs to 'Range' op must be of the same typeShape input must be a one-dimensional tensor.The value for the elements of the output tensor.The value for the elements of the output tensor in sparse format.Output tensor containing the same value of the provided tensor./onnxruntime_src/cmake/external/onnx/onnx/defs/generator/defs.cc(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float321D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar.Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.Constrain output types to be numerics.(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.2D input tensor to copy shape, and optionally, type information from.Output tensor, same shape as input tensor T1.Constrain input types. Strings and complex are not supported.Constrain output types. Strings and complex are not supported.Lower boundary of the output values.Upper boundary of the output values.(Optional) Seed to the random generator, if not specified we will auto generate one.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.The shape of the output tensor.Output tensor of random values drawn from uniform distributionConstrain output types to float tensors.The mean of the normal distribution.The standard deviation of the normal distribution.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.Output tensor of random values drawn from normal distribution(Optional) The data type for the elements of the output tensor, if not specified, we will usethe data type of the input tensor.Input tensor to copy shape and optionally type information from.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.Constrain input types to float tensors.Constrain output types to integral tensors.Output type must be int32 or int64Scalar. First entry for the range of output values.Scalar. Exclusive upper limit for the range of output values.A 1-D tensor with same type as the inputs containing generated range of values.Constrain input types to common numeric type tensors.*ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25ConstantOfShape_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17EyeLike_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23RandomUniform_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22RandomNormal_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27RandomUniformLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26RandomNormalLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21Multinomial_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Range_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Attribute 'value' of Constant node must exist with 'Tensor' data./onnxruntime_src/cmake/external/onnx/onnx/defs/generator/old.cc*ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Returns the tensor resulted from performing the `{name}` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). {broadcast_doc} This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).First input operand for the logical operator.Second input operand for the logical operator.Constrains input to boolean tensor.Constrains output to boolean tensor./onnxruntime_src/cmake/external/onnx/onnx/defs/logical/defs.ccConstrains input types to all numeric tensors. @@ -6240,11 +6194,11 @@ - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0 - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0 - Ht = (1 - zt) (.) ht + zt (.) Ht-1 -A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0First input tensor must have rank 3*ZN4onnx15RNNDocGeneratorEPKcEUlRNS_8OpSchemaEE_fowardoutput_sequenceOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.The sequence output for the hidden is optional if 0. Default 0.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0./onnxruntime_src/cmake/external/onnx/onnx/defs/rnn/old.cc*ZN4onnx18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_*ZN4onnx16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_Attributes:(no type)(no doc)(unnamed)Outputs:Defined at (no documentation yet)Operator ') has input size not in range [min=, max= not in allowed input sizes.) has output size not in allowed output sizes.Attribute ' has unknown expected type' is missing.Required attribute ') than declared () has more inputs () in op definition.)'s input ) has more outputs ()'s output ' appeared multiple times. for operator Unrecognized attribute: ONNX Schema (inputs_.size() - 1) == i(outputs_.size() - 1) == i!(it.GetName().empty()) (no explicit description available)' has been deprecated since version Mismatched attribute type in '' is expected to have field 't'' is expected to have field 'sparse_tensor'' is expected to have field 'g'' is expected to have field 'floats'' is expected to have field 'ints'' is expected to have field 'strings'' is expected to have field 'tensors'' is expected to have field 'graphs' is marked single but has an empty string in the graph: failed validating the check: Duplicate type constraint nameAttribute specification type mismatch.```H`` X`*ZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_*ZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_N4onnx16OpSchemaRegistryEseq(tensor(uint8))seq(tensor(uint16))seq(tensor(uint32))seq(tensor(uint64))seq(tensor(int8))seq(tensor(int16))seq(tensor(int32))seq(tensor(int64))seq(tensor(float16))seq(tensor(float))seq(tensor(double))seq(tensor(string))seq(tensor(bool))seq(tensor(complex64))seq(tensor(complex128))], Value= sum of split values=Empty sequence.Tensors.Constrain to any tensor type.Input sequence.input_sequencepositionThe tensor to splitConcatenated tensorconcat_result Tensor=Attribute dtype should be of integer type and specify a type.Required attribute axis is missingnew_axis must be either 0 or 1Invalid value of attribute 'axis'. Accepted range=[Invalid value of attribute 'axis'. Rank=Only supports `int32_t` or `int64_t` inputs for splitInput 'split' can not be empty.Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size=Constrain output types to any tensor type.(Optional) The data type of the tensors in the output sequence. The default type is 'float'./onnxruntime_src/cmake/external/onnx/onnx/defs/sequence/defs.ccConstrain input types to any tensor type.Sequence enclosing the input tensors.Input tensor to be inserted into the input sequence.Position in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Output sequence that contains the inserted tensor at given position.Constrain position to integral tensor. It must be a scalar(tensor of empty shape).Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Output tensor at the specified position in the input sequence.Output sequence that has the tensor at the specified position removed.Length of input sequence. It must be a scalar(tensor of empty shape).Constrain output to integral tensor. It must be a scalar(tensor of empty shape).Constrain output types to all tensor types.Constrain input types to all tensor types.Length of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be positive. One or more outputs forming a sequence of tensors after splittingConstrain split size to integral tensor.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. Insert and concatenate on a new axis or not, default 0 means do not insert new axis.Sequence of tensors for concatenationInput Sequence and Tensor are expected to have the same elem type. Sequence=SequenceConstruct is expected to have at least 1 input.Element type of inputs are expected to be the same.*ZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_axis must be in [-r, r-1]'input' must have rank >= 2Invalid attribute perm {}, input shape = {Invalid position of 0Invalid dimension value: Input tensor to be cast.Specified shape for output.Reshaped data.reshapedShape of the input tensorlength of each outputstepsTransposed output.transposedupdatesTensor of rank q + (r - 1).squeezedOriginal tensorexpandedInput tensor of any shape.repeatsN-D tensorN-D tensor after resizingroisizesTensor to copy input into.conditiondepthConstrain to boolean tensors.output (always 2D tensor)Tensor of rank r >= 2.inverse_indicesconstant_valueInput axes has invalid data'step' cannot be 0Blocksize must be positive'Repeats' input must be 1D tensor of type int64'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.axis must be in [-rank, rank-1].All inputs to Concat must have same rankdata tensor must have rank >= 1Indices tensor must have rank >= 1OneHot node must have three inputs.Input 'depth' must be a scalar or rank 1 tensor.Input 'depth' must have exactly one element.Input 'values' must be rank 1 tensor.Input 'values' must have exactly two elements.'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64Pads has incorrect number of values'sequence_lens' must have rank of 1Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0.Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensorTarget shape may not have multiple -1 dimensionsInvalid Target shape product of 0Dimension could not be inferred: incompatible shapesvalues in 'axes' are beyond the bounds of the computed output shape'axes' attribute must not contain any duplicatesOnly supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsThe data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoOutput tensor with the same shape as input with type specified by the 'to' argumentConstrain input types. Casting from complex is not supported.Constrain output types. Casting to complex is not supported./onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/defs.ccInput tensor can be of arbitrary type.Constrain output to int64 tensor.Total number of elements of the input tensorConstrain output to int64 tensor, which should be a scalar though.Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..List of tensors for concatenationOne or more outputs forming list of tensors after splittingWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).1-D tensor of ending indices (exclusive) of corresponding axis in `axes`1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).1-D tensor of slice step of corresponding axis in `axes`. Default to 1. A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.Input and output types can be of any tensor type.Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of rank r >=1 (same rank and shape as indices)Tensor of rank r >= 1 (same rank as input).Tensor of rank q + r - indices_shape[-1] - 1.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of the same shape as indices.List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensors with at least max(dims) dimensions.Reshaped tensor with same data as input.List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).Blocks of [blocksize, blocksize] are moved.Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.Output tensor of the same dimension and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]Constrain repeat's type to int64 tensors.Constrain input 'X' and output 'Y' to all tensor types.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0. +A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0First input tensor must have rank 3*ZN4onnx15RNNDocGeneratorEPKcEUlRNS_8OpSchemaEE_fowardoutput_sequenceOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.The sequence output for the hidden is optional if 0. Default 0.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0./onnxruntime_src/cmake/external/onnx/onnx/defs/rnn/old.cc*ZN4onnx18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_*ZN4onnx16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_Attributes:(no type)(no doc)(unnamed)Outputs:Defined at (no documentation yet)Operator ') has input size not in range [min=, max= not in allowed input sizes.) has output size not in allowed output sizes.Attribute ' has unknown expected type' is missing.Required attribute ') than declared () has more inputs () in op definition.)'s input ) has more outputs ()'s output ' appeared multiple times. for operator Unrecognized attribute: ONNX Schema (inputs_.size() - 1) == i(outputs_.size() - 1) == i!(it.GetName().empty()) (no explicit description available)' has been deprecated since version Mismatched attribute type in '' is expected to have field 't'' is expected to have field 'sparse_tensor'' is expected to have field 'g'' is expected to have field 'floats'' is expected to have field 'ints'' is expected to have field 'strings'' is expected to have field 'tensors'' is expected to have field 'graphs' is marked single but has an empty string in the graph: failed validating the check: Duplicate type constraint nameAttribute specification type mismatch.حPP*ZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_*ZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_N4onnx16OpSchemaRegistryEseq(tensor(uint8))seq(tensor(uint16))seq(tensor(uint32))seq(tensor(uint64))seq(tensor(int8))seq(tensor(int16))seq(tensor(int32))seq(tensor(int64))seq(tensor(float16))seq(tensor(float))seq(tensor(double))seq(tensor(string))seq(tensor(bool))seq(tensor(complex64))seq(tensor(complex128))], Value= sum of split values=Empty sequence.Tensors.Constrain to any tensor type.Input sequence.input_sequencepositionThe tensor to splitConcatenated tensorconcat_result Tensor=Attribute dtype should be of integer type and specify a type.Required attribute axis is missingnew_axis must be either 0 or 1Invalid value of attribute 'axis'. Accepted range=[Invalid value of attribute 'axis'. Rank=Only supports `int32_t` or `int64_t` inputs for splitInput 'split' can not be empty.Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size=Constrain output types to any tensor type.(Optional) The data type of the tensors in the output sequence. The default type is 'float'./onnxruntime_src/cmake/external/onnx/onnx/defs/sequence/defs.ccConstrain input types to any tensor type.Sequence enclosing the input tensors.Input tensor to be inserted into the input sequence.Position in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Output sequence that contains the inserted tensor at given position.Constrain position to integral tensor. It must be a scalar(tensor of empty shape).Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Output tensor at the specified position in the input sequence.Output sequence that has the tensor at the specified position removed.Length of input sequence. It must be a scalar(tensor of empty shape).Constrain output to integral tensor. It must be a scalar(tensor of empty shape).Constrain output types to all tensor types.Constrain input types to all tensor types.Length of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be positive. One or more outputs forming a sequence of tensors after splittingConstrain split size to integral tensor.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. Insert and concatenate on a new axis or not, default 0 means do not insert new axis.Sequence of tensors for concatenationInput Sequence and Tensor are expected to have the same elem type. Sequence=SequenceConstruct is expected to have at least 1 input.Element type of inputs are expected to be the same.*ZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_axis must be in [-r, r-1]'input' must have rank >= 2Invalid attribute perm {}, input shape = {Invalid position of 0Invalid dimension value: Input tensor to be cast.Specified shape for output.Reshaped data.reshapedShape of the input tensorlength of each outputstepsTransposed output.transposedupdatesTensor of rank q + (r - 1).squeezedOriginal tensorexpandedInput tensor of any shape.repeatsN-D tensorN-D tensor after resizingroisizesTensor to copy input into.conditiondepthConstrain to boolean tensors.output (always 2D tensor)Tensor of rank r >= 2.inverse_indicesconstant_valueInput axes has invalid data'step' cannot be 0Blocksize must be positive'Repeats' input must be 1D tensor of type int64'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.axis must be in [-rank, rank-1].All inputs to Concat must have same rankdata tensor must have rank >= 1Indices tensor must have rank >= 1OneHot node must have three inputs.Input 'depth' must be a scalar or rank 1 tensor.Input 'depth' must have exactly one element.Input 'values' must be rank 1 tensor.Input 'values' must have exactly two elements.'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64Pads has incorrect number of values'sequence_lens' must have rank of 1Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0.Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensorTarget shape may not have multiple -1 dimensionsInvalid Target shape product of 0Dimension could not be inferred: incompatible shapesvalues in 'axes' are beyond the bounds of the computed output shape'axes' attribute must not contain any duplicatesOnly supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsThe data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoOutput tensor with the same shape as input with type specified by the 'to' argumentConstrain input types. Casting from complex is not supported.Constrain output types. Casting to complex is not supported./onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/defs.ccInput tensor can be of arbitrary type.Constrain output to int64 tensor.Total number of elements of the input tensorConstrain output to int64 tensor, which should be a scalar though.Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..List of tensors for concatenationOne or more outputs forming list of tensors after splittingWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).1-D tensor of ending indices (exclusive) of corresponding axis in `axes`1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).1-D tensor of slice step of corresponding axis in `axes`. Default to 1. A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.Input and output types can be of any tensor type.Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of rank r >=1 (same rank and shape as indices)Tensor of rank r >= 1 (same rank as input).Tensor of rank q + r - indices_shape[-1] - 1.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of the same shape as indices.List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensors with at least max(dims) dimensions.Reshaped tensor with same data as input.List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).Blocks of [blocksize, blocksize] are moved.Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.Output tensor of the same dimension and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]Constrain repeat's type to int64 tensors.Constrain input 'X' and output 'Y' to all tensor types.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor. <br/> The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original, <br/> @@ -6262,26 +6216,29 @@ if coordinate_transformation_mode is "tf_half_pixel_for_nn", <br/> x_original = (x_resized + 0.5) / scale, <br/> if coordinate_transformation_mode is "tf_crop_and_resize", <br/> -x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified. If 'size' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.The size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified.Constrain roi type to float or double.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.Constrains to boolean tensors.(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).Input tensor containing indices. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [-depth, depth-1]. In case 'depth' is of non-integer type, it will be casted to int64 before use.Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor. Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used.Constrain output types to boolean tensors.(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.When True (nonzero), yield X, otherwise yield Yvalues selected at indices where condition is Truevalues selected at indices where condition is FalseTensor of shape equal to the broadcasted shape of condition, X, and Y.Constrain to all tensor types.(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.Tensor with same shape of input.(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).A N-D input tensor that is to be processed.A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X'A 1-D INT64 tensor containing indices of 'Y' elements' first occurance in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor. A 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'. A 1-D INT64 tensor containing the count of each element of 'Y' in input 'X'Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Constrains input and output to only numeric types.Supported modes: `constant`(default), `reflect`, `edge`Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`.(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0).Slice op must have either three, four or five inputs.Incorrect or missing input value for starts and endsInput axes has incorrect lengthInput steps has incorrect lengthInput tensor must be 4-dimensional*ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Ranks inferred (Which axis to concat onoutputs...Which axis to split onpaddingsNew shapetilesAxis along which to repeat.width_scaleheight_scale4-D tensor, [N,C,H,W]Which axis to split on. 'axis' must be in [-rank(indices)-1, rank(indices)]rank must be greater than axis) is not equal to the existing rank value (Number of elements of attribute 'scales' must be same as rank of input 'X'Attribute 'scales' must have floats type.Attribute 'scales' is required.Attribute value for pads is requiredAttribute pads has incorrect lengthIncorrect or missing attribute value for starts and endsAttribute axes has incorrect lengthInvalid value of attribute 'axis'Constrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported./onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/old.ccWhich axis to concat on. Default value is 1.Optional list of output lengths (see also arg 'split')List of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.Three modes: constant(default), reflect, edgeOne float, indicates the value to be filled, default is 0Number of repeated copies to make of the input tensor.Output tensor of same shape and type as input.Constrain tiles and axis's type to int64 tensors.The scale along width dimension. It takes value greater than or equal to 1.The scale along height dimension. It takes value greater than or equal to 1.Two interpolation modes: nearest(default), bilinear4-D tensor after resizing, [N,C,H,W]Constrain output types to bool, int32, int64, float16, float, double tensors.The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'.Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Starting indices of corresponding axis in `axes`Ending indices (exclusive) of corresponding axis in axes`Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of r >= 1 (same rank as input).Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. It is an error if any of the index values are out of bounds.List of non-negative integers, indicate the dimensions to squeeze.List of non-negative integers, indicate the dimensions to be inserted(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor.Input tensor containing indices. The values must be non-negative integers. Any entries in the 'indices' input tensor with values outside the range [0, depth) will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [0, depth). In case 'depth' is of non-integer type, it will be casted to int64 before use.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length alone the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.List of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.One float, indicates the value to be filled.*ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Split_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_13Pad_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_) is not equal to the existing dim value (Number of elements of input 'sizes' must be same as rank of input 'X'Input 'sizes' must have int64 element type.Number of elements of input 'scales' must be same as rank of input 'X'Input 'scales' must have float element type.Dimension value inferred (Data to be selectedData to be binarizedBinarized output dataInput dataA dictionary.Data to be processed.Imputed output dataValue(s) to change toA value that needs replacing.Value(s) to change to.Output data.A list of ints.A list of floats.A string.An integer.A float.Data to be classified.A collection of intercepts.Data to be regressed.Weights of the model(s).Encoded output dataOne of 'MAX,' 'L1,' 'L2'Data to be encoded.Data to be scaled.Scaled output data.Chosen support vectorsSupport vector coefficients.Input of shape [N,F]N, Top class for each pointTree id for each node.Feature id for each node.N classesThe node id of each weightThe weight for each targetThe total number of targets.The input valuesThe output mapseq(map(string, float))seq(map(int64, float))map(string, int64)map(int64, string)map(int64, float)map(int64, double)map(string, float)map(string, double)Label encoder has only one input.Label encoder has only one output.Only one of keys_*'s can be set in label encoder.Input type is not string tensor but key_strings is setInput type is not int64 tensor but keys_int64s is setInput type is not float tensor but keys_floats is setOnly one of values_*'s can be set in label encoder.The indices, based on 0 as the first index of any dimension.Selected output data as an arrayThe input must be a tensor of a numeric type or string. The output will be of the same tensor type./onnxruntime_src/cmake/external/onnx/onnx/defs/traditionalml/defs.ccThe input must be a tensor of a numeric type. The output will be of the same tensor type.Values greater than this are mapped to 1, others to 0.The input map that is to be cast to a tensorA tensor representing the same data as the input map, ordered by their keysThe input must be an integer map to either string or float.The output is a 1-D tensor of string, float, or integer.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).<br>One of 'DENSE', 'SPARSE'.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.Output data. If strings are input, the output values are integers, and vice versa.The input must be a tensor of strings or integers, either [N,C] or [C].The output is a tensor of strings or integers. Its shape will be the same as the input shape.The strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceThe integers of the map. This sequence must be the same length as the 'cats_strings' sequence.A string to use when an input integer value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined.An integer to use when an input string value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined.A 1-D tensor holding values from the input dictionary.The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.A string vocabulary array.<br>One and only one of the vocabularies must be defined.An integer vocabulary array.<br>One and only one of the vocabularies must be defined.The input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.Input data. It can be either tensor or scalar.The input type is a tensor of any shape.Output type is determined by the specified 'values_*' attribute.A list of strings. One and only one of 'keys_*'s should be set.A list of strings. One and only one of 'value_*'s should be set.Classification outputs (one class per example).Classification scores ([N,E] - one score for each class and exampleThe input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]The output will be a tensor of strings or integers.A collection of weights of the model(s).Indicates whether to do OvR or multinomial (0=OvR is the default).Class labels when using string labels. One and only one 'classlabels' attribute must be defined.Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Indicates the transform to apply to the scores vector.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Regression outputs (one per target, per example).The input must be a tensor of a numeric type.Indicates the transform to apply to the regression output vector.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Weights of the intercepts, if used.The total number of regression targets, 1 if not defined.Data to be encoded, a tensor of shape [N,C] or [C]Encoded output data, having one more dimension than X.List of categories, ints.<br>One and only one of the 'cats_*' attributes must be defined.List of categories, strings.<br>One and only one of the 'cats_*' attributes must be defined.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.First, offset by this.<br>Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.Second, multiply by this.<br>Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.<br>Must be same length as 'offset'Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores.The input must be a tensor of a numeric type, either [C] or [N,C].The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Its size will match the bactch size of the input.The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.First set of probability coefficients.Second set of probability coefficients. This array must be same size as prob_a.<br>If these are provided then output Z are probability estimates, otherwise they are raw scores.Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Class labels if using string labels.<br>One and only one of the 'classlabels_*' attributes must be defined.Class labels if using integer labels.<br>One and only one of the 'classlabels_*' attributes must be defined.Regression outputs (one score per target per example).The input type must be a tensor of a numeric type, either [C] or [N,C].Flag indicating whether the regression is a one-class SVM or not.The number of support vectors.Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'The class score for each class, for each point, a tensor of shape [N,E].The input type must be a tensor of a numeric type.The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.Node id for each node. Ids may restart at zero for each tree, but it not required to.Thresholds to do the splitting on for each node.Popularity of each node, used for performance and may be omitted.The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.<br>One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'Child node if expression is true.Child node if expression is false.For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.<br>This attribute may be left undefined, and the defalt value is false (0) for all nodes.The id of the tree that this node is in.node id that this weight is for.The index of the class list that each weight is for.The weight for the class in class_id.Indicates the transform to apply to the score. <br> One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)Node id for each node. Node ids must restart at zero for each tree and increase sequentially.Child node if expression is trueChild node if expression is falseFor each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.<br>This attribute may be left undefined and the defalt value is false (0) for all nodes.The id of the tree that each node is in.The index of the target that each weight is forDefines how to aggregate leaf values within a target. <br>One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The output will be a sequence of string or integer maps to float.The keys when using string keys.<br>One and only one of the 'classlabels_*' attributes must be defined.The keys when using int keys.<br>One and only one of the 'classlabels_*' attributes must be defined.An ordered collection of tensors, all with the same element type.The output array, elements ordered as the inputs.The size of each input in the input listInput's shape should be 1D or 2D*ZN4onnx11GetOpSchemaINS_19CastMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26CategoryMapper_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26DictVectorizer_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28LinearClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25SVMClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_34TreeEnsembleClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18ZipMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Input data.A list of labels.The input type must be a tensor of integers or strings, of any shape.The output type will be a tensor of strings or integers, and will have the same shape as the input./onnxruntime_src/cmake/external/onnx/onnx/defs/traditionalml/old.cc*ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ is out of bounds) vs ( inferred=type case mismatch. existing=type mismatch. existing= does not contain a graph.no name(op_type:, name: inputs but Graph has were providedGraph input #Inferred elem type differs from existing elem type: (Inferred shape and existing shape differ in rank: (Inferred shape and existing shape differ in dimension type case unsupported. existing=GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. is tensor type, but provided type is N4onnx15shape_inference19GraphInferencerImplEN4onnx15shape_inference20InferenceContextImplEFunc_Input for function node Output for function node onnx.AttributeProtoonnx.ValueInfoProtoonnx.NodeProtoonnx.ModelProtoonnx.StringStringEntryProtoonnx.TensorAnnotationonnx.GraphProtoonnx.TensorProto.Segmentonnx.TensorProtoonnx.SparseTensorProtoonnx.TensorShapeProtoonnx.TypeProto.Tensoronnx.TypeProto.Sequenceonnx.TypeProto.Maponnx.TypeProto.SparseTensoronnx.TypeProto.Opaqueonnx.TypeProtoonnx.OperatorSetIdProtoonnx.FunctionProtoonnx.TensorShapeProto.Dimension/build/Release/onnx/onnx-ml.pb.ccllllL_P__PP_PP\\\LLxEF@FGFEEIXI I(HHKIJhE,Z]L]^] ]\\l\<\[\[,Z[,Z,Z,Z,Z,Z,ZZ|ZTZZcdlcccc<cc|fdh,h|f|fi|f|f|f|fhgglgflf<l<mmmlmll\l,lo8rrrprqqpHpoooooqooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooď|ďď4ď\@ +x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified. If 'size' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.The size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified.Constrain roi type to float or double.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.Constrains to boolean tensors.(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).Input tensor containing indices. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [-depth, depth-1]. In case 'depth' is of non-integer type, it will be casted to int64 before use.Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor. Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used.Constrain output types to boolean tensors.(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.When True (nonzero), yield X, otherwise yield Yvalues selected at indices where condition is Truevalues selected at indices where condition is FalseTensor of shape equal to the broadcasted shape of condition, X, and Y.Constrain to all tensor types.(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.Tensor with same shape of input.(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).A N-D input tensor that is to be processed.A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X'A 1-D INT64 tensor containing indices of 'Y' elements' first occurance in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor. A 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'. A 1-D INT64 tensor containing the count of each element of 'Y' in input 'X'Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Constrains input and output to only numeric types.Supported modes: `constant`(default), `reflect`, `edge`Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`.(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0).Slice op must have either three, four or five inputs.Incorrect or missing input value for starts and endsInput axes has incorrect lengthInput steps has incorrect lengthInput tensor must be 4-dimensional*ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Ranks inferred (Which axis to concat onoutputs...Which axis to split onpaddingsNew shapetilesAxis along which to repeat.width_scaleheight_scale4-D tensor, [N,C,H,W]Which axis to split on. 'axis' must be in [-rank(indices)-1, rank(indices)]rank must be greater than axis) is not equal to the existing rank value (Number of elements of attribute 'scales' must be same as rank of input 'X'Attribute 'scales' must have floats type.Attribute 'scales' is required.Attribute value for pads is requiredAttribute pads has incorrect lengthIncorrect or missing attribute value for starts and endsAttribute axes has incorrect lengthInvalid value of attribute 'axis'Constrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported./onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/old.ccWhich axis to concat on. Default value is 1.Optional list of output lengths (see also arg 'split')List of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.Three modes: constant(default), reflect, edgeOne float, indicates the value to be filled, default is 0Number of repeated copies to make of the input tensor.Output tensor of same shape and type as input.Constrain tiles and axis's type to int64 tensors.The scale along width dimension. It takes value greater than or equal to 1.The scale along height dimension. It takes value greater than or equal to 1.Two interpolation modes: nearest(default), bilinear4-D tensor after resizing, [N,C,H,W]Constrain output types to bool, int32, int64, float16, float, double tensors.The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'.Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Starting indices of corresponding axis in `axes`Ending indices (exclusive) of corresponding axis in axes`Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of r >= 1 (same rank as input).Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. It is an error if any of the index values are out of bounds.List of non-negative integers, indicate the dimensions to squeeze.List of non-negative integers, indicate the dimensions to be inserted(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor.Input tensor containing indices. The values must be non-negative integers. Any entries in the 'indices' input tensor with values outside the range [0, depth) will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [0, depth). In case 'depth' is of non-integer type, it will be casted to int64 before use.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length alone the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.List of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.One float, indicates the value to be filled.*ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Split_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_13Pad_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_) is not equal to the existing dim value (Number of elements of input 'sizes' must be same as rank of input 'X'Input 'sizes' must have int64 element type.Number of elements of input 'scales' must be same as rank of input 'X'Input 'scales' must have float element type.Dimension value inferred (Data to be selectedData to be binarizedBinarized output dataInput dataA dictionary.Data to be processed.Imputed output dataValue(s) to change toA value that needs replacing.Value(s) to change to.Output data.A list of ints.A list of floats.A string.An integer.A float.Data to be classified.A collection of intercepts.Data to be regressed.Weights of the model(s).Encoded output dataOne of 'MAX,' 'L1,' 'L2'Data to be encoded.Data to be scaled.Scaled output data.Chosen support vectorsSupport vector coefficients.Input of shape [N,F]N, Top class for each pointTree id for each node.Feature id for each node.N classesThe node id of each weightThe weight for each targetThe total number of targets.The input valuesThe output mapseq(map(string, float))seq(map(int64, float))map(string, int64)map(int64, string)map(int64, float)map(int64, double)map(string, float)map(string, double)Label encoder has only one input.Label encoder has only one output.Only one of keys_*'s can be set in label encoder.Input type is not string tensor but key_strings is setInput type is not int64 tensor but keys_int64s is setInput type is not float tensor but keys_floats is setOnly one of values_*'s can be set in label encoder.The indices, based on 0 as the first index of any dimension.Selected output data as an arrayThe input must be a tensor of a numeric type or string. The output will be of the same tensor type./onnxruntime_src/cmake/external/onnx/onnx/defs/traditionalml/defs.ccThe input must be a tensor of a numeric type. The output will be of the same tensor type.Values greater than this are mapped to 1, others to 0.The input map that is to be cast to a tensorA tensor representing the same data as the input map, ordered by their keysThe input must be an integer map to either string or float.The output is a 1-D tensor of string, float, or integer.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).<br>One of 'DENSE', 'SPARSE'.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.Output data. If strings are input, the output values are integers, and vice versa.The input must be a tensor of strings or integers, either [N,C] or [C].The output is a tensor of strings or integers. Its shape will be the same as the input shape.The strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceThe integers of the map. This sequence must be the same length as the 'cats_strings' sequence.A string to use when an input integer value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined.An integer to use when an input string value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined.A 1-D tensor holding values from the input dictionary.The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.A string vocabulary array.<br>One and only one of the vocabularies must be defined.An integer vocabulary array.<br>One and only one of the vocabularies must be defined.The input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.Input data. It can be either tensor or scalar.The input type is a tensor of any shape.Output type is determined by the specified 'values_*' attribute.A list of strings. One and only one of 'keys_*'s should be set.A list of strings. One and only one of 'value_*'s should be set.Classification outputs (one class per example).Classification scores ([N,E] - one score for each class and exampleThe input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]The output will be a tensor of strings or integers.A collection of weights of the model(s).Indicates whether to do OvR or multinomial (0=OvR is the default).Class labels when using string labels. One and only one 'classlabels' attribute must be defined.Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Indicates the transform to apply to the scores vector.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Regression outputs (one per target, per example).The input must be a tensor of a numeric type.Indicates the transform to apply to the regression output vector.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Weights of the intercepts, if used.The total number of regression targets, 1 if not defined.Data to be encoded, a tensor of shape [N,C] or [C]Encoded output data, having one more dimension than X.List of categories, ints.<br>One and only one of the 'cats_*' attributes must be defined.List of categories, strings.<br>One and only one of the 'cats_*' attributes must be defined.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.First, offset by this.<br>Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.Second, multiply by this.<br>Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.<br>Must be same length as 'offset'Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores.The input must be a tensor of a numeric type, either [C] or [N,C].The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Its size will match the bactch size of the input.The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.First set of probability coefficients.Second set of probability coefficients. This array must be same size as prob_a.<br>If these are provided then output Z are probability estimates, otherwise they are raw scores.Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Class labels if using string labels.<br>One and only one of the 'classlabels_*' attributes must be defined.Class labels if using integer labels.<br>One and only one of the 'classlabels_*' attributes must be defined.Regression outputs (one score per target per example).The input type must be a tensor of a numeric type, either [C] or [N,C].Flag indicating whether the regression is a one-class SVM or not.The number of support vectors.Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'The class score for each class, for each point, a tensor of shape [N,E].The input type must be a tensor of a numeric type.The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.Node id for each node. Ids may restart at zero for each tree, but it not required to.Thresholds to do the splitting on for each node.Popularity of each node, used for performance and may be omitted.The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.<br>One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'Child node if expression is true.Child node if expression is false.For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.<br>This attribute may be left undefined, and the defalt value is false (0) for all nodes.The id of the tree that this node is in.node id that this weight is for.The index of the class list that each weight is for.The weight for the class in class_id.Indicates the transform to apply to the score. <br> One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)Node id for each node. Node ids must restart at zero for each tree and increase sequentially.Child node if expression is trueChild node if expression is falseFor each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.<br>This attribute may be left undefined and the defalt value is false (0) for all nodes.The id of the tree that each node is in.The index of the target that each weight is forDefines how to aggregate leaf values within a target. <br>One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The output will be a sequence of string or integer maps to float.The keys when using string keys.<br>One and only one of the 'classlabels_*' attributes must be defined.The keys when using int keys.<br>One and only one of the 'classlabels_*' attributes must be defined.An ordered collection of tensors, all with the same element type.The output array, elements ordered as the inputs.The size of each input in the input listInput's shape should be 1D or 2D*ZN4onnx11GetOpSchemaINS_19CastMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26CategoryMapper_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26DictVectorizer_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28LinearClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25SVMClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_34TreeEnsembleClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18ZipMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Input data.A list of labels.The input type must be a tensor of integers or strings, of any shape.The output type will be a tensor of strings or integers, and will have the same shape as the input./onnxruntime_src/cmake/external/onnx/onnx/defs/traditionalml/old.cc*ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ is out of bounds) vs ( inferred=type case mismatch. existing=type mismatch. existing= does not contain a graph.no name(op_type:, name: inputs but Graph has were providedGraph input #Inferred elem type differs from existing elem type: (Inferred shape and existing shape differ in rank: (Inferred shape and existing shape differ in dimension type case unsupported. existing=GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. is tensor type, but provided type is N4onnx15shape_inference19GraphInferencerImplEN4onnx15shape_inference20InferenceContextImplEFunc_Input for function node Output for function node onnx.AttributeProtoonnx.ValueInfoProtoonnx.NodeProtoonnx.ModelProtoonnx.StringStringEntryProtoonnx.TensorAnnotationonnx.GraphProtoonnx.TensorProto.Segmentonnx.TensorProtoonnx.SparseTensorProtoonnx.TensorShapeProtoonnx.TypeProto.Tensoronnx.TypeProto.Sequenceonnx.TypeProto.Maponnx.TypeProto.SparseTensoronnx.TypeProto.Opaqueonnx.TypeProtoonnx.OperatorSetIdProtoonnx.FunctionProtoonnx.TensorShapeProto.Dimension/build/Release/onnx/onnx-ml.pb.cc$4Dܻ L ,\ DED8F8EHD(DhHGGFI(HXICX\[\\\[\[,[ZZ\ZYXYXXXXXXLY YXXa<caLbbbaa eff e eg e e e edgflfe,edjkk,lktkTkjjxnpp8qqXppxonxnxnxnxnxnoxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnxnhnT TT|čT4PP   d    -  N4onnx14AttributeProtoEN4onnx14ValueInfoProtoEN4onnx9NodeProtoEN4onnx10ModelProtoEN4onnx22StringStringEntryProtoEN4onnx16TensorAnnotationEN4onnx10GraphProtoEN4onnx19TensorProto_SegmentEN4onnx11TensorProtoEN4onnx17SparseTensorProtoEN4onnx26TensorShapeProto_DimensionEN4onnx16TensorShapeProtoEN4onnx16TypeProto_TensorEN4onnx18TypeProto_SequenceEN4onnx13TypeProto_MapEN4onnx22TypeProto_SparseTensorEN4onnx16TypeProto_OpaqueEN4onnx9TypeProtoEN4onnx18OperatorSetIdProtoEN4onnx13FunctionProtoEN6google8protobuf8internal29InternalMetadataWithArenaBaseISsNS1_29InternalMetadataWithArenaLiteEE9ContainerE/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits<size_t>::max() - kBlockHeaderSize): /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/generated_message_util.ccCHECK failed: (scc->visit_status.load(std::memory_order_relaxed)) == (SCCInfoBase::kRunning): Not implemented field number with type .32b21w1&10}0 0w/.p.--7-,b,+33+6+*z**)^))(x('-'&j&%%p%%$338$#r#"R"!f!!  (4"8J332:z(8*33F  O '  V - -b - P Y338\4444556L6666$7d7748889T9D]D]9 :<:l::4;;;<4<<==L>t>>?D?t?D]D]?PtQRRST$UUdVZ<YY|WdX$\\ NND]D]O@t@@tABBCtCCdDDTEEdFGdGGHD]D]TIIJ4JdJJJJ$KKKTLLLLMDMtMMD]D]M$4/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/coded_stream.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): /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug.N6google8protobuf2io20ZeroCopyOutputStreamE/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl_lite.ccCHECK failed: (last_returned_size_) > (0): BackUp() can only be called after a successful Next().CHECK failed: (count) <= (last_returned_size_): CHECK failed: (count) >= (0): CHECK failed: target_ != NULL: CHECK failed: (buffer_used_) == (buffer_size_): BackUp() can only be called after Next().CHECK failed: (count) <= (buffer_used_): Can't back up over more bytes than were returned by the last call to Next().CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: Parameter to BackUp() can't be negative.CHECK failed: (count) <= (target_->size()): Cannot allocate buffer larger than kint32max for CHECK failed: (backup_bytes_) == (0): StringOutputStream.N6google8protobuf2io16ArrayInputStreamEN6google8protobuf2io17ArrayOutputStreamEN6google8protobuf2io18StringOutputStreamEN6google8protobuf2io18CopyingInputStreamEN6google8protobuf2io25CopyingInputStreamAdaptorEN6google8protobuf2io26CopyingOutputStreamAdaptorE(cannot determine missing fields for lite message)/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/message_lite.ccCHECK failed: !coded_out.HadError(): " because it is missing required fields: CHECK failed: (byte_size_before_serialization) == (byte_size_after_serialization): was modified concurrently during serialization.CHECK failed: (bytes_produced_by_serialization) == (byte_size_before_serialization): Byte size calculation and serialization were inconsistent. This may indicate a bug in protocol buffers or it may be caused by concurrent modification of This shouldn't be called if all the sizes are equal.Exceeded maximum protobuf size of 2GB: Can't parse message of type "N6google8protobuf11MessageLiteE[libprotobuf %s %s:%d] %s -%d.%d.%d%c%g%p%lld%llu".)INFOWARNINGFATAL/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/stubs/common.ccThis program requires version of the Protocol Buffer runtime library, but the installed version is . Please update your library. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in "This program was compiled against version of the Protocol Buffer runtime library, which is not compatible with the installed version (). Contact the program author for an update. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in "N6google8protobuf7ClosureEN6google8protobuf8internal16FunctionClosure0EN6google8protobuf14FatalExceptionE, lo=/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/stubs/int128.ccDivision or mod by zero: dividend.hi=CANCELLEDUNKNOWNDEADLINE_EXCEEDEDNOT_FOUNDALREADY_EXISTSPERMISSION_DENIEDUNAUTHENTICATEDRESOURCE_EXHAUSTEDFAILED_PRECONDITIONABORTEDOUT_OF_RANGEUNIMPLEMENTEDINTERNALUNAVAILABLEDATA_LOSS@` @` \\%;Qg}up`X +  N4onnx14AttributeProtoEN4onnx14ValueInfoProtoEN4onnx9NodeProtoEN4onnx10ModelProtoEN4onnx22StringStringEntryProtoEN4onnx16TensorAnnotationEN4onnx10GraphProtoEN4onnx19TensorProto_SegmentEN4onnx11TensorProtoEN4onnx17SparseTensorProtoEN4onnx26TensorShapeProto_DimensionEN4onnx16TensorShapeProtoEN4onnx16TypeProto_TensorEN4onnx18TypeProto_SequenceEN4onnx13TypeProto_MapEN4onnx22TypeProto_SparseTensorEN4onnx16TypeProto_OpaqueEN4onnx9TypeProtoEN4onnx18OperatorSetIdProtoEN4onnx13FunctionProtoEN6google8protobuf8internal29InternalMetadataWithArenaBaseISsNS1_29InternalMetadataWithArenaLiteEE9ContainerE/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits<size_t>::max() - kBlockHeaderSize): /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/generated_message_util.ccCHECK failed: (scc->visit_status.load(std::memory_order_relaxed)) == (SCCInfoBase::kRunning): Not implemented field number with type 1z1100/o//..m--,&,+++}*'2'2'*)))(G(''m''o&%x% +%$W$$#-#'2'2"v""! h  DH6xT'2'28b1(H"h0j '2'27 * ? + +u + + x  ?r'2'2J2,3d33D444$5\555666D7|777[[t888 9t99D:t:::\;4<d<<====>[[>OPP|Q\R<SSTTUYWXVWZT[LM[[tN>?t?@@TAABBC|CClDEEFdFDG[[GlHHHI4IdIIIDJtJJ$KTKKKKLDL[[tL2/onnxruntime_src/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): /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug.N6google8protobuf2io20ZeroCopyOutputStreamE/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl_lite.ccCHECK failed: (last_returned_size_) > (0): BackUp() can only be called after a successful Next().CHECK failed: (count) <= (last_returned_size_): CHECK failed: (count) >= (0): CHECK failed: target_ != NULL: CHECK failed: (buffer_used_) == (buffer_size_): BackUp() can only be called after Next().CHECK failed: (count) <= (buffer_used_): Can't back up over more bytes than were returned by the last call to Next().CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: Parameter to BackUp() can't be negative.CHECK failed: (count) <= (target_->size()): Cannot allocate buffer larger than kint32max for CHECK failed: (backup_bytes_) == (0): StringOutputStream.N6google8protobuf2io16ArrayInputStreamEN6google8protobuf2io17ArrayOutputStreamEN6google8protobuf2io18StringOutputStreamEN6google8protobuf2io18CopyingInputStreamEN6google8protobuf2io25CopyingInputStreamAdaptorEN6google8protobuf2io26CopyingOutputStreamAdaptorE(cannot determine missing fields for lite message)/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/message_lite.ccCHECK failed: !coded_out.HadError(): " because it is missing required fields: CHECK failed: (byte_size_before_serialization) == (byte_size_after_serialization): was modified concurrently during serialization.CHECK failed: (bytes_produced_by_serialization) == (byte_size_before_serialization): Byte size calculation and serialization were inconsistent. This may indicate a bug in protocol buffers or it may be caused by concurrent modification of This shouldn't be called if all the sizes are equal.Exceeded maximum protobuf size of 2GB: Can't parse message of type "N6google8protobuf11MessageLiteE[libprotobuf %s %s:%d] %s +%d.%d.%d%c%g%p%lld%llu".)INFOWARNINGFATAL/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/stubs/common.ccThis program requires version of the Protocol Buffer runtime library, but the installed version is . Please update your library. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in "This program was compiled against version of the Protocol Buffer runtime library, which is not compatible with the installed version (). Contact the program author for an update. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in "N6google8protobuf7ClosureEN6google8protobuf8internal16FunctionClosure0EN6google8protobuf14FatalExceptionE, lo=/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/stubs/int128.ccDivision or mod by zero: dividend.hi=CANCELLEDUNKNOWNDEADLINE_EXCEEDEDNOT_FOUNDALREADY_EXISTSPERMISSION_DENIEDUNAUTHENTICATEDRESOURCE_EXHAUSTEDFAILED_PRECONDITIONABORTEDOUT_OF_RANGEUNIMPLEMENTEDINTERNALUNAVAILABLEDATA_LOSS @` `Am3IW+@h0 -  /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/wire_format_lite.ccCHECK failed: (value.size()) <= (kint32max): buffer. Use the 'bytes' type if you intend to send raw parsingserializing '%s'String field contains invalid UTF-8 data when a protocol bytes. N6google8protobuf8internal12FieldSkipperEN6google8protobuf8internal29CodedOutputStreamFieldSkipperE/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/extension_set.ccNon-primitive types can't be packed.Don't lookup extension types if they aren't present (1). Don't lookup extension types if they aren't present (2). CHECK failed: extension != NULL: Index out-of-bounds (field is empty).Multiple extension registrations for type "CHECK failed: type == WireFormatLite::TYPE_MESSAGE || type == WireFormatLite::TYPE_GROUP: CHECK failed: (type) == (WireFormatLite::TYPE_ENUM): CHECK failed: (type) != (WireFormatLite::TYPE_ENUM): CHECK failed: (type) != (WireFormatLite::TYPE_MESSAGE): CHECK failed: (type) != (WireFormatLite::TYPE_GROUP): can't reach here.Can't get here.Extension not found.", field number X<| L,t\L80H`x(@XpGW` #? hrh&hy2\,, LpL |\QcJX((,p@ --t..../$/--.(>BDChDBB=HCxBA(A@@@?h?>>,>EE;o:;%FkE6;====:W=DrD<]<MNNNNNOO0O@ONtMlPLL4OtR|STUVWQN6google8protobuf8internal20RepeatedPtrFieldBaseEN6google8protobuf8internal15ExtensionFinderEN6google8protobuf8internal24GeneratedExtensionFinderEN6google8protobuf8internal12ExtensionSet8KeyValueEN6google8protobuf13RepeatedFieldIiEEN6google8protobuf13RepeatedFieldIlEEN6google8protobuf13RepeatedFieldIjEEN6google8protobuf13RepeatedFieldImEEN6google8protobuf13RepeatedFieldIfEEN6google8protobuf13RepeatedFieldIdEEN6google8protobuf13RepeatedFieldIbEEN6google8protobuf16RepeatedPtrFieldISsEEN6google8protobuf16RepeatedPtrFieldINS0_11MessageLiteEEESt3mapIiN6google8protobuf8internal12ExtensionSet9ExtensionESt4lessIiESaISt4pairIKiS4_EEEN6google8protobuf8internal19ImplicitWeakMessageE/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/stubs/stringprintf.ccCHECK failed: (v.size()) <= (kStringPrintfVectorMaxArgs): StringPrintfVector currently only supports up to Feel free to add support for more if you need it. arguments. Error reverse compiling 'Unknown encoding Error parsing '': Error compiling '\x00Invalid RE2: startpos: endpos: text size: DFA out of memory: size bytemap range list count SearchDFA inconsistencySearchOnePass inconsistencySearchBitState inconsistencySearchNFA inconsistencyrequested group in regexp invalid rewrite pattern: /onnxruntime_src/cmake/external/re2/re2/re2.ccpattern too large - reverse compile failedpattern too large - compile failedRE2: invalid startpos, endpos pair. [Rewrite schema error: '\' not allowed at end.Rewrite schema error: '\' must be followed by a digit or '\'.Rewrite schema requests %d matches, but the regexp only has %d parenthesized subexpressions. - Regexp not destroyed.Stack not empty.Bad reference count Walk NULLno errorunexpected errorinvalid escape sequenceinvalid character classinvalid character class rangemissing ]missing )trailing \invalid repetition sizebad repetition operatorinvalid perl operatorinvalid UTF-8invalid named capture group/onnxruntime_src/cmake/external/re2/re2/regexp.ccUnexpected op in Regexp::Equal: NumCapturesWalker::ShortVisit calledNamedCapturesWalker::ShortVisit calledCaptureNamesWalker::ShortVisit called/onnxruntime_src/cmake/external/re2/re2/walker-inl.hno argument for repetition operatorlLLL,LLLLLLL\<<4224333333d3D32222222,321N3re26Regexp6WalkerIiEEN3re217NumCapturesWalkerEN3re219NamedCapturesWalkerEN3re218CaptureNamesWalkerE\%03obasic_string::eraseGrowStack() failed: njob_ = job_.size() = Unexpected opcode: arg Bad arg in kInstAltMatch: Bad arg in kInstCapture: /onnxruntime_src/cmake/external/re2/re2/bitstate.cc\l$TCompiler::Copy called!should never happenNo ranges in char classMissing case in Compiler: hello, world/onnxruntime_src/cmake/external/re2/re2/compile.cc$!t !$! l T D tT4 N3re26Regexp6WalkerINS_4FragEEEN3re28CompilerE/onnxruntime_src/cmake/external/re2/re2/dfa.ccStateSaver failed to restore state.Unexpected special state in RunStateOnByteRunStateOnByteUnlocked failed after ResetCacheRunStateOnByteUnlocked failed after ResetFailed to analyze start state.%s%d(%p) flag=%#x||unhandled opcode: DeadState in RunStateOnByteNULL state in RunStateOnBytecontext does not contain text`XWxW(XWxW(Xunhandled in AddToThreadqUnhandled in step(?,?)(%d,?)(%d,%d)Bad args: nsubmatch= in ComputeFirstByte in Prog::Fanout()/onnxruntime_src/cmake/external/re2/re2/nfa.ccUnexpected opcode in short circuit: hPPPX  /onnxruntime_src/cmake/external/re2/re2/onepass.ccCannot use SearchOnePass for unanchored matches. $R%$$$$s#0000Bad hex digit Concat of AnyRE2: unexpected op: unknown round: /onnxruntime_src/cmake/external/re2/re2/parse.ccRepetitionWalker::ShortVisit calledAddFoldedRange recurses too much.Bad call to ParseState::ParsePerlFlags~Z[[[[[[[y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\y\[y\y\y\y\[y\y\y\y\y\y\y\[y\y\y\g\y\U\y\[y\Zܩ\,|ܪ̪D4)xN3re216RepetitionWalkerE09AFaf09AZ__azAZ !/:@[`{~ ~az!~09 AZaz09AZaz09AZ__az +  /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/wire_format_lite.ccCHECK failed: (value.size()) <= (kint32max): buffer. Use the 'bytes' type if you intend to send raw parsingserializing '%s'String field contains invalid UTF-8 data when a protocol bytes. N6google8protobuf8internal12FieldSkipperEN6google8protobuf8internal29CodedOutputStreamFieldSkipperE/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/extension_set.ccNon-primitive types can't be packed.Don't lookup extension types if they aren't present (1). Don't lookup extension types if they aren't present (2). CHECK failed: extension != NULL: Index out-of-bounds (field is empty).Multiple extension registrations for type "CHECK failed: type == WireFormatLite::TYPE_MESSAGE || type == WireFormatLite::TYPE_GROUP: CHECK failed: (type) == (WireFormatLite::TYPE_ENUM): CHECK failed: (type) != (WireFormatLite::TYPE_ENUM): CHECK failed: (type) != (WireFormatLite::TYPE_MESSAGE): CHECK failed: (type) != (WireFormatLite::TYPE_GROUP): can't reach here.Can't get here.Extension not found.", field number R````````$,4l<\|0H`x@( oy8FFFF7]YLcmmmm\L\LGTPL\'eH0<H0N6q0HYT44444444DD@@@@@@@@wXX},4,-<-d----4,,,<ABHBC@8Ax<AAP@??0?>`>>=0=<gDC:9;:D D9<<<<{9;bCCv;:@L0M@MXMpMMMMMMLL OJMQR4SDTtUV$PN6google8protobuf8internal20RepeatedPtrFieldBaseEN6google8protobuf8internal15ExtensionFinderEN6google8protobuf8internal24GeneratedExtensionFinderEN6google8protobuf8internal12ExtensionSet8KeyValueEN6google8protobuf13RepeatedFieldIiEEN6google8protobuf13RepeatedFieldIlEEN6google8protobuf13RepeatedFieldIjEEN6google8protobuf13RepeatedFieldImEEN6google8protobuf13RepeatedFieldIfEEN6google8protobuf13RepeatedFieldIdEEN6google8protobuf13RepeatedFieldIbEEN6google8protobuf16RepeatedPtrFieldISsEEN6google8protobuf16RepeatedPtrFieldINS0_11MessageLiteEEESt3mapIiN6google8protobuf8internal12ExtensionSet9ExtensionESt4lessIiESaISt4pairIKiS4_EEEN6google8protobuf8internal19ImplicitWeakMessageE/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/stubs/stringprintf.ccCHECK failed: (v.size()) <= (kStringPrintfVectorMaxArgs): StringPrintfVector currently only supports up to Feel free to add support for more if you need it. arguments. Error reverse compiling 'Unknown encoding Error parsing '': Error compiling '\x00Invalid RE2: startpos: endpos: text size: DFA out of memory: size bytemap range list count SearchDFA inconsistencySearchOnePass inconsistencySearchBitState inconsistencySearchNFA inconsistencyrequested group in regexp invalid rewrite pattern: /onnxruntime_src/cmake/external/re2/re2/re2.ccpattern too large - reverse compile failedpattern too large - compile failedRE2: invalid startpos, endpos pair. [Rewrite schema error: '\' not allowed at end.Rewrite schema error: '\' must be followed by a digit or '\'.Rewrite schema requests %d matches, but the regexp only has %d parenthesized subexpressions. + Regexp not destroyed.Stack not empty.Bad reference count Walk NULLno errorunexpected errorinvalid escape sequenceinvalid character classinvalid character class rangemissing ]missing )trailing \invalid repetition sizebad repetition operatorinvalid perl operatorinvalid UTF-8invalid named capture group/onnxruntime_src/cmake/external/re2/re2/regexp.ccUnexpected op in Regexp::Equal: NumCapturesWalker::ShortVisit calledNamedCapturesWalker::ShortVisit calledCaptureNamesWalker::ShortVisit called/onnxruntime_src/cmake/external/re2/re2/walker-inl.hno argument for repetition operator \LL2002d2L2L24242422100000001|10N3re26Regexp6WalkerIiEEN3re217NumCapturesWalkerEN3re219NamedCapturesWalkerEN3re218CaptureNamesWalkerE\%03obasic_string::eraseGrowStack() failed: njob_ = job_.size() = Unexpected opcode: arg Bad arg in kInstAltMatch: Bad arg in kInstCapture: /onnxruntime_src/cmake/external/re2/re2/bitstate.cc DCompiler::Copy called!should never happenNo ranges in char classMissing case in Compiler: hello, world/onnxruntime_src/cmake/external/re2/re2/compile.cc4DdD$d<$ N3re26Regexp6WalkerINS_4FragEEEN3re28CompilerE/onnxruntime_src/cmake/external/re2/re2/dfa.ccStateSaver failed to restore state.Unexpected special state in RunStateOnByteRunStateOnByteUnlocked failed after ResetCacheRunStateOnByteUnlocked failed after ResetFailed to analyze start state.%s%d(%p) flag=%#x||unhandled opcode: DeadState in RunStateOnByteNULL state in RunStateOnBytecontext does not contain textW!VVVHVVVunhandled in AddToThreadqUnhandled in step(?,?)(%d,?)(%d,%d)Bad args: nsubmatch= in ComputeFirstByte in Prog::Fanout()/onnxruntime_src/cmake/external/re2/re2/nfa.ccUnexpected opcode in short circuit: h PH-P e`x   /onnxruntime_src/cmake/external/re2/re2/onepass.ccCannot use SearchOnePass for unanchored matches.L##t#t#)#t#"0000Bad hex digit Concat of AnyRE2: unexpected op: unknown round: /onnxruntime_src/cmake/external/re2/re2/parse.ccRepetitionWalker::ShortVisit calledAddFoldedRange recurses too much.Bad call to ParseState::ParsePerlFlagsYZZZZZZZ[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[~Z[[[[lZ[[[[[[[ZZ[[[[[Z[HZ[NY|̧|l$ԨɯVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVNVVVVFVVVVVVV>VVV6V.VVN3re216RepetitionWalkerE09AFaf09AZ__azAZ !/:@[`{~ ~az!~09 AZaz09AZaz09AZ__az 09[:alnum:][:^alnum:][:alpha:][:^alpha:][:ascii:][:^ascii:][:blank:][:^blank:][:cntrl:][:^cntrl:][:digit:][:^digit:][:graph:][:^graph:][:lower:][:^lower:][:print:][:^print:][:punct:][:^punct:][:space:][:^space:][:upper:][:^upper:][:word:][:^word:][:xdigit:][:^xdigit:]\d\D\s\S\w\W/ialt -> %d | %daltmatch -> %d | %dbyte%s [%02x-%02x] -> %dcapture %d -> %demptywidth %#x -> %dmatch! %dnop -> %dfail%d. %s %d+ %s [%02x-%02x] -> %d -/onnxruntime_src/cmake/external/re2/re2/prog.ccUnexpected opcode in IsMatch: Hh((Malformed repeat Simplify case not handled: Simplify failed on /onnxruntime_src/cmake/external/re2/re2/simplify.ccCoalesceWalker::ShortVisit calledSimplifyWalker::ShortVisit calledCase not handled in ComputeSimple: DoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is 0HH0H   86666667778@86666666666N3re26Regexp6WalkerIPS0_EEN3re214CoalesceWalkerEN3re214SimplifyWalkerE[]^-\\r\t\n\f\x%02x\x{%x}(){}[]*+?|.^$\[^\x00-\x{10ffff}](?:)Bad final char: ?+{%d,}{%d}{%d,%d}\C^$(?-m:^)(?-m:$)\z\b\B(?HaveMatch:%d)(?:kRegexpCapture cap() == 0?P< [truncated]/onnxruntime_src/cmake/external/re2/re2/tostring.ccTbeeetbbbggtgghg<hdfffff<f femmm n n n nnN3re214ToStringWalkerEAZ  .@26@9G@Jv@xxy}@@O@@@@@@  ~"2@::+*;;==]>>(*AACC=DDEEEGFN@EEtpr@vvt&%@?  @~P/ `@@@.@1V0```É@@@A@(/8?HMYY[[]]__ho&!&!*!*!A+!+!2!2!`!o!!!$$,.,0`,`,b,b, c,c,d,d,g,k,@m,m,n,n,o,o,p,p,r,r,u,u,~,,,,@,,@,,@l@@".@2n@y{@}}u~@Z@@ZZZZZZZZ@p0h!: '((  @ @n_n !"]AZ ajkk lrss tz F y/279HJwxxy~Oa8  ~"3::+*;<==]>>(*?@?*ABCC=DDEEEGFOPP*QQ*RR*SS.TT2VW3YY6[[5\\O``3aaKcc1ee(ffDhh/ii-jjDkk)llAoo-qq)rr+uu*}})&&*&'%EETpsvw{}t&%@?  @60]#~P/ 0122N3344M5=>>D?@ABBCIJJ<KOP_`bcc$d/1V0a```  З‰@@yy}}`aa:bA '(/078?@EHMQQSSUUWWYY[[]]__`ghopqJruVvwdxyz{p|}~   &!&!*!*!!+!+!2!2!N!N!`!o!p!!!!$$$$,.,00,^,`,a,b,b, c,c,d,d,e,e,f,f,g,l,m,m,n,n,o,o,p,p,r,s,u,v,~,,,,,,,,-%-'-'-----@JKK=vLm"/2oy|}}u~ZZZZZZZZZSS`p0h!: AZ'((O(  @   @n_n `nn!""C      68<>>@ACDFO 5 9 ? P`h8j`~!"$$'')24799;;BBGGIIKKMOQRTTWWYY[[]]__abddgjlrtwy|~~   ?AJVoqPP=Pptv46F,,,,37?&)fjzV`xTV +/onnxruntime_src/cmake/external/re2/re2/prog.ccUnexpected opcode in IsMatch: (h88 (((Malformed repeat Simplify case not handled: Simplify failed on /onnxruntime_src/cmake/external/re2/re2/simplify.ccCoalesceWalker::ShortVisit calledSimplifyWalker::ShortVisit calledCase not handled in ComputeSimple: DoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is ,,,,888,,,,,,,,,HHHHHHHHH@705050505p5p5060606660505050505050505X505N3re26Regexp6WalkerIPS0_EEN3re214CoalesceWalkerEN3re214SimplifyWalkerE[]^-\\r\t\n\f\x%02x\x{%x}(){}[]*+?|.^$\[^\x00-\x{10ffff}](?:)Bad final char: ?+{%d,}{%d}{%d,%d}\C^$(?-m:^)(?-m:$)\z\b\B(?HaveMatch:%d)(?:kRegexpCapture cap() == 0?P< [truncated]/onnxruntime_src/cmake/external/re2/re2/tostring.cc`ldDd$daNatatfLffefffeedeDe$edddll\llllllN3re214ToStringWalkerEAZ  .@26@9G@Jv@xxy}@@O@@@@@@  ~"2@::+*;;==]>>(*AACC=DDEEEGFN@EEtpr@vvt&%@?  @~P/ `@@@.@1V0```É@@@A@(/8?HMYY[[]]__ho&!&!*!*!A+!+!2!2!`!o!!!$$,.,0`,`,b,b, c,c,d,d,g,k,@m,m,n,n,o,o,p,p,r,r,u,u,~,,,,@,,@,,@l@@".@2n@y{@}}u~@Z@@ZZZZZZZZ@p0h!: '((  @ @n_n !"]AZ ajkk lrss tz F y/279HJwxxy~Oa8  ~"3::+*;<==]>>(*?@?*ABCC=DDEEEGFOPP*QQ*RR*SS.TT2VW3YY6[[5\\O``3aaKcc1ee(ffDhh/ii-jjDkk)llAoo-qq)rr+uu*}})&&*&'%EETpsvw{}t&%@?  @60]#~P/ 0122N3344M5=>>D?@ABBCIJJ<KOP_`bcc$d/1V0a```  З‰@@yy}}`aa:bA '(/078?@EHMQQSSUUWWYY[[]]__`ghopqJruVvwdxyz{p|}~   &!&!*!*!!+!+!2!2!N!N!`!o!p!!!!$$$$,.,00,^,`,a,b,b, c,c,d,d,e,e,f,f,g,l,m,m,n,n,o,o,p,p,r,s,u,v,~,,,,,,,,-%-'-'-----@JKK=vLm"/2oy|}}u~ZZZZZZZZZSS`p0h!: AZ'((O(  @   @n_n `nn!""C      68<>>@ACDFO 5 9 ? 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AABA 4v8xBAD G -ABA 4wJBDA o +AAG tvpmA~ +A lvmA~ +A lTusyBBB B(A0A8G +8A0A(B BBBG 44vh=yAAD  +DAA 4lv)yAAD$ +DAA 4vyAAD +DAA Lv*yBDB A(A0L +0A(A BBBE T,w@.xBBB B(A0A8J* +8A0A(B BBBA \xx.K^4wgyBDD G~ + AABA 4w(5yBAD G +ABA 4xJBDA o ABG A -ABA Lv88jyBBB I(A0G -0D(A BBBA <LwX:_yBAI G| - DABA <w<8yBAI G - DABA <w=0yBAI G - DABA < x?0xBAI G - DABA TLxA4xBBB B(I0A8Gg -8D0A(B BBBA TxD4xBBB B(I0A8Gg -8D0A(B BBBA <xG;ixBAI G - DABA 4zhvBDA D0a - AABA LLzBBB B(A0A8Dp -8A0A(B BBBA Ly@zwBBB E(D0D8GP -8A0A(B BBBC <zHwBBK A(DPn -(A ABBA <TzPIwBBK A(DPn -(A ABBA TzI_wBBB B(A0D8G -8A0A(B BBBD TzQwBBB B(A0A8G4 -8A0A(B BBBA DD{@m|;yBBI A(G -(D ABBA ,d|bD\ +ABA LDxH5j|zBBB I(A0G +0D(A BBBA <xh7WzBAI G| + DABA <x90zBAI G + DABA <y:0 zBAI G + DABA <Ty<0yBAI G + DABA Ty>4yBBB B(I0A8Gg +8D0A(B BBBA TyA4yBBB B(I0A8Gg +8D0A(B BBBA <DzD;ayBAI G + DABA 4\{xvBDA D0a + AABA L{BBB B(A0A8Dp +8A0A(B BBBA L {PzxBBB E(D0D8GP +8A0A(B BBBC <\{ExBBK A(DPn +(A ABBA <{`FxBBK A(DPn +(A ABBA T{F_xBBB B(A0D8G +8A0A(B BBBD T4|NxBBB B(A0A8G4 +8A0A(B BBBA D|Pj|3zBBI A(G +(D ABBA ,}bD\ H P H Q -G I,|8bD\ +G I,}HbD\ H P H Q -G IT{xxBBB B(A0A8G -8A0A(B BBBJ TD|exBEE B(A0A8G -8A0A(B BBBF T|4xBBB B(A0A8G{ -8A0A(B BBBJ }.K^T }4+zBBB B(A0A8G -8A0A(B BBBA Td}pzBBB B(A0A8Gu -8A0A(B BBBA L~AAG ] +G IT4}yBBB B(A0A8G +8A0A(B BBBJ T}eyBEE B(A0A8G +8A0A(B BBBF T}ȕ4yBBB B(A0A8G{ +8A0A(B BBBJ .K^TT~4#{BBB B(A0A8G +8A0A(B BBBA T~{BBB B(A0A8Gu +8A0A(B BBBA LAAG ] CAF [ ACA R -CAJ JCA,~bD\ +CAJ JCA,,bD\ H P H Q -G I,bD\ +G I,\(bD\ H P H Q -G ILl~XyBBB A(A0N -0A(A BBBA ,~eyADG r -DAA D~B6LzBCA GPg +G ILhzBBB A(A0N +0A(A BBBA ,exzADG r +DAA D4?6D{BCA GPg  AABH C - AABI T4Ee0zBEE B(A0A8G -8A0A(B BBBF <dKBBG A(G@ -(A ABBG .K^T@{BIB B(D0A8G -8D0A(B BBBA T<{BIB B(D0A8G -8D0A(B BBBA Tw{BBB B(I0A8G' -8D0A(B BBBC TH{{BIE B(A0A8G -8D0A(B BBBF TD7{BBB B(A0A8G -8A0A(B BBBA T {BIB B(A0A8G -8A0A(B BBBA TP{BIB B(A0A8G -8A0A(B BBBA TL {BIB B(A0A8G -8A0A(B BBBA T- {BBB B(A0A8M -8A0A(B BBBA L6 {BBB B(A0A8Mi -8A0A(B BBBA L$8IAAG ] + AABI T|Be({BEE B(A0A8G +8A0A(B BBBF <IBBG A(G@ +(A ABBG H.K^T,P|BIB B(D0A8G +8D0A(B BBBA T|BIB B(D0A8G +8D0A(B BBBA T܁o|BBB B(I0A8G' +8D0A(B BBBC T4Xs|BIE B(A0A8G +8D0A(B BBBF T7|BBB B(A0A8G +8A0A(B BBBA T |BIB B(A0A8G +8A0A(B BBBA T<`{|BIB B(A0A8G +8A0A(B BBBA T |BIB B(A0A8G +8A0A(B BBBA T+ |BBB B(A0A8M +8A0A(B BBBA LD3 |BBB B(A0A8Mi +8A0A(B BBBA LlHFAAG ] CAF [ ACA R -CAJ JCALthIAAG ] +CAJ JCALxFAAG ] CAF [ ACA R -CAJ JCA,ĄIbD\ +CAJ JCA, FbD\ H P H Q -G IDIzBBA A(N -(A ABBA Ld LzBBB A(A0N -0A(A BBBA ,NezADG r -DAA ,OezADG r -DAA D 6zBCA GPg +G IDdF{BBA A(N +(A ABBA L0I{BBB A(A0N +0A(A BBBA ,Ke{ADG r +DAA ,,Le{ADG r +DAA D\06{BCA GPg  AABH C - AABI 4.K^TtN |BIB B(A0A8G -8A0A(B BBBA T̅H\ |BIB B(A0A8G -8A0A(B BBBA T$i |BIB B(A0A8G -8A0A(B BBBA T|w q|BIB B(A0A8G -8A0A(B BBBA TԆ [|BIB B(A0A8G -8A0A(B BBBA T, E|BIB B(A0A8G -8A0A(B BBBA TP /|BIB B(A0A8G -8A0A(B BBBA T܇ |BIB B(A0A8G -8A0A(B BBBA T4 |BIB B(A0A8G -8A0A(B BBBA L {BBB B(A0A8Mi -8A0A(B BBBA D܈{BBA K(D| -(A ABBA L`AAG ] + AABI |ۓ.K^TK }BIB B(A0A8G +8A0A(B BBBA TXY }BIB B(A0A8G +8A0A(B BBBA Tlf }BIB B(A0A8G +8A0A(B BBBA Tć(t i}BIB B(A0A8G +8A0A(B BBBA T S}BIB B(A0A8G +8A0A(B BBBA Tt =}BIB B(A0A8G +8A0A(B BBBA T̈` '}BIB B(A0A8G +8A0A(B BBBA T$ȩ }BIB B(A0A8G +8A0A(B BBBA T|0 |BIB B(A0A8G +8A0A(B BBBA Lԉ |BBB B(A0A8Mi +8A0A(B BBBA D$|BBA K(D| +(A ABBA LDpAAG ] CAF [ ACA R -CAJ JCA,LbD\ +CAJ JCA,bD\ H P H Q -G IL|AAG ] +G ILċAAG ] CAF c CAI R -CAJ JCA,̊bD\ +CAJ JCA, bD\ H P H Q -G I,PbD\ +G I,D`bD\ H P H Q -G I,,bD\ +G I,tbD\ H P H Q -G IL\AAG ] +G ILAAG ] CAF [ ACA R -CAJ JCA,bD\ +CAJ JCA,bD\ H P H Q -G I,܋@bD\ +G I,$PbD\ H P H Q -G I, bD\ +G I,TbD\ H P H Q -G I,<bD\ +G I,bD\ H P H Q -G I,lbD\ +G I,bD\ H P H Q -G I,@bD\ +G I,PbD\ H P H Q -G I,̌bD\ +G I,bD\ H P H Q -G I,bD\ +G I,DbD\ H P H Q -G I,mA~ -A lTtPxBBB B(K0A8G -8A0A(B BBBA ĽxBBB B(A0A8DW -8D0A(B BBBF LXcxBLE A(D0D -0D(A BBBA ,lHeAxADG r -DAA ,hxBDD y -ABA ,̍ewADG r -DAA L`xBLB B(A0A8G\ -8A0A(B BBBG TLx{xBBB L(A0A8G -8A0A(B BBBA TxBBB B(A0A8G -8A0A(B BBBG LXaxBDB J(A0A8Ib -8A0A(B BBBG TLqxBBB B(A0A8G -8A0A(B BBBG T;xBBB B(A0A8G -8A0A(B BBBG ThxBBB B(A0A8G -8A0A(B BBBG ,D.K^4yBKA G - DABA 4HyBKA G - DABA TeyBBB B(I0A8GM -8D0A(B BBBA 4LiyBKA G - DABA 4HyBKA G - DABA <'yBAD GC - DABA TxBBB B(D0A8G -8D0A(B BBBA <T`xBAD GC - DABA <xBAD GC - DABA <ԒqxBAD GC - DABA <PExBAD GC - DABA <TxBAD GC - DABA <wBAD Gk - DABA <ԓpwBAD Gk - DABA <wBAD G - DABA <TiwBAD Gk - DABA <=wBAD GC - DABA <Ԕ`wBAD GC - DABA <vBAD GC - DABA DTvBBA I(G) -(D ABBI DvvBBA D(G -(D ABBA < -zvBAD GC - DABA <$@ NvBAD G - DABA 4d "vBKA G - DABA 4vBKA G - DABA 4ԖPuBKA G - DABA 4 uBKA G - DABA <DuBAD G - DABA 4ruBRA G - DABA 48QuBRA G - DABA 40uBRA G - DABA <,uBAD GC - DABA <ltBAD GC - DABA DHtBBA I(G -(D ABBF <tBAI Gp - DABA T4PtBBB B(I0A8G -8D0A(B BBBA <![tBAD GC - DABA <̙h"/tBAD GC - DABA < #tBAD GC - DABA <L%sBAD GC - DABA <X&sBAD GC - DABA <̚'sBAD GC - DABA T (ySsBBB B(I0A8GQ -8D0A(B BBBI <d /GsBAD GC - DABA <p0sBAD GC - DABA <1rBAD GC - DABA <$3rBAD GC - DABA <d`4rBAD GC - DABA T5krBBB B(D0A8G -8D0A(B BBBA T889rBBB B(D0A8G -8D0A(B BBBA <T:YrBAI G - DABA <=qBAI G" - DABA <ԝ0@5qBAD G - DABA <0BqBAD GC - DABA <TCaqBAI G< - DABA Tn:qBDB E(A0C8G -8A0A(B BBBA <%qBEA D(D`s -(A ABBG D,XDpBDB A(I0F`K -0A(A BBBE TtEVpBDA C(L`^ +G ItmA~ +A lT`yBBB B(K0A8G +8A0A(B BBBA LyBBB B(A0A8DW +8D0A(B BBBF Ldh[yBLE A(D0D +0D(A BBBA ,Xe9yADG r +DAA ,hyBDD y +ABA ,exADG r +DAA LDXyBLB B(A0A8G\ +8A0A(B BBBG TsyBBB L(A0A8G +8A0A(B BBBA TyBBB B(A0A8G +8A0A(B BBBG LDhYyBDB J(A0A8Ib +8A0A(B BBBG TȔiyBBB B(A0A8G +8A0A(B BBBG T 3yBBB B(A0A8G +8A0A(B BBBG TDxxBBB B(A0A8G +8A0A(B BBBG tОѓ.K^4̑zBKA G + DABA 4XzBKA G + DABA T<ezBBB B(I0A8GM +8D0A(B BBBA 4(azBKA G + DABA 4̒@zBKA G + DABA <zBAD GC + DABA TDyBBB B(D0A8G +8D0A(B BBBA <pyBAD GC + DABA <ܓyBAD GC + DABA <iyBAD GC + DABA <\`=yBAD GC + DABA <yBAD GC + DABA <ܔxBAD Gk + DABA <xBAD Gk + DABA <\xBAD G + DABA <axBAD Gk + DABA <ܕ 5xBAD GC + DABA <p xBAD GC + DABA <\wBAD GC + DABA DwBBA I(G) +(D ABBI DvwBBA D(G +(D ABBA <,rwBAD GC + DABA <lP FwBAD G + DABA 4 +wBKA G + DABA 4 vBKA G + DABA 4` vBKA G + DABA 4T vBKA G + DABA < vBAD G + DABA 4̘jvBRA G + DABA 4HIvBRA G + DABA 4<(vBRA G + DABA <tvBAD GC + DABA <uBAD GC + DABA DXuBBA I(G +(D ABBF <<uBAI Gp + DABA T|`uBBB B(I0A8G +8D0A(B BBBA <Ԛ(SuBAD GC + DABA <x'uBAD GC + DABA <T tBAD GC + DABA <"tBAD GC + DABA <ԛh#tBAD GC + DABA <$wtBAD GC + DABA TT&yKtBBB B(I0A8GQ +8D0A(B BBBI <0,?tBAD GC + DABA <-tBAD GC + DABA <,.sBAD GC + DABA <l 0sBAD GC + DABA <p1sBAD GC + DABA T2csBBB B(D0A8G +8D0A(B BBBA TDH51sBBB B(D0A8G +8D0A(B BBBA <7YrBAI G + DABA <ܞ:rBAI G" + DABA <@=5rBAD G + DABA <\@?rBAD GC + DABA <@YrBAI G< + DABA Tܟn2rBDB E(A0C8G +8A0A(B BBBA <4rBEA D(D`s +(A ABBG DthAqBDB A(I0F`K +0A(A BBBE TBVqBDA C(L`^ (A ABBF  -(A ABBE T̟GVpBDA C(L`^ +(A ABBE TDV~qBDA C(L`^ (A ABBF  -(A ABBE T$IVMpBDA C(L`^ +(A ABBE TlFVEqBDA C(L`^ (A ABBF  -(A ABBE L|KQpBDB B(A0A8OZ -8A0A(B BBBA L̠SQpBDB B(A0A8Or -8A0A(B BBBI TckpBDJ B(A0A8G -8A0A(B BBBA Lti [pBBB B(D0G8JP -8A0A(B BBBI  w AZ4 wTpBAA J@ - AABB <HyapBBA A(JP -(A ABBI L\[pBBB A(K0D -0A(A BBBA D/pBBB A(A0Np -0A(A BBBA ḶAAG ] +(A ABBE LġHQ qBDB B(A0A8OZ +8A0A(B BBBA LPQqBDB B(A0A8Or +8A0A(B BBBI TdacqBDJ B(A0A8G +8A0A(B BBBA Lf SqBBB B(D0G8JP +8A0A(B BBBI 0t AZ4,0tTyqBAA J@ + AABB <dXvazqBBA A(JP +(A ABBI LSqBBB A(K0D +0A(A BBBA Dȕ'qBBB A(A0Np +0A(A BBBA LAAG ] CAF c CAI R -CAJ JCALAAG ] +CAJ JCALdЗAAG ] CAF [ ACA R -CAJ JCALlAAG ] +CAJ JCALAAG ] CAF [ ACA R -CAJ JCA, bD\ +CAJ JCA,0bD\ H P H Q -G I,`bD\ +G I,4pbD\ H P H Q -G I,bD\ +G I,dbD\ H P H Q -G I,LbD\ +G I,bD\ H P H Q -G I,| bD\ +G I,Ħ0bD\ H P H Q -G I,`bD\ +G I,pbD\ H P H Q -G IܥmA~ -A lT$hmBBL B(A0A8G\ -8A0A(B BBBG ,|hnBDD y -ABA ,HemADG r -DAA ,ܥemADG r -DAA ȤLnBDB B(A0C8Lpo +G I$mA~ +A lTlhnBBL B(A0A8G\ +8A0A(B BBBG ,ĦhoBDD y +ABA ,XenADG r +DAA ,$enADG r +DAA TءDoBDB B(A0C8Lpo 8A0A(B BBBE 4 8A0A(B BBBJ / -8A0A(B BBBG T` mnBBB B(A0A8G -8A0A(B BBBG L(7nBDB B(A0C8OZ -8A0A(B BBBG L<(5nBDB B(A0C8OZ -8A0A(B BBBG dԓ.K^4pBRA G - DABA 4ܧhoBRA G - DABA 4 oBRA G - DABA 4LزoBKA G - DABA 4oBKA G - DABA 4HwoBKA G - DABA 4VoBKA G - DABA 4,5oBKA G - DABA 4dpoBKA G - DABA 4(nBKA G - DABA 4ԩnBKA G - DABA D nBBA D(G8 -(D ABBA 4T-nADG -DAA 4-dnADG -DAA 4Ī-?nADG -DAA 4-nADG -DAA 44-mADG -DAA 4l-mADG -DAA 4-mADG -DAA 4ܫUmADG0 -DAA 4}amADGX -DAA 4LHU<mADG0 -DAA 4p-mADG -DAA 4h-lADG -DAA 4`-lADG -DAA 4,XalADG< -DAA <dlBAD Gw - DABA D vWlBBA D(G -(D ABBA 4X-/lADG -DAA 4$P} -lADGX -DAA 4\0kADG  -DAA 40kADG  -DAA 4̮0kADG  -DAA 40vkADG  -DAA 4<xiQkADGD -DAA 4tB,kADG -DAA <kBAI Gr - DABA DhjBBA I(G -(D ABBA D4jBBA I(G -(D ABBA <|jBAD GF - DABA <HsjBAD GF - DABA <GjBAD GF - DABA <<jBAD GF - DABA <|8iBAI Gp - DABA <IiBAI G - DABA <\iBAI G - DABA <<|iBAD G - DABA L|iBDB B(A0A8O| -8A0A(B BBBG L̲xiBDB B(A0A8O| -8A0A(B BBBG TjBFB B(A0A8J -8A0A(B BBBD LpdxLjBBB A(A0Nf -0A(A BBBA DjBBA A(N -(A ABBA D<(5iBBA A(N -(A ABBA D qiBBA A(N -(A ABBA D̴X"iBBA K(DU -(A ABBA D#NiBBA K(D -(A ABBA D\%DiBBA A(N -(A ABBA L|(AAG e +8A0A(B BBBG TܧpeoBBB B(A0A8G +8A0A(B BBBG L4(/oBDB B(A0C8OZ +8A0A(B BBBG L(-oBDB B(A0C8OZ +8A0A(B BBBG .K^4qBRA G + DABA 4$xpBRA G + DABA 4\0pBRA G + DABA 4pBKA G + DABA 4̩pBKA G + DABA 4XopBKA G + DABA 4<NpBKA G + DABA 4tȲ-pBKA G + DABA 4 pBKA G + DABA 48oBKA G + DABA 4oBKA G + DABA DToBBA D(G8 +(D ABBA 4 -oADG +DAA 4ԫ-\oADG +DAA 4 -7oADG +DAA 4D-oADG +DAA 4|-nADG +DAA 4-nADG +DAA 4-nADG +DAA 4$U~nADG0 +DAA 4\}YnADGX +DAA 4XU4nADG0 +DAA 4̭-nADG +DAA 4x-mADG +DAA 4<p-mADG +DAA 4thamADG< +DAA <{mBAD Gw + DABA D0vOmBBA D(G +(D ABBA 44h-'mADG +DAA 4l`}mADGX +DAA 40lADG  +DAA 4ܯ0lADG  +DAA 40lADG  +DAA 4L0nlADG  +DAA 4iIlADGD +DAA 4B$lADG +DAA <kBAI Gr + DABA D4xkBBA I(G +(D ABBA D|kBBA I(G +(D ABBA <ıkBAD GF + DABA <XkkBAD GF + DABA <D?kBAD GF + DABA <kBAD GF + DABA <IJHjBAI Gp + DABA <IjBAI G + DABA <D\jBAI G + DABA <tjBAD G + DABA LijjBDB B(A0A8O| +8A0A(B BBBG LjBDB B(A0A8O| +8A0A(B BBBG TdjBFB B(A0A8J +8A0A(B BBBD LkBBB A(A0Nf +0A(A BBBA D<jBBA A(N +(A ABBA D85jBBA A(N +(A ABBA D̵0qjBBA A(N +(A ABBA DhjBBA K(DU +(A ABBA D\!NjBBA K(D +(A ABBA D#DjBBA A(N +(A ABBA Lķ%AAG e CAF s ACA R -CAJ JCAL̶P(AAG ] +CAJ JCAL`%AAG ] CAF Z ACB R -CAJ JCA,(bD\ +CAJ JCA,d%bD\ H P H Q -G ILL(AAG ] +G IL%AAG ] CAF [ ACA R -CAJ JCA,(bD\ +CAJ JCA,&bD\ H P H Q -G IL̷0)AAG ] +G IL@&AAG ] CAF [ ACA R -CAJ JCA,`)bD\ +CAJ JCA,dp&bD\ H P H Q -G ILL)AAG ] +G IL&AAG ] CAF [ ACA R -CAJ JCA,)bD\ +CAJ JCA,&bD\ H P H Q -G I,̸*bD\ +G I, 'bD\ H P H Q -G I,P*bD\ +G I,D`'bD\ H P H Q -G IL,*AAG ] +G ILt'AAG ] CAF [ ACA R -CAJ JCA,|*bD\ +CAJ JCA,ĺ'bD\ H P H Q -G IL+AAG ] +G IL(AAG ] CAF c CAI R -CAJ JCA,@+bD\ +CAJ JCA,DP(bD\ H P H Q -G IL,+AAG ] +G ILt(AAG ] CAF c CAI R -CAJ JCA,|+bD\ +CAJ JCA,Ļ(bD\ H P H Q -G I,,bD\ +G I,)bD\ H P H Q -G I,ܺ@,bD\ +G I,$P)bD\ H P H Q -G I, ,bD\ +G I,T)bD\ H P H Q -G I,<,bD\ +G I,)bD\ H P H Q -G I,l-bD\ +G I,*bD\ H P H Q -G I,@-bD\ +G I,P*bD\ H P H Q -G I,̻-bD\ +G I,*bD\ H P H Q -G I-mA~ -A l4D.cBAD D -ABA <|h.cBBD A(G@a -(A ABBA L/cBBB F(A0A8J@X -8A0A(B BBBA D 0~ncBEE D(D0A -(A BBBA ,T0e2cADG r -DAA ,1ecADG r -DAA ,@1ebADG r -DAA ,1ebADG r -DAA ,1hbBDD y -ABA ,D2h~bBDD y -ABA L@2A` -G bl2A` -G b 3A` -G b<Խ3bACLPo +G ID*mA~ +A l4 +dBAD D +ABA <ļx+dBBD A(G@a +(A ABBA L,dBBB F(A0A8J@X +8A0A(B BBBA DT-~fdBEE D(D0A +(A BBBA ,-e*dADG r +DAA ,̽.edADG r +DAA ,P.ecADG r +DAA ,,.ecADG r +DAA ,\.hcBDD y +ABA ,/hvcBDD y +ABA P/A` +G b/A` +G bԿ00A` +G b<0cACLPo AAG  -AAE DbBED D(G$ -(D ABBE D\xbBED D(G$ -(D ABBE DbBED D(G$ -(D ABBE LĿHBBB B(A0A8Gp8A0A(B BBB,ADD Z -AAA $l(cA -E DbBPR M(D0c -(A BBBA 4ܿ3dB\A G - DABA <4cB_A G5 - DABA <T5cBAI G, - DABA 4@8cBKA G - DABA 48cBKA G - DABA 49jcBKA G - DABA D<h:IcBBA I(G4 -(D ABBA D> cBBA I(G -(D ABBA 4HBbBKA G - DABA 4CbBRA G - DABA 4<CbBRA G - DABA 4tpDbBRA G - DABA T(EsbBBB B(A0A8G -8A0A(B BBBJ <J9GbBAI G - DABA <DL bBAI G - DABA D`NaBBA I(G2 -(D ABBA TPaBBE B(A0A8G< -8D0A(B BBBC T$ VaBBJ B(A0A8G -8D0A(B BBBE <|[&aBAD G - DABA <x]aBAI G~ - DABA T(`iaBBB B(A0I8G -8D0A(B BBBA TTpcR{aBBE E(D0D8L  -8A0A(B BBBK ,xyBCA x -ABO y(A[z(A[4zdaBAA J@ - AABJ <TP|aaBBA A(JP -(A ABBA T}-aBBB B(A0D8G -8A0A(B BBBG XLP%GbBFB B(A0C8J] -8A0A(B BBBG ,0dl({TbBFB B(A0A8Gp_ +AAE D\ cBED D(G$ +(D ABBE DcBED D(G$ +(D ABBE DcBED D(G$ +(D ABBE L XBBB B(A0A8Gp8A0A(B BBB,\ADD Z +AAA $h dA +E D cBPR M(D0c +(A BBBA 4$0eB\A G + DABA <\1dB_A G5 + DABA <2dBAI G, + DABA 4P5dBKA G + DABA 46dBKA G + DABA 4L6bdBKA G + DABA Dx7AdBBA I(G4 +(D ABBA D<dBBA I(G +(D ABBA 4X?cBKA G + DABA 4L@cBRA G + DABA 4@cBRA G + DABA 4AcBRA G + DABA T8BkcBBB B(A0A8G +8A0A(B BBBJ <LG9?cBAI G + DABA <IcBAI G + DABA DpKbBBA I(G2 +(D ABBA TNbBBE B(A0A8G< +8D0A(B BBBC Tl0SbBBJ B(A0A8G +8D0A(B BBBE <X&bBAD G + DABA <ZbBAI G~ + DABA TD8]abBBB B(A0I8G +8D0A(B BBBA T`RsbBBE E(D0D8L  +8A0A(B BBBK ,vBCA x +ABO w(A[w(A[4d(wdbBAA J@ + AABJ <`yabBBA A(JP +(A ABBA Tz-bBBB B(A0D8G +8A0A(B BBBG  hLL`%?cBFB B(A0C8J] +8A0A(B BBBG t@d8{LcBFB B(A0A8Gp_ 8A0A(B BBBB  -8A0A(B BBBA @8T01aBBA A(MT +8A0A(B BBBA P HTL@1bBBA A(MT (A ABBC \ -(A ABBF L\aBAA JV +(A ABBF L(bBAA JV  AABH  - AABD L aBFB B(A0C8J] -8A0A(B BBBG T-bBBE E(A0C8G^ -8D0A(B BBBD LlHaBDB B(A0A8O\ -8A0A(B BBBG TaBBB E(A0G8G -8D0A(B BBBJ T0bBIB B(A0A8G -8A0A(B BBBD DlXocBBA A(N -(A ABBA L]cBBB A(A0Nf -0A(A BBBA DDcBBA A(N -(A ABBA DL52cBBA A(N -(A ABBA Dq cBBA A(N -(A ABBA LAAG ] + AABD L bBFB B(A0C8J] +8A0A(B BBBG T\%cBBE E(A0C8G^ +8D0A(B BBBD LXbBDB B(A0A8O\ +8A0A(B BBBG TbBBB E(A0G8G +8D0A(B BBBJ T\@bBIB B(A0A8G +8A0A(B BBBD DhgdBBA A(N +(A ABBA LUdBBB A(A0Nf +0A(A BBBA DL<dBBA A(N +(A ABBA D5*dBBA A(N +(A ABBA DqdBBA A(N +(A ABBA L(AAG ] CAF k ACA R -CAJ JCALXAAG e +CAJ JCALLhAAG e CAF s ACA R -CAJ JCA,TbD\ +CAJ JCA,bD\ H P H Q -G ILAAG ] +G ILAAG ] CAF Z ACB R -CAJ JCA,bD\ +CAJ JCA,(bD\ H P H Q -G ILXAAG ] +G ILLhAAG ] CAF [ ACA R -CAJ JCA,TbD\ +CAJ JCA,bD\ H P H Q -G ILAAG ] +G ILAAG ] CAF [ ACA R -CAJ JCA,bD\ +CAJ JCA,bD\ H P H Q -G IL8AAG ] +G ILLHAAG ] CAF [ ACA R -CAJ JCA,ThbD\ +CAJ JCA,xbD\ H P H Q -G I,bD\ +G I,bD\ H P H Q -G I,bD\ +G I,bD\ H P H Q -G I,(bD\ +G I,,8bD\ H P H Q -G I,hbD\ +G I,\xbD\ H P H Q -G IDmA~ -A l<{_BED D(G0z -(D ABBA D8~S_BEE D(D0A -(A BBBA ,pe_ADG r -DAA ,De^ADG r -DAA ,te^ADG r -DAA D028_BCA L@Z +G ImA~ +A l<{`BED D(G0z +(D ABBA DH~K`BEE D(D0A +(A BBBA ,\e`ADG r +DAA ,e_ADG r +DAA ,e_ADG r +DAA D@20`BCA L@Z  AABH  - AABA D(2 _BCA L@Z + AABA D482`BCA L@Z  AABH  - AABA T4n_BEJ E(D0D8GP -8D0A(B BBBF dث.K^4`BYA G - DABA 4h`BYA G - DABA 4Pr`B\A G - DABA 4L8Q`BYA G - DABA <t0`BYA G - DABA <0`B_A G5 - DABA <_BAI G, - DABA LD _BIB A(D0G -0D(A BBBA 4_BKA G - DABA 4h`_BKA G - DABA 4 ?_BKA G - DABA 4<7_ADG -DAA Tt^BDB I(A0A8Np -8A0A(B BBBE 4h -^ADG -DAA 4 ^ADG -DAA <<x s^BAD G - DABA D|XG^BBA D(G -(D ABBA TV^BBB B(A0A8G -8A0A(B BBBD <]BAI Ge - DABA T\XX]BBJ B(A0A8G -8D0A(B BBBI T`]BBB B(A0A8G. -8A0A(B BBBG T "P]BEE B(D0D8L -8A0A(B BBBK ,<:ACL d -JAQ ,l:BCA x -ABO ;(A[;(A[T ;"]BBB B(A0D8G -8A0A(B BBBE 4NLtN `^BFB B(A0C8J] -8A0A(B BBBG ^L^M^BDB B(A0A8O\ -8A0A(B BBBG L,bM^BDB B(A0A8O\ -8A0A(B BBBG ,ThbD\ + AABA T|l`BEJ E(D0D8GP +8D0A(B BBBF .K^4aBYA G + DABA 4$xaBYA G + DABA 4\`jaB\A G + DABA 4HIaBYA G + DABA <t(aBYA G + DABA < @aB_A G5 + DABA <L`BAI G, + DABA L `BIB A(D0G +0D(A BBBA 4y`BKA G + DABA 4xX`BKA G + DABA 4L07`BKA G + DABA 47`ADG +DAA T_BDB I(A0A8Np +8A0A(B BBBE 4x_ADG +DAA 4L _ADG +DAA < +k_BAD G + DABA Dh ?_BBA D(G +(D ABBA T V_BBB B(A0A8G +8A0A(B BBBD <d^BAI Ge + DABA ThX^BBJ B(A0A8G +8D0A(B BBBI Tp^BBB B(A0A8G. +8A0A(B BBBG TTP^BEE B(D0D8L +8A0A(B BBBK ,7ACL d +JAQ ,7BCA x +ABO 8(A[ 8(A[TL08"^BBB B(A0D8G +8A0A(B BBBE |LLL X_BFB B(A0C8J] +8A0A(B BBBG [L$[M_BDB B(A0A8O\ +8A0A(B BBBG Lt_M_BDB B(A0A8O\ +8A0A(B BBBG ,ebD\ H P H Q -G I,hbD\ +G I,fbD\ H P H Q -G IThEy^BBE E(A0A8G6 -8A0A(B BBBI T4`r^BBE E(A0A8G -8A0A(B BBBK TEj^BBE E(A0A8G6 -8A0A(B BBBI .K^ThpK^BBB B(A0A8G -8A0A(B BBBA TT0x7=^BBB B(A0A8G -8A0A(B BBBA T{)^BDB B(A0A8J -8A0A(B BBBH @<8_BBA A(J`x -(A ABBB 4hQAz -E P,TbD\ +G IT$xEq_BBE E(A0A8G6 +8A0A(B BBBI T|pj_BBE E(A0A8G +8A0A(B BBBK TEb_BBE E(A0A8G6 +8A0A(B BBBI .K^TD(epC_BBB B(A0A8G +8A0A(B BBBA T@u75_BBB B(A0A8G +8A0A(B BBBA T({!_BDB B(A0A8J +8A0A(B BBBH $P<dH`BBA A(J`x +(A ABBB |xQAz +E P,bD\ H P H Q -G I.K^Tз7^BBB B(A0A8G -8A0A(B BBBA ,bD\ +G IX.K^T 7_BBB B(A0A8G +8A0A(B BBBA ,<bD\ H P H Q -G I,$bD\ +G I,lbD\ H P H Q -G I4|8^BAD D -ABA D7_BAA Jp +G I4H_BAD D +ABA D/`BAA Jp  AABH  - 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@R8R,4t AAG } -AAA 4;Au<44AAG y +DAA ԎPOHO,,1t AAG } +AAA 41;Au<|2 AAG y AAD Y -DAA LQdQ,5t\AAG } -AAA `5;Au<5AAG y +DAA NN, 2tT AAG } +AAA p2;Au<<2 AAG y AAD Y -DAA  Q$P,d5tAAG } -AAA l5;Au<6tAAG y +DAA TNlN,2tAAG } +AAA 3;Au< 3lAAG y AAD Y -DAA ̏`PXP,$06tAAG } -AAA <T6AAG y +DAA pM,hM,l@3t AAG } +AAA <3AAG y AAD Y -DAA lOO,ď6tAAG } -AAA <6lAAG y +DAA L̑L, 3tAAG } +AAA <<4dAAG y AAD Y -DAA  `O$XO,d7t AAG } -AAA l`7;Au<7AAG y +DAA TpLlhL, 4tAAG } +AAA p4;Au<4AAG y AAD Y -DAA ̑NN,$7thAAG } -AAA ,7;Au<t8$AAG y +DAA K,K,l4t`AAG } +AAA t5;Au< 5AAG y AAD Y -DAA  NN,08tAAG } -AAA $8ARD hAA<<8xAAG y +DAA ԓ0K(K,,@5tAAG } +AAA $45ARD hAA<5pAAG y AAD Y -DAA TxMlpM,9tAAG } -AAA X9;Au<x9AAG y +DAA JJ,6tAAG } +AAA h6;Au<D6AAG y AAD Y -DAA L,L<l9tBBA A(G0 -(A ABBK (;AL,̓(;AAG  -AAA <;AAG y +DAA \ItI<6lBBA A(G0 +(A ABBK ̕88AL,88AAG  +AAA <D9AAG y AAD Y -DAA K,K<l<BBA A(G0b -(A ABBK <AL,̔<;AAG [ -AAA ԕ=;Au<(=AAG y +DAA \ItI<(9BBA A(G0b +(A ABBK ̖9AL,93AAG [ +AAA :;Au<d8:AAG y AAD Y -DAA 4JLJ,H=AAG  -AAA >;Au<ܕ8>WAAG y +DAA |HH,ԖX:AAG  +AAA ܗ(;;Au<$H;OAAG y AAD Y -DAA XJ PJ<LX>BBA A(G0o -(A ABBF d(?AL,(?AAG  -AAA @;Au<8@ZAAG y +DAA <hGT`G<h;BBA A(G0o +(A ABBF 8<AL,8<AAG  +AAA (=;Au<DH=RAAG y AAD Y -DAA XI,PI<lX@BBA A(G0o -(A ABBF @AL,̗@AAG 0 -AAA <B}AAG y +DAA \hFt`F<h=BBA A(G0o +(A ABBF ̙=AL,=AAG 0 +AAA <D?uAAG y AAD Y -DAA xH,pH<lBGBBA A(G0 -(A ABBD CAL,̘CAAG [ -AAA ԙ(D;Au<HD~AAG y +DAA \EtE<?GBBA A(G0 +(A ABBD ̚@AL,@AAG [ +AAA 8A;Au<dXAvAAG y AAD Y -DAA 4xGLpGdhG%|G%,8DAAG S -AAA ĚD;Au< DAAG y +DAA |DDxD%ěD%,HAAAG S +AAA  A;Au<TAAAG y AAD Y -DAA $G<G,|DpKAAG  -AAA $(FARD hAA<ԚFAAG y +DAA lDD,ěApCAAG  +AAA $̜8CARD hAA<CAAG y AAD Y -DAA `FXFPF%4hF%LF%,hFZAAG  -AAA G;Au<ܛHAAG y +DAA 4pCLhCd`C%|xC%C%,ԜxCRAAG  +AAA ܝD;Au<$EAAG y AAD Y -DAA F F,$EADD c -ADG TxF2W VF0qD] -E ,GAG s +DAA <CTC,lCADD c +ADG C2W VܝC0iD] +E ,ԞDAG s AD X -AE `J$hFAG0y -AA <$FaAAG0_ +AE pG$DxC AG0y +AA <lCaAAG0_ AAF Q -AAA LdJBBB B(D0A8K -8C0A(B BBBI 4FjBAD a -ADC rABD(KOBBB D(A0Kp -0C(A BBBB D4LP$BBB D(A0Kp -0C(A BBBG L|NBBB B(D0A8K -8C0A(B BBBI D̞PBBB D(A0Kp -0C(A BBBH TQaBBB B(D0A8K~ -8C0A(B BBBA TlSdBBB B(D0A8K> -8C0A(B BBBF TğU)BBB B(D0A8K -8C0A(B BBBE THWBBB B(D0A8K -8C0A(B BBBE DtYBBB D(A0Kp -0C(A BBBG LZBBB B(D0A8N -8C0A(B BBBK 4CjBAD a -ADC rABLD^BBB B(D0A8K -8C0A(B BBBH 4lCjBAD a -ADC rABD̡(`QBBB D(A0Kp -0C(A BBBB ,@aAG C +AAA LGBBB B(D0A8K +8C0A(B BBBI 4ԟCjBAD a +ADC rABD48HGBBB D(A0Kp +0C(A BBBB D|IPBBB D(A0Kp +0C(A BBBG LğKBBB B(D0A8K +8C0A(B BBBI D(MBBB D(A0Kp +0C(A BBBH T\NaBBB B(D0A8K~ +8C0A(B BBBA TP\BBB B(D0A8K> +8C0A(B BBBF T R!BBB B(D0A8K +8C0A(B BBBE TdXTBBB B(D0A8K +8C0A(B BBBE D VBBB D(A0Kp +0C(A BBBG LWBBB B(D0A8N +8C0A(B BBBK 4,@jBAD a +ADC rABL[BBB B(D0A8K +8C0A(B BBBH 4@jBAD a +ADC rABD8]QBBB D(A0Kp +0C(A BBBB ,4P^AG C AD X -AE a,4aAG s +AE d^,|^AG s AD X -AE db,| bAG C +AE (_,Ĥ0_AG C AD X -AE b,ģbAG C +AE _, _AG C AD X -AE c, cAG  +AE <`,T `AG  AH X -AE <c,TcAG S +AE `,`AG S AD X -AE d,dAG  +AE ̥a, aAG  AH X -AE ̤d,dAG  +AE a,,aAG  AH X -AE d,,eAG C +AE \b,tbAG C AD X -AE \pe,txeAG O +AE b,bAG O AH X -AE e,fAG O +AE c,cAG O AH X -AE f,fAG K +AE 4c,LcAG K AD X -AE 4g,LgAG [ +AE |d, dAG [ AD X -AE |g,gAG k +AE ħd,ܧdAG k AD X -AE ĦHh,ܦPh=AG  +AE  Xe,$`e=AG  AH X -AE  `i,$hiAG 3 +AE Tpf,lxfAG 3 AD X -AE Tj<j -BBA D(G0d -(A ABBA ,0k}AG # +AE g<ܧg BBA D(G0d +(A ABBA ,@h}AG # AD X -AE ܧl,l~ADG0h -AAJ $l,<l=AG  +AE $i,<i~ADG0h +AAJ li,i=AG  AD X -AE ln4nBDD D0U - AABI o,ԨxoAG S +AE k4̩kBDD D0U + AABI l,lAG S AD X -AE qTrBDA G0e +AE LoTdoBDA G0e  AABI D  AABH T - FABE trTrBDA G0e + FABE oTԪoBDA G0e  AABI D  AABH T - FABE (s<$ sBBE D(G0Z -(A ABBA D<s&ADG0p + FABE ,8p<l0p~BBE D(G0Z +(A ABBA Dp&ADG0p DAO a AAE Q -DAJ t<ĩtBFA D(G0p -(A ABBA <uBFA D(G0p -(A ABBA DuL44u7ADG N -GAN DAATuDBFA D(G0 +DAJ ̫q< qBFA D(G0p +(A ABBA <L rBFA D(G0p +(A ABBA rL4|r7ADG N +GAN DAATܫr<BFA D(G0 (A ABBI [ -(A ABBA $vUAc -D d4v7ADG N -GAN DAA4LvAAG i -AAD KAA4\@w7ADG N -GAN DAA4Hw3AAG i -AAD KAA4̬w7ADG N -GAN DAA$,wUAc -D d4,w7ADG N -GAN DAA$wUoAc -D d4 xOAAG o -AAF [AA$xEA[ -D \$xMAb -E \L<xyBEA A(D0@ -(A ABBE V(A ABB$yMAb -E \$@yMwAb -E \LܭhyWBEA A(D0G -(A ABBF z(A ABBT,yBFA H(D0 +(A ABBA $4sUAc +D d44s7ADG N +GAN DAA4sAAG i +AAD KAA4Pt7ADG N +GAN DAA4Xt+AAG i +AAD KAA4t7ADG N +GAN DAA$ttUAc +D d4tt7ADG N +GAN DAA$ԭtUgAc +D d40uGAAG o +AAF [AA$4uEA[ +D \$\uMAb +E \LuyBEA A(D0@ +(A ABBE V(A ABB$Ԯ(vMAb +E \$PvMoAb +E \L$xvOBEA A(D0G +(A ABBF z(A ABBTtvBFA H(D0 (A ABBE p -(A ABBJ 4{zAAG p -AAE sAATH{LBFA H(D0 +(A ABBJ 4̯xzAAG p +AAE sAATXxLBFA H(D0 (A ABBE ~ -(A ABBL L@|BBA D(D0 +(A ABBL L4PyBBA D(D0 (A ABBJ d -(E ABBJ Td}BFA H(D0 +(E ABBJ T{BFA H(D0 (A ABBA \ -(E ABBJ T'BFA H(D0 +(E ABBJ T|'BFA H(D0 (A ABBB z -(A ABBH TZBBA D(D0 +(A ABBH T\}RBBA D(D0 (A ABBG g -(E ABBO LDH5BBB B(A0D8G`c -8A0A(B BBBG L6BBA D(G0a -(C ABBG (F ABBT XkBBB B(D0A8Nz -8D0A(B BBBF 4< 7jBAD a -ADC rABLBBB B(D0A8K -8C0A(B BBBE 4IJ7jBAD a -ADC rABT$@U BBB B(D0A8N= -8D0A(B BBBA T|HtaBBB B(D0A8K -8C0A(B BBBE 46jBAD a -ADC rABT 8BBB B(D0A8K -8C0A(B BBBA 4<p6jBAD a -ADC rABT}BBB B(D0A8K -8D0A(B BBBK L CBBB B(D0A8N -8A0A(B BBBG d6BBB B(A0A8D`P +(E ABBO LX2BBB B(A0D8G`c +8A0A(B BBBG Lܲ3BBA D(G0a +(C ABBG (F ABBTTh~cBBB B(D0A8Nz +8D0A(B BBBF 404jBAD a +ADC rABLBBB B(D0A8K +8C0A(B BBBE 4 4jBAD a +ADC rABTlPU BBB B(D0A8N= +8D0A(B BBBA TijXtYBBB B(D0A8K +8C0A(B BBBE 43jBAD a +ADC rABTTHBBB B(D0A8K +8C0A(B BBBA 43jBAD a +ADC rABTuBBB B(D0A8K +8D0A(B BBBK L<0;BBB B(D0A8N +8A0A(B BBBG dd3BBB B(A0A8D`P 8A0A(B BBBH  -8A0A(B BBBA h7`7|BEB B(D0A8D`E +8A0A(B BBBA ̶x4p4|BEB B(D0A8D`E 8A0A(B BBBM D 8A0A(B BBBB  -8F0A(B BBBE 4H4L@7ADG N -GAN DAALH!BBB E(D0A8D` -8A0A(B BBBK LԶ(BBB B(A0D8G` -8A0A(B BBBG $X4<P7ADG N -GAN DAADtXRBAD G0 +8F0A(B BBBE |X4P7ADG N +GAN DAALX!BBB E(D0A8D` +8A0A(B BBBK L8BBB B(A0D8G` +8A0A(B BBBG lh4`7ADG N +GAN DAADhRBAD G0  AABF D - AABH p4Էh7ADG N -GAN DAA 5TLX)+BEB E(A0A8Dy -8A0A(B BBBI T02BIF E(D0d + AABH 4x7ADG N +GAN DAAT2Th)#BEB E(A0A8Dy +8A0A(B BBBI T@*BIF E(D0d (A BBBK  -(A BBBA ԸH4d`fBBB B(A0A8G`^ +(A BBBA X1d4pfBBB B(A0A8G`^ 8A0A(B BBBG  -8A0A(B BBBA Th4l`7ADG N -GAN DAA4hBDD D0G - AABG ܹ47ADG N -GAN DAA4,BDD D0G - AABG d4|7ADG N -GAN DAAdADG0 +8A0A(B BBBA x4p7ADG N +GAN DAA4xBDD D0G + AABG $4<7ADG N +GAN DAA4tBDD D0G + AABG 4Ļ7ADG N +GAN DAAdADG0 DAH { DAH { DAH { DAH { DAH D -AAJ 4BDD D0G - AABG T4l7ADG N -GAN DAAD/BAD G0 +AAJ 4dBDD D0G + AABG 47ADG N +GAN DAAD/BAD G0  AABL D - AABH 4x7ADG N -GAN DAA|<BBB B(A0D8G` + AABH 44L7ADG N +GAN DAA|BBB B(A0D8G` 8A0A(B BBBM D 8A0A(B BBBB  -8H0A(B BBBE 4Լ7ADG N -GAN DAA  0L$BBB B(A0D8G`w -8A0A(B BBBK tX4P7ADG N -GAN DAAĽx/|ܽ@BBB B(A0D8Gp +8H0A(B BBBE 47ADG N +GAN DAAT0-LlBBB B(A0D8G`w +8A0A(B BBBK h4Ծ`7ADG N +GAN DAA ,|$PBBB B(A0D8Gp 8A0A(B BBBH D 8A0A(B BBBB  -8E0A(B BBBE \4t7ADG N -GAN DAALBBB B(D0A8Gp -8A0A(B BBBH 4x7ADG N -GAN DAAL.Ldh BBB B(A0D8G`X -8A0A(B BBBJ (4̿ 7ADG N -GAN DAAL,(8BBB E(A0A8G`? -8A0A(B BBBC L|S -BBB E(A0A8G` -8A0A(B BBBG L̿({BBB E(A0A8G`h -8A0A(B BBBJ LXBBB B(D0A8GpK -8A0A(B BBBG LlYBBB E(A0A8G`1 -8A0A(B BBBA 4 ADG0j +8E0A(B BBBE 47ADG N +GAN DAALBBB B(D0A8Gp +8A0A(B BBBH D4\7ADG N +GAN DAA+Lx BBB B(A0D8G`X +8A0A(B BBBJ 8407ADG N +GAN DAALt88BBB E(A0A8G`? +8A0A(B BBBC L(S +BBB E(A0A8G` +8A0A(B BBBG L8{BBB E(A0A8G`h +8A0A(B BBBJ LdhBBB B(D0A8GpK +8A0A(B BBBG LYBBB E(A0A8G`1 +8A0A(B BBBA 4 +ADG0j AAH D -AAJ @487ADG N -GAN DAA@4487ADG N -GAN DAA@YBBE D(G0 +AAJ P 4,H 7ADG N +GAN DAAdP 4|H 7ADG N +GAN DAAP YBBE D(G0 (A ABBA y (A ABBA y (A ABBA y (A ABBA u -(A ABBE TeBFA D(G0 +(A ABBE Td( ]BFA D(G0 (A ABBC a -(A ABBA Lt'BBE D(G0} +(A ABBA L BBE D(G0} (A ABBI a -(A ABBA <pBBE D(G0C -(A ABBC lBBB B(D0A8D`d +(A ABBA < BBE D(G0C +(A ABBC lLBBB B(D0A8D`d 8A0A(B BBBA " -8A0A(B BBBA <tuBBE D(G0C -(A ABBC < NBBE D(G0C -(A ABBC .K^x(D$p(BEB D(A0GpY -0A(A BBBJ $D()[ADG LAA4l`)BDA G0x - AABF ))6$*.ACD `DA< *LBBA A(D0t -(D ABBA ,<0*<BDD kAE4l@*BEA A(D0(A ABB**#Aa*6AL*D,*AFG | -AAD $Dh++AAG \DA4lp+BDD D0I - AABE +QAd -A c(,.Ac -A D<8,oADG x +8A0A(B BBBA <mBBE D(G0C +(A ABBC <0FBBE D(G0C +(A ABBC @.K^,%Dl%BEB D(A0GpY +0A(A BBBJ $H&[ADG LAA4&BDA G0x + AABF ''6$8'.ACD `DA<D@'LBBA A(D0t +(D ABBA ,P'<BDD kAE4`'BEA A(D0(A ABB''#AaL'.ALD'D,\(AFG | +AAD $(+AAG \DA4(BDD D0I + AABE )QAd +A c H).Ac +A D<,X)oADG x AAA P -GAE ADA4$h,vBDD D0| - AABA L\,BED D(D@Q +GAE ADA4l)vBDD D0| + AABA L)BED D(D@Q (A ABBA S -(G ABEE 4 -BADG b -AAA JAAD8-xBDD z +(G ABEE 4@*BADG b +AAA JAAD,X*xBDD z ABA Y -ABE AGB4,p-OAAG n -DDI DAAd-"|.K^xp-Jn N-4-8Dsd-BBB B(A0A8DP +ABE AGB4t*OAAG n +DDI DAA*".K^*Jn N *4$*8DsdD+BBB B(A0A8DP 8A0A(B BBBA h -8A0A(B BBBA dȑM|p1h1 `1 $X1tRQQ +8A0A(B BBBA M.. . $4x.tJQQ F b -S 1xZk44ADG ` +S 4.LZdk4|8ADG ` AAJ D -AAJ lP1 H1DT,H1l~ATDP} -AAA 1 1x1p14h1L`1 dX1|`1%,x14AAG a -AAD <1EnADD`r +AAJ p. h.DT,h.lvATDP} +AAA . 4.L.d.|.. x..%,.4AAG a +AAD <4.EfADD`r AAC ~ -AAA ,AG w -DL EDL\h2-BBB B(A0A8G -8A0A(B BBBA _AT,(ADG  -AAA 4_LAAG d -DAF NDAD$x'BDD G0a +AAA ,LAG w +DL EDL/-BBB B(A0A8G +8A0A(B BBBA \AT,HADG  +AAA Ȏ44\LAAG d +DAF NDADl'BDD G0a  IABR t - GABE ,l`~ADG0R -AAH L _,BBB B(A0A8D -8A0A(B BBBG @H(<Ћ BDD G0 - AABH | DAB\в.K^tFRCAA8-@5ВEN$ВAAG QACDȒALd&|ВM&, |9ARDPO -AAA 4pBAA S + GABE ,~ADG0R +AAH L @\,BBB B(A0A8D +8A0A(B BBBG 4@Lh(<d BDD G0 + AABH | DAB.K^(FR(CAA$X%`,ȏ5DEN$dAAG QACAL؏&M(&,@|1ARDPO +AAA 4$BAA S CBD | -CBG <<ؓBBA A(Gn -(A ABBG <T(lBEE A(D0S(A BBB,XNAAG0f -AAG ,xNAAG0h -AAE $PAG ] -AJ LBED C(G0T +CBG <BBA A(Gn +(A ABBG <HlBEE A(D0S(A BBB,xNAAG0f +AAG , NAAG0h +AAE $<PAG ] +AJ LdBED C(G0T (C ABBG L -(A ABBA $lD X +(A ABBA $0D X D Y -G AO$D b +G ؔAO$ؔD b J  E K -E $TAg +E $$ЕTAg H F J K -E "D],4xAc +E L"D]d ,|ȗxAc D O I o -A GdND0BEB D(A0 -(A BBBA <sFAD I -ABI AAB(+AiL8<0AY$\0LAK e -AF RA4XQADF r -FAD DCA$HAG Y -AF 4eBBD C(G0L(A ABB$JAG p -AG DAD9d T\0:d UtX?F xAG yKHr -F J -5h.K^ `$X,dbARDPs -CAA l .K^X:( :HD@bBBB A(C0G B -0C(A BBBA Dh \` txph`hpx4DbBAC G`a - AABF 4|BAC G`k - AABD 4`bBAC G`a - AABF ,AAG`T -AAA DBAC G`q +A GNDPBEB D(A0 +(A BBBA < sFAD I +ABI AABLH+AiXPAY$PLAK e +AF RA4xQADF r +FAD DCA$HAG Y +AF 4,ȚeBBD C(G0L(A ABB$dJAG p +AG DA(9d TP:d Ux?F xAG y؛KHr +F J  +$5<.K^Tlx,bARDPs +CAA `.K^x :H@:,hDD`bBBB A(C0G B +0C(A BBBA   4L4Лb BAC G`a + AABF 4BAC G`k + AABD 4bBAC G`a + AABF ,4AAG`T +AAA DdBAC G`q  AABF ] - AABG Dd@BAC G`b + AABG D`BAC G`b  AABE  - AABA 4dBAD G`k - AABC lХBBE E(A0D8Du + AABA 4dBAD G`k + AABC l,BBE E(A0D8Du 8F0A(B BBBE U -8C0A(B BBBG ,MHDL ALD nBAC G`a +8C0A(B BBBG tMHD@ALD@fBAC G`a  AABF e - AABG ,,ااF,,AK -D W,t AAG`h -AAE L|BBA A(G0O + AABG Ȥ,,,D\Ft0,AK +D W,@AAG`h +AAE LBBA A(G0O (C ABBD z -(A ABBH ?8,AK -D WH",`fAI V +(A ABBH 0?,X,AK +D WLh",dfAI V AG e -AB tRHIlAL4ةVBDD v -ABE JAB॒.K^x p$x"AN NDD!$\3ADG SDKLBBBB B(D0D8Jq -8A0A(B BBBK DdBGB A(K0G` -0D(A BBBA LDy;BEE D(A0Dj -0A(A BBBA 40GBAD D`P - CABG 4&BAD G`\ - CABH $'ADG WAA,,AAGp -CAA ,\XAAGp -CAA 4BDD Dp - CABG 4BDD Dp - CABG <h]BBA A(J -(D ABBJ ,<ش8AAGp -CAA <lxBEA A(J -(D ABBA ,ضADGp -CAA \hMBBB D(A0G`} +AB RHIAL4VBDD v +ABE JAB$ .K^<Tl"AN ND!$3ADG SDKLاBBBB B(D0D8Jq +8A0A(B BBBK DDب\BGB A(K0G` +0D(A BBBA L y3BEE D(A0Dj +0A(A BBBA 4P?BAD D`P + CABG 4BAD G`\ + CABH $$Э'ADG WAA,tحAAGp +CAA ,xAAGp +CAA 4BDD Dp + CABG 4 ЯyBDD Dp + CABG <DUBBA A(J +(D ABBJ ,0AAGp +CAA <BEA A(J +(D ABBA ,ADGp +CAA \$MBBB D(A0G`} 0A(A BBBI  -0C(A BBBA X,TPKlADDPl -AAA ,pKLADDPl -AAA T ,BBD A(G`j +0C(A BBBA \x,pKdADDPl +AAA ,KDADDPl +AAA T $BBD A(G`j (D ABBE d -(D ABBA H,$@0AFJ RGA,T@0AFJ RGAL\@tBEE A(C0R -(C BBBE z(C BBBp,h_AAM0n -DAA ,_AAM0n -DAA $ȹ <Tȹl $,AAG ]DA@.K^LBBB A(A0 +(D ABBA ,h,l`0AFJ RGA,`0AFJ RGAL`tBEE A(C0R +(C BBBE z(C BBB,4_AAM0n +DAA ,d_AAM0n +DAA l  $ض,AAG ]DA.K^ عL$BBB A(A0 (A BBGG Q -(A FBGG ,DB4\кMADG T +(A FBGG tB4MADG T AAF  -AAG .K^ػлػ л$ DZ< D[TALtALBEs4X7AAG Z -AAK DAA$gAG k -AA AL,4PAQqDAdhlD ] -A AP$-ADJ XDA,&AAG@C -DAG ,$PAAG@o -DAA TTBBE B(A0A8G -8D0A(B BBBH ,(AVGaDA,ATGaDA,ATGaDA,HAUGaDA,DAUGaDA,tAUGaDA,hAUGaDA,AUGaDA,(AUGaDA4LKo N4BAA S -ABA LgBEB B(D0A8FY -8A0A(B BBBG )$CAm +AAG ȃ.K^ $<Tl DZи D[ظALظALظBEs4x7AAG Z +AAK DAA$4иgAG k +AA \AL,|PAQqDAlD ] +A عAP$ع-ADJ XDA,<AAG@C +DAG ,lpAAG@o +DAA TкBBE B(A0A8G +8D0A(B BBBH ,HAVGaDA,ATGaDA,,ATGaDA,\hAUGaDA,AUGaDA,(AUGaDA,AUGaDA,AUGaDA,LHAUGaDA|Ko N4BAA S +ABA L$gBEB B(D0A8FY +8A0A(B BBBG L)$dCAm E H -A $lCaAm +A $CYAm E H -A l$4 l RAA  -AIH <xBBG A(G@ -(A ABBF D<ZBEE A(A0D@ -0A(A BBBF \0t(LDC.K^lo)BEE E(D0D8G +A 0($4 @lRAA  +AIH <BBG A(G@ +(A ABBF D8ZBEE A(A0D@ +0A(A BBBF PHLDC}.K^lo!BEE E(D0D8G 8A0A(B BBBJ  -8A0A(B BBBE _AG0UA4`AG0VAT|( BBE B(A0D8G -8A0A(B BBBF Ж.K^Ko Np4xlBAA G@Y - AABH , D(AYd(5fN<PBBA A(G`p -(D ABBJ $AG0q -DA ,ȕBOA AB , Dh\ t ,0AG  -AA aAkAe -A 4 AFD0{ +8A0A(B BBBE \_AG0UA|`AG0VATH BBE B(A0D8G +8A0A(B BBBF |.K^ Ko N$4ddBAA G@Y + AABH t@HAYH5fN<pBBA A(G`p +(D ABBJ $,0AG0q +DA ,,{BOA AB\t,PAG  +AA aAkAe +A 4$@AFD0{ AAH I -FAH x -,p4DhADG Z +FAH \ +t4ADG Z AAH D -AFA <!BIA A(FPK -(C ABBJ 4Ld|,AM  -AA aD,AN  -AA cG,8AM } -AD ^F,$AN  -AB ^G,ThAM  -AK iF,(AN  -AA dG,AM F -AK VC,8AN N -AJ VD,AM F -AK VC,DAN N -AJ VD4tXAAN0Q -AAM LFA,zAAN0L -GAL ,AM Z -AG ^G, pAM  -AA aD4dMvBHD G` - AABB D(m^BHD G` +AFA <0BIA A(FPK +(C ABBJ 4Ld| ,AM  +AA aD, AN  +AA cG,<XAM } +AD ^F,lAN  +AB ^G,AM  +AK iF,HAN  +AA dG,AM F +AK VC,,XAN N +AJ VD,\AM F +AK VC,AN N +AJ VD4xAAN0Q +AAM LFA,zAAN0L +GAL ,$ AM Z +AG ^G,TAM  +AA aD40MnBHD G` + AABB DHmVBHD G`  AABK I - AABC 4PM6BHD G` - AABB DhmBHD G` + AABC 4,pM.BHD G` + AABB DdmBHD G`  AABK I - AABC 4<BDJ  -ABA TAB,t(AKG  -AAB <BIJ  + AABC 4BDJ  +ABA TAB,HAKG  +AAB <BIJ  ABE _ -ABN 4:AKG0 +ABN 4,:AKG0 AAK R -AAL DBAD h +AAL DdBAD h ADD L ADG L -CDE TdX$BAA G0| +CDE Tx$BAA G0|  CABC J  FADK O - CADI T0vBBE B(A0C8D: -8C0A(B BBBA C, 1DH;\pCt44BL0BBE D(A0j + CADI T,PnBBE B(A0C8D: +8C0A(B BBBA \Ct@1h;C44BLPBBE D(A0j (A BBDG  -(A BBGH d BBE E(D0D8GPj +(A BBGH dTBBE E(D0D8GPj 8C0A(B BBBE  -8F0A(B BBBF t.K^xxP HS PAL4,0wADD [ -DAG DAAdx%|%%%؇%%%<  tBBA D(D0] -(A ABBH $L`ED +8F0A(B BBBF 8r.K^4p HSTpAL4tPwADD [ +DAG DAA%%Ȅ%% %$%<(%<T@tBBA D(D0] +(A ABBH $ED I A -G LtBBB B(D0A8DP^ -8A0A(B BBBG L8BBB B(D0A8DPk -8A0A(B BBBJ $hXY -G p<T,lsAAD y -CAA <PBED H(G`o -(A ABBA T_vBBA A(G0q +G LBBB B(D0A8DP^ +8A0A(B 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+    +9"5,<?FH!W@A,C?AA=2;A<A<A<A<?==@>@>@>@@BDBCBCBCCLDGDGDGEFEEMFLFLFLGGJGIGIHIHIHIIIIIJLJLJLKLKMKMLMLMNPNPNONOOPOPOPOOQSQRQRQRRdRdRcRcSVSUSUTUTUTUUUUUVXVXVXWXWeWeXdXdY[Y[YZYZZ[Z[Z[ZZ\^\^\]\]]d]d]d]d^a^a_`_`_`_`````acacbcbcbebecece}($66dz66dzC-3g,b-3g,i'1s$'0o$'0o$'1s$'1s$'1s$'1s$'1s$'1s$'1s$'0o$'1s$'0o$-1s    $ '0o  $ '0o  $ '1s$'1s$'1s$'1s$'1s$'1s$'1s$'0o$'0o$'1s$'1s$-1s    $ N     +HV"5,P !XKC,JJII7KJ K K=NEJJ;C,IKKK3,CCBC5BA;LBKC!="F"K#N#K%>N(K+O-N.=/G0G2N3=3=4L4L5L5L6H6H6H7H7I8I8I8I9OK:=;N;;<<;=8==P=>D>D?D?D??D@D@D@D@DADADADAAABBDIKMPMPMOMONKNNNK}$z +HV"5,P yml )   $   U}$ +HV/8   +, N   uhz'#$}L$e *S8fa *S8fa *S8fa *S8fa *S8fa *S8fa *S8fa *S8fa *S8fa *S8fa *S8fa *S8fa *S8fa++++++++++++++++++++++++++/LC/LC/LC/LC/LC/LC/LC/LC/LC/LC/LC/LC/LC!/cW!/cW!/cW!/cW!/cW!/cW!/cW!/cW!/cW!/cW!/cW!/cW!/cW!/cP!/cP!/cP!/cP!/cP!/cP!/cP!/cP!/cP!/cP!/cP!/cP!/cP/%%/]}F60 "<0     "kANqc2?  t    ~         @@ -16110,15 +16086,15 @@ %     `6gz  %     "5,<"5,<"5,C*CCHC )        -           }l#!)     +           }#!)        -            }#C6 u  +            }#C6 u         C6 u         nR      F:  @@ -16153,18 +16129,18 @@           - qd)    -e$       }#:    +e$       }p#:      &   "5,H"5,O%3#,    -    $y}d#%3 +HV: +    $y}#%3 +HV:    & /8   ,N   g) ) &%    -       }#: +       } #:    & :  @@ -16176,11 +16152,11 @@  @   u<++ ) +$&%`+&'*'*'*'*(+(*(*(*)))++M   %      jj           *Q6d_", -  v !!!""" r$  "}#&Ob%3q"5,< %} %}[!(D     +  v !!!""" r$  "}#&Ob%3q"5,< %} %}[!(D         R.    !& ".$.$.$.%.%.%.&.&.,-!(      R ?(.''$&'''''''''V'!2$"##$$$'$%),(121211111 ,  E,*,-/./.&, ,",",",#,#,$,$,$,)*--.1'}(      C0o.000*0 /!/"/"/#/#/#/#/$/'/0(((0))/-.!c76n76n" )   2 @@ -16190,11 +16166,11 @@     - $    P}#2X"5,U "5,N "5,C"5,C"5,C0          + $    P}#2X"5,U "5,N "5,C"5,C"5,C0                   0}Y0}Y0}Y.8T.8T @@ -16202,23 +16178,23 @@        I~,,---- - + , +,+,+-,++++,,!+"+*****+++,-+*_N@              I~,,---- - , , +,,,+++++,,,, ,!,**++++++*,,-**_N@        -      I +HV +HV +HV +HV"3,<"3,<"3,<"3,<&)cpDKz$ %)'&&)&! 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AR      @@ -16419,18 +16395,18 @@  +HV"5,H xW<#   $  N     -_}w# +HV +HV"5,<"5,<: +_}x# +HV +HV"5,<"5,<:    & ``W,"##",##, !!#,$# $ "   -#     """ ""!!$$$$!!!! d!}t#W$$,($($,&&'','''(,& & $ % +#     """ ""!!$$$$!!!! d!}0v#W$$,($($,&&'','''(,& & $ %  ,% -& & &  $ ( $!!$#"""%##""""""####%%%%$! !#!!!!!# !$"}r#: +& & &  $ ( $!!$#"""%##""""""####%%%%$! !#!!!!!# !$"}ht#:    & :    & @@ -16441,21 +16417,21 @@ )    z       M  - }o# +HV"5,< - 1_}= Y,00 -  /$i}|m#09f{  + }p# +HV"5,< - 1_}= Y,00 +  /$i}o#09f{    c"5,P![(,((),))'',('((,&&))  ) -  ) $%$$#)))&'&&&****&&%%# # # " $!#!#!#!#"$%}j# +HV"5,P #yX) +  ) $%$$#)))&'&&&****&&%%# # # " $!#!#!#!#"$%}Dl# +HV"5,P #yX)    $       - ,    }i##     }u  + ,    }|k##     }u      ;      9Ik\$?]A8$4>&   ]UJ          @@ -16474,11 +16450,11 @@        1#I        Uj   C      -        #m5/332 2 3 '3 , ,:..---,,+))(('5'%%3/!=!-"=-"-#-$3%%%&%)+)+)+))*+**+4....../3/0/0/30202021211717262634464656565656_#;ANq=>Knf+ +        #m5/332 2 3 '3 , ,:..---,,+))(('5'%%3/!=!-"=-"-#-$3%%%&%)+)+)+))*+**+4....../3/0/0/30202021211717262634464656565656a#;ANq=>Knf+    :          #|$4pqoorqq  tsvtvu:=<<::vsssrs;=">%C&7&3'S(k*7*L6,k.>.4/403051l1l2l2k2>25345454489899>:;<;<;:>=>?>>>9?A?A@>@B@B@B@AA>BCBCC7C>FQFOGOHRIMKOKMOMNPQPQQOQRRRR>ReS>SgT>TgTgUgVeXdXd\d\a^a^`^#`_`bcccdddddde>fhfhfhhijijiIjj)kklnlnmnmnmnooooqpqpppqsrtvwQl_(   < @@ -16491,11 +16467,11 @@    & $ @2WR ?s @2WR ?s @2WR ?s @2WR0=`u0=`u#m)pk)pk)pk$$<Ip}=>u>F=;kx>C~0[h(}().#q%/]}E?]$?]$?]$?]$?]$?]$?]$?]$?]$?]$?]$?]$?]$?]$(@|@@r?]$?]$?]$?]$?]$?]$?]$?]$?]$?]$?]$?]$?]$-Wu&-Wu&-Wu&-Wu&-Wu&-Wu&-Wu&-Wu&-Wu&-Wu&-Wu&-Wu&-Wu&-Wu&@-WuQ-WuQ-WuQ-Wu&-WuQ-Wu&-WuQ-WuQ-WuQ-WuQ&:,'X %&%& "#   %  -!   , '"#"#"$($'$&$%$%% &'}}M#[E;e$ '  }M#-#JVaCPsANqUb[MD((`:1^G":0.X,  ") +!   , '"#"#"$($'$&$%$%% &'}}O#[E;e$ '  }N#-#JVaCPsANqUb[MD((`:1^G":0.X,  ")    Dz#V08    @@ -16566,11 +16542,11 @@  & :    & -#   #    =}}5#5# FQz>Kn  I-:   & ;       +#   #    =}}P7#D7# FQz>Kn  I-:   & ;        *Yiny%/]}#    :n!/jM       [%/]}J#%/]} aW ,         @@ -16584,18 +16560,18 @@ 8(3#(n^NG!   $.C8M%""  % $  """ $$##! # $c?  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