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If the dtype attribute is not provided this must be a valid output type.Exception caught: MemcpyToHostP1bad castAttempt to use DefaultLogger but none has been registered.)CUDAExecutionProviderProvider has already been registered.C:\agent\_work\18\s\onnxruntime\core/framework/execution_providers.honnxruntime::ExecutionProviders::Add allocator already registered.Create_State_Compute_Release_State_onnxruntime::DummyArena::Usedonnxruntime::DummyArena::MaxMemcpyTransformerCastFloat16Transformer%Y-%m-%d_%H-%M-%SEnvironment::IsInitialized()Environment must be initialized before creating an InferenceSession.C:\agent\_work\18\s\onnxruntime\core\session\inference_session.cconnxruntime::InferenceSession::InferenceSessionError during EndProfiling(): SESSIONUnknown error during EndProfiling()onnxruntime::InferenceSession::~InferenceSessionReceived nullptr for exec providerReceived nullptr for custom registryonnxruntime::InferenceSession::LoadThis session already contains a loaded model.Unknown exception in Load()Exception during loading: Encountered unknown exception in Load()Failed to load model because protobuf parsing failed.Could not find an implementation for the node model_loading_arrayMain Graph instance should have populated all subgraphs when being resolved.(onnxruntime::InferenceSession::CreateSubgraphSessionStatesubgraphsubgraph_session_stateCreateSubgraphSessionState should have created an entry earlier.Initializing session.onnxruntime::InferenceSession::InitializeSubgraphSessionsModel was not loadedonnxruntime::InferenceSession::InitializeSession has already been initialized.Model was not loaded.Parallel execution is currently not supported for the registered CUDA Execution Provider.Adding default CPU execution provider.Exception during initialization: Session successfully initialized.session_initializationEncountered unknown exception in Initialize() Expected: Please fix either the inputs or the model.Invalid rank for input: Got: for the following indices onnxruntime::InferenceSession::CheckShapes index: Got invalid dimensions for input: Unexpected input data type. Actual: () , expected: (elements, but feeds has elements.Invalid Feed Input Name:Size mismatch: feed_names has Input with name: is not expected to be of type tensor.At least one output should be requested.Output vector pointer is NULLOutput vector incorrectly sized: output_names.size(): p_fetches->size(): Session was not initializedInvalid Output Name:Session not initialized.onnxruntime::InferenceSession::RunEncountered unknown exception in Run()Running with tag: model_runonnxruntime::InferenceSession::GetModelOutputsonnxruntime::InferenceSession::GetModelInputsCould not write a profile because no model was loaded.onnxruntime::InferenceSession::EndProfilingrun_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::CreateLoggerForRunInvalid session log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::InitLoggersession_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)graph_optimization_level < TransformerLevel::MaxTransformerLevelAllowed values are 1 and 2. Current level is set to onnxruntime::InferenceSession::AddPredefinedTransformersGSL: Postcondition failure at C:\agent\_work\18\s\cmake\external\gsl\include\gsl/pointers: 91invalid hash bucket countinvalid vector subscript != onnxruntime::DataTypeImpl::GetType() == type_.jsonC:\agent\_work\18\s\include\onnxruntime\core/framework/ml_value.hmodel_loading_uri_Load model from failed:OrtValue::Getonnxruntime::`anonymous-namespace'::GetCurrentTimeStringlocaltime_s(&local_tm, &in_time_t) == 0!=C:\agent\_work\18\s\include\onnxruntime\core/framework/tensor.h0.5.0input array is too shortshape is invalidtype is not supported in this functionoutput name cannot be emptyinput name cannot be emptyparse input tensor proto failedspace is not enoughout of indexInvalid alignment, which can only be 0 or 256index out of rangeinternal errorInput is not of type sequence or map.Input is not of one of the supported sequence types.Each element of the sequence should be either tensor or map.Input is not of one of the supported map types.Type not supported.At least one element in the sequence is of a type different from others.For map type num_values MUST be 2Unsupported input typeKey and value tensors have unequal number of elements.Either the key tensor or the value tensor has NumDimensions > 1Number of values should be at least 1.Key type is not supported yet.OrtValue::GetMutableDataTypeImpl::GetType() == dtype_Tensor type mismatch. onnxruntime::Tensor::MutableDataonnxruntime::Tensor::Data, got size overflowInvalid index requested for map type.not enough space: expected Value type is not supported yet.Encountered nullptr.node_index < nodes_.size()Validating no unexpected access using an invalid node_index.C:\agent\_work\18\s\include\onnxruntime\core/graph/graph.honnxruntime::Graph::NodeAtIndexImplCastDef_%dCastcast node to cast from float16 to float32 on cpuRemoveDuplicateCastTransformertofalseIdentityEliminateIdentitySliceEliminateSliceConvConvMulFusionConvAddFusionConvBNFusionConstantFoldingRandomNormalRandomUniformRandomNormalLikeRandomUniformLikeUnsqueezeEliminationMultinomialConvActivationFusionUnsqueezeMatMulAddFusionGemmActivationFusionDropoutEliminateDropoutReluFuseReluClipShapeShapeToInitializer_RuleBasedTransformerNchwcTransformerUnsupported levelLevelC:\agent\_work\18\s\onnxruntime\core\optimizer\graph_transformer_utils.cconnxruntime::transformer_utils::GenerateRewriteRulesonnxruntime::transformer_utils::GenerateTransformersUnsupported level MKLDNNExecutionProviderNupharExecutionProviderNGRAPHExecutionProviderOpenVINOExecutionProviderTensorrtExecutionProviderC:\agent\_work\18\s\onnxruntime\core\optimizer\transformer_memcpy.cc' doesn't support memcpy status.IsOK()onnxruntime::TransformerMemcpyImpl::ProcessDefsCopy from/to host memorydup_replacements.find(&arg) == dup_replacements.end()Execution type 'Memcpyonnxruntime::TransformerMemcpyImpl::ProcessInitializers::::operator ()onnxruntime::TransformerMemcpyImpl::ProcessInitializersThis transformer is already registered ai.onnxRequested size is too large to fit into size_t.'Could not find OrtValue with name 'fetches.size() == node->OutputDefs().size()onnxruntime::ConstantFolding::ApplyImplC:\agent\_work\18\s\onnxruntime\core\optimizer\constant_folding.ccort_value.IsTensor()data type is not supportedsize_ == sizeC:\agent\_work\18\s\onnxruntime\core/optimizer/initializer.honnxruntime::Initializer::Initializersize is differentbn_scale_tensor_protoonnxruntime::ConvBNFusion::Applyonnxruntime::Initializer::ToProtoepsilonbn_mean_tensor_protobn_var_tensor_protoC:\agent\_work\18\s\onnxruntime\core\optimizer\conv_bn_fusion.ccbn_B_tensor_protoBatchNormalizationconv_W_tensor_protoconv_B_tensor_protoSigmoidalphaClipTanhLeakyReluwith activation fused Conv minmaxactivationactivation_paramsFusedConvfused deque too longonnxruntime::UnsqueezeElimination::Applytensor_protoaxesC:\agent\_work\18\s\onnxruntime\core\optimizer\unsqueeze_elimination.ccstartsendsfused Matmul and Add AddMatMulgemmGemmleaky_relu_FusedGemmfused Gemm mul_B_tensor_protoC:\agent\_work\18\s\onnxruntime\core\optimizer\conv_mul_fusion.cconnxruntime::ConvMulFusion::ApplyMulreorderdilationsstridespadsReorderInputSAME_UPPERNOTSETauto_padkernel_shape_nchwcgroupVALIDSAME_LOWERGlobalMaxPoolAveragePoolMaxPoolaxisReorderOutputConcatSumGlobalAveragePoolchannelsonnxruntime::ConvAddFusion::ApplyC:\agent\_work\18\s\onnxruntime\core\optimizer\conv_add_fusion.ccadd_B_tensor_protoFailed to get allocator for optimizerallocator_ptr_ != nullptronnxruntime::OptimizerExecutionFrame::Info::InfoC:\agent\_work\18\s\onnxruntime\core\optimizer\optimizer_execution_frame.ccTried to allocate without valid type information, ort_value index=invalid unordered_map keyGSL: Precondition failure at C:\agent\_work\18\s\cmake\external\gsl\include\gsl/span: 311onnxruntime::ReverseSequenceOp::ReverseSequenceOpc:\agent\_work\18\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.htime_axisinfo.GetAttr("time_axis", &time_axis).IsOK()C:\agent\_work\18\s\include\onnxruntime\core/framework/op_kernel.hbatch_axisinfo.GetAttr("batch_axis", &batch_axis).IsOK()time_axis < 2time_axis and batch_axis must have different values but both are batch_axis != time_axis. Must be 0 or 1Invalid batch_axis of batch_axis < 2Invalid time_axis of ReverseSequence. batch_size=sequence_lens shape must be {batch_size}. Got:Unknown tensor type of onnxruntime::ReverseSequenceOp::ComputeC:\agent\_work\18\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.ccMissing Input: GSL: Precondition failure at C:\agent\_work\18\s\cmake\external\gsl\include\gsl/span: 561onnxruntime::OpKernelContext::Inputonnxruntime::Tensor::DataAsSpanGSL: Precondition failure at C:\agent\_work\18\s\cmake\external\gsl\include\gsl/span: 475GSL: Precondition failure at C:\agent\_work\18\s\cmake\external\gsl\include\gsl/span: 555GSL: Precondition failure at C:\agent\_work\18\s\cmake\external\gsl\include\gsl/span: 523GSL: Precondition failure at C:\agent\_work\18\s\cmake\external\gsl\include\gsl/span: 524onnxruntime::Tensor::MutableDataAsSpanGSL: Precondition failure at C:\agent\_work\18\s\cmake\external\gsl\include\gsl/gsl_algorithm: 53GSL: Precondition failure at C:\agent\_work\18\s\cmake\external\gsl\include\gsl/span: 516T2Number 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 matchRoiAlignNull input X ptrNull rois_ptrNull batch_indices_ptronnxruntime::RoiAlign::RoiAlignc:\agent\_work\18\s\onnxruntime\core\providers\cpu\object_detection\roialign.houtput_heightoutput_widthavgmode specified. It should be either avg or maxInvalid mode of value onnxruntime::RoiAlign::RoiAlignsampling_ratioSampling ratio should be >=0, but it was sampling_ratio_ >= 0spatial_scaledetect_positiveIsInfonnxruntime::IsInf::IsInfFailed to obtain detect_positiveT1onnxruntime::IsInf::Computedetect_negativeC:\agent\_work\18\s\onnxruntime\core\providers\cpu\tensor\isinf.ccData type X must be float or double, but instead got Failed to obtain detect_negativecenter_point_box only support 0 or 1center_point_boxonnxruntime::nms_helpers::GetThresholdsFromInputsiou_threshold must be in range [0, 1].Not satsified: (iou_threshold >= 0 && iou_threshold <= 1.f) C:\agent\_work\18\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.cconnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase0 == center_point_box_ || 1 == center_point_box_NonMaxSuppressionc:\agent\_work\18\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.hscores must be a 3D tensor.Not satsified: boxes_shape.NumDimensions() == 3 boxes and scores should have same num_batches.Not satsified: scores_shape.NumDimensions() == 3 onnxruntime::NonMaxSuppressionBase::PrepareComputeboxes_tensorboxes must be a 3D tensor.scores_tensoronnxruntime::NonMaxSuppression::ComputeNot satsified: boxes_dims[2] == 4 output != nullptrNot satsified: ret.IsOK() boxes and scores should have same spatial_dimension.Not satsified: boxes_dims[0] == scores_dims[0] The most inner dimension in boxes must have 4 data.Not satsified: boxes_dims[1] == scores_dims[2] type == dtype_Tensor type mismatch.onnxruntime::Tensor::DataRawonnxruntime::Tensor::MutableDataRawonnxruntime::IdentityOp<1>::ComputeX != nullptrC:\agent\_work\18\s\onnxruntime\core/providers/cpu/tensor/identity_op.honnxruntime::IdentityOp<0>::Compute!has_axes || attr_axes_.size() == attr_starts_.size()Invalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributesonnxruntime::SliceSkips::SliceSkipsstatic_cast(dims.size()) == extents.size() && static_cast(dims.size()) >= steps.size()has_starts && has_ends && attr_starts_.size() == attr_ends_.size()Missing or invalid starts and ends attributeC:\agent\_work\18\s\onnxruntime\core/providers/cpu/tensor/slice.honnxruntime::SliceBase::SliceBaseStarts must be a 1-D array'step' value cannot be 0onnxruntime::SliceBase::FillVectorsFromInputnullptr != start_tensor && start_tensor->Shape().NumDimensions() == 1TindC:\agent\_work\18\s\onnxruntime\core/providers/cpu/tensor/utils.h'axes' has duplicates'axes' has an axis outside of the tensor dimension countStarts and axes shape mismatchstart_tensor->Shape() == ends_tensor->Shape()Starts and steps shape mismatchnullptr == axes_tensor || start_tensor->Shape() == axes_tensor->Shape()Ends must be a 1-D arrayC:\agent\_work\18\s\onnxruntime\core\providers\cpu\tensor\slice.ccStarts and ends shape mismatchnullptr != ends_tensor && ends_tensor->Shape().NumDimensions() == 1Data type for starts and ends inputs' need to be int32_t or int64_t, but instead got nullptr == steps_tensor || start_tensor->Shape() == steps_tensor->Shape()onnxruntime::Slice::ComputeCannot slice scalarsonnxruntime::Slice::Computeonnxruntime::Slice::ComputeMissing input tensor to be processedonnxruntime::Slice::Computeinput_tensor_ptr != nullptronnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice<__int64,0>::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice,class std::allocator >,0>::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice,class std::allocator >,1>::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::Slice<__int64,1>::Computeonnxruntime::Slice::Computeonnxruntime::Slice::Computeonnxruntime::SliceIterator::Initonnxruntime::SliceIterator::Initonnxruntime::SliceIterator::Initonnxruntime::SliceIterator::Initonnxruntime::SliceIterator,class std::allocator > >::Initstatic_cast(dims.size()) == starts.size() && static_cast(dims.size()) == extents_.size() && static_cast(dims.size()) >= steps.size()onnxruntime::SliceIterator::Initonnxruntime::SliceIterator::Initonnxruntime::SliceIterator::Initonnxruntime::SliceIterator::Initonnxruntime::SliceIterator::Initonnxruntime::SliceIterator<__int64>::Initonnxruntime::SliceIterator::Initonnxruntime::SliceIterator::Initonnxruntime::StringToAutoPadTypeUnknown AutoPadType String W: C:\agent\_work\18\s\onnxruntime\core/providers/cpu/nn/autopad_type.h group: X num_dims does not match W num_dims. C: kernel channels: kernel_shape num_dims is not compatible with W num_dims. kernel_shape: X: kernel_shape is not compatible with W shape.WidthMajorallocation failure M: Input channels C is not equal to kernel channels * group.gemmlowp error: %s Output channels M is not divisible by group.QLinearConvT3zeropoint->Shape().NumDimensions() == 0 || (zeropoint->Shape().NumDimensions() == 1 && zeropoint->Shape().GetDims().size() == 1)zeropoint must be a scalaronnxruntime::math::Im2colNd::operator ()d_iter[d_i] < d_maxscale->Shape().NumDimensions() == 0 || (scale->Shape().NumDimensions() == 1 && scale->Shape().GetDims().size() == 1)scale must be a scalarC:\agent\_work\18\s\onnxruntime\core\providers\cpu\nn\qlinearconv.cconnxruntime::QLinearConv::ScaleAndZeropointPairValidationHelperreference(Lhs: %d cells %dx%d %s, Rhs: %d cells %dx%d %s)C:\agent\_work\18\s\onnxruntime\core/util/math.hOut of bound access to arrayDilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.onnxruntime::ComputePadAndOutputShapedilation == 1Invalid input shape: pad type not supported.C:\agent\_work\18\s\onnxruntime\core/providers/cpu/nn/conv_base.hNon per-tensor quantization is not supported now.ConvIntegerMatMul dimension mismatchNot satsified: left_num_dims >= 1 && right_num_dims >= 1 left operand cannot broadcast on dim Not satsified: K_ == right_shape[right_num_dims - 2] C:\agent\_work\18\s\onnxruntime\core/providers/cpu/math/matmul_helper.honnxruntime::MatMulComputeHelper::ComputeNot satsified: num_dims_with_pad - 1 == num_output_dims Not satsified: M_ == 1 && N_ == 1 Not satsified: K_ == right_shape[0] Not satsified: num_dims_with_pad - 2 == num_output_dims right operand cannot broadcast on dim Not satsified: left_padded_dims_[idx_dim] == 1 Not satsified: num_dims_with_pad == num_output_dims Not satsified: right_padded_dims_[idx_dim] == 1 a_zero_point->Shape().NumDimensions() == 0 || (a_zero_point->Shape().NumDimensions() == 1 && a_zero_point->Shape().GetDims().size() == 1)Currently only scalar zero_point is supported. TODO: add per channel zero point support.b_zero_point->Shape().NumDimensions() == 0 || (b_zero_point->Shape().NumDimensions() == 1 && b_zero_point->Shape().GetDims().size() == 1)a != nullptr && b != nullptrMatMulIntegerC:\agent\_work\18\s\onnxruntime\core\providers\cpu\math\matmul_integer.cconnxruntime::MatMulInteger::Computeonnxruntime::QLinearMatMul::Computeonnxruntime::ScaleAndZeropointPairValidationHelperC:\agent\_work\18\s\onnxruntime\core\providers\cpu\math\quantize_linear_matmul.ccQLinearMatMuly_scaley_zero_pointyDequantizeLinearx_scalex_zero_pointxzero_point_shape.NumDimensions() == 0 || (zero_point_shape.NumDimensions() == 1 && zero_point_shape.GetDims().size() == 1)C:\agent\_work\18\s\onnxruntime\core\providers\cpu\tensor\quantize_linear.ccx_zero_point must be a scalar.scale_shape.NumDimensions() == 0 || (scale_shape.NumDimensions() == 1 && scale_shape.GetDims().size() == 1)onnxruntime::QuantizeLinear::ComputeQuantizeLinearx_scale must be a scalar.onnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear::ComputeThresholdedRelubetaSoftplusSoftsignHardSigmoidSeluElugammanearestlinear(default) or . It can only be info.GetAttrs("scales", scales_).IsOK().C:\agent\_work\18\s\onnxruntime\core/providers/cpu/tensor/upsample.hscalesinfo.GetAttr("mode", &mode).IsOK()onnxruntime::UpsampleBase::UpsampleBasescale > 0Upsample: linear mode upsample only support bilinear with 4 dimension.onnxruntime::UpsampleBase::ScalesValidationScale value should be greater than 0.Scale value should be greater than or equal to 1.scale >= 1mode attribute is onnxruntime::UpsampleBase::StringToUpsampleModeResizescales_size > 0onnxruntime::UpsampleBase::ParseScalesData((scales[0] == 1) && (scales[1] == 1))scales size should be greater than 0.scales.size() == 4Upsample: linear mode upsample only support bilinear, the first 2 scales should be 1.Please fetch output tensor with specified shape.p_ml_valueonnxruntime::OpKernelContext::OutputAttempting to broadcast an axis by a dimension other than 1. axis == 1 || axis == largest by NegFloorDivAbsonnxruntime::BroadcastIterator::AppendSubonnxruntime::BroadcastIterator::InitC:\agent\_work\18\s\onnxruntime\core/providers/cpu/math/element_wise_ops.hMinMaxExpLogSqrtPowCeilReciprocalEqualMeanLessGreaterOrXorNotAnddata_1.Shape() == shapedata_n.Shape() == shapeC:\agent\_work\18\s\onnxruntime\core\providers\cpu\math\element_wise_ops.ccAll inputs must have the same shapeinput_count >= 1onnxruntime::Sum_6::ComputeErfMust have 1 or more inputsTanAsinSinCosonnxruntime::Max_6::Computeonnxruntime::Mean_6::ComputeinputCount >= 1onnxruntime::Min_6::ComputeAtanhPReluAsinhAcoshSinhCoshAcosAtanonnxruntime::Mod::ModModfmod must have value either 0 or 1(fmod == 0) || (fmod == 1)onnxruntime::Erf::ComputefmodExpandX_ptr != nullptronnxruntime::Mod::ComputeUnsupported data typefmod attribute must be true for float, float16 and double typesfmod_ vs X and Y input types do not match: failed to get first output!onnxruntime::mod_internal::BroadCastMFloat16FModtensor_shape.Shape().GetDims().size() == 1onnxruntime::Expand_8::Computeonnxruntime::BroadcastVariadicShape must be 1 dimensional as it's tensor data is a shapeonnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::Expand_8<__int64>::Computeonnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::Expand_8::ComputeGSL: Precondition failure at C:\agent\_work\18\s\cmake\external\gsl\include\gsl/span: 162GSL: Precondition failure at C:\agent\_work\18\s\cmake\external\gsl\include\gsl/span: 150onnxruntime::mod_internal::BroadCastModonnxruntime::TensorAllocator::TensorAllocatoronnxruntime::Expand_8::Computeonnxruntime::mod_internal::BroadCastFModonnxruntime::Expand_8::Computeonnxruntime::Expand_8::Computeonnxruntime::TensorAllocator::TensorAllocatorGSL: Precondition failure at C:\agent\_work\18\s\cmake\external\gsl\include\gsl/span: 211((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOC:\agent\_work\18\s\onnxruntime\core/providers/cpu/nn/pool_base.honnxruntime::PoolProcessContext::initinfo.GetAttr("p", &p_).IsOK()pinfo.GetAttr("count_include_pad", &temp).IsOK()count_include_padceil_modeinfo.GetAttr("auto_pad", &auto_padding).IsOK()onnxruntime::PoolBase::PoolBaseinfo.GetAttrs("kernel_shape", kernel_shape_).IsOK()No kernel shape is set.GlobalLpPoolinput_shape.Size() > 0dilations_.size() == kernel_shape_.size()Dilations dimensions should match kernel shapestrides_.size() == kernel_shape_.size()pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]Pad should be smaller than kernel.kernel_shape_[dim] > 0storage_orderC:\agent\_work\18\s\onnxruntime\core\providers\cpu\nn\pool.cconnxruntime::PoolBase::ComputeInput dimension cannot be less than 3.onnxruntime::PoolBase::ComputeSizePadDilationsUnsupported AutoPad Type.onnxruntime::PoolBase::InferOutputSizeinput_dims.size() >= 2onnxruntime::PoolBase::SetOutputSizeIUnsupported pooling size : Not satsified: x_shape.NumDimensions() >= 3 onnxruntime::Pool >::ComputeNot satsified: pooling_dims == kernel_shape_.size() kernel_shape num_dims is not compatible with X num_dims.Unsupported pooling size.Not satsified: input_dims >= 3 LpPoolonnxruntime::Pool::Compute] is not in valid range [-,C:\agent\_work\18\s\onnxruntime\core/providers/common.honnxruntime::HandleNegativeAxisaxis >= -tensor_rank && axis <= tensor_rank - 1axis k <= dims.size()onnxruntime::TopK<9,float>::TopKop_kernel_info.GetAttr("k", &k_temp).IsOK()kk argment [] should not be greater than specified axis dim value [onnxruntime::SizeFromDimC:\agent\_work\18\s\onnxruntime\core\providers\cpu\math\top_k.cconnxruntime::SizeToDimTopKvalue of k must not be negativek tensor should be a 1D tensor of size 1input count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valueonnxruntime::TopK<10,float>::TopKinput count mismatch, expected 1 input - the tensor to be processedop_kernel_info.GetAttr("axis", &axis_temp).IsOK()k_temp > 0*en-USC:\agent\_work\18\s\onnxruntime\core\providers\cpu\nn\string_normalizer.cconnxruntime::string_normalizer::Locale::LocaleFailed to construct locale with name::Please, install necessary language-pack-XX and configure localesConversion ErrorConversion ErrorStringNormalizerNONEUPPERLOWERattribute case_change_action is not setcase_change_actiononnxruntime::StringNormalizer::StringNormalizerattribute is_case_sensitive is not setis_case_sensitiveStopword contains invalid utf8 charsp.secondDuplicate stopwords not allowed!sw.empty()Empty stopwords not allowedstopwordslocaleattribute case_change_action has invalid valueInput contains invalid utf8 chars at: Input dimensions are either[C > 0] or [1][C > 0] allowedSingle dimension value must be greater than 0input count mismatchwstr != wconv_errorbad conversionWhereonnxruntime::TensorAllocator::TensorAllocatoronnxruntime::TensorAllocator,class std::allocator > >::TensorAllocatoronnxruntime::Where,class std::allocator > >::Computeonnxruntime::Where::ComputeC:\agent\_work\18\s\onnxruntime\core\providers\cpu\tensor\where_op.cconnxruntime::Where::Computecondition && X && Ycondition, X, and Y inputs are required!NonZeroonnxruntime::NonZero::Computeonnxruntime::NonZero<__int64>::Computeonnxruntime::NonZero::ComputeC:\agent\_work\18\s\onnxruntime\core\providers\cpu\tensor\nonzero_op.cconnxruntime::NonZero::ComputeX input is required! is unrecognized, acceptable values are TF,IDF,TFIDFTFIDFIDFTFC:\agent\_work\18\s\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.cconnxruntime::TfIdfVectorizer::TfIdfVectorizermode is requiredTfIdfVectorizer >= max_gram_lengthimpl_->min_gram_length_ > 0Required min_gram_length must be positive: min_gram_length is requiredmin_gram_lengthimpl_->weighting_criteria_ != kNonemode: Non-empty ngram_counts is requiredngram_countsimpl_->max_skip_count_ >= 0max_skip_count must be non-negative: max_skip_count is requiredmax_skip_countimpl_->max_gram_length_ >= impl_->min_gram_length_min_gram_length >= max_gram_length required: Non-empty ngram_indexes is requiredngram_indexessize_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()max_gram_length must be inbounds of ngram_counts: size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()min_gram_length must be inbounds of ngram_counts: <= status.IsOK() && !impl_->ngram_counts_.empty()impl_->weights_.size() == impl_->ngram_indexes_.size()Got weights of size: but ngram_indexes size: must be of equal sizeweightsstd::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; })Negative ngram_indexes values are not allowedstatus.IsOK() && !impl_->ngram_indexes_.empty()n-gram counts out of bounds for -gramsstatus.IsOK() && !pool_int64s.empty()non-empty pool_int64s is required if pool_strings not providedpool_int64s!impl_->pool_strings_.empty()pool_strings must not be empty if specifiedpool_strings(before_insert + ngrams) == impl_->str_set_.size()poll_strings duplicate (before_insert + ngrams) == impl_->int64_set_.size()pool_int64s duplicate -grams detected(items % ngram_size == 0)Number of items must compose whole end_idx >= start_idx && end_idx <= total_itemsInvalid type of the input argumentInput shape must have either [C] or [B,C] dimensions with B > 0.onnxruntime::Tensor::Sizetensor size overflowdata type is different from updates typeScatterC:\agent\_work\18\s\onnxruntime\core\providers\cpu\tensor\scatter.cconnxruntime::Scatter::Scatterinfo.GetAttr("axis", &axis_).IsOK()Missing/Invalid 'axis' attribute valueonnxruntime::Scatter::ComputeIndices dim= at pos= is greater than input dim=Indices must have the same rank as Input. Indices rank=. Input rank=Indices vs updates dimensions differs at position=Indices and updates must have the same rankExpecting indices to be either int32_t or int64_tindices element out of data bounds, idx= data_dim=Attempt to retrieve final output before it was set.final_output_mlvalue_onnxruntime::scan::detail::OutputIterator::GetOutputC:\agent\_work\18\s\onnxruntime\core/providers/cpu/controlflow/scan_utils.hscan_input_directionsscan_output_directionsscan_input_axes but expected Number of entries in 'scan_input_axes' was gsl::narrow_cast(input_axes_.size()) == num_scan_inputs_scan_output_axesNumber of entries in 'scan_output_axes' was bodyinfo.GetAttr("body", &proto).IsOK()onnxruntime::Scan<9>::ScanC:\agent\_work\18\s\onnxruntime\core\providers\cpu\controlflow\scan_9.ccnum_scan_inputsinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()' dimension but input 'Scan inputs have inconsistent sequence lengths. Previous value was inputs but Scan was only given The subgraph in 'body' requires . Input tensor rank was of Invalid value in scan_input_axes for input gsl::narrow_cast(output_axes_.size()) == num_scan_outputsSubgraph SessionState was not found for 'body' attribute.session_stateonnxruntime::Scan<9>::Compute dimensions or more but input had shape of Expected Invalid scan input: has length of onnxruntime::ScanImpl::TransposeOutputVScan outputs but Scan expects Subgraph in 'body' produces Output OrtValue has not been created for loop state variable output output_mlvalueonnxruntime::ScanImpl::CreateLoopStateVariables. Output tensor rank was Invalid value in scan_output_axes for output Outputs from Scan are not optional and should never be null.onnxruntime::MaxUnpool::MaxUnpoolC:\agent\_work\18\s\onnxruntime\core/providers/cpu/nn/unpool.hNot satsified: inferredOutputShape[dim] <= shape[dim] Not satsified: inferredPad <= kernel_shape_[dim - 2] GSL: Precondition failure at C:\agent\_work\18\s\cmake\external\gsl\include\gsl/span: 176MaxUnpoolonnxruntime::MaxUnpool::ComputeC:\agent\_work\18\s\onnxruntime\core\providers\cpu\nn\Unpool.ccNot satsified: X_shape.NumDimensions() >= 3 Index tensor shape should be same as that of the input data tensor to unpool.Not satsified: I_shape == X_shape Not satsified: tensor_shape->Shape().GetDims().size() == 1 Incorrect output shapebad allocationOneHotInvalid argument for depth; it's not a scalar.Invalid argument for values; either it's rank is more than 1 or it has more than 2 elementsDepth is negative.'axis' attribute must have a value in the range [onnxruntime::Shrink::ShrinkC:\agent\_work\18\s\onnxruntime\core/providers/cpu/nn/shrink.hlambdop_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()biasop_kernel_info.GetAttr("bias", &bias_temp).IsOK()ShrinkInput types for the Shrink operator are constrained to all numeric types only. Got bool type here.Input types for the Shrink operator are constrained to all numeric types only. Got std::string type here.onnxruntime::Shrink::ComputeC:\agent\_work\18\s\onnxruntime\core\providers\cpu\nn\shrink.ccSignUnsupported input datatypeIsNaNNull input ptrinvalid stoi argumentstoi argument out of rangeinvalid stol argumentstol argument out of rangeinvalid stoul argumentstoul argument out of rangeinvalid stoll argumentstoll argument out of rangeinvalid stoull argumentstoull argument out of rangeinvalid stof argumentstof argument out of rangeinvalid stod argumentstod argument out of rangeCast op must have 'to' argument of type DataTypeonnxruntime::Cast::ComputeC:\agent\_work\18\s\onnxruntime\core\providers\cpu\tensor\cast_op.ccUnexpected 'to' argument value: onnxruntime::Cast::Computeonnxruntime::Cast::ComputeCasting to and from strings is not supported yet.onnxruntime::Cast::ComputeInput is missing. The operator Cast expects one and only one inputonnxruntime::Cast,class std::allocator > >::ComputeAttribute to is not set.onnxruntime::Cast,class std::allocator > >::Castonnxruntime::Cast::Castonnxruntime::Cast::Computeonnxruntime::Cast::Computeonnxruntime::Cast::Computeonnxruntime::Cast::Computeonnxruntime::Cast::Computeonnxruntime::Cast<__int64>::Computeonnxruntime::Cast::Computeonnxruntime::Cast::Computeonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast<__int64>::Castonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast::Castonnxruntime::Cast::Castallocator != nullptronnxruntime::CastFloat16Datalen > 0bufferonnxruntime::CastToStringDataNaN-INFINFonnxruntime::CastFromStringDatadtypeEyeLikeonnxruntime::EyeLike::ComputeT1 != nullptrUnsupported 'dtype' value: C:\agent\_work\18\s\onnxruntime\core\providers\cpu\tensor\eye_like.ccEyeLike : Input tensor dimension is not 2MeanVarianceNormalizationInput is expected to have four dimensions corresponding to [N,C,H,W]info.GetAttr("across_channels", &across_channels_).IsOK()across_channelsC:\agent\_work\18\s\onnxruntime\core/providers/cpu/tensor/mean_variance_normalization.honnxruntime::MeanVarianceNormalization_0::MeanVarianceNormalization_0info.GetAttr("normalize_variance", &normalize_variance_).IsOK()normalize_varianceUnsupported value attribute datatype with sizeof=: C:\agent\_work\18\s\onnxruntime\core/providers/cpu/generator/constant_of_shape.honnxruntime::ConstantOfShapeBase::SetValuet_proto.has_data_type()ConstantOfShapeC:\agent\_work\18\s\onnxruntime\core\providers\cpu\generator\constant_of_shape.cconnxruntime::ConstantOfShapeBase::SetValueFromTensorProtovalueMust have a valid input shape.t_proto.dims()[0] == 1onnxruntime::ConstantOfShapeBase::PrepareComputeinput_shape.NumDimensions() > 0onnxruntime::ConstantOfShape::ComputeValue attribute unpacking failed:TensorProto::DataType_IsValid(t_proto.data_type())Unsupported value attribute datatype: unpack_status.IsOK()t_proto.dims_size() == 1Must have a single dimensionMust have a single dimension of 1onnxruntime::ConstantOfShapeBase::ConstantOfShapeBaseaxis greater than input data dimension!Compressonnxruntime::Compress::Computeaxis_ < static_cast(rank)axes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()C:\agent\_work\18\s\onnxruntime\core\providers\cpu\tensor\compress.ccLoopBC:\agent\_work\18\s\onnxruntime\core\providers\cpu\controlflow\loop.cconnxruntime::Loop::ComputeGraph in 'body' attribute of Loop should have inputs. Found:onnxruntime::Loop::LoopC:\agent\_work\18\s\onnxruntime\core/providers/cpu/controlflow/loop.h Got:onnxruntime::LoopImpl::CreateInitialFeedsInconsistent shape in loop output for output Expected:Loop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}.onnxruntime::LoopImpl::Execute outputs so the subgraph requires but has 'Loop' input 'M' should be a scalar tensor. Got shape of 'Loop' node has did not.'Loop' input 'cond' should be a scalar tensor. Got shape of entry.secondAll implicit inputs should have OrtValue instances by now. info.GetAttr("then_branch", &proto).IsOK()then_branchC:\agent\_work\18\s\onnxruntime\core/providers/cpu/controlflow/if.honnxruntime::If::Ifinfo.GetAttr("else_branch", &proto).IsOK()else_branch' attribute.Ifonnxruntime::If::ComputeSubgraph SessionState was not found for ' outputs.C:\agent\_work\18\s\onnxruntime\core\providers\cpu\controlflow\if.cc'If' node has outputs which doesn't match the subgraph's Failed to create output tensor for Subgraph must have the shape set for all outputs but feed_mlvalueFailed to create output tensor for If output onnxruntime::IfImpl::ExecuteC:\agent\_work\18\s\onnxruntime\core\providers\cpu\controlflow\scan_8.cconnxruntime::Scan<8>::Scanonnxruntime::Scan<8>::Computedirections but has batch size of The subgraph in 'body' expects Scan inputs have inconsistent batch size. Previous value was sequence_lens length of did not match batch size of p_mlvalueInvalid entries in sequence_lens. Max sequence length was position_ >= 0 && position_ < sequence_length_onnxruntime::Scan8Impl::CreateLoopStateVariablesC:\agent\_work\18\s\onnxruntime\core/framework/ort_value_tensor_slicer.honnxruntime::OrtValueTensorSlicer::Iterator::operator *GSL: Precondition failure at C:\agent\_work\18\s\cmake\external\gsl\include\gsl/span: 196Upsampleonnxruntime::Upsample::BaseComputeUpsample: unexpected modeonnxruntime::Upsample::BaseComputeUpsample: input tensor's dimension does not match the scales.Upsample: linear mode upsample only support 4-D tensor with NCHW layoutscales != nullptronnxruntime::Upsample::Computeonnxruntime::Upsample::Computeonnxruntime::Upsample::ComputeC:\agent\_work\18\s\onnxruntime\core\providers\cpu\tensor\upsample.ccUpsample: input/output value is nullptrUpsample: input/output value's dimension mismatchonnxruntime::Upsample::BaseComputeUpsample: input shape needs to be at least a single dimension.Missing/Invalid 'axes' attribute valueC:\agent\_work\18\s\onnxruntime\core/providers/cpu/tensor/unsqueeze.hinfo.GetAttrs("axes", axes_).IsOK()onnxruntime::UnsqueezeBase::UnsqueezeBase'axes' has an out of range axis'axes' has a duplicate axisonnxruntime::UnsqueezeBase::PrepareComputeC:\agent\_work\18\s\onnxruntime\core\providers\cpu\tensor\unsqueeze.ccpermv >= 0 && static_cast(v) <= std::numeric_limits::max()onnxruntime::Transpose::ComputeC:\agent\_work\18\s\onnxruntime\core\providers\cpu\tensor\transpose.ccMismatched data types between input and output Tensors. is outside range.Attribute perm of Transpose has an invalid value. Value onnxruntime::TransposeBase::TransposeBaseC:\agent\_work\18\s\onnxruntime\core/providers/cpu/tensor/transpose.h does not align with rank of input data: perm: is repeated.[ vector too longTransposeInput count of Tile OP mismatch, the second one is emptythe tensor to be tiled using Tile OP must be atleast 1 dimensionalTileInput count of Tile OP mismatch, the first one is emptyTile doesn't have an implementation yet for the type: onnxruntime::Tile::Compute'repeat' input tensor must be 1 dimensional'repeat' input tensor must have the same length as the 'input' tensorC:\agent\_work\18\s\onnxruntime\core\providers\cpu\tensor\tile.cconnxruntime::SqueezeBase::SqueezeBaseC:\agent\_work\18\s\onnxruntime\core/providers/cpu/tensor/squeeze.hAttribute axes is not set.Dimension of input input_shape[i] == 1. shape= must be 1 instead of onnxruntime::SqueezeBase::ComputeOutputShapeSqueezesplitonnxruntime::SplitBase::SplitBaseC:\agent\_work\18\s\onnxruntime\core/providers/cpu/tensor/split.hInvalid value in 'split' attribute. All values must be > 0std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value > 0; })SplitInput cannot be split evenly on selected axis. Input shape= Sum of sizes in 'split' (must equal size of selected axis) was NumOutputs= Axis=Cannot split using values in 'split' attribute. Axis= yet Num entries in 'split' (must equal number of outputs) was Input shape=C:\agent\_work\18\s\onnxruntime\core\providers\cpu\tensor\split.ccSplit operator does not support onnxruntime::Split::ComputeC:\agent\_work\18\s\onnxruntime\core/providers/cpu/tensor/space_depth_ops.hinfo.GetAttr("blocksize", &blocksize_).IsOK()onnxruntime::SpaceDepthBase::SpaceDepthBaseblocksizeAttribute blocksize is not set.DepthToSpaceinput.Shape().NumDimensions() == 4SpaceToDepthinput_height % this->blocksize_ == 0input_width % this->blocksize_ == 0onnxruntime::SpaceToDepth::ComputeC:\agent\_work\18\s\onnxruntime\core\providers\cpu\tensor\space_depth_ops.cconnxruntime::DepthToSpace::Computeinput_depth % (blocksize_ * blocksize_) == 0Size, requested shape:i < input_shape.NumDimensions()The dimension with value zero exceeds the dimension size of the input tensor.unknown_dim == -1gsl::narrow_cast(input_shape.Size()) == size(input_shape.Size() % size) == 0The input tensor cannot be reshaped to the requested shape. Input shape:A dimension cannot be less than -1.At most one dimension can be -1.c:\agent\_work\18\s\onnxruntime\core\providers\cpu\tensor\reshape_helper.honnxruntime::ReshapeHelper::ReshapeHelperrequested_shape[i] >= -1Reshape_1C:\agent\_work\18\s\onnxruntime\core/providers/cpu/tensor/reshape.honnxruntime::Reshape::ComputeshapeTensor->Shape().NumDimensions() == 1A shape tensor must be a vector tensor.Reshapeonnxruntime::Reshape_1::Reshape_1Attribute shape is not set.shapeconstant'pads' has wrong number of valuesC:\agent\_work\18\s\onnxruntime\core\providers\cpu\tensor\pad.cconnxruntime::PadCpuImpldimension_count > 0dimension_count * 2 == pads.size()onnxruntime::PadBase::PadBaseInvalid 'mode' attribute valueedgereflectInput tensor has no dimensionsPadInvalid 'pads' attribute valueC:\agent\_work\18\s\onnxruntime\core/providers/cpu/tensor/pad.hC:\agent\_work\18\s\onnxruntime\core/providers/cpu/tensor/gather.honnxruntime::GatherBase::GatherBaseType for Tind not supported yet in Gather.Gather and Cannot concatenate scalarsNot satsified: input_count >= 1 C:\agent\_work\18\s\onnxruntime\core\providers\cpu\tensor\concat.cconnxruntime::ConcatBase::PrepareForComputeinputs_n_rank == inputs_0_rankRanks of input data are different, cannot concatenate them, expected rank: got: Not satsified: inputs_0_rank > 0 C:\agent\_work\18\s\onnxruntime\core/providers/cpu/tensor/concat.honnxruntime::ConcatBase::ConcatBaseMust have valid 'axis' attributeNot satsified: data_n.DataType() == concat_result.DataType() Not satsified: inputs_n_dims[axis_index] == inputs_0_dims[axis_index] Non concat axis dimensions must match: Axis has mismatched dimensions of bidirectionalreverseforwardRNNinfo.GetAttrs("activations", activations_).IsOK()activationsactivation_betaactivation_alphaallowed_activations.find(activations_[direction]) != allowed_activations.end()activations_.size() == static_cast(num_directions)info.GetAttr("hidden_size", &hidden_size_).IsOK()hidden_sizedirectionclipScaledTanhAffineallowed_directions.find(direction_) != allowed_directions.end()C:\agent\_work\18\s\onnxruntime\core/providers/cpu/rnn/rnn.honnxruntime::RNN::RNNinfo.GetAttr("direction", &direction_).IsOK()info.GetAttr("direction", &direction).IsOK()DEEPCPU_LSTMC:\agent\_work\18\s\onnxruntime\core/providers/cpu/rnn/rnn_helpers.honnxruntime::rnn::detail::MakeDirectionclip_ > 0.finfo.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0C:\agent\_work\18\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_lstm.honnxruntime::DeepCpuLstmOp::DeepCpuLstmOpInvalid 'direction' argument of ''. Must be one of 'forward', 'reverse', or 'bidirectional'.Input P must have shape {Input initial_c must have shape {}. Actual:C:\agent\_work\18\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_lstm.ccactivation_func_names.size() == static_cast(num_directions_) * 3tanhsigmoidinput_forgetonnxruntime::DeepCpuLstmOp::ComputeInvalid data type for LSTM operator of LSTM operator does not support double yetLSTM..Processing batchonnxruntime::rnn::detail::SafeRawConstPointeroffset + size <= size_t(span.size())A + (M * lda - (lda - K)) <= A_endonnxruntime::rnn::detail::ComputeGemmlda >= K && ldb >= K && ldc >= N rows[onnxruntime::rnn::detail::SafeRawPointercur + size <= endC + (M * ldc - (ldc - N)) <= C_endB + (N * ldb - (ldb - K)) <= B_endinfo.GetAttr("linear_before_reset", &int64_value).IsOK()linear_before_resetC:\agent\_work\18\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.honnxruntime::DeepCpuGruOp::DeepCpuGruOpInvalid data type for GRU operator of GRU operator does not support double yetGRUactivation_func_names.size() == static_cast(num_directions_) * 2C:\agent\_work\18\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.cconnxruntime::DeepCpuGruOp::Compute * Rh^Trt (.) Ht-1rt (.) (Ht-1 * (Rh^T) + Rbh) [seqno=ReduceL1ReduceL2ReduceLogSumReduceLogSumExpkeepdimsReduceSumReduceSumSquareArgMaxArgMinReduceMaxReduceMeanReduceMinReduceProdonnxruntime::PrepareForReduceC:\agent\_work\18\s\onnxruntime\core\providers\cpu\reduction\reduction_ops.ccinfo.GetAttr("keepdims", &keepdims).IsOK()onnxruntime::ReduceKernelBase<0>::ReduceKernelBaseC:\agent\_work\18\s\onnxruntime\core/providers/cpu/reduction/reduction_ops.honnxruntime::ReduceKernelBase<1>::ReduceKernelBaseinfo.GetAttrs("pooled_shape", pooled_shape).IsOK()onnxruntime::RoiPool::RoiPoolC:\agent\_work\18\s\onnxruntime\core/providers/cpu/nn/roi_pool.hpooled_shape.size() == 2C:\agent\_work\18\s\onnxruntime\core\providers\cpu\nn\roi_pool.ccroi_batch_id >= 0roi_batch_id < batch_sizepooled_shapeMaxRoiPoolR->Shape()[1] == 5onnxruntime::RoiPool::Computepooled_height_ > 0pooled_width_ > 0info.GetAttr("spatial_scale", &spatial_scale_).IsOK()spatial_scale_ > 0C:\agent\_work\18\s\onnxruntime\core\providers\cpu\nn\lrn.ccLRNsizeinfo.GetAttr("size", &size).IsOK()X->Shape().NumDimensions() == 4onnxruntime::LRN::Computeinfo.GetAttr("alpha", &alpha_).IsOK()alpha_ > 0.0finfo.GetAttr("beta", &beta_).IsOK()beta_ > 0.0fonnxruntime::LRN::LRNC:\agent\_work\18\s\onnxruntime\core/providers/cpu/nn/lrn.hsize_ > 0size_ % 2 == 1onnxruntime::LpNorm::LpNormC:\agent\_work\18\s\onnxruntime\core/providers/cpu/nn/lp_norm.hop_kernel_info.GetAttr("p", &p_).IsOK()p_ == 1 || p_ == 2LpNormalizationop_kernel_info.GetAttr("axis", &axis_).IsOK()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 Invalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: vs. onnxruntime::InstanceNorm::InstanceNormC:\agent\_work\18\s\onnxruntime\core/providers/cpu/nn/instance_norm.hInstanceNormalizationop_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()onnxruntime::Flatten::ComputeFlattenonnxruntime::Flatten::FlattenC:\agent\_work\18\s\onnxruntime\core/providers/cpu/nn/flatten.hThe rank of input tensor must be >= axisgsl::narrow_cast(X_shape.NumDimensions()) >= axis_output_paddingoutput_shapeOutput height cannot be smaller than input height.output_height >= Honnxruntime::ConvTransposeBase::ComputePadsAndOutputShapeOutput width cannot be smaller than input width. num_input_channels: filter_number: filter number not equal to input channel number.Input channels is not divisible by group.C:\agent\_work\18\s\onnxruntime\core\providers\cpu\nn\conv_transpose.ccpad type not supportedgroup count is <= 0Input X must be 4-dimensional.ConvTranspose*out_size >= 0onnxruntime::ComputeTransposePadAndOutputShapeoutput_width >= Wonnxruntime::math::Im2colNd::operator ()Invalid input X: Empty dimensionsC:\agent\_work\18\s\onnxruntime\core/providers/cpu/nn/batch_norm.honnxruntime::BatchNorm::BatchNormBatchNormalization kernel for CPU provider does not support non-spatial casesspatialmeanscalest.IsOK()varInvalid input mean: 0th dimension != Invalid input mean: NumDimensions() != Invalid input var: 0th dimension != Invalid input var: NumDimensions() != Invalid input scale: 0th dimension != Invalid input scale: NumDimensions() != Invalid input B: 0th dimension != Invalid input B: NumDimensions() != spatial == 1SoftmaxLogSoftmaxC:\agent\_work\18\s\onnxruntime\core/providers/cpu/math/hardmax.hInvalid axis provided.Hardmaxonnxruntime::Hardmax::Hardmaxaxis_ >= 0, D=Hardmax inputs N, D and N * D must be < . N=left.NumDimensions() == 2 || left.NumDimensions() == 1info.GetAttr("transB", &temp).IsOK()transBinfo.GetAttr("transA", &temp).IsOK()transAC:\agent\_work\18\s\onnxruntime\core/providers/cpu/math/gemm.honnxruntime::Gemm::GemmGEMM: Dimension mismatch, W: K: M_ >= 0 && K_ > 0 && N_ >= 0Gemm: Invalid bias shape for broadcastc:\agent\_work\18\s\onnxruntime\core\providers\cpu\math\gemm_helper.honnxruntime::GemmHelper::GemmHelper N:right.NumDimensions() == 2Not implemented fused activation: info.GetAttrs("shape", shape).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDhighonnxruntime::RandomNormalLike::RandomNormalLikeseedinfo.GetAttr("scale", &scale_).IsOK()Invalid dtype of info.GetAttr("dtype", &dtype).IsOK()info.GetAttr("mean", &mean_).IsOK()C:\agent\_work\18\s\onnxruntime\core/providers/cpu/generator/random.honnxruntime::RandomNormal::RandomNormalONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDsample_sizeonnxruntime::RandomUniformLike::RandomUniformLikeonnxruntime::Multinomial::Multinomialinfo.GetAttr("sample_size", &num_samples_).IsOK()onnxruntime::RandomUniform::RandomUniforminfo.GetAttr("high", &high_).IsOK()info.GetAttr("low", &low_).IsOK()lowFLOAT16 is not supportedInvalid data type of C:\agent\_work\18\s\onnxruntime\core\providers\cpu\generator\random.cconnxruntime::RandomNormalComputebatch_size is < 1Empty dimensions for input tensornum_samples is < 1num_classes is < 1Could not infer data type from input tensor with data type onnxruntime::RandomUniformComputeonnxruntime::Clip::ClipC:\agent\_work\18\s\onnxruntime\core/providers/cpu/math/clip.hinfo.GetAttr("max", &max_).IsOK()info.GetAttr("min", &min_).IsOK()] != number of classlabels[Input features_per_batch[Zipmap does not support empty dim countZipmap only supports 1D or 2D input tensorsonnxruntime::ml::ZipMapOp::ZipMapOpC:\agent\_work\18\s\onnxruntime\core\providers\cpu\ml\zipmap.ccMust provide classlabels_strings or classlabels_int64s but not both.classlabels_strings_.empty() ^ classlabels_int64s_.empty()classlabels_int64sclasslabels_stringsZipMapLOGISTICSOFTMAXBRANCH_GTBRANCH_EQBRANCH_LTBRANCH_GTEBRANCH_LEQLEAFc:\agent\_work\18\s\onnxruntime\core\providers\cpu\ml\ml_common.hSUMMINSOFTMAX_ZEROAVERAGETreeEnsembleRegressortarget_treeidstarget_idsnodes_missing_value_tracks_truetarget_nodeidsnodes_truenodeidsnodes_falsenodeidsnodes_valuesnodes_hitratesnodes_nodeidsnodes_featureidsnodes_treeidsnodes_id_size == nodes_modes_.size()nodes_id_size == nodes_truenodeids_.size()nodes_id_size == nodes_featureids_.size()nodes_id_size == nodes_values_.size()target_nodeids_.size() == target_ids_.size()target_nodeids_.size() == target_weights_.size()!nodes_treeids_.empty()nodes_modesonnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressorC:\agent\_work\18\s\onnxruntime\core\providers\cpu\ml\treeregressor.ccn_targetsinfo.GetAttr("n_targets", &n_targets_).IsOK()post_transformaggregate_functiontarget_weightsbase_valuesonnxruntime::ml::write_scoreslength overflowtreeindex evaluated to a negative value, which should not happen.base_values_.empty() || base_values_.size() == static_cast(n_targets_)Input shape needs to be at least a single dimension.it != parents.end()it != indices.end()nodes_id_size == nodes_falsenodeids_.size()(nodes_id_size == nodes_hitrates_.size()) || (nodes_hitrates_.empty())TreeEnsembleClassifiernodes_nodeids_.size() == nodes_truenodeids_.size()nodes_nodeids_.size() == nodes_falsenodeids_.size()nodes_nodeids_.size() == nodes_modes_names_.size()nodes_nodeids_.size() == nodes_values_.size()class_nodeids_.size() == class_weights_.size()nodes_nodeids_.size() == nodes_featureids_.size()C:\agent\_work\18\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_classifier.ccclass_nodeids_.size() == class_ids_.size()class_weightsonnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifierclass_treeidsclass_idsclass_nodeidstreeindex >= 0onnxruntime::ml::TreeEnsembleClassifier::ProcessTreeNodebase_values_.empty() || base_values_.size() == static_cast(class_count_) || base_values_.size() == weights_classes_.size()Invalid mode of value: onnxruntime::ml::TreeEnsembleClassifier::Initializeonnxruntime::ml::TreeEnsembleClassifier<__int64>::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier0onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifierX dims is empty.1(nodes_nodeids_.size() == nodes_hitrates_.size()) || (nodes_hitrates_.empty())std::all_of( std::begin(missing_tracks_true_), std::end(missing_tracks_true_), [](int64_t elem) { return elem >= 0; })onnxruntime::ml::TreeEnsembleClassifier::Initializeonnxruntime::ml::TreeEnsembleClassifier::ProcessTreeNodeonnxruntime::ml::TreeEnsembleClassifier<__int64>::Initializeonnxruntime::ml::TreeEnsembleClassifier<__int64>::ProcessTreeNodeonnxruntime::ml::TreeEnsembleClassifier::Initializeonnxruntime::ml::TreeEnsembleClassifier::ProcessTreeNodeSVMRegressorRBFLINEARPOLYone_classkernel_typeinfo.GetAttrs("coefficients", coefficients_).IsOK()!coefficients_.empty()C:\agent\_work\18\s\onnxruntime\core\providers\cpu\ml\svmregressor.cccoefficientsinfo.GetAttrs("rho", rho_).IsOK()onnxruntime::ml::SVMRegressor::SVMRegressorsupport_vectorsrhon_supportsonnxruntime::ml::SVMCommon::SVMCommonc:\agent\_work\18\s\onnxruntime\core\providers\cpu\ml\svmclassifier.hkernel_paramsinfo.GetAttrs("kernel_params", kernel_params).IsOK()SVMClassifiercoefficients_.size() > 0No support vectors.info.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0proba_.size() == probb_.size()classlabels_intsonnxruntime::ml::SVMClassifier::SVMClassifierC:\agent\_work\18\s\onnxruntime\core\providers\cpu\ml\svmclassifier.ccprob_aprob_bvectors_per_classonnxruntime::ml::SVMCommon::SVMCommonC:\agent\_work\18\s\onnxruntime\core/providers/cpu/ml/svmclassifier.honnxruntime::ml::SVMCommon<__int64>::SVMCommononnxruntime::ml::SVMClassifier::SVMClassifieronnxruntime::ml::SVMCommon::SVMCommononnxruntime::ml::SVMClassifier::SVMClassifieronnxruntime::ml::SVMClassifier<__int64>::SVMClassifierScalerscale_.size() == offset_.size()Invalid argument: input has empty dimensions.) != (Scale size: (onnxruntime::ml::ScalerOp::ScalerOpC:\agent\_work\18\s\onnxruntime\core\providers\cpu\ml\scaler.ccEmpty scale in attributes!scale_.empty()offsetonnxruntime::ml::ScalerOp::ScalerOponnxruntime::ml::ScalerOp::ScalerOponnxruntime::ml::ScalerOp<__int64>::ScalerOp) or 1Either both scale and offset can be of feature size (Unknown Category and zeros = 0.OneHotEncoderonnxruntime::ml::OneHotEncoderOp,class std::allocator > >::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOpnum_categories_ > 0C:\agent\_work\18\s\onnxruntime\core\providers\cpu\ml\onehotencoder.cconnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOptmp_cats_int64s.empty() || tmp_cats_strings.empty()One and only one of the 'cats_*' attributes must be definedcats_stringscats_int64szerosC:\agent\_work\18\s\onnxruntime\core/providers/cpu/ml/ml_common.hInvalid input type of C:\agent\_work\18\s\onnxruntime\core\providers\cpu\ml\normalizer.cconnxruntime::ml::Normalizer::ComputeNormalizerC:\agent\_work\18\s\onnxruntime\core/providers/cpu/ml/normalizer.honnxruntime::ml::Normalizer::Normalizerinfo.GetAttr("norm", &norm).IsOK()normonnxruntime::ml::MakeNormalizeInvalid normalize value of L2L1MAXonnxruntime::ml::Normalizer::NormalizeUnexpected NORMALIZE value of LinearRegressorC:\agent\_work\18\s\onnxruntime\core\providers\cpu\ml\linearregressor.cconnxruntime::ml::LinearRegressor::LinearRegressorinfo.GetAttr("targets", &targets_).IsOK()targetsinterceptsLinearClassifieronnxruntime::ml::LinearClassifier<__int64>::LinearClassifieronnxruntime::ml::LinearClassifier::LinearClassifierC:\agent\_work\18\s\onnxruntime\core\providers\cpu\ml\linearclassifier.cconnxruntime::ml::LinearClassifier::LinearClassifiermulti_classonnxruntime::ml::LinearClassifier::LinearClassifierInput of tensor(int64) must have output of tensor(string)Input of tensor(string) must have output of tensor(int64)LabelEncoderinfo.GetAttr("default_int64", &default_int_).IsOK()default_int64info.GetAttr("default_string", &default_string_).IsOK()default_stringC:\agent\_work\18\s\onnxruntime\core/providers/cpu/ml/label_encoder.honnxruntime::ml::LabelEncoder::LabelEncoderinfo.GetAttrs("classes_strings", string_classes).IsOK()classes_stringsonnxruntime::ml::ImputerOp::ImputerOpExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedreplaced_value_floatimputed_value_int64simputed_value_floatsImputerInvalid typeonnxruntime::ml::ImputerOp::Computeimputed_values_float_.empty() ^ imputed_values_int64_.empty()Must provide imputed_values_float_ or imputed_values_int64_ but not both.Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedreplaced_value_int64C:\agent\_work\18\s\onnxruntime\core\providers\cpu\ml\imputer.ccEmpty input dimensions.Empty value of imputed values.Invalid input type:C:\agent\_work\18\s\onnxruntime\core\providers\cpu\ml\feature_vectorizer.cconnxruntime::ml::FeatureVectorizer::Computeinput_count >= 0 && static_cast(input_count) == input_dimensions_.size()Number of inputs () does not match number of inputdimensions values ().FeatureVectorizerC:\agent\_work\18\s\onnxruntime\core/providers/cpu/ml/feature_vectorizer.honnxruntime::ml::FeatureVectorizer::FeatureVectorizerstatus.IsOK() && !input_dimensions_.empty()inputdimensions attribute must be providedinputdimensionsonnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string,class std::allocator > >::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOpC:\agent\_work\18\s\onnxruntime\core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOpinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()int64_vocabularystring_vocabularyDictVectorizeronnxruntime::ml::DictVectorizerOp,class std::allocator >,__int64>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,double>::DictVectorizerOpinfo.GetAttrs("cats_strings", string_categories).IsOK()onnxruntime::ml::CategoryMapper::CategoryMapperC:\agent\_work\18\s\onnxruntime\core/providers/cpu/ml/category_mapper.hinfo.GetAttrs("cats_int64s", int_categories).IsOK()num_entries == int_categories.size()CategoryMapperInput of string must have output of int64Input of int64 must have output of string %fcast_toinfo.GetAttr("cast_to", &attr).IsOK()onnxruntime::ml::CastMap::CastMapC:\agent\_work\18\s\onnxruntime\core/providers/cpu/ml/cast_map.hmap_forminfo.GetAttr("map_form", &attr).IsOK()max_mapinfo.GetAttr("max_map", &max_map_).IsOK()max_map must be > 0 if map_form is SPARSEmap_form_ != PACK_MAP::SPARSE || max_map_ > 0CastMap Expected std::map or std::mapInvalid input type of value: 0.fUnexpected CAST_TO value of TO_FLOATTO_STRINGTO_INT64 Expected TO_FLOAT, TO_STRING or TO_INT64Invalid CAST_TO value of onnxruntime::ml::MakeCastDENSESPARSE Expected DENSE or SPARSEInvalid PACK_MAP value of onnxruntime::ml::MakePackNegative index values are not permitted. First entry in map has index value of cur_input == end_input || cur_input->first >= 0onnxruntime::ml::CastMap::ComputeImplC:\agent\_work\18\s\onnxruntime\core\providers\cpu\ml\cast_map.ccInput data with index: Binarizerthreshold is NaNInvalid argument: X input has empty dimensions.Invalid Y argument: num_indices = 0) >=] (Invalid Y argument: index is out of range: Y[ArrayFeatureExtractorC:\agent\_work\18\s\onnxruntime\core\providers\cpu\controlflow\scan_utils.cc'. 0 == forward. 1 == reverse.Invalid values in 'validGraph::InferAndVerifySubgraphTypes should have already validated that num_variadic_inputs matched the subgraph inputs or required inputs.static_cast(num_variadic_inputs) == graph_inputs->size()onnxruntime::scan::detail::CreateFeedsFetchesManageronnxruntime::scan::detail::IterateSequenceMisuse of LoopStateVariable. Attempt to move beyond end of sequenceiteration_num_ < sequence_len_onnxruntime::scan::detail::LoopStateVariable::Next is not compatible with Mismatch between expected shape and shape from first outputFailed to create output tensor for output #If shape was concrete we shouldn't be using a custom allocator!is_concrete_shape_' was Number of entries in 'num_entries < 0 || gsl::narrow_cast(directions.size()) == num_entriesonnxruntime::scan::detail::ReadDirectionsonnxruntime::scan::detail::OutputIterator::AllocateSubgraphOutputcur_iteration_ < num_iterations_onnxruntime::scan::detail::OutputIterator::operator *Expected AllocateSubgraphOutput to have been called to before we read the OrtValue from the iterator.is_concrete_shape_Expected AllocateSubgraphOutput to have been called to before we increment the iteratoronnxruntime::scan::detail::OutputIterator::operator ++onnxruntime::OrtValueTensorSlicer::Iterator::operator *leakyreluthresholdedreluscaledtanhhardsigmoidelusoftsignsoftplusExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCountC:\agent\_work\18\s\onnxruntime\core\providers\cpu\rnn\rnn_helpers.ccInvalid activation function of onnxruntime::rnn::detail::deepcpu::ActivationFuncByNameInvalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNameInvalid GRU reset gate activation function: Input X must have 3 dimensions only. Actual:Input W must have shape {Input R must have shape {Input B must have shape {Input sequence_lens must have shape {Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input initial_h must have shape {affinereluonnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameInvalid GRU hidden gate activation function: onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByNameSoftmaxCPU inputs N, D and N * D must be < info.GetAttr("scale", &scale_).IsOK()onnxruntime::contrib::Scale::Scalec:\agent\_work\18\s\onnxruntime\contrib_ops\cpu\element_wise_ops.hinfo.GetAttr("alpha", &alpha_).IsOK()onnxruntime::contrib::Affine::Affineinfo.GetAttr("beta", &beta_).IsOK()ScaleParametricSoftplusInput is expected to have four dimensions corresponding to [N,C,H,W], got ImageScalerBias size () does not match the number of channels (c:\agent\_work\18\s\onnxruntime\contrib_ops\cpu\image_scaler.honnxruntime::contrib::ImageScaler::ImageScalerinfo.GetAttrs("bias", bias_).IsOK()DynamicSliceAttribute border needs to be specified with four border elements, got border) + bottomBorder ( input dimensions insteadInput's height () needs to be greater than or equal to the topBorder () needs to be greater than or equal to the leftBorder () + rightBorder () + scale_[0] (Input's width (Crop) + scale_[1] (CropAndResize specified. It should be either bilinear or nearestbilinearC:\agent\_work\18\s\onnxruntime\contrib_ops/cpu/crop_and_resize.honnxruntime::contrib::CropAndResize::CropAndResizeextrapolation_valueNull crop_size_ptrNumber of dimensions for crop size should be exactly 1ConvTransposeWithDynamicPadsInput tensor to Unique op should be 1DUniquePads tensor should be an INT64 tensoronnxruntime::contrib::Pad::Computepads_tensor.DataType() == DataTypeImpl::GetType()Pads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]C:\agent\_work\18\s\onnxruntime\contrib_ops\cpu\pad.ccPads tensor size should be equal to twice the input dimension count pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensorpads_size == 2 * dimension_countvalue_tensor->DataType() == DataTypeImpl::GetType() && value_tensor->Shape().Size() == 1c:\agent\_work\18\s\onnxruntime\contrib_ops\cpu\maxpool_with_mask.hMaxpoolWithMaskonnxruntime::contrib::MaxpoolWithMask::ComputeC:\agent\_work\18\s\onnxruntime\contrib_ops\cpu\murmur_hash3.cckeysonnxruntime::contrib::MurmurHash3::ComputepositiveMurmurHash3GatherNDinput_tensor != nullptrindice_tensor != nullptrindices tensor must has rank larger than 0onnxruntime::contrib::GatherNDBase::PrepareForComputeC:\agent\_work\18\s\onnxruntime\contrib_ops\cpu\gather_nd.cclast dimension of indices must not be larger than rank of input tensorinvalid indice found, indice = char_embedding_sizeembedding_sizeconv_window_sizeChar embedding size does not match char_embedding_size attribute. Conv kernal size 2 : Char embedding size: char_embedding_size attribute: Char embedding size does not match conv kernal size 2.WordConvEmbedding conv filter size: embedding_size attribute: conv_window_size attribute: Conv kernal size 1 does not match conv_window_size attribute .Conv filter size does not match embedding_size attribute. conv kernal size 1: start in Range operator should be scalar like tensor, yet got shape:delta in Range operator can not be zero!limit in Range operator should be scalar like tensor, yet got shape:delta in Range operator should be scalar like tensor, yet got shape:RangeUnsupportted tensor data type:attribute mark is not setonnxruntime::contrib::Tokenizer::TokenizerTokenizermarkattribute pad_value is not setmincharnumC:\agent\_work\18\s\onnxruntime\contrib_ops\cpu\tokenizer.ccpad_valuemincharnum_ > 0separatorsattribute mincharnum is not setattribute mincharnum must have a positive valueExpecting a non-empty tokenexp!tokenexp.empty()tokenexpEither one of the separators OR tokenexp attributes required but none is setmincharnum is too big for char level tokenezation!char_tokenezation_ || mincharnum_ < 2separators must not be empty!separators.empty()Input string contains invalid utf8 chars: Match contains invalid utf8 chars: Can not digest separators: Can not digest tokenexp: tensor(string) expected as inputInput dimensions are either [C] or [N][C] allowedstatic_cast(activation_func_names.size()) == num_directions_ * 3AttnLSTMonnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOpc:\agent\_work\18\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.h}, actural: Attention mechanism memory shape error! Expected: {onnxruntime::contrib::DeepCpuAttnLstmOp::ComputeC:\agent\_work\18\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.cc], while Attention mechanism memory sequence lengths value must in (0, Attention mechanism memory sequence lengths must have shape { found!}, Got: Attention query layer weight shape error! Expected:{, am_attn_size}, Got:Attention memory layer weight shape error! Expected:{DEEPCPU_ATTN_LSTM, aw_attn_size}. Got:Attention layer weight shape error! Expected: {}. Got: Attention v weight shape error! Expected:{leaky_relu_alphaC:\agent\_work\18\s\onnxruntime\contrib_ops\cpu\fused_conv.cconnxruntime::contrib::FusedConvFloat::FusedConvFloatGetFusedActivationAttr(info, activation_).IsOK()onnxruntime::contrib::ExpandDims::Computeaxis_tensor->Shape().IsScalar()An axis tensor must be a scalar tensor.Axis must be within range []. Axis is c:\agent\_work\18\s\onnxruntime\contrib_ops\cpu\expand_dims.hExpandDimsaxis <= X_NumDims && axis >= -X_NumDimsSampleOponnxruntime::contrib::NchwcConv::NchwcConvonnxruntime::contrib::ReorderInput::Computechannels_ <= Y_shape[1]onnxruntime::contrib::ReorderOutput::ComputeC:\agent\_work\18\s\onnxruntime\contrib_ops\cpu\nchwc_ops.cconnxruntime::contrib::NchwcConv::ComputeX_shape.NumDimensions() == 4c:\agent\_work\18\s\onnxruntime\contrib_ops\cpu\nchwc_ops.hNot satsified: Y->Shape() == sum_shape output and sum shape must matchUnsupported convolution size.(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)info.GetAttr("channels", &channels_).IsOK()Not satsified: kernel_shape_.size() == 2 (X_shape[1] % MlasNchwcGetBlockSize()) == 0onnxruntime::contrib::NchwcPoolBase::NchwcPoolchannels_ > 0invalid channel countonnxruntime::contrib::ReorderOutput::ReorderOutput row[onnxruntime::contrib::BahdanauAttention::PrepareMemorymem_steps <= max_memory_steps_ && mem_steps > 0!normalize_not support normalize yet.Real memory steps is not in (0, C:\agent\_work\18\s\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.cconnxruntime::contrib::BahdanauAttention::BahdanauAttentionactivation_params count mismatchunimplemented activation: CHECK failed: !is_closed_: close() failed: C:\agent\_work\18\s\onnxruntime\core\util\protobuf_parsing_utils.cconnxruntime::SessionState::AddInitializedTensor. Do you have duplicated calls to SessionState::AddInitializedTensor function?duplicated ort_value index: (nullptr != graph_vieweronnxruntime::SessionState::SetGraphViewerC:\agent\_work\18\s\onnxruntime\core\framework\session_state.ccort_value_index >= 0 && ort_value_index <= ort_value_name_idx_map_.MaxIdx() for attribute Entry exists in node existing_entries.find(attribute_name) == existing_entries.cend()onnxruntime::SessionState::AddSubgraphSessionState) and node is used by node Using an input in multiple nodes on different devices is not supported currently. Input:Failed to find input name in the mapping: onnxruntime::SessionState::GetNodeIndexInfograph_viewer_onnxruntime::SessionState::CalculateNodeIndexInfoCalculateNodeIndexInfo must be called prior to GetExecutionInfo.node_index_info_device_copy_checks.status == DeviceCopyCheck::Unknownonnxruntime::utils::ExecuteGraphonnxruntime::utils::CachedSetupFetchesForExecutecopy_to_new_fetches_cached_values.size() == num_outputsnum_outputs == copy_info.size()onnxruntime::utils::CachedCopyOutputsAcrossDevicesinvalid allocatorp_input_provideronnxruntime::utils::CopyOneInputAcrossDevicesC:\agent\_work\18\s\onnxruntime\core\framework\utils.cconnxruntime::utils::CachedCopyInputsAcrossDevicesnew_fetches.empty()onnxruntime::utils::SetupFetchesForExecutep_graphrequired_providerfeed_names.size() == num_feedsonnxruntime::utils::CopyInputsAcrossDevicescopy_info.size() == num_feedsonnxruntime::SparseTensorTypeBase::GetElementTypeonnxruntime::TensorTypeBase::GetElementTypethisProto->tensor_type().has_elem_type()thisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeonnxruntime::SparseTensorTypeBase::IsCompatiblethisProto->sparse_tensor_type().has_elem_type()onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypeWe do not expect duplicate registration of types for: thisProto->value_case() == TypeProto::ValueCase::kTensorTypeonnxruntime::TensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kSequenceTypeonnxruntime::NonTensorTypeBase::IsSequenceCompatiblethisProto->sequence_type().has_elem_type()thisProto->value_case() == TypeProto::ValueCase::kMapTypeonnxruntime::NonTensorTypeBase::IsMapCompatiblethisProto->map_type().has_key_type()thisProto->map_type().has_value_type()onnxruntime::data_types_internal::IsCompatibleC:\agent\_work\18\s\onnxruntime\core\framework\data_types.ccOnly ONNX MLDataType can be registeredproto != nullptr expected to be a registered ONNX type is not currently registered or supported(null)onnxruntime::data_types_internal::SetMapTypes,class std::allocator >,float>::Setonnxruntime::data_types_internal::SetMapTypes,class std::allocator >,double>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,class std::basic_string,class std::allocator > >::Setonnxruntime::data_types_internal::SetMapTypes<__int64,__int64>::Setvalue_proto != nullptronnxruntime::data_types_internal::SetMapTypes,class std::allocator >,class std::basic_string,class std::allocator > >::SetC:\agent\_work\18\s\include\onnxruntime\core/framework/data_types.honnxruntime::data_types_internal::SetMapTypes,class std::allocator >,__int64>::Settensor(bfloat16) is not supportedtensor type sparse tensor type tensor(MLFloat16) value type: Map with key type: type != nullptrtype: tensor_type.has_elem_type()onnxruntime::DataTypeImpl::TypeFromProtosparse_tensor_type.has_elem_type() is not registeredonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType<__int64>::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > >::Setonnxruntime::data_types_internal::SetMapTypes<__int64,float>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,double>::Setelem_proto != nullptronnxruntime::data_types_internal::SetSequenceType,class std::allocator > >::Setonnxruntime::data_types_internal::SetSequenceType,class std::allocator > > >::SetNot implemented'is defined.No attribute with name:'Attibute name and type don't matchNo attribute with this name is defined.GSL: Precondition failure at C:\agent\_work\18\s\cmake\external\gsl\include\gsl/pointers: 77onnxruntime::OpKernelInfo::GetAllocatorInfoC:\agent\_work\18\s\onnxruntime\core\framework\op_kernel_info.cccannot find allocatorC:\agent\_work\18\s\onnxruntime\core\framework\tensor_shape.cconnxruntime::TensorShape::SizeFromDimensionInvalid tensor shape slice argument.dimstart <= dimend && dimend <= size() for SizeFromDimension. Tensor has Invalid dimension of dimension <= num_dimsonnxruntime::TensorShape::SizeToDimensiononnxruntime::TensorShape::Slice{} dimensions.Execution frame was nullonnxruntime::OpKernelContext::OutputMLValuekernel != nullptrInvalid arg_num of . Num args is onnxruntime::OpKernelContext::OpKernelContextframe != nullptrOpKernel was nullC:\agent\_work\18\s\onnxruntime\core\framework\op_kernel.ccnode_offsets_index < node_offsets_.size()C:\agent\_work\18\s\onnxruntime\core/framework/node_index_info.honnxruntime::NodeIndexInfo::GetNodeOffsetonnxruntime::OpKernelContext::NumVariadicInputsarg_num < arg_counts.size()onnxruntime::OpKernelContext::GetOrCreateOutputMLValueTempSpace allocator not found id: mem_type:OrtAllocatorInfo: [ name: type:Specified device is not supported.CudaPinnedCudasrc.Size() == dst.Size()C:\agent\_work\18\s\onnxruntime\core\framework\data_transfer.cconnxruntime::CPUDataTransfer::CopyTensorSaveMLValueNameIndexMappingC:\agent\_work\18\s\onnxruntime\core\framework\session_state_initializer.cconnxruntime::SaveMLValueNameIndexMappingExecution plan was not found in SessionState. CreatePlan must be called first.onnxruntime::SessionStateInitializer::InitializeAndSaveexec_plan_ptrGraphSubgraphonnxruntime::SaveInputOutputNamesToNodeMappingDone saving kernels.onnxruntime::SaveKernels input with name is not used by any node.Done saving OrtValue mappings.string tensor is not supported for copying between allocatorsInvalid allocation info. Provider name = with error: Could not create kernel for node: Saving kernels.Kernel creation failed for node: Internal error. The preallocated buffer is too small. Requires , Got as there's no execution provider allocated.Failed to copy tensor to execution provider: OrtValue indexes should have been populated.onnxruntime::SaveInitializedTensors failed.ort_value_name_idx_map.MaxIdx() > 0Saving initialized tensors.Deserialize tensor Done saving initialized tensors in KernelRegistryManager (node The node is not placed on any Execution Providerfound duplicated provider Failed to find kernel for The node is not placed on any Execution Provider, therefore, can't find a suitable kernel for itonnxruntime::utils::TensorToTensorProtoIsLittleEndianOrder()C:\agent\_work\18\s\onnxruntime\core\framework\tensorprotoutils.ccThe buffer planner is not consistent with tensor buffer size, expected tensor can't contain negative dimsInitialized tensor with unexpected type: initialize preallocated buffer failedUnpackTensor: the pre-allocate size does not match the size in protocorrupted protobuf data: tensor shape size(string tensor can not have raw datadata overflow) does not match the data size() in protoInvalid TensorProtoUnpackTensor: the pre-allocated size does not match the raw data size, expected nullptr != type_protoC:\agent\_work\18\s\onnxruntime\core\framework\mldata_type_utils.cconnxruntime::utils::GetMLDataTypeC:\agent\_work\18\s\onnxruntime\core\framework\execution_provider.cconnxruntime::IExecutionProvider::InsertAllocatorduplicated allocatoronnxruntime::FeedsFetchesManager::SetDeviceCopyChecksC:\agent\_work\18\s\onnxruntime\core\framework\feeds_fetches_manager.ccError mapping output names: checks.input_copy_needed != DeviceCopyCheck::Unknown && checks.output_copy_needed != DeviceCopyCheck::UnknownError mapping feeds: NnapiExecutionProvideronnxruntime::GraphPartitioner::Partitiononnxruntime::FunctionKernel::FunctionKernelC:\agent\_work\18\s\onnxruntime\core/framework/func_kernel.hProvider doesn't return correct number of compiled functionsnode_compute_funcs.size() == nodes_need_compile.size()No provider specified.onnxruntime::PlaceNodeC:\agent\_work\18\s\onnxruntime\core\framework\graph_partitioner.cccreate_func(&context, &func_state_) == 01 == capability->nodes.size()Device: [ device_id: memory_type:data_transfer registered is nullptr.Tensor size mismatch to There's no data transfer registered for copying tensors from Unloading DSO onnxruntime::ExLibLoader::~ExLibLoaderA dso with name Caught exception while loading custom ops with message: Caught exception while destructing CustomOpsLoader with message: has already been loaded.C:\agent\_work\18\s\onnxruntime\core\framework\ex_lib_loader.ccFailed to unload DSO: :tensor type ElementTypeFromProtoonnxruntime::Tensor::InitC:\agent\_work\18\s\onnxruntime\core\framework\tensor.ccshape.Size() must >=0p_type != nullptronnxruntime::Tensor::Tensoroffset >= 0 && static_cast(offset) < node_values_.size()onnxruntime::NodeIndexInfo::GetMLValueIndexort_value_idx == NodeIndexInfo::kInvalidEntry || (ort_value_idx >= 0 && static_cast(ort_value_idx) < all_values_.size())onnxruntime::IExecutionFrame::GetNodeIdxToMLValueIdxonnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValueinvalid index OrtValue shape verification failed. Current shape:shape && tensor.Shape() == *shapenull Requested shape:ort_value.Fence() == nullptronnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelperTrying to allocate memory for unused optional inputs/outputsTensor shape cannot contain any negative valuebuffers_.find(mem_patterns_->locations[i]) == buffers_.end()onnxruntime::ExecutionFrame::ExecutionFrame entries which doesn't match the number of fetches the frame was initialized with of Fetches vector passed to GetOutputs contains C:\agent\_work\18\s\onnxruntime\core\framework\execution_frame.ccfetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size()feeds.size() == feed_mlvalue_idxs.size()onnxruntime::IExecutionFrame::IExecutionFrameonnxruntime::IExecutionFrame::GetMLValueC:\agent\_work\18\s\onnxruntime\core/framework/execution_frame.hort_value_index >= 0 && static_cast(ort_value_index) < all_values_.size()TraceFree for ort_value_idx=Memory pattern planner is not enabled on this execution framework.onnxruntime::ExecutionFrame::TraceAllocateonnxruntime::ExecutionFrame::TraceFree size=TraceAllocation for ort_value_idx=onnxruntime::ExecutionFrame::GetAllocationPlan failed: Shape mismatch attempting to re-use buffer. onnxruntime::ExecutionFrame::AllocateMLValueTensorPreAllocateBuffer, block not found in target location. fall back to default allocation behavior. 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., block in memory pattern size is: For ort_value with index: , fall back to default allocation behavior but the actually size is: Invalid allocation kind: ort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()ort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()Allocation of tensor types requires a shape.We don't expect custom allocators for non-tensor types, so a shape is mandatory here.onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlanmlvalue.Fence() == nullptronnxruntime::AllocateSparseTensor Version mismatch.: Conflicting with a registered kernel with op versions. kernel start version: node_version: Execution provider mismatch. kernel_end_version: Actual: Domain mismatch: kernel is not supported in C:\agent\_work\18\s\onnxruntime\core\framework\kernel_registry.cc Encountered following errors: Failed to create kernel for op: onnxruntime::KernelRegistry::TryFindKernelFailed to add kernel for since it was ill-formed during registration Incompatible types.Op: onnxruntime::`anonymous-namespace'::TraverseFormalParametersWithTypeProtolen <= op_schema.inputs().size()onnxruntime::NodeIndexInfo::Init::::operator ()onnxruntime::NodeIndexInfo::Init::::operator ()C:\agent\_work\18\s\onnxruntime\core\framework\node_index_info.cc. Shape:ort_value.IsAllocated()OrtValue has not been allocated so can't be sliced.C:\agent\_work\18\s\onnxruntime\core\framework\ort_value_tensor_slicer.cconnxruntime::OrtValueTensorSlicer::CreateCan't slice a non-tensor OrtValue. Type was onnxruntime::OrtValueTensorSlicer::Createdim0_offset < dim0_sizeInvalid dim0_offset of . Dimension 0 is gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensionInsufficient dimensions to slice on EXECUTORException running nodes starting at node ''. Unknown exception was caught by catch-all handler._fence_after_kernel_timeonnxruntime::ParallelExecutor::RunNodeAsyncBegin executionParallelExecutor::ExecuteC:\agent\_work\18\s\onnxruntime\core\framework\parallel_executor.cconnxruntime::ParallelExecutor::ExecuteMultiple errors were found.providerCompute failed for node: Error:_fence_beforeop_nameGot nullptr from GetKernel for node: Exiting due to terminate flag being set to true.onnxruntime::SequentialExecutor::ExecuteSequentialExecutor::ExecuteNon-zero status code returned while running Node: Status Message: C:\agent\_work\18\s\onnxruntime\core\framework\sequential_executor.cc already exist. not found.Can't use func with null ptrfunc info for node: Get preallocated buffer for initializer '' failedWeight buffer for initializer '' is not foundMem pattern for initializer is not foundInternal error.duplicated locationFailed to get allocator for location: : Can not find the node onnxruntime::PlannerImpl::GetElementSizenullptr != tensor_type_baseonnxruntime::PlannerImpl::Reusereused != reused_foronnxruntime::PlannerImpl::ProcessDefid >= 0 && static_cast(id) < ort_value_info_.size()onnxruntime::PlannerImpl::AllocPlankernel_create_info != nullptr && kernel_create_info->kernel_def != nullptronnxruntime::PlannerImpl::GetLocationForNodeInputCan not find the execution provider No suitable kernel definition found for op ] C:\agent\_work\18\s\onnxruntime\core\framework\allocation_planner.ccn >= 0 && static_cast(n) < plan_.allocation_plan.size()onnxruntime::PlannerImpl::Bufferonnxruntime::PlannerImpl::UseCountn >= 0 && static_cast(n) < ort_value_info_.size()onnxruntime::PlannerImpl::Index[illegal input path:. .unexpected failuremodel format error! Missing 'location'model format error!checksumlengthparsing failedlocationmodel format error! Need a value for the external data infomodel format error! Need a key for the external data infoonnxruntime::BFCArena::AllocationRegion::IndexForp_int >= base_intC:\agent\_work\18\s\onnxruntime\core/framework/bfc_arena.honnxruntime::BFCArena::AllocationRegion::AllocationRegion0 == memory_size % kMinAllocationSizeh != kInvalidChunkHandleonnxruntime::BFCArena::Reservereserved_chunks_.find(ptr) == reserved_chunks_.end()Allocated memory at !c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::FindChunkPtr!chunk->in_use()onnxruntime::BFCArena::AllocateRawInternaltried to allocate 0 bytesBinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size) == BinFromIndex(b)C:\agent\_work\18\s\onnxruntime\core\framework\bfc_arena.cconnxruntime::BFCArena::BFCArenaCreating bin of max chunk size onnxruntime::BFCArena::RegionManager::RegionForCould not find Region for p_int < base_int + memory_size_Total allocated bytes: onnxruntime::BFCArena::ExtendExtending allocation by bytes.onnxruntime::BFCArena::ChunkFromHandleh < chunks_.size()BinForSize(bin_size * 2) != BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b) was onnxruntime::BFCArena::RemoveFreeChunkIterFromBin!c->in_use() && (c->bin_num != kInvalidBinNum)onnxruntime::BFCArena::InsertFreeChunkIntoBinc2->prev == h1onnxruntime::BFCArena::Merge!c1->in_use() && !c2->in_use()onnxruntime::BFCArena::DeallocateRawInternalonnxruntime::BFCArena::SplitChunkonnxruntime::BFCArena::FreeAndMaybeCoalescec->in_use() && (c->bin_num == kInvalidBinNum)BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0Could not find chunk in binonnxruntime::BFCArena::RemoveFreeChunkFromBinFailed to get allocator for initializer 'invalid ort_value_index:', location: NoOpConstant Target=Can't merge shape info. Both source and target dimension have values but they differ. Source=Mismatch between number of source and target dimensions. Source=[ShapeInferenceError] Dimension=This is an invalid model. Error: two nodes with same node name (This is an invalid model. Error: Duplicate definition of name (Invalid node indexes specified when adding edge.onnxruntime::Graph::AddEdgeInvalid source node arg slot specified when adding edge.Invalid destination node arg slot specified when adding edge.Argument type mismatch when adding edge.Invalid node indexes specified when removing edge.Type mismatch. Current=Tensor element type mismatch. SparseTensor element type mismatch. This is an invalid model. The sum of input arg count is not equal to size of input defs in node (graph_proto cannot be nullgraph_proto != nullptronnxruntime::Graph::GraphC:\agent\_work\18\s\onnxruntime\core\graph\graph.ccOutput: Input=This is an invalid model. Type Error: Type ' in node () bound to different types (Type Error: Type parameter () type inference failed) output arg (Node () does not match expected type (Subgraph input missing type.) does not have type information set by parent node.) input arg (This is an invalid model. Node () is invalid.) in node () of operator (' of input parameter (Graph attribute inferencing failed: in node No Graph instance was found for attribute inputs. Either provide all subgraph inputs, or just the required inputs. inputs and requires inputs but subgraph has Size mismatch validating subgraph inputs. Got Node:onnxruntime::Graph::RemoveEdgeInvalid source node arg slot specified when removing edge.Invalid destination node arg slot specified when removing edge.Argument mismatch when removing edge. Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.This is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name=This is an invalid model. Failed to find NodeArg in all parent graphs. Name=This is an invalid model. Error: the graph is not acyclic.'. It is no longer used by any node.Graph ctor should have created NodeArg for initializer.onnxruntime::Graph::SetGraphInputsOutputs) does not exist in the graph.This is an invalid model. Graph output ( must be either specified in graph inputs or graph initializers.nodes_.size() < static_cast(std::numeric_limits::max())onnxruntime::Graph::AllocateNodeonnxruntime::Graph::Resolvenode_arg'. It is not used by any node and should be removed from the model.Removing initializer 'onnxruntime::Graph::CleanUnusedInitializersThis is an invalid model. Error in Node: is not a registered function/opFatal error: ) is required but not specified.) attribute (Error: Duplicate definition-site for (Shouldn't be possible to have NodeArgs that haven't been handled already.outer_scope_node_args_consumed.empty()) of node () of output arg (Type Error: Type () does not have type information.This is an invalid model. Model input ( does not match its type.Type Error: Value of initializer Type Error: Shape of initializer C:\agent\_work\18\s\include\onnxruntime\core/common/const_pointer_container.hnullptr != sub_graph && nullptr != sub_graph->GetMetaDef()onnxruntime::Graph::FuseSubGraphThis API is not supported when model is loaded from proto file right now.onnxruntime::Graph::SetInputsonnxruntime::Graph::SetOutputsindex < data_.size()onnxruntime::ConstPointerContainer > >::atDomain already set in registryknown by the checker.than the operator set version , but it its version is higherCFor internal use.Constrain input and output types to float tensorstensor rank too smallC:\agent\_work\18\s\onnxruntime\core\graph\contrib_ops\contrib_defs.ccConstrain input and output types to float/quantized tensorsWAttribute should specify a shapeNegative values are not allowed in a shape specificationboth data and indices tensor need to have rank larger than zero.last dimension of indices must not be larger and rank of data tensor'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64first input tensor has wrong dimensionrois input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensioncrop_size shape input tensor has wrong dimensionValue of alpha) + right_border () needs to be greater than or equal to the left_border () + scale[0] () + scale[1] (First input does not have rank 2Second input does not have rank 2Input axis is invalid: Tokenizer divides each string in X into a vector of strings along the last axis. Allowed input shapes are [C] and [N, C]. If the maximum number of tokens found per input string is D, the output shape would be [N, C, D] when input shape is [N, C]. Similarly, if input shape is [C] then the output should be [C, D]. Tokenizer has two different operation modes. The first mode is selected when "tokenexp" is not set and "separators" is set. If "tokenexp" is set and "separators" is not set, the second mode will be used. The first mode breaks each input string into tokens by matching and removing separators. "separators" is a list of strings which are regular expressions. "tokenexp" is a single regular expression. Let's assume "separators" is [" "] and consider an example. If input is ["Hello World", "I love computer science !"] whose shape is [2], then the output would be [["Hello", "World", padvalue, padvalue, padvalue], ["I", "love", "computer", "science", "!"]] whose shape is [2, 5] because you can find at most 5 tokens per input string. Note that the input at most can have two axes, so 3-D and higher dimension are not supported. If "separators" contains a single empty string, the Tokenizer will enter into character tokenezation mode. This means all strings will be broken part into individual characters. For each input string, the second mode searches matches of "tokenexp" and each match will be a token in Y. The matching of "tokenexp" is conducted greedily (i.e., a match should be as long as possible). This operator searches for the first match starting from the beginning of the considered string, and then launches another search starting from the first remained character after the first matched token. If no match found, this operator will remove the first character from the remained string and do another search. This procedure will be repeated until reaching the end of the considered string. Let's consider another example to illustrate the effect of setting "mark" to true. If input is ["Hello", "World"], then the corresponding output would be [0x02, "Hello", "World", 0x03]. This implies that if mark is true, [C]/[N, C] - input's output shape becomes [C, D+2]/[N, C, D+2]. If tokenizer removes the entire content of [C]-input, it will produce [[]]. I.e. the output shape should be [C][0] or [N][C][0] if input shape was [N][C]. If the tokenizer receives empty input of [0] then the output is [0] if empty input of [N, 0] then [N, 0]. Scale takes one input data (Tensor) and produces one output data (Tensor) whose value is the input data tensor scaled element-wise. GRUUnit computes the activations of a standard GRU, in a sequence-length aware fashion. Concretely, given the (fused) inputs X (TxNxD), the previous hidden state (NxD), and the sequence lengths (N), computes the GRU activations, avoiding computation if the input is invalid (as in, the value at X[t][n] >= seqLengths[n]. Experimental allowing ATen operations to be accessed directly from Caffe2 to allow for quick prototyping when ONNX is missing standard versions of and opInput's shape must be 4-D'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)'Scale' must contain exactly 2 values - (height, width)) + bottom_border () needs to be greater than or equal to the top_border ( Affine takes one input data (Tensor) and produces one output data (Tensor) where the affine function, y = alpha * x + beta, is applied to the tensor elementwise. ParametricSoftplus takes one input data (Tensor) and produces one output data (Tensor) where the softplus function, y = alpha * ln(exp(beta * x) + 1), is applied to the tensor elementwise. Scale and bias the input image. Bias values are stored in the same ordering as the image pixel format.Crop and image to the specified spatial dimensions. If scale is given, then optionally start the crop offset by the left/top border amounts. If scale is not provided, crop the borders as provided. Produces a slice of the input tensor along multiple axes. Similar to numpy: https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html Slices uses `axes`, `starts` and `ends` inputs to specify the start and end dimension for each axis in the list of axes, it uses this information to slice the input `data` tensor. If a negative value is passed for any of the start or end indices, it represent number of elements before the end of that dimension. If the value passed to start or end is larger than the `n` (the number of elements in this dimension), it represents `n`. For slicing to the end of a dimension with unknown size, it is recommended to pass in `INT_MAX`. If `axes` are omitted, they are set to `[0, ..., ndim-1]`. Example 1: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] result = [ [5, 6, 7], ] Example 2: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000] result = [ [2, 3, 4], ] input_as_shapeextra_shapeThe filled tensorvaluesInput data to be scaledOutput data after scalingGRUUnitBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.drop_statesThe previous GRU hidden state.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.Sliced data tensor.Constrain input and output types to all tensor types.Constrain indices to integer typesGivenTensorFillThe shape of filled tensorA 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.Threshold valueInput tensorOutput tensorTensor of data to extract slices from.dataValue of beta1D input tensor1D output tensorConstrain input and output types to float tensors.Bias applied to each channel, same size as C.The scale to apply.Input tensor of shape [N,C,H,W]Result, has same shape and type as input The FusedGemm operator schema is the same as Gemm besides it includes attributes activation and leaky_relu_alpha.Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.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 transposedScaling valueThe scaled hyperbolic tangent values of the input tensor computed element-wise Sample echo operator.maskMConstrain input0 and output types to float tensorsPads The fused convolution operator schema is the same as Conv besides it includes an attribute activation.hiddenATenArbitrary inputArbitrary outputConstrain output types to bool, int32, int64, float16, float, double tensors.Perform mean variance normalization.If 1, mean and variance are computed across channels. Default is 0.If 0, normalize the mean only. Default is 1.hidden_prevUnactivated gate outputs from forget, update, and output gates, pre-activation.gatesArray of sequence lengths. len(seq_lengths) should equal batch size N.seq_lengthsThe timestep for this operation.tThe new GRU hidden state calculated by this op.Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.Constrain output type to unsigned and signed 32-bit integer tensor.Seed for the hashing algorithm, unsigned 32-bit integer, default to 0.If value is 1, output type is uint32_t, else int32_t. Default value is 1.Tensor of rank r >= 1.Tensor of rank q >= 1.indicesTensor of rank q-1+r-indices[-1].reducedConstrain input type to 8-bit integer tensor.Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8).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.The underlying implementation is MurmurHash3_x86_32 generating low latency 32bits hash suitable for implementing lookup tables, Bloom filters, count min sketch or feature hashing.An input tensor to hash.32-bit hash value.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 ignoredReduceSumInteger Computes the sum of the low-precision input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulting tensor have the reduced dimension pruned. The above behavior is similar to numpy, with the exception that numpy default keepdims to False instead of True.An input tensor.Reduced output tensor.Scalar multiplier for the product of input tensors A * B.Scalar multiplier for input tensor C.Specified axis to insert a dimensionExpandDims echo operator.Strings to tokenizeTokenized stringsInput/Output is a string tensorBoolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).A 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x'countsInput can be of any tensor type. Finds all the unique values (deduped list) present in the given input tensor. This operator returns 3 outputs. The first output tensor 'uniques' contains all of the unique elements of the input, sorted in the same order that they occur in the input. The second output tensor 'idx' is the same size as the input and it contains the index of each value of the input in 'uniques'. The third output tensor 'counts' contains the count of each element of 'uniques' in the input. Example: input_x = [2, 1, 1, 3, 4, 3] output_uniques = [2, 1, 3, 4] output_idx = [0, 1, 1, 2, 3, 2] output_counts = [1, 2, 2, 1] The 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.Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]. `pads` format (1D example) should be as follow [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.(Optional) A scalar or rank 1 tensor containing a single value to be filled if the mode chosen is `constant` (by default it is 0.0).Tensor after padding. Given `data` tensor, pads, mode, and value. Example: Insert 0 pads to the beginning of the second dimension. data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] output = [ [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ], ] A 1-D input tensor that is to be processed.A 1-D tensor of the same type as 'x' containing all the unique values in 'x' sorted in the same order that they occur in the input 'x'A 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques'idxSpecify weights of convSpecify bias of convSpecify embedding vector of charConstrain to tensor(int32).Constrain to tensor(float).The WordConvEmbedding takes in a batch of sequence words and embed each word to a vector.Three modes: `constant`(default) - pads with a given constant value, `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis, `edge` - pads with the edge values of arrayInput tensor.Constrain input and output types to any tensor type.Constrain indice type to int32 or int64 Given `data` tensor of rank r >= 1, and `indices` tensor of rank q >= 1, gather slices of `data` into an output tensor of rank q - 1 + r - indices[-1]. Example 1: data = [[0,1],[2,3]] indices = [[0,0],[1,1]] output = [0,3] Example 2: data = [[0,1],[2,3]] indices = [[1],[0]] output = [[2,3],[0,1]] Example 3: data = [[[0,1],[2,3]],[[4,5],[6,7]]] indices = [[0,1],[1,0]] output = [[2,3],[4,5]] Example 4: data = [[[0,1],[2,3]],[[4,5],[6,7]]] indices = [[[0,1]],[[1,0]]] output = [[[2,3]],[[4,5]]] Integer representing the embedding vector size for each word.If not provide, use the fileter size of conv weightThis operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernal shape.Integer representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.Specify batchs of sequence words to embeddingSequence Extracts crops from the input image tensor and resizes them using bilinear sampling or nearest neighbor sampling (possibly with aspect ratio change) to a common output size specified by crop_height and crop_width. Returns a tensor with crops from the input image at positions defined at the bounding box locations in boxes. The cropped boxes are all resized (with bilinear or nearest neighbor interpolation) to a fixed size = [crop_height, crop_width]. The result is a 4-D tensor [num_boxes, crop_height, crop_width, depth]. The resizing is corner aligned.rois1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.batch_indices1-D tensor of 2 elements: [crop_height, crop_width]. All cropped image patches are resized to this size. Both crop_height and crop_width need to be positive.crop_sizeRoI pooled output, 4-D tensor of shape (num_rois, C, crop_height, crop_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].Constrain types to float tensors.Constrain types to int tensors.ModelProto was null. failed. File doesn't existLoad model system error number No graph was found in the protobuf.Failed to load model with error: Protobuf parsing failed. less than 0.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. model may run depending upon legacy support of some older opset version operators.ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset onnxruntime::Model::ModelC:\agent\_work\18\s\onnxruntime\core\graph\model.cconnxruntime::graph_utils::RemoveNodeWithSingleInitializerIn Invalid value for input index of node Attempting to get an input that does not exist.index >= 0 && static_cast(index) < inputs.size()onnxruntime::graph_utils::GetNodeInputNameAttempting to get an output that does not exist.index >= 0 && static_cast(index) < outputs.size()onnxruntime::graph_utils::GetNodeOutputNamestd::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraphC:\agent\_work\18\s\onnxruntime\core\graph\graph_utils.cc cannot be safely updated in one of the subgraphs. Implicit input name onnxruntime::graph_utils::CanUpdateImplicitInputNameInSubgraphsOutgoing node could not be found.output_nodeFailed to replace initializer. Original initializer was not found. Name:ReplaceInitializerImpl(graph, original_name, initializer, check_outer_scope)onnxruntime::graph_utils::ReplaceInitializerResolve subgraph failed:inoutfused_function_subgraphinput_arg->Type() != nullptronnxruntime::FunctionImpl::FunctionImplC:\agent\_work\18\s\onnxruntime\core\graph\function.ccUnsupported type:Can not get shape initializer data!Tensor(scalar, or dims=[1]). First entry in the range.Constrain input and output types.Tensor(scalar, or dims=[1]). Upper limit of sequence, exclusive.startUnsupported non-raw-data data type!Tensor(scalar, or dims=[1]). Number that increments start. Defaults to 1.limit1-D Tensor of the range.deltaThe weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`RThe recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]` The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.sequence_lensOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.Number of neurons in the hidden layer.Couple the input and forget gates if 1, default 0.Constrain seq_lens to integral tensors.Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Y_hThe last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`. Y_cThe last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.The attention_v tensor in the attention mechanism. Should be of shape `[num_directions, am_attn_size]` MWThe sequence length of the input memory for the attention mechanism. Should be of `[batch_size]` The sequence of the memory (input) for attention mechanism. Should be of `[batch_size, max_memory_step, memory_depth]` 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.` memory_seq_lensA tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`AWOptional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.initial_hThe weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.initial_cThe weight tensor of the query layer in the attention mechanism. Should be of shape `[num_directions, am_query_depth(hidden_size of lstm), am_attn_size]` PThe weight tensor of the memory layer in the attention mechanism. Should be of shape `[num_directions, memory_depth, am_attn_size]` QWcode != static_cast(MLStatus::OK)C:\agent\_work\18\s\onnxruntime\core\common\status.cconnxruntime::common::Status::StatusSystemErrorOK[ONNXRuntimeError]MODEL_LOADEDINVALID_PROTOBUFINVALID_GRAPHNOT_IMPLEMENTEDREQUIREMENT_NOT_REGISTEREDSHAPE_INFERENCE_NOT_REGISTEREDGENERAL ERRORINVALID_ARGUMENTSUCCESSNO_MODELNO_SUCHFILERUNTIME_EXCEPTIONENGINE_ERRORonnxruntimedefault_logger_id must be provided if instance_type is InstanceType::DefaultISink must be provided.Default logger already set. Only one instance of LoggingManager created with InstanceType::Default can exist at any point in time.r4r4"name" :""ph" : "X",""args" : {} " : "] }, [ onnxruntime::profiling::Profiler::EndTimeAndRecordEvent{"cat" : "","tid" :"pid" :"ts" :"dur" :NodeSessiononnxruntime::profiling::Profiler::Initializesession_logger != nullptrC:\agent\_work\18\s\onnxruntime\core\common\profiler.ccMaximum number of events reached, could not record profile event.: genericonnxruntime::TaskThreadPool::~TaskThreadPoolException joining threads in TaskThreadPool: c:\agent\_work\18\s\onnxruntime\core\common\task_thread_pool.hbroken promisepromise already satisfiedfuture already retrievedfutureno state is not a valid date is not a valid year is not a valid day [Failed to find symbol in libraryFailed to unload librarykernel32.dllonnxruntime::`anonymous-namespace'::WindowsEnv::FormatLibraryFileNameGetSystemTimePreciseAsFileTimeReadFileAsString: 'fname' cannot be NULLopen file ReadFileAsString: please specify length to readReadFile SetFilePointerEx fail: unexpected endonnxruntime::`anonymous-namespace'::WindowsEnv::GetNumCpuCoresFatal error: 0 count processors from GetSystemInfoFatal error: 0 count processors from GetLogicalProcessorInformationC:\agent\_work\18\s\onnxruntime\core\platform\windows\env.ccGetFileSizeEx fail, errcode =AQ5@.N3;A8X} <2?~>++{16?:>=4?~??A%~3a*7A\3JCy7 ':Y;Ӡ5֕8;Y;ffn@l?A ;Eۼ# =pu>7fȹ~;ƼX=P"?>?B;?r1mL:e9 <*=*>>??@K?@@@' has been deprecated since version Operator '' is expected to have field 'strings'' is expected to have field 'tensors'' is expected to have field 'floats'' is expected to have field 'ints'' is missing.Required attribute '' is expected to have field 'graphs' has unknown expected typeMismatched attribute type in '' is expected to have field 'f' for operator Unrecognized attribute: ' is expected to have field 't'' is expected to have field 'g'' is expected to have field 'i'' is expected to have field 's' is marked single but has an empty string in the graph)'s input ) than declared () has more inputs (' appeared multiple times.Attribute ') has more outputs ()'s output ) has input size not in allowed input sizes., max= not in range [min=has output size ) in op definition.) has output size not in allowed output sizes.(inputs_.size() - 1) == i: failed validating the check: Duplicate type constraint name!(it.GetName().empty())ONNX Schema (outputs_.size() - 1) == iData to be binarizedThe input must be a tensor of a numeric type or string. The output will be of the same tensor type.C:\agent\_work\18\s\cmake\external\onnx\onnx\defs\traditionalml\defs.ccValues greater than this are mapped to 1, others to 0.Binarized output dataThe input must be a tensor of a numeric type. The output will be of the same tensor type.Selected output data as an arrayZData to be selectedThe indices, based on 0 as the first index of any dimension.map(string, double)The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.map(int64, double)map(string, float)An ordered collection of tensors, all with the same element type.A string vocabulary array.
One and only one of the vocabularies must be defined.An integer vocabulary array.
One and only one of the vocabularies must be defined.An integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined._UnusedA string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.map(string, int64)A dictionary.A 1-D tensor holding values from the input dictionary.Input dataOutput data. If strings are input, the output values are integers, and vice versa.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.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.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 input must be an integer map to either string or float.map(int64, string)The input map that is to be cast to a tensorA tensor representing the same data as the input map, ordered by their keysA 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).
One of 'DENSE', 'SPARSE'.map(int64, float)The output is a 1-D tensor of string, float, or integer.The input type is a tensor of any shape.Output type is determined by the specified 'values_*' attribute.Input data. It can be either tensor or scalar.Output data.A list of floats.A list of strings. One and only one of 'value_*'s should be set.A list of strings. One and only one of 'keys_*'s should be set.A list of ints.Input type is not int64 tensor but keys_int64s is setInput type is not float tensor but keys_floats is setOnly one of keys_*'s can be set in label encoder.Input type is not string tensor but key_strings is setvalues_floatsOnly one of values_*'s can be set in label encoder.values_stringsvalues_int64sLabel encoder has only one input.A value that needs replacing.Value(s) to change to.keys_int64skeys_floatsLabel encoder has only one output.keys_stringsThe size of each input in the input listThe output array, elements ordered as the inputs.The input type must be a tensor of a numeric type.The input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.Value(s) to change toData to be processed.Imputed output dataData to be encoded, a tensor of shape [N,C] or [C]Encoded output dataThe total number of regression targets, 1 if not defined.Data to be encoded.Encoded output data, having one more dimension than X.One of 'MAX,' 'L1,' 'L2'Data to be regressed.Regression outputs (one per target, per example).Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Weights of the model(s).Weights of the intercepts, if used.The input must be a tensor of a numeric type.Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'The output will be a tensor of strings or integers.A collection of weights of the model(s).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]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.A collection of intercepts.Indicates whether to do OvR or multinomial (0=OvR is the default).A float.default_floatA string.An integer.Data to be classified.Classification outputs (one class per example).Input's shape should be 1D or 2DIndicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Support vector coefficients.The number of support vectors.The class score for each class, for each point, a tensor of shape [N,E].The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.Input of shape [N,F]N, Top class for each pointClass labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.Chosen support vectorsFlag indicating whether the regression is a one-class SVM or not.Regression outputs (one score per target per example).The input type must be a tensor of a numeric type, either [C] or [N,C].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.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.First set of probability coefficients.Second set of probability coefficients. This array must be same size as prob_a.
If these are provided then output Z are probability estimates, otherwise they are raw scores.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.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.List of categories, ints.
One and only one of the 'cats_*' attributes must be defined.List of categories, strings.
One and only one of the 'cats_*' attributes must be defined.First, offset by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.Second, multiply by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
Must be same length as 'offset'Data to be scaled.Scaled output data.Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The weight for each targetThe total number of targets.The output will be a sequence of string or integer maps to float.seq(map(string, float))The input valuesThe output mapChild node if expression is trueChild node if expression is falseN classesNode id for each node. Node ids must restart at zero for each tree and increase sequentially.The node id of each weightThe index of the target that each weight is forFor each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the defalt value is false (0) for all nodes.The id of the tree that each node is in.node id that this weight is for.The index of the class list that each weight is for.For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the defalt value is false (0) for all nodes.The id of the tree that this node is in.Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)The weight for the class in class_id.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Feature id for each node.Thresholds to do the splitting on for each node.Tree id for each node.Node id for each node. Ids may restart at zero for each tree, but it not required to.Child node if expression is true.Child node if expression is false.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.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.seq(map(int64, float))The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.C:\agent\_work\18\s\cmake\external\onnx\onnx\defs\traditionalml\old.ccThe output type will be a tensor of strings or integers, and will have the same shape as the input.A list of labels.Input data.The input type must be a tensor of integers or strings, of any shape.Second input operand for the logical operator.Result tensor.{broadcast_doc}First input operand for the logical operator.Constrains output to boolean tensor.C:\agent\_work\18\s\cmake\external\onnx\onnx\defs\logical\defs.ccandConstrains input to boolean tensor.This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). 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} {name}Incompatible dimensionsSecond operand, amounts of shift.Constrain input and output types to integer tensors. Bitwise shift operator performs element-wise operation. For each input element, if the attribute "direction" is "RIGHT", this operator moves its binary representation toward the right side so that the input value is effectively decreased. If the attribute "direction" is "LEFT", bits of binary representation moves toward the left side, which results the increase of its actual value. The input X is the tensor to be shifted and another input Y specifies the amounts of shifting. For example, if "direction" is "Right", X is [1, 4], and S is [1, 1], the corresponding output Z would be [0, 2]. If "direction" is "LEFT" with X=[1, 2] and S=[1, 2], the corresponding output Y would be [2, 8]. Because this operator supports Numpy-style broadcasting, X's and Y's shapes are not necessarily identical. First operand, input to be shifted.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).BitShiftgreaterConstrains input types to all numeric tensors.orxorConstrains input/output to boolean tensors.lessequalConstrain input and output types to all numeric tensors.Constrain input and output types to signed numeric tensors.Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentDividend tensorsubtractionmultiplicationdivisionDivisor tensorRemainder tensor{description}Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizeConstrain input and output types to high-precision numeric tensors. The operator computes the {name} ({description}) values for each layer in the batch of the given input. The input is a 2-D tensor (Tensor) of size (batch_size x input_feature_dimensions). The output tensor has the same shape and contains the {name} values of the corresponding input. Input does not need to explicitly be a 2D vector; rather, it will be coerced into one. For an arbitrary n-dimensional tensor input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is the axis provided, then input will be coerced into a 2-dimensional tensor with dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default case where axis=1, this means the input tensor will be coerced into a 2D tensor of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size. In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D. Each of these dimensions must be matched correctly, or else the operator will throw errors. additionC:\agent\_work\18\s\cmake\external\onnx\onnx\defs\math\defs.ccThe input tensor that's coerced into a 2D matrix of size (NxD) as described above.The output values with the same shape as input tensor (the original size without coercion).); for more details please check [the doc](Broadcasting.md). should be unidirectional broadcastable to This operator supports **unidirectional broadcasting** (Second operand.Result, has same element type as two inputs Performs element-wise binary {name} (with Numpy-style broadcasting support). {broadcast_doc} First operand.Value of alpha.Value of beta.data_0Output tensor. Element-wise {name} of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. {broadcast_doc} List of tensors for Output tensor (same size as X) PRelu takes input data (Tensor) and slope tensor as input, and produces one output data (Tensor) where the function `f(x) = slope * x for x < 0`, `f(x) = x for x >= 0`., is applied to the data tensor elementwise. First operand, base of the exponent.Second operand, power of the exponent.Slope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to Xslopeinput tensor Xtensor slopeThe exponential of the input tensor computed element-wiseCoefficient of ELU.The hyperbolic tangent values of the input tensor computed element-wise Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. The natural log of the input tensor computed element-wiseCoefficient of leakage.Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).K input must be a one-dimensional tensor of size 1.K input must be of type int64.Invalid value for attribute axisA 1-D tensor containing a single positive value corresponding to the number of top elements to retrieveKAxis has less than the requested k elements.Tensor of shape [a_1, a_2, ..., a_n, r]Input tensors of wrong rank (0).Incompatible dimensions for matrix multiplicationtensor A * Btensor CN-dimensional matrix BMatrix multiply results from A * BN-dimensional matrix A1 for the first maximum value, and 0 for all othershardmaxlog of softmaxlogsoftmaxGeneral Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3 A' = transpose(A) if transA else A B' = transpose(B) if transB else B Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M), input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), and output tensor Y has shape (M, N). A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB. The softsign (x/(1+|x|)) values of the input tensor computed element-wiseMinimum value, under which element is replaced by minMaximum value, above which element is replaced by maxsumnormalized exponentialsoftmaxInput tensor whose elements to be clippedOutput tensor with clipped input elementsThe hyperbolic arcsine values of the input tensor computed element-wiseThe hyperbolic cosine values of the input tensor computed element-wiseThe hyperbolic arctangent values of the input tensor computed element-wiseThe hyperbolic arccosine values of the input tensor computed element-wise'shape' input must be 1D tensor of type INT64The arctangent of the input tensor computed element-wiseThe hyperbolic sine values of the input tensor computed element-wiseA 1-D tensor indicates the shape you want to expand to, following the broadcast ruleConstrain input and output types to all tensors.The tangent of the input tensor computed element-wiseThe cosine of the input tensor computed element-wiseThe arccosine of the input tensor computed element-wiseThe arcsine of the input tensor computed element-wiseTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values.IndicesTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensorValuesThe sine of the input tensor computed element-wiseConstrain index tensor to int64Dimension on which to do the sort.inputs are expected to have tensor type and output type should not be null.Constrain input b and its zero point data type to 8-bit integer tensor.Constrain output y and its zero point data type to 8-bit integer tensor.Constrain input A data type to 8-bit integer tensor.Constrain input B data type to 8-bit integer tensor.Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-row quantization. If it's a 1-D tensor, its number of elements should be equal to the number of rows of input 'A'.Scale tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.zero point of quantized input bb_zero_pointscale of quantized input bb_scaleQuantized matrix multiply results from a * bConstrain input a and its zero point data type to 8-bit integer tensor.scale of quantized output yzero point of quantized output yscale of quantized input aa_scaleN-dimensional quantized matrix aaN-dimensional quantized matrix bbzero point of quantized input aa_zero_pointThe error function of the input tensor computed element-wise. It has the same shape and type of the input.The sign of the input tensor computed element-wise. It has the same shape and type of the input.input and zero_point pair is expected to have be same type.input and zero_point pair is expected to have same type.inputs are expected to have tensor type.Roundaxis tensor can be int32 or int64 onlyCumSumIf set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.exclusiveConstrain output Y data type as 32-bit integer tensor.(Optional) A 0-D tensor. Must be in the range [-rank(x), rank(x))Output tensor of the same type as 'x' with cumulative sums of the x's elementsIf set to 1 will perform the sums in reverse direction.An input tensor that is to be processed. is invalid for a tensor of rank axis value Input element type of Output was expected to have tensor type. Got Element type of input was unknown does not match existing output type of Input type was nullInput was expected to have tensor type. Got Only boolC:\agent\_work\18\s\cmake\external\onnx\onnx\defs\controlflow\defs.ccGraph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.All Tensor typesA boolean termination condition. Optional. Pass empty string to skip.The initial values of any loop-carried dependencies (values that change across loop iterations)A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.Condition for the ifMismatched tensor element type for output Loop 'body' subgraph outputs should all be tensors but output outputsGraph 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.condValues 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.then_branch and else_branch produce different number of outputs. but subgraphs produce was notScan 'body' subgraph outputs should all be tensors but output then=Mismatched type for output If node has else=) is not equal to number of scan outputs (Number of scan output axes specified () is not equal to number of scan inputs (Number of scan input axes specified ( outputs. Expected Graph attribute inferencing returned type information for was not a tensor.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.Int64 tensorfinal_state_and_scan_outputsThe graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.initial_state_and_scan_inputsFinal values of the loop's N state variables followed by K scan_outputsAn 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.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.v_final_and_scan_outputsThe graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.v_initialFinal N loop carried dependency values then K scan_outputsInitial values of the loop's N state variables followed by M scan_inputstensor of int64, which should be a scalar.tensor of bool, which should be a scalar.RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.An input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.boxesC:\agent\_work\18\s\cmake\external\onnx\onnx\defs\object_detection\defs.ccDimension mismatch in unification between expected to have rank >default 1; Pooled output Y's height.default 1; Pooled output Y's width.Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. but has rank expected to have rank Integer indicate the format of the box data. The default is 0.0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box cornersand the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models.1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytoch models.selected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].selected_indicesInteger representing the maximum number of boxes to be selected per batch per class. It is a scalar.max_output_boxes_per_classAn input tensor with shape [num_batches, num_classes, spatial_dimension]scoresFloat representing the threshold for deciding when to remove boxes based on score. It is a scalarscore_thresholdFloat representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1].iou_thresholdConstrain input types. Casting from complex is not supported.Constrain output types. Casting to complex is not supported.Input tensor to be cast.Output tensor with the same shape as input with type specified by the 'to' argumentTarget shape may not have multiple -1 dimensionsInvalid position of 0C:\agent\_work\18\s\cmake\external\onnx\onnx\defs\tensor\defs.cc should be of integer type and specify a type. does not specify a valid type. not specifiedValue of attribute The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoOne or more outputs forming list of tensors after splittingWhich axis to split on.Invalid value of attribute 'axis'The tensor to splitOnly supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsSlice op must have either three, four or five inputs.length of each outputList of tensors for concatenationinputsWhich axis to concat onConstrain output types to any tensor type.Concatenated tensorconcat_resultTotal number of elements of the input tensorInput tensor can be of arbitrary type.Constrain output to int64 tensor.rank must be greater than axisAll inputs to Concat must have same rankConstrain output to int64 tensor, which should be a scalar though.Required attribute axis is missingDimension could not be inferred: incompatible shapesSpecified shape for output.Invalid dimension value: Invalid Target shape product of 0Shape of the input tensorReshaped data.reshapedTensor of rank q + (r - 1).Which axis to gather on. Negative value means counting dimensions from the back. Accepted range in [-r, r-1]Tensor of int32/int64 indices, of any rank q.Reshaped tensor with same data as input.squeezedList of non-negative integers, indicate the dimensions to squeeze.Tensors with at least max(dims) dimensions.updatesTensor of rank r >= 1 (same rank as input).Tensor of int32/int64 indices, of r >= 1 (same rank as input).Tensor of rank r >=1 (same rank and shape as indices)data tensor must have rank >= 1axis must be in [-r, r-1]Input and output types can be of any tensor type.}, input shape = {A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.Invalid attribute perm {Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range in [-r, r-1]Transposed output.transposedInput steps has incorrect lengthInput axes has invalid dataIncorrect or missing input value for starts and endsInput axes has incorrect length1-D tensor of slice step of corresponding axis in `axes`. Default to 1. steps'step' cannot be 01-D tensor of ending indices (exclusive) of corresponding axis in `axes`Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)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.N-D tensor after resizingConstrain input 'X' and output 'Y' to all tensor types.N-D tensorThe 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'.'Repeats' input must be 1D tensor of type int64Output 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.repeats'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.Input tensor of any shape.Blocksize must be positiveThree modes: constant(default), reflect, edgeOne float, indicates the value to be filled.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].Input tensor must be 4-dimensionalBlocks of [blocksize, blocksize] are moved.Original tensorexpandedList of non-negative integers, indicate the dimensions to be insertedAttribute pads has incorrect lengthList of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.Attribute value for pads is requiredConstrain input types to float tensors.Constrains input to only numeric types.Constrain to any tensor type.(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.Constrain output types 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.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.Indices tensor must have rank >= 1'axis' must be in [-rank(indices)-1, rank(indices)]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.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 and the values in the 'indices' input tensor are expected to be in the range [0, depth). TheIn case 'depth' is of non-integer type, it will be casted to int64 before use.depthOneHot node must have three inputs.Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.Constrains to boolean tensors.Input 'values' must be rank 1 tensor.Input 'values' must have exactly two elements.Input 'depth' must be rank 1 tensor.Input 'depth' must have exactly one element.Tensor to copy input into.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'.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.condition(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.Tensor with same shape of input.'input' must have rank >= 2'sequence_lens' must have rank of 1Constrain to all tensor types.Tensor of rank r >= 2.Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.(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.values selected at indices where condition is Truevalues selected at indices where condition is FalseWhen True (nonzero), yield X, otherwise yield Youtput (always 2D tensor)Tensor of shape equal to the broadcasted shape of condition, X, and Y.Constrain to boolean tensors.C:\agent\_work\18\s\cmake\external\onnx\onnx\defs\quantization\defs.ccConstrain 'x' to float or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.Zero point for input 'x'. It's a scalar, which means a per-tensor/layer quantization. It's optional. 0 is the default value when it's not specified.N-D full precision output tensor. It has same shape as input 'x'.N-D quantized input tensor to be de-quantized.Scale for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Zero point for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization. Default value is 0 if it's not specified.N-D quantized output tensor. It has same shape as input 'x'.N-D full precision Input tensor to be quantized.Scale for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization.Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.Attribute dilations has incorrect sizeAttribute strides has incorrect sizeauto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.Input tensor must have atleast 2 dimensionsInput tensor X must have atleast 2 dimensions.Attribute pads has incorrect size.Dilation value along each spatial axis of filter.MaxUnpool op must have either two or three inputs.Attribute kernel_shape must be specified.'output_shape' must be rank 1 tensor.Attribute strides has incorrect size.Attribute kernel_shape has incorrect size.C:\agent\_work\18\s\cmake\external\onnx\onnx\defs\nn\defs.ccThe output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero).The output of each pooling window is divided by the number of elements exclude pad.averageThe storage order of the tensor. 0 is row major, and 1 is column major.Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.The output of each pooling window is maximum number of elements exclude pad.Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used {name} consumes an input tensor X and applies {opName} pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. {opName} pooling consisting of computing the {opName} on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following: ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled ``` * pad_shape[i] is sum of pads along axis i ``` `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - {kernelSpatialShape} + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + {kernelSpatialShape} - input_spatial_shape[i] ``` {additionalDescription} Stride along each spatial axis.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].{kernelSpatialShape}Wether to use ceil or floor (default) to compute the output shape.((kernel_spatial_shape[i] - 1) * dilations[i] + 1)kernel_spatial_shape[i]Attribute pads has incorrect sizeSecond input tensor has wrong dimensionAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specified{additionalDescription}The size of the kernel along each axis. {name} consumes an input tensor X and applies {opName} pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. {opName} pooling consisting of computing the {opName} on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following: ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1) * pad_shape[i] is sum of pads along axis i ``` `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i] ``` {additionalDescription} {opName}The shape of the convolution kernel. If not present, should be inferred from input W.dilation value along each spatial axis of the filter.Optional 1D bias to be added to the convolution, has size of M.Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.weight and zero_point pair is expected to have same type.number of groups input channels and output channels are divided into.a filterRoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...].RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. The convolution operator consumes an input tensor and {filter_desc}, and computes the output.{filter_desc}Input tensor must have at least 2 dimensionsRoIs tensor must have 2 dimensionsInput data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. ROI {name} pool consumes an input tensor X and region of interests (RoIs) to apply {name} pooling across each RoI, to produce output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).ROI pool output shape (height, width).Attribute pooled_shape has incorrect lengthAttribute pooled_shape must be specifiedInput data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).'output_shape' must have same number of elements as the shape of input tensor X. {name} consumes an input tensor X and applies Lp pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. Lp pooling consisting of computing the Lp norm on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing.p value of the Lp norm used to pool over the input data.The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored.Output data tensor that contains the result of the unpooling.The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the weight shape will be (C x M/group x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, pad lengths and group count. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array) The convolution transpose operator consumes an input tensor and {filter_desc}, and computes the output. If the pads parameter is provided the shape of the output is calculated via the following equation: output_shape[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - pads[start_i] - pads[end_i] output_shape can also be explicitly specified in which case pads values are auto generated using these equations: total_padding[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - output_shape[i] If (auto_pads != SAME_UPPER): pads[start_i] = total_padding[i]/2; pads[end_i] = total_padding[i] - (total_padding[i]/2) Else: pads[start_i] = total_padding[i] - (total_padding[i]/2); pads[end_i] = (total_padding[i]/2). Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn) Global{op_type} consumes an input tensor X and applies {op} pooling across the values in the same channel. This is equivalent to {op_type} with kernel size equal to the spatial dimension of input tensor.{op_type}The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate padsThe zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.Scale tensor for input 'w'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M)Constrain input x and its zero point data type to 8-bit integer tensor.Zero point tensor for input 'x'. It's optional and default value is 0. It's a scalar, which means a per-tensor/layer quantization.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain output y data type to 32-bit integer tensor.Constrain bias type to 32-bit integer tensor.The shape of the convolution kernel. If not present, should be inferred from input 'w'.Constrain filter type to 8-bit integer tensor.Constrain output type to 8-bit integer tensor.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.number of groups input channels and output channels are divided into. default is 1.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.wScale tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).Scale tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.T4w_scalew_zero_pointThe axis on which to apply normalization, -1 mean last axis.The order of the normalization, only 1 or 2 are supported.Input matrixMatrix after normalizationratioThe input data as Tensor. Dropout takes one input floating tensor and produces two tensor outputs, output (floating tensor) and mask (`Tensor`). Depending on whether it is in test mode or not, the output Y will either be a random dropout, or a simple copy of the input. Note that our implementation of Dropout does scaling in the training phase, so during testing nothing needs to be done. The ratio of random dropoutsaved_varsaved_meanSaved variance used during training to speed up gradient computation.The output tensor of the same shape as input.The input 1-dimensional scale tensor of size C.The input 1-dimensional bias tensor of size C.running (training) or estimated (testing) mean tensor of shape (C).running (training) or estimated (testing) variance tensor of shape (C).Scale tensor of shape (C).Bias tensor of shape (C).The running variance after the BatchNormalization operator.Saved mean used during training to speed up gradient computation.The output tensor of the same shape as XThe running mean after the BatchNormalization operator.lp pool Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C*D1*D2 ..*Dn) before a BatchNormalization Op. {op}Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.momentumInput data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1The epsilon value to use to avoid division by zero.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).Maximum n-gram length. If this value is 3, 3-grams will be used to generate the output.Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.Input is ether string UTF-8 or int32/int641-D tensor of floatsList of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.Constrain input and output types to float tensors.The exponent.Output tensor, which has the shape and type as input tensorInput for n-gram extractionNgram resultsngram_indexes must be non-empty with no negative valuesInput tensor must have rank 1 or 2A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.Constrain input and output to all tensor types.Invalid value(A tensor of rank >= axis.The number of channels to sum overScaling parameter.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). Constrain output mask types to boolean tensors.The output.The output mask.) for attribute 'axis'The lambd value for the Shrink formulation. Default is 0.5.The bias value added to output. Default is 0.E_XsquaredVarianceEX_squaredX_squaredProcessed_STDSTDX_varianceEnvironment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation.Boolean. Whether the identification of stop words in X is case-sensitive. Default is falseList of stop words. If not set, no word would be removed from X.EpsilonX_RMA list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.ExponentThe weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.UTF-8 Normalized stringsstring enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"Input shape must have either [C] or [1,C] dimensions where C > 0UTF-8 strings to normalizeOutput tensor, same shape as input tensor T1.2D input tensor to copy shape, and optionally, type information from.(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.Lower boundary of the output values.Constrain output types. Strings and complex are not supported.Constrain input types. Strings and complex are not supported.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 float32.1D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar.(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32Invalid shape value: Input tensor must be 2-dimensionalConstrain output types to be numerics.Constrain input types.The value for the elements of the output tensor.Attribute 'value' of Constant node must exist with 'Tensor' data.Shape input must be a one-dimensional tensor.C:\agent\_work\18\s\cmake\external\onnx\onnx\defs\generator\defs.ccOutput tensor containing the same value of the provided tensor.Attribute expected to have a one-dim tensorAttribute expected to have tensor type(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Number of times to sample.Input tensor must have rank 2Output type must be int32 or int64Constrain output types to integral tensors.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.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 of random values drawn from normal distributionThe data type for the elements of the output tensor. Default is TensorProto::FLOAT.The standard deviation of the normal distribution.Input tensor to copy shape and optionally type information from.(Optional) The data type for the elements of the output tensor, if not specified, we will usethe data type of the input tensor.The shape of the output tensor.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.(Optional) Seed to the random generator, if not specified we will auto generate one.Upper boundary of the output values.The mean of the normal distribution.Constrain output types to float tensors.Output tensor of random values drawn from uniform distributionconsumed_inputslegacy optimization attribute.New shapetilesNumber of repeated copies to make of the input tensor.Which axis to split onoutputs...Optional list of output lengths (see also arg 'split')One float, indicates the value to be filled, default is 0paddingsList 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`.Which axis to concat on. Default value is 1.C:\agent\_work\18\s\cmake\external\onnx\onnx\defs\tensor\old.ccConstrain output types. Casting to strings and complex are not supported.Constrain input types. Casting from strings and complex are not supported.Starting indices of corresponding axis in `axes`Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Attribute axes has incorrect lengthIncorrect or missing attribute value for starts and endsEnding indices (exclusive) of corresponding axis in axes`Ranks inferred () is not equal to the existing rank value (4-D tensor after resizing, [N,C,H,W]4-D tensor, [N,C,H,W]Attribute 'scales' is required.Attribute 'scales' must have floats type.Number of elements of attribute 'scales' must be same as rank of input 'X'The scale along width dimension. It takes value greater than or equal to 1.Constrain tiles and axis's type to int64 tensors.Output tensor of same shape and type as input.Axis along which to repeat.Two interpolation modes: nearest(default), bilinearheight_scaleThe scale along height dimension. It takes value greater than or equal to 1.width_scaleOptional tensor specifying lengths of the sequences in a batch. If this input is not specified, all sequences are assumed to be of the maximum sequence length (the dimension of the sequence axis of the scan_input tensors).C:\agent\_work\18\s\cmake\external\onnx\onnx\defs\controlflow\old.ccList of tensors for MinOutput tensor. Same dimension as inputs.List of tensors for Mean.List of tensors for Sum.Slope tensor. If `Slope` is of size 1, the value is sharedacross different channels1-D input tensorCoefficient of ELU default to 1.0.List of tensors for Max.Value of beta default to 0.5Value of alpha default to 0.2Input tensor of any shape, base of the exponent.C:\agent\_work\18\s\cmake\external\onnx\onnx\defs\math\old.ccResult, has same dimensions and type as ASecond operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.Coefficient of SELU default to 1.0507.Coefficient of SELU default to 1.6732.Coefficient of leakage default to 0.01.Input tensor of any shape broadcastable to X shape, the exponent component. Performs element-wise binary {name} (with limited broadcast support). {broadcast_doc} If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. First operand, should share the type with the second operand.If set, defines the broadcast dimensions. See doc for details.broadcastPass 1 to enable broadcastingNumber of top elements to retrieveInvalid value for attribute kWhether C should be broadcastedInput tensor C, can be inplace.Input tensor BInput tensor AInput tensor CScalar multiplier for input tensor C, the default value is 1.0.Scalar multiplier for the product of input tensors A * B, the default value is 1.0.Number of neurons in the hidden layerFirst input tensor must have rank 3Couple the input and forget gates if 1. Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `o` - output gate `f` - forget gate `c` - cell gate `t` - time step (t-1 means previous time step) `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates `P[iof]` - P peephole weight vector for input, output, and forget gates `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates `PB[iof]` - P peephole weight vector for backward input, output, and forget gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) - Ct = ft (.) Ct-1 + it (.) ct - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) - Ht = ot (.) h(Ct) The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0 Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `z` - update gate `r` - reset gate `h` - hidden gate `t` - time step (t-1 means previous time step) `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates `Wb[zrh]` - W bias vectors for update, reset, and hidden gates `Rb[zrh]` - R bias vectors for update, reset, and hidden gates `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh): - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz) - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr) - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0 - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0 - Ht = (1 - zt) (.) ht + zt (.) Ht-1 C:\agent\_work\18\s\cmake\external\onnx\onnx\defs\rnn\defs.ccThe bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0.The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified. Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `t` - time step (t-1 means previous time step) `Wi` - W parameter weight matrix for input gate `Ri` - R recurrence weight matrix for input gate `Wbi` - W parameter bias vector for input gate `Rbi` - R parameter bias vector for input gate `WBi` - W parameter weight matrix for backward input gate `RBi` - R recurrence weight matrix for backward input gate `WBbi` - WR bias vectors for backward input gate `RBbi` - RR bias vectors for backward input gate `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Tanh): - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) Constrain seq_lens to integer tensor.Left input tensor for the logical operator.If set, defines the broadcast dimensions.Enable broadcasting Returns the tensor resulted from performing the `{name}` logical operation elementwise on the input tensors `A` and `B`. If broadcasting is enabled, the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. See the doc of `Add` for a detailed description of the broadcasting rules. Constrains input to integral tensors.Constrains input to float tensors.C:\agent\_work\18\s\cmake\external\onnx\onnx\defs\logical\old.ccRight input tensor for the logical operator.The epsilon value to use to avoid division by zero, default is 1e-5f.is_testIf set to nonzero, run spatial batch normalization in test mode, default is 0.If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.The bias as a 1-dimensional tensor of size C to be applied to the output.The scale as a 1-dimensional tensor of size C to be applied to the output.The input 4-dimensional tensor of shape NCHW.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimension are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.p value of the Lp norm used to pool over the input data, default is 2.0.Stride along each axis.Output data tensor from pooling across the input tensor. Dimensions will be N x C x 1 x 1C:\agent\_work\18\s\cmake\external\onnx\onnx\defs\nn\old.ccauto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute.Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.If spatial is true, the dimension of the running variance(training) or the estimated variance (testing) is (C). If spatial is false, the dimensions of the running variance(training) or the estimated variance (testing) are (C x D1 x ... x Dn).If spatial is true, the dimension of the running mean (training) or the estimated mean (testing) is (C). If spatial is false, the dimensions of the running mean (training) or the estimated mean (testing) are (C x D1 x ... x Dn).If spatial is true, the dimension of bias is (C). If spatial is false, the dimensions of bias are (C x D1 x ... x Dn)If spatial is true, the dimension of scale is (C). If spatial is false, the dimensions of scale are (C x D1 x ... x Dn)The output mask. If is_test is nonzero, this output is not filled.(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.(float, default 0.5) the ratio of random dropout Dropout takes one input data (Tensor) and produces two Tensor outputs, output (Tensor) and mask (Tensor). Depending on whether it is in test mode or not, the output Y will either be a random dropout, or a simple copy of the input. Note that our implementation of Dropout does scaling in the training phase, so during testing nothing needs to be done. If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch. Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) The output tensor of the same shape as X.The running mean after the BatchNormalization operator. Must be in-place with the input mean. Should not be used for testing.The output 4-dimensional tensor of the same shape as X.The running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C.The running mean (training) or the estimated mean (testing) as a 1-dimensional tensor of size C.The output 4-dimensional tensor of the same shape as input.Saved variance used during training to speed up gradient computation. Should not be used for testing.Saved mean used during training to speed up gradient computation. Should not be used for testing.The running variance after the BatchNormalization operator. Must be in-place with the input var. Should not be used for testing.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.output_sequenceThe sequence output for the hidden is optional if 0. Default 0.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.C:\agent\_work\18\s\cmake\external\onnx\onnx\defs\rnn\old.ccfowardReduced output tensor with integer data type.The axis in which to compute the arg indices. Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulted tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. The type of the output tensor is integer.L2 normsum squareC:\agent\_work\18\s\cmake\external\onnx\onnx\defs\reduction\defs.cc Computes the {name} of the input tensor's element along the provided axes. The resulted tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. The above behavior is similar to numpy, with the exception that numpy default keepdims to False instead of True.L1 normlog sum exponentlog sumproductC:\agent\_work\18\s\cmake\external\onnx\onnx\defs\generator\old.ccsparse_tensoropaquemapseqbfloat16float16floattensortensor(sparse_tensor(opaque(map(seq(stringcomplex128complex64uint64boolint32int16int8doubleuint32uint16uint8int64 is required but missing.Field 'name' of value_typekey_typeelem_typetypevalue_info is required to be non-empty. ==> Context: attrUnrecognized data_type (tensor name: values of data_type '' should be stored in field ') should refer to attribute in parent node.Attribute (name: ) should not contain more than one value field.type field and data field mismatch in attribute TensorProto (tensor name: ) is 0-element but contains data!TensorProto ( tensor name: ) is stored externally but doesn't have a location.' instead of 'STRING data (tensor name: ) should not be stored in raw_data field) should contain one and only one value field.uint64_datadouble_dataraw_dataint64_data) should be stored in , but it doesn't exist or is not accessible.Data of TensorProto ( tensor name: ) is stored externally and should not have data field.) to UNDEFINED is not alloweddata_typeUnrecognized type value case (value_info name: ): string_dataint32_datafloat_datasetting data_type field (tensor name: ' has been used as output names multiple times. in initializer but not in graph inputGraph must be in single static assignment (SSA) form, however '' has been used as graph input names multiple times.graphBad node spec: Nodes in a graph must be topologically sorted, however input '' of node: is not output of any previous nodes.No opset import for domain 'Warning: was a removed experimental ops. In the future, we may directly Op registered for is deprecated in domain_version of No Op registered for with domain_version of , type: ) has zero input and zero output.op_typenodereject this operator. Please update your model as soon as possible.ConstantFillNodeProto (name: inputs but Graph has is tensor type, but provided type is Graph input #no name, name:(op_type: were provided: (Inferred shape and existing shape differ in dimension inferred=type mismatch. existing= does not contain a graph.) vs (Inferred elem type differs from existing elem type: (Inferred shape and existing shape differ in rank: ( is out of boundsinput output GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance.Number of elements of input 'scales' must be same as rank of input 'X'Input 'scales' must have float element type.) is not equal to the existing dim value (Dimension value inferred (onnx.TypeProto.SparseTensoronnx.TypeProtoonnx.OperatorSetIdProtoonnx.FunctionProtoonnx.TypeProto.Sequenceonnx.TypeProto.Maponnx.TypeProto.Opaqueonnx.TensorShapeProtoonnx.TypeProto.Tensoronnx.TensorProto.Segmentonnx.TensorProtoonnx.TensorShapeProto.Dimensiononnx.ModelProtoonnx.StringStringEntryProtoonnx.TensorAnnotationonnx.GraphProtoonnx.ValueInfoProtoonnx.NodeProtoonnx.AttributeProto%I64u%uFATAL[libprotobuf %s %s:%d] %s %dINFOWARNINGERROR5555CHECK failed: (last_returned_size_) > (0): BackUp() can only be called after a successful Next().CHECK failed: (count) <= (last_returned_size_): CHECK failed: (count) >= (0): C:\agent\_work\18\s\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream_impl_lite.cc Parameter to BackUp() can't be negative.CHECK failed: (backup_bytes_) == (0): CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: BackUp() can only be called after Next().CHECK failed: (count) <= (buffer_used_): Can't back up over more bytes than were returned by the last call to Next().CHECK failed: target_ != NULL: Cannot allocate buffer larger than kint32max for StringOutputStream.CHECK failed: (count) <= (target_->size()): Exceeded maximum protobuf size of 2GB: CHECK failed: !coded_out.HadError(): " because it is missing required fields: parseByte size calculation and serialization were inconsistent. This may indicate a bug in protocol buffers or it may be caused by concurrent modification of This shouldn't be called if all the sizes are equal.Can't message of type "C:\agent\_work\18\s\cmake\external\protobuf\src\google\protobuf\message_lite.ccCHECK failed: (byte_size_before_serialization) == (byte_size_after_serialization): was modified concurrently during serialization.CHECK failed: (bytes_produced_by_serialization) == (byte_size_before_serialization): (cannot determine missing fields for lite message) with type Not implemented field number CHECK failed: (scc->visit_status.load(std::memory_order_relaxed)) == (SCCInfoBase::kRunning): C:\agent\_work\18\s\cmake\external\protobuf\src\google\protobuf\generated_message_util.ccC:\agent\_work\18\s\cmake\external\protobuf\src\google\protobuf\arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - kBlockHeaderSize): C:\agent\_work\18\s\cmake\external\protobuf\src\google\protobuf\repeated_field.ccCHECK failed: (new_size) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(old_rep->elements[0])): C:\agent\_work\18\s\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug.A protocol message was rejected because it was too big (more than bytes). To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h.C:\agent\_work\18\s\cmake\external\protobuf\src\google\protobuf\io\coded_stream.ccCHECK failed: (buffer_size) >= (0): CHECK failed: (value.size()) <= (kint32max): C:\agent\_work\18\s\cmake\external\protobuf\src\google\protobuf\wire_format_lite.ccendpos: text size: RE2: invalid startpos, endpos pair. [startpos: pattern too large - reverse compile failedError reverse compiling 'Invalid RE2: Error parsing '': pattern too large - compile failedError compiling '...C:\agent\_work\18\s\cmake\external\re2\re2\re2.ccUnknown encoding SearchBitState inconsistencySearchOnePass inconsistencySearchNFA inconsistencybytemap range list count SearchDFA inconsistencyDFA out of memory: size invalid UTF-8invalid perl operatorNumCapturesWalker::ShortVisit calledinvalid named capture groupno argument for repetition operatortrailing \bad repetition operatorinvalid repetition sizeinvalid character class rangeinvalid character classmissing )missing ]no errorUnexpected op in Regexp::Equal: invalid escape sequenceunexpected errorRegexp not destroyed.Bad reference count C:\agent\_work\18\s\cmake\external\re2\re2\regexp.ccC:\agent\_work\18\s\cmake\external\re2\re2/walker-inl.hStack not empty.Walk NULL555p5P5555585 55x55Missing case in Compiler: C:\agent\_work\18\s\cmake\external\re2\re2\compile.ccshould never happenNo ranges in char classCompiler::Copy called!Bad hex digit Bad call to ParseState::ParsePerlFlagsAnyConcat of RepetitionWalker::ShortVisit calledRE2: unexpected op: unknown round: C:\agent\_work\18\s\cmake\external\re2\re2\parse.ccAddFoldedRange recurses too much. in ComputeFirstByteUnexpected opcode in short circuit: Bad args: nsubmatch=context does not contain textUnhandled C:\agent\_work\18\s\cmake\external\re2\re2\nfa.ccunhandled in step in AddToThreadq|RunStateOnByteUnlocked failed after ResetFailed to analyze start state.RunStateOnByteUnlocked failed after ResetCacheUnexpected special state in RunStateOnByteNULL state in RunStateOnByteStateSaver failed to restore state.C:\agent\_work\18\s\cmake\external\re2\re2\dfa.ccunhandled opcode: DeadState in RunStateOnByte arg C:\agent\_work\18\s\cmake\external\re2\re2\bitstate.ccBad arg in kInstAltMatch: Unexpected opcode: job_.size() = GrowStack() failed: njob_ = Bad arg in kInstCapture: C:\agent\_work\18\s\cmake\external\re2\re2\onepass.ccCannot use SearchOnePass for unanchored matches.C:\agent\_work\18\s\cmake\external\re2\re2\prog.ccUnexpected opcode in IsMatch: DoCoalesce failed: r2->op() is DoCoalesce failed: r1->op() is Simplify case not handled: SimplifyWalker::ShortVisit calledC:\agent\_work\18\s\cmake\external\re2\re2\simplify.ccCoalesceWalker::ShortVisit calledCase not handled in ComputeSimple: Malformed repeat p5L5(5L5$55055,55555X55X555555H55H55555x55555D5H5D5855h55X5P55P555(555h55h55x55x55555x555555855\d[:word:][:^upper:][:xdigit:][:^word:][:space:][:^punct:][:upper:][:^space:][:print:][:^lower:][:punct:][:^print:][:graph:][:^digit:][:lower:][:^graph:][:cntrl:][:^blank:][:digit:][:^cntrl:][:ascii:][:^alpha:][:blank:][:^ascii:][:alnum:]\W[:alpha:][:^alnum:]\s\D\w\S[:^xdigit:]0909az09AZaz!/:@[`{~ !~09AZ__az ~09AZ__azAZaz09AFafAZd6PU6H6pY606(Y6\6PB6`'6#P666`z66i6$6h6@?66P?660V66Pa660z66]66pz66 z66a660a6 4J6B6x6&6p6~66`+66[66pe6 z66 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