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chema19TypeConstraintParamESaIS2_EED2Ev_ZNSt6vectorIN10onnx_torch8OpSchema19TypeConstraintParamESaIS2_EED1Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED2Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED1Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED0Ev_ZN10onnx_torch10MakeStringIJA22_cA8_cmA9_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA22_cA8_cmA49_cNS_9TypeProto9ValueCaseEEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch14getOutputShapeERNS_16InferenceContextEmNS_9TypeProto9ValueCaseE_ZTIN10onnx_torch14InferenceErrorE_ZN10onnx_torch10MakeStringIJA23_cA111_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA41_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA50_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA40_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA42_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA52_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA92_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_S6_ISt13unordered_setIPS7_St4hashIS9_ESt8equal_toIS9_ESaIS9_EES5_EESaISH_ENSt8__detail10_Select1stESC_IS5_ESA_IS5_ENSJ_18_Mod_range_hashingENSJ_20_Default_ranged_hashENSJ_20_Prime_rehash_policyENSJ_17_Hashtable_traitsILb1ELb0ELb1EEEE5clearEv_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_S6_ISt13unordered_setIPS7_St4hashIS9_ESt8equal_toIS9_ESaIS9_EES5_EESaISH_ENSt8__detail10_Select1stESC_IS5_ESA_IS5_ENSJ_18_Mod_range_hashingENSJ_20_Default_ranged_hashENSJ_20_Prime_rehash_policyENSJ_17_Hashtable_traitsILb1ELb0ELb1EEEED2Ev_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_S6_ISt13unordered_setIPS7_St4hashIS9_ESt8equal_toIS9_ESaIS9_EES5_EESaISH_ENSt8__detail10_Select1stESC_IS5_ESA_IS5_ENSJ_18_Mod_range_hashingENSJ_20_Default_ranged_hashENSJ_20_Prime_rehash_policyENSJ_17_Hashtable_traitsILb1ELb0ELb1EEEED1Ev_ZNSt13_Bvector_baseISaIbEE13_M_deallocateEv_ZN10onnx_torch8OpSchema15FormalParameterD2Ev_ZN10onnx_torch8OpSchema15FormalParameterD1Ev_ZN10onnx_torch8OpSchemaC2Ev_ZN10onnx_torch8OpSchemaC1Ev_ZNK10onnx_torch14InferenceError4whatEv_ZN10onnx_torch8OpSchemaC2ERKS0__ZN10onnx_torch8OpSchemaC1ERKS0__ZN10onnx_torch8OpSchemaD2Ev_ZN10onnx_torch8OpSchemaD1Ev_ZN10onnx_torch11GetOpSchemaINS_19Constant_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Constant_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Constant_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19Constant_Onnx_ver11EEENS_8OpSchemaEv_ZTSN10onnx_torch14InferenceErrorE_ZTSN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_E_ZTSN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_E_ZN10onnx_torch35dbg_count_check_Constant_Onnx_ver11E_ZN10onnx_torch34dbg_count_check_Constant_Onnx_ver9E_ZN10onnx_torch34dbg_count_check_Constant_Onnx_ver1E_ZN10onnx_torch35dbg_count_check_Constant_Onnx_ver12E_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EE9_M_invokeERKSt9_Any_dataOi_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EE10_M_managerERSt9_Any_dataRKS5_St18_Manager_operation_ZTIN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_E_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EE9_M_invokeERKSt9_Any_dataOi_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EE10_M_managerERSt9_Any_dataRKS5_St18_Manager_operation_ZTIN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_E_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEPS3_E9_M_invokeERKSt9_Any_dataS2__ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EED2Ev_ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EED1Ev_ZN10onnx_torch14InferenceErrorD2Ev_ZTVN10onnx_torch14InferenceErrorE_ZN10onnx_torch14InferenceErrorD1Ev_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEPS3_E10_M_managerERSt9_Any_dataRKS6_St18_Manager_operation_ZTIPFvRN10onnx_torch16InferenceContextEEDW.ref.__gxx_personality_v0_ZN10onnx_torch14InferenceErrorD0Ev_ZN10onnx_torch8OpSchema17all_numeric_typesB5cxx11Ev_ZGVZN10onnx_torch8OpSchema17all_numeric_typesB5cxx11EvE17all_numeric_types_ZZN10onnx_torch8OpSchema17all_numeric_typesB5cxx11EvE17all_numeric_types_ZN10onnx_torch23BinaryLogicDocGeneratorEPKc_ZNSt10_HashtableIPKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_SaIS7_ENSt8__detail9_IdentityESt8equal_toIS7_ESt4hashIS7_ENS9_18_Mod_range_hashingENS9_20_Default_ranged_hashENS9_20_Prime_rehash_policyENS9_17_Hashtable_traitsILb0ELb1ELb1EEEED2Ev_ZNSt10_HashtableIPKNSt7__cxx1112basic_stringIcSt11char_tra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Ev_ZN10onnx_torch11GetOpSchemaINS_14Less_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Equal_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Equal_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Greater_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Less_Onnx_ver7EEENS_8OpSchemaEv_ZTSFvRN10onnx_torch16InferenceContextEE_ZTIFvRN10onnx_torch16InferenceContextEE_ZTSN10onnx_torch14InferenceErrorE_ZTSN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_E_ZTSN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_E_ZTSPFvRN10onnx_torch16InferenceContextEE_ZN10onnx_torch30dbg_count_check_Less_Onnx_ver7E_ZN10onnx_torch33dbg_count_check_Greater_Onnx_ver7E_ZN10onnx_torch31dbg_count_check_Equal_Onnx_ver7E_ZN10onnx_torch31dbg_count_check_Equal_Onnx_ver1E_ZN10onnx_torch30dbg_count_check_Less_Onnx_ver1E_ZN10onnx_torch33dbg_count_check_Greater_Onnx_ver1E_ZN10onnx_torch29dbg_count_check_Xor_Onnx_ver1E_ZN10onnx_torch28dbg_count_check_Or_Onnx_ver1E_ZN10onnx_torch29dbg_count_check_And_Onnx_ver1E_ZN10onnx_torch32dbg_count_check_Equal_Onnx_ver11E_ZN10onnx_torch30dbg_count_check_Less_Onnx_ver9E_ZN10onnx_torch33dbg_count_check_Greater_Onnx_ver9E_ZNK10onnx_torch28FunctionBodyBuildContextImpl12getInputTypeEi_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EE9_M_invokeERKSt9_Any_dataOi_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EE10_M_managerERSt9_Any_dataRKS5_St18_Manager_operation_ZTIN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_E_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EE9_M_invokeERKSt9_Any_dataOi_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EE10_M_managerERSt9_Any_dataRKS5_St18_Manager_operation_ZTIN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_E_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEPS3_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFbRKN10onnx_torch24FunctionBodyBuildContextERKNS0_8OpSchemaERNS0_13FunctionProtoEEPS9_E9_M_invokeERKSt9_Any_dataS3_S6_S8__ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EED2Ev_ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EED1Ev_ZN10onnx_torch14InferenceErrorD2Ev_ZTVN10onnx_torch14InferenceErrorE_ZN10onnx_torch14InferenceErrorD1Ev_ZNK10onnx_torch28FunctionBodyBuildContextImpl8hasInputEi_ZNK10onnx_torch28FunctionBodyBuildContextImpl9hasOutputEi_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEPS3_E10_M_managerERSt9_Any_dataRKS6_St18_Manager_operation_ZTIPFvRN10onnx_torch16InferenceContextEE_ZNSt17_Function_handlerIFbRKN10onnx_torch24FunctionBodyBuildContextERKNS0_8OpSchemaERNS0_13FunctionProtoEEPS9_E10_M_managerERSt9_Any_dataRKSC_St18_Manager_operation_ZTIPFbRKN10onnx_torch24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEE_ZNK10onnx_torch28FunctionBodyBuildContextImpl12getAttributeERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDW.ref.__gxx_personality_v0_ZN10onnx_torch14InferenceErrorD0Ev_ZN10onnx_torch8OpSchema6SetDocEPKc_ZN10onnx_torch8OpSchema29all_numeric_types_with_bfloatB5cxx11Ev_ZGVZN10onnx_torch8OpSchema29all_numeric_types_with_bfloatB5cxx11EvE29all_numeric_types_with_bfloat_ZZN10onnx_torch8OpSchema29all_numeric_types_with_bfloatB5cxx11EvE29all_numeric_types_with_bfloat_ZN10onnx_torch26GenerateBroadcastingDocUniB5cxx11EPKcS1__ZN10onnx_torch18FunctionBodyHelper7NodeDefC2ESt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS8_EES8_SA_S2_INS0_21AttributeProtoWrapperESaISB_EES8__ZN10onnx_torch18FunctionBodyHelper7NodeDefC1ESt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS8_EES8_SA_S2_INS0_21AttributeProtoWrapperESaISB_EES8__ZN10onnx_torch16MathDocGeneratorEPKc_ZN10onnx_torch25SoftmaxFamilyDocGeneratorEPKcS1_S1__ZN10onnx_torch30ElementwiseMultiOpDocGeneratorEPKc_ZN10onnx_torch13hasInputShapeINS_16InferenceContextEEEbRT_m_ZNSt10_HashtableIPKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_SaIS7_ENSt8__detail9_IdentityESt8equal_toIS7_ESt4hashIS7_ENS9_18_Mod_range_hashingENS9_20_Default_ranged_hashENS9_20_Prime_rehash_policyENS9_17_Hashtable_traitsILb0ELb1ELb1EEEED2Ev_ZNSt10_HashtableIPKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_SaIS7_ENSt8__detail9_IdentityESt8equal_toIS7_ESt4hashIS7_ENS9_18_Mod_range_hashingENS9_20_Default_ranged_hashENS9_20_Prime_rehash_policyENS9_17_Hashtable_traitsILb0ELb1ELb1EEEED1Ev_ZNSt6vectorIN10onnx_torch8OpSchema15FormalParameterESaIS2_EED2Ev_ZNSt6vectorIN10onnx_torch8OpSchema15FormalParameterESaIS2_EED1Ev_ZNSt6vectorIN10onnx_torch8OpSchema19TypeConstraintParamESaIS2_EED2Ev_ZNSt6vectorIN10onnx_torch8OpSchema19TypeConstraintParamESaIS2_EED1Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED2Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED1Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED0Ev_ZN10onnx_torch10MakeStringIJA22_cA56_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch13getInputS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deDefESaIS2_EE17_M_realloc_insertIJS2_EEEvN9__gnu_cxx17__normal_iteratorIPS2_S4_EEDpOT__ZNSt6vectorIN10onnx_torch18FunctionBodyHelper7NodeDefESaIS2_EE12emplace_backIJS2_EEEvDpOT__ZN10onnx_torch8OpSchemaC2Ev_ZN10onnx_torch8OpSchemaC1Ev_ZN10onnx_torch20MathOpDataPropagatorERNS_22DataPropagationContextENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN10onnx_torch8OpSchemaC2ERKS0__ZN10onnx_torch8OpSchemaC1ERKS0__ZN10onnx_torch8OpSchemaD2Ev_ZN10onnx_torch8OpSchemaD1Ev_ZN10onnx_torch11GetOpSchemaINS_14Add_Onnx_ver14EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Sub_Onnx_ver14EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Mod_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Mul_Onnx_ver14EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Div_Onnx_ver14EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Neg_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Abs_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_21Reciprocal_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Floor_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Ceil_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Sqrt_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Relu_Onnx_ver14EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19LeakyRelu_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26ThresholdedRelu_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Selu_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Elu_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Celu_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch37BuildContextDependentFunctionBodyCeluERKNS_24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoE_ZN10onnx_torch11GetOpSchemaINS_14Exp_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Log_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Tanh_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Pow_Onnx_ver15EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15PRelu_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Sigmoid_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_21HardSigmoid_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20HardSwish_Onnx_ver14EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Max_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Min_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Sum_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Mean_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Clip_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Softmax_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_21LogSoftmax_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Hardmax_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Softsign_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Softplus_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Gemm_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17MatMul_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15TopK_Onnx_ver11EEENS_8OpSchemaEv_ZGVZN10onnx_torch8OpSchema17all_numeric_typesB5cxx11EvE17all_numeric_types_ZZN10onnx_torch8OpSchema17all_numeric_typesB5cxx11EvE17all_numeric_types_ZN10onnx_torch11GetOpSchemaINS_13Sin_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Cos_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Tan_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Asin_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Acos_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Atan_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Expand_Onnx_ver13EEENS_8OpSchemaEv_ZGVZN10onnx_torch8OpSchema28all_tensor_types_with_bfloatB5cxx11EvE28all_tensor_types_with_bfloat_ZZN10onnx_torch8OpSchema28all_tensor_types_with_bfloatB5cxx11EvE28all_tensor_types_with_bfloat_ZN10onnx_torch11GetOpSchemaINS_14Sinh_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Cosh_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Asinh_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Acosh_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Atanh_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Sign_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Erf_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_24QLinearMatMul_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_24MatMulInteger_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17CumSum_Onnx_ver14EEENS_8OpSchemaEv_ZGVZN10onnx_torch8OpSchema44numeric_types_for_math_reduction_with_bfloatB5cxx11EvE44numeric_types_for_math_reduction_with_bfloat_ZZN10onnx_torch8OpSchema44numeric_types_for_math_reduction_with_bfloatB5cxx11EvE44numeric_types_for_math_reduction_with_bfloat_ZN10onnx_torch11GetOpSchemaINS_16Round_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Det_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch33BuildContextDependentFunctionBodyERKNS_24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoE_ZN10onnx_torch11GetOpSchemaINS_17Einsum_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch17reduction_doc_sceE_ZN10onnx_torch36BuildContextDependentFunctionBodySCEERKNS_24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoE_ZN10onnx_torch25ToDimensionOneFloatTensorEf_ZN10onnx_torch20ToDimensionOneTensorEi_ZN10onnx_torch25ToDimensionOneInt64TensorEl_ZN10onnx_torch25ToDimensionOneInt64TensorESt6vectorIlSaIlEE_ZNSt6vectorIN10onnx_torch9NodeProtoESaIS1_EED2Ev_ZNSt6vectorIN10onnx_torch9NodeProtoESaIS1_EED1Ev_ZN10onnx_torch20matmulShapeInferenceERNS_16InferenceContextEii_ZTSFvRN10onnx_torch16InferenceContextEE_ZTIFvRN10onnx_torch16InferenceContextEE_ZTSFbRKN10onnx_torch24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEE_ZTIFbRKN10onnx_torch24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEE_ZTSN10onnx_torch14InferenceErrorE_ZTSN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_E_ZTSN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_E_ZTSPFvRN10onnx_torch16InferenceContextEE_ZTSPFbRKN10onnx_torch24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEE_ZN10onnx_torch50dbg_count_check_SoftmaxCrossEntropyLoss_Onnx_ver13E_ZN10onnx_torch33dbg_count_check_Einsum_Onnx_ver12E_ZN10onnx_torch52dbg_count_check_NegativeLogLikelihoodLoss_Onnx_ver13E_ZN10onnx_torch30dbg_count_check_Det_Onnx_ver11E_ZN10onnx_torch32dbg_count_check_Round_Onnx_ver11E_ZN10onnx_torch33dbg_count_check_CumSum_Onnx_ver14E_ZN10onnx_torch40dbg_count_check_MatMulInteger_Onnx_ver10E_ZN10onnx_torch40dbg_count_check_QLinearMatMul_Onnx_ver10E_ZN10onnx_torch30dbg_count_check_Erf_Onnx_ver13E_ZN10onnx_torch31dbg_count_check_Sign_Onnx_ver13E_ZN10onnx_torch31dbg_count_check_Atanh_Onnx_ver9E_ZN10onnx_torch31dbg_count_check_Acosh_Onnx_ver9E_ZN10onnx_torch31dbg_count_check_Asinh_Onnx_ver9E_ZN10onnx_torch30dbg_count_check_Cosh_Onnx_ver9E_ZN10onnx_torch30dbg_count_check_Sinh_Onnx_ver9E_ZN10onnx_torch33dbg_count_check_Expand_Onnx_ver13E_ZN10onnx_torch30dbg_count_check_Atan_Onnx_ver7E_ZN10onnx_torch30dbg_count_check_Acos_Onnx_ver7E_ZN10onnx_torch30dbg_count_check_Asin_Onnx_ver7E_ZN10onnx_torch29dbg_count_check_Tan_Onnx_ver7E_ZN10onnx_torch29dbg_count_check_Cos_Onnx_ver7E_ZN10onnx_torch29dbg_count_check_Sin_Onnx_ver7E_ZN10onnx_torch31dbg_count_check_TopK_Onnx_ver11E_ZN10onnx_torch33dbg_count_check_MatMul_Onnx_ver13E_ZN10onnx_torch31dbg_count_check_Gemm_Onnx_ver13E_ZN10onnx_torch34dbg_count_check_Softplus_Onnx_ver1E_ZN10onnx_torch34dbg_count_check_Softsign_Onnx_ver1E_ZN10onnx_torch34dbg_count_check_Hardmax_Onnx_ver13E_ZN10onnx_torch37dbg_count_check_LogSoftmax_Onnx_ver13E_ZN10onnx_torch34dbg_count_check_Softmax_Onnx_ver13E_ZN10onnx_torch31dbg_count_check_Clip_Onnx_ver13E_ZN10onnx_torch31dbg_count_check_Mean_Onnx_ver13E_ZN10onnx_torch30dbg_count_check_Sum_Onnx_ver13E_ZN10onnx_torch30dbg_count_check_Min_Onnx_ver13E_ZN10onnx_torch30dbg_count_check_Max_Onnx_ver13E_ZN10onnx_torch36dbg_count_check_HardSwish_Onnx_ver14E_ZN10onnx_torch37dbg_count_check_HardSigmoid_Onnx_ver6E_ZN10onnx_torch34dbg_count_check_Sigmoid_Onnx_ver13E_ZN10onnx_torch31dbg_count_check_PRelu_Onnx_ver9E_ZN10onnx_torch30dbg_count_check_Pow_Onnx_ver15E_ZN10onnx_torch31dbg_count_check_Tanh_Onnx_ver13E_ZN10onnx_torch30dbg_count_check_Log_Onnx_ver13E_ZN10onnx_torch30dbg_count_check_Exp_Onnx_ver13E_ZN10onnx_torch31dbg_count_check_Celu_Onnx_ver12E_ZN10onnx_torch29dbg_count_check_Elu_Onnx_ver6E_ZN10onnx_torch30dbg_count_check_Selu_Onnx_ver6E_ZN10onnx_torch42dbg_count_check_ThresholdedRelu_Onnx_ver10E_ZN10onnx_torch35dbg_count_check_LeakyRelu_Onnx_ver6E_ZN10onnx_torch31dbg_count_check_Relu_Onnx_ver14E_ZN10onnx_torch31dbg_count_check_Sqrt_Onnx_ver13E_ZN10onnx_torch31dbg_count_check_Ceil_Onnx_ver13E_ZN10onnx_torch32dbg_count_check_Floor_Onnx_ver13E_ZN10onnx_torch37dbg_count_check_Reciprocal_Onnx_ver13E_ZN10onnx_torch30dbg_count_check_Abs_Onnx_ver13E_ZN10onnx_torch30dbg_count_check_Neg_Onnx_ver13E_ZN10onnx_torch30dbg_count_check_Div_Onnx_ver14E_ZN10onnx_torch30dbg_count_check_Mul_Onnx_ver14E_ZN10onnx_torch30dbg_count_check_Mod_Onnx_ver13E_ZN10onnx_torch30dbg_count_check_Sub_Onnx_ver14E_ZN10onnx_torch30dbg_count_check_Add_Onnx_ver14E_ZNK10onnx_torch28FunctionBodyBuildContextImpl12getInputTypeEi_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EE9_M_invokeERKSt9_Any_dataOi_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EE10_M_managerERSt9_Any_dataRKS5_St18_Manager_operation_ZTIN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_E_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EE9_M_invokeERKSt9_Any_dataOi_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EE10_M_managerERSt9_Any_dataRKS5_St18_Manager_operation_ZTIN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_E_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEPS3_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFbRKN10onnx_torch24FunctionBodyBuildContextERKNS0_8OpSchemaERNS0_13FunctionProtoEEPS9_E9_M_invokeERKSt9_Any_dataS3_S6_S8__ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EED2Ev_ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EED1Ev_ZN10onnx_torch14InferenceErrorD2Ev_ZTVN10onnx_torch14InferenceErrorE_ZN10onnx_torch14InferenceErrorD1Ev_ZNK10onnx_torch28FunctionBodyBuildContextImpl8hasInputEi_ZNK10onnx_torch28FunctionBodyBuildContextImpl9hasOutputEi_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEPS3_E10_M_managerERSt9_Any_dataRKS6_St18_Manager_operation_ZTIPFvRN10onnx_torch16InferenceContextEE_ZNSt17_Function_handlerIFbRKN10onnx_torch24FunctionBodyBuildContextERKNS0_8OpSchemaERNS0_13FunctionProtoEEPS9_E10_M_managerERSt9_Any_dataRKSC_St18_Manager_operation_ZTIPFbRKN10onnx_torch24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEEDW.ref.__gxx_personality_v0_ZNK10onnx_torch28FunctionBodyBuildContextImpl12getAttributeERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN10onnx_torch14InferenceErrorD0Ev_ZN10onnx_torch17kBroadcastDoc_oldE_ZN10onnx_torch35propagateShapeAndTypeFromFirstInputERNS_16InferenceContextE_ZN10onnx_torch9TypeProto29_internal_mutable_tensor_typeEv_ZN10onnx_torch8OpSchema6SetDocEPKc_ZN10onnx_torch8OpSchema17all_numeric_typesB5cxx11Ev_ZGVZN10onnx_torch8OpSchema17all_numeric_typesB5cxx11EvE17all_numeric_types_ZZN10onnx_torch8OpSchema17all_numeric_typesB5cxx11EvE17all_numeric_types_ZN10onnx_torch26GenerateBroadcastingDocUniB5cxx11EPKcS1__ZN10onnx_torch18FunctionBodyHelper7NodeDefC2ESt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS8_EES8_SA_S2_INS0_21AttributeProtoWrapperESaISB_EES8__ZN10onnx_torch18FunctionBodyHelper7NodeDefC1ESt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS8_EES8_SA_S2_INS0_21AttributeProtoWrapperESaISB_EES8__ZN10onnx_torch24MathDocGenerator_opset13EPKc_ZN10onnx_torch24MathDocGenerator_opset_7EPKc_ZN10onnx_torch34SoftmaxFamilyDocGenerator_opset_11EPKcS1__ZN10onnx_torch37ElementwiseMultiOpDocGenerator_opset8EPKc_ZN10onnx_torch32SoftmaxFamilyDocGenerator_opset1EPKcS1__ZN10onnx_torch20MathDocGenerator_oldEPKc_ZN10onnx_torch27MathDocGenerator_old_opset6EPKc_ZN10onnx_torch34ElementwiseMultiOpDocGenerator_oldEPKc_ZStplIcSt11char_traitsIcESaIcEENSt7__cxx1112basic_stringIT_T0_T1_EEOS8_S9__ZN10onnx_torch13hasInputShapeINS_16InferenceContextEEEbRT_m_ZNSt10_HashtableIPKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_SaIS7_ENSt8__detail9_IdentityESt8equal_toIS7_ESt4hashIS7_ENS9_18_Mod_range_hashingENS9_20_Default_ranged_hashENS9_20_Prime_rehash_policyENS9_17_Hashtable_traitsILb0ELb1ELb1EEEED2Ev_ZNSt10_HashtableIPKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_SaIS7_ENSt8__detail9_IdentityESt8equal_toIS7_ESt4hashIS7_ENS9_18_Mod_range_hashingENS9_20_Default_ranged_hashENS9_20_Prime_rehash_policyENS9_17_Hashtable_traitsILb0ELb1ELb1EEEED1Ev_ZNSt6vectorIN10onnx_torch8OpSchema15FormalParameterESaIS2_EED2Ev_ZNSt6vectorIN10onnx_torch8OpSchema15FormalParameterESaIS2_EED1Ev_ZNSt6vectorIN10onnx_torch8OpSchema19TypeConstraintParamESaIS2_EED2Ev_ZNSt6vectorIN10onnx_torch8OpSchema19TypeConstraintParamESaIS2_EED1Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED2Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED1Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED0Ev_ZN10onnx_torch10MakeStringIJA22_cA7_cmA43_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch40propagateElemTypeFromTensorInputToOutputERNS_16InferenceContextEmm_ZTIN10onnx_torch14InferenceErrorE_ZN10onnx_torch10MakeStringIJA22_cA7_cmA32_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA22_cA32_cmA9_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch14propagateShapeEPKNS_9TypeProtoEPS0__ZN10onnx_torch10MakeStringIJA22_cA56_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch13getInputShapeERNS_16InferenceContextEm_ZN10onnx_torch14getOutputShapeERNS_16InferenceContextEmNS_9TypeProto9ValueCaseE_ZN10onnx_torch10MakeStringIJA22_cA8_cmA9_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA22_cA8_cmA49_cNS_9TypeProto9ValueCaseEEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch20updateOutputElemTypeERNS_16InferenceContextEmiNS_9TypeProto9ValueCaseE_ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EEC2ERKS7__ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EEC1ERKS7__ZGVZN10onnx_torch8OpSchema44numeric_types_for_math_reduction_with_bfloatB5cxx11EvE44numeric_types_for_math_reduction_with_bfloat_ZZN10onnx_torch8OpSchema44numeric_types_for_math_reduction_with_bfloatB5cxx11EvE44numeric_types_for_math_reduction_with_bfloat_ZGVZN10onnx_torch8OpSchema32numeric_types_for_math_reductionB5cxx11EvE32numeric_types_for_math_reduction_ZZN10onnx_torch8OpSchema32numeric_types_for_math_reductionB5cxx11EvE32numeric_types_for_math_reduction_ZN10onnx_torch10MakeStringIJA23_cA33_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA34_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA50_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA25_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA48_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA43_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cS1_EEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA45_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_S6_ISt13unordered_setIPS7_St4hashIS9_ESt8equal_toIS9_ESaIS9_EES5_EESaISH_ENSt8__detail10_Select1stESC_IS5_ESA_IS5_ENSJ_18_Mod_range_hashingENSJ_20_Default_ranged_hashENSJ_20_Prime_rehash_policyENSJ_17_Hashtable_traitsILb1ELb0ELb1EEEE5clearEv_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_S6_ISt13unordered_setIPS7_St4hashIS9_ESt8equal_toIS9_ESaIS9_EES5_EESaISH_ENSt8__detail10_Select1stESC_IS5_ESA_IS5_ENSJ_18_Mod_range_hashingENSJ_20_Default_ranged_hashENSJ_20_Prime_rehash_policyENSJ_17_Hashtable_traitsILb1ELb0ELb1EEEED2Ev_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_S6_ISt13unordered_setIPS7_St4hashIS9_ESt8equal_toIS9_ESaIS9_EES5_EESaISH_ENSt8__detail10_Select1stESC_IS5_ESA_IS5_ENSJ_18_Mod_range_hashingENSJ_20_Default_ranged_hashENSJ_20_Prime_rehash_policyENSJ_17_Hashtable_traitsILb1ELb0ELb1EEEED1Ev_ZNSt6vectorIPKN10onnx_torch16TensorShapeProtoESaIS3_EE17_M_realloc_insertIJS3_EEEvN9__gnu_cxx17__normal_iteratorIPS3_S5_EEDpOT__ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EE17_M_realloc_insertIJS5_EEEvN9__gnu_cxx17__normal_iteratorIPS5_S7_EEDpOT__ZN10onnx_torch8OpSchema15FormalParameterD2Ev_ZN10onnx_torch8OpSchema15FormalParameterD1Ev_ZN6google8protobuf8internal18GenericTypeHandlerIN10onnx_torch26TensorShapeProto_DimensionEE5MergeERKS4_PS4__ZN6google8protobuf8internal20RepeatedPtrFieldBase18MergeFromInnerLoopINS0_16RepeatedPtrFieldIN10onnx_torch26TensorShapeProto_DimensionEE11TypeHandlerEEEvPPvSA_ii_ZNK10onnx_torch14InferenceError4whatEv_ZN10onnx_torch34propagateElemTypeFromInputToOutputERNS_16InferenceContextEmm_ZN10onnx_torch39multidirectionalBroadcastShapeInferenceERKSt6vectorIPKNS_16TensorShapeProtoESaIS3_EERS1__ZN10onnx_torch18FunctionBodyHelper21AttributeProtoWrapperC2ERKNS_14AttributeProtoE_ZN10onnx_torch18FunctionBodyHelper21AttributeProtoWrapperC1ERKNS_14AttributeProtoE_ZNSt6vectorIN10onnx_torch18FunctionBodyHelper21AttributeProtoWrapperESaIS2_EED2Ev_ZNSt6vectorIN10onnx_torch18FunctionBodyHelper21AttributeProtoWrapperESaIS2_EED1Ev_ZNSt6vectorIN10onnx_torch18FunctionBodyHelper21AttributeProtoWrapperESaIS2_EE17_M_realloc_insertIJS2_EEEvN9__gnu_cxx17__normal_iteratorIPS2_S4_EEDpOT__ZNSt6vectorIN10onnx_torch18FunctionBodyHelper7NodeDefESaIS2_EED2Ev_ZNSt6vectorIN10onnx_torch18FunctionBodyHelper7NodeDefESaIS2_EED1Ev_ZN10onnx_torch18FunctionBodyHelper7NodeDefD2Ev_ZN10onnx_torch18FunctionBodyHelper7NodeDefD1Ev_ZNSt6vectorIN10onnx_torch18FunctionBodyHelper7NodeDefESaIS2_EE17_M_realloc_insertIJS2_EEEvN9__gnu_cxx17__normal_iteratorIPS2_S4_EEDpOT__ZNSt6vectorIN10onnx_torch18FunctionBodyHelper7NodeDefESaIS2_EE12emplace_backIJS2_EEEvDpOT__ZN10onnx_torch8OpSchemaC2Ev_ZN10onnx_torch8OpSchemaC1Ev_ZN10onnx_torch8OpSchemaC2ERKS0__ZN10onnx_torch8OpSchemaC1ERKS0__ZN10onnx_torch8OpSchemaD2Ev_ZN10onnx_torch8OpSchemaD1Ev_ZN10onnx_torch11GetOpSchemaINS_14Add_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Sub_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Mul_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Div_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Add_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Sub_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Mul_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Div_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Softmax_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_21LogSoftmax_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Hardmax_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Mod_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Neg_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Abs_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20Reciprocal_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Floor_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Ceil_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Sqrt_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Relu_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Relu_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Exp_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Log_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Tanh_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Pow_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Pow_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Sigmoid_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Max_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Min_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Sum_Onnx_ver8EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Mean_Onnx_ver8EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Clip_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Gemm_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16MatMul_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Expand_Onnx_ver8EEENS_8OpSchemaEv_ZGVZN10onnx_torch8OpSchema16all_tensor_typesB5cxx11EvE16all_tensor_types_ZZN10onnx_torch8OpSchema16all_tensor_typesB5cxx11EvE16all_tensor_types_ZN10onnx_torch11GetOpSchemaINS_14Sign_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Erf_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17CumSum_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch41BuildContextDependentFunctionBody_opset12ERKNS_24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoE_ZN10onnx_torch11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch25reduction_doc_sce_opset12E_ZN10onnx_torch44BuildContextDependentFunctionBodySCE_opset12ERKNS_24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoE_ZN10onnx_torch11GetOpSchemaINS_17Softmax_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20LogSoftmax_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Hardmax_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Add_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Sub_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Mul_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Div_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Add_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Sub_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Mul_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Div_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Pow_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Pow_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Neg_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Abs_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20Reciprocal_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Floor_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Ceil_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Sqrt_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Relu_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19LeakyRelu_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Selu_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Elu_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Exp_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Log_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Tanh_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15PRelu_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15PRelu_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15PRelu_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Sigmoid_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_21HardSigmoid_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Max_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Min_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Sum_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Mean_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Clip_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Gemm_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Gemm_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Gemm_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Gemm_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Max_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Min_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Sum_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Mean_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16MatMul_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14TopK_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15TopK_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Clip_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Clip_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Max_Onnx_ver8EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Min_Onnx_ver8EEENS_8OpSchemaEv_ZN10onnx_torch28matmulShapeInference_opset_9ERNS_16InferenceContextEii_ZN10onnx_torch29ToDimensionOneFloatTensor_oldEf_ZN10onnx_torch24ToDimensionOneTensor_oldEi_ZN10onnx_torch29ToDimensionOneInt64Tensor_oldEl_ZN10onnx_torch29ToDimensionOneInt64Tensor_oldESt6vectorIlSaIlEE_ZNSt6vectorIN10onnx_torch9NodeProtoESaIS1_EED2Ev_ZNSt6vectorIN10onnx_torch9NodeProtoESaIS1_EED1Ev_ZTSFbRKN10onnx_torch24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEE_ZTIFbRKN10onnx_torch24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEE_ZTSFvRN10onnx_torch16InferenceContextEE_ZTIFvRN10onnx_torch16InferenceContextEE_ZTSN10onnx_torch14InferenceErrorE_ZTSN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_E_ZTSN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_E_ZTSPFvRN10onnx_torch16InferenceContextEE_ZTSPFbRKN10onnx_torch24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEE_ZN10onnx_torch29dbg_count_check_Min_Onnx_ver8E_ZN10onnx_torch29dbg_count_check_Max_Onnx_ver8E_ZN10onnx_torch31dbg_count_check_Clip_Onnx_ver11E_ZN10onnx_torch30dbg_count_check_Clip_Onnx_ver6E_ZN10onnx_torch31dbg_count_check_TopK_Onnx_ver10E_ZN10onnx_torch30dbg_count_check_TopK_Onnx_ver1E_ZN10onnx_torch32dbg_count_check_MatMul_Onnx_ver1E_ZN10onnx_torch30dbg_count_check_Mean_Onnx_ver6E_ZN10onnx_torch29dbg_count_check_Sum_Onnx_ver6E_ZN10onnx_torch29dbg_count_check_Min_Onnx_ver6E_ZN10onnx_torch29dbg_count_check_Max_Onnx_ver6E_ZN10onnx_torch30dbg_count_check_Gemm_Onnx_ver9E_ZN10onnx_torch30dbg_count_check_Gemm_Onnx_ver7E_ZN10onnx_torch30dbg_count_check_Gemm_Onnx_ver6E_ZN10onnx_torch30dbg_count_check_Gemm_Onnx_ver1E_ZN10onnx_torch30dbg_count_check_Clip_Onnx_ver1E_ZN10onnx_torch30dbg_count_check_Mean_Onnx_ver1E_ZN10onnx_torch29dbg_count_check_Sum_Onnx_ver1E_ZN10onnx_torch29dbg_count_check_Min_Onnx_ver1E_ZN10onnx_torch29dbg_count_check_Max_Onnx_ver1E_ZN10onnx_torch37dbg_count_check_HardSigmoid_Onnx_ver1E_ZN10onnx_torch33dbg_count_check_Sigmoid_Onnx_ver1E_ZN10onnx_torch31dbg_count_check_PRelu_Onnx_ver7E_ZN10onnx_torch31dbg_count_check_PRelu_Onnx_ver6E_ZN10onnx_torch31dbg_count_check_PRelu_Onnx_ver1E_ZN10onnx_torch30dbg_count_check_Tanh_Onnx_ver1E_ZN10onnx_torch29dbg_count_check_Log_Onnx_ver1E_ZN10onnx_torch29dbg_count_check_Exp_Onnx_ver1E_ZN10onnx_torch29dbg_count_check_Elu_Onnx_ver1E_ZN10onnx_torch30dbg_count_check_Selu_Onnx_ver1E_ZN10onnx_torch35dbg_count_check_LeakyRelu_Onnx_ver1E_ZN10onnx_torch30dbg_count_check_Relu_Onnx_ver1E_ZN10onnx_torch30dbg_count_check_Sqrt_Onnx_ver1E_ZN10onnx_torch30dbg_count_check_Ceil_Onnx_ver1E_ZN10onnx_torch31dbg_count_check_Floor_Onnx_ver1E_ZN10onnx_torch36dbg_count_check_Reciprocal_Onnx_ver1E_ZN10onnx_torch29dbg_count_check_Abs_Onnx_ver1E_ZN10onnx_torch29dbg_count_check_Neg_Onnx_ver1E_ZN10onnx_torch29dbg_count_check_Pow_Onnx_ver7E_ZN10onnx_torch29dbg_count_check_Pow_Onnx_ver1E_ZN10onnx_torch29dbg_count_check_Div_Onnx_ver6E_ZN10onnx_torch29dbg_count_check_Mul_Onnx_ver6E_ZN10onnx_torch29dbg_count_check_Sub_Onnx_ver6E_ZN10onnx_torch29dbg_count_check_Add_Onnx_ver6E_ZN10onnx_torch29dbg_count_check_Div_Onnx_ver1E_ZN10onnx_torch29dbg_count_check_Mul_Onnx_ver1E_ZN10onnx_torch29dbg_count_check_Sub_Onnx_ver1E_ZN10onnx_torch29dbg_count_check_Add_Onnx_ver1E_ZN10onnx_torch33dbg_count_check_Hardmax_Onnx_ver1E_ZN10onnx_torch36dbg_count_check_LogSoftmax_Onnx_ver1E_ZN10onnx_torch33dbg_count_check_Softmax_Onnx_ver1E_ZN10onnx_torch50dbg_count_check_SoftmaxCrossEntropyLoss_Onnx_ver12E_ZN10onnx_torch52dbg_count_check_NegativeLogLikelihoodLoss_Onnx_ver12E_ZN10onnx_torch33dbg_count_check_CumSum_Onnx_ver11E_ZN10onnx_torch29dbg_count_check_Erf_Onnx_ver9E_ZN10onnx_torch30dbg_count_check_Sign_Onnx_ver9E_ZN10onnx_torch32dbg_count_check_Expand_Onnx_ver8E_ZN10onnx_torch32dbg_count_check_MatMul_Onnx_ver9E_ZN10onnx_torch31dbg_count_check_Gemm_Onnx_ver11E_ZN10onnx_torch31dbg_count_check_Clip_Onnx_ver12E_ZN10onnx_torch30dbg_count_check_Mean_Onnx_ver8E_ZN10onnx_torch29dbg_count_check_Sum_Onnx_ver8E_ZN10onnx_torch30dbg_count_check_Min_Onnx_ver12E_ZN10onnx_torch30dbg_count_check_Max_Onnx_ver12E_ZN10onnx_torch33dbg_count_check_Sigmoid_Onnx_ver6E_ZN10onnx_torch30dbg_count_check_Pow_Onnx_ver12E_ZN10onnx_torch30dbg_count_check_Pow_Onnx_ver13E_ZN10onnx_torch30dbg_count_check_Tanh_Onnx_ver6E_ZN10onnx_torch29dbg_count_check_Log_Onnx_ver6E_ZN10onnx_torch29dbg_count_check_Exp_Onnx_ver6E_ZN10onnx_torch31dbg_count_check_Relu_Onnx_ver13E_ZN10onnx_torch30dbg_count_check_Relu_Onnx_ver6E_ZN10onnx_torch30dbg_count_check_Sqrt_Onnx_ver6E_ZN10onnx_torch30dbg_count_check_Ceil_Onnx_ver6E_ZN10onnx_torch31dbg_count_check_Floor_Onnx_ver6E_ZN10onnx_torch36dbg_count_check_Reciprocal_Onnx_ver6E_ZN10onnx_torch29dbg_count_check_Abs_Onnx_ver6E_ZN10onnx_torch29dbg_count_check_Neg_Onnx_ver6E_ZN10onnx_torch30dbg_count_check_Mod_Onnx_ver10E_ZN10onnx_torch34dbg_count_check_Hardmax_Onnx_ver11E_ZN10onnx_torch37dbg_count_check_LogSoftmax_Onnx_ver11E_ZN10onnx_torch34dbg_count_check_Softmax_Onnx_ver11E_ZN10onnx_torch29dbg_count_check_Div_Onnx_ver7E_ZN10onnx_torch29dbg_count_check_Mul_Onnx_ver7E_ZN10onnx_torch29dbg_count_check_Sub_Onnx_ver7E_ZN10onnx_torch29dbg_count_check_Add_Onnx_ver7E_ZN10onnx_torch30dbg_count_check_Div_Onnx_ver13E_ZN10onnx_torch30dbg_count_check_Mul_Onnx_ver13E_ZN10onnx_torch30dbg_count_check_Sub_Onnx_ver13E_ZN10onnx_torch30dbg_count_check_Add_Onnx_ver13E_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EE9_M_invokeERKSt9_Any_dataOi_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EE10_M_managerERSt9_Any_dataRKS5_St18_Manager_operation_ZTIN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_E_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EE9_M_invokeERKSt9_Any_dataOi_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EE10_M_managerERSt9_Any_dataRKS5_St18_Manager_operation_ZTIN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_E_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEPS3_E9_M_invokeERKSt9_Any_dataS2__ZNSt6vectorIlSaIlEED2Ev_ZNSt6vectorIlSaIlEED1Ev_ZN10onnx_torch14InferenceErrorD2Ev_ZTVN10onnx_torch14InferenceErrorE_ZN10onnx_torch14InferenceErrorD1Ev_ZN6google8protobuf8internal21arena_destruct_objectINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEvPv_ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EED2Ev_ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EED1Ev_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEPS3_E10_M_managerERSt9_Any_dataRKS6_St18_Manager_operation_ZTIPFvRN10onnx_torch16InferenceContextEEDW.ref.__gxx_personality_v0_ZN10onnx_torch14InferenceErrorD0Ev_ZN10onnx_torch17conv_auto_pad_docE_ZN10onnx_torch8pads_docE_ZN10onnx_torch27conv_transpose_auto_pad_docE_ZN6google8protobuf8internal14ArenaStringPtr14CreateInstanceEPNS0_5ArenaEPKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZNK6google8protobuf11MessageLite8GetArenaEv_ZN10onnx_torch9TypeProto29_internal_mutable_tensor_typeEv_ZN10onnx_torch8OpSchema6SetDocEPKc_ZN10onnx_torch34GetSupportedDataTypesForPoolingOpsB5cxx11Eb_ZN10onnx_torch21PoolOpSchemaGeneratorEPKcS1_S1_bb_ZN10onnx_torch23LpPoolOpSchemaGeneratorEPKc_ZN10onnx_torch24RoiPoolOpSchemaGeneratorEPKc_ZN10onnx_torch21ConvOpSchemaGeneratorEPKc_ZN10onnx_torch30ConvTransposeOpSchemaGeneratorEPKc_ZN10onnx_torch30GlobalPoolingOpSchemaGeneratorEPKcS1__ZN10onnx_torch32GlobalLpPoolingOpSchemaGeneratorEPKcS1__ZN10onnx_torch13hasInputShapeINS_16InferenceContextEEEbRT_m_ZNSt10_HashtableIPKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_SaIS7_ENSt8__detail9_IdentityESt8equal_toIS7_ESt4hashIS7_ENS9_18_Mod_range_hashingENS9_20_Default_ranged_hashENS9_20_Prime_rehash_policyENS9_17_Hashtable_traitsILb0ELb1ELb1EEEED2Ev_ZNSt10_HashtableIPKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_SaIS7_ENSt8__detail9_IdentityESt8equal_toIS7_ESt4hashIS7_ENS9_18_Mod_range_hashingENS9_20_Default_ranged_hashENS9_20_Prime_rehash_policyENS9_17_Hashtable_traitsILb0ELb1ELb1EEEED1Ev_ZNSt6vectorIN10onnx_torch8OpSchema15FormalParameterESaIS2_EED2Ev_ZNSt6vectorIN10onnx_torch8OpSchema15FormalParameterESaIS2_EED1Ev_ZNSt6vectorIN10onnx_torch8OpSchema19TypeConstraintParamESaIS2_EED2Ev_ZNSt6vectorIN10onnx_torch8OpSchema19TypeConstraintParamESaIS2_EED1Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED2Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED1Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED0Ev_ZN10onnx_torch10MakeStringIJA22_cA56_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch13getInputShapeERNS_16InferenceContextEm_ZTIN10onnx_torch14InferenceErrorE_ZN10onnx_torch10MakeStringIJA22_cA7_cmA43_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch40propagateElemTypeFromTensorInputToOutputERNS_16InferenceContextEmm_ZN10onnx_torch10MakeStringIJA22_cA7_cmA32_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA22_cA32_cmA9_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch14propagateShapeEPKNS_9TypeProtoEPS0__ZN10onnx_torch10MakeStringIJA23_cA43_clA6_clEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch16checkDimEqualityEll_ZN10onnx_torch14getOutputShapeERNS_16InferenceContextEmNS_9TypeProto9ValueCaseE_ZN10onnx_torch10MakeStringIJA23_cA7_cmA24_ciA15_ciEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch14checkInputRankERNS_16InferenceContextEmi_ZN10onnx_torch10MakeStringIJA23_cA7_cmA25_ciA15_ciEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch13unifyInputDimERNS_16InferenceContextEmiRNS_26TensorShapeProto_DimensionE_ZN10onnx_torch10MakeStringIJA23_cA44_c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DequantizeLinear_Onnx_ver10E_ZN10onnx_torch41dbg_count_check_QuantizeLinear_Onnx_ver10E_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EE9_M_invokeERKSt9_Any_dataOi_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EE10_M_managerERSt9_Any_dataRKS5_St18_Manager_operation_ZTIN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_E_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EE9_M_invokeERKSt9_Any_dataOi_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EE10_M_managerERSt9_Any_dataRKS5_St18_Manager_operation_ZTIN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_E_ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EED2Ev_ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EED1Ev_ZN10onnx_torch14InferenceErrorD2Ev_ZTVN10onnx_torch14InferenceErrorE_ZN10onnx_torch14InferenceErrorD1EvDW.ref.__gxx_personality_v0_ZN10onnx_torch14InferenceErrorD0Ev_ZGVZN10onnx_torch8OpSchema29all_numeric_types_with_bfloatB5cxx11EvE29all_numeric_types_with_bfloat_ZZN10onnx_torch8OpSchema29all_numeric_types_with_bfloatB5cxx11EvE29all_numeric_types_with_bfloat_ZN10onnx_torch8OpSchema44numeric_types_for_math_reduction_with_bfloatB5cxx11Ev_ZGVZN10onnx_torch8OpSchema44numeric_types_for_math_reduction_with_bfloatB5cxx11EvE44numeric_types_for_math_reduction_with_bfloat_ZZN10onnx_torch8OpSchema44numeric_types_for_math_reduction_with_bfloatB5cxx11EvE44numeric_types_for_math_reduction_with_bfloat_ZN10onnx_torch18ReduceDocGeneratorEPKcbb_ZN10onnx_torch21ArgReduceDocGeneratorEPKc_ZNSt10_HashtableIPKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_SaIS7_ENSt8__detail9_IdentityESt8equal_toIS7_ESt4hashIS7_ENS9_18_Mod_range_hashingENS9_20_Default_ranged_hashENS9_20_Prime_rehash_policyENS9_17_Hashtable_traitsILb0ELb1ELb1EEEED2Ev_ZNSt10_HashtableIPKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_SaIS7_ENSt8__detail9_IdentityESt8equal_toIS7_ESt4hashIS7_ENS9_18_Mod_range_hashingENS9_20_Default_ranged_hashENS9_20_Prime_rehash_policyENS9_17_Hashtable_traitsILb0ELb1ELb1EEEED1Ev_ZNSt6vectorIN10onnx_torch8OpSchema15FormalParameterESaIS2_EED2Ev_ZNSt6vectorIN10onnx_torch8OpSchema15FormalParameterESaIS2_EED1Ev_ZNSt6vectorIN10onnx_torch8OpSchema19TypeConstraintParamESaIS2_EED2Ev_ZNSt6vectorIN10onnx_torch8OpSchema19TypeConstraintParamESaIS2_EED1Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED2Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED1Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED0Ev_ZN10onnx_torch10MakeStringIJA22_cA7_cmA43_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch40propagateElemTypeFromTensorInputToOutputERNS_16InferenceContextEmm_ZTIN10onnx_torch14InferenceErrorE_ZN10onnx_torch10MakeStringIJA22_cA7_cmA32_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA22_cA32_cmA9_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch14propagateShapeEPKNS_9TypeProtoEPS0__ZN10onnx_torch10MakeStringIJA22_cA8_cmA49_cNS_9TypeProto9ValueCaseEEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA69_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA49_clEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA52_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_S6_ISt13unordered_setIPS7_St4hashIS9_ESt8equal_toIS9_ESaIS9_EES5_EESaISH_ENSt8__detail10_Select1stESC_IS5_ESA_IS5_ENSJ_18_Mod_range_hashingENSJ_20_Default_ranged_hashENSJ_20_Prime_rehash_policyENSJ_17_Hashtable_traitsILb1ELb0ELb1EEEE5clearEv_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_S6_ISt13unordered_setIPS7_St4hashIS9_ESt8equal_toIS9_ESaIS9_EES5_EESaISH_ENSt8__detail10_Select1stESC_IS5_ESA_IS5_ENSJ_18_Mod_range_hashingENSJ_20_Default_ranged_hashENSJ_20_Prime_rehash_policyENSJ_17_Hashtable_traitsILb1ELb0ELb1EEEED2Ev_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_S6_ISt13unordered_setIPS7_St4hashIS9_ESt8equal_toIS9_ESaIS9_EES5_EESaISH_ENSt8__detail10_Select1stESC_IS5_ESA_IS5_ENSJ_18_Mod_range_hashingENSJ_20_Default_ranged_hashENSJ_20_Prime_rehash_policyENSJ_17_Hashtable_traitsILb1ELb0ELb1EEEED1Ev_ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EE17_M_realloc_insertIJS5_EEEvN9__gnu_cxx17__normal_iteratorIPS5_S7_EEDpOT__ZN10onnx_torch36GetSupportedDataTypesForReductionOpsB5cxx11Eb_ZN10onnx_torch8OpSchema15FormalParameterD2Ev_ZN10onnx_torch8OpSchema15FormalParameterD1Ev_ZNK10onnx_torch14InferenceError4whatEv_ZN10onnx_torch8OpSchemaC2Ev_ZN10onnx_torch8OpSchemaC1Ev_ZN10onnx_torch8OpSchemaC2ERKS0__ZN10onnx_torch8OpSchemaC1ERKS0__ZN10onnx_torch8OpSchemaD2Ev_ZN10onnx_torch8OpSchemaD1Ev_ZN10onnx_torch11GetOpSchemaINS_20ReduceMax_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ReduceMin_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ReduceSum_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26ReduceSumSquare_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_21ReduceMean_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_21ReduceProd_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_23ReduceLogSum_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26ReduceLogSumExp_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19ReduceL1_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19ReduceL2_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17ArgMax_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17ArgMin_Onnx_ver13EEENS_8OpSchemaEv_ZTSN10onnx_torch14InferenceErrorE_ZTSN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_E_ZTSN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_E_ZN10onnx_torch33dbg_count_check_ArgMin_Onnx_ver13E_ZN10onnx_torch33dbg_count_check_ArgMax_Onnx_ver13E_ZN10onnx_torch35dbg_count_check_ReduceL2_Onnx_ver13E_ZN10onnx_torch35dbg_count_check_ReduceL1_Onnx_ver13E_ZN10onnx_torch42dbg_count_check_ReduceLogSumExp_Onnx_ver13E_ZN10onnx_torch39dbg_count_check_ReduceLogSum_Onnx_ver13E_ZN10onnx_torch37dbg_count_check_ReduceProd_Onnx_ver13E_ZN10onnx_torch37dbg_count_check_ReduceMean_Onnx_ver13E_ZN10onnx_torch42dbg_count_check_ReduceSumSquare_Onnx_ver13E_ZN10onnx_torch36dbg_count_check_ReduceSum_Onnx_ver13E_ZN10onnx_torch36dbg_count_check_ReduceMin_Onnx_ver13E_ZN10onnx_torch36dbg_count_check_ReduceMax_Onnx_ver13E_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EE9_M_invokeERKSt9_Any_dataOi_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EE10_M_managerERSt9_Any_dataRKS5_St18_Manager_operation_ZTIN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_E_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EE9_M_invokeERKSt9_Any_dataOi_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EE10_M_managerERSt9_Any_dataRKS5_St18_Manager_operation_ZTIN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_E_ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EED2Ev_ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EED1Ev_ZN10onnx_torch14InferenceErrorD2Ev_ZTVN10onnx_torch14InferenceErrorE_ZN10onnx_torch14InferenceErrorD1EvDW.ref.__gxx_personality_v0_ZN10onnx_torch14InferenceErrorD0Ev_ZGVZN10onnx_torch8OpSchema32numeric_types_for_math_reductionB5cxx11EvE32numeric_types_for_math_reduction_ZZN10onnx_torch8OpSchema32numeric_types_for_math_reductionB5cxx11EvE32numeric_types_for_math_reduction_ZN10onnx_torch8OpSchema32numeric_types_for_math_reductionB5cxx11Ev_ZN10onnx_torch8OpSchema17all_numeric_typesB5cxx11Ev_ZGVZN10onnx_torch8OpSchema17all_numeric_typesB5cxx11EvE17all_numeric_types_ZZN10onnx_torch8OpSchema17all_numeric_typesB5cxx11EvE17all_numeric_types_ZN10onnx_torch26ReduceDocGenerator_opset12EPKcb_ZN10onnx_torch29ArgReduceDocGenerator_opset12EPKc_ZN10onnx_torch25ReduceDocGenerator_opset1EPKci_ZN10onnx_torch28ArgReduceDocGenerator_opset1EPKc_ZN10onnx_torch29ArgReduceDocGenerator_opset11EPKc_ZNSt10_HashtableIPKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_SaIS7_ENSt8__detail9_IdentityESt8equal_toIS7_ESt4hashIS7_ENS9_18_Mod_range_hashingENS9_20_Default_ranged_hashENS9_20_Prime_rehash_policyENS9_17_Hashtable_traitsILb0ELb1ELb1EEEED2Ev_ZNSt10_HashtableIPKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_SaIS7_ENSt8__detail9_IdentityESt8equal_toIS7_ESt4hashIS7_ENS9_18_Mod_range_hashingENS9_20_Default_ranged_hashENS9_20_Prime_rehash_policyENS9_17_Hashtable_traitsILb0ELb1ELb1EEEED1Ev_ZNSt6vectorIN10onnx_torch8OpSchema15FormalParameterESaIS2_EED2Ev_ZNSt6vectorIN10onnx_torch8OpSchema15FormalParameterESaIS2_EED1Ev_ZNSt6vectorIN10onnx_torch8OpSchema19TypeConstraintParamESaIS2_EED2Ev_ZNSt6vectorIN10onnx_torch8OpSchema19TypeConstraintParamESaIS2_EED1Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED2Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED1Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED0Ev_ZN10onnx_torch10MakeStringIJA22_cA7_cmA43_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch40propagateElemTypeFromTensorInputToOutputERNS_16InferenceContextEmm_ZTIN10onnx_torch14InferenceErrorE_ZN10onnx_torch10MakeStringIJA22_cA7_cmA32_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA22_cA32_cmA9_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA22_cA8_cmA9_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA22_cA8_cmA49_cNS_9TypeProto9ValueCaseEEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA52_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_S6_ISt13unordered_setIPS7_St4hashIS9_ESt8equal_toIS9_ESaIS9_EES5_EESaISH_ENSt8__detail10_Select1stESC_IS5_ESA_IS5_ENSJ_18_Mod_range_hashingENSJ_20_Default_ranged_hashENSJ_20_Prime_rehash_policyENSJ_17_Hashtable_traitsILb1ELb0ELb1EEEE5clearEv_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_S6_ISt13unordered_setIPS7_St4hashIS9_ESt8equal_toIS9_ESaIS9_EES5_EESaISH_ENSt8__detail10_Select1stESC_IS5_ESA_IS5_ENSJ_18_Mod_range_hashingENSJ_20_Default_ranged_hashENSJ_20_Prime_rehash_policyENSJ_17_Hashtable_traitsILb1ELb0ELb1EEEED2Ev_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_S6_ISt13unordered_setIPS7_St4hashIS9_ESt8equal_toIS9_ESaIS9_EES5_EESaISH_ENSt8__detail10_Select1stESC_IS5_ESA_IS5_ENSJ_18_Mod_range_hashingENSJ_20_Default_ranged_hashENSJ_20_Prime_rehash_policyENSJ_17_Hashtable_traitsILb1ELb0ELb1EEEED1Ev_ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EE17_M_realloc_insertIJS5_EEEvN9__gnu_cxx17__normal_iteratorIPS5_S7_EEDpOT__ZN10onnx_torch44GetSupportedDataTypesForReductionOps_opset12B5cxx11Eb_ZN10onnx_torch8OpSchema15FormalParameterD2Ev_ZN10onnx_torch8OpSchema15FormalParameterD1Ev_ZNK10onnx_torch14InferenceError4whatEv_ZN10onnx_torch34propagateElemTypeFromInputToOutputERNS_16InferenceContextEmm_ZN10onnx_torch8OpSchemaC2Ev_ZN10onnx_torch8OpSchemaC1Ev_ZN10onnx_torch8OpSchemaC2ERKS0__ZN10onnx_torch8OpSchemaC1ERKS0__ZN10onnx_torch8OpSchemaD2Ev_ZN10onnx_torch8OpSchemaD1Ev_ZN10onnx_torch11GetOpSchemaINS_20ReduceMax_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ReduceMin_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ReduceSum_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26ReduceSumSquare_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_21ReduceMean_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_21ReduceProd_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_23ReduceLogSum_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26ReduceLogSumExp_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19ReduceL1_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19ReduceL2_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17ArgMax_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17ArgMin_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19ReduceMax_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19ReduceMin_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19ReduceSum_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_25ReduceSumSquare_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ReduceMean_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ReduceProd_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_22ReduceLogSum_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_25ReduceLogSumExp_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18ReduceL1_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18ReduceL2_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ReduceMax_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ReduceMin_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16ArgMax_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16ArgMin_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17ArgMax_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17ArgMin_Onnx_ver11EEENS_8OpSchemaEv_ZTSN10onnx_torch14InferenceErrorE_ZTSN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_E_ZTSN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_E_ZN10onnx_torch33dbg_count_check_ArgMin_Onnx_ver11E_ZN10onnx_torch33dbg_count_check_ArgMax_Onnx_ver11E_ZN10onnx_torch32dbg_count_check_ArgMin_Onnx_ver1E_ZN10onnx_torch32dbg_count_check_ArgMax_Onnx_ver1E_ZN10onnx_torch36dbg_count_check_ReduceMin_Onnx_ver11E_ZN10onnx_torch36dbg_count_check_ReduceMax_Onnx_ver11E_ZN10onnx_torch34dbg_count_check_ReduceL2_Onnx_ver1E_ZN10onnx_torch34dbg_count_check_ReduceL1_Onnx_ver1E_ZN10onnx_torch41dbg_count_check_ReduceLogSumExp_Onnx_ver1E_ZN10onnx_torch38dbg_count_check_ReduceLogSum_Onnx_ver1E_ZN10onnx_torch36dbg_count_check_ReduceProd_Onnx_ver1E_ZN10onnx_torch36dbg_count_check_ReduceMean_Onnx_ver1E_ZN10onnx_torch41dbg_count_check_ReduceSumSquare_Onnx_ver1E_ZN10onnx_torch35dbg_count_check_ReduceSum_Onnx_ver1E_ZN10onnx_torch35dbg_count_check_ReduceMin_Onnx_ver1E_ZN10onnx_torch35dbg_count_check_ReduceMax_Onnx_ver1E_ZN10onnx_torch33dbg_count_check_ArgMin_Onnx_ver12E_ZN10onnx_torch33dbg_count_check_ArgMax_Onnx_ver12E_ZN10onnx_torch35dbg_count_check_ReduceL2_Onnx_ver11E_ZN10onnx_torch35dbg_count_check_ReduceL1_Onnx_ver11E_ZN10onnx_torch42dbg_count_check_ReduceLogSumExp_Onnx_ver11E_ZN10onnx_torch39dbg_count_check_ReduceLogSum_Onnx_ver11E_ZN10onnx_torch37dbg_count_check_ReduceProd_Onnx_ver11E_ZN10onnx_torch37dbg_count_check_ReduceMean_Onnx_ver11E_ZN10onnx_torch42dbg_count_check_ReduceSumSquare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_torch11GetOpSchemaINS_16Shape_Onnx_ver15EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Split_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20Transpose_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ScatterND_Onnx_ver16EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26ScatterElements_Onnx_ver16EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_25GatherElements_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_23SpaceToDepth_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_23DepthToSpace_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Tile_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Resize_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19Identity_Onnx_ver16EEENS_8OpSchemaEv_ZGVZN10onnx_torch8OpSchema25all_tensor_sequence_typesB5cxx11EvE25all_tensor_sequence_types_ZZN10onnx_torch8OpSchema25all_tensor_sequence_typesB5cxx11EvE25all_tensor_sequence_types_ZGVZN10onnx_torch8OpSchema18all_optional_typesB5cxx11EvE18all_optional_types_ZZN10onnx_torch8OpSchema18all_optional_typesB5cxx11EvE18all_optional_types_ZN10onnx_torch35propagateShapeAndTypeFromFirstInputERNS_16InferenceContextE_ZN10onnx_torch11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16IsNaN_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Where_Onnx_ver16EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18NonZero_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19GatherND_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Pad_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Trilu_Onnx_ver14EEENS_8OpSchemaEv_ZN10onnx_torch42propagateElemTypeFromSequenceInputToOutputERNS_16InferenceContextEmm_ZN10onnx_torch42propagateElemTypeFromOptionalInputToOutputERNS_16InferenceContextEmm_ZN10onnx_torch34propagateElemTypeFromInputToOutputERNS_16InferenceContextEmm_ZTSFvRN10onnx_torch16InferenceContextEE_ZTIFvRN10onnx_torch16InferenceContextEE_ZTSN10onnx_torch14InferenceErrorE_ZTSN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_E_ZTSN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_E_ZTSPFvRN10onnx_torch16InferenceContextEE_ZN10onnx_torch32dbg_count_check_Trilu_Onnx_ver14E_ZN10onnx_torch30dbg_count_check_Pad_Onnx_ver13E_ZN10onnx_torch35dbg_count_check_GatherND_Onnx_ver13E_ZN10onnx_torch33dbg_count_check_Unique_Onnx_ver11E_ZN10onnx_torch42dbg_count_check_ReverseSequence_Onnx_ver10E_ZN10onnx_torch34dbg_count_check_NonZero_Onnx_ver13E_ZN10onnx_torch32dbg_count_check_Where_Onnx_ver16E_ZN10onnx_torch32dbg_count_check_IsInf_Onnx_ver10E_ZN10onnx_torch32dbg_count_check_IsNaN_Onnx_ver13E_ZN10onnx_torch33dbg_count_check_OneHot_Onnx_ver11E_ZN10onnx_torch35dbg_count_check_Compress_Onnx_ver11E_ZN10onnx_torch35dbg_count_check_Identity_Onnx_ver16E_ZN10onnx_torch33dbg_count_check_Resize_Onnx_ver13E_ZN10onnx_torch35dbg_count_check_Upsample_Onnx_ver10E_ZN10onnx_torch31dbg_count_check_Tile_Onnx_ver13E_ZN10onnx_torch39dbg_count_check_DepthToSpace_Onnx_ver13E_ZN10onnx_torch39dbg_count_check_SpaceToDepth_Onnx_ver13E_ZN10onnx_torch36dbg_count_check_Unsqueeze_Onnx_ver13E_ZN10onnx_torch34dbg_count_check_Squeeze_Onnx_ver13E_ZN10onnx_torch41dbg_count_check_GatherElements_Onnx_ver13E_ZN10onnx_torch33dbg_count_check_Gather_Onnx_ver13E_ZN10onnx_torch42dbg_count_check_ScatterElements_Onnx_ver16E_ZN10onnx_torch36dbg_count_check_ScatterND_Onnx_ver16E_ZN10onnx_torch34dbg_count_check_Scatter_Onnx_ver11E_ZN10onnx_torch36dbg_count_check_Transpose_Onnx_ver13E_ZN10onnx_torch32dbg_count_check_Slice_Onnx_ver13E_ZN10onnx_torch32dbg_count_check_Split_Onnx_ver13E_ZN10onnx_torch33dbg_count_check_Concat_Onnx_ver13E_ZN10onnx_torch31dbg_count_check_Size_Onnx_ver13E_ZN10onnx_torch32dbg_count_check_Shape_Onnx_ver15E_ZN10onnx_torch34dbg_count_check_Reshape_Onnx_ver14E_ZN10onnx_torch35dbg_count_check_CastLike_Onnx_ver15E_ZN10onnx_torch31dbg_count_check_Cast_Onnx_ver13E_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EE9_M_invokeERKSt9_Any_dataOi_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EE10_M_managerERSt9_Any_dataRKS5_St18_Manager_operation_ZTIN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_E_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EE9_M_invokeERKSt9_Any_dataOi_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EE10_M_managerERSt9_Any_dataRKS5_St18_Manager_operation_ZTIN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_E_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEPS3_E9_M_invokeERKSt9_Any_dataS2__ZN6google8protobuf8internal21arena_destruct_objectINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEvPv_ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EED2Ev_ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EED1Ev_ZN10onnx_torch14InferenceErrorD2Ev_ZTVN10onnx_torch14InferenceErrorE_ZN10onnx_torch14InferenceErrorD1Ev_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEPS3_E10_M_managerERSt9_Any_dataRKS6_St18_Manager_operation_ZTIPFvRN10onnx_torch16InferenceContextEEDW.ref.__gxx_personality_v0_ZN10onnx_torch14InferenceErrorD0Ev_ZN6google8protobuf8internal14ArenaStringPtr14CreateInstanceEPNS0_5ArenaEPKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN10onnx_torch26TensorShapeProto_Dimension23_internal_set_dim_paramERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN10onnx_torch16TypeProto_Tensor23_internal_mutable_shapeEv_ZN10onnx_torch9TypeProto29_internal_mutable_tensor_typeEv_ZN10onnx_torch8OpSchema6SetDocEPKc_ZN10onnx_torch8OpSchema17all_numeric_typesB5cxx11Ev_ZGVZN10onnx_torch8OpSchema17all_numeric_typesB5cxx11EvE17all_numeric_types_ZZN10onnx_torch8OpSchema17all_numeric_typesB5cxx11EvE17all_numeric_types_ZN10onnx_torch8OpSchema16all_tensor_typesB5cxx11Ev_ZGVZN10onnx_torch8OpSchema16all_tensor_typesB5cxx11EvE16all_tensor_types_ZZN10onnx_torch8OpSchema16all_tensor_typesB5cxx11EvE16all_tensor_types_ZN10onnx_torch8OpSchema28all_tensor_types_with_bfloatB5cxx11Ev_ZGVZN10onnx_torch8OpSchema28all_tensor_types_with_bfloatB5cxx11EvE28all_tensor_types_with_bfloat_ZZN10onnx_torch8OpSchema28all_tensor_types_with_bfloatB5cxx11EvE28all_tensor_types_with_bfloat_ZN10onnx_torch13hasInputShapeINS_16InferenceContextEEEbRT_m_ZNSt10_HashtableIPKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_SaIS7_ENSt8__detail9_IdentityESt8equal_toIS7_ESt4hashIS7_ENS9_18_Mod_range_hashingENS9_20_Default_ranged_hashENS9_20_Prime_rehash_policyENS9_17_Hashtable_traitsILb0ELb1ELb1EEEED2Ev_ZNSt10_HashtableIPKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_SaIS7_ENSt8__detail9_IdentityESt8equal_toIS7_ESt4hashIS7_ENS9_18_Mod_range_hashingENS9_20_Default_ranged_hashENS9_20_Prime_rehash_policyENS9_17_Hashtable_traitsILb0ELb1ELb1EEEED1Ev_ZNSt6vectorIN10onnx_torch8OpSchema15FormalParameterESaIS2_EED2Ev_ZNSt6vectorIN10onnx_torch8OpSchema15FormalParameterESaIS2_EED1Ev_ZNSt6vectorIN10onnx_torch8OpSchema19TypeConstraintParamESaIS2_EED2Ev_ZNSt6vectorIN10onnx_torch8OpSchema19TypeConstraintParamESaIS2_EED1Ev_ZN10onnx_torch10MakeStringIJA22_cA20_cNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEA15_cEEES8_DpRKT__ZN10onnx_torch10MakeStringIJA22_cA11_cNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEA47_cEEES8_DpRKT__ZN10onnx_torch10MakeStringIJA22_cA11_cNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEA32_cEEES8_DpRKT__ZN10onnx_torch20updateOutputElemTypeERNS_16InferenceContextEmiNS_9TypeProto9ValueCaseE_ZTIN10onnx_torch14InferenceErrorE_ZN10onnx_torch14propagateShapeEPKNS_9TypeProtoEPS0__ZN10onnx_torch10MakeStringIJA22_cA7_cmA43_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch40propagateElemTypeFromTensorInputToOutputERNS_16InferenceContextEmm_ZN10onnx_torch10MakeStringIJA22_cA7_cmA32_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA22_cA32_cmA9_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA22_cA56_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch13getInputShapeERNS_16InferenceContextEm_ZN10onnx_torch14getOutputShapeERNS_16InferenceContextEmNS_9TypeProto9ValueCaseE_ZN10onnx_torch10MakeStringIJA23_cA94_clA9_clA12_ciEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EEC2ERKS7__ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EEC1ERKS7__ZN10onnx_torch10MakeStringIJA23_cA49_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA22_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA53_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA35_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA33_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA41_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch20getRepeatedAttributeIlEEbRNS_16InferenceContextENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERSt6vectorIT_SaISA_EE_ZN10onnx_torch10MakeStringIJA22_cA54_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA32_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZNSt10_HashtableIllSaIlENSt8__detail9_IdentityESt8equal_toIlESt4hashIlENS1_18_Mod_range_hashingENS1_20_Default_ranged_hashENS1_20_Prime_rehash_policyENS1_17_Hashtable_traitsILb0ELb1ELb1EEEED2Ev_ZNSt10_HashtableIllSaIlENSt8__detail9_IdentityESt8equal_toIlESt4hashIlENS1_18_Mod_range_hashingENS1_20_Default_ranged_hashENS1_20_Prime_rehash_policyENS1_17_Hashtable_traitsILb0ELb1ELb1EEEED1Ev_ZN10onnx_torch10MakeStringIJA23_cA28_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA19_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA22_cNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEES7_DpRKT__ZN10onnx_torch10MakeStringIJA23_cA20_ciS1_lEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA27_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA88_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA106_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA36_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_S6_ISt13unordered_setIPS7_St4hashIS9_ESt8equal_toIS9_ESaIS9_EES5_EESaISH_ENSt8__detail10_Select1stESC_IS5_ESA_IS5_ENSJ_18_Mod_range_hashingENSJ_20_Default_ranged_hashENSJ_20_Prime_rehash_policyENSJ_17_Hashtable_traitsILb1ELb0ELb1EEEE5clearEv_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_S6_ISt13unordered_setIPS7_St4hashIS9_ESt8equal_toIS9_ESaIS9_EES5_EESaISH_ENSt8__detail10_Select1stESC_IS5_ESA_IS5_ENSJ_18_Mod_range_hashingENSJ_20_Default_ranged_hashENSJ_20_Prime_rehash_policyENSJ_17_Hashtable_traitsILb1ELb0ELb1EEEED2Ev_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_S6_ISt13unordered_setIPS7_St4hashIS9_ESt8equal_toIS9_ESaIS9_EES5_EESaISH_ENSt8__detail10_Select1stESC_IS5_ESA_IS5_ENSJ_18_Mod_range_hashingENSJ_20_Default_ranged_hashENSJ_20_Prime_rehash_policyENSJ_17_Hashtable_traitsILb1ELb0ELb1EEEED1Ev_ZNSt6vectorIlSaIlEE12emplace_backIJlEEEvDpOT__ZNSt6vectorIPKN10onnx_torch16TensorShapeProtoESaIS3_EE17_M_realloc_insertIJS3_EEEvN9__gnu_cxx17__normal_iteratorIPS3_S5_EEDpOT__ZNSt13_Bvector_baseISaIbEE13_M_deallocateEv_ZN10onnx_torch8OpSchema15FormalParameterD2Ev_ZN10onnx_torch8OpSchema15FormalParameterD1Ev_ZNSt10_HashtableIllSaIlENSt8__detail9_IdentityESt8equal_toIlESt4hashIlENS1_18_Mod_range_hashingENS1_20_Default_ranged_hashENS1_20_Prime_rehash_policyENS1_17_Hashtable_traitsILb0ELb1ELb1EEEE9_M_rehashEmRKm_ZN10onnx_torch8OpSchemaC2Ev_ZN10onnx_torch8OpSchemaC1Ev_ZNK10onnx_torch14InferenceError4whatEv_ZN10onnx_torch8OpSchemaC2ERKS0__ZN10onnx_torch8OpSchemaC1ERKS0__ZN10onnx_torch8OpSchemaD2Ev_ZN10onnx_torch8OpSchemaD1Ev_ZN10onnx_torch11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Reshape_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ScatterND_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26ScatterElements_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19Identity_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch35propagateShapeAndTypeFromFirstInputERNS_16InferenceContextE_ZN10onnx_torch11GetOpSchemaINS_18Identity_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Cast_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Concat_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Split_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Pad_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Reshape_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Upsample_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Compress_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Split_Onnx_ver2EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Pad_Onnx_ver2EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19Identity_Onnx_ver14EEENS_8OpSchemaEv_ZGVZN10onnx_torch8OpSchema25all_tensor_sequence_typesB5cxx11EvE25all_tensor_sequence_types_ZZN10onnx_torch8OpSchema25all_tensor_sequence_typesB5cxx11EvE25all_tensor_sequence_types_ZN10onnx_torch11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch42propagateElemTypeFromSequenceInputToOutputERNS_16InferenceContextEmm_ZN10onnx_torch42propagateElemTypeFromOptionalInputToOutputERNS_16InferenceContextEmm_ZTSFvRN10onnx_torch16InferenceContextEE_ZTIFvRN10onnx_torch16InferenceContextEE_ZTSN10onnx_torch14InferenceErrorE_ZTSN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_E_ZTSN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_E_ZTSPFvRN10onnx_torch16InferenceContextEE_ZN10onnx_torch31dbg_count_check_Where_Onnx_ver9E_ZN10onnx_torch35dbg_count_check_Identity_Onnx_ver14E_ZN10onnx_torch35dbg_count_check_GatherND_Onnx_ver11E_ZN10onnx_torch29dbg_count_check_Pad_Onnx_ver2E_ZN10onnx_torch31dbg_count_check_Split_Onnx_ver2E_ZN10onnx_torch34dbg_count_check_Compress_Onnx_ver9E_ZN10onnx_torch32dbg_count_check_OneHot_Onnx_ver9E_ZN10onnx_torch35dbg_count_check_Unsqueeze_Onnx_ver1E_ZN10onnx_torch33dbg_count_check_Squeeze_Onnx_ver1E_ZN10onnx_torch32dbg_count_check_Gather_Onnx_ver1E_ZN10onnx_torch38dbg_count_check_DepthToSpace_Onnx_ver1E_ZN10onnx_torch33dbg_count_check_Scatter_Onnx_ver9E_ZN10onnx_torch32dbg_count_check_Slice_Onnx_ver10E_ZN10onnx_torch31dbg_count_check_Slice_Onnx_ver1E_ZN10onnx_torch33dbg_count_check_Resize_Onnx_ver10E_ZN10onnx_torch34dbg_count_check_Upsample_Onnx_ver9E_ZN10onnx_torch34dbg_count_check_Upsample_Onnx_ver7E_ZN10onnx_torch34dbg_count_check_Upsample_Onnx_ver1E_ZN10onnx_torch30dbg_count_check_Tile_Onnx_ver1E_ZN10onnx_torch33dbg_count_check_Reshape_Onnx_ver1E_ZN10onnx_torch29dbg_count_check_Pad_Onnx_ver1E_ZN10onnx_torch31dbg_count_check_Split_Onnx_ver1E_ZN10onnx_torch32dbg_count_check_Concat_Onnx_ver4E_ZN10onnx_torch32dbg_count_check_Concat_Onnx_ver1E_ZN10onnx_torch30dbg_count_check_Cast_Onnx_ver6E_ZN10onnx_torch30dbg_count_check_Cast_Onnx_ver1E_ZN10onnx_torch30dbg_count_check_Pad_Onnx_ver11E_ZN10onnx_torch35dbg_count_check_GatherND_Onnx_ver12E_ZN10onnx_torch33dbg_count_check_NonZero_Onnx_ver9E_ZN10onnx_torch31dbg_count_check_IsNaN_Onnx_ver9E_ZN10onnx_torch34dbg_count_check_Identity_Onnx_ver1E_ZN10onnx_torch35dbg_count_check_Identity_Onnx_ver13E_ZN10onnx_torch33dbg_count_check_Resize_Onnx_ver11E_ZN10onnx_torch30dbg_count_check_Tile_Onnx_ver6E_ZN10onnx_torch39dbg_count_check_DepthToSpace_Onnx_ver11E_ZN10onnx_torch38dbg_count_check_SpaceToDepth_Onnx_ver1E_ZN10onnx_torch36dbg_count_check_Unsqueeze_Onnx_ver11E_ZN10onnx_torch34dbg_count_check_Squeeze_Onnx_ver11E_ZN10onnx_torch41dbg_count_check_GatherElements_Onnx_ver11E_ZN10onnx_torch33dbg_count_check_Gather_Onnx_ver11E_ZN10onnx_torch42dbg_count_check_ScatterElements_Onnx_ver11E_ZN10onnx_torch42dbg_count_check_ScatterElements_Onnx_ver13E_ZN10onnx_torch36dbg_count_check_ScatterND_Onnx_ver11E_ZN10onnx_torch36dbg_count_check_ScatterND_Onnx_ver13E_ZN10onnx_torch35dbg_count_check_Transpose_Onnx_ver1E_ZN10onnx_torch32dbg_count_check_Slice_Onnx_ver11E_ZN10onnx_torch32dbg_count_check_Split_Onnx_ver11E_ZN10onnx_torch33dbg_count_check_Concat_Onnx_ver11E_ZN10onnx_torch30dbg_count_check_Size_Onnx_ver1E_ZN10onnx_torch31dbg_count_check_Shape_Onnx_ver1E_ZN10onnx_torch32dbg_count_check_Shape_Onnx_ver13E_ZN10onnx_torch33dbg_count_check_Reshape_Onnx_ver5E_ZN10onnx_torch34dbg_count_check_Reshape_Onnx_ver13E_ZN10onnx_torch30dbg_count_check_Cast_Onnx_ver9E_ZN10onnx_torch14InferenceErrorD2Ev_ZTVN10onnx_torch14InferenceErrorE_ZN10onnx_torch14InferenceErrorD1Ev_ZN10onnx_torch14InferenceErrorD0Ev_ZN10onnx_torch26resizeShapeInferenceHelperERKNS_16TensorShapeProtoERKSt6vectorIlSaIlEEPS0__ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED2Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED1Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED0Ev_ZN10onnx_torch10MakeStringIJA22_cA7_cmA43_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT_DW.ref.__gxx_personality_v0_ZN10onnx_torch40propagateElemTypeFromTensorInputToOutputERNS_16InferenceContextEmm_ZTIN10onnx_torch14InferenceErrorE_ZN10onnx_torch10MakeStringIJA22_cA7_cmA32_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA22_cA32_cmA9_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA22_cA56_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch14getOutputShapeERNS_16InferenceContextEmNS_9TypeProto9ValueCaseE_ZN10onnx_torch26resizeShapeInferenceHelperERKNS_16TensorShapeProtoERKSt6vectorIfSaIfEEPS0__ZN10onnx_torch39resizeShapeInferenceHelper_opset7_to_10ERKNS_16TensorShapeProtoERKSt6vectorIfSaIfEEPS0__ZN10onnx_torch10MakeStringIJA23_cA17_ciA44_ciA3_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA70_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA44_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA71_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA45_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZNK10onnx_torch14InferenceError4whatEv_ZN10onnx_torch34propagateElemTypeFromInputToOutputERNS_16InferenceContextEmm_ZN10onnx_torch20resizeShapeInferenceERNS_16InferenceContextEb_ZN10onnx_torch33resizeShapeInference_opset7_to_10ERNS_16InferenceContextE_ZTSN10onnx_torch14InferenceErrorE_ZN10onnx_torch14InferenceErrorD2Ev_ZTVN10onnx_torch14InferenceErrorE_ZN10onnx_torch14InferenceErrorD1Ev_ZN10onnx_torch14InferenceErrorD0Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED2Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED1Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED0Ev_ZN10onnx_torch10MakeStringIJA23_cA21_cNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEA38_cEEES8_DpRKT_DW.ref.__gxx_personality_v0_ZN10onnx_torch10MakeStringIJA23_cA49_cA46_cNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEES9_DpRKT__ZN10onnx_torch10MakeStringIJA23_cA37_cNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEA12_cS8_A9_cS8_EEES8_DpRKT__ZN10onnx_torch10MakeStringIJA23_cA29_cNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEA16_ciA32_ciEEES8_DpRKT__ZNSt6vectorIiSaIiEE17_M_default_appendEm_ZN10onnx_torch9ParseDataIiEEKSt6vectorIT_SaIS2_EEPKNS_11TensorProtoE_ZTIN10onnx_torch14InferenceErrorE_ZNSt6vectorIlSaIlEE17_M_default_appendEm_ZN10onnx_torch9ParseDataIlEEKSt6vectorIT_SaIS2_EEPKNS_11TensorProtoE_ZNSt6vectorIfSaIfEE17_M_default_appendEm_ZN10onnx_torch9ParseDataIfEEKSt6vectorIT_SaIS2_EEPKNS_11TensorProtoE_ZNSt6vectorIdSaIdEE17_M_default_appendEm_ZN10onnx_torch9ParseDataIdEEKSt6vectorIT_SaIS2_EEPKNS_11TensorProtoE_ZN10onnx_torch8ToTensorIfEENS_11TensorProtoERKT__ZN10onnx_torch8ToTensorIbEENS_11TensorProtoERKT__ZN10onnx_torch8ToTensorIiEENS_11TensorProtoERKT__ZN10onnx_torch8ToTensorIlEENS_11TensorProtoERKT__ZN10onnx_torch8ToTensorImEENS_11TensorProtoERKT__ZN10onnx_torch8ToTensorIdEENS_11TensorProtoERKT__ZN10onnx_torch8ToTensorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEENS_11TensorProtoERKT__ZN10onnx_torch8ToTensorIfEENS_11TensorProtoERKSt6vectorIT_SaIS3_EE_ZN10onnx_torch8ToTensorIbEENS_11TensorProtoERKSt6vectorIT_SaIS3_EE_ZN10onnx_torch8ToTensorIiEENS_11TensorProtoERKSt6vectorIT_SaIS3_EE_ZN10onnx_torch8ToTensorIlEENS_11TensorProtoERKSt6vectorIT_SaIS3_EE_ZN10onnx_torch8ToTensorImEENS_11TensorProtoERKSt6vectorIT_SaIS3_EE_ZN10onnx_torch8ToTensorIdEENS_11TensorProtoERKSt6vectorIT_SaIS3_EE_ZN10onnx_torch8ToTensorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEENS_11TensorProtoERKSt6vectorIT_SaIS9_EE_ZNK10onnx_torch14InferenceError4whatEv_ZTSN10onnx_torch14InferenceErrorE_ZNSt6vectorIiSaIiEE17_M_default_appendEm_ZN10onnx_torch9ParseDataIiEEKSt6vectorIT_SaIS2_EEPKNS_6TensorEDW.ref.__gxx_personality_v0_ZNSt6vectorIlSaIlEE17_M_default_appendEm_ZN10onnx_torch9ParseDataIlEEKSt6vectorIT_SaIS2_EEPKNS_6TensorE_ZNSt6vectorIfSaIfEE17_M_default_appendEm_ZN10onnx_torch9ParseDataIfEEKSt6vectorIT_SaIS2_EEPKNS_6TensorE_ZNSt6vectorIdSaIdEE17_M_default_appendEm_ZN10onnx_torch9ParseDataIdEEKSt6vectorIT_SaIS2_EEPKNS_6TensorE_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EE9_M_invokeERKSt9_Any_dataOi_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EE10_M_managerERSt9_Any_dataRKS5_St18_Manager_operation_ZTIN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_E_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EE9_M_invokeERKSt9_Any_dataOi_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EE10_M_managerERSt9_Any_dataRKS5_St18_Manager_operation_ZTIN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_E_ZN10onnx_torch14InferenceErrorD2Ev_ZTVN10onnx_torch14InferenceErrorE_ZN10onnx_torch14InferenceErrorD1EvDW.ref.__gxx_personality_v0_ZN10onnx_torch14InferenceErrorD0Ev_ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EED2Ev_ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EED1Ev_ZNSt10_HashtableIPKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_SaIS7_ENSt8__detail9_IdentityESt8equal_toIS7_ESt4hashIS7_ENS9_18_Mod_range_hashingENS9_20_Default_ranged_hashENS9_20_Prime_rehash_policyENS9_17_Hashtable_traitsILb0ELb1ELb1EEEED2Ev_ZNSt10_HashtableIPKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_SaIS7_ENSt8__detail9_IdentityESt8equal_toIS7_ESt4hashIS7_ENS9_18_Mod_range_hashingENS9_20_Default_ranged_hashENS9_20_Prime_rehash_policyENS9_17_Hashtable_traitsILb0ELb1ELb1EEEED1Ev_ZNSt6vectorIN10onnx_torch8OpSchema15FormalParameterESaIS2_EED2Ev_ZNSt6vectorIN10onnx_torch8OpSchema15FormalParameterESaIS2_EED1Ev_ZNSt6vectorIN10onnx_torch8OpSchema19TypeConstraintParamESaIS2_EED2Ev_ZNSt6vectorIN10onnx_torch8OpSchema19TypeConstraintParamESaIS2_EED1Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED2Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED1Ev_ZNSt7__cxx1115basic_stringbufIcSt11char_traitsIcESaIcEED0Ev_ZN10onnx_torch14propagateShapeEPKNS_9TypeProtoEPS0__ZTIN10onnx_torch14InferenceErrorE_ZN10onnx_torch10MakeStringIJA22_cA8_cmA40_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA22_cA8_cmA30_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA34_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA35_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA50_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA55_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA54_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA52_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZN10onnx_torch10MakeStringIJA23_cA33_cEEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEDpRKT__ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_S6_ISt13unordered_setIPS7_St4hashIS9_ESt8equal_toIS9_ESaIS9_EES5_EESaISH_ENSt8__detail10_Select1stESC_IS5_ESA_IS5_ENSJ_18_Mod_range_hashingENSJ_20_Default_ranged_hashENSJ_20_Prime_rehash_policyENSJ_17_Hashtable_traitsILb1ELb0ELb1EEEE5clearEv_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_S6_ISt13unordered_setIPS7_St4hashIS9_ESt8equal_toIS9_ESaIS9_EES5_EESaISH_ENSt8__detail10_Select1stESC_IS5_ESA_IS5_ENSJ_18_Mod_range_hashingENSJ_20_Default_ranged_hashENSJ_20_Prime_rehash_policyENSJ_17_Hashtable_traitsILb1ELb0ELb1EEEED2Ev_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_S6_ISt13unordered_setIPS7_St4hashIS9_ESt8equal_toIS9_ESaIS9_EES5_EESaISH_ENSt8__detail10_Select1stESC_IS5_ESA_IS5_ENSJ_18_Mod_range_hashingENSJ_20_Default_ranged_hashENSJ_20_Prime_rehash_policyENSJ_17_Hashtable_traitsILb1ELb0ELb1EEEED1Ev_ZN10onnx_torch8OpSchema15FormalParameterD2Ev_ZN10onnx_torch8OpSchema15FormalParameterD1Ev_ZN10onnx_torch8OpSchemaC2Ev_ZN10onnx_torch8OpSchemaC1Ev_ZN10onnx_torch8OpSchemaC2ERKS0__ZN10onnx_torch8OpSchemaC1ERKS0__ZN10onnx_torch8OpSchemaD2Ev_ZN10onnx_torch8OpSchemaD1Ev_ZN10onnx_torch11GetOpSchemaINS_33ArrayFeatureExtractor_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_21Binarizer_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19CastMap_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26CategoryMapper_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26DictVectorizer_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_29FeatureVectorizer_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19Imputer_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_24LabelEncoder_OnnxML_ver2EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_28LinearClassifier_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_27LinearRegressor_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_22Normalizer_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_25OneHotEncoder_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Scaler_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_25SVMClassifier_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_24SVMRegressor_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_34TreeEnsembleClassifier_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_33TreeEnsembleRegressor_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18ZipMap_OnnxML_ver1EEENS_8OpSchemaEv_ZNK10onnx_torch14InferenceError4whatEv_ZTSN10onnx_torch14InferenceErrorE_ZTSN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_E_ZTSN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_E_ZN10onnx_torch34dbg_count_check_ZipMap_OnnxML_ver1E_ZN10onnx_torch49dbg_count_check_TreeEnsembleRegressor_OnnxML_ver1E_ZN10onnx_torch50dbg_count_check_TreeEnsembleClassifier_OnnxML_ver1E_ZN10onnx_torch40dbg_count_check_SVMRegressor_OnnxML_ver1E_ZN10onnx_torch41dbg_count_check_SVMClassifier_OnnxML_ver1E_ZN10onnx_torch34dbg_count_check_Scaler_OnnxML_ver1E_ZN10onnx_torch41dbg_count_check_OneHotEncoder_OnnxML_ver1E_ZN10onnx_torch38dbg_count_check_Normalizer_OnnxML_ver1E_ZN10onnx_torch43dbg_count_check_LinearRegressor_OnnxML_ver1E_ZN10onnx_torch44dbg_count_check_LinearClassifier_OnnxML_ver1E_ZN10onnx_torch40dbg_count_check_LabelEncoder_OnnxML_ver2E_ZN10onnx_torch35dbg_count_check_Imputer_OnnxML_ver1E_ZN10onnx_torch45dbg_count_check_FeatureVectorizer_OnnxML_ver1E_ZN10onnx_torch42dbg_count_check_DictVectorizer_OnnxML_ver1E_ZN10onnx_torch42dbg_count_check_CategoryMapper_OnnxML_ver1E_ZN10onnx_torch35dbg_count_check_CastMap_OnnxML_ver1E_ZN10onnx_torch37dbg_count_check_Binarizer_OnnxML_ver1E_ZN10onnx_torch49dbg_count_check_ArrayFeatureExtractor_OnnxML_ver1E_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EE9_M_invokeERKSt9_Any_dataOi_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EE10_M_managerERSt9_Any_dataRKS5_St18_Manager_operation_ZTIN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_E_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EE9_M_invokeERKSt9_Any_dataOi_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EE10_M_managerERSt9_Any_dataRKS5_St18_Manager_operation_ZTIN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EDW.ref.__gxx_personality_v0_ZNSt10_HashtableIPKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_SaIS7_ENSt8__detail9_IdentityESt8equal_toIS7_ESt4hashIS7_ENS9_18_Mod_range_hashingENS9_20_Default_ranged_hashENS9_20_Prime_rehash_policyENS9_17_Hashtable_traitsILb0ELb1ELb1EEEED2Ev_ZNSt10_HashtableIPKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_SaIS7_ENSt8__detail9_IdentityESt8equal_toIS7_ESt4hashIS7_ENS9_18_Mod_range_hashingENS9_20_Default_ranged_hashENS9_20_Prime_rehash_policyENS9_17_Hashtable_traitsILb0ELb1ELb1EEEED1Ev_ZNSt6vectorIN10onnx_torch8OpSchema15FormalParameterESaIS2_EED2Ev_ZNSt6vectorIN10onnx_torch8OpSchema15FormalParameterESaIS2_EED1Ev_ZNSt6vectorIN10onnx_torch8OpSchema19TypeConstraintParamESaIS2_EED2Ev_ZNSt6vectorIN10onnx_torch8OpSchema19TypeConstraintParamESaIS2_EED1Ev_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_S6_ISt13unordered_setIPS7_St4hashIS9_ESt8equal_toIS9_ESaIS9_EES5_EESaISH_ENSt8__detail10_Select1stESC_IS5_ESA_IS5_ENSJ_18_Mod_range_hashingENSJ_20_Default_ranged_hashENSJ_20_Prime_rehash_policyENSJ_17_Hashtable_traitsILb1ELb0ELb1EEEE5clearEv_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_S6_ISt13unordered_setIPS7_St4hashIS9_ESt8equal_toIS9_ESaIS9_EES5_EESaISH_ENSt8__detail10_Select1stESC_IS5_ESA_IS5_ENSJ_18_Mod_range_hashingENSJ_20_Default_ranged_hashENSJ_20_Prime_rehash_policyENSJ_17_Hashtable_traitsILb1ELb0ELb1EEEED2Ev_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_S6_ISt13unordered_setIPS7_St4hashIS9_ESt8equal_toIS9_ESaIS9_EES5_EESaISH_ENSt8__detail10_Select1stESC_IS5_ESA_IS5_ENSJ_18_Mod_range_hashingENSJ_20_Default_ranged_hashENSJ_20_Prime_rehash_policyENSJ_17_Hashtable_traitsILb1ELb0ELb1EEEED1Ev_ZN10onnx_torch8OpSchema15FormalParameterD2Ev_ZN10onnx_torch8OpSchema15FormalParameterD1Ev_ZN10onnx_torch8OpSchemaC2ERKS0__ZN10onnx_torch8OpSchemaC1ERKS0__ZN10onnx_torch8OpSchemaD2Ev_ZN10onnx_torch8OpSchemaD1Ev_ZN10onnx_torch11GetOpSchemaINS_24LabelEncoder_OnnxML_ver1EEENS_8OpSchemaEv_ZTSN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_E_ZTSN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_E_ZN10onnx_torch40dbg_count_check_LabelEncoder_OnnxML_ver1E_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EE9_M_invokeERKSt9_Any_dataOi_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EE10_M_managerERSt9_Any_dataRKS5_St18_Manager_operation_ZTIN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_E_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EE9_M_invokeERKSt9_Any_dataOi_ZNSt17_Function_handlerIFbiEN10onnx_torch8OpSchema20num_outputs_allowed_MU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0onnx_torch18version_conversion7AdapterE_ZN10onnx_torch18version_conversion10Concat_3_4D1Ev_ZN10onnx_torch18version_conversion9TopK_9_10D2Ev_ZN10onnx_torch18version_conversion9TopK_9_10D1Ev_ZN10onnx_torch18version_conversion13Upsample_9_10D2Ev_ZN10onnx_torch18version_conversion13Upsample_9_10D1Ev_ZN10onnx_torch18version_conversion10Clip_10_11D2Ev_ZN10onnx_torch18version_conversion10Clip_10_11D1Ev_ZN10onnx_torch18version_conversion9Pad_10_11D2Ev_ZN10onnx_torch18version_conversion9Pad_10_11D1Ev_ZN10onnx_torch18version_conversion12Resize_10_11D2Ev_ZN10onnx_torch18version_conversion12Resize_10_11D1Ev_ZN10onnx_torch18version_conversion13Scatter_10_11D2Ev_ZN10onnx_torch18version_conversion13Scatter_10_11D1Ev_ZN10onnx_torch18version_conversion13Dropout_11_12D2Ev_ZN10onnx_torch18version_conversion13Dropout_11_12D1Ev_ZN10onnx_torch18version_conversion18ArgMaxArgMin_12_11D2Ev_ZN10onnx_torch18version_conversion18ArgMaxArgMin_12_11D1Ev_ZN10onnx_torch18version_conversion20AxesAttributeToInputD2Ev_ZN10onnx_torch18version_conversion20AxesAttributeToInputD1Ev_ZN10onnx_torch18version_conversion11Split_12_13D2Ev_ZN10onnx_torch18version_conversion11Split_12_13D1Ev_ZN10onnx_torch18version_conversion13Softmax_12_13D2Ev_ZN10onnx_torch18version_conversion13Softmax_12_13D1Ev_ZN10onnx_torch18version_conversion20AxesInputToAttributeD2Ev_ZN10onnx_torch18version_conversion20AxesInputToAttributeD1Ev_ZN10onnx_torch18version_conversion11Split_13_12D2Ev_ZN10onnx_torch18version_conversion11Split_13_12D1Ev_ZN10onnx_torch18version_conversion9AddLayoutD2Ev_ZN10onnx_torch18version_conversion9AddLayoutD1Ev_ZN10onnx_torch18version_conversion24BatchNormalization_13_14D2Ev_ZN10onnx_torch18version_conversion24BatchNormalization_13_14D1Ev_ZN10onnx_torch18version_conversion12RemoveLayoutD2Ev_ZN10onnx_torch18version_conversion12RemoveLayoutD1Ev_ZN10onnx_torch18version_conversion20RemoveConsumedInputsD2Ev_ZN10onnx_torch18version_conversion20RemoveConsumedInputsD1Ev_ZN10onnx_torch18version_conversion11Reshape_4_5D2Ev_ZN10onnx_torch18version_conversion11Reshape_4_5D1Ev_ZN10onnx_torch18version_conversion11Dropout_6_7D2Ev_ZN10onnx_torch18version_conversion11Dropout_6_7D1Ev_ZN10onnx_torch18version_conversion12Upsample_6_7D2Ev_ZN10onnx_torch18version_conversion12Upsample_6_7D1Ev_ZN10onnx_torch18version_conversion30BroadcastBackwardCompatibilityD2Ev_ZN10onnx_torch18version_conversion30BroadcastBackwardCompatibilityD1Ev_ZN10onnx_torch18version_conversion9SetIsTestD2Ev_ZN10onnx_torch18version_conversion9SetIsTestD1Ev_ZN10onnx_torch18version_conversion8Gemm_7_6D2Ev_ZN10onnx_torch18version_conversion8Gemm_7_6D1Ev_ZN10onnx_torch18version_conversion15AveragePool_7_6D2Ev_ZN10onnx_torch18version_conversion15AveragePool_7_6D1Ev_ZN10onnx_torch18version_conversion7Sum_8_7D2Ev_ZN10onnx_torch18version_conversion7Sum_8_7D1Ev_ZN10onnx_torch18version_conversion11MaxPool_8_7D2Ev_ZN10onnx_torch18version_conversion11MaxPool_8_7D1Ev_ZN10onnx_torch18version_conversion22BatchNormalization_8_9D2Ev_ZN10onnx_torch18version_conversion22BatchNormalization_8_9D1Ev_ZN10onnx_torch18version_conversion8Scan_8_9D2Ev_ZN10onnx_torch18version_conversion8Scan_8_9D1Ev_ZN10onnx_torch18version_conversion12Upsample_8_9D2Ev_ZN10onnx_torch18version_conversion12Upsample_8_9D1Ev_ZN10onnx_torch18version_conversion20ExtendSupportedTypesD2Ev_ZN10onnx_torch18version_conversion20ExtendSupportedTypesD1Ev_ZN10onnx_torch18version_conversion8Cast_9_8D2Ev_ZN10onnx_torch18version_conversion8Cast_9_8D1Ev_ZN10onnx_torch18version_conversion8Scan_9_8D2Ev_ZN10onnx_torch18version_conversion8Scan_9_8D1Ev_ZN10onnx_torch18version_conversion12Upsample_9_8D2Ev_ZN10onnx_torch18version_conversion12Upsample_9_8D1Ev_ZN10onnx_torch18version_conversion10Slice_9_10D2Ev_ZN10onnx_torch18version_conversion10Slice_9_10D1Ev_ZN10onnx_torch18version_conversion14RoiAlign_15_16D2Ev_ZN10onnx_torch18version_conversion14RoiAlign_15_16D1Ev_ZN10onnx_torch18version_conversion11Reshape_5_4D2Ev_ZN10onnx_torch18version_conversion11Reshape_5_4D1Ev_ZN10onnx_torch18version_conversion17CompatibleAdapterD2Ev_ZN10onnx_torch18version_conversion17CompatibleAdapterD1Ev_ZN10onnx_torch18version_conversion22BatchNormalization_6_5D2Ev_ZN10onnx_torch18version_conversion22BatchNormalization_6_5D1Ev_ZN10onnx_torch18version_conversion29BroadcastForwardCompatibilityD2Ev_ZN10onnx_torch18version_conversion29BroadcastForwardCompatibilityD1Ev_ZN10onnx_torch18version_conversion8Gemm_6_7D2Ev_ZN10onnx_torch18version_conversion8Gemm_6_7D1Ev_ZN10onnx_torch18version_conversion22BatchNormalization_6_7D2Ev_ZN10onnx_torch18version_conversion22BatchNormalization_6_7D1Ev_ZN10onnx_torch18version_conversion24NoPreviousVersionAdapterD2Ev_ZN10onnx_torch18version_conversion24NoPreviousVersionAdapterD1Ev_ZN10onnx_torch18version_conversion11MaxPool_8_7D0Ev_ZN10onnx_torch18version_conversion20ExtendSupportedTypesD0Ev_ZN10onnx_torch18version_conversion8Cast_9_8D0Ev_ZN10onnx_torch18version_conversion8Scan_8_9D0Ev_ZN10onnx_torch18version_conversion12Upsample_8_9D0Ev_ZN10onnx_torch18version_conversion15AveragePool_7_6D0Ev_ZN10onnx_torch18version_conversion7Sum_8_7D0Ev_ZN10onnx_torch18version_conversion22BatchNormalization_8_9D0Ev_ZN10onnx_torch18version_conversion9AddLayoutD0Ev_ZN10onnx_torch18version_conversion24BatchNormalization_13_14D0Ev_ZN10onnx_torch18version_conversion20AxesInputToAttributeD0Ev_ZN10onnx_torch18version_conversion11Split_13_12D0Ev_ZN10onnx_torch18version_conversion20RemoveConsumedInputsD0Ev_ZN10onnx_torch18version_conversion13Upsample_9_10D0Ev_ZN10onnx_torch18version_conversion10Clip_10_11D0Ev_ZN10onnx_torch18version_conversion10Slice_9_10D0Ev_ZN10onnx_torch18version_conversion9TopK_9_10D0Ev_ZN10onnx_torch18version_conversion8Gemm_6_7D0Ev_ZN10onnx_torch18version_conversion24NoPreviousVersionAdapterD0Ev_ZN10onnx_torch18version_conversion22BatchNormalization_6_7D0Ev_ZN10onnx_torch18version_conversion11Dropout_6_7D0Ev_ZN10onnx_torch18version_conversion9SetIsTestD0Ev_ZN10onnx_torch18version_conversion8Gemm_7_6D0Ev_ZN10onnx_torch18version_conversion12Upsample_6_7D0Ev_ZN10onnx_torch18version_conversion30BroadcastBackwardCompatibilityD0Ev_ZN10onnx_torch18version_conversion11Split_12_13D0Ev_ZN10onnx_torch18version_conversion13Softmax_12_13D0Ev_ZN10onnx_torch18version_conversion18ArgMaxArgMin_12_11D0Ev_ZN10onnx_torch18version_conversion20AxesAttributeToInputD0Ev_ZN10onnx_torch18version_conversion13Scatter_10_11D0Ev_ZN10onnx_torch18version_conversion13Dropout_11_12D0Ev_ZN10onnx_torch18version_conversion9Pad_10_11D0Ev_ZN10onnx_torch18version_conversion12Resize_10_11D0Ev_ZN10onnx_torch18version_conversion10Concat_3_4D0Ev_ZN10onnx_torch18version_conversion12RemoveLayoutD0Ev_ZN10onnx_torch18version_conversion11Reshape_4_5D0Ev_ZN10onnx_torch18version_conversion11Reshape_5_4D0Ev_ZN10onnx_torch18version_conversion17CompatibleAdapterD0Ev_ZN10onnx_torch18version_conversion22BatchNormalization_6_5D0Ev_ZN10onnx_torch18version_conversion29BroadcastForwardCompatibilityD0Ev_ZN10onnx_torch18version_conversion8Scan_9_8D0Ev_ZN10onnx_torch18version_conversion12Upsample_9_8D0Ev_ZN10onnx_torch18version_conversion14RoiAlign_15_16D0Ev_ZN10onnx_torch18version_conversion15TypeRestrictionD2Ev_ZTVN10onnx_torch18version_conversion15TypeRestrictionE_ZN10onnx_torch18version_conversion15TypeRestrictionD1Ev_ZN10onnx_torch18version_conversion15TypeRestrictionD0Ev_ZNK10onnx_torch20VectorAttributeValueIlLNS_13AttributeKindE3EE5cloneEv_ZZNSt8__detail18__to_chars_10_implImEEvPcjT_E8__digits_ZN10onnx_torch20ScalarAttributeValueINS_6TensorELNS_13AttributeKindE6EED2Ev_ZTVN10onnx_torch20ScalarAttributeValueINS_6TensorELNS_13AttributeKindE6EEE_ZN10onnx_torch20ScalarAttributeValueINS_6TensorELNS_13AttributeKindE6EED1Ev_ZN10onnx_torch20ScalarAttributeValueINS_6TensorELNS_13AttributeKindE6EED0Ev_ZNK10onnx_torch5Value10uniqueNameB5cxx11Ev_ZN10onnx_torch4Node6outputEv_ZN10onnx_torch4Node11removeInputEm_ZNK10onnx_torch7OpSetID8toStringB5cxx11Ev_ZN10onnx_torch6TensorD2Ev_ZN10onnx_torch6TensorD1Ev_ZN10onnx_torch5GraphD2Ev_ZN10onnx_torch5GraphD1Ev_ZNSt19_Sp_counted_deleterIPN10onnx_torch5GraphESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZN10onnx_torch18version_conversion7AdapterC2ERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKNS_7OpSetIDESC__ZN10onnx_torch18version_conversion7AdapterC1ERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKNS_7OpSetIDESC__ZN10onnx_torch18version_conversion7AdapterD2Ev_ZN10onnx_torch18version_conversion7AdapterD1Ev_ZN10onnx_torch18version_conversion7AdapterD0Ev_ZN10onnx_torch18version_conversion15TypeRestrictionC2ERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKNS_7OpSetIDESC_RKSt6vectorINS_20TensorProto_DataTypeESaISE_EE_ZN10onnx_torch18version_conversion15TypeRestrictionC1ERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKNS_7OpSetIDESC_RKSt6vectorINS_20TensorProto_DataTypeESaISE_EE_ZN10onnx_torch18version_conversion9AddLayoutC2ERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZTVN10onnx_torch18version_conversion9AddLayoutE_ZN10onnx_torch18version_conversion9AddLayoutC1ERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN10onnx_torch18version_conversion12RemoveLayoutC2ERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZTVN10onnx_torch18version_conversion12RemoveLayoutE_ZN10onnx_torch18version_conversion12RemoveLayoutC1ERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN10onnx_torch18version_conversion13Softmax_12_13C2ERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZTVN10onnx_torch18version_conversion13Softmax_12_13E_ZN10onnx_torch18version_conversion13Softmax_12_13C1ERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EED2Ev_ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EED1Ev_ZSteqIcEN9__gnu_cxx11__enable_ifIXsrSt9__is_charIT_E7__valueEbE6__typeERKNSt7__cxx1112basic_stringIS3_St11char_traitsIS3_ESaIS3_EEESE__ZNSt6vectorIlSaIlEEC2ERKS1__ZNSt6vectorIlSaIlEEC1ERKS1__ZNSt6vectorIN10onnx_torch9DimensionESaIS1_EED2Ev_ZNSt6vec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ESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion9AddLayout5adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZN10onnx_torch10AttributesINS_4NodeEE3setINS_20VectorAttributeValueIlLNS_13AttributeKindE3EEEEEPS1_NS_6SymbolENT_15ConstructorTypeE_ZNK10onnx_torch18version_conversion22BatchNormalization_6_55adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion11Reshape_5_417adapt_reshape_5_4ESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion11Reshape_5_45adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion20AxesInputToAttribute5adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion11Split_13_125adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion8Scan_8_914adapt_scan_8_9ESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion8Scan_8_95adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion12Upsample_9_818adapt_upsample_9_8ESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion12Upsample_9_85adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion12Upsample_6_75adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion14RoiAlign_15_165adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_St13unordered_mapIS5_S8_IS5_St10unique_ptrIN10onnx_torch18version_conversion7AdapterESt14default_deleteISC_EESt4hashIS5_ESt8equal_toIS5_ESaIS6_IS7_SF_EEESH_SJ_SaIS6_IS7_SM_EEEESaISQ_ENSt8__detail10_Select1stESJ_SH_NSS_18_Mod_range_hashingENSS_20_Default_ranged_hashENSS_20_Prime_rehash_policyENSS_17_Hashtable_traitsILb1ELb0ELb1EEEE9_M_rehashEmRKm_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_St13unordered_mapIS5_St10unique_ptrIN10onnx_torch18version_conversion7AdapterESt14default_deleteISC_EESt4hashIS5_ESt8equal_toIS5_ESaIS6_IS7_SF_EEEESaISN_ENSt8__detail10_Select1stESJ_SH_NSP_18_Mod_range_hashingENSP_20_Default_ranged_hashENSP_20_Prime_rehash_policyENSP_17_Hashtable_traitsILb1ELb0ELb1EEEE9_M_rehashEmRKm_ZNSt8__detail9_Map_baseINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS6_St13unordered_mapIS6_St10unique_ptrIN10onnx_torch18version_conversion7AdapterESt14default_deleteISD_EESt4hashIS6_ESt8equal_toIS6_ESaIS7_IS8_SG_EEEESaISO_ENS_10_Select1stESK_SI_NS_18_Mod_range_hashingENS_20_Default_ranged_hashENS_20_Prime_rehash_policyENS_17_Hashtable_traitsILb1ELb0ELb1EEELb1EEixEOS6__ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_St10unique_ptrIN10onnx_torch18version_conversion7AdapterESt14default_deleteISB_EEESaISF_ENSt8__detail10_Select1stESt8equal_toIS5_ESt4hashIS5_ENSH_18_Mod_range_hashingENSH_20_Default_ranged_hashENSH_20_Prime_rehash_policyENSH_17_Hashtable_traitsILb1ELb0ELb1EEEE9_M_rehashEmRKm_ZNSt8__detail9_Map_baseINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS6_St10unique_ptrIN10onnx_torch18version_conversion7AdapterESt14default_deleteISC_EEESaISG_ENS_10_Select1stESt8equal_toIS6_ESt4hashIS6_ENS_18_Mod_range_hashingENS_20_Default_ranged_hashENS_20_Prime_rehash_policyENS_17_Hashtable_traitsILb1ELb0ELb1EEELb1EEixEOS6__ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_St13unordered_mapIS5_St3mapIlPKN10onnx_torch8OpSchemaESt4lessIlESaIS6_IKlSD_EEESt4hashIS5_ESt8equal_toIS5_ESaIS6_IS7_SJ_EEEESaISR_ENSt8__detail10_Select1stESN_SL_NST_18_Mod_range_hashingENST_20_Default_ranged_hashENST_20_Prime_rehash_policyENST_17_Hashtable_traitsILb1ELb0ELb1EEEE9_M_rehashEmRKm_ZNSt8__detail9_Map_baseINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS6_St13unordered_mapIS6_St3mapIlPKN10onnx_torch8OpSchemaESt4lessIlESaIS7_IKlSE_EEESt4hashIS6_ESt8equal_toIS6_ESaIS7_IS8_SK_EEEESaISS_ENS_10_Select1stESO_SM_NS_18_Mod_range_hashingENS_20_Default_ranged_hashENS_20_Prime_rehash_policyENS_17_Hashtable_traitsILb1ELb0ELb1EEELb1EEixERS8__ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_St3mapIlPKN10onnx_torch8OpSchemaESt4lessIlESaIS6_IKlSC_EEEESaISJ_ENSt8__detail10_Select1stESt8equal_toIS5_ESt4hashIS5_ENSL_18_Mod_range_hashingENSL_20_Default_ranged_hashENSL_20_Prime_rehash_policyENSL_17_Hashtable_traitsILb1ELb0ELb1EEEE9_M_rehashEmRKm_ZNSt8__detail9_Map_baseINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS6_St3mapIlPKN10onnx_torch8OpSchemaESt4lessIlESaIS7_IKlSD_EEEESaISK_ENS_10_Select1stESt8equal_toIS6_ESt4hashIS6_ENS_18_Mod_range_hashingENS_20_Default_ranged_hashENS_20_Prime_rehash_policyENS_17_Hashtable_traitsILb1ELb0ELb1EEELb1EEixERS8__ZNSt10_HashtableIPKN10onnx_torch5ValueES3_SaIS3_ENSt8__detail9_IdentityESt8equal_toIS3_ESt4hashIS3_ENS5_18_Mod_range_hashingENS5_20_Default_ranged_hashENS5_20_Prime_rehash_policyENS5_17_Hashtable_traitsILb0ELb1ELb1EEEE9_M_rehashEmRKm_ZN10onnx_torch5ValueC2EPNS_4NodeEm_ZN10onnx_torch5ValueC1EPNS_4NodeEm_ZNK10onnx_torch18version_conversion8Scan_9_814adapt_scan_9_8ESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion8Scan_9_85adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNSt10_HashtableIPKN10onnx_torch4NodeES3_SaIS3_ENSt8__detail9_IdentityESt8equal_toIS3_ESt4hashIS3_ENS5_18_Mod_range_hashingENS5_20_Default_ranged_hashENS5_20_Prime_rehash_policyENS5_17_Hashtable_traitsILb0ELb1ELb1EEEE9_M_rehashEmRKm_ZNK10onnx_torch18version_conversion20AxesAttributeToInput5adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion29BroadcastForwardCompatibility37adapt_broadcast_forward_compatibilityESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion29BroadcastForwardCompatibility5adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion13Dropout_11_1219adapt_dropout_11_12ESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion13Dropout_11_125adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion11Reshape_4_55adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion11Split_12_135adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion12Upsample_8_918adapt_upsample_8_9ESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion12Upsample_8_95adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion12Resize_10_1118adapt_resize_10_11ESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion12Resize_10_115adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion9TopK_9_1015adapt_topk_9_10ESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion9TopK_9_105adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion9Pad_10_1115adapt_pad_10_11ESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion9Pad_10_115adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion13Scatter_10_115adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion13Softmax_12_1319adapt_softmax_12_13ESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion13Softmax_12_135adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion13Upsample_9_105adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion10Slice_9_105adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion10Clip_10_115adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZN10onnx_torch4Node16allocNewInstanceEPNS_5GraphE_ZN10onnx_torch8OpSchema15FormalParameterD2Ev_ZN10onnx_torch8OpSchema15FormalParameterD1Ev_ZNSt10_HashtableIiiSaIiENSt8__detail9_IdentityESt8equal_toIiESt4hashIiENS1_18_Mod_range_hashingENS1_20_Default_ranged_hashENS1_20_Prime_rehash_policyENS1_17_Hashtable_traitsILb0ELb1ELb1EEEE9_M_rehashEmRKm_ZNSt10_HashtableIiiSaIiENSt8__detail9_IdentityESt8equal_toIiESt4hashIiENS1_18_Mod_range_hashingENS1_20_Default_ranged_hashENS1_20_Prime_rehash_policyENS1_17_Hashtable_traitsILb0ELb1ELb1EEEEC2IPKiEET_SG_mRKS6_RKS4_RKS0_St17integral_constantIbLb1EE_ZNSt10_HashtableIiiSaIiENSt8__detail9_IdentityESt8equal_toIiESt4hashIiENS1_18_Mod_range_hashingENS1_20_Default_ranged_hashENS1_20_Prime_rehash_policyENS1_17_Hashtable_traitsILb0ELb1ELb1EEEEC1IPKiEET_SG_mRKS6_RKS4_RKS0_St17integral_constantIbLb1EE_ZNK10onnx_torch18version_conversion20ExtendSupportedTypes20adapt_type_extensionESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNK10onnx_torch18version_conversion20ExtendSupportedTypes5adaptESt10shared_ptrINS_5GraphEEPNS_4NodeE_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_St13unordered_mapIS5_St10unique_ptrIN10onnx_torch18version_conversion7AdapterESt14default_deleteISC_EESt4hashIS5_ESt8equal_toIS5_ESaIS6_IS7_SF_EEEESaISN_ENSt8__detail10_Select1stESJ_SH_NSP_18_Mod_range_hashingENSP_20_Default_ranged_hashENSP_20_Prime_rehash_policyENSP_17_Hashtable_traitsILb1ELb0ELb1EEEE5clearEv_ZNSt8__detail9_Map_baseINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS6_St13unordered_mapIS6_S9_IS6_St10unique_ptrIN10onnx_torch18version_conversion7AdapterESt14default_deleteISD_EESt4hashIS6_ESt8equal_toIS6_ESaIS7_IS8_SG_EEESI_SK_SaIS7_IS8_SN_EEEESaISR_ENS_10_Select1stESK_SI_NS_18_Mod_range_hashingENS_20_Default_ranged_hashENS_20_Prime_rehash_policyENS_17_Hashtable_traitsILb1ELb0ELb1EEELb1EEixERS8__ZN10onnx_torch18version_conversion20BaseVersionConverter15registerAdapterESt10unique_ptrINS0_7AdapterESt14default_deleteIS3_EE_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_St13unordered_mapIS5_S8_IS5_St10unique_ptrIN10onnx_torch18version_conversion7AdapterESt14default_deleteISC_EESt4hashIS5_ESt8equal_toIS5_ESaIS6_IS7_SF_EEESH_SJ_SaIS6_IS7_SM_EEEESaISQ_ENSt8__detail10_Select1stESJ_SH_NSS_18_Mod_range_hashingENSS_20_Default_ranged_hashENSS_20_Prime_rehash_policyENSS_17_Hashtable_traitsILb1ELb0ELb1EEEED2Ev_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_St13unordered_mapIS5_S8_IS5_St10unique_ptrIN10onnx_torch18version_conversion7AdapterESt14default_deleteISC_EESt4hashIS5_ESt8equal_toIS5_ESaIS6_IS7_SF_EEESH_SJ_SaIS6_IS7_SM_EEEESaISQ_ENSt8__detail10_Select1stESJ_SH_NSS_18_Mod_range_hashingENSS_20_Default_ranged_hashENSS_20_Prime_rehash_policyENSS_17_Hashtable_traitsILb1ELb0ELb1EEEED1Ev_ZN10onnx_torch18version_conversion23DefaultVersionConverterD2Ev_ZTVN10onnx_torch18version_conversion20BaseVersionConverterE_ZN10onnx_torch18version_conversion23DefaultVersionConverterD1Ev_ZN10onnx_torch18version_conversion23DefaultVersionConverterD0Ev_ZN10onnx_torch8OpSchemaC2ERKS0__ZN10onnx_torch8OpSchemaC1ERKS0__ZN10onnx_torch8OpSchemaD2Ev_ZN10onnx_torch8OpSchemaD1Ev_ZNSt6vectorIN10onnx_torch8OpSchemaESaIS1_EE17_M_realloc_insertIJRS1_EEEvN9__gnu_cxx17__normal_iteratorIPS1_S3_EEDpOT__ZNSt6vectorIN10onnx_torch8OpSchemaESaIS1_EED2Ev_ZNSt6vectorIN10onnx_torch8OpSchemaESaIS1_EED1Ev_ZN10onnx_torch18version_conversion23DefaultVersionConverterC2Ev_ZTVN10onnx_torch18version_conversion23DefaultVersionConverterE_ZTVN10onnx_torch18version_conversion9SetIsTestE_ZTVN10onnx_torch18version_conversion24NoPreviousVersionAdapterE_ZN10onnx_torch18version_conversion23DefaultVersionConverterC1Ev_ZNK10onnx_torch18version_conversion23DefaultVersionConverter15convert_versionERKNS_10ModelProtoERKNS_7OpSetIDES7__ZTVSt19_Sp_counted_deleterIPN10onnx_torch5GraphESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZN10onnx_torch18version_conversion14ConvertVersionERKNS_10ModelProtoEi_ZTSSt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EE_ZTISt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EE_ZTSN10onnx_torch10AttributesINS_4NodeEEE_ZTIN10onnx_torch10AttributesINS_4NodeEEE_ZTSSt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EE_ZTISt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EE_ZTSN10onnx_torch14AttributeValueE_ZTIN10onnx_torch14AttributeValueE_ZTSN10onnx_torch20VectorAttributeValueIdLNS_13AttributeKindE1EEE_ZTIN10onnx_torch20VectorAttributeValueIdLNS_13AttributeKindE1EEE_ZTSN10onnx_torch20ScalarAttributeValueINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEELNS_13AttributeKindE4EEE_ZTIN10onnx_torch20ScalarAttributeValueINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEELNS_13AttributeKindE4EEE_ZTSN10onnx_torch20ScalarAttributeValueIlLNS_13AttributeKindE2EEE_ZTIN10onnx_torch20ScalarAttributeValueIlLNS_13AttributeKindE2EEE_ZTSN10onnx_torch20VectorAttributeValueIlLNS_13AttributeKindE3EEE_ZTIN10onnx_torch20VectorAttributeValueIlLNS_13AttributeKindE3EEE_ZTSN10onnx_torch20ScalarAttributeValueINS_6TensorELNS_13AttributeKindE6EEE_ZTIN10onnx_torch20ScalarAttributeValueINS_6TensorELNS_13AttributeKindE6EEE_ZTSN10onnx_torch4NodeE_ZTIN10onnx_torch4NodeE_ZTSN10onnx_torch18version_conversion7AdapterE_ZTIN10onnx_torch18version_conversion7AdapterE_ZTSN10onnx_torch18version_conversion20BaseVersionConverterE_ZTIN10onnx_torch18version_conversion20BaseVersionConverterE_ZTSN10onnx_torch18version_conversion18ArgMaxArgMin_12_11E_ZTIN10onnx_torch18version_conversion18ArgMaxArgMin_12_11E_ZTSN10onnx_torch18version_conversion15AveragePool_7_6E_ZTIN10onnx_torch18version_conversion15AveragePool_7_6E_ZTSN10onnx_torch18version_conversion20AxesAttributeToInputE_ZTIN10onnx_torch18version_conversion20AxesAttributeToInputE_ZTSN10onnx_torch18version_conversion20AxesInputToAttributeE_ZTIN10onnx_torch18version_conversion20AxesInputToAttributeE_ZTSN10onnx_torch18version_conversion22BatchNormalization_6_5E_ZTIN10onnx_torch18version_conversion22BatchNormalization_6_5E_ZTSN10onnx_torch18version_conversion22BatchNormalization_6_7E_ZTIN10onnx_torch18version_conversion22BatchNormalization_6_7E_ZTSN10onnx_torch18version_conversion22BatchNormalization_8_9E_ZTIN10onnx_torch18version_conversion22BatchNormalization_8_9E_ZTSN10onnx_torch18version_conversion30BroadcastBackwardCompatibilityE_ZTIN10onnx_torch18version_conversion30BroadcastBackwardCompatibilityE_ZTSN10onnx_torch18version_conversion29BroadcastForwardCompatibilityE_ZTIN10onnx_torch18version_conversion29BroadcastForwardCompatibilityE_ZTSN10onnx_torch18version_conversion8Cast_9_8E_ZTIN10onnx_torch18version_conversion8Cast_9_8E_ZTSN10onnx_torch18version_conversion10Clip_10_11E_ZTIN10onnx_torch18version_conversion10Clip_10_11E_ZTSN10onnx_torch18version_conversion17CompatibleAdapterE_ZTIN10onnx_torch18version_conversion17CompatibleAdapterE_ZTSN10onnx_torch18version_conversion10Concat_3_4E_ZTIN10onnx_torch18version_conversion10Concat_3_4E_ZTSN10onnx_torch18version_conversion13Dropout_11_12E_ZTIN10onnx_torch18version_conversion13Dropout_11_12E_ZTSN10onnx_torch18version_conversion11Dropout_6_7E_ZTIN10onnx_torch18version_conversion11Dropout_6_7E_ZTSN10onnx_torch18version_conversion20ExtendSupportedTypesE_ZTIN10onnx_torch18version_conversion20ExtendSupportedTypesE_ZTSN10onnx_torch18version_conversion8Gemm_6_7E_ZTIN10onnx_torch18version_conversion8Gemm_6_7E_ZTSN10onnx_torch18version_conversion8Gemm_7_6E_ZTIN10onnx_torch18version_conversion8Gemm_7_6E_ZTSN10onnx_torch18version_conversion11MaxPool_8_7E_ZTIN10onnx_torch18version_conversion11MaxPool_8_7E_ZTSN10onnx_torch18version_conversion24NoPreviousVersionAdapterE_ZTIN10onnx_torch18version_conversion24NoPreviousVersionAdapterE_ZTSN10onnx_torch18version_conversion20RemoveConsumedInputsE_ZTIN10onnx_torch18version_conversion20RemoveConsumedInputsE_ZTSN10onnx_torch18version_conversion11Reshape_4_5E_ZTIN10onnx_torch18version_conversion11Reshape_4_5E_ZTSN10onnx_torch18version_conversion11Reshape_5_4E_ZTIN10onnx_torch18version_conversion11Reshape_5_4E_ZTSN10onnx_torch18version_conversion8Scan_8_9E_ZTIN10onnx_torch18version_conversion8Scan_8_9E_ZTSN10onnx_torch18version_conversion8Scan_9_8E_ZTIN10onnx_torch18version_conversion8Scan_9_8E_ZTSN10onnx_torch18version_conversion9SetIsTestE_ZTIN10onnx_torch18version_conversion9SetIsTestE_ZTSN10onnx_torch18version_conversion11Split_12_13E_ZTIN10onnx_torch18version_conversion11Split_12_13E_ZTSN10onnx_torch18version_conversion11Split_13_12E_ZTIN10onnx_torch18version_conversion11Split_13_12E_ZTSN10onnx_torch18version_conversion7Sum_8_7E_ZTIN10onnx_torch18version_conversion7Sum_8_7E_ZTSN10onnx_torch18version_conversion10Slice_9_10E_ZTIN10onnx_torch18version_conversion10Slice_9_10E_ZTSN10onnx_torch18version_conversion15TypeRestrictionE_ZTIN10onnx_torch18version_conversion15TypeRestrictionE_ZTSN10onnx_torch18version_conversion12Upsample_6_7E_ZTIN10onnx_torch18version_conversion12Upsample_6_7E_ZTSN10onnx_torch18version_conversion12Upsample_8_9E_ZTIN10onnx_torch18version_conversion12Upsample_8_9E_ZTSN10onnx_torch18version_conversion12Upsample_9_8E_ZTIN10onnx_torch18version_conversion12Upsample_9_8E_ZTSN10onnx_torch18version_conversion9AddLayoutE_ZTIN10onnx_torch18version_conversion9AddLayoutE_ZTSN10onnx_torch18version_conversion12RemoveLayoutE_ZTIN10onnx_torch18version_conversion12RemoveLayoutE_ZTSN10onnx_torch18version_conversion12Resize_10_11E_ZTIN10onnx_torch18version_conversion12Resize_10_11E_ZTSN10onnx_torch18version_conversion9TopK_9_10E_ZTIN10onnx_torch18version_conversion9TopK_9_10E_ZTSN10onnx_torch18version_conversion9Pad_10_11E_ZTIN10onnx_torch18version_conversion9Pad_10_11E_ZTSN10onnx_torch18version_conversion13Scatter_10_11E_ZTIN10onnx_torch18version_conversion13Scatter_10_11E_ZTSN10onnx_torch18version_conversion13Softmax_12_13E_ZTIN10onnx_torch18version_conversion13Softmax_12_13E_ZTSN10onnx_torch18version_conversion24BatchNormalization_13_14E_ZTIN10onnx_torch18version_conversion24BatchNormalization_13_14E_ZTSN10onnx_torch18version_conversion13Upsample_9_10E_ZTIN10onnx_torch18version_conversion13Upsample_9_10E_ZTSN10onnx_torch18version_conversion14RoiAlign_15_16E_ZTIN10onnx_torch18version_conversion14RoiAlign_15_16E_ZTSN10onnx_torch18version_conversion23DefaultVersionConverterE_ZTIN10onnx_torch18version_conversion23DefaultVersionConverterE_ZTSZN10onnx_torch5Graph11forEachNodeESt8functionIFvPNS_4NodeEEEEUlPS0_E__ZTSZNK10onnx_torch5Graph11forEachNodeESt8functionIFvPKNS_4NodeEEEEUlPS2_E__ZTSZN10onnx_torch5Value13setUniqueNameERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEbEUlPNS_4NodeEE__ZTSZN10onnx_torch5Value18replaceAllUsesWithEPS0_EUlPNS_4NodeEE__ZTSZNK10onnx_torch5Value4usesEvEUlPKNS_4NodeEE__ZTSSt19_Sp_counted_deleterIPN10onnx_torch5GraphESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTISt19_Sp_counted_deleterIPN10onnx_torch5GraphESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZN10onnx_torch18version_conversion50check_numpy_unibroadcastable_and_require_broadcastERKSt6vectorINS_9DimensionESaIS2_EES6__ZN10onnx_torch18version_conversion31assert_numpy_multibroadcastableERKSt6vectorINS_9DimensionESaIS2_EES6_DW.ref.__gxx_personality_v0_ZN10onnx_torch18version_conversion15assertNotParamsERKSt6vectorINS_9DimensionESaIS2_EE_ZN10onnx_torch18version_conversion21assertInputsAvailableERKNS_8ArrayRefIPNS_5ValueEEEPKcm// 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does not equal NNZ.) second dimension size does not match rank of tensor.] not in lexicographic sorted order.Unrecognized type value case (value_info name: setting data_type field (tensor name: Data of TensorProto ( tensor name: ) is stored externally and should not have data field., but it doesn't exist or is not accessible.) is stored externally but doesn't have a location.) is 0-element but contains data!) should contain one and only one value field.) should not be stored in raw_data fieldUnrecognized data_type (tensor name: ) dimensions are not positive.) is not have a valid element type.setting key_type field (map name: ) to invalid TensorProto key_type ) should not contain more than one keys field.Length of map keys and map values are not the same (map name: ) has zero input and zero output.Warning: Checker does not support models with experimental ops: is deprecated in domain_version of Graph must be in single static assignment (SSA) form, however '' has been used as graph input names multiple times.Tensor initializers must have a non-empty name initializer name is not unique in initializer but not in graph inputSparse tensor initializers must have a non-empty name sparse initializer name is not unique across initializers and sparse_initializersNodes in a graph must be topologically sorted, however input ' is not output of any previous nodes.' has been used as output names multiple times.Bad node spec for node. Name: type field and data field mismatch in attribute ) should not contain more than one value field.) should refer to attribute in parent node.No Opset registered for domain is not compatible with the version imported by model. FunctionOp imports version whereas model imports version ' has been used multiple times.) should not have duplicate outputs specified.) should not have duplicate attributes specified.Nodes in a function must be topologically sorted, however input ' is neither output of any previous nodes nor input of the function.Function must be in single static assignment (SSA) form, however 'The model does not have an ir_version set properly.Your model ir_version is higher than the checker's.Your model has duplicate keys in metadata_props.model with IR version >= 3 must specify opset_import for ONNXmodel with IR version < 3 cannot have opset_import specifiedH‹UH‰ýHƒÀH‰H‹HE H9ÇtèH‰ïèH‰ï]éÿ›%-úSò úÆê®<}ÿ› a$ª¯º}ÿÿÿÿHauÿÿEÿÿ`› 5:¯}–Ïð‘‰Y„ðÇÂøóWæýÿxå…‚ Óøôà:ÅÕ¬§9ÿÿcÿÿJ Š9s…ºƒŒþ´þÍdã¿Ê‹¬¬ÀYƒ§±ùþ§Wÿÿ3M`ÊØÿÿ3M`ÊØÿÿÿÿØ8†CßªÚÃ`¿±§ý—¢°`‡žïêœ¤Ò ½B· Ÿ Ù–š ¡óîdì œ… `ó é ¿Ùä ò`É à± ¬ pÙžä÷±îòùËÆXÛôæÍ³ð ûùÈXÿÿ3IÆ‘Ÿÿÿ3IÆ‘ŸÿÿƒØìêÿÿÁVå!cº!´×Ö ùI» ¸÷Ž±’ÓIÛ ’ž èŽ” š¶ I÷ õÚ Ë G¶éÎxÜÔ²¥ä€×Ž‡ßSÄŽÓã¤ÿŒ+á*Þ0±‡ÓIú’’è„õ¦yÚ½ƒr±µ!ÓXš!¹ý êø ÷Í È ˆ"„)ª"I÷(é$Ç(¿%ì%ò(Ž&v¬(’'(è'…(¬(Â(0ß)¼+î)‘+¿*}ÿÿ2ãÿÿ‹¦ 58‹ Ñó ž¿Þ Ø8Ã ô« Àáç ú8å — ã Ù MÀ êÙ [åÂÑèà8ÛÇÖ‹ ±¤ 8¬Á§çÀ€8»¶ÿÿ3M`ÊØÿÿ3M[ÅÓÿÿjÅÿÿ 8ÜCµØˆãá°XÿÿN7ÿÿˆž.[ƒ—ëä‚æ›8Ëá³®p¼ Õ8æ ñ¶ ¾Ø¨ñ[† ÚÎ § z¾ ‹× Wð¼‰§ëÀ8ÐÜ¸©bÿÿTwÿÿ 8‹CäÏ’Ú†ÿÿ3M`ÊØÿÿ(>7ßíÿÿ >NŒ ÿÿÿÿÿ›Œ€·ÎçÉÎÁÛ;§¦[¢ŽéÇ…’ÔØèÖ® §¶ “éæ‚E˜É¢ÛŽ¥¤—À=ÿˆÓ’ÿ›5Óˆ˜¨ÿ›) s˜’»—©¾¹ÉÿÿsÚÆÔÿ›5Óˆ˜¨ÿ›5Óˆ˜¨ÿ›5Óˆ˜¨ÿ›x¿À·ÿ›+0;ÿ›5Óˆ˜¨ÿÿÅöWÿÿÿ›!ëÅ‰À×òíÿÿ•Ï¥ÿÿ%ÿÿÿÿÿ›)!}¢ôøï†˜¨ÿÿÿ›)!}¢äî§ßöˆ˜ÿÿ-M}Ï í2ÈˆÂÿÿ”ü1úHø[öƒÖŽÖ¹×ñïì†ç™âÁàÌÛÑžèµãÝªèõ Þ±®l¯¨´ ‹ ð ¬ pÕ ª Ó ë ÿ›)[EÐÅÉµÀ¹µÅÆÀ}ÿ›´¨$Ú @–Â ä£ ¸®éŸ€š–•±ÏÄC¤“»©Ÿ… þ ý© ý´ Ñž®ºÌÅ¢®’ ß© mÚ¤Õø®¡ÐÌË×ÆÁ®ƒ²®¹¨£®ÎÖÙÑ±®éäôßÌ®÷ý“@ðáÆ¹¾šÚ¶h¿¬ „¡›½A€€áÒ®Õ}ÿÿ#£ì½ëùóú ƒÓ ûÿÿÅ+·6ƒ³Õ¾®‹,çŸòø¿ïóúÌÇXÁ©Ú8ŽööÃå— þ8üšäçÕç óò Ì¿ï…úÞÇ,¦`’”úåHÝ•èîµåéðÂ½XÅ±ÐŠÍÙØ²¥Õžà÷™ñ©ü‚Éùý„ÖÑXÙòäË±îÊù£Æ„ÿÿ·ßmÿÿ#]ûì¨õê° ŠÿÿZ” - I‡ `ÿx÷’ï¨çÂßÕ×ˆÏ“Ï¹ÞÊ‘Åœ†Îÿÿ$¦ê÷€Ý‚@ò¶GØÊíÿÿ¥ý4mî±Á…i Ö·—¯ÞÛªô‹ª§G›€ÙÔ‹È…äEãŠ…Š££¿=þèÔô»ü×>÷—òãôžÙºh¼²†Sÿÿrã§Ä½¢NµÿÿÿÿÊ ®†“¹˜³“¾‹ÿÿ`*€‚£ä®ºüµ°4µš‹îƒÙîäÁ¼2’ûöñƒ¨ÿÿX ƒ¨ÿÿ ÿÿ+D³O«Á»„ŒÃÿÿUSHƒìH‹oH…ít>H‹}èH‹} HE0H‹]H9ÇtèH‰ïèH…ÛtH‰ÝëÏH‰ïèH…ÛuîHƒÄ[]ÃAVA‰öAUAÁîI‰ýATU‰õ÷ÝSHîƒý †Šƒýc†£ýç†·‰ëH‰Úý'†–H¿KY†8ÖÅm4¹ë(€ƒúc†Çúç†ú'†H‰ÐH‰ÖH÷ç‰ÈƒÁHÁêHþŸ†wÄD`IEA4º-L‰ïI‰EèA¶ÎIMH‹5ëfD‰ëHiÓ…ëQ‰èHÁê%kúd)ø‰ï‰ÕD‰âÀD@¶F¶DˆAT$ÿAƒìˆÿ'w¾ÿçwOƒÅ0L‰è@ˆ)[]A\A]A^Ã@ƒÀA‰ÌIUA4L‰ï‰ëI‰Uº-èA¶ÎIMƒýc‡_ÿÿÿH‹5íE¶,.¶@ˆ)ˆAL‰è[]A\A]A^Ã„HD`éÿÿÿ@HD`éôþÿÿHGAvº-H‰èA¶ÎIMéLÿÿÿ¸A¼éXÿÿÿA¼¹é²þÿÿA¼¹‰ëé þÿÿATI‰üUID$`SH‹PH9ÇtèI‹l$H…íu!ë=f.„èH‰ïèH…Ût!H‰ÝH‹}HEH‹]H9ÇuÚH‰ïèH…ÛußI‹D$I‹|$1öIƒÄ8HÅèI‹|$ÐIÇD$èIÇD$àL9çt[]A\é[]A\Ãbasic_string::appendATI‰üUH‰ÕSH_H‹VH‰L‹LÂL‰ÆèH‰ïèH‰ÂH¸ÿÿÿÿÿÿÿ?I+D$H9ÂwH‰îL‰çèL‰à[]A\ÃH=èH‰ÅI‹<$H9ûtèH‰ïèATI‰ÑL‹FI‰üH‹RH‹H‰÷HƒÆIH9ñ„©L‹WI‹1L9ÐvMQL9Ö„ M‹QL9ÐvGèIT$I‰$H‹HPH9Ñt]I‰$H‹HI‰L$H‹HI‰L$H‰HÇ@Æ@L‰àA\Ãf1Ò1öL‰ÏèIT$I‰$H‹HPH9Ñu²óoHAL$ë²fóo@AD$ë£AºéPÿÿÿDAºéYÿÿÿATI‰üUSH‹oH…íuë9fDèH‰ïèH…Ût!H‰ÝH‹}HEH‹]H9ÇuÚH‰ïèH…ÛußI‹D$I‹<$1öIƒÄ0HÅèI‹|$ÐIÇD$èIÇD$àL9çt []A\é@[]A\ÃSparse tensor indices (Sparse tensor () index value at position [,] out of range.) has values, but NNZ is ] out of range [0, ]] not in sorted order.Field 'name' of value_info is required to be non-empty.type is required but missing.elem_typeshapekey_typevalue_type): data_typetensor) to UNDEFINED is not allowedfloat_dataint32_datastring_dataint64_dataraw_datadouble_datauint64_datalocation) should be stored in TensorProto ( tensor name: TensorProto (tensor name: STRING data (tensor name: ' instead of '' should be stored in field 'values of data_type ''valuessparse_tensor_protoSparse tensor values () must have rank 1.) must have a dense-rank > 0) must have INT64 type.) must have rank 1 or 2.) has no index values.sequence, elem_type: Sequence ( Structure name: map is not allowedMap (name: )optionalOptional ( Structure name: /ATenAffineConstantFillCropDynamicSliceGRUUnitGivenTensorFillImageScalerParametricSoftplusScaleScaledTanhop_typenodeNodeProto (name: , type: No opset import for domain 'ai.onnx.mlai.onnxai.onnx.trainingNo Op registered for with domain_version of Op registered for graphinit' of node: name: OpType: ==> Context: attr.Attribute (name: Opset import for domain in function op functiondomainfunction (Name: \/AUI‰ÕATI‰üH‰×UH‰õèH‰ÂH¸ÿÿÿÿÿÿÿ?H+EH9ÂwaL‰îH‰ïèIT$I‰$H‹HPH9Ñt4I‰$H‹HI‰L$H‹HH‰HÇ@I‰L$Æ@L‰à]A\A]ÃfDóo@AD$ëÌH=èATI‰üUSH‹oH…íuë9fDèH‰ïèH…Ût!H‰ÝH‹}HEH‹]H9ÇuÚH‰ïèH…ÛußI‹D$I‹<$1öIƒÄ0HÅèI‹|$ÐIÇD$èIÇD$àL9çt []A\é@[]A\ÃAWM‰ÇAVI‰ÖAUM‰ÍATI‰üUSHì¨H\$H‰t$Hl$ H‰ßH‰$èH‹|$èH‹t$H‰ïH‰ÂèL‰÷èL‰öH‰ïH‰ÂèH‹4$H‰ïèL‰ÿèL‰þH‰ïH‰ÂèL‰ïèL‰îH‰ïH‰ÂèHt$(L‰çèH‰ßèHÄ¨L‰à[]A\A]A^A_ÃH‰ÅH‰ßèH‰ïèAWI‰ÏAVM‰ÆAUM‰ÍATI‰üUSHì¨H\$H‰t$Hl$ H‰ßH‰$èH‹|$èH‹t$H‰ïH‰ÂèH‹4$H‰ïèL‰ÿèL‰þH‰ïH‰ÂèL‰÷èL‰öH‰ïH‰ÂèL‰ïèL‰îH‰ïH‰ÂèHt$(L‰çèH‰ßèHÄ¨L‰à[]A\A]A^A_ÃH‰ÅH‰ßèH‰ïèAWI‰÷AVM‰ÆAUM‰ÍATI‰üUSHì¨H\$H‰$Hl$ H‰ßH‰L$èL‰ÿèL‰þH‰ïH‰ÂèH‹$H‰ï‹0èH‹t$H‰ïèL‰÷èL‰öH‰ïH‰ÂèL‰ïèL‰îH‰ïH‰ÂèH‹„$àL‹(M…ítSL‰ïèL‰îH‰ïH‰ÂèH‹´$èH‰ïèHt$(L‰çèH‰ßèHÄ¨L‰à[]A\A]A^A_ÃH‹D$ H‹xèHï‹w ƒÎèëªH‰ÅH‰ßèH‰ïèAWI‰÷AVM‰ÆAUM‰ÍATI‰üUSHì¨H\$H‰$Hl$ H‰ßH‰L$èL‰ÿèL‰þH‰ïH‰ÂèH‹$H‰ï‹0èH‹t$H‰ïèL‰÷èL‰öH‰ïH‰ÂèL‰ïèL‰îH‰ïH‰ÂèH‹„$àL‹(M…ítSL‰ïèL‰îH‰ïH‰ÂèH‹´$èH‰ïèHt$(L‰çèH‰ßèHÄ¨L‰à[]A\A]A^A_ÃH‹D$ H‹xèHï‹w ƒÎèëªH‰ÅH‰ßèH‰ïèAWM‰ÏAVI‰ÖAUI‰ÍATI‰üUSHì¨H\$H‰t$Hl$ H‰ßL‰$èH‹|$èH‹t$H‰ïH‰ÂèL‰÷èL‰öH‰ïH‰ÂèL‰ïèL‰îH‰ïH‰ÂèH‹4$H‰ïèL‰ÿèL‰þH‰ïH‰ÂèHt$(L‰çèH‰ßèHÄ¨L‰à[]A\A]A^A_ÃH‰ÅH‰ßèH‰ïèAWAVI‰ÎAUM‰ÍATI‰üUSH‰ÓHì¨L|$H‰t$Hl$ L‰ÿL‰$èH‹|$èH‹t$H‰ïH‰ÂèH‹SH‹3H‰ïèL‰÷èL‰öH‰ïH‰ÂèH‹$H‰ï‹0èL‰ïèL‰îH‰ïH‰ÂèHt$(L‰çèL‰ÿèHÄ¨L‰à[]A\A]A^A_ÃH‰ÅL‰ÿèH‰ïèAWM‰ÏAVI‰ÖAUI‰ÍATI‰üUSHì¨H\$H‰t$Hl$ H‰ßL‰$èH‹|$èH‹t$H‰ïH‰ÂèL‰÷èL‰öH‰ïH‰ÂèL‰ïèL‰îH‰ïH‰ÂèH‹4$H‰ïèL‰ÿèL‰þH‰ïH‰ÂèHt$(L‰çèH‰ßèHÄ¨L‰à[]A\A]A^A_ÃH‰ÅH‰ßèH‰ïèAWAVAUI‰ÍATI‰üUH‰õSH‰ÓHì˜I‰çLt$L‰ÿèH‰ïèH‰îL‰÷H‰ÂèH‹SH‹3L‰÷èL‰ïèL‰îL‰÷H‰ÂèHt$L‰çèH‹H‹|$`HPH‰$HPhHƒÀ@fHnÀH‹H‰”$€HƒÀfHnÈHD$pflÁ)D$H9ÇtèH‹H|$PHƒÀH‰D$èH‹H¼$€H‹PH‹H0H‹RèH‰H‹P H‹H(H‰T$H‹RèH‰LH‹PH‹@H‰$H‹RèH‰H‹HÇD$HƒÀH‰„$€èHÄ˜L‰à[]A\A]A^A_ÃH‰ÅL‰ÿèH‰ïèAVAUATI‰üH‰÷UMt$H‰õSH‰ÓèAÆD$L‰çIÇD$H‹sI‰ÅM‰4$HÆèH¸ÿÿÿÿÿÿÿ?I+D$I9Åw)L‰êH‰îL‰çèH‹SH‹3L‰çè[L‰à]A\A]A^ÃH=èH‰ÅI‹<$I9þtèH‰ïèAUI‰ýATUSHƒìH‹oH…í„£fDH‹}`HEpL‹eH9ÇtèH‹]8H…ÛtH‰ßH‹èH…ÛuðH‹E0H‹}(1öHÅèH‹}(HEXHÇE@HÇE8H9ÇtèH‹}HEH9ÇtèH‰ïèM…ätL‰åépÿÿÿH‰ïèM…äuëI‹EI‹}1öHÅèIÇEIÇEHƒÄ[]A\A]ÃAVºiÇAUI‰õATI‰üI‹}USH‹vèI‹\$1ÒH‰ÅH÷óI‹$L‹$ÐM…ät/M‹$$I‰ÖI‹L$(H9Ít-M‹$$M…ätI‹L$(1ÒH‰ÈH÷óI9ÖtàE1ä[L‰à]A\A]A^ÃI‹UI;T$uÈH…ÒtáI‹t$I‹}è…Àu±[L‰à]A\A]A^ÃUH‰õSH‰ûHƒìH‰îH‰ßèH…ÀtHƒÄ¸[]Ã„H‹[8H…ÛtH‰îH‰ßèH…ÀuÓH‹[8H…ÛuºHƒÄ1À[]ÃAVºiÇAUI‰õATI‰üI‹}USH‹vèI‹\$1ÒH‰ÅH÷óI‹$L‹$ÐM…ät/M‹$$I‰ÖI‹L$0H9Ít-M‹$$M…ätI‹L$01ÒH‰ÈH÷óI9ÖtàE1ä[L‰à]A\A]A^ÃI‹UI;T$uÈH…ÒtáI‹t$I‹}è…Àu±[L‰à]A\A]A^ÃUH‹oH…ítH‹}HEH9ÇtèH‰ï]é]ÃUnable to open proto file: . Please check if it is a valid proto. Unable to parse proto from file: . Please check if it is a valid protobuf file of proto. 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Ž´™@æ£Á@ Ž´‚Aá£ªA Ž´ðA¬£˜B Ž´êB§£’C Ž´ßCò¢‡D Ž´ÔDí¢üD Ž´ÂE¸¢êE Ž´¼F³¢äF Ž´¸Gþ¡àG Ž´«Hù¡ÓH Ž´¥IÄ¡ÍI2Ž´…J¿¡JŽ´ñJŠ¡™K Ž´ÚK…¡‚L Ž´ÃLÐ ëL Ž´¬MË ÔM Ž´šN– ÂN Ž´ƒO‘ «O Ž´ñOÜŸ™P2Ž´ÑP×ŸùPŽ´¶Q¢ŸÞQ2Ž´–RŸ¾RŽ´ýRèž¥S Ž´ëSãž“T Ž´ÛT®žƒU Ž´ÄU©žìU Ž´²VôÚV2Ž´’WïºWŽ´÷WºŸX2Ž´×XµÿXŽ´¾Y€æY Ž´©ZûœÑZ Ž´”[Æœ¼[ Ž´ý[Áœ¥\ Ž´ð\Œœ˜] Ž´â]‡œŠ^ Ž´Ð^Ò›ø^2Ž´°_Í›Ø_Ž´ž`˜›Æ` Ž´a“›¸a Ž´ýaÞš¥b Ž´æbÙšŽc Ž´Øc¤š€d Ž´ÒdŸšúd Ž´Çeê™ïe2Ž´§få™ÏfŽ´‘g°™¹g Ž´h«™©h Ž´êhö˜’i Ž´Óiñ˜ûi Ž´Ãj¼˜ëj Ž´¶k·˜Þk Ž´¦l‚˜Îl Ž´”mý—¼m Ž´‚nÈ—ªn2Ž´ânÃ—Šo.Ž´¾oŽ—æo.Ž´šp‰—Âp.Ž´öpÔ–žqŽ´ÛqÏ–ƒr Ž´Ærš–îr2Ž´¦s•–ÎsŽ´¡tà•Ét Ž´ uÛ•Èu2Ž´€v¦•¨v<Ž´—w¡•¿w;Ž´±xì”Ùx Ž´®yç”Öy Ž´£z²”Ëz2Ž´ƒ{”«{Ž´ï{ø“—| Ž´ç|ó“} Ž´î}¾“–~2Ž´Î~¹“ö~Ž´Æ„“î Ž´»€ÿ’ã€ Ž´·Ê’ß Ž´¬‚Å’Ô‚ Ž´µƒ’Ýƒ Ž´ž„‹’Æ„ Ž´‹…Ö‘³… Ž´ù…Ñ‘¡† Ž´ó†œ‘›‡ Ž´Ü‡—‘„ˆ Ž´Ïˆâ÷ˆ Ž´¿‰Ýç‰ Ž´¯Š¯×Š2Ž´‹ª·‹Ž´ñ‹µ™Œ Ž´éŒ«‘ Ž´ð°˜Ž2Ž´ÐŽ¦øŽŽ´áit != sym_to_string_.end()customString/opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/common/interned_strings.cc%s:%u: %s: Assertion `%s` 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H9Ç„(L‰öèAƒÅD;m8ŒÿÿÿHƒÄL‰à[]A\A]A^A_Ã„A‹t$$9ò„¸ƒÂI‹|$‰èH‰ÃIcD$ I‹T$(HA‰L$ H‰\ÂA‹F¨„PþÿÿH‹CM‹~ƒKH‰ÆHƒæþH‰4$¨…ÿèH‹{H9ÇtqL‰þèA‹F¨…þÿÿAƒÅD;m Œ¿ýÿÿéþÿÿf.„A‹t$graph_ == this && !n->inGraphList()!inGraphList() && n->inGraphList()/opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/common/ir_pb_converter.cc%s:%u: %s: Assertion `%s` failed: Warning: onnx version converter is unable to parse input model. 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L‰ùL9ûuàH‹ðúÿÿH…ÉtH‰ÏèH‹½0ûÿÿH;½°úÿÿtèH‹½ÐúÿÿèH‹…ÀúÿÿHeØ[A\A]A^A_]ÃDH‰ÈHƒÀ H‰ÁH9ÃuëŸ€HÇ…¸úÿÿéZýÿÿ„L‰ïè…À„éüÿÿH5L‰ÿè‘¶ÿÿH½pûÿÿH5è~¶ÿÿH½ûÿÿH5èk¶ÿÿH½°ûÿÿH5èX¶ÿÿH½ÐûÿÿH5èE¶ÿÿH½ðûÿÿH5è2¶ÿÿH½üÿÿH5è¶ÿÿH½0üÿÿH5è¶ÿÿH½PüÿÿH5èùµÿÿH½püÿÿH5èæµÿÿH½üÿÿH5èÓµÿÿH½°üÿÿH5èÀµÿÿH½ÐüÿÿH5èµÿÿH½ðüÿÿH5èšµÿÿH…ýÿÿH5H‰ÇH‰…¸úÿÿè}µÿÿH‹fïÀ¿àHÇCèI‰ÆH‰H€àM‰üH‰CL‰µØúÿÿfH‹ØúÿÿI‹4$I‹T$HAH‰ÏH‰HòèÞµÿÿHƒ…Øúÿÿ IƒÄ L;¥ÐúÿÿuÉH‹…ØúÿÿHH‰ÞH=H‰CèL‰ïèH‹•¸úÿÿë€èIFàM9÷„ûÿÿH‰ÂH‹:HBI‰ÖH9ÇuÜHBàI9×uåéîúÿÿM…äyèèH=èI‰ÄéH‰ÇéH‰ÃéH‰ÇéH‰ÃéI‰ÆéI‰ÆéI‰ÄéH‰Ãé¸Ã…ÒtƒúuH‰71ÀÃH‹H‰1ÀÃ¸Ã…ÒtƒúuH‰71ÀÃH‹H‰1ÀÃI‰øH‰÷Aÿ H‹UH‰ýHƒÀH‰H‹HE H9ÇtèH‰ï]éL‹HƒÇI9øtL‰Çé@ÃATI‰üUSH‹_H‹/H9ët$DH‹}HEH9Çt+èHƒÅ H9ëuåI‹,$H…ít$[H‰ï]A\é„HƒÅ H9ëu¿ëØD[]A\Ã…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃfH‹H‰ëÖAUE1íATUH‰õSH‰ûHƒìHƒ?„³H‹mH…í„‘¿èHÇI‰ÄH‹E1ÒHKL‰cI‰D$H÷sH‹H‰ÐH‹mH…íuëTf„H‹mH…ítBI‰Ì¿èHÇH‰ÁH‹E1ÒH‰AH÷sH‹I‰$HÐHƒ8uÁL‰ H‹mH…íu¾HƒÄ[]A\A]Ãf.„H‹WHƒút1H‰ÐHÁè]() %b = Constant[value = ]() %keepgoing = Constant[value = ]() %max_trip_count = Constant[value = ]() %keepgoing_out, %b_out, %user_defined_vals = Loop[body = ](%max_trip_count, %keepgoing, %b) return } graph body-net ( %i[INT32, scalar] // iteration number %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used %b_in[INT32, scalar] // incoming value of loop-carried-dependency b ) { %my_local = Add(%a, %b_in) %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated return %keepgoing_out, %b_out, %user_defined_val } *Sample equivalent C code* { /* User-defined code (enclosing scope) */ int a = 3, b = 6; bool keepgoing = true; // Analogous to input cond /* End user-defined code */ /* Implicitly-defined code */ const int max_trip_count = 10; // Analogous to input M int user_defined_vals[]; // Imagine this is resizable /* End implicitly-defined code */ /* initialize loop-carried variables and scan-output variables */ bool keepgoing_out = keepgoing int b_out = b for (int i=0; i < max_trip_count && keepgoing_out; ++i) { /* Implicitly-defined code: bind actual parameter values to formal parameter variables of loop-body */ bool keepgoing_in = keepgoing_out; bool b_in = b_out; /* User-defined code (loop body) */ int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine b_out = a - b_in; keepgoing_out = my_local > b_out; user_defined_val = b_in + b_in; // b_in and b_out are different variables /* End user-defined code */ /* Implicitly defined-code */ user_defined_vals[i] = user_defined_val // accumulate scan-output values } // int t = my_local; // Can't do this. my_local is not accessible here. // The values below are bound to the output variables of the loop and therefore accessible // b_out; user_defined_vals; keepgoing_out; } There are several things of note in this code snippet: 1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can be referenced in the inputs of the loop. 2) Any values computed in the loop body that needs to be used in a subsequent iteration or after the loop are modelled using a pair of variables in the loop-body, consisting of an input variable (eg., b_in) and an output variable (eg., b_out). These are referred to as loop-carried dependences. The loop operation node supplies the input value of the input variable for the first iteration, and returns the output value of the output variable produced by the final iteration. 3) Scan_output variables are used to implicitly concatenate values computed across all the iterations. In the above example, the value of user_defined_val computed over all iterations are concatenated and returned as the value of user_defined_vals after the loop. 4) Values created in the body cannot be accessed in the enclosing scope, except using the mechanism described above. Note that the semantics of this op support "diagonal" or "wavefront" execution. (See Step 3 here for an example: https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/). Frontends should emit multi-layer RNNs as a series of While operators (with time being the inner looping dimension), with each successive layer consuming the scan_outputs from the previous layer, possibly going through several point-wise operators (e.g. dropout, residual connections, linear layer). The input/output of subgraph (produced by loop node) matching is based on order instead of name. The implementation will figure out the names based on this order. A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.A boolean termination condition. Optional. Pass empty string to skip.The initial values of any loop-carried dependencies (values that change across loop iterations)Final N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.tensor of int64, which should be a scalar.tensor of bool, which should be a scalar. Scan can be used to iterate over one or more scan_input tensors, constructing zero or more scan_output tensors. It combines ideas from general recurrences, functional programming constructs such as scan, fold, map, and zip and is intended to enable generalizations of RNN-like constructs for sequence-to-sequence processing. Other tensors (referred to as state_variables here) can be used to carry a state when iterating from one element to another (similar to hidden-state in RNNs, also referred to as loop-carried dependences in the context of loops). Many common usages involve a single scan_input tensor (where functionality similar to scan, fold and map can be obtained). When more than one scan_input is used, a behavior similar to zip is obtained. The attribute body must be a graph, specifying the computation to be performed in every iteration. It takes as input the current values of the state_variables and the current iterated element of the scan_inputs. It must return the (updated) values of the state_variables and zero or more scan_output_element tensors. The values of the scan_output_element tensors are concatenated over all the iterations to produce the scan_output values of the scan construct (similar to the concatenated intermediate hidden-state values of RNN-like constructs). All the output tensors (state_variables as well as scan_output_element tensors) are required to have the same shape in each iteration of the loop (a restriction imposed to enable efficient memory allocation). Note that the iterated element passed to the body subgraph does not have a sequence axis. It will have a rank one less than the rank of the corresponding scan_input. The scan operation returns the final values of the state_variables as well as the scan_outputs. The optional attribute scan_input_directions specifies the direction (forward or backward) for each scan input. If this attribute is omitted, all sequences are scanned in the forward direction. A bidirectional scan may be performed by specifying the same tensor input twice in the scan_inputs, once with a forward direction, and once with a backward direction. The scan_output of the operation is produced by concatenating the scan_output_element values produced by the body in each iteration. The optional attribute scan_output_directions specifies the direction in which scan_output is constructed (by appending or prepending the scan_output_element to scan_output in each iteration) for each scan_output. If this attribute is omitted, the scan_output_element is appended to the scan_output in each iteration. The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input. If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1. Note that scanning a non-zero axis may be less efficient than scanning axis zero. The optional attribute scan_output_axes specifies the axis along which the scan_outputs are accumulated for each scan_output. For example, if axis 1 is the time axis (to be scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis value of 1. Note that because of the ONNX restriction that only the last parameter of an operator can be variadic, the initial-states and scan-inputs are listed together as one input parameter. Similarly, the final-states and scan-outputs are listed together as one output parameter. The attribute num_scan_inputs indicates the number M of scan-inputs. The behavior of Scan < num_scan_inputs = m, body = loop-body, scan_input_axes = [axis_1, ..., axis_m] > (init_1, ..., init_n, scan_1, ..., scan_m) is equivalent to the following pseudo-code: // scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i // scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j. sequence_length = scan_1.shape[axis_1]; // initialize state-variables st_1 = init_1; ... st_n = init_n; // initialize scan-output variables: [] denotes an empty tensor scan_out_1 = []; ...; scan_out_k = []; // identify number of iterations: // execute loop for (int t = 0; t < sequence_length; ++t) { // generate the scan-input elements: the notation T[t] indicates the sub-tensor // of rank one less than T obtained by indexing T at position t along axis k. si_1 = scan_1[t]; ... ; si_m = scan_m[t]; // execute loop-body st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m) // accumulate the scan-output elements scan_out_1 = Concat(scan_out_1, so_1); ... ; scan_out_k = Concat(scan_out_k, so_k); } return st_1, ..., st_n, scan_out_1, ..., scan_out_k; *Sample usage: Encoding RNN using a Scan* The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi, recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes %Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these values are computed in the outer graph, they need to be passed in as extra state_variables. graph rnn-encoding { %H_0 = ... %X = ... %Y_h, %Y = Scan[body = , num_scan_inputs=1](%H_0, %X) return %Y, %Y_h } graph rnn-cell-1 ( %H_tminus1[FLOAT, tensor] %X_t[FLOAT, tensor] ) { %Wi = ... %Ri = ... %Wbi = ... %Rbi = ... %t1 = X_t * (Wi^T) %t2 = H_tminus1*(Ri^T) %t3 = Add(%t1, %t2) %t4 = Add(%t3, %Wbi) %t5 = Add(%t4, %Rbi) %Ht = Tanh(%t5) %Accumulate = Identity(%Ht) return %Ht, %Accumulate } Initial values of the loop's N state variables followed by M scan_inputsFinal values of the loop's N state variables followed by K scan_outputsThe graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.An attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].H‹UH‰ýHƒÀH‰H‹HE H9ÇtèH‰ïèH‰ï]évector::_M_range_insert != If node has but subgraphs produce axis value ).scan_output_axes was not a tensor.Scan input outputs. 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H‹¼$ÐHƒÀ)¬$€H‰D$pH‹HƒÀhH‰„$ðH;|$0tèH‹H‹|$PHƒÀH‰„$ˆèH‹D$H‹T$L‰ÿH‹L$H‹@èH‰TpI‹FèL‰´$€H‹$H‰Œ€H‹EèH‰l$pH‰TpH‹HÇD$xHƒÀH‰„$ðèHÄL‰à[]A\A]A^A_ÃfDI‰Àéÿÿÿ„H´$ÐL‰çèéüþÿÿH‰ÅëH‰ÅëI‰Äë*I‰ÄëcI‰Äë;I‹<$H9ûtèH‹|$hèH‰ïèH‹EèH‹$H‰l$pH‰LpHÇD$xH‹L‰ÿHƒÀH‰„$ðèL‰çèH‰ßèH‹D$H‹L$H‹@èH‰LpI‹FèH‹L$L‰´$€H‰Œ€ëŒAWAVAUATI‰üUSHìøH‹L´$àL|$`H‰t$PHƒÀ@L‰÷H‰T$@fHnÀH‹L‰|$XfoÈfoÐHƒÀfHnØH‹flËHƒÀ)L$ fHnàflÔ)T$0èH‹H‹1öHÇ„$¸fïÀH‹kHƒÀH‹K„$ÈH‰„$à1Àf‰„$À„$ØH‹EèH‰l$`H‰L`HÇD$hH‹}èH‰$LÿèL‹k H‹C(L|$p1öI‹}èL‰l$pH‰D$LÿH‰èH‹CH‹K0fïÀH\$xfoT$0H‰D$H‹@èH‰L$H‰L`H‹)T$pHƒÀ)„$€H‰D$`H‹)„$HƒÀh)„$ H‰„$àH„$°H‰ÇH‰D$HèH‹H‰ÞL‰÷Ç„$¸HƒÀÆ„$ÐH‰D$xH„$ÐH‰D$0H‰„$ÀHÇ„$ÈèH‹\$PH‰ßèH‰ÞL‰ÿH‰ÂèH‹\$@H‰ßèH‰ÞL‰ÿH‰ÂèH‹„$ I\$IÇD$I‰$AÆD$H…À„L‹„$M…À„óL9À‡êH‹Œ$˜1Ò1öL‰çI)ÈèH‹fol$ H‹¼$ÀHƒÀ)l$pH‰D$`H‹HƒÀhH‰„$àH;|$0tèH‹H‹|$HHƒÀH‰D$xèH‹D$H‹T$L‰÷H‹t$H‹@èH‰T`I‹EèL‰l$pH‹$H‰tpH‹EèH‰l$`H‰T`H‹HÇD$hHƒÀH‰„$àèHÄøL‰à[]A\A]A^A_ÃfDI‰Àéÿÿÿ„H´$ÀL‰çèéÿÿÿH‰ÅëH‰ÅëI‰Äë*I‰ÄëcI‰Äë;I‹<$H9ûtèH‹|$XèH‰ïèH‹EèH‹4$H‰l$`H‰t`HÇD$hH‹L‰÷HƒÀH‰„$àèL‰çèH‰ßèH‹D$H‹t$H‹@èH‰t`I‹EèH‹t$L‰l$pH‰tpë’Input type was null[TypeInferenceError] Input Element type of input unknownOutput expected to have tensor or sparse tensor type. Got: AWAVAUI‰ÕATI‰ôUH‰ýSHì¸H‹ÿPH…À„ÔD‹p(Aƒþt Aƒþ…~H‹@ ‹X …Û„XH‹EH‰ïL‰îÿP(D‹`(H‰ÅAƒüt2Aƒüt,E…ä…¨AƒþteAƒþt/HÄ¸[]A\A]A^A_ÃDH‹U ‹B‰Z ƒÈ‰Bë×fDH‰ÇèH‹EÇE(H‰ÇHƒçþ¨uEèH‰E ƒH‰X ë¡H‰ÇèH‹EÇE(H‰ÇHƒçþ¨uèH‰E ƒH‰X énÿÿÿH‹?ë¶H‹?ëá¿0L|$ èL‰ÿI‰ÆèHl$0ºH5H‰ïèºH5H‰ïèL‰îH‰ïèº5H5H‰ïèD‰æH‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰÷èH‹H‹<$AÆF IÇFHƒÀI‰IF I‰FHD$H9ÇtèH‹5HL‰÷è¿0L|$ èHH5L‰ÿH‰ÅèL‰þH‰ïèH‹H‹|$ HÇEÆE HƒÀH‰EHE H‰EHD$0H9ÇtèH‹5HH‰ïèI‰ÄëI‰ÄëH‹|$ HD$0H9ÇtèH‰ïèL‰çèH‰Åé¡H‰Åé®¿0L|$ èL‰ÿI‰ÅèHl$0ºH5H‰ïèºH5H‰ïèL‰æH‰ïèº5H5H‰ïèD‰öH‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰ïèH‹AÆE IÇEHƒÀI‰EIE I‰EH‹<$HD$H9ÇtèH‹5HL‰ïèH‰ÅéÍ¿0L|$ èL‰ÿI‰ÅèHl$0ºH5H‰ïèºH5H‰ïèL‰æH‰ïèºH5H‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰ïèéÿÿÿH‰ÅëH‰ÅëL‰ÿèL‰ïèH‰ïèH‹<$HD$H9ÇtèëL‰ÿèL‰÷èH‰ïèH‰Åëëùë¨ë«H‹<$HD$H9Çtªèë£AWAVAUATI‰üUSHìH‹L¼$ðLt$pH‰t$XHƒÀ@L‰ÿH‰L$HfHnÀH‹H‰T$@foÈfoÐL‰D$`HƒÀL‰t$hfHnØH‹flËHƒÀ)L$ fHnàflÔ)T$0èH‹H‹1öHÇ„$ÈfïÀH‹kHƒÀH‹K„$ØH‰„$ð1Àf‰„$Ð„$èH‹EèH‰l$pH‰LpHÇD$xH‹}èH‰$L÷èL‹s H‹C(L¬$€1öI‹~èL‰´$€H‰D$LïH‰èH‹CH‹s0fïÀHœ$ˆfoT$0H‰D$H‹@èH‰t$H‰tpH‹)”$€HƒÀ)„$H‰D$pH‹)„$ HƒÀh)„$°H‰„$ðH„$ÀH‰ÇH‰D$PèH‹H‰ÞL‰ÿÇ„$ÈHƒÀÆ„$àH‰„$ˆH„$àH‰D$0H‰„$ÐHÇ„$ØèH‹\$XH‰ßèH‰ÞL‰ïH‰ÂèH‹\$@H‰ßèH‰ÞL‰ïH‰ÂèH‹D$HL‰ïH‹0èH‹\$`H‰ßèH‰ÞL‰ïH‰ÂèH‹„$°I\$IÇD$I‰$AÆD$H…À„ L‹„$ M…À„ÿL9À‡öH‹Œ$¨1Ò1öL‰çI)ÈèH‹fol$ H‹¼$ÐHƒÀ)¬$€H‰D$pH‹HƒÀhH‰„$ðH;|$0tèH‹H‹|$PHƒÀH‰„$ˆèH‹D$H‹T$L‰ÿH‹L$H‹@èH‰TpI‹FèL‰´$€H‹$H‰Œ€H‹EèH‰l$pH‰TpH‹HÇD$xHƒÀH‰„$ðèHÄL‰à[]A\A]A^A_ÃfDI‰Àéÿÿÿ„H´$ÐL‰çèéüþÿÿH‰ÅëH‰ÅëI‰Äë*I‰ÄëcI‰Äë;I‹<$H9ûtèH‹|$hèH‰ïèH‹EèH‹$H‰l$pH‰LpHÇD$xH‹L‰ÿHƒÀH‰„$ðèL‰çèH‰ßèH‹D$H‹L$H‹@èH‰LpI‹FèH‹L$L‰´$€H‰Œ€ëŒAWAVAUATI‰üUSHìH‹L¼$ðLt$pH‰t$XHƒÀ@L‰ÿH‰L$HfHnÀH‹H‰T$@foÈfoÐL‰D$`HƒÀL‰t$hfHnØH‹flËHƒÀ)L$ fHnàflÔ)T$0èH‹H‹1öHÇ„$ÈfïÀH‹kHƒÀH‹K„$ØH‰„$ð1Àf‰„$Ð„$èH‹EèH‰l$pH‰LpHÇD$xH‹}èH‰$L÷èL‹s H‹C(L¬$€1öI‹~èL‰´$€H‰D$LïH‰èH‹CH‹s0fïÀHœ$ˆfoT$0H‰D$H‹@èH‰t$H‰tpH‹)”$€HƒÀ)„$H‰D$pH‹)„$ HƒÀh)„$°H‰„$ðH„$ÀH‰ÇH‰D$PèH‹H‰ÞL‰ÿÇ„$ÈHƒÀÆ„$àH‰„$ˆH„$àH‰D$0H‰„$ÐHÇ„$ØèH‹\$XH‰ßèH‰ÞL‰ïH‰ÂèH‹\$@H‰ßèH‰ÞL‰ïH‰ÂèH‹D$HL‰ïH‹0èH‹\$`H‰ßèH‰ÞL‰ïH‰ÂèH‹„$°I\$IÇD$I‰$AÆD$H…À„ L‹„$ M…À„ÿL9À‡öH‹Œ$¨1Ò1öL‰çI)ÈèH‹fol$ H‹¼$ÐHƒÀ)¬$€H‰D$pH‹HƒÀhH‰„$ðH;|$0tèH‹H‹|$PHƒÀH‰„$ˆèH‹D$H‹T$L‰ÿH‹L$H‹@èH‰TpI‹FèL‰´$€H‹$H‰Œ€H‹EèH‰l$pH‰TpH‹HÇD$xHƒÀH‰„$ðèHÄL‰à[]A\A]A^A_ÃfDI‰Àéÿÿÿ„H´$ÐL‰çèéüþÿÿH‰ÅëH‰ÅëI‰Äë*I‰ÄëcI‰Äë;I‹<$H9ûtèH‹|$hèH‰ïèH‹EèH‹$H‰l$pH‰LpHÇD$xH‹L‰ÿHƒÀH‰„$ðèL‰çèH‰ßèH‹D$H‹L$H‹@èH‰LpI‹FèH‹L$L‰´$€H‰Œ€ëŒ[ShapeInferenceError] Target=Mismatch between source and target type. Source=Unsupported Source/Target type=AWAVAUATUSHì¸‹o(D‹n(D9íuwI‰üH‰óƒý„ôƒý„‹ƒý„Jƒý …ƒAƒý …ßH‹k H‹]ƒMH…Û„ Aƒ|$( „.H‹L‹`M…ä„,A‹l$(D‹k(D9ít¿0Lt$ èL‰÷I‰ÄèL|$0ºH5L‰ÿèº0H5L‰ÿè‰îL‰ÿèºH5L‰ÿèD‰îL‰ÿèH‰åHt$8H‰ïèL‰÷èH‰îL‰çèé@H‰ßèH‹CÇC( H‰ÇHƒçþ¨…èH‰ÅH‰C H‹]ƒMH…Û…÷þÿÿH‹EH‰ÇHƒçþ¨…&èAƒ|$( H‰EH‰Ã…ØþÿÿfDI‹D$ L‹`M…ä…ÔþÿÿL‹%éÈþÿÿfƒýt[I‹D$ ö@„œL‹`M…ä„WAƒý…H‹k H‹}ƒMH…ÿ„HHÄ¸L‰æ[]A\A]A^A_é€I‹D$ ö@tEL‹`M…ä„ðAƒýtH‰ßèH‹CÇC(H‰ÇHƒçþ¨…]èH‰C H‰ÅëfHÄ¸[]A\A]A^A_ÃfDAƒýu2H‹k H‹]ƒMH…Û„ÝAƒ|$(„ñþÿÿH‹é¾ýÿÿDH‰ßèH‹CÇC(H‰ÇHƒçþ¨…¾èH‰C H‰Åë¢H‰ßèH‹CÇC(H‰ÇHƒçþ¨…žèH‰C H‰ÅéÌþÿÿDL‹%éÿÿÿ@L‹%éþÿÿ@H‹EH‰ÇHƒçþ¨uaèH‰EH‰Çé˜þÿÿ„H‹?éÚýÿÿ„H‹EH‰ÇHƒçþ¨u6èH‰EH‰ÃéÿÿÿH‹?é:ÿÿÿH‹?éÒýÿÿH‹?éZÿÿÿH‹?ëšH‹?é›þÿÿH‹?ëÅ¿0Lt$ èL‰÷I‰ÄèL|$0ºH5L‰ÿèºH5L‰ÿè‰îL‰ÿèH‰åHt$8H‰ïèL‰÷èH‰îL‰çèH‹AÆD$ IÇD$HƒÀI‰$ID$ I‰D$H‹<$HD$H9ÇtèH‹5HL‰çèH‰Åë H‰ÅëH‰ÅëH‹<$HD$H9ÇtèëL‰÷èL‰çèH‰ïèëÂëÅëÈElement type of tensor or sparse tensor input was unknownInput was expected to have tensor or sparse tensor type. Got does not match existing output type of Output was expected to have tensor type. Got Input element type of AWAVAUATUSHì¸H…ÿ„tD‹g(H‰óAƒüt Aƒü…˜H‹G D‹h E…í„Ì‹k(…í„³ƒýu.H‹C D‹` E…ä„ÝE9ì…kHÄ¸[]A\A]A^A_ÃfƒýtÍ¿0Lt$ èL‰÷I‰ÄèL|$0ºH5L‰ÿèº-H5L‰ÿè‰îL‰ÿèH‰åHt$8H‰ïèL‰÷èH‰îL‰çèézfH‰ßAƒütOèH‹CÇC(H‰ÇHƒçþ¨u)èH‰C ƒHD‰h é2ÿÿÿfDƒHD‰h éÿÿÿH‹?ëÒfDèH‹CÇC(H‰ÇHƒçþ¨uèH‰C ƒHD‰h éãþÿÿH‹?ëå¿0Lt$ èHH5L‰÷H‰ÅèL‰öH‰ïèH‹H‹|$ HÇEÆE HƒÀH‰EHE H‰EHD$0H9ÇtèH‹5HH‰ïè¿0Lt$ èL‰÷I‰ÄèL|$0ºH5L‰ÿèº9H5L‰ÿèH‰åHt$8H‰ïèL‰÷èH‰îL‰çèH‹AÆD$ IÇD$HƒÀI‰$ID$ I‰D$H‹<$HD$H9ÇtèH‹5HL‰çè¿0Lt$ èL‰÷I‰ÅèL|$0ºH5L‰ÿèº=H5L‰ÿèD‰æL‰ÿèH‰åHt$8H‰ïèL‰÷èH‰îL‰ïèH‹H‹<$AÆE IÇEHƒÀI‰EIE I‰EHD$H9ÇtèH‹5HL‰ïèH‰Åë H‰Åë0H‰ÅëH‹<$HD$H9ÇtèL‰ïèH‰ïèH‰ÅéTL‰÷èëÞH‰Åé¿0Lt$ èL‰÷H‰ÃèL|$0ºH5L‰ÿèºH5L‰ÿèD‰îL‰ÿèº(H5L‰ÿèD‰æL‰ÿèH‰åHt$8H‰ïèL‰÷èH‰îH‰ßèH‹H‹<$HÇCÆC HƒÀH‰HC H‰CHD$H9ÇtèH‹5HH‰ßèéÿÿÿéÿÿÿH‰Åë.H‰ÅH‹<$HD$H9ÇtèH‰ßèH‰ïèL‰÷èL‰çèH‰ïèH‰ÅëH‰ÅëÎL‰÷èëÄë§H‹<$HD$H9ÇtÌèëÅI‰ÄëI‰ÄëH‹|$ HD$0H9ÇtèH‰ïèL‰çèAWAVAUATI‰üUSHìH‹L¼$ðLt$pH‰t$`HƒÀ@L‰ÿH‰L$PfHnÀH‹H‰T$HfoÈfoÐL‰t$hHƒÀfHnØH‹flËHƒÀ)L$ fHnàflÔ)T$0èH‹H‹1öHÇ„$ÈfïÀH‹kHƒÀH‹K„$ØH‰„$ð1Àf‰„$Ð„$èH‹EèH‰l$pH‰LpHÇD$xH‹}èH‰L$L÷èL‹k H‹C(L´$€1öI‹}èL‰¬$€H‰D$L÷H‰èH‹CH‹S0fïÀHœ$ˆfoT$0H‰D$H‹@èH‰T$@H‰TpH‹)”$€HƒÀ)„$H‰D$pH‹)„$ HƒÀh)„$°H‰„$ðH„$ÀH‰ÇH‰D$XèH‹H‰ÞL‰ÿÇ„$ÈHƒÀÆ„$àH‰„$ˆH„$àH‰D$0H‰„$ÐHÇ„$ØèH‹\$`H‰ßèH‰ÞL‰÷H‰ÂèH‹\$HH‰ßèH‰ÞL‰÷H‰ÂèH‹D$PL‰÷‹0èH‹„$°I\$IÇD$I‰$AÆD$H…À„ L‹„$ M…À„ÿL9À‡öH‹Œ$¨1Ò1öL‰çI)ÈèH‹fol$ H‹¼$ÐHƒÀ)¬$€H‰D$pH‹HƒÀhH‰„$ðH;|$0tèH‹H‹|$XHƒÀH‰„$ˆèH‹D$H‹t$@L‰ÿH‹L$H‹@èH‰tpI‹EèL‰¬$€H‹t$H‰Œ€H‹EèH‰l$pH‰tpH‹HÇD$xHƒÀH‰„$ðèHÄL‰à[]A\A]A^A_ÃDI‰Àéÿÿÿ„H´$ÐL‰çèéüþÿÿH‰ÅëH‰ÅëI‰Äë*I‰ÄëdI‰Äë¿0Hl$@èHL$H‰ïLHH5I‰ÄèH‰îL‰çèH‹H‹|$@IÇD$AÆD$ HƒÀI‰$ID$ I‰D$HD$PH9ÇtèH‹5HL‰çè¿0Hl$@èHL$H‰ïLHH5I‰ÄèH‰îL‰çèH‹AÆD$ IÇD$HƒÀI‰$ID$ I‰D$H‹|$@HD$PH9ÇtèH‹5HL‰çèH‹|$@HD$PH9ÇtèL‰çèL‰ïèH‰ïèH‰ÅëH‰ÅëH‹|$@HD$PH9ÇtèL‰çèH‰ïèH‰Åë¡H‰Åë°H‰Åé¿0Hl$@èHL$H‰ïLHH5I‰ÄèH‰îL‰çèH‹H‹|$@IÇD$AÆD$ HƒÀI‰$ID$ I‰D$HD$PH9ÇtèH‹5HL‰çèH‹|$@HD$PH9ÇtèL‰çèL‰ïèH‰ïèH‰ÅëÏH‰ÅëÞ¿0Hl$@èHL$H‰ïLHH5I‰ÄèH‰îL‰çèé7þÿÿ¿0Hl$@èHL$H‰ïLHH5I‰ÄèH‰îL‰çèéóýÿÿélþÿÿélþÿÿébþÿÿébþÿÿAWAVAUATUH‰õSH‰û¿ˆHƒì8èH‹s H‹S(Lh0Hx I‰ÄL‰h 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init_1, ..., init_n, scan_1, ..., scan_m) is equivalent to the following pseudo-code: // T.shape[0] denotes the batch-size of T // The batch-size of scan_1, ..., scan_m are all required to be equal batch_size = scan_1.shape[0]; // scan_i.shape[1] denotes the (max) sequence-length of scan_i // scan_i.shape[1] is required to be equal to scan_j.shape[1] for all i,j. max_sequence_length = scan_1.shape[1]; for (int batch = 0; batch < batch_size; ++batch) { // initialize state-variables st_1 = init_1; ... st_n = init_n; // initialize scan-output variables: [] denotes an empty tensor scan_out_1 = []; ...; scan_out_k = []; // identify number of iterations: N = (sequence_lengths specified) ? sequence_lengths[batch] : max_sequence_length; // execute loop for (int t = 0; t < N; ++t) { // generate the scan-input elements: the notation T[t] indicates the sub-tensor // of rank one less than T obtained by indexing T at position t along axis k. si_1 = (scan_1[batch])[t]; ... ; si_m = (scan_m[batch])[t]; // execute loop-body st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m) // accumulate the scan-output elements scan_out_1 = Concat(scan_out_1, so_1); ... ; scan_out_k = Concat(scan_out_k, so_k); } // accumulate the outputs for this batch: bst_1[batch] = st_1; ..., bst_n[batch] = st_n; // Note scan-outputs will have size max_sequence_length, but only first N values will be meaningful. // The remaining values have an undefined value. b_scan_out_1[batch] = scan_out_1; ...; b_scan_out_k[batch] = scan_out_k; } return bst_1, ..., bst_n, b_scan_out_1, ..., b_scan_out_k; *Sample usage: Encoding RNN using a Scan* The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi, recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes %Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these values are computed in the outer graph, they need to be passed in as extra state_variables. graph rnn-encoding { %H_0 = ... %X = ... %Y_h, %Y = Scan[body = , num_scan_inputs=1]("", %H_0, %X) return %Y, %Y_h } graph rnn-cell-1 ( %H_tminus1[FLOAT, tensor] %X_t[FLOAT, tensor] ) { %Wi = ... %Ri = ... %Wbi = ... %Rbi = ... %t1 = X_t * (Wi^T) %t2 = H_tminus1*(Ri^T) %t3 = Add(%t1, %t2) %t4 = Add(%t3, %Wbi) %t5 = Add(%t4, %Rbi) %Ht = Tanh(%t5) %Accumulate = Identity(%Ht) return %Ht, %Accumulate } Optional tensor specifying lengths of the sequences in a batch. If this input is not specified, all sequences are assumed to be of the maximum sequence length (the dimension of the sequence axis of the scan_input tensors).Initial values of the loop's N state variables followed by M scan_inputsFinal values of the loop's N state variables followed by K scan_outputsThe graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.An attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction./opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/defs/controlflow/old.cc Generic Looping construct. This loop has multiple termination conditions: 1) Trip count. Iteration count specified at runtime. Set by specifying the input M. Optional. Set to empty string to omit. Note that a static trip count (specified at graph construction time) can be specified by passing in a constant node for input M. 2) Loop termination condition. This is an input to the op that determines whether to run the first iteration and also a loop-carried dependency for the body graph. The body graph must yield a value for the condition variable, whether this input is provided or not. This table summarizes the operating modes of this operator with equivalent C-style code: Operator inputs defined as (max_trip_count, condition_var). input ("", ""): for (int i=0; ; ++i) { cond = ... // Note this value is ignored, but is required in the body } input ("", cond) // Note this is analogous to a while loop bool cond = ...; for (int i=0; cond; ++i) { cond = ...; } input ("", 1) // Note this is analogous to a do-while loop bool cond = true for (int i=0; cond; ++i) { cond = ...; } input (trip_count, "") // Note this is analogous to a for loop int trip_count = ... for (int i=0; i < trip_count; ++i) { cond = ...; // ignored } input (trip_count, cond) int trip_count = ...; bool cond = ...; for (int i=0; i < trip_count && cond; ++i) { cond = ...; } *Sample usage - cond as well as trip count* graph predict-net { %a = Constant[value = ]() %b = Constant[value = ]() %keepgoing = Constant[value = ]() %max_trip_count = Constant[value = ]() %keepgoing_out, %b_out, %user_defined_vals = Loop[body = ](%max_trip_count, %keepgoing, %b) return } graph body-net ( %i[INT32, scalar] %keepgoing[BOOL, scalar] %b[INT32, scalar] ) { %my_local = Add(%a, %b) %b_out = Sub(%a, %b) %keepgoing_out = Greater(%my_local, %b_out) %user_defined_vals = Add(%b, %b) return %keepgoing_out, %b_out, %user_defined_vals } *Sample equivalent C code* { /* User-defined code (enclosing scope) */ int a = 3, b = 6; bool keepgoing = true; // Analogous to input cond /* End user-defined code */ /* Implicitly-defined code */ const int max_trip_count = 10; // Analogous to input M int user_defined_vals[]; // Imagine this is resizable /* End implicitly-defined code */ for (int i=0; i < max_trip_count && keepgoing; ++i) { /* User-defined code (loop body) */ int my_local = a + b; // Reading values in the enclosing scope is fine b = a - b; // writes fine if we specify b as a loop-carried dependency keepgoing = my_local > b; // keepgoing is a loop-carried dependency user_defined_vals[i] = b + b; /* End user-defined code */ } // my_local = 123; // Can't do this. my_local was defined in the the body // These below values are live-out from the loop and therefore accessible b_out; user_defined_vals; keepgoing_out; } There are several things of note in this code snippet: 1) Values from the enclosing scope (i.e. variable a here) are in scope and can be referenced in the inputs of the loop. 2) Any variables which you wish to make available in the enclosing scope (i.e. the variables b and keepgoing) must be declared as either loop-carried dependencies (both at the op inputs and output and at the body net input and output) or scan_outputs. 3) Values created in the body cannot be accessed in the enclosing scope. Note that the semantics of this op support "diagonal" or "wavefront" execution. (See Step 3 here for an example: https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/). Frontends should emit multi-layer RNNs as a series of While operators (with time being the inner looping dimension), with each successive layer consuming the scan_outputs from the previous layer, possibly going through several point-wise operators (e.g. dropout, residual connections, linear layer). A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.A boolean termination condition. Optional. Pass empty string to skip.The initial values of any loop-carried dependencies (values that change across loop iterations)Final N loop carried dependency values then K scan_outputsThe graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.tensor of int64, which should be a scalar.tensor of bool, which should be a scalar. Generic Looping construct. This loop has multiple termination conditions: 1) Trip count. Iteration count specified at runtime. Set by specifying the input M. Optional. Set to empty string to omit. Note that a static trip count (specified at graph construction time) can be specified by passing in a constant node for input M. 2) Loop termination condition. This is an input to the op that determines whether to run the first iteration and also a loop-carried dependency for the body graph. The body graph must yield a value for the condition variable, whether this input is provided or not. This table summarizes the operating modes of this operator with equivalent C-style code: Operator inputs defined as (max_trip_count, condition_var). input ("", ""): for (int i=0; ; ++i) { cond = ... // Note this value is ignored, but is required in the body } input ("", cond) // Note this is analogous to a while loop bool cond = ...; for (int i=0; cond; ++i) { cond = ...; } input ("", 1) // Note this is analogous to a do-while loop bool cond = true for (int i=0; cond; ++i) { cond = ...; } input (trip_count, "") // Note this is analogous to a for loop int trip_count = ... for (int i=0; i < trip_count; ++i) { cond = ...; // ignored } input (trip_count, cond) int trip_count = ...; bool cond = ...; for (int i=0; i < trip_count && cond; ++i) { cond = ...; } *Sample usage - cond as well as trip count* graph predict-net { %a = Constant[value = ]() %b = Constant[value = ]() %keepgoing = Constant[value = ]() %max_trip_count = Constant[value = ]() %keepgoing_out, %b_out, %user_defined_vals = Loop[body = ](%max_trip_count, %keepgoing, %b) return } graph body-net ( %i[INT32, scalar] // iteration number %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used %b_in[INT32, scalar] // incoming value of loop-carried-dependency b ) { %my_local = Add(%a, %b_in) %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated return %keepgoing_out, %b_out, %user_defined_val } *Sample equivalent C code* { /* User-defined code (enclosing scope) */ int a = 3, b = 6; bool keepgoing = true; // Analogous to input cond /* End user-defined code */ /* Implicitly-defined code */ const int max_trip_count = 10; // Analogous to input M int user_defined_vals[]; // Imagine this is resizable /* End implicitly-defined code */ /* initialize loop-carried variables and scan-output variables */ bool keepgoing_out = keepgoing int b_out = b for (int i=0; i < max_trip_count && keepgoing_out; ++i) { /* Implicitly-defined code: bind actual parameter values to formal parameter variables of loop-body */ bool keepgoing_in = keepgoing_out; bool b_in = b_out; /* User-defined code (loop body) */ int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine b_out = a - b_in; keepgoing_out = my_local > b_out; user_defined_val = b_in + b_in; // b_in and b_out are different variables /* End user-defined code */ /* Implicitly defined-code */ user_defined_vals[i] = user_defined_val // accumulate scan-output values } // int t = my_local; // Can't do this. my_local is not accessible here. // The values below are bound to the output variables of the loop and therefore accessible // b_out; user_defined_vals; keepgoing_out; } There are several things of note in this code snippet: 1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can be referenced in the inputs of the loop. 2) Any values computed in the loop body that needs to be used in a subsequent iteration or after the loop are modelled using a pair of variables in the loop-body, consisting of an input variable (eg., b_in) and an output variable (eg., b_out). These are referred to as loop-carried dependences. The loop operation node supplies the input value of the input variable for the first iteration, and returns the output value of the output variable produced by the final iteration. 3) Scan_output variables are used to implicitly concatenate values computed across all the iterations. In the above example, the value of user_defined_val computed over all iterations are concatenated and returned as the value of user_defined_vals after the loop. 4) Values created in the body cannot be accessed in the enclosing scope, except using the mechanism described above. Note that the semantics of this op support "diagonal" or "wavefront" execution. (See Step 3 here for an example: https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/). Frontends should emit multi-layer RNNs as a series of While operators (with time being the inner looping dimension), with each successive layer consuming the scan_outputs from the previous layer, possibly going through several point-wise operators (e.g. dropout, residual connections, linear layer). Scan can be used to iterate over one or more scan_input tensors, constructing zero or more scan_output tensors. It combines ideas from general recurrences, functional programming constructs such as scan, fold, map, and zip and is intended to enable generalizations of RNN-like constructs for sequence-to-sequence processing. Other tensors (referred to as state_variables here) can be used to carry a state when iterating from one element to another (similar to hidden-state in RNNs, also referred to as loop-carried dependences in the context of loops). Many common usages involve a single scan_input tensor (where functionality similar to scan, fold and map can be obtained). When more than one scan_input is used, a behavior similar to zip is obtained. The attribute body must be a graph, specifying the computation to be performed in every iteration. It takes as input the current values of the state_variables and the current iterated element of the scan_inputs. It must return the (updated) values of the state_variables and zero or more scan_output_element tensors. The values of the scan_output_element tensors are concatenated over all the iterations to produce the scan_output values of the scan construct (similar to the concatenated intermediate hidden-state values of RNN-like constructs). All the output tensors (state_variables as well as scan_output_element tensors) are required to have the same shape in each iteration of the loop (a restriction imposed to enable efficient memory allocation). Note that the iterated element passed to the body subgraph does not have a sequence axis. It will have a rank one less than the rank of the corresponding scan_input. The scan operation returns the final values of the state_variables as well as the scan_outputs. The optional attribute scan_input_directions specifies the direction (forward or backward) for each scan input. If this attribute is omitted, all sequences are scanned in the forward direction. A bidirectional scan may be performed by specifying the same tensor input twice in the scan_inputs, once with a forward direction, and once with a backward direction. The scan_output of the operation is produced by concatenating the scan_output_element values produced by the body in each iteration. The optional attribute scan_output_directions specifies the direction in which scan_output is constructed (by appending or prepending the scan_output_element to scan_output in each iteration) for each scan_output. If this attribute is omitted, the scan_output_element is appended to the scan_output in each iteration. The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input. If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1. Note that scanning a non-zero axis may be less efficient than scanning axis zero. The optional attribute scan_output_axes specifies the axis along which the scan_outputs are accumulated for each scan_output. For example, if axis 1 is the time axis (to be scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis value of 1. Note that because of the ONNX restriction that only the last parameter of an operator can be variadic, the initial-states and scan-inputs are listed together as one input parameter. Similarly, the final-states and scan-outputs are listed together as one output parameter. The attribute num_scan_inputs indicates the number M of scan-inputs. The behavior of Scan < num_scan_inputs = m, body = loop-body, scan_input_axes = [axis_1, ..., axis_m] > (init_1, ..., init_n, scan_1, ..., scan_m) is equivalent to the following pseudo-code: // scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i // scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j. sequence_length = scan_1.shape[axis_1]; // initialize state-variables st_1 = init_1; ... st_n = init_n; // initialize scan-output variables: [] denotes an empty tensor scan_out_1 = []; ...; scan_out_k = []; // identify number of iterations: // execute loop for (int t = 0; t < sequence_length; ++t) { // generate the scan-input elements: the notation T[t] indicates the sub-tensor // of rank one less than T obtained by indexing T at position t along axis k. si_1 = scan_1[t]; ... ; si_m = scan_m[t]; // execute loop-body st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m) // accumulate the scan-output elements scan_out_1 = Concat(scan_out_1, so_1); ... ; scan_out_k = Concat(scan_out_k, so_k); } return st_1, ..., st_n, scan_out_1, ..., scan_out_k; *Sample usage: Encoding RNN using a Scan* The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi, recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes %Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these values are computed in the outer graph, they need to be passed in as extra state_variables. graph rnn-encoding { %H_0 = ... %X = ... %Y_h, %Y = Scan[body = , num_scan_inputs=1](%H_0, %X) return %Y, %Y_h } graph rnn-cell-1 ( %H_tminus1[FLOAT, tensor] %X_t[FLOAT, tensor] ) { %Wi = ... %Ri = ... %Wbi = ... %Rbi = ... %t1 = X_t * (Wi^T) %t2 = H_tminus1*(Ri^T) %t3 = Add(%t1, %t2) %t4 = Add(%t3, %Wbi) %t5 = Add(%t4, %Rbi) %Ht = Tanh(%t5) %Accumulate = Identity(%Ht) return %Ht, %Accumulate } An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output.Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same shape and same data type.Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible. Generic Looping construct. This loop has multiple termination conditions: 1) Trip count. Iteration count specified at runtime. Set by specifying the input M. Optional. Set to empty string to omit. Note that a static trip count (specified at graph construction time) can be specified by passing in a constant node for input M. 2) Loop termination condition. This is an input to the op that determines whether to run the first iteration and also a loop-carried dependency for the body graph. The body graph must yield a value for the condition variable, whether this input is provided or not. This table summarizes the operating modes of this operator with equivalent C-style code: Operator inputs defined as (max_trip_count, condition_var). input ("", ""): for (int i=0; ; ++i) { cond = ... // Note this value is ignored, but is required in the body } input ("", cond) // Note this is analogous to a while loop bool cond = ...; for (int i=0; cond; ++i) { cond = ...; } input ("", 1) // Note this is analogous to a do-while loop bool cond = true for (int i=0; cond; ++i) { cond = ...; } input (trip_count, "") // Note this is analogous to a for loop int trip_count = ... for (int i=0; i < trip_count; ++i) { cond = ...; // ignored } input (trip_count, cond) int trip_count = ...; bool cond = ...; for (int i=0; i < trip_count && cond; ++i) { cond = ...; } *Sample usage - cond as well as trip count* graph predict-net { %a = Constant[value = ]() %b = Constant[value = ]() %keepgoing = Constant[value = ]() %max_trip_count = Constant[value = ]() %keepgoing_out, %b_out, %user_defined_vals = Loop[body = ](%max_trip_count, %keepgoing, %b) return } graph body-net ( %i[INT32, scalar] // iteration number %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used %b_in[INT32, scalar] // incoming value of loop-carried-dependency b ) { %my_local = Add(%a, %b_in) %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated return %keepgoing_out, %b_out, %user_defined_val } *Sample equivalent C code* { /* User-defined code (enclosing scope) */ int a = 3, b = 6; bool keepgoing = true; // Analogous to input cond /* End user-defined code */ /* Implicitly-defined code */ const int max_trip_count = 10; // Analogous to input M int user_defined_vals[]; // Imagine this is resizable /* End implicitly-defined code */ /* initialize loop-carried variables and scan-output variables */ bool keepgoing_out = keepgoing int b_out = b for (int i=0; i < max_trip_count && keepgoing_out; ++i) { /* Implicitly-defined code: bind actual parameter values to formal parameter variables of loop-body */ bool keepgoing_in = keepgoing_out; bool b_in = b_out; /* User-defined code (loop body) */ int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine b_out = a - b_in; keepgoing_out = my_local > b_out; user_defined_val = b_in + b_in; // b_in and b_out are different variables /* End user-defined code */ /* Implicitly defined-code */ user_defined_vals[i] = user_defined_val // accumulate scan-output values } // int t = my_local; // Can't do this. my_local is not accessible here. // The values below are bound to the output variables of the loop and therefore accessible // b_out; user_defined_vals; keepgoing_out; } There are several things of note in this code snippet: 1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can be referenced in the inputs of the loop. 2) Any values computed in the loop body that needs to be used in a subsequent iteration or after the loop are modelled using a pair of variables in the loop-body, consisting of an input variable (eg., b_in) and an output variable (eg., b_out). These are referred to as loop-carried dependences. The loop operation node supplies the input value of the input variable for the first iteration, and returns the output value of the output variable produced by the final iteration. 3) Scan_output variables are used to implicitly concatenate values computed across all the iterations. In the above example, the value of user_defined_val computed over all iterations are concatenated and returned as the value of user_defined_vals after the loop. 4) Values created in the body cannot be accessed in the enclosing scope, except using the mechanism described above. Note that the semantics of this op support "diagonal" or "wavefront" execution. (See Step 3 here for an example: https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/). Frontends should emit multi-layer RNNs as a series of While operators (with time being the inner looping dimension), with each successive layer consuming the scan_outputs from the previous layer, possibly going through several point-wise operators (e.g. dropout, residual connections, linear layer). The input/output of subgraph (produced by loop node) matching is based on order instead of name. The implementation will figure out the names based on this order. Final N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.Loop 'body' subgraph outputs should all be tensors but output Loop 'body' subgraph outputs should all be tensors or sequences but output Loop 'body' subgraph scan outputs should all be tensors but output H‹UH‰ýHƒÀH‰H‹HE H9ÇtèH‰ïèH‰ï]évector::_M_range_insert axis value then_branchelse_branch != but subgraphs produce If node has then=Mismatched type for output else=num_scan_inputsbody was not a tensor.Scan input outputs. Expected was notscan_input_axes).scan_output_axesIsequence_lensVinitial_state_and_scan_inputsfinal_state_and_scan_outputsdirectionsInt64 tensorAll Tensor typesScanMBcondv_initialv_final_and_scan_outputsLoopscan_input_directionsscan_output_directionsCondition for the ifoutputsOnly boolIfIf conditionalAll Tensor and Sequence typesseq(tensor(uint8))seq(tensor(uint16))seq(tensor(uint32))seq(tensor(uint64))seq(tensor(int8))seq(tensor(int16))seq(tensor(int32))seq(tensor(int64))seq(tensor(float16))seq(tensor(float))seq(tensor(double))seq(tensor(string))seq(tensor(bool))seq(tensor(complex64))seq(tensor(complex128)) was èL9ótIFH9ÇtèL;µØúÿÿuâH‹=èL‰çèH‹½0ûÿÿH…@ûÿÿH9ÇtèL‰ïèH‰ßèH‹½PûÿÿH…`ûÿÿH9ÇtÝèëÖH‹…`ûÿÿH…ÀtH‹µØúÿÿºH‰÷ÿÐH‹½ûÿÿH… ûÿÿH9ÇtèL‰çèé|ÿÿÿH‹½ØúÿÿèécÿÿÿèH;¸úÿÿt"H‹…¸úÿÿH‹8HƒÀH9ÇtèHƒ…¸úÿÿ ëÕèèé÷þÿÿH‹|$ èH‹|$èH‹|$0H‰ÝèH‹|$8èH‹|$pH…ÿtèH‰ïèH‹¼$°H…ÿtÅèë¾H‰ÃHƒ|$u©ë±L‹5H‹¿0IV@HƒÀfHnÊfHnÐflÊ)$èH‹|$ E‹d$(I‰ÅèH‹|$XºH5èH‹|$Xº>H5èH‹|$XH‰îèH‹|$XºH5èH‹|$XD‰æèH‹„$@Hœ$ÀHÇ„$¸H‰œ$°Æ„$ÀH…À…’H‹|$HH´$`èIFfo$H‹¼$`H‰„$IFhH‰„$€H„$p)œ$H9Ç…=H‹H¼$PHƒÀH‰„$èH‹H¼$€H‹PH‹H0H‹RèH‰ŒH‹P H‹H(H‰”$H‹RèH‰ŒH‹PH‹@H‰”$H‹RèH‰„H‹HÇ„$HƒÀH‰„$€èH‹t$HL‰ïèH‹H‹¼$°AÆE IÇEHƒÀI‰EIE I‰EH9ßtèH‹5HL‰ïèH‹¼$H„$H9Ç„{ýÿÿèéqýÿÿH‰Ãé_ýÿÿH‰ÃH‹|$ èL‰ïèéEýÿÿH‰ÃëîL‹„$0M…Àu!I‰ÀH‹Œ$8H‹|$H1Ò1öI)ÈèéRþÿÿL9ÀwÚëÛ¿0èL‹L$HH‹|$ HL$hLH‰\$hH‰ÅHH5L‰¤$°èH‹t$ H‰ïèH‹HÇEÆE H‹¼$HƒÀH‰EHE H‰EH;|$XtèH‹5HH‰ïèH‹¼$H‰ÃH;|$XtèH‰ïèé™üÿÿH‰Ãëîèé¹ýÿÿH‹¼$°H‰ÅH9ßtèH‰ëéÂþÿÿH‹¼$°H‰ÅH9ßtèH‰ëé¯þÿÿ¿0èL‹L$XH‹|$0HL$xLH‰l$xI‰ÄHH5L‰¬$ÀèH‹t$0L‰çèH‹AÆD$ IÇD$H‹¼$HƒÀI‰$ID$ I‰D$H;|$htèH‹5HL‰çèH‰ÃHƒ|$t H‹|$èH‰ÝH‹|$@èH‹|$HèH‹¼$€H…ÿtèH‰ïèH‹¼$H‰ÃH;|$htèL‰çèëœH‰ÃëñH‹H‹¿0HS@HƒÀfHnÚfHnèflÝ)$èH‹|$0E‹m(I‰ÄèH‹|$hºH5èH‹|$hºKH5èH‹|$hH‰îèH‹|$hºH5èH‹|$hD‰îèH‹„$PH¬$ÐHÇ„$ÈH‰¬$ÀÆ„$ÐH…À…TH‹|$XH´$pèHCfo4$H‹¼$pH‰„$HChH‰„$H„$€)´$ H9Ç…2H‹H¼$`HƒÀH‰„$(èH‹H¼$H‹PH‹H0H‹RèH‰ŒH‹P H‹H(H‰”$ H‹RèH‰Œ H‹PH‹@H‰”$H‹RèH‰„H‹HÇ„$HƒÀH‰„$èH‹t$XL‰çèH‹H‹¼$ÀIÇD$AÆD$ HƒÀI‰$ID$ I‰D$H9ïtèH‹5HL‰çèH‰ÃéwýÿÿL‹„$@M…Àu!I‰ÀH‹Œ$HH‹|$X1Ò1öI)ÈèéþÿÿL9ÀwÚëÛH‹¼$H„$ H9Ç….ýÿÿé.ýÿÿH‹¼$ÀH…ÿ…ýÿÿéýÿÿH‹H‹¿0HS@HƒÀfHnâfHnøflç)$$èH‹|$0E‹m(I‰ÄèH‹|$hºH5èH‹|$hºCH5èH‹|$hH‰îèH‹|$hºH5èH‹|$hD‰îèH‹„$PH¬$ÐHÇ„$ÈH‰¬$ÀÆ„$ÐH…À…fH‹|$XH´$pèHCfo,$H‹¼$pH‰„$HChH‰„$H„$€)¬$ H9Ç…_H‹H¼$`HƒÀH‰„$(èH‹H¼$H‹PH‹H0H‹RèH‰ŒH‹P H‹H(H‰”$ H‹RèH‰Œ H‹PH‹@H‰”$H‹RèH‰„H‹HÇ„$HƒÀH‰„$èH‹t$XL‰çèH‹H‹¼$ÀIÇD$AÆD$ HƒÀI‰$ID$ I‰D$H9ïtèH‹5HL‰çèH‰ÃH‹|$0èL‰çèéÔúÿÿL‹„$@M…Àu0I‰ÀH‹Œ$HH‹|$X1Ò1öI)Èèé~þÿÿH‹|$0èé—úÿÿL9ÀwËëÌH‰Ãëªèé—þÿÿH‹¼$ÀH‰ÃH9ït†èé|ÿÿÿH‹¼$ÀH‰ÃH9ï„rÿÿÿèéhÿÿÿèéÄûÿÿë¼éJÿÿÿë¦ëÍÿ›!ºÕŒõ„Î¶}ÿ›!FKV[`œ¡¬}ÿÿ)A‚³¤"ÿ›%[œÇ•ÑÍÌ³¸ÿ›- 7²€¼Ÿ·ãèü}ÿÿWmAŠú†…¨ÿÿOeÇÌñÂèÿÿV¶œ¦SÉ‡ÓÓíÑ†SÁç¹´æ—ÿH’Õß‚éäÚwÿÿWmAŠú†…¨ÿÿWmAŠú†…¨ÿÿ/®ì ÇSç ¨â ôÁù° ’4« Ô¦ ¡ }ÿÿh«Â Ä6À ˆ¾ ¤T¨œ ÁU— ¤’ öÆ ©6Ä í¼ ŒÜ¥+×ÞÒ¬Ô©ß¤Í8ÿÿ\rƒÚÁÕøÿÿŸŠ¬±ö»ÿÿ)£¿°º€ªßöú[ñãìµxÿÿWmAŠú†…¨ÿÿ\rQª‘¥ÈÿÿWmAŠú†…¨ÿÿ\raºŸ‘µÚÿÿÿÿ6’C¥ÿ›) FùéÓ äé–›³ÿÿ\rQª‘¥Èÿÿ\rQª‘¥Èÿÿ.M²X¢–’´¿ªõˆ’´÷šÿÿ'/Ã:ÁŠýÏ¨…øýÿÿ\r‚ÚÀÕøÿÿ\r‚ÚÀÕøÿÿ)[¨fÇ¥½ÈµÓ¿ŽÏŸØ°ÿÿK7ÊFÈ–¿ÒÆáÄ±¿ú×½è»¸¿›¯¬ü¿ÿÿ)[˜f¨¥¸È°Ó 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[Ãf.„H‹?ëætensor(uint8)tensor(uint16)tensor(uint32)tensor(uint64)tensor(int8)tensor(int16)tensor(int32)tensor(int64)tensor(float16)tensor(float)tensor(double)tensor(string)tensor(bool)tensor(complex64)tensor(complex128)AWAVAUATUSHìøL‹-A¶E„ÀtL‹%HÄøL‰à[]A\A]A^A_ÃL‰ïè…ÀtÕLt$H5L‰÷èH|$0H5èH|$PH5èH|$pH5èH¼$H5èH¼$°H5èH¼$ÐH5èH¼$ðH5èH¼$H5èH¼$0H5èH¼$PH5èH¼$pH5èH¼$H5èH¼$°H5èH„$ÐH5H‰ÇH‰D$èL‹%fïÀ¿àIÇD$A$èI‰ÇI‰$H€àL‰õI‰D$L‰ûDHCH‹uH‹UH‰ßH‰HòèHƒÅ H„$ðHƒÃ H9ÅuÑHL‰æI‰\$H=èL‰ïèH‹\$ë€èHCàI9Þ„þýÿÿH‰ÃH‹;HCH9ÇußHCàI9ÞuèéáýÿÿH‰Ãë6H‰ÇëAH‰ÃëèI‹<$H…ÿuYH¬$ðHƒí H‹}HEH9ÇuGL9õuêL‰ïèH‰ßèèL9ûtI‹?IGH9ÇtèIƒÇ ëäèH‰Ãë™èë èë²UH‰ýSHƒìH‹_H…ÛtfDH‰ßH‹èH…ÛuðH‹EH‹}1öHƒÅ0HÅèH‹}ÐHÇEèHÇEàH9ïtHƒÄ[]éf„HƒÄ[]ÃAUATUSH‰û¿ˆHƒìèH‹3H‹SHhHÇHxI‰ÄH‰hHòèH‹C(óoC@Ml$(Hs IÇD$(L‰ïI‰D$0H‹C8IÇD$8I‰D$@IÇD$XAD$HèH‹sXH‹S`ID$pI|$`I‰D$`HòèHƒÄL‰à[]A\A]ÃH‰ÇëH‰ÃëH‰ÃëL‰ïèI‹|$H9ýuH‰ßèL‰çèèH‰ÅëèëßèH‰ïèAUI‰ýATUSHƒìL‹gH‹/I9ì„¢H‹}xH…ˆH9ÇtèH‹}XHEhH9ÇtèH‹]0H…ÛtH‰ßH‹èH…ÛuðH‹E(H‹} 1öHÅèH‹} HEPHÇE8HÇE0H9ÇtèH‹}HEH9Çt:èHÅ¨I9ì…eÿÿÿI‹mH…ít.HƒÄH‰ï[]A\A]éf.„HÅ¨I9ì…0ÿÿÿëÉHƒÄ[]A\A]ÃAVI‰þAUATUSL‹oL‹'M9åt}f„I‹|$8ID$HH9ÇtèI‹\$(I‹l$ H9ët%DH‹}HEH9Çt[èHƒÅ H9ëuåI‹l$ H…ítH‰ïèI‹<$ID$H9Çt=èIƒÄXM9åuM‹&M…ät6[L‰ç]A\A]A^é€HƒÅ H9ëuë¨DIƒÄXM9å…SÿÿÿëÂ[]A\A]A^ÃAWAVM‰ÆAUATI‰üUH‰ÕSH‰óHì¸H‹L|$ H‰L$Ll$0HP@H‹L‰ÿfHnÂHƒÀfHnÈflÁ)$èH‰ßèH‰ÞL‰ïH‰ÂèH‰ïèH‰îL‰ïH‰ÂèH‹D$L‰ïH‹0èL‰÷èL‰öL‰ïH‰ÂèH‹D$`I\$IÇD$I‰$AÆD$H…À„L‹D$PM…À„L9À‡ýH‹L$X1Ò1öL‰çI)ÈèH‹fo$H‹¼$€HƒÀ)T$0H‰D$ H‹HƒÀhH‰„$ H„$H9ÇtèH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èHÄ¸L‰à[]A\A]A^A_ÃfDI‰ÀéûþÿÿH´$€L‰çèéúþÿÿH‰ÅëH‰ÅëI‹<$H9ûtèL‰ÿèH‰ïèAWAVAUI‰õATI‰üUH‰ÕSHì¨H‹H‹L|$Lt$ HS@HƒÀL‰ÿfHnÂfHnÈflÁ)$èL‰ïèL‰îL‰÷H‰ÂèH‰ïèH‰îL‰÷H‰ÂèH‹D$PIl$IÇD$I‰,$AÆD$H…À„ L‹D$@M…À„ôL9À‡ëH‹L$H1Ò1öL‰çI)ÈèHCfo$H‹|$pH‰D$HChH‰„$H„$€)T$ H9ÇtèH‹H|$`HƒÀH‰D$(èH‹H¼$H‹PH‹H0H‹RèH‰LH‹P H‹H(H‰T$ H‹RèH‰L H‹PH‹@H‰T$H‹RèH‰DH‹HÇD$HƒÀH‰„$èHÄ¨L‰à[]A\A]A^A_ÃDI‰Àé ÿÿÿHt$pL‰çèéÿÿÿH‰ÃëH‰ÅëI‹<$H9ýtèH‰ÝL‰ÿèH‰ïèInput type was null[TypeInferenceError] Input Element type of input unknownOutput expected to have tensor or sparse tensor type. Got: AWAVAUI‰ÕATI‰ôUH‰ýSHì¸H‹ÿPH…À„6D‹p(Aƒþt Aƒþ…›H‹@ ‹X …Û„…H‹EH‰ïL‰îÿP(D‹`(H‰ÅAƒüt2AƒüttE…ä…ƒAƒþtAƒþt/HÄ¸[]A\A]A^A_ÃDH‰ïèƒH‰X ëØ€H‰ÇèH‹EÇE(H‰ÇHƒçþ¨u%èH‰E ƒH‰X ë¡Aƒüt²H‹@ ƒH‰X ëŽH‹?ëÖ¿0L|$ èL‰ÿI‰ÆèHl$0ºH5H‰ïèºH5H‰ïèL‰îH‰ïèº5H5H‰ïèD‰æH‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰÷èH‹H‹<$AÆF IÇFHƒÀI‰IF I‰FHD$H9ÇtèH‹5HL‰÷è¿0L|$ èL‰ÿI‰ÅèHl$0ºH5H‰ïèºH5H‰ïèL‰æH‰ïèº5H5H‰ïèD‰öH‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰ïèH‹AÆE IÇEHƒÀI‰EIE I‰EH‹<$HD$H9ÇtèH‹5HL‰ïèH‰Åé—H‰ÅépH‰ÅéP¿0L|$ èL‰ÿI‰ÅèHl$0ºH5H‰ïèºH5H‰ïèL‰æH‰ïèºH5H‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰ïèé ÿÿÿ¿0L|$ èHH5L‰ÿH‰ÅèL‰þH‰ïèH‹H‹|$ HÇEÆE HƒÀH‰EHE H‰EHD$0H9ÇtèH‹5HH‰ïèéÕþÿÿI‰ÄëI‰ÄëH‹|$ HD$0H9ÇtèH‰ïèL‰çèéªþÿÿH‰Åë%H‰ÅëL‰ÿèL‰÷èH‰ïèL‰ÿèL‰ïèH‰ïèëÄH‰ÅëH‹<$HD$H9ÇtÛèëÔH‹<$HD$H9Çt®èë§AWAVM‰ÆAUATI‰üUH‰ÕSH‰óHì¸H‹L|$ H‰L$Ll$0HP@H‹L‰ÿfHnÂHƒÀfHnÈflÁ)$èH‰ßèH‰ÞL‰ïH‰ÂèH‰ïèH‰îL‰ïH‰ÂèH‹D$L‰ïH‹0èL‰÷èL‰öL‰ïH‰ÂèH‹D$`I\$IÇD$I‰$AÆD$H…À„L‹D$PM…À„L9À‡ýH‹L$X1Ò1öL‰çI)ÈèH‹fo$H‹¼$€HƒÀ)T$0H‰D$ H‹HƒÀhH‰„$ H„$H9ÇtèH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èHÄ¸L‰à[]A\A]A^A_ÃfDI‰ÀéûþÿÿH´$€L‰çèéúþÿÿH‰ÅëH‰ÅëI‹<$H9ûtèL‰ÿèH‰ïèAWAVM‰ÆAUATI‰üUH‰ÕSH‰óHì¸H‹L|$ H‰L$Ll$0HP@H‹L‰ÿfHnÂHƒÀfHnÈflÁ)$èH‰ßèH‰ÞL‰ïH‰ÂèH‰ïèH‰îL‰ïH‰ÂèH‹D$L‰ïH‹0èL‰÷èL‰öL‰ïH‰ÂèH‹D$`I\$IÇD$I‰$AÆD$H…À„L‹D$PM…À„L9À‡ýH‹L$X1Ò1öL‰çI)ÈèH‹fo$H‹¼$€HƒÀ)T$0H‰D$ H‹HƒÀhH‰„$ H„$H9ÇtèH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èHÄ¸L‰à[]A\A]A^A_ÃfDI‰ÀéûþÿÿH´$€L‰çèéúþÿÿH‰ÅëH‰ÅëI‹<$H9ûtèL‰ÿèH‰ïè[ShapeInferenceError] Target=Mismatch between source and target type. Source=Unsupported Source/Target type=AWAVAUATUSHì¸D‹g(D‹n(E9ìu{H‰ûH‰õAƒü„BAƒü„ØAƒü„–Aƒü …˜Aƒý …*L‹e I‹l$AƒL$H…í„Tƒ{( „xH‹H‹XH…Û„uD‹c(D‹m(E9ìt‹¿0Lt$ èL‰÷I‰ÇèHl$0ºH5H‰ïèº0H5H‰ïèD‰æH‰ïèºH5H‰ïèD‰îH‰ïèH‰åHt$8H‰ïèL‰÷èH‰îL‰ÿèH‹H‹<$AÆG IÇGHƒÀI‰IG I‰GHD$H9ÇtèH‹5HL‰ÿè€H‰ïèH‹EÇE( H‰ÇHƒçþ¨…îèI‰ÄH‰E I‹l$AƒL$H…í…¬þÿÿI‹D$H‰ÇHƒçþ¨…ôèƒ{( I‰D$H‰Å…‹þÿÿH‹C H‹XH…Û…‹þÿÿH‹éþÿÿAƒütZH‹C ö@„œL‹`M…ä„OAƒý…H‹] H‹{ƒKH…ÿuSH‹CH‰ÇHƒçþ¨…xèH‰CH‰Çë2H‹C ö@tFL‹`M…ä„éH‰ïèH‹xƒHH‰ÃH…ÿtHÄ¸L‰æ[]A\A]A^A_é„HÄ¸[]A\A]A^A_ÃfDAƒýu2L‹e I‹l$AƒL$H…í„ªƒ{(„ðþÿÿH‹ésýÿÿ@H‰ïèH‹EÇE(H‰ÇHƒçþ¨…èH‰E I‰Äë¢H‰ïèH‹EÇE(H‰ÇHƒçþ¨utèH‰E H‰ÃéÐþÿÿL‹%éÿÿÿ@L‹%é¥þÿÿ@H‹?é þÿÿ„I‹D$H‰ÇHƒçþ¨u/èI‰D$H‰Åé4ÿÿÿH‹?éhÿÿÿH‹?éþÿÿH‹?ë‡H‹?é€þÿÿH‹?ëÌ¿0Lt$ èL‰÷I‰ÅèHl$0ºH5H‰ïèºH5H‰ïèD‰æH‰ïèH‰åHt$8H‰ïèL‰÷èH‰îL‰ïèH‹H‹<$AÆE IÇEHƒÀI‰EIE I‰EHD$H9ÇtèH‹5HL‰ïèH‰Åë H‰ÅëH‰ÅëH‹<$HD$H9ÇtèëL‰÷èL‰ïèH‰ïèH‰Åë H‰ÅëH‰ÅëH‹<$HD$H9ÇtèëL‰÷èL‰ÿèH‰ïèElement type of tensor or sparse tensor input was unknownInput was expected to have tensor or sparse tensor type. Got does not match existing output type of Output was expected to have tensor type. Got Input element type of AWAVAUATUHìÀH…ÿ„˜D‹o(Aƒýt Aƒý…NH‹G D‹p E…ö„¹D‹f(E…ä„õAƒüu?H‹F D‹h Aƒü„µE…í„½E9õ…HÄÀ]A\A]A^A_Ãf.„Aƒüt»¿0Hl$0èH‰ïI‰ÅèL|$@ºH5L‰ÿèº-H5L‰ÿèD‰æL‰ÿèLt$Ht$HL‰÷èH‰ïèL‰öL‰ïèéQfDE…í…KÿÿÿH‰÷èƒHD‰p é?ÿÿÿf.„H‰÷AƒýtÛH‰t$èH‹t$H‹FÇF(H‰ÇHƒçþ¨u H‰t$èH‹t$H‰F ƒHD‰p éçþÿÿH‹?ëÛ¿0Hl$0èH‰ïI‰ÄèL|$@ºH5L‰ÿèºH5L‰ÿèD‰öL‰ÿèº(H5L‰ÿèD‰îL‰ÿèLt$Ht$HL‰÷èH‰ïèL‰öL‰çèH‹AÆD$ IÇD$HƒÀI‰$ID$ I‰D$H‹|$HD$ H9ÇtèH‹5HL‰çè¿0Hl$0èH‰ïI‰ÄèL|$@ºH5L‰ÿèº=H5L‰ÿèD‰îL‰ÿèLt$Ht$HL‰÷èH‰ïèL‰öL‰çèé2ÿÿÿ¿0Hl$0èH‰ïI‰ÅèL|$@ºH5L‰ÿèº9H5L‰ÿèLt$Ht$HL‰÷èH‰ïèL‰öL‰ïèH‹AÆE IÇEHƒÀI‰EIE I‰EH‹|$HD$ H9ÇtèH‹5HL‰ïè¿0Hl$0èHH5H‰ïI‰ÄèH‰îL‰çèH‹H‹|$0IÇD$AÆD$ HƒÀI‰$ID$ I‰D$HD$@H9Ç…AþÿÿéAþÿÿH‰ÅëH‰ÅëH‹|$0HD$@H9ÇtèL‰çèH‰ïèH‰Åë I‰Äë)H‰ÅëH‹|$HD$ H9ÇtèL‰ïèH‰ïèH‰ïL‰åèëãH‰ÅëI‰Åëë‹H‹|$HD$ H9Çuë“H‰ïL‰íèë†ëÔë’ë•ë˜ëÑé^ÿÿÿAWAVM‰ÆAUI‰ÕATI‰üUSH‰óHì¸H‹L|$ H‰L$Hl$0HP@H‹L‰ÿL‰L$fHnÂHƒÀfHnÈflÁ)$èH‰ßèH‰ÞH‰ïH‰ÂèL‰ïèL‰îH‰ïH‰ÂèH‹D$H‰ïH‹0èL‰÷èL‰öH‰ïH‰ÂèH‹D$H‰ïH‹0èH‹¼$ðèH‹´$ðH‰ïH‰ÂèH‹„$øH‰ï‹0èH‹D$`I\$IÇD$I‰$AÆD$H…À„%L‹D$PM…À„L9À‡þH‹L$X1Ò1öL‰çI)ÈèH‹fo$H‹¼$€HƒÀ)T$0H‰D$ H‹HƒÀhH‰„$ H„$H9ÇtèH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èHÄ¸L‰à[]A\A]A^A_Ã€I‰Àéúþÿÿ„H´$€L‰çèéñþÿÿH‰ÅëH‰ÅëI‹<$H9ûtèL‰ÿèH‰ïèCan't merge shape info. Both source and target dimension have values but they differ. Source= Dimension=UH‰åAVATSH‰óHƒìH‰U¬‹W(‹F(ƒút"ƒèƒøv ƒú„ñHeè[A\A^]ÃfDH‹O H‰M°I‰Ìƒø…¬H‹F H‰E¸H9ÁtÎ¿0LeÀèHM°LM¸L‰çH‰ÃHE¬LPHHPH5èXL‰æZH‰ßèH‹H‹}ÀHÇCÆC HƒÀH‰HC H‰CHEÐH9ÇtèH‹5HH‰ßèDH‰÷èÇC(L‰c Heè[A\A^]ÃfL‹w H‰÷èÇC(èH‰C H‹CI‰ÄIƒäþ¨u9èH‹{ H9ÇtL‰öèéÉþÿÿf.„H{ L‰òL‰æèé«þÿÿM‹$$ëÁI‰ÄëH‹}ÀHEÐH9ÇtèH‰ßèL‰çèI‰ÄëÙMismatch between number of source and target dimensions. Source=UH‰åAWI‰ÿAVAUATSH‰óHìØ‹FL‹v‰ÂƒÊ‰V¨„M…ö„A‹FE‹g‰ÃA9Ä…{1ÛE…ä-HeØ[A\A]A^A_]ÃDƒèƒøv ƒù„_ƒÃA9ÜtÓI‹W HcÃI‹N ‰þÿÿH‹TÂH‹DÁ‹J(H‰…þÿÿ‹@(ƒùuºL‹j L‰þÿÿƒø…ÆH‹…þÿÿH‹@ H‰… þÿÿI9Åt ¿0Lµ@þÿÿèHþÿÿL‰÷L þÿÿH‰ÃH…þÿÿLPHHPH5èXL‰öZH‰ßèH‹H‹½@þÿÿHÇCÆC HƒÀH‰HC H‰CH…PþÿÿH9ÇtèH‹5HH‰ßèH‹½þÿÿèH‹…þÿÿÇ@(L‰h éÎþÿÿDM…ö„·HeØL‰þL‰÷[A\A]A^A_]éfDH‹½þÿÿL‹j èH‹…þÿÿÇ@(èH‹µþÿÿH‰F H‹FH‰ÆHƒæþH‰µþÿÿ¨…èH‹½þÿÿH‹ H9Çt[L‰îèé7þÿÿfDH‹FH‰ÇHƒçþ¨ulèH‰CI‰ÆéÒýÿÿ„H‹FH‰ÇHƒçþ¨u?èH‰CI‰Æé)ÿÿÿH‹½þÿÿH‹µþÿÿL‰êHƒÇ èéÊýÿÿH‹H‰…þÿÿébÿÿÿH‹?ë¼H‹?ë¿0Lµ@þÿÿèL‰÷I‰ÅèL½PþÿÿºH5L‰ÿèº@H5L‰ÿèD‰æL‰ÿèºH5L‰ÿè‰ÞL‰ÿèH… þÿÿHµXþÿÿH‰ÇH‰ÃèL‰÷èH‰ÞL‰ïèH‹H‹½ þÿÿAÆE IÇEHƒÀI‰EIE I‰EH…0þÿÿH9ÇtèH‹5HL‰ïèI‰ÄëI‰ÄëH‹½@þÿÿH…PþÿÿH9ÇtèH‰ßèL‰çèH‰Ãë H‰ÃëH‰Ãë"H‹½ þÿÿH…0þÿÿH9ÇtèëL‰÷èL‰ïèH‰ßèAWAVM‰ÆAUATI‰üUH‰ÕSH‰óHì¸H‹L|$ H‰L$Ll$0HP@H‹L‰ÿfHnÂHƒÀfHnÈflÁ)$èH‰ßèH‰ÞL‰ïH‰ÂèH‰ïèH‰îL‰ïH‰ÂèH‹D$L‰ïH‹0èL‰÷èL‰öL‰ïH‰ÂèH‹D$`I\$IÇD$I‰$AÆD$H…À„L‹D$PM…À„L9À‡ýH‹L$X1Ò1öL‰çI)ÈèH‹fo$H‹¼$€HƒÀ)T$0H‰D$ H‹HƒÀhH‰„$ H„$H9ÇtèH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èHÄ¸L‰à[]A\A]A^A_ÃfDI‰ÀéûþÿÿH´$€L‰çèéúþÿÿH‰ÅëH‰ÅëI‹<$H9ûtèL‰ÿèH‰ïèAWAVM‰ÆAUI‰ÕATI‰üUSH‰óHì¸H‹L|$ H‰L$Hl$0HP@H‹L‰ÿL‰L$fHnÂHƒÀfHnÈflÁ)$èH‰ßèH‰ÞH‰ïH‰ÂèL‰ïèL‰îH‰ïH‰ÂèH‹D$H‰ïH‹0èL‰÷èL‰öH‰ïH‰ÂèH‹D$H‰ïH‹0èH‹D$`I\$IÇD$I‰$AÆD$H…À„'L‹D$PM…À„ L9À‡H‹L$X1Ò1öL‰çI)ÈèH‹fo$H‹¼$€HƒÀ)T$0H‰D$ H‹HƒÀhH‰„$ H„$H9ÇtèH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èHÄ¸L‰à[]A\A]A^A_Ãf„I‰Àéøþÿÿ„H´$€L‰çèéïþÿÿH‰ÅëH‰ÅëI‹<$H9ûtèL‰ÿèH‰ïèAWAVM‰ÆAUATI‰üUH‰ÕSH‰óHì¸H‹L|$ H‰L$Ll$0HP@H‹L‰ÿfHnÂHƒÀfHnÈflÁ)$èH‰ßèH‰ÞL‰ïH‰ÂèH‰ïèH‰îL‰ïH‰ÂèH‹D$L‰ïH‹0èL‰÷èL‰öL‰ïH‰ÂèH‹D$`I\$IÇD$I‰$AÆD$H…À„L‹D$PM…À„L9À‡ýH‹L$X1Ò1öL‰çI)ÈèH‹fo$H‹¼$€HƒÀ)T$0H‰D$ H‹HƒÀhH‰„$ H„$H9ÇtèH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èHÄ¸L‰à[]A\A]A^A_ÃfDI‰ÀéûþÿÿH´$€L‰çèéúþÿÿH‰ÅëH‰ÅëI‹<$H9ûtèL‰ÿèH‰ïèAWI‰÷AVI‰ÎAUM‰ÍATI‰üUSH‰ÓHì¸H‹H|$ L‰D$Hl$0HP@H‹H‰|$fHnÂHƒÀfHnÈflÁ)$èL‰ÿèL‰þH‰ïH‰ÂèH‹SH‹3H‰ïèL‰÷èL‰öH‰ïH‰ÂèH‹D$H‰ï‹0èL‰ïèL‰îH‰ïH‰ÂèH‹„$ðH‰ï‹0èH‹D$`I\$IÇD$I‰$AÆD$H…À„'L‹D$PM…À„ L9À‡H‹L$X1Ò1öL‰çI)ÈèH‹fo$H‹¼$€HƒÀ)T$0H‰D$ H‹HƒÀhH‰„$ H„$H9ÇtèH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èHÄ¸L‰à[]A\A]A^A_Ãf„I‰Àéøþÿÿ„H´$€L‰çèéïþÿÿH‰ÅëH‰ÅëI‹<$H9ûtèH‹|$èH‰ïèAWfïÀI‰ÿAVAUATUH‰õSHƒì(H‹^H+HÇG„H¸àÿÿÿÿÿÿH9Ã‡H‰ßèH‰D$H‹D$fHnÀHÃflÀI‰_H‰ÃAL‹uH‹mL9õ„«HD$H‰$ë&„A¶EˆCHƒÅ L‰cHƒÃ BÆ'I9îtzH{L‹eH‰;L‹mL‰èLàt M…í„L‰d$IƒüwIƒüt±M…ät´ë'f.„H‹4$1ÒH‰ßèH‰H‰ÇH‹D$H‰CL‰âL‰îèL‹d$H‹;ésÿÿÿI‰_HƒÄ([]A\A]A^A_ÃDHÇD$éÿÿÿH…ÛyèèH=èH‰ÇèH;\$tH‹D$H‹8HƒÀH9ÇtèHƒD$ ëÛèH‰ÅèI‹?H…ÿtèH‰ïèAWAVM‰ÆAUI‰ÕATI‰üUSH‰óHì¸H‹L|$ H‰L$Hl$0HP@H‹L‰ÿL‰L$fHnÂHƒÀfHnÈflÁ)$èH‰ßèH‰ÞH‰ïH‰ÂèL‰ïèL‰îH‰ïH‰ÂèH‹D$H‰ïH‹0èL‰÷èL‰öH‰ïH‰ÂèH‹D$H‰ïH‹0èH‹D$`I\$IÇD$I‰$AÆD$H…À„'L‹D$PM…À„ L9À‡H‹L$X1Ò1öL‰çI)ÈèH‹fo$H‹¼$€HƒÀ)T$0H‰D$ H‹HƒÀhH‰„$ H„$H9ÇtèH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ 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Got AVAUATUSHìÀH‹H‹HS@HƒÀfHnÂfHnÈflÁ)$H…ÿtH‹o(ƒýt,ƒýt'ƒý…²HÄÀ[]A\A]A^éf.„HÄÀ[]A\A]A^é¿0Ll$0èHH5L‰ïH‰ÅèL‰îH‰ïèH‹H‹|$0HÇEÆE HƒÀH‰EHE H‰EHD$@H9ÇtèH‹5HH‰ïè¿0Ll$0èL‰ïI‰ÄèLt$@ºH5L‰÷èº?H5L‰÷è‰îL‰÷èH‹D$pLt$ ÆD$ Hl$L‰t$HÇD$H…À…XH´$H‰ïèHCfo$H‹¼$H‰D$0HChH‰„$°H„$ )T$@H9Ç…=H‹H¼$€HƒÀH‰D$HèH‹H¼$°H‹PH‹H0H‹RèH‰L0H‹P H‹H(H‰T$@H‹RèH‰L@H‹PH‹@H‰T$0H‹RèH‰D0H‹HÇD$8HƒÀH‰„$°èH‰îL‰çèH‹H‹|$IÇD$AÆD$ HƒÀI‰$ID$ I‰D$L9÷tèH‹5HL‰çèI‰ÄëI‰ÄëH‹|$0HD$@H9ÇtèH‰ïèL‰çèL‹D$`M…ÀuI‰ÀH‹L$h1Ò1öH‰ïI)Èèé’þÿÿL9Àwßëàèé¹þÿÿH‰Åë H‰ÅëH‰ÅëH‹|$L9÷uL‰ïèL‰çèH‰ïèèëáH‰ÅH‹|$L9÷tÜèëÕ expected to have type but instead is null expected to have sequence typeElement type of sequence input expected to have optional typeElement type of optional input AUATI‰ÔUH‰ýHƒì`H‹H‰t$ÿPH…À„‹@(ƒøt%ƒøt ƒø„×ƒø t2HƒÄ`]A\A]Ã„H‹t$L‰âH‰ïèHƒÄ`]A\A]ÃfDH‹t$H‹EH‰ïH‰t$ÿPH…À„dƒx( …ZLl$ H‹p L‰ïèöD$0„wH‹EL‰æH‰ïÿP(ƒx( H‰Å…L‹` I‹|$AƒL$H…ÿ„{H‹t$8H…ö„5èL‰ïèHƒÄ`]A\A]ÃfDH‹t$H‹EH‰ïH‰t$ÿPH…À„ƒx(…öLl$ H‹p L‰ïèöD$0„IH‹EL‰æH‰ïÿP(ƒx(H‰ÅuGL‹` 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H‰D$@H9ÇtèH‹¼$¨èH‹¼$0H„$@H‰D$HH9ÇtèH‹¼$PH;|$`tèH‹|$xèH‹¼$pH„$€H‰$H9ÇtèH‹¼$H;|$XtèL´$L¬$°L‰÷èL‰ïèH‹¼$`H„$pH9ÇtèH‹¼$@H„$PH9ÇtèH‹¼$°H;|$ptèH‹¼$€èH‹¼$@H„$PH9ÇtèH‹D$0H…À„ŽH‹¼$°H5‹˜øH¬$èî²ÿÿH‹´$°ºL‰÷èi²ÿÿH‹¼$ÈH5èÅ²ÿÿH‹¬$àH5H‰ïè®²ÿÿH‹|$xH‰îºè,²ÿÿH‹¬$ØH5Ç„$€?H‰ïèz²ÿÿH‹”$ðH‹|$8H‰îèH‹l$(1öH‰ïèH‹t$8H‰ïèH‹¬$ÐH5Ç„$H‰ïè'²ÿÿH‹|$PH”$H‰îèH‹¬$è1öH‰ïèH‹t$PH‰ïè‰œ$H‹\$H5H‰ßèØ±ÿÿH‰ÞH‹œ$ H”$H‰ßèH¬$@1öH‰ïèH‰ÞH‰ïèH‹œ$€Hc„$H5H‰ßH‰„$0èy±ÿÿH‰ÞH‹œ$ˆL‰âH‰ßèH¬$@1öH‰ïèH‰ÞH‰ïèfïÀ¿HÇ„$`)„$PèI‰ÄH‰„$PH‹\$(H€H‰D$L‰åH‰„$`DH‰ÞH‰ïèHÃH„$@ HÅH9ÃuÚfHníH5L‰ïH‰¬$Xl$)l$è¬°ÿÿH‹„$ fïÀfo¬$HÇ„$ )„$H‰„$ÀH„$ØH‰„$ÈH‹„$p)¬$°H;$„x H‰„$ÈH‹„$€H‰„$ØH‹„$xfInÔó~Œ$pHœ$Àó~„$€fol$HÇ„$xH‰„$ÐH‹$Œ$xflÂfïÒÆ„$€H‰„$pH„$(H‰„$H‹„$°HÇ„$€HÇ„$`)”$pŒ$è„$ø¬$)”$PH9Ø„°H‰„$H‹„$ÀH‰„$(L‹¬$ÀH‹„$¸H¬$H‰œ$°H‹´$H‰ïHÇ„$¸L‰êH‰„$ Æ„$ÀèL‰ïèH‹¼$°H9ßtèH‹¼$¨Hœ$@ èL‹d$(HëH‰ßèL9ãuìH‹¼$ˆèH‹¼$ÐH„$àH9ÇtèH‹¼$ èH‹¼$ðH;|$htèH‹|$PèH‹¼$H;|$@tèH‹|$8èH‹¼$0H;|$HtèH‹|$xèH‹¼$PH;|$`tèH‹¼$pH;<$tèL‰÷èH‹¼$H;|$XtèH‹|$PH‰îèL‹¤$HH‹œ$@Mo`L9ãu*é˜„QH‹|ÈA‰WhH‰ÞèHƒë€I9ÜtsI‹GpH…ÀtJIcOh‹9Ñ|ÐA‹wl9òt<ƒÂI‹`‰èH‰ÇIcGhI‹WpHA‰OhH‰|ÂëHÇ$é.øÿÿA‹wlƒÆL‰ïèI‹Gp‹ë±f„H‹¼$¸L‰þèH‹œ$HH‹¬$@H9ëtH‰ïHƒí€èH9ëuïH‹¬$@H…ítH‰ïèH‹œ$H‹¬$H9ët!DH‰ïHÅˆèH9ëuìH‹¬$H…ítH‰ïè¸ébðÿÿfo¤$ ¤$¸ éóÿÿfo¼$À¼$h éPòÿÿfo¼$)¼$@ éöÿÿfo”$`)”$ð éõÿÿH…í‰ŒèH‰ÆI9Ít/H‹6H…ö„¿H‹N01ÒH‰ÈH÷óI9ÐtÞHÇD$0éóïÿÿ@Hƒ~uÊH‰t$0H‰ðéñîÿÿH‹UH‹BH9Øt1öH‰ïÿÐH‹U‹H(étëÿÿH‹…ÀH;…È…UëÿÿéèIƒ|$…Ðîÿÿé%ïÿÿH‹t$(H‰ïAÿÔéŽìÿÿH‹œ$ÀH5H¬$H‰ßèc«ÿÿH‹¼$€H‰ÞºèÞªÿÿH‹¼$°H5è:«ÿÿH‹œ$ÈH5H‰ßè#«ÿÿH‰ÞºL‰ïè£ªÿÿH‹œ$àH5Ç„$P€?H‰ßèñªÿÿH‹l$(H‹”$¨H‰ÞH‰ïèH‹\$81öH‰ßèH‰îH‰ßèH‹œ$ØH5Ç„$0H‰ßèªÿÿH‰ÞH‹\$PL‰âH‰ßèL¤$@1öL‰çèH‰ÞL‰çèH‹œ$ÐHc„$H5H‰ßH‰„$pèFªÿÿH‹¬$ H‹T$xH‰ÞH‰ïèH„$@1öH‰ÇH‰D$0H‰ÃèH‰îH‰ßèfïÀ¿HÇ„$ )„$èH‰D$xH‰ÅH‹\$8H‰„$H€H‰„$H‰„$ H‰ÞH‰ïèHÃHÅH;\$(uàfHnýH‹|$H5H‰¬$˜¼$)¼$èj©ÿÿH‹„$àfïÀfo¬$ÐHÇ„$à)„$ÐH‰„$PH„$hH‰„$XH‹„$)¬$@H;D$X„œH‰„$XH‹„$ H‰„$hH‹„$˜fïÒó~Œ$°HÇ„$˜ó~„$Àfo¤$Æ„$ H‰„$`H‹D$XŒ$¸D$x)”$°H‰„$H„$¸H‰„$¨H‹„$ðHÇ„$ÀHÇ„$ Œ$x„$ˆ¤$˜)”$H;D$h„®H‰„$¨H‹„$H‰„$¸H‹„$øH‹\$hH¬$HÇ„$øH‹”$ˆH‹´$H‰ïÆ„$H‰„$°H‰œ$ðèH‹¼$ˆèH‹¼$ðH9ßtèL‰÷èH‹|$0èL‰çèH‹|$8èH‹¼$ èH‹¼$H;|$@tèH‹|$PèH‹¼$0H;|$HtèH‹|$(èH‹¼$PH;|$`tèL‰ïèH‹¼$pH;<$tèH‹¼$H;|$XtèH‹¼$€èH‹¼$°H;|$p…æøÿÿéæøÿÿfo´$€´$ØéŠöÿÿfo”$À”$(éR÷ÿÿH‹t$8H‰ïAÿÔH‹¼$@é-êÿÿH‰\$0M‰Ìéêÿÿfo¼$¼$¸éTþÿÿfo¬$ ¬$héfýÿÿH‰t$0éIêÿÿI‰ÄéH‰ÅéH‰ÃéH‰ÃH„$`H‰D$`éH‰ÃH„$`H‰D$`éH‰ÃH„$`H‰D$`éH‰ÃéH‰ÃH„$`H‰D$`éH‰ÃH„$`H‰D$`éH‰ÃéH‰ÇéH‰ÃH„$ÀH‰D$péH‰ÃéH‰ÃH„$`H‰D$`éH‰ÃéH‰ÇéH‰ÃH¬$éH‰ÃéI‰ÄH¬$éH‰ÇéH‰ÃH¬$éH‰ÃH¬$éH‰ÃH¬$éH‰ÃH¬$éH‰ÃéH‰ÃH¬$éH‰ÃéH‰ÃéH‰ÃéH‰ÇéH‰ÃéH‰ÃH¬$éH‰ÅéI‰ÄH¬$éH‰ÃH¬$éH‰ÃH¬$éH‰ÃH¬$éH‰ÃéH‰ÃH¬$éH‰ÃH¬$éH‰ÃéH‰ÅéI‰ÄéI‰ÄéH‰ÃéH‰ÃH¬$éH‰ÃéH‰ÃH¬$éH‰ÃH¬$éH‰ÃéH‰ÃH¬$éH‰ÃéH‰ÃH¬$éH‰ÃH¬$éI‰ÄH¬$éI‰ÄH¬$éH‰ÅéH‰ÃH¬$éH‰ÃH¬$éH‰ÃH¬$éH‰ÃH„$ÀH‰D$péH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÇééH‰ÃH¬$éH‰ÇéH‰ÇéH‰ÃéDH‰÷H‰ÖH‰Êé"âÿÿ…öxLH‹‡ÀH‹—ÈHcöH¹«ªªªªªªªH)ÂHÁúH¯ÑH9Ös!HvHÁâHÐ1Ò‹H(…ÉHDÂÃf.„1ÀÃ¸Ã…ÒtƒúuH‰71ÀÃH‹H‰1ÀÃ¸Ã…ÒtƒúuH‰71ÀÃH‹H‰1ÀÃATI‰üUSH‹_H‹/H9ët$DH‹}HEH9Çt+èHƒÅ H9ëuåI‹,$H…ít$[H‰ï]A\é„HƒÅ H9ëu¿ëØD[]A\ÃH‹UH‰ýHƒÀH‰H‹HE H9ÇtèH‰ï]éAVºiÇAUI‰õATI‰üI‹}USH‹vèI‹\$1ÒH‰ÅH÷óI‹D$L‹$ÐM…ät/M‹$$I‰ÖI‹L$0H9Ít4M‹$$M…ätI‹L$01ÒH‰ÈH÷óI9ÖtàE1ä[L‰à]A\A]A^Ãf.„I‹UI;T$uÁH…ÒtI‹t$I‹}è…ÀuªM‹d$([]L‰àA\A]A^ÃÿÿAUE1íATUH‰õSH‰ûHƒìHƒ?„³H‹mH…í„‘¿èHÇI‰ÄH‹E1ÒHKL‰cI‰D$H÷sH‹H‰ÐH‹mH…íuëTf„H‹mH…ítBI‰Ì¿èHÇH‰ÁH‹E1ÒH‰AH÷sH‹I‰$HÐHƒ8uÁL‰ H‹mH…íu¾HƒÄ[]A\A]Ãf.„H‹WHƒút1H‰ÐHÁè= 0.Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.Constrain output types to be numerics. Generate a 2D tensor (matrix) with ones on the diagonal and zeros everywhere else. Only 2D tensors are supported, i.e. input T1 must be of rank 2. The shape of the output tensor is the same as the input tensor. The data type can be specified by the 'dtype' argument. If 'dtype' is not specified, then the type of input tensor is used. By default, the main diagonal is populated with ones, but attribute 'k' can be used to populate upper or lower diagonals. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message and be valid as an output type. (Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.2D input tensor to copy shape, and optionally, type information from.Output tensor, same shape as input tensor T1.Constrain input types. Strings and complex are not supported.Constrain output types. Strings and complex are not supported. Generate a tensor with random values drawn from a uniform distribution. The shape of the tensor is specified by the `shape` argument and the range by `low` and `high`. The data type is specified by the 'dtype' argument. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. Lower boundary of the output values.Upper boundary of the output values.(Optional) Seed to the random generator, if not specified we will auto generate one.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.The shape of the output tensor.Output tensor of random values drawn from uniform distributionConstrain output types to float tensors. Generate a tensor with random values drawn from a normal distribution. The shape of the tensor is specified by the `shape` argument and the parameter of the normal distribution specified by `mean` and `scale`. The data type is specified by the 'dtype' argument. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. The mean of the normal distribution.The standard deviation of the normal distribution.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.Output tensor of random values drawn from normal distribution Generate a tensor with random values drawn from a uniform distribution. The shape of the output tensor is copied from the shape of the input tensor, and the parameters of the uniform distribution are specified by `low` and `high`. The data type is specified by the 'dtype' argument, or copied from the input tensor if not provided. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message and be valid as an output type. (Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.Input tensor to copy shape and optionally type information from.Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type. Generate a tensor with random values drawn from a normal distribution. The shape of the output tensor is copied from the shape of the input tensor, and the parameters of the normal distribution are specified by `mean` and `scale`. The data type is specified by the 'dtype' argument, or copied from the input tensor if not provided. The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message, and be valid as an output type. Generate a tensor of samples from a multinomial distribution according to the probabilities of each of the possible outcomes. (Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.Constrain input types to float tensors.Constrain output types to integral tensors. Generate a tensor containing a sequence of numbers that begin at `start` and extends by increments of `delta` up to `limit` (exclusive). The number of elements in the output of range is computed as below- `number_of_elements = max( ceil( (limit - start) / delta ) , 0 )` The pseudocode determining the contents of the output is shown below- `for(int i=0; i (sub_result) delta_casted = Cast (delta) div_result = Div (sub_result_casted, delta_casted) ceil_result = Ceil (div_result) ceil_result_relu = Relu (ceil_result) ceil_result_relu_int = Cast (ceil_result_relu) ceil_result_relu_bool = Cast (ceil_result_relu) variadic_output, output = Loop (ceil_result_relu_int, ceil_result_relu_bool, start) (cond_out, current, range) { cond_out = Identity (cond) current = Add (prev, delta) range = Identity (prev) }> } Draws binary random numbers (0 or 1) from a Bernoulli distribution. The input tensor should be a tensor containing probabilities p (a value in the range [0,1]) to be used for drawing the binary random number, where an output of 1 is produced with probability p and an output of 0 is produced with probability (1-p). This operator is non-deterministic and may not produce the same values in different implementations (even if a seed is specified). The data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.All values in input have to be in the range:[0, 1].The returned output tensor only has values 0 or 1, same shape as input tensor.Constrain output types to all numeric tensors and bool tensors.All inputs to 'Range' op must be of the same typeInput tensor must be 2-dimensionalOutput type must be int32 or int64H‰ÅèH‹;H…ÿtèH‰ïèèè¿0èL‰ÿI‰ÄèH‹|$ºH5èH‹|$º-H5èHl$@Ht$xH‰ïèH‹H‹¼$ÀHPH‰T$`HPhHƒÀ@fHnÀH‹H‰”$àHƒÀfHnøH„$ÐflÇ)D$pH9Ç…€H‹H¼$°HƒÀH‰D$xèH‹H¼$àH‹PH‹H0H‹RèH‰L`H‹P H‹H(H‰T$pH‹RèH‰LpH‹PH‹@H‰T$`H‹RèH‰D`H‹HÇD$hHƒÀH‰„$àèH‰îL‰çèH‹H‹|$@IÇD$AÆD$ HƒÀI‰$ID$ I‰D$HD$PH9ÇtèH‹5HL‰çè¿0èL‰ÿI‰ÄèH‹|$ºH5èH‹|$º2H5èLl$PÆD$PHl$@H‹„$ L‰l$@HÇD$HH…À…TH´$ÀH‰ïèHCfo4$H‹¼$ÀH‰D$`HChH‰„$àH„$Ð)t$pH9Ç…?H‹H¼$°HƒÀH‰D$xèH‹H¼$àH‹PH‹H0H‹RèH‰L`H‹P H‹H(H‰T$pH‹RèH‰LpH‹PH‹@H‰T$`H‹RèH‰D`H‹HÇD$hHƒÀH‰„$àèH‰îL‰çèH‹H‹|$@IÇD$AÆD$ HƒÀI‰$ID$ 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tensor(uint8)tensor(uint16)tensor(uint32)tensor(uint64)tensor(int8)tensor(int16)tensor(int32)tensor(int64)tensor(float16)tensor(float)tensor(double)tensor(string)tensor(bool)tensor(complex64)tensor(complex128)AWAVAUATUSHìøL‹-A¶E„ÀtL‹%HÄøL‰à[]A\A]A^A_ÃL‰ïè…ÀtÕLt$H5L‰÷èH|$0H5èH|$PH5èH|$pH5èH¼$H5èH¼$°H5èH¼$ÐH5èH¼$ðH5èH¼$H5èH¼$0H5èH¼$PH5èH¼$pH5èH¼$H5èH¼$°H5èH„$ÐH5H‰ÇH‰D$èL‹%fïÀ¿àIÇD$A$èI‰ÇI‰$H€àL‰õI‰D$L‰ûDHCH‹uH‹UH‰ßH‰HòèHƒÅ H„$ðHƒÃ H9ÅuÑHL‰æI‰\$H=èL‰ïèH‹\$ë€èHCàI9Þ„þýÿÿH‰ÃH‹;HCH9ÇußHCàI9ÞuèéáýÿÿH‰Ãë6H‰ÇëAH‰ÃëèI‹<$H…ÿuYH¬$ðHƒí H‹}HEH9ÇuGL9õuêL‰ïèH‰ßèèL9ûtI‹?IGH9ÇtèIƒÇ ëäèH‰Ãë™èë èë²ATI‰ôUH‰ýHƒìH‹ÿPL9àvhH‹EL‰æH‰ïÿPH…ÀtVH‹EL‰æH‰ïÿP‹P(ƒúuëO€‹P(ƒút@ƒút;ƒútƒú u!H‹@ ö@tH‹@H…ÀuÖH‹ëÍDHƒÄ1À]A\ÃfDH‹@ ‹@HƒÄ]A\ƒàÃUH‰ýSHƒìH‹_H…ÛtfDH‰ßH‹èH…ÛuðH‹EH‹}1öHƒÅ0HÅèH‹}ÐHÇEèHÇEàH9ïtHƒÄ[]éf„HƒÄ[]ÃAUATUSH‰û¿ˆHƒìèH‹3H‹SHhHÇHxI‰ÄH‰hHòèH‹C(óoC@Ml$(Hs IÇD$(L‰ïI‰D$0H‹C8IÇD$8I‰D$@IÇD$XAD$HèH‹sXH‹S`ID$pI|$`I‰D$`HòèHƒÄL‰à[]A\A]ÃH‰ÇëH‰ÃëH‰ÃëL‰ïèI‹|$H9ýuH‰ßèL‰çèèH‰ÅëèëßèH‰ïèAUI‰ýATUSHƒìL‹gH‹/I9ì„¢H‹}xH…ˆH9ÇtèH‹}XHEhH9ÇtèH‹]0H…ÛtH‰ßH‹èH…ÛuðH‹E(H‹} 1öHÅèH‹} HEPHÇE8HÇE0H9ÇtèH‹}HEH9Çt:èHÅ¨I9ì…eÿÿÿI‹mH…ít.HƒÄH‰ï[]A\A]éf.„HÅ¨I9ì…0ÿÿÿëÉHƒÄ[]A\A]ÃAVI‰þAUATUSL‹oL‹'M9åt}f„I‹|$8ID$HH9ÇtèI‹\$(I‹l$ H9ët%DH‹}HEH9Çt[èHƒÅ H9ëuåI‹l$ H…ítH‰ïèI‹<$ID$H9Çt=èIƒÄXM9åuM‹&M…ät6[L‰ç]A\A]A^é€HƒÅ H9ëuë¨DIƒÄXM9å…SÿÿÿëÂ[]A\A]A^Ã[TypeInferenceError] Output is null expected to have tensor or sparse tensor type: AWAVAUA‰ÕATI‰ôU‰ÍSHì¸H‹ÿP(H…À„nH‰Ã‹@(9èt…À…¦ƒýt!ƒýtdHÄ¸[]A\A]A^A_Ãf.„ƒøuH‹C ƒHD‰h ëÑDH‰ßèH‹CÇC(H‰ÇHƒçþ¨u@èH‰C ëÈ€ƒøt¸H‰ßèH‹CÇC(H‰ÇHƒçþ¨uèH‰C ë“H‹?ë»H‹?ëë¿0L|$ èL‰ÿI‰ÅèLt$0ºH5L‰÷èºH5L‰÷èL‰æL‰÷èº0H5L‰÷è‰îL‰÷èH‰åHt$8H‰ïèH‹H‹¼$€HPH‰T$ HPhHƒÀ@fHnÀH‹H‰”$ HƒÀfHnÐH„$flÂ)D$0H9Ç…DH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èH‰îL‰ïèH‹AÆE IÇEHƒÀI‰EIE I‰EH‹<$HD$H9ÇtèH‹5HL‰ïè¿0L|$ èL‰ÿI‰ÅèLt$0ºH5L‰÷èºH5L‰÷èL‰æL‰÷èºH5L‰÷èH‰åHt$8H‰ïèH‹H‹¼$€HPH‰T$ HPhHƒÀ@fHnÀH‹H‰”$ HƒÀfHnÈH„$flÁ)D$0H9ÇtèH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èH‰îL‰ïèéNþÿÿèé²ýÿÿH‰Åë H‰ÅëH‰ÅëH‹<$HD$H9ÇtèëL‰ÿèL‰ïèH‰ïèëÂëÅëÈ expected to have tensor or sparse type expected to have tensor typeAVAUATI‰ôUS‰ÓHì°H‹ÿP(H…À„œH‰Å‹@(ƒø„Žƒøt)…À…«ƒût!1ÀƒûtHÄ°[]A\A]A^ÃDƒøt[H‰ïèH‹EÇE(H‰ÇHƒçþ¨u]èH‰E H‰ÃƒHH‹@H…Àu¯H‹CH‰ÇHƒçþ¨u:èH‰Cë•f.„H‰ïèƒHH‰ÃH‹@H…À…oÿÿÿë¾fH‹?ëžH‹?ëÁ¿0Ll$ èL‰ïI‰ÆèHl$0ºH5H‰ïèºH5H‰ïèL‰æH‰ïèºH5H‰ïèH‰åHt$8H‰ïèL‰ïèH‰îL‰÷èH‹AÆF IÇFHƒÀI‰IF I‰FH‹<$HD$H9ÇtèH‹5HL‰÷è¿0Ll$ èL‰ïI‰ÆèHl$0ºH5H‰ïèºH5H‰ïèL‰æH‰ïèº'H5H‰ïèH‰åHt$8H‰ïèL‰ïèH‰îL‰÷èé&ÿÿÿH‰Åë H‰ÅëH‰ÅëH‹<$HD$H9ÇtèëL‰ïèL‰÷èH‰ïèëÂëÅëÈAWAVAUI‰õATI‰üUH‰ÕSHì¨H‹H‹L|$Lt$ HS@HƒÀL‰ÿfHnÂfHnÈflÁ)$èL‰ïèL‰îL‰÷H‰ÂèH‰ïèH‰îL‰÷H‰ÂèH‹D$PIl$IÇD$I‰,$AÆD$H…À„ L‹D$@M…À„ôL9À‡ëH‹L$H1Ò1öL‰çI)ÈèHCfo$H‹|$pH‰D$HChH‰„$H„$€)T$ H9ÇtèH‹H|$`HƒÀH‰D$(èH‹H¼$H‹PH‹H0H‹RèH‰LH‹P H‹H(H‰T$ H‹RèH‰L H‹PH‹@H‰T$H‹RèH‰DH‹HÇD$HƒÀH‰„$èHÄ¨L‰à[]A\A]A^A_ÃDI‰Àé ÿÿÿHt$pL‰çèéÿÿÿH‰ÃëH‰ÅëI‹<$H9ýtèH‰ÝL‰ÿèH‰ïèAttribute expected to have tensor or sparse tensor typeATUHƒì(H‹ÿP‹P(ƒútƒúu%H‹@ H‹@H…Àt HƒÄ(]A\ÃH‹HƒÄ(]A\Ã¿0I‰äèHH5L‰çH‰ÅèL‰æH‰ïèH‹H‹<$HÇEÆE HƒÀH‰EHE H‰EHD$H9ÇtèH‹5HH‰ïèI‰ÄëH‹<$HD$H9ÇtèH‰ïèL‰çèI‰ÄëØValue of attribute not specifiedAttribute should be of integer type and specify a type. does not specify a valid type.AVE‰ÆAUA‰ÍATI‰ÔUH‰ýSH‰óHì°H‹ÿH…ÀtHö@„`D‹°ðD‰÷è„À„•HÄ°D‰éD‰òL‰æ[H‰ï]A\A]A^é€E…öuÔ¿0Ll$ èL‰ïI‰ÄèHl$0ºH5H‰ïèºH5H‰ïèH‹SH‹3H‰ïèºH5H‰ïèH‰åHt$8H‰ïèH‹H‹¼$€HPH‰T$ HPhHƒÀ@fHnÀH‹H‰”$ HƒÀfHnÈH„$flÁ)D$0H9ÇtèH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èH‰îL‰çèéc¿0Ll$ èL‰ïI‰ÄèHl$0ºH5H‰ïèº H5H‰ïèH‹SH‹3H‰ïèºH5H‰ïèH‰åHt$8H‰ïèH‹H‹¼$€HPH‰T$ HPhHƒÀ@fHnÀH‹H‰”$ HƒÀfHnØH„$flÃ)D$0H9Ç…LH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èH‰îL‰çèH‹AÆD$ IÇD$HƒÀI‰$ID$ I‰D$H‹<$HD$H9ÇtèH‹5HL‰çè¿0Ll$ èL‰ïI‰ÄèHl$0ºH5H‰ïèº H5H‰ïèH‹SH‹3H‰ïèº.H5H‰ïèH‰åHt$8H‰ïèH‹H‹¼$€HPH‰T$ HPhHƒÀ@fHnÀH‹H‰”$ HƒÀfHnÐH„$flÂ)D$0H9ÇtèH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èH‰îL‰çèéFþÿÿèéªýÿÿH‰Åë H‰ÅëH‰ÅëH‹<$HD$H9ÇtèëL‰ïèL‰çèH‰ïèëÂëÅëÈë¼ë¿ëÂAWAVM‰ÆAUATI‰üUH‰ÕSH‰óHì¨L|$H‰L$Ll$ L‰ÿèH‰ßèH‰ÞL‰ïH‰ÂèH‰ïèH‰îL‰ïH‰ÂèH‹D$L‰ïH‹0èL‰÷èL‰öL‰ïH‰ÂèHt$(L‰çèH‹H‹|$pHPH‰T$HPhHƒÀ@fHnÀH‹H‰”$HƒÀfHnÈH„$€flÁ)D$ H9ÇtèH‹H|$`HƒÀH‰D$(èH‹H¼$H‹PH‹H0H‹RèH‰LH‹P H‹H(H‰T$ H‹RèH‰L H‹PH‹@H‰T$H‹RèH‰DH‹HÇD$HƒÀH‰„$èHÄ¨L‰à[]A\A]A^A_ÃH‰ÅL‰ÿèH‰ïèInput type was nullInput Element type of input unknown expected to have tensor or sparse tensor type. Got: AWAVAUI‰ÕATI‰ôUH‰ýSHì¸H‹ÿPH…À„ÔD‹p(Aƒþt Aƒþ…“H‹@ ‹X …Û„eH‹EH‰ïL‰îÿP(D‹`(H‰ÅAƒüt2Aƒüt,E…ä…¨AƒþteAƒþt/HÄ¸[]A\A]A^A_ÃDH‹U ‹B‰Z ƒÈ‰Bë×fDH‰ÇèH‹EÇE(H‰ÇHƒçþ¨uEèH‰E ƒH‰X ë¡H‰ÇèH‹EÇE(H‰ÇHƒçþ¨uèH‰E ƒH‰X énÿÿÿH‹?ë¶H‹?ëá¿0L|$ èL‰ÿI‰ÆèHl$0ºH5H‰ïèºH5H‰ïèL‰îH‰ïèº5H5H‰ïèD‰æH‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰÷èH‹H‹<$AÆF IÇFHƒÀI‰IF I‰FHD$H9ÇtèH‹5HL‰÷è¿0L|$ èL‰ÿI‰ÄèHl$0ºH5H‰ïèºH5H‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰çèH‹H‹<$AÆD$ IÇD$HƒÀI‰$ID$ I‰D$HD$H9ÇtèH‹5HL‰çèH‰Åé½H‰Åé!H‰ÅéÕ¿0L|$ èL‰ÿI‰ÅèHl$0ºH5H‰ïèºH5H‰ïèL‰æH‰ïèº5H5H‰ïèD‰öH‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰ïèH‹AÆE IÇEHƒÀI‰EIE I‰EH‹<$HD$H9ÇtèH‹5HL‰ïè¿0L|$ èL‰ÿI‰ÅèHl$0ºH5H‰ïèºH5H‰ïèL‰æH‰ïèºH5H‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰ïèé%ÿÿÿH‰Åë H‰ÅéH‰ÅëH‹<$HD$H9ÇtèL‰ïèH‰ïèëÉëÌH‰ÅëH‹<$HD$H9ÇtèL‰çèH‰ïèH‰ÅëL‰ÿèL‰÷èH‰ïèH‹<$HD$H9ÇtâèëÛH‰ÅëÖépÿÿÿL‰ÿèé{ÿÿÿL‰ÿèëAWAVM‰ÆAUATI‰üUH‰ÕSH‰óHì¨L|$H‰L$Ll$ L‰ÿèH‰ßèH‰ÞL‰ïH‰ÂèH‰ïèH‰îL‰ïH‰ÂèH‹D$L‰ïH‹0èL‰÷èL‰öL‰ïH‰ÂèHt$(L‰çèH‹H‹|$pHPH‰T$HPhHƒÀ@fHnÀH‹H‰”$HƒÀfHnÈH„$€flÁ)D$ H9ÇtèH‹H|$`HƒÀH‰D$(èH‹H¼$H‹PH‹H0H‹RèH‰LH‹P H‹H(H‰T$ H‹RèH‰L H‹PH‹@H‰T$H‹RèH‰DH‹HÇD$HƒÀH‰„$èHÄ¨L‰à[]A\A]A^A_ÃH‰ÅL‰ÿèH‰ïèAWAVM‰ÆAUATI‰üUH‰ÕSH‰óHì¨L|$H‰L$Ll$ L‰ÿèH‰ßèH‰ÞL‰ïH‰ÂèH‰ïèH‰îL‰ïH‰ÂèH‹D$L‰ïH‹0èL‰÷èL‰öL‰ïH‰ÂèHt$(L‰çèH‹H‹|$pHPH‰T$HPhHƒÀ@fHnÀH‹H‰”$HƒÀfHnÈH„$€flÁ)D$ H9ÇtèH‹H|$`HƒÀH‰D$(èH‹H¼$H‹PH‹H0H‹RèH‰LH‹P H‹H(H‰T$ H‹RèH‰L H‹PH‹@H‰T$H‹RèH‰DH‹HÇD$HƒÀH‰„$èHÄ¨L‰à[]A\A]A^A_ÃH‰ÅL‰ÿèH‰ïè[ShapeInferenceError] Target=Mismatch between source and target type. Source=Unsupported Source/Target type=AWAVAUATUSHì¸D‹g(D‹n(E9ìu{H‰ûH‰õAƒü„BAƒü„ØAƒü„–Aƒü …˜Aƒý …*L‹e I‹l$AƒL$H…í„Tƒ{( „xH‹H‹XH…Û„uD‹c(D‹m(E9ìt‹¿0Lt$ èL‰÷I‰ÇèHl$0ºH5H‰ïèº0H5H‰ïèD‰æH‰ïèºH5H‰ïèD‰îH‰ïèH‰åHt$8H‰ïèL‰÷èH‰îL‰ÿèH‹H‹<$AÆG IÇGHƒÀI‰IG I‰GHD$H9ÇtèH‹5HL‰ÿè€H‰ïèH‹EÇE( H‰ÇHƒçþ¨…îèI‰ÄH‰E I‹l$AƒL$H…í…¬þÿÿI‹D$H‰ÇHƒçþ¨…ôèƒ{( I‰D$H‰Å…‹þÿÿH‹C H‹XH…Û…‹þÿÿH‹éþÿÿAƒütZH‹C ö@„œL‹`M…ä„OAƒý…H‹] H‹{ƒKH…ÿuSH‹CH‰ÇHƒçþ¨…xèH‰CH‰Çë2H‹C ö@tFL‹`M…ä„éH‰ïèH‹xƒHH‰ÃH…ÿtHÄ¸L‰æ[]A\A]A^A_é„HÄ¸[]A\A]A^A_ÃfDAƒýu2L‹e I‹l$AƒL$H…í„ªƒ{(„ðþÿÿH‹ésýÿÿ@H‰ïèH‹EÇE(H‰ÇHƒçþ¨…èH‰E I‰Äë¢H‰ïèH‹EÇE(H‰ÇHƒçþ¨utèH‰E H‰ÃéÐþÿÿL‹%éÿÿÿ@L‹%é¥þÿÿ@H‹?é þÿÿ„I‹D$H‰ÇHƒçþ¨u/èI‰D$H‰Åé4ÿÿÿH‹?éhÿÿÿH‹?éþÿÿH‹?ë‡H‹?é€þÿÿH‹?ëÌ¿0Lt$ èL‰÷I‰ÅèHl$0ºH5H‰ïèºH5H‰ïèD‰æH‰ïèH‰åHt$8H‰ïèL‰÷èH‰îL‰ïèH‹H‹<$AÆE IÇEHƒÀI‰EIE I‰EHD$H9ÇtèH‹5HL‰ïèH‰Åë H‰ÅëH‰ÅëH‹<$HD$H9ÇtèëL‰÷èL‰ïèH‰ïèH‰Åë H‰ÅëH‰ÅëH‹<$HD$H9ÇtèëL‰÷èL‰ÿèH‰ïèAWI‰÷AVM‰ÆAUATI‰üUH‰ÕSH‰ËHì¨H|$Ll$ H‰|$èL‰ÿèL‰þL‰ïH‰ÂèH‰ïèH‰îL‰ïH‰ÂèH‹SH‹3L‰ïèL‰÷èL‰öL‰ïH‰ÂèHt$(L‰çèH‹H‹|$pHPH‰T$HPhHƒÀ@fHnÀH‹H‰”$HƒÀfHnÈH„$€flÁ)D$ H9ÇtèH‹H|$`HƒÀH‰D$(èH‹H¼$H‹PH‹H0H‹RèH‰LH‹P H‹H(H‰T$ H‹RèH‰L H‹PH‹@H‰T$H‹RèH‰DH‹HÇD$HƒÀH‰„$èHÄ¨L‰à[]A\A]A^A_ÃH‰ÅH‹|$èH‰ïèAWAVAUI‰õATI‰üUH‰ÕHìI‰çLt$L‰ÿèL‰ïèL‰îL‰÷H‰ÂèH‰ïèH‰îL‰÷H‰ÂèHt$L‰çèH‹H‹|$`HPH‰$HPhHƒÀ@fHnÀH‹H‰”$€HƒÀfHnÈHD$pflÁ)D$H9ÇtèH‹H|$PHƒÀH‰D$èH‹H¼$€H‹PH‹H0H‹RèH‰H‹P H‹H(H‰T$H‹RèH‰LH‹PH‹@H‰$H‹RèH‰H‹HÇD$HƒÀH‰„$€èHÄL‰à]A\A]A^A_ÃH‰ÅL‰ÿèH‰ïèAWAVM‰ÆAUI‰ÕATI‰üUSH‰óHì¨L|$H‰$Hl$ L‰ÿL‰L$èH‰ßèH‰ÞH‰ïH‰ÂèL‰ïèL‰îH‰ïH‰ÂèH‹$H‰ïH‹0èL‰÷èL‰öH‰ïH‰ÂèH‹D$H‰ï‹0èH‹¼$àèH‹´$àH‰ïH‰ÂèH‹„$èH‰ï‹0èHt$(L‰çèL‰ÿèHÄ¨L‰à[]A\A]A^A_ÃH‰ÅL‰ÿèH‰ïè expected to have: or UNDEFINED. Got: UH‰åAVATA‰ôH‰ÖSHƒìHH‹H‰U¨‰M¤ÿP(H‰Ã‹@(‰E¼…Àt#‹U¤9Â…«ƒútSƒútNHeè[A\A^]Ã‹E¤ƒøtXƒøuåH‰ßèH‹CÇC(H‰ÇHƒçþ¨ucèH‰C ëf.„H‹C ƒHD‰` Heè[A\A^]Ãf„H‰ßèH‹CÇC(H‰ÇHƒçþ¨uèH‰C ë»H‹?ëðH‹?ë˜¿0LeÀèHM¨LM¤L‰çI‰ÆHE¼LPHHPH5èXL‰æZL‰÷èH‹H‹}ÀAÆF IÇFHƒÀI‰IF I‰FHEÐH9ÇtèH‹5HL‰÷èH‰ÃëH‹}ÀHEÐH9ÇtèL‰÷èH‰ßèH‰ÃëÙvalueInvalid shape value: dtypeshape should specify a shapesparse_valuevalue_intvalue_intsvalue_floatvalue_floatsvalue_stringvalue_stringsToutputtensor(bfloat16)ConstantT1inputT2Constrain input types.ConstantOfShapekEyeLikelowhighseedRandomUniformmeanscaleRandomNormalRandomUniformLikeRandomNormalLikeNumber of times to sample.sample_sizeMultinomialstartlimitScalar. 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[]A\A]A^é[]A\A]A^ÃN10onnx_torch14InferenceErrorEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_E*ZN10onnx_torch11GetOpSchemaINS_19Constant_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_18Constant_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_18Constant_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_19Constant_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_tensor(complex64tensor(complex12 This operator produces a constant tensor. Exactly one of the provided attributes, either value, sparse_value, or value_* must bConstrain input and output types to all tensor tA constant tenso A constant tensor. 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HƒÀh)„$°H‰„$ðH„$ÀH‰ÇH‰D$PèH‹H‰ÞL‰ÿÇ„$ÈHƒÀÆ„$àH‰„$ˆH„$àH‰D$0H‰„$ÐHÇ„$ØèH‹\$XH‰ßèH‰ÞL‰ïH‰ÂèH‹\$@H‰ßèH‰ÞL‰ïH‰ÂèH‹D$HL‰ïH‹0èH‹\$`H‰ßèH‰ÞL‰ïH‰ÂèH‹„$°I\$IÇD$I‰$AÆD$H…À„ L‹„$ M…À„ÿL9À‡öH‹Œ$¨1Ò1öL‰çI)ÈèH‹fol$ H‹¼$ÐHƒÀ)¬$€H‰D$pH‹HƒÀhH‰„$ðH;|$0tèH‹H‹|$PHƒÀH‰„$ˆèH‹D$H‹T$L‰ÿH‹L$H‹@èH‰TpI‹FèL‰´$€H‹$H‰Œ€H‹EèH‰l$pH‰TpH‹HÇD$xHƒÀH‰„$ðèHÄL‰à[]A\A]A^A_ÃfDI‰Àéÿÿÿ„H´$ÐL‰çèéüþÿÿH‰ÅëH‰ÅëI‰Äë*I‰ÄëcI‰Äë;I‹<$H9ûtèH‹|$hèH‰ïèH‹EèH‹$H‰l$pH‰LpHÇD$xH‹L‰ÿHƒÀH‰„$ðèL‰çèH‰ßèH‹D$H‹L$H‹@èH‰LpI‹FèH‹L$L‰´$€H‰Œ€ëŒAWAVAUATI‰üUSHìH‹L´$H¬$€H‰t$HƒÀ@L‰÷H‰L$`fHnÀH‹H‰T$foÈfoÐL‰D$HƒÀL‰L$hfHnØH‹H‰l$pflËHƒÀ)L$@fHnàflÔ)T$PèL‹=H‹1öHÇ„$ØfïÀI‹_HƒÀI‹O„$èH‰„$1Àf‰„$à„$øH‹CèH‰œ$€H‰Œ€HÇ„$ˆH‹{èH‰L$ HïèM‹o I‹G(H¬$1öI‹}èL‰¬$H‰D$(HïH‰èI‹GI‹W0L¼$ÐfïÀfoT$PL‰ÿH‰D$0H‹@èH‰T$8H‰”€H‹)”$HƒÀ)„$ H‰„$€H‹)„$°HƒÀh)„$ÀH‰„$èH‹L‰÷Ç„$ØHÇ„$èHƒÀÆ„$ðH‰„$˜H„$ðH‰D$PH‰„$àH„$˜H‰ÆH‰D$xèH‹|$èH‹t$H‰ïH‰ÂèH‹|$èH‹t$H‰ïH‰ÂèH‹D$`H‰ïH‹0èH‹|$èH‹t$H‰ïH‰ÂèH‹D$hH‰ï‹0èH‹„$ÀIl$IÇD$I‰,$AÆD$H…À„,L‹„$°M…À„L9À‡H‹Œ$¸1Ò1öL‰çI)ÈèH‹fol$@H‹¼$àHƒÀ)¬$H‰„$€H‹HƒÀhH‰„$H;|$PtèH‹L‰ÿHƒÀH‰„$˜èH‹D$0H‹L$8L‰÷H‹t$(H‹@èH‰Œ€I‹EèL‰¬$H‰´H‹CèH‰œ$€H‹\$ H‰œ€H‹HÇ„$ˆHƒÀH‰„$èHÄL‰à[]A\A]A^A_Ã@I‰Àéöþÿÿ„H´$àL‰çèéðþÿÿH‰ÃëH‰Åë&H‰Åé±H‰Åë+H‰ÅéI‹<$H9ýtèH‰ÝH‹|$pèH‰ïèH‹|$xèH‹D$0H‹L$8H‹@èH‰Œ€I‹EèH‹L$(L‰¬$H‰ŒH‹CèH‰œ$€H‹\$ H‰œ€HÇ„$ˆH‹L‰÷HƒÀH‰„$èH‰ïèë¶[ShapeInferenceError] Target=Mismatch between source and target type. Source=Unsupported Source/Target type=AWAVAUATUSHì¸‹o(D‹n(D9íuwI‰üH‰óƒý„ôƒý„‹ƒý„Jƒý …ƒAƒý …ßH‹k H‹]ƒMH…Û„ Aƒ|$( „.H‹L‹`M…ä„,A‹l$(D‹k(D9ít¿0Lt$ èL‰÷I‰ÄèL|$0ºH5L‰ÿèº0H5L‰ÿè‰îL‰ÿèºH5L‰ÿèD‰îL‰ÿèH‰åHt$8H‰ïèL‰÷èH‰îL‰çèé@H‰ßèH‹CÇC( H‰ÇHƒçþ¨…èH‰ÅH‰C H‹]ƒMH…Û…÷þÿÿH‹EH‰ÇHƒçþ¨…&èAƒ|$( H‰EH‰Ã…ØþÿÿfDI‹D$ L‹`M…ä…ÔþÿÿL‹%éÈþÿÿfƒýt[I‹D$ ö@„œL‹`M…ä„WAƒý…H‹k H‹}ƒMH…ÿ„HHÄ¸L‰æ[]A\A]A^A_é€I‹D$ ö@tEL‹`M…ä„ðAƒýtH‰ßèH‹CÇC(H‰ÇHƒçþ¨…]èH‰C H‰ÅëfHÄ¸[]A\A]A^A_ÃfDAƒýu2H‹k H‹]ƒMH…Û„ÝAƒ|$(„ñþÿÿH‹é¾ýÿÿDH‰ßèH‹CÇC(H‰ÇHƒçþ¨…¾èH‰C H‰Åë¢H‰ßèH‹CÇC(H‰ÇHƒçþ¨…žèH‰C H‰ÅéÌþÿÿDL‹%éÿÿÿ@L‹%éþÿÿ@H‹EH‰ÇHƒçþ¨uaèH‰EH‰Çé˜þÿÿ„H‹?éÚýÿÿ„H‹EH‰ÇHƒçþ¨u6èH‰EH‰ÃéÿÿÿH‹?é:ÿÿÿH‹?éÒýÿÿH‹?éZÿÿÿH‹?ëšH‹?é›þÿÿH‹?ëÅ¿0Lt$ èL‰÷I‰ÄèL|$0ºH5L‰ÿèºH5L‰ÿè‰îL‰ÿèH‰åHt$8H‰ïèL‰÷èH‰îL‰çèH‹AÆD$ IÇD$HƒÀI‰$ID$ I‰D$H‹<$HD$H9ÇtèH‹5HL‰çèH‰Åë H‰ÅëH‰ÅëH‹<$HD$H9ÇtèëL‰÷èL‰çèH‰ïèëÂëÅëÈ is nullOutput [TypeInferenceError] expected to have tensor or sparse tensor type: AT1öUH‰ýSHƒì0H‹HÇD$ÇD$ÿP(H…À„‹P(H‰Ã‹D$…Òt9Â…oƒø„”ƒø„H‹EH‰ïÿPH…ÀtlH‹E1öH‰ïÿPH…Àt[H‹E1öH‰ïÿP‹P(ƒúué¢f‹P(ƒú„”ƒú„‹ƒútƒú u!H‹@ ö@tH‹@H…ÀuÎH‹ëÅDHƒÄ0[]A\Ã€ƒúuH‹C ƒHÇ@ é\ÿÿÿ€H‰ßèH‹CÇC(H‰ÇHƒçþ¨…ƒèH‰C ë¼H‹@ ‹@ƒà„Àt’H‹EH‰ï1öÿP(H‰ï1öI‰ÄH‹EÿPHƒÄ0L‰æ[H‰Ç]A\é@ƒú„lÿÿÿH‰ßèH‹CÇC(H‰ÇHƒçþ¨uèH‰C éDÿÿÿH‹?éuÿÿÿH‹?ëå¿0Ld$èHL$L‰çLL$LHH‰ÅH5èL‰æH‰ïèH‹ÆE HÇEHƒÀH‰EHE H‰EH‹|$HD$ H9ÇtèH‹5HH‰ïè¿0Ld$èHL$L‰çLHH5H‰ÅèL‰æH‰ïèéoÿÿÿI‰ÄëI‰ÄëH‹|$HD$ H9ÇtèH‰ïèL‰çèëÐëÓAWAVAUATI‰üUSHìH‹L¼$ðLt$pH‰t$XHƒÀ@L‰ÿH‰L$HfHnÀH‹H‰T$@foÈfoÐL‰D$`HƒÀL‰t$hfHnØH‹flËHƒÀ)L$ fHnàflÔ)T$0èH‹H‹1öHÇ„$ÈfïÀH‹kHƒÀH‹K„$ØH‰„$ð1Àf‰„$Ð„$èH‹EèH‰l$pH‰LpHÇD$xH‹}èH‰$L÷èL‹s H‹C(L¬$€1öI‹~èL‰´$€H‰D$LïH‰èH‹CH‹s0fïÀHœ$ˆfoT$0H‰D$H‹@èH‰t$H‰tpH‹)”$€HƒÀ)„$H‰D$pH‹)„$ HƒÀh)„$°H‰„$ðH„$ÀH‰ÇH‰D$PèH‹H‰ÞL‰ÿÇ„$ÈHƒÀÆ„$àH‰„$ˆH„$àH‰D$0H‰„$ÐHÇ„$ØèH‹\$XH‰ßèH‰ÞL‰ïH‰ÂèH‹\$@H‰ßèH‰ÞL‰ïH‰ÂèH‹D$HL‰ïH‹0èH‹\$`H‰ßèH‰ÞL‰ïH‰ÂèH‹„$°I\$IÇD$I‰$AÆD$H…À„ L‹„$ M…À„ÿL9À‡öH‹Œ$¨1Ò1öL‰çI)ÈèH‹fol$ H‹¼$ÐHƒÀ)¬$€H‰D$pH‹HƒÀhH‰„$ðH;|$0tèH‹H‹|$PHƒÀH‰„$ˆèH‹D$H‹T$L‰ÿH‹L$H‹@èH‰TpI‹FèL‰´$€H‹$H‰Œ€H‹EèH‰l$pH‰TpH‹HÇD$xHƒÀH‰„$ðèHÄL‰à[]A\A]A^A_ÃfDI‰Àéÿÿÿ„H´$ÐL‰çèéüþÿÿH‰ÅëH‰ÅëI‰Äë*I‰ÄëcI‰Äë;I‹<$H9ûtèH‹|$hèH‰ïèH‹EèH‹$H‰l$pH‰LpHÇD$xH‹L‰ÿHƒÀH‰„$ðèL‰çèH‰ßèH‹D$H‹L$H‹@èH‰LpI‹FèH‹L$L‰´$€H‰Œ€ëŒInput type was nullInput Element type of input unknown expected to have tensor or sparse tensor type. Got: AWAVAUI‰ÕATI‰ôUH‰ýSHì¸H‹ÿPH…À„ÔD‹p(Aƒþt Aƒþ…“H‹@ ‹X …Û„eH‹EH‰ïL‰îÿP(D‹`(H‰ÅAƒüt2Aƒüt,E…ä…¨AƒþteAƒþt/HÄ¸[]A\A]A^A_ÃDH‹U ‹B‰Z ƒÈ‰Bë×fDH‰ÇèH‹EÇE(H‰ÇHƒçþ¨uEèH‰E ƒH‰X ë¡H‰ÇèH‹EÇE(H‰ÇHƒçþ¨uèH‰E ƒH‰X énÿÿÿH‹?ë¶H‹?ëá¿0L|$ èL‰ÿI‰ÆèHl$0ºH5H‰ïèºH5H‰ïèL‰îH‰ïèº5H5H‰ïèD‰æH‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰÷èH‹H‹<$AÆF IÇFHƒÀI‰IF I‰FHD$H9ÇtèH‹5HL‰÷è¿0L|$ èL‰ÿI‰ÄèHl$0ºH5H‰ïèºH5H‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰çèH‹H‹<$AÆD$ IÇD$HƒÀI‰$ID$ I‰D$HD$H9ÇtèH‹5HL‰çèH‰Åé½H‰Åé!H‰ÅéÕ¿0L|$ èL‰ÿI‰ÅèHl$0ºH5H‰ïèºH5H‰ïèL‰æH‰ïèº5H5H‰ïèD‰öH‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰ïèH‹AÆE IÇEHƒÀI‰EIE I‰EH‹<$HD$H9ÇtèH‹5HL‰ïè¿0L|$ èL‰ÿI‰ÅèHl$0ºH5H‰ïèºH5H‰ïèL‰æH‰ïèºH5H‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰ïèé%ÿÿÿH‰Åë H‰ÅéH‰ÅëH‹<$HD$H9ÇtèL‰ïèH‰ïèëÉëÌH‰ÅëH‹<$HD$H9ÇtèL‰çèH‰ïèH‰ÅëL‰ÿèL‰÷èH‰ïèH‹<$HD$H9ÇtâèëÛH‰ÅëÖépÿÿÿL‰ÿèé{ÿÿÿL‰ÿèëAWAVAUATI‰üUSHìH‹L¼$ðLt$pH‰t$XHƒÀ@L‰ÿH‰L$HfHnÀH‹H‰T$@foÈfoÐL‰D$`HƒÀL‰t$hfHnØH‹flËHƒÀ)L$ fHnàflÔ)T$0èH‹H‹1öHÇ„$ÈfïÀH‹kHƒÀH‹K„$ØH‰„$ð1Àf‰„$Ð„$èH‹EèH‰l$pH‰LpHÇD$xH‹}èH‰$L÷èL‹s 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doc](BroadcastiConstrains input types to all nutensor(bfloat16) Returns the negation of the input tensor elemenGCC: (Debian 11.2.0-16) 11.2.0zRx0DXl €”4¨eBŒD†A ƒv DBMQABà-H†`ü88zPLRx›<$šBEŒA †D(ƒG0¬ (A ABBK„5H†h8€KA†R BIŽEŒHƒ>. ’.w A ¼<ƒ†ŒŽLàçBBŽB B(ŒA0†A8ƒGÀ\ 8D0A(B BBBFP.0dwA†DƒD W AANDAALx)BBŽB B(ŒA0†A8ƒLPù 8A0A(B BBBGHèýBEŒA †A(ƒD0» (D ABBOV(A ABBH4ØBŽEB ŒA(†A0ƒ (D BBBL`(A BBB€<HƒoœEH†xP˜rBBŽB B(ŒD0†A8ƒGÀx 8D0A(B BBBGPìºBBŽB B(ŒD0†A8ƒGÐª 8D0A(B BBBEh@ÑBBŽB B(ŒA0†A8ƒGðö 8D0A(B BBBLW 8A0A(B BBBGD¬¹BŒC†D ƒDPÇ AABH} DDBIPôrBBŽB B(ŒD0†A8ƒGÀx 8D0A(B BBBGLHóBBŽB E(ŒD0†D8ƒGðn 8A0A(B BBBFP˜rBBŽB B(ŒD0†A8ƒGÀx 8D0A(B BBBGPìrBBŽB B(ŒD0†A8ƒGÀx 8D0A(B BBBG4`ïBEŒA †A(ƒD0Û(A ABB˜"AƒV IAH¸GBOŽB B(ŒA0†A8ƒDP• 8A0A(B BBBF0¯A†DƒK Š AALDAA8'A†ZThH:ZEŽB B(ŒD0†D8ƒG€Á 8A0A(B BBBCÃÆÌÍÎÏA€ƒ†ŒŽP´SBBŒA †D(ƒG`c (A ABBGŒ (A ABBE!`ƒ†Œ4HMBŒA†A ƒr DBLAABL€sBŽBE ŒA(†A0ƒ (D BBBH{(A BBB4ÐBŒA†A ƒØ DBNaABH ëBBŽB B(ŒA0†A8ƒD`Ò8A0A(B BBBL4¹BBŽB B(ŒA0†A8ƒLp 8D0A(B BBBKP„µBPŽO O(ŒA0†A8ƒG ] 8A0A(B BBBAPØBFŽB B(ŒA0†A8ƒGÐt 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with the same shape as the input tensor.Constrain input and output types to float tensors. 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} Performs element-wise binary {name} (with Numpy-style broadcasting support). {broadcast_doc} (Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16. Result, has same element type as two inputsNumber of input tensors does not match the operands in the equation.Ellipsis represents incompatible dimensions. does not match the equation indices.%s: __pos (which is %zu) > this->size() (which is %zu)Target rank must be 1 less than the input rank.Input and target dimension value mismatch.The inner-most 2 dimensions must have the same size (mat_w:First input does not have rank 2Second input does not have rank 2'shape' input must be 1D tensor of type INT64 expected to have tensor or sparse tensor type: Invalid value for attribute axisK input must be a one-dimensional tensor of size 1.K input must be of type int64.Axis has less than the requested k elements./opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/defs/math/defs.cc Performs element-wise binary modulus (with Numpy-style broadcasting support). The sign of the remainder is the same as that of the Divisor. Mod operator can also behave like C fmod() or numpy.fmod. In this case, the sign of the remainder however, will be the same as the Dividend (in contrast to integer mod). To force a behavior like numpy.fmod() an 'fmod' Attribute is provided. This attribute is set to 0 by default causing the behavior to be like integer mod. Setting this attribute to 1 causes the remainder to be calculated similar to that of numpy.fmod(). If the input type is floating point, then `fmod` attribute must be set to 1. In case of dividend being zero, the results will be platform dependent. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentConstrain input and output types to signed numeric tensors.Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.Constrain input and output types to float32 tensors.The exponential of the input tensor computed element-wiseThe natural log of the input tensor computed element-wiseThe hyperbolic tangent values of the input tensor computed element-wiseFirst operand, base of the exponent.Second operand, power of the exponent.Constrain input X and output types to float/int tensors.Constrain input Y types to float/int tensors.Slope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to XOutput tensor (same size as X)Constrain input and output types to float/int tensors. HardSwish takes one input data (Tensor) and produces one output data (Tensor) where the HardSwish function, y = x * max(0, min(1, alpha * x + beta)) = x * HardSigmoid(x), where alpha = 1/6 and beta = 0.5, is applied to the tensor elementwise. { HS_X = HardSigmoid(X) Y = Mul (X, HS_X) } Clip operator limits the given input within an interval. The interval is specified by the inputs 'min' and 'max'. They default to numeric_limits::lowest() and numeric_limits::max(), respectively. Input tensor whose elements to be clippedMinimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).Output tensor with clipped input elementsSoftmax(input, axis) = Exp(input) / ReduceSum(Exp(input), axis=axis, keepdims=1) LogSoftmax(input, axis) = Log(Softmax(input, axis=axis))Hardmax(element in input, axis) = 1 if the element is the first maximum value along the specified axis, 0 otherwiseThe softsign (x/(1+|x|)) values of the input tensor computed element-wiseThis 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. General 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. 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.Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N).Output tensor of shape (M, N).Whether A should be transposedWhether B should be transposedScalar multiplier for the product of input tensors A * B.Scalar multiplier for input tensor C.Matrix multiply results from A * B Retrieve the top-K largest or smallest elements along a specified axis. Given an input tensor of shape [a_1, a_2, ..., a_n, r] and integer argument k, return two outputs: -Value tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which contains the values of the top k elements along the specified axis -Index tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which contains the indices of the top k elements (original indices from the input tensor). If "largest" is 1 (the default value) then the k largest elements are returned. If "sorted" is 1 (the default value) then the resulting k elements will be sorted. If "sorted" is 0, order of returned 'Values' and 'Indices' are undefined. Given two equivalent values, this operator uses the indices along the axis as a tiebreaker. That is, the element with the lower index will appear first. Tensor of shape [a_1, a_2, ..., a_n, r]A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieveTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensorTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values.Constrain index tensor to int64Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Whether to return the top-K largest or smallest elements.Whether to return the elements in sorted order.The sine of the input tensor computed element-wiseThe cosine of the input tensor computed element-wiseThe tangent of the input tensor computed element-wiseThe arcsine of the input tensor computed element-wiseThe arccosine of the input tensor computed element-wiseThe arctangent of the input tensor computed element-wise Broadcast the input tensor following the given shape and the broadcast rule. The broadcast rule is similar to numpy.array(input) * numpy.ones(shape): Dimensions are right alignment; Two corresponding dimension must have the same value, or one of them is equal to 1. Also, this operator is similar to numpy.broadcast_to(input, shape), but the major difference is numpy.broadcast_to() does not allow shape to be smaller than input.size(). It is possible that the output.shape is not equal to shape, when some dimensions in shape is equal to 1, or the shape.ndim < input.shape.ndim. A 1-D tensor indicates the shape you want to expand to, following the broadcast ruleThe hyperbolic sine values of the input tensor computed element-wiseThe hyperbolic cosine values of the input tensor computed element-wiseThe hyperbolic arcsine values of the input tensor computed element-wiseThe hyperbolic arccosine values of the input tensor computed element-wiseThe hyperbolic arctangent values of the input tensor computed element-wiseThe sign of the input tensor computed element-wise. It has the same shape and type of the input.The error function of the input tensor computed element-wise. It has the same shape and type of the input. Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. It consumes two quantized input tensors, their scales and zero points, scale and zero point of output, and computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point). For (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. Scale and zero point must have same shape. They must be either scalar (per tensor) or N-D tensor (per row for 'a' and per column for 'b'). Scalar refers to per tensor quantization whereas N-D refers to per row or per column quantization. If the input is 2D of shape [M, K] then zero point and scale tensor may be an M element vector [v_1, v_2, ..., v_M] for per row quantization and K element vector of shape [v_1, v_2, ..., v_K] for per column quantization. If the input is N-D tensor with shape [D1, D2, M, K] then zero point and scale tensor may have shape [D1, D2, M, 1] for per row quantization and shape [D1, D2, 1, K] for per column quantization. Production must never overflow, and accumulation may overflow if and only if in 32 bits. N-dimensional quantized matrix azero point of quantized input aN-dimensional quantized matrix bzero point of quantized input bzero point of quantized output yQuantized matrix multiply results from a * bConstrain input a and its zero point data type to 8-bit integer tensor.Constrain input b and its zero point data type to 8-bit integer tensor.Constrain output y and its zero point 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 N-D tensor. Scalar refers to per tensor quantization whereas N-D refers to per row quantization. If the input is 2D of shape [M, K] then zero point tensor may be an M element vector [zp_1, zp_2, ..., zp_M]. If the input is N-D tensor with shape [D1, D2, M, K] then zero point tensor may have shape [D1, D2, M, 1]. Zero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a N-D tensor, Scalar refers to per tensor quantization whereas N-D refers to per col quantization. If the input is 2D of shape [K, N] then zero point tensor may be an N element vector [zp_1, zp_2, ..., zp_N]. If the input is N-D tensor with shape [D1, D2, K, N] then zero point tensor may have shape [D1, D2, 1, N]. Constrain input A data type to 8-bit integer tensor.Constrain input B data type to 8-bit integer tensor.Constrain output Y data type as 32-bit integer tensor. Performs cumulative sum of the input elements along the given axis. By default, it will do the sum inclusively meaning the first element is copied as is. Through an `exclusive` attribute, this behavior can change to exclude the first element. It can also perform summation in the opposite direction of the axis. For that, set `reverse` attribute to 1. Example: ``` input_x = [1, 2, 3] axis=0 output = [1, 3, 6] exclusive=1 output = [0, 1, 3] exclusive=0 reverse=1 output = [6, 5, 3] exclusive=1 reverse=1 output = [5, 3, 0] ``` If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.If set to 1 will perform the sums in reverse direction.An input tensor that is to be processed.A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.Output tensor of the same type as 'x' with cumulative sums of the x's elementsaxis tensor can be int32 or int64 only Round takes one input Tensor and rounds the values, element-wise, meaning it finds the nearest integer for each value. In case of halfs, the rule is to round them to the nearest even integer. The output tensor has the same shape and type as the input. Examples: ``` round([0.9]) = [1.0] round([2.5]) = [2.0] round([2.3]) = [2.0] round([1.5]) = [2.0] round([-4.5]) = [-4.0] ``` Det calculates determinant of a square matrix or batches of square matrices. Det takes one input tensor of shape `[*, M, M]`, where `*` is zero or more batch dimensions, and the inner-most 2 dimensions form square matrices. The output is a tensor of shape `[*]`, containing the determinants of all input submatrices. e.g., When the input is 2-D, the output is a scalar(shape is empty: `[]`). Constrain input and output types to floating-point tensors. A NegativeLogLikelihoodLoss operator computes (weighted) negative log likelihood loss. Its "input" tensor has the shape of (N, C, d1, d2, ..., dk) where k >= 0. The "input" tensor contains log-probabilities for input[n, :, d_1, d_2,..., d_k] being in a class of [0, C). The operator's "target" input tensor has the shape of (N, d1, d2, ..., dk). It encodes class labels (one of C classes) or it may contain a special value (indicated by an attribute ignore_index) for N x d1 x d2 x ... x dk samples. The loss value for input[n, :, d_1, d_2,...d_k] being classified as class c = target[n][d_1][d_2]...[d_k] is computed as: loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k]. When an optional "weight" is provided, the sample loss is calculated as: loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k] * weight[c]. loss is zero for the case when target-value equals ignore_index. loss[n][d_1][d_2]...[d_k] = 0, when target[n][d_1][d_2]...[d_k] = ignore_index If "reduction" attribute is set to "none", the operator's output will be the above loss with shape (N, d1, d2, ..., dk). If "reduction" attribute is set to "mean" (the default attribute value), the output loss is (weight) averaged: mean(loss), if "weight" is not provided, or if weight is provided, sum(loss) / sum(weight[target[n][d_1][d_2]...[d_k]]]), for all samples. If "reduction" attribute is set to "sum", the output is a scalar: sum(loss). See also https://pytorch.org/docs/stable/nn.html#torch.nn.NLLLoss. Example 1: // negative log likelihood loss, "none" reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] loss = np.zeros((N, d1)) for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] // print(loss) // [[-3. -2.] // [-0. -2.]] Example 2: // weighted negative log likelihood loss, sum reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] weight = [0.2, 0.3, 0.1] loss = np.zeros((N, d1)) for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] * weight[c] loss = np.sum(loss) // print(loss) // -1.1 Example 3: // weighted negative log likelihood loss, mean reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] weight = [0.2, 0.3, 0.1] loss = np.zeros((N, d1)) weight_total = 0 for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] * weight[c] weight_total = weight_total + weight[c] loss = np.sum(loss) / weight_total // print(loss) // -1.57 Input tensor of shape (N, C) or (N, C, d1, d2, ..., dk).Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the target values should either be in the range [0, C) or have the value ignore_index.Optional rescaling weight tensor. If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones.The negative log likelihood lossType of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.Constrain input, weight, and output types to floating-point tensors.Constrain target to integer types An einsum of the form ```term1, term2 -> output-term``` produces an output tensor using the following equation ```output[output-term] = reduce-sum( input1[term1] * input2[term] )``` where the reduce-sum performs a summation over all the indices occurring in the input terms (term1, term2) that do not occur in the output-term. The Einsum operator evaluates algebraic tensor operations on a sequence of tensors, using the Einstein summation convention. The equation string contains a comma-separated sequence of lower case letters. Each term corresponds to an operand tensor, and the characters within the terms correspond to operands dimensions. This sequence may be followed by "->" to separate the left and right hand side of the equation. If the equation contains "->" followed by the right-hand side, the explicit (not classical) form of the Einstein summation is performed, and the right-hand side indices indicate output tensor dimensions. In other cases, output indices are (implicitly) set to the alphabetically sorted sequence of indices appearing exactly once in the equation. When a dimension character is repeated in the left-hand side, it represents summation along the dimension. The equation may contain ellipsis ("...") to enable broadcasting. Ellipsis must indicate a fixed number of dimensions. Specifically, every occurrence of ellipsis in the equation must represent the same number of dimensions. The right-hand side may contain exactly one ellipsis. In implicit mode, the ellipsis dimensions are set to the beginning of the output. The equation string may contain space (U+0020) character. Loss function that measures the softmax cross entropy between 'scores' and 'labels'. This operator first computes a loss tensor whose shape is identical to the labels input. If the input is 2-D with shape (N, C), the loss tensor may be a N-element vector L = (l_1, l_2, ..., l_N). If the input is N-D tensor with shape (N, C, D1, D2, ..., Dk), the loss tensor L may have (N, D1, D2, ..., Dk) as its shape and L[i,][j_1][j_2]...[j_k] denotes a scalar element in L. After L is available, this operator can optionally do a reduction operator. shape(scores): (N, C) where C is the number of classes, or (N, C, D1, D2,..., Dk), with K >= 1 in case of K-dimensional loss. shape(labels): (N) where each value is 0 <= labels[i] <= C-1, or (N, D1, D2,..., Dk), with K >= 1 in case of K-dimensional loss. The loss for one sample, l_i, can caculated as follows: l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk], where i is the index of classes. or l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk] * weights[c], if 'weights' is provided. loss is zero for the case when label-value equals ignore_index. l[i][d1][d2]...[dk] = 0, when labels[n][d1][d2]...[dk] = ignore_index where: p = Softmax(scores) y = Log(p) c = labels[i][d1][d2]...[dk] Finally, L is optionally reduced: If reduction = 'none', the output is L with shape (N, D1, D2, ..., Dk). If reduction = 'sum', the output is scalar: Sum(L). If reduction = 'mean', the output is scalar: ReduceMean(L), or if weight is provided: ReduceSum(L) / ReduceSum(W), where tensor W is of shape (N, D1, D2, ..., Dk) and W[n][d1][d2]...[dk] = weights[labels[i][d1][d2]...[dk]]. The predicted outputs with shape [batch_size, class_size], or [batch_size, class_size, D1, D2 , ..., Dk], where K is the number of dimensions.The ground truth output tensor, with shape [batch_size], or [batch_size, D1, D2, ..., Dk], where K is the number of dimensions. Labels element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the label values should either be in the range [0, C) or have the value ignore_index.A manual rescaling weight given to each class. If given, it has to be a 1D Tensor assigning weight to each of the classes. Otherwise, it is treated as if having all ones.Weighted loss float Tensor. If reduction is 'none', this has the shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of K-dimensional loss. Otherwise, it is a scalar.Log probability tensor. If the output of softmax is prob, its value is log(prob).input_gather_element_transformInput tensors of wrong rank (0).Incompatible dimensions for matrix multiplicationinputs are expected to have tensor type and output type should not be null.inputs are expected to have tensor type.input and zero_point pair is expected to have be same type.input and zero_point pair is expected to have same type.Type of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 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for more details please check [the doc](Broadcasting.md).AWAVI‰öAULoATI‰üUH‰Õ1ÒSH»ting** (HìˆL‰/Ht$`I‰çHÇD$`8èH‹T$`foL‰ÿI‰$I‰T$foH‰X0H\$@fo@ H‹D$`I‹$I‰D$ÆI‹4$I‹T$H‰$HòèL‰÷èH‰ÂH¸ÿÿÿÿÿÿÿ?H+D$H9Â‡œL‰öL‰ÿèH¸ÿÿÿÿÿÿÿ?H+D$Hƒø*†º+H5L‰ÿèLt$0HPL‰t$ H‹H9Ñ„ÄH‰L$ H‹HH‰L$0H‹HÆ@H‰ïH‰L$(H‰HÇ@èH‰ÂH¸ÿÿÿÿÿÿÿ?H+D$(H9Â‡ H|$ H‰îèHl$PHPH‰l$@H‹H9Ñ„dH‰L$@H‹HH‰L$PH‹HÆ@H‰L$HHÇ@H‰H¸ÿÿÿÿÿÿÿ?H+D$HHƒø;†H|$@º<H5èL|$pHPL‰|$`H‹H9Ñ„H‰L$`H‹HH‰L$pH‹HH‰L$hH‰Æ@I‹<$HÇ@H‹D$`L9ø„ÝóoD$hI9ý„~I‹T$I‰$AD$H…ÿttH‰|$`H‰T$pHÇD$hÆH‹|$`L9ÿtèH‹|$@H9ïtèH‹|$ L9÷tèH‹<$H9ßtèHÄˆL‰à[]A\A]A^A_Ã@I‰$AD$L‰|$`L|$pL‰ÿë‡€óoH)L$0é;þÿÿóoP)T$Pé›þÿÿóoX)\$péúþÿÿH‹T$hH…ÒtHƒút(L‰þèH‹T$hI‹<$I‰T$ÆH‹|$`éÿÿÿ@¶D$pˆH‹T$hI‹<$ë×H=èH=èH=èH=èH‰Åë+H‰ÅëëôI‰ÇH‹|$@H9ïtèL‰ýH‹|$ L9÷tèH‹<$H9ßtèI‹<$L9ïtèH‰ïèH‰ÅëåóoH‰øfïÀH‹~HÇFHp(H‰pHrH‰xH‹:H9÷„ŸH‰xH‹zH‰x(H‹zH‰2fïÀHÇBH‰x ÆBH‹QóoóAoH‰PHI‹PHÇAH‰P`HPxP8IÇ@AH‰PhI‹ IQXPH9ÊtDH‰HhI‹IH‰HxI‹II‰IÇAH‰HpAÆAÃ€óob`(é_ÿÿÿf.„óAoihxë¼ATI‰ôUH‰ýHƒìH‹ÿPL9àvhH‹EL‰æH‰ïÿPH…ÀtVH‹EL‰æH‰ïÿP‹P(ƒúuëO€‹P(ƒút@ƒút;ƒútƒú u!H‹@ ö@tH‹@H…ÀuÖH‹ëÍDHƒÄ1À]A\ÃfDH‹@ ‹@HƒÄ]A\ƒàÃUH‰ýSHƒìH‹_H…ÛtfDH‰ßH‹èH…ÛuðH‹EH‹}1öHƒÅ0HÅèH‹}ÐHÇEèHÇEàH9ïtHƒÄ[]éf„HƒÄ[]ÃAUATUSH‰û¿ˆHƒìèH‹3H‹SHhHÇHxI‰ÄH‰hHòèH‹C(óoC@Ml$(Hs IÇD$(L‰ïI‰D$0H‹C8IÇD$8I‰D$@IÇD$XAD$HèH‹sXH‹S`ID$pI|$`I‰D$`HòèHƒÄL‰à[]A\A]ÃH‰ÇëH‰ÃëH‰ÃëL‰ïèI‹|$H9ýuH‰ßèL‰çèèH‰ÅëèëßèH‰ïèAUI‰ýATUSHƒìL‹gH‹/I9ì„¢H‹}xH…ˆH9ÇtèH‹}XHEhH9ÇtèH‹]0H…ÛtH‰ßH‹èH…ÛuðH‹E(H‹} 1öHÅèH‹} HEPHÇE8HÇE0H9ÇtèH‹}HEH9Çt:èHÅ¨I9ì…eÿÿÿI‹mH…ít.HƒÄH‰ï[]A\A]éf.„HÅ¨I9ì…0ÿÿÿëÉHƒÄ[]A\A]ÃAVI‰þAUATUSL‹oL‹'M9åt}f„I‹|$8ID$HH9ÇtèI‹\$(I‹l$ H9ët%DH‹}HEH9Çt[èHƒÅ H9ëuåI‹l$ H…ítH‰ïèI‹<$ID$H9Çt=èIƒÄXM9åuM‹&M…ät6[L‰ç]A\A]A^é€HƒÅ H9ëuë¨DIƒÄXM9å…SÿÿÿëÂ[]A\A]A^ÃH‹SH‰ûHƒÀH‰H‹HHCXH9ÇtèH‹H{8HƒÀH‰[éH‹UH‰ýHƒÀH‰H‹HHEXH9ÇtèH‹H}8HƒÀH‰EèH‰ï]éAWAVAUATI‰üUSHìøH‹L´$àL|$`H‰t$PHƒÀ@L‰÷H‰T$@fHnÀH‹L‰|$XfoÈfoÐHƒÀfHnØH‹flËHƒÀ)L$ fHnàflÔ)T$0èH‹H‹1öHÇ„$¸fïÀH‹kHƒÀH‹K„$ÈH‰„$à1Àf‰„$À„$ØH‹EèH‰l$`H‰L`HÇD$hH‹}èH‰$LÿèL‹k H‹C(L|$p1öI‹}èL‰l$pH‰D$LÿH‰èH‹CH‹K0fïÀH\$xfoT$0H‰D$H‹@èH‰L$H‰L`H‹)T$pHƒÀ)„$€H‰D$`H‹)„$HƒÀh)„$ H‰„$àH„$°H‰ÇH‰D$HèH‹H‰ÞL‰÷Ç„$¸HƒÀÆ„$ÐH‰D$xH„$ÐH‰D$0H‰„$ÀHÇ„$ÈèH‹\$PH‰ßèH‰ÞL‰ÿH‰ÂèH‹\$@H‰ßèH‰ÞL‰ÿH‰ÂèH‹„$ I\$IÇD$I‰$AÆD$H…À„L‹„$M…À„óL9À‡êH‹Œ$˜1Ò1öL‰çI)ÈèH‹fol$ H‹¼$ÀHƒÀ)l$pH‰D$`H‹HƒÀhH‰„$àH;|$0tèH‹H‹|$HHƒÀH‰D$xèH‹D$H‹T$L‰÷H‹t$H‹@èH‰T`I‹EèL‰l$pH‹$H‰tpH‹EèH‰l$`H‰T`H‹HÇD$hHƒÀH‰„$àèHÄøL‰à[]A\A]A^A_ÃfDI‰Àéÿÿÿ„H´$ÀL‰çèéÿÿÿH‰ÅëH‰ÅëI‰Äë*I‰ÄëcI‰Äë;I‹<$H9ûtèH‹|$XèH‰ïèH‹EèH‹4$H‰l$`H‰t`HÇD$hH‹L‰÷HƒÀH‰„$àèL‰çèH‰ßèH‹D$H‹t$H‹@èH‰t`I‹EèH‹t$L‰l$pH‰tpë’Attribute expected to have tensor or sparse tensor type[TypeInferenceError] ATUHƒì(H‹ÿP‹P(ƒútƒúu%H‹@ H‹@H…Àt HƒÄ(]A\ÃH‹HƒÄ(]A\Ã¿0I‰äèHH5L‰çH‰ÅèL‰æH‰ïèH‹H‹<$HÇEÆE HƒÀH‰EHE H‰EHD$H9ÇtèH‹5HH‰ïèI‰ÄëH‹<$HD$H9ÇtèH‰ïèL‰çèI‰ÄëØAWAVAUATI‰üUSHìH‹L¼$ðLt$pH‰t$XHƒÀ@L‰ÿH‰L$HfHnÀH‹H‰T$@foÈfoÐL‰D$`HƒÀL‰t$hfHnØH‹flËHƒÀ)L$ fHnàflÔ)T$0èH‹H‹1öHÇ„$ÈfïÀH‹kHƒÀH‹K„$ØH‰„$ð1Àf‰„$Ð„$èH‹EèH‰l$pH‰LpHÇD$xH‹}èH‰$L÷èL‹s H‹C(L¬$€1öI‹~èL‰´$€H‰D$LïH‰èH‹CH‹s0fïÀHœ$ˆfoT$0H‰D$H‹@èH‰t$H‰tpH‹)”$€HƒÀ)„$H‰D$pH‹)„$ HƒÀh)„$°H‰„$ðH„$ÀH‰ÇH‰D$PèH‹H‰ÞL‰ÿÇ„$ÈHƒÀÆ„$àH‰„$ˆH„$àH‰D$0H‰„$ÐHÇ„$ØèH‹\$XH‰ßèH‰ÞL‰ïH‰ÂèH‹\$@H‰ßèH‰ÞL‰ïH‰ÂèH‹D$HL‰ïH‹0èH‹\$`H‰ßèH‰ÞL‰ïH‰ÂèH‹„$°I\$IÇD$I‰$AÆD$H…À„ L‹„$ M…À„ÿL9À‡öH‹Œ$¨1Ò1öL‰çI)ÈèH‹fol$ H‹¼$ÐHƒÀ)¬$€H‰D$pH‹HƒÀhH‰„$ðH;|$0tèH‹H‹|$PHƒÀH‰„$ˆèH‹D$H‹T$L‰ÿH‹L$H‹@èH‰TpI‹FèL‰´$€H‹$H‰Œ€H‹EèH‰l$pH‰TpH‹HÇD$xHƒÀH‰„$ðèHÄL‰à[]A\A]A^A_ÃfDI‰Àéÿÿÿ„H´$ÐL‰çèéüþÿÿH‰ÅëH‰ÅëI‰Äë*I‰ÄëcI‰Äë;I‹<$H9ûtèH‹|$hèH‰ïèH‹EèH‹$H‰l$pH‰LpHÇD$xH‹L‰ÿHƒÀH‰„$ðèL‰çèH‰ßèH‹D$H‹L$H‹@èH‰LpI‹FèH‹L$L‰´$€H‰Œ€ëŒAWAVAUATI‰üUSHìH‹L¼$ðLt$pH‰t$XHƒÀ@L‰ÿH‰L$HfHnÀH‹H‰T$@foÈfoÐL‰D$`HƒÀL‰t$hfHnØH‹flËHƒÀ)L$ fHnàflÔ)T$0èH‹H‹1öHÇ„$ÈfïÀH‹kHƒÀH‹K„$ØH‰„$ð1Àf‰„$Ð„$èH‹EèH‰l$pH‰LpHÇD$xH‹}èH‰$L÷èL‹s H‹C(L¬$€1öI‹~èL‰´$€H‰D$LïH‰èH‹CH‹s0fïÀHœ$ˆfoT$0H‰D$H‹@èH‰t$H‰tpH‹)”$€HƒÀ)„$H‰D$pH‹)„$ HƒÀh)„$°H‰„$ðH„$ÀH‰ÇH‰D$PèH‹H‰ÞL‰ÿÇ„$ÈHƒÀÆ„$àH‰„$ˆH„$àH‰D$0H‰„$ÐHÇ„$ØèH‹\$XH‰ßèH‰ÞL‰ïH‰ÂèH‹\$@H‰ßèH‰ÞL‰ïH‰ÂèH‹D$HL‰ïH‹0èH‹\$`H‰ßèH‰ÞL‰ïH‰ÂèH‹„$°I\$IÇD$I‰$AÆD$H…À„ L‹„$ M…À„ÿL9À‡öH‹Œ$¨1Ò1öL‰çI)ÈèH‹fol$ H‹¼$ÐHƒÀ)¬$€H‰D$pH‹HƒÀhH‰„$ðH;|$0tèH‹H‹|$PHƒÀH‰„$ˆèH‹D$H‹T$L‰ÿH‹L$H‹@èH‰TpI‹FèL‰´$€H‹$H‰Œ€H‹EèH‰l$pH‰TpH‹HÇD$xHƒÀH‰„$ðèHÄL‰à[]A\A]A^A_ÃfDI‰Àéÿÿÿ„H´$ÐL‰çèéüþÿÿH‰ÅëH‰ÅëI‰Äë*I‰ÄëcI‰Äë;I‹<$H9ûtèH‹|$hèH‰ïèH‹EèH‹$H‰l$pH‰LpHÇD$xH‹L‰ÿHƒÀH‰„$ðèL‰çèH‰ßèH‹D$H‹L$H‹@èH‰LpI‹FèH‹L$L‰´$€H‰Œ€ëŒAWAVAUATI‰üUSHìH‹L¼$ðLt$pH‰t$XHƒÀ@L‰ÿH‰L$HfHnÀH‹H‰T$@foÈfoÐL‰D$`HƒÀL‰t$hfHnØH‹flËHƒÀ)L$ fHnàflÔ)T$0èH‹H‹1öHÇ„$ÈfïÀH‹kHƒÀH‹K„$ØH‰„$ð1Àf‰„$Ð„$èH‹EèH‰l$pH‰LpHÇD$xH‹}èH‰$L÷èL‹s H‹C(L¬$€1öI‹~èL‰´$€H‰D$LïH‰èH‹CH‹s0fïÀHœ$ˆfoT$0H‰D$H‹@èH‰t$H‰tpH‹)”$€HƒÀ)„$H‰D$pH‹)„$ HƒÀh)„$°H‰„$ðH„$ÀH‰ÇH‰D$PèH‹H‰ÞL‰ÿÇ„$ÈHƒÀÆ„$àH‰„$ˆH„$àH‰D$0H‰„$ÐHÇ„$ØèH‹\$XH‰ßèH‰ÞL‰ïH‰ÂèH‹\$@H‰ßèH‰ÞL‰ïH‰ÂèH‹D$HL‰ïH‹0èH‹\$`H‰ßèH‰ÞL‰ïH‰ÂèH‹„$°I\$IÇD$I‰$AÆD$H…À„ L‹„$ M…À„ÿL9À‡öH‹Œ$¨1Ò1öL‰çI)ÈèH‹fol$ H‹¼$ÐHƒÀ)¬$€H‰D$pH‹HƒÀhH‰„$ðH;|$0tèH‹H‹|$PHƒÀH‰„$ˆèH‹D$H‹T$L‰ÿH‹L$H‹@èH‰TpI‹FèL‰´$€H‹$H‰Œ€H‹EèH‰l$pH‰TpH‹HÇD$xHƒÀH‰„$ðèHÄL‰à[]A\A]A^A_ÃfDI‰Àéÿÿÿ„H´$ÐL‰çèéüþÿÿH‰ÅëH‰ÅëI‰Äë*I‰ÄëcI‰Äë;I‹<$H9ûtèH‹|$hèH‰ïèH‹EèH‹$H‰l$pH‰LpHÇD$xH‹L‰ÿHƒÀH‰„$ðèL‰çèH‰ßèH‹D$H‹L$H‹@èH‰LpI‹FèH‹L$L‰´$€H‰Œ€ëŒ[ShapeInferenceError] Target=Mismatch between source and target type. Source=Unsupported Source/Target type=AWAVAUATUSHì¸‹o(D‹n(D9íuwI‰üH‰óƒý„ôƒý„‹ƒý„Jƒý …ƒAƒý …ßH‹k H‹]ƒMH…Û„ Aƒ|$( „.H‹L‹`M…ä„,A‹l$(D‹k(D9ít¿0Lt$ èL‰÷I‰ÄèL|$0ºH5L‰ÿèº0H5L‰ÿè‰îL‰ÿèºH5L‰ÿèD‰îL‰ÿèH‰åHt$8H‰ïèL‰÷èH‰îL‰çèé@H‰ßèH‹CÇC( H‰ÇHƒçþ¨…èH‰ÅH‰C H‹]ƒMH…Û…÷þÿÿH‹EH‰ÇHƒçþ¨…&èAƒ|$( H‰EH‰Ã…ØþÿÿfDI‹D$ L‹`M…ä…ÔþÿÿL‹%éÈþÿÿfƒýt[I‹D$ ö@„œL‹`M…ä„WAƒý…H‹k H‹}ƒMH…ÿ„HHÄ¸L‰æ[]A\A]A^A_é€I‹D$ ö@tEL‹`M…ä„ðAƒýtH‰ßèH‹CÇC(H‰ÇHƒçþ¨…]èH‰C H‰ÅëfHÄ¸[]A\A]A^A_ÃfDAƒýu2H‹k H‹]ƒMH…Û„ÝAƒ|$(„ñþÿÿH‹é¾ýÿÿDH‰ßèH‹CÇC(H‰ÇHƒçþ¨…¾èH‰C H‰Åë¢H‰ßèH‹CÇC(H‰ÇHƒçþ¨…žèH‰C H‰ÅéÌþÿÿDL‹%éÿÿÿ@L‹%éþÿÿ@H‹EH‰ÇHƒçþ¨uaèH‰EH‰Çé˜þÿÿ„H‹?éÚýÿÿ„H‹EH‰ÇHƒçþ¨u6èH‰EH‰ÃéÿÿÿH‹?é:ÿÿÿH‹?éÒýÿÿH‹?éZÿÿÿH‹?ëšH‹?é›þÿÿH‹?ëÅ¿0Lt$ èL‰÷I‰ÄèL|$0ºH5L‰ÿèºH5L‰ÿè‰îL‰ÿèH‰åHt$8H‰ïèL‰÷èH‰îL‰çèH‹AÆD$ IÇD$HƒÀI‰$ID$ I‰D$H‹<$HD$H9ÇtèH‹5HL‰çèH‰Åë H‰ÅëH‰ÅëH‹<$HD$H9ÇtèëL‰÷èL‰çèH‰ïèëÂëÅëÈAWAVAUATI‰üUSHìH‹L´$H¬$€H‰t$HƒÀ@L‰÷H‰L$`fHnÀH‹H‰T$foÈfoÐL‰D$HƒÀL‰L$hfHnØH‹H‰l$pflËHƒÀ)L$@fHnàflÔ)T$PèL‹=H‹1öHÇ„$ØfïÀI‹_HƒÀI‹O„$èH‰„$1Àf‰„$à„$øH‹CèH‰œ$€H‰Œ€HÇ„$ˆH‹{èH‰L$ HïèM‹o I‹G(H¬$1öI‹}èL‰¬$H‰D$(HïH‰èI‹GI‹W0L¼$ÐfïÀfoT$PL‰ÿH‰D$0H‹@èH‰T$8H‰”€H‹)”$HƒÀ)„$ H‰„$€H‹)„$°HƒÀh)„$ÀH‰„$èH‹L‰÷Ç„$ØHÇ„$èHƒÀÆ„$ðH‰„$˜H„$ðH‰D$PH‰„$àH„$˜H‰ÆH‰D$xèH‹|$èH‹t$H‰ïH‰ÂèH‹|$èH‹t$H‰ïH‰ÂèH‹D$`H‰ïH‹0èH‹|$èH‹t$H‰ïH‰ÂèH‹D$hH‰ï‹0èH‹„$ÀIl$IÇD$I‰,$AÆD$H…À„,L‹„$°M…À„L9À‡H‹Œ$¸1Ò1öL‰çI)ÈèH‹fol$@H‹¼$àHƒÀ)¬$H‰„$€H‹HƒÀhH‰„$H;|$PtèH‹L‰ÿHƒÀH‰„$˜èH‹D$0H‹L$8L‰÷H‹t$(H‹@èH‰Œ€I‹EèL‰¬$H‰´H‹CèH‰œ$€H‹\$ H‰œ€H‹HÇ„$ˆHƒÀH‰„$èHÄL‰à[]A\A]A^A_Ã@I‰Àéöþÿÿ„H´$àL‰çèéðþÿÿH‰ÃëH‰Åë&H‰Åé±H‰Åë+H‰ÅéI‹<$H9ýtèH‰ÝH‹|$pèH‰ïèH‹|$xèH‹D$0H‹L$8H‹@èH‰Œ€I‹EèH‹L$(L‰¬$H‰ŒH‹CèH‰œ$€H‹\$ H‰œ€HÇ„$ˆH‹L‰÷HƒÀH‰„$èH‰ïèë¶Output expected to have tensor type expected to have tensor or sparse typeAVAUATI‰ôU‰ÕSHì°H‹ÿP(H…À„äH‰Ã‹@(ƒø„Žƒøt)…À…sƒýt!1ÀƒýtHÄ°[]A\A]A^ÃDƒøt[H‰ßèH‹CÇC(H‰ÇHƒçþ¨uuèH‰C H‰ÅƒHH‹@H…Àu¯H‹EH‰ÇHƒçþ¨uRèH‰Eë•f.„H‹[ H‹CƒKH…À…vÿÿÿH‹CH‰ÇHƒçþ¨uèH‰CéYÿÿÿfDH‹?ë†H‹?ë©H‹?ëÝ¿0Ll$ èL‰ïI‰ÆèHl$0ºH5H‰ïèºH5H‰ïèL‰æH‰ïèº'H5H‰ïèH‰åHt$8H‰ïèH‹H‹¼$€HPH‰T$ HPhHƒÀ@fHnÀH‹H‰”$ HƒÀfHnÈH„$flÁ)D$0H9Ç…CH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èH‰îL‰÷èH‹AÆF IÇFHƒÀI‰IF I‰FH‹<$HD$H9ÇtèH‹5HL‰÷è¿0Ll$ èL‰ïI‰ÆèHl$0ºH5H‰ïèºH5H‰ïèL‰æH‰ïèºH5H‰ïèH‰åHt$8H‰ïèH‹H‹¼$€HPH‰T$ HPhHƒÀ@fHnÀH‹H‰”$ HƒÀfHnÐH„$flÂ)D$0H9ÇtèH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èH‰îL‰÷èéOþÿÿèé³ýÿÿH‰Åë H‰ÅëH‰ÅëH‹<$HD$H9ÇtèëL‰ïèL‰÷èH‰ïèëÂëÅëÈInput type was nullInput Element type of input unknown expected to have tensor or sparse tensor type. 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operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcastipports **unidirectional broadcasConstrain input and output types to all numeric to high-precision numeric tenso Neg takes one input data (Tensor) and produces one output data (Tensor) where each element flipped sign, y = -x, is applied to the tensor Absolute takes one input data (Tensor) and produces one output data (Tensor) where the absolute is, y = abs(x), is applied to the tensor Reciprocal takes one input data (Tensor) and produces one output data (Tensor) where the reciprocal is, y = 1/x, is appli Floor takes one input data (Tensor) and produces one output data (Tensor) where the floor is, y = floor(x), is applied to the tensor elem Ceil takes one input data (Tensor) and produces one output data (Tensor) where the ceil is, y = ceil(x), is applied to the tensor element Square root takes one input data (Tensor) and produces one output data (Tensor) where the square root is, y = x^0.5, is applied to the tensor elementwise. If x is negative, then it will Relu takes one where the rectified linear function, y = max(0, x), is applied to the tensor ele LeakyRelu takessor) and an argument alpha, and produces one output data (Tensor) where the function `f(x) = alpha * x for x < 0`, `f(x) = x for x >= 0`, is applied to the data tensor el ThresholdedRelu takes one input data (Tensor) and produces one output data (Tensor) where the rectified linear function, y = x for x > alpha, y = 0 otherwise, is applied to the tensor e Selu takes one where the scaled exponential linear unit function, `y = gamma * (alpha * e^x - alpha) for x <= 0`, `y = gamma * x for x > 0`, is applied to the tensor elementwi Elu takes one ihere the function `f(x) = alpha * (exp(x) - 1.) for x < 0`, `f(x) = x for x >= 0`., is applied to the tensor ele Continuously Differentiable Exponential Linear Units: Perform the linear unit element-wise on the input tensor X using formula: ``` max(0,x) + min(0,alpha*(exp(x/alpha)-1)) ` Calculates the exponential of the given input tensor, element-wnatural log of thyperbolic tangent of the given input tensor ele Pow takes input) and exponent Tensor, and produata (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwis PRelu takes input data (Tensor) and slope tensor as input, a = slope * x for x for x >= 0`., is applied to the data tensor e Sigmoid takes one input data (Tensor) and produces one output data (Tensor) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the tensor e HardSigmoid tak HardSigmoid function, y = max(0, min(1, alpha * x + beta)), is applied to the t to numeric tenssoftsign (x/(1+|x|)) of the given input tensor e Softplus takes T>) where the softplus function, y = ln(exp(x) + 1), is applied to the tensor el Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpsine of the given input tensor, cosine of the given input tensor, element-wise. tangent of the given input tensor, element-wise.arcsine (inverse of sine) of the given input tensor, element-wisarccosine (inverse of cosine) of the given input tensor, elementarctangent (inverse of tangent) of the given input tensor, eleme to all tensors.hyperbolic sine ut tensor elemenhyperbolic cosine of the given input tensor elemhyperbolic arcsine of the given hyperbolic arccohyperbolic arctangent of the given input tensor Calculate the sign of the given input tensor element-wise. 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prop.0_ZZN10onnx_torch11GetOpSchemaINS_15TopK_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_15TopK_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_14Add_Onnx_ver14EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Sub_Onnx_ver14EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Mod_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Mul_Onnx_ver14EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Div_Onnx_ver14EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Neg_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Abs_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_21Reciprocal_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_16Floor_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15Ceil_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15Sqrt_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15Relu_Onnx_ver14EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_19LeakyRelu_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_26ThresholdedRelu_Onnx_ver10EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Selu_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Elu_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torchL18celu_default_alphaE_ZN10onnx_torch11GetOpSchemaINS_15Celu_Onnx_ver12EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Exp_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Log_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15Tanh_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Pow_Onnx_ver15EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15PRelu_Onnx_ver9EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_18Sigmoid_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_21HardSigmoid_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_20HardSwish_Onnx_ver14EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Max_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Min_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Sum_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15Mean_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15Clip_Onnx_ver13EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFbRKN10onnx_torch24FunctionBodyBuildContextERKNS0_8OpSchemaERNS0_13FunctionProtoEEZNS0_11GetOpSchemaINS0_18Softmax_Onnx_ver13EEES4_vEUlS3_S6_S8_E_E9_M_invokeERKSt9_Any_dataS3_S6_S8__ZN10onnx_torch11GetOpSchemaINS_18Softmax_Onnx_ver13EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFbRKN10onnx_torch24FunctionBodyBuildContextERKNS0_8OpSchemaERNS0_13FunctionProtoEEZNS0_11GetOpSchemaINS0_21LogSoftmax_Onnx_ver13EEES4_vEUlS3_S6_S8_E_E9_M_invokeERKSt9_Any_dataS3_S6_S8__ZN10onnx_torch11GetOpSchemaINS_21LogSoftmax_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_18Hardmax_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_18Softsign_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_18Softplus_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15Gemm_Onnx_ver13EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_17MatMul_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_17MatMul_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15TopK_Onnx_ver11EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Sin_Onnx_ver7EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Cos_Onnx_ver7EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Tan_Onnx_ver7EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Asin_Onnx_ver7EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Acos_Onnx_ver7EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Atan_Onnx_ver7EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_17Expand_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Sinh_Onnx_ver9EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Cosh_Onnx_ver9EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15Asinh_Onnx_ver9EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15Acosh_Onnx_ver9EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15Atanh_Onnx_ver9EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15Sign_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Erf_Onnx_ver13EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_24QLinearMatMul_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_24QLinearMatMul_Onnx_ver10EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_24MatMulInteger_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_24MatMulInteger_Onnx_ver10EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_17CumSum_Onnx_ver14EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_16Round_Onnx_ver11EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Det_Onnx_ver11EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_17Einsum_Onnx_ver12EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch25ToDimensionOneFloatTensorEf.cold_ZN10onnx_torch20ToDimensionOneTensorEi.cold_ZN10onnx_torch25ToDimensionOneInt64TensorEl.cold_ZN10onnx_torch25ToDimensionOneInt64TensorESt6vectorIlSaIlEE.cold_ZN10onnx_torch37BuildContextDependentFunctionBodyCeluERKNS_24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoE.cold_ZZN10onnx_torch11GetOpSchemaINS_18Softmax_Onnx_ver13EEENS_8OpSchemaEvENKUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_clES5_S7_S9_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_18Softmax_Onnx_ver13EEENS_8OpSchemaEvENKUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_clES5_S7_S9_.constprop.0.cold_ZZN10onnx_torch11GetOpSchemaINS_21LogSoftmax_Onnx_ver13EEENS_8OpSchemaEvENKUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_clES5_S7_S9_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_21LogSoftmax_Onnx_ver13EEENS_8OpSchemaEvENKUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_clES5_S7_S9_.constprop.0.cold_ZN10onnx_torch36BuildContextDependentFunctionBodySCEERKNS_24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoE.cold_ZN10onnx_torch33BuildContextDependentFunctionBodyERKNS_24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoE.cold_ZN10onnx_torch20matmulShapeInferenceERNS_16InferenceContextEii.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_24MatMulInteger_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_24QLinearMatMul_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZTSZZN10onnx_torch16MathDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE__ZTSZN10onnx_torch16MathDocGeneratorEPKcEUlRNS_8OpSchemaEE__ZTSZZN10onnx_torch25SoftmaxFamilyDocGeneratorEPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE__ZTSZN10onnx_torch25SoftmaxFamilyDocGeneratorEPKcS1_S1_EUlRNS_8OpSchemaEE__ZTSZN10onnx_torch11GetOpSchemaINS_14Add_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_14Sub_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_14Mod_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_14Mul_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_14Pow_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZZN10onnx_torch30ElementwiseMultiOpDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE__ZTSZN10onnx_torch30ElementwiseMultiOpDocGeneratorEPKcEUlRNS_8OpSchemaEE__ZTSZN10onnx_torch11GetOpSchemaINS_18Softmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE__ZTSZN10onnx_torch11GetOpSchemaINS_21LogSoftmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE__ZTSZN10onnx_torch11GetOpSchemaINS_15Gemm_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_17MatMul_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_15TopK_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_17Expand_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_24QLinearMatMul_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_24MatMulInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_14Det_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_17Einsum_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_.LC1.LC2.LC10.LC11.LC12.LC13.LC14.LC15.LC16.LC17.LC18.LC19.LC7.LC6.LC9.LC8.LC20.LC22.LC28.LC23.LC29.LC30.LC31.LC32.LC33.LC34.LC35.LC24.LC26.LC27.LC25.LC64.LC65.LC66.LC67.LC68.LC69.LC70.LC71.LC72.LC73.LC75.LC76.LC81.LC82.LC0.LC96.LC105.LC106.LC107.LC116.LC113.LC136.LC142.LC124.LC143.LC145.LC125.LC147.LC148.LC149.LC150.LC151.LC152.LC154.LC155.LC153.LC157.LC126.LC159.LC160.LC168.LC169.LC170.LC171.LC172.LC173.LC174.LC175.LC176.LC162.LC163.LC164.LC165.LC43.LC41.L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H‰îºL‰÷è§ŸþÿH5H‰ßè8VþÿH‹t$H‹L$(L‰ïI‰ÙH‹$M‰ðèH‹|$8L‰îèé3ôÿÿH‰ÃéH‰ÃéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÃéH‰ÃéH‰ÅéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéH‰ÅéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéH‰ÃéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÃéH‰ÅéH‰ÅéH‰ÃH„$PH‰D$(H„$ðH‰D$8éH‰ÃH„$PH‰D$(H„$ðH‰D$8éI‰ÆéH‰ÅéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéI‰ÆéH‰ÅéH‰ÅéH‰ÃéH‰ÃéH‰ÃH„$PH‰D$(H„$ðH‰D$8éH‰ÃH„$PH‰D$(H„$ðH‰D$8éH‰ÃH„$PH‰D$(H„$ðH‰D$8éH‰ÃH„$PH‰D$(H„$ðH‰D$8éH‰ÅH„$ðH‰D$8éH‰ÅH„$ðH‰D$8éH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃééH‰ÃéH‰ÅéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéI‰ÆéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéI‰ÆéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéI‰ÆéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéH‰ÃéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéH‰ÃéH‰Ãé…öxLH‹‡ÀH‹—ÈHcöH¹«ªªªªªªªH)ÂHÁúH¯ÑH9Ös!HvHÁâHÐ1Ò‹H(…ÉHDÂÃf.„1ÀÃ¸Ã…ÒtƒúuH‰71ÀÃH‹H‰1ÀÃ¸Ã…ÒtƒúuH‰71ÀÃH‹H‰1ÀÃI‰øH‰÷Aÿ I‰øH‰÷H‰ÖH‰ÊAÿ ATI‰üUSH‹_H‹/H9ët$DH‹}HEH9Çt+èHƒÅ H9ëuåI‹,$H…ít$[H‰ï]A\é„HƒÅ H9ëu¿ëØD[]A\ÃH‹UH‰ýHƒÀH‰H‹HE H9ÇtèH‰ï]éATA‰ôUHoXH‰ïHƒìèA‰À1ÀE9Ä}D‰æH‰ïèH5H‰Çè…À•ÀHƒÄ]A\ÃATA‰ôUHopH‰ïHƒìèA‰À1ÀE9Ä}D‰æH‰ïèH5H‰Çè…À•ÀHƒÄ]A\Ã…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃfH‹H‰ëÖ…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃfH‹H‰ëÖcannot create std::vector larger than max_size()basic_string::_M_construct null not valid Performs element-wise binary {name} (with limited broadcast support). {broadcast_doc}legacy optimization attribute.If set, defines the broadcast dimensions. See doc for details.First operand, should share the type with the second operand.Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.Result, has same dimensions and type as AConstrain input and output types to float tensors. The operator computes the {name} ({description}) values for each layer in the batch of the given input. The input does not need to explicitly be a 2D vector; rather, it will be coerced into one. For an arbitrary n-dimensional tensor input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is the axis provided, then input will be coerced into a 2-dimensional tensor with dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default case where axis=1, this means the input tensor will be coerced into a 2D tensor of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size. In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D. Each of these dimensions must be matched correctly, or else the operator will throw errors. The output tensor has the same shape and contains the {name} values of the corresponding input. Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).The 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). The operator computes the {name} ({description}) values for each layer in the batch of the given input. The input is a 2-D tensor (Tensor) of size (batch_size x input_feature_dimensions). The output tensor has the same shape and contains the {name} values of the corresponding input. Input does not need to explicitly be a 2D vector; rather, it will be coerced into one. For an arbitrary n-dimensional tensor input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is the axis provided, then input will be coerced into a 2-dimensional tensor with dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default case where axis=1, this means the input tensor will be coerced into a 2D tensor of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size. In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D. Each of these dimensions must be matched correctly, or else the operator will throw errors. Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size 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} Performs element-wise binary {name} (with Numpy-style broadcasting support). {broadcast_doc} Result, has same element type as two inputsTarget rank must be 1 less than the input rank.Input and target dimension value mismatch.First input does not have rank 2Second input does not have rank 2/opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/defs/math/old.cc1 for the first maximum value, and 0 for all others Performs element-wise binary modulus (with Numpy-style broadcasting support). The sign of the remainder is the same as that of the Divisor. Mod operator can also behave like C fmod() or numpy.fmod. In this case, the sign of the remainder however, will be the same as the Dividend (in contrast to integer mod). To force a behavior like numpy.fmod() an 'fmod' Attribute is provided. This attribute is set to 0 by default causing the behavior to be like integer mod. Setting this attribute to 1 causes the remainder to be calculated similar to that of numpy.fmod(). If the input type is floating point, then `fmod` attribute must be set to 1. In case of dividend being zero, the results will be platform dependent. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentConstrain input and output types to signed numeric tensors. Absolute takes one input data (Tensor) and produces one output data (Tensor) where the absolute is, y = abs(x), is applied to the tensor elementwise. The exponential of the input tensor computed element-wiseThe natural log of the input tensor computed element-wiseThe 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. First operand, base of the exponent.Second operand, power of the exponent.Constrain input X and output types to float/int tensors.Constrain input Y types to float/int tensors. Clip operator limits the given input within an interval. The interval is specified by the inputs 'min' and 'max'. They default to numeric_limits::lowest() and numeric_limits::max(), respectively. Input tensor whose elements to be clippedMinimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).Output tensor with clipped input elementsThis 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. General 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. 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.Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N).Output tensor of shape (M, N).Constrain input and output types to float/int tensors.Whether A should be transposedWhether B should be transposedScalar multiplier for the product of input tensors A * B.Scalar multiplier for input tensor C.Matrix multiply results from A * B Broadcast the input tensor following the given shape and the broadcast rule. The broadcast rule is similar to numpy.array(input) * numpy.ones(shape): Dimensions are right alignment; Two corresponding dimension must have the same value, or one of them is equal to 1. Also, this operator is similar to numpy.broadcast_to(input, shape), but the major difference is numpy.broadcast_to() does not allow shape to be smaller than input.size(). It is possible that the output.shape is not equal to shape, when some dimensions in shape is equal to 1, or the shape.ndim < input.shape.ndim. A 1-D tensor indicates the shape you want to expand to, following the broadcast rule Calculate the sign of the given input tensor element-wise. If input > 0, output 1. if input < 0, output -1. if input == 0, output 0. The sign of the input tensor computed element-wise. It has the same shape and type of the input. Computes the error function of the given input tensor element-wise. The error function of the input tensor computed element-wise. It has the same shape and type of the input. Performs cumulative sum of the input elements along the given axis. By default, it will do the sum inclusively meaning the first element is copied as is. Through an `exclusive` attribute, this behavior can change to exclude the first element. It can also perform summation in the opposite direction of the axis. For that, set `reverse` attribute to 1. Example: ``` input_x = [1, 2, 3] axis=0 output = [1, 3, 6] exclusive=1 output = [0, 1, 3] exclusive=0 reverse=1 output = [6, 5, 3] exclusive=1 reverse=1 output = [5, 3, 0] ``` If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.If set to 1 will perform the sums in reverse direction.An input tensor that is to be processed.A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.Output tensor of the same type as 'x' with cumulative sums of the x's elementsInput can be of any tensor type.axis tensor can be int32 or int64 only A NegativeLogLikelihoodLoss operator computes (weighted) negative log likelihood loss. Its "input" tensor has the shape of (N, C, d1, d2, ..., dk) where k >= 0. The "input" tensor contains log-probabilities for input[n, :, d_1, d_2,..., d_k] being in a class of [0, C). The operator's "target" input tensor has the shape of (N, d1, d2, ..., dk). It encodes class labels (one of C classes) or it may contain a special value (indicated by an attribute ignore_index) for N x d1 x d2 x ... x dk samples. The loss value for input[n, :, d_1, d_2,...d_k] being classified as class c = target[n][d_1][d_2]...[d_k] is computed as: loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k]. When an optional "weight" is provided, the sample loss is calculated as: loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k] * weight[c]. loss is zero for the case when target-value equals ignore_index. loss[n][d_1][d_2]...[d_k] = 0, when target[n][d_1][d_2]...[d_k] = ignore_index If "reduction" attribute is set to "none", the operator's output will be the above loss with shape (N, d1, d2, ..., dk). If "reduction" attribute is set to "mean" (the default attribute value), the output loss is (weight) averaged: mean(loss), if "weight" is not provided, or if weight is provided, sum(loss) / sum(weight[target[n][d_1][d_2]...[d_k]]]), for all samples. If "reduction" attribute is set to "sum", the output is a scalar: sum(loss). See also https://pytorch.org/docs/stable/nn.html#torch.nn.NLLLoss. Example 1: // negative log likelihood loss, "none" reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] loss = np.zeros((N, d1)) for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] // print(loss) // [[-3. -2.] // [-0. -2.]] Example 2: // weighted negative log likelihood loss, sum reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] weight = [0.2, 0.3, 0.1] loss = np.zeros((N, d1)) for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] * weight[c] loss = np.sum(loss) // print(loss) // -1.1 Example 3: // weighted negative log likelihood loss, mean reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] target = [[2, 1], [0, 2]] weight = [0.2, 0.3, 0.1] loss = np.zeros((N, d1)) weight_total = 0 for n in range(N): for d_1 in range(d1): c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] * weight[c] weight_total = weight_total + weight[c] loss = np.sum(loss) / weight_total // print(loss) // -1.57 Input tensor of shape (N, C) or (N, C, d1, d2, ..., dk).Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the target values should either be in the range [0, C) or have the value ignore_index.Optional rescaling weight tensor. If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones.The negative log likelihood lossType of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.Constrain input, weight, and output types to floating-point tensors.Constrain target to integer typesLoss function that measures the softmax cross entropy between 'scores' and 'labels'. This operator first computes a loss tensor whose shape is identical to the labels input. If the input is 2-D with shape (N, C), the loss tensor may be a N-element vector L = (l_1, l_2, ..., l_N). If the input is N-D tensor with shape (N, C, D1, D2, ..., Dk), the loss tensor L may have (N, D1, D2, ..., Dk) as its shape and L[i,][j_1][j_2]...[j_k] denotes a scalar element in L. After L is available, this operator can optionally do a reduction operator. shape(scores): (N, C) where C is the number of classes, or (N, C, D1, D2,..., Dk), with K >= 1 in case of K-dimensional loss. shape(labels): (N) where each value is 0 <= labels[i] <= C-1, or (N, D1, D2,..., Dk), with K >= 1 in case of K-dimensional loss. The loss for one sample, l_i, can caculated as follows: l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk], where i is the index of classes. or l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk] * weights[c], if 'weights' is provided. loss is zero for the case when label-value equals ignore_index. l[i][d1][d2]...[dk] = 0, when labels[n][d1][d2]...[dk] = ignore_index where: p = Softmax(scores) y = Log(p) c = labels[i][d1][d2]...[dk] Finally, L is optionally reduced: If reduction = 'none', the output is L with shape (N, D1, D2, ..., Dk). If reduction = 'sum', the output is scalar: Sum(L). If reduction = 'mean', the output is scalar: ReduceMean(L), or if weight is provided: ReduceSum(L) / ReduceSum(W), where tensor W is of shape (N, D1, D2, ..., Dk) and W[n][d1][d2]...[dk] = weights[labels[i][d1][d2]...[dk]]. The predicted outputs with shape [batch_size, class_size], or [batch_size, class_size, D1, D2 , ..., Dk], where K is the number of dimensions.The ground truth output tensor, with shape [batch_size], or [batch_size, D1, D2, ..., Dk], where K is the number of dimensions. Labels element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the label values should either be in the range [0, C) or have the value ignore_index.A manual rescaling weight given to each class. If given, it has to be a 1D Tensor assigning weight to each of the classes. Otherwise, it is treated as if having all ones.Weighted loss float Tensor. If reduction is 'none', this has the shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of K-dimensional loss. Otherwise, it is a scalar.Log probability tensor. If the output of softmax is prob, its value is log(prob).Input tensor of any shape, base of the exponent.Input tensor of any shape broadcastable to X shape, the exponent component.Output tensor (same size as X)Coefficient of leakage default to 0.01.Coefficient of SELU default to 1.6732.Coefficient of SELU default to 1.0507.Coefficient of ELU default to 1.0.Slope tensor. If `Slope` is of size 1, the value is sharedacross different channelsSlope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to XOutput tensor. Same dimension as inputs.Minimum value, under which element is replaced by minMaximum value, above which element is replaced by maxGeneral Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3 Compute Y = alpha * A * B + beta * C, where input tensor A has dimension (M X K), input tensor B has dimension (K X N), input tensor C and output tensor Y have dimension (M X N). If attribute broadcast is non-zero, input tensor C will be broadcasted to match the dimension requirement. A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB. Input tensor C, can be inplace.Whether C should be broadcastedScalar multiplier for the product of input tensors A * B, the default value is 1.0.Scalar multiplier for input tensor C, the default value is 1.0.Input tensor C. The shape of C should be unidirectional broadcastable to (M, N). Retrieve the top-K elements along a specified axis. Given an input tensor of shape [a_1, a_2, ..., a_n, r] and integer argument k, return two outputs: -Value tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which contains the values of the top k elements along the specified axis -Index tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which contains the indices of the top k elements (original indices from the input tensor). Given two equivalent values, this operator uses the indices along the axis as a tiebreaker. That is, the element with the lower index will appear first. Tensor of shape [a_1, a_2, ..., a_n, r]Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensorTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values.Constrain index tensor to int64Number of top elements to retrieveDimension on which to do the sort. Retrieve the top-K elements along a specified axis. Given an input tensor of shape [a_1, a_2, ..., a_n, r] and integer argument k, return two outputs: -Value tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which contains the values of the top k elements along the specified axis -Index tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which contains the indices of the top k elements (original indices from the input tensor). Given two equivalent values, this operator uses the indices along the axis as a tiebreaker. That is, the element with the lower index will appear first. A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieveInvalid value for attribute axisK input must be a one-dimensional tensor of size 1.K input must be of type int64.Axis has less than the requested k elements.'shape' input must be 1D tensor of type INT64Input tensors of wrong rank (0).Incompatible dimensions for matrix multiplicationinput_gather_element_transform 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. Type of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 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for more details please check [the doc](Broadcasting.md).AWAVI‰öAULoATI‰üUH‰Õ1ÒSH»ting** (HìˆL‰/Ht$`I‰çHÇD$`8èH‹T$`foL‰ÿI‰$I‰T$foH‰X0H\$@fo@ H‹D$`I‹$I‰D$ÆI‹4$I‹T$H‰$HòèL‰÷èH‰ÂH¸ÿÿÿÿÿÿÿ?H+D$H9Â‡œL‰öL‰ÿèH¸ÿÿÿÿÿÿÿ?H+D$Hƒø*†º+H5L‰ÿèLt$0HPL‰t$ H‹H9Ñ„ÄH‰L$ H‹HH‰L$0H‹HÆ@H‰ïH‰L$(H‰HÇ@èH‰ÂH¸ÿÿÿÿÿÿÿ?H+D$(H9Â‡ H|$ H‰îèHl$PHPH‰l$@H‹H9Ñ„dH‰L$@H‹HH‰L$PH‹HÆ@H‰L$HHÇ@H‰H¸ÿÿÿÿÿÿÿ?H+D$HHƒø;†H|$@º<H5èL|$pHPL‰|$`H‹H9Ñ„H‰L$`H‹HH‰L$pH‹HH‰L$hH‰Æ@I‹<$HÇ@H‹D$`L9ø„ÝóoD$hI9ý„~I‹T$I‰$AD$H…ÿttH‰|$`H‰T$pHÇD$hÆH‹|$`L9ÿtèH‹|$@H9ïtèH‹|$ L9÷tèH‹<$H9ßtèHÄˆL‰à[]A\A]A^A_Ã@I‰$AD$L‰|$`L|$pL‰ÿë‡€óoH)L$0é;þÿÿóoP)T$Pé›þÿÿóoX)\$péúþÿÿH‹T$hH…ÒtHƒút(L‰þèH‹T$hI‹<$I‰T$ÆH‹|$`éÿÿÿ@¶D$pˆH‹T$hI‹<$ë×H=èH=èH=èH=èH‰Åë+H‰ÅëëôI‰ÇH‹|$@H9ïtèL‰ýH‹|$ L9÷tèH‹<$H9ßtèI‹<$L9ïtèH‰ïèH‰ÅëåóoH‰øfïÀH‹~HÇFHp(H‰pHrH‰xH‹:H9÷„ŸH‰xH‹zH‰x(H‹zH‰2fïÀHÇBH‰x ÆBH‹QóoóAoH‰PHI‹PHÇAH‰P`HPxP8IÇ@AH‰PhI‹ IQXPH9ÊtDH‰HhI‹IH‰HxI‹II‰IÇAH‰HpAÆAÃ€óob`(é_ÿÿÿf.„óAoihxë¼ATI‰ÑL‹FI‰üH‹RH‹H‰÷HƒÆIH9ñ„©L‹WI‹1L9ÐvMQL9Ö„ M‹QL9ÐvGèIT$I‰$H‹HPH9Ñt]I‰$H‹HI‰L$H‹HI‰L$H‰HÇ@Æ@L‰àA\Ãf1Ò1öL‰ÏèIT$I‰$H‹HPH9Ñu²óoHAL$ë²fóo@AD$ë£AºéPÿÿÿDAºéYÿÿÿATI‰ôUH‰ýHƒìH‹ÿPL9àvhH‹EL‰æH‰ïÿPH…ÀtVH‹EL‰æH‰ïÿP‹P(ƒúuëO€‹P(ƒút@ƒút;ƒútƒú u!H‹@ ö@tH‹@H…ÀuÖH‹ëÍDHƒÄ1À]A\ÃfDH‹@ ‹@HƒÄ]A\ƒàÃUH‰ýSHƒìH‹_H…ÛtfDH‰ßH‹èH…ÛuðH‹EH‹}1öHƒÅ0HÅèH‹}ÐHÇEèHÇEàH9ïtHƒÄ[]éf„HƒÄ[]ÃAUATUSH‰û¿ˆHƒìèH‹3H‹SHhHÇHxI‰ÄH‰hHòèH‹C(óoC@Ml$(Hs IÇD$(L‰ïI‰D$0H‹C8IÇD$8I‰D$@IÇD$XAD$HèH‹sXH‹S`ID$pI|$`I‰D$`HòèHƒÄL‰à[]A\A]ÃH‰ÇëH‰ÃëH‰ÃëL‰ïèI‹|$H9ýuH‰ßèL‰çèèH‰ÅëèëßèH‰ïèAUI‰ýATUSHƒìL‹gH‹/I9ì„¢H‹}xH…ˆH9ÇtèH‹}XHEhH9ÇtèH‹]0H…ÛtH‰ßH‹èH…ÛuðH‹E(H‹} 1öHÅèH‹} HEPHÇE8HÇE0H9ÇtèH‹}HEH9Çt:èHÅ¨I9ì…eÿÿÿI‹mH…ít.HƒÄH‰ï[]A\A]éf.„HÅ¨I9ì…0ÿÿÿëÉHƒÄ[]A\A]ÃAVI‰þAUATUSL‹oL‹'M9åt}f„I‹|$8ID$HH9ÇtèI‹\$(I‹l$ H9ët%DH‹}HEH9Çt[èHƒÅ H9ëuåI‹l$ H…ítH‰ïèI‹<$ID$H9Çt=èIƒÄXM9åuM‹&M…ät6[L‰ç]A\A]A^é€HƒÅ H9ëuë¨DIƒÄXM9å…SÿÿÿëÂ[]A\A]A^ÃH‹SH‰ûHƒÀH‰H‹HHCXH9ÇtèH‹H{8HƒÀH‰[éH‹UH‰ýHƒÀH‰H‹HHEXH9ÇtèH‹H}8HƒÀH‰EèH‰ï]éAWAVAUATI‰üUSHìH‹L¼$ðLt$pH‰t$XHƒÀ@L‰ÿH‰L$HfHnÀH‹H‰T$@foÈfoÐL‰D$`HƒÀL‰t$hfHnØH‹flËHƒÀ)L$ fHnàflÔ)T$0èH‹H‹1öHÇ„$ÈfïÀH‹kHƒÀH‹K„$ØH‰„$ð1Àf‰„$Ð„$èH‹EèH‰l$pH‰LpHÇD$xH‹}èH‰$L÷èL‹s H‹C(L¬$€1öI‹~èL‰´$€H‰D$LïH‰èH‹CH‹s0fïÀHœ$ˆfoT$0H‰D$H‹@èH‰t$H‰tpH‹)”$€HƒÀ)„$H‰D$pH‹)„$ HƒÀh)„$°H‰„$ðH„$ÀH‰ÇH‰D$PèH‹H‰ÞL‰ÿÇ„$ÈHƒÀÆ„$àH‰„$ˆH„$àH‰D$0H‰„$ÐHÇ„$ØèH‹\$XH‰ßèH‰ÞL‰ïH‰ÂèH‹\$@H‰ßèH‰ÞL‰ïH‰ÂèH‹D$HL‰ïH‹0èH‹\$`H‰ßèH‰ÞL‰ïH‰ÂèH‹„$°I\$IÇD$I‰$AÆD$H…À„ L‹„$ M…À„ÿL9À‡öH‹Œ$¨1Ò1öL‰çI)ÈèH‹fol$ H‹¼$ÐHƒÀ)¬$€H‰D$pH‹HƒÀhH‰„$ðH;|$0tèH‹H‹|$PHƒÀH‰„$ˆèH‹D$H‹T$L‰ÿH‹L$H‹@èH‰TpI‹FèL‰´$€H‹$H‰Œ€H‹EèH‰l$pH‰TpH‹HÇD$xHƒÀH‰„$ðèHÄL‰à[]A\A]A^A_ÃfDI‰Àéÿÿÿ„H´$ÐL‰çèéüþÿÿH‰ÅëH‰ÅëI‰Äë*I‰ÄëcI‰Äë;I‹<$H9ûtèH‹|$hèH‰ïèH‹EèH‹$H‰l$pH‰LpHÇD$xH‹L‰ÿHƒÀH‰„$ðèL‰çèH‰ßèH‹D$H‹L$H‹@èH‰LpI‹FèH‹L$L‰´$€H‰Œ€ëŒ[TypeInferenceError] Input type was nullInput Element type of input unknownOutput expected to have tensor or sparse tensor type. Got: AWAVAUI‰ÕATI‰ôUH‰ýSHì¸H‹ÿPH…À„÷D‹p(Aƒþt Aƒþ…¶H‹@ ‹X …Û„ˆH‹EH‰ïL‰îÿP(D‹`(H‰ÅAƒü„~Aƒüt`E…ä…ÇAƒþ„}AƒþtHÄ¸[]A\A]A^A_ÃDH‰ÇèH‹EÇE(H‰ÇHƒçþ¨uuèH‰E ƒH‰X ë¹AƒütH‹@ ƒH‰X ë¦DH‰ïèƒH‰X ë€H‰ÇèH‹EÇE(H‰ÇHƒçþ¨uèH‰E ƒH‰X éVÿÿÿH‹?éƒÿÿÿH‹?ëÞ¿0L|$ èL‰ÿI‰ÆèHl$0ºH5H‰ïèºH5H‰ïèL‰îH‰ïèº5H5H‰ïèD‰æH‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰÷èH‹H‹<$AÆF IÇFHƒÀI‰IF I‰FHD$H9ÇtèH‹5HL‰÷è¿0L|$ èL‰ÿI‰ÄèHl$0ºH5H‰ïèºH5H‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰çèH‹H‹<$AÆD$ IÇD$HƒÀI‰$ID$ I‰D$HD$H9ÇtèH‹5HL‰çèH‰Åé½H‰Åé!H‰ÅéÕ¿0L|$ èL‰ÿI‰ÅèHl$0ºH5H‰ïèºH5H‰ïèL‰æH‰ïèº5H5H‰ïèD‰öH‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰ïèH‹AÆE IÇEHƒÀI‰EIE I‰EH‹<$HD$H9ÇtèH‹5HL‰ïè¿0L|$ èL‰ÿI‰ÅèHl$0ºH5H‰ïèºH5H‰ïèL‰æH‰ïèºH5H‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰ïèé%ÿÿÿH‰Åë H‰ÅéH‰ÅëH‹<$HD$H9ÇtèL‰ïèH‰ïèëÉëÌH‰ÅëH‹<$HD$H9ÇtèL‰çèH‰ïèH‰ÅëL‰ÿèL‰÷èH‰ïèH‹<$HD$H9ÇtâèëÛH‰ÅëÖépÿÿÿL‰ÿèé{ÿÿÿL‰ÿèëAWAVAUATI‰üUSHìH‹L¼$ðLt$pH‰t$XHƒÀ@L‰ÿH‰L$HfHnÀH‹H‰T$@foÈfoÐL‰D$`HƒÀL‰t$hfHnØH‹flËHƒÀ)L$ fHnàflÔ)T$0èH‹H‹1öHÇ„$ÈfïÀH‹kHƒÀH‹K„$ØH‰„$ð1Àf‰„$Ð„$èH‹EèH‰l$pH‰LpHÇD$xH‹}èH‰$L÷èL‹s H‹C(L¬$€1öI‹~èL‰´$€H‰D$LïH‰èH‹CH‹s0fïÀHœ$ˆfoT$0H‰D$H‹@èH‰t$H‰tpH‹)”$€HƒÀ)„$H‰D$pH‹)„$ HƒÀh)„$°H‰„$ðH„$ÀH‰ÇH‰D$PèH‹H‰ÞL‰ÿÇ„$ÈHƒÀÆ„$àH‰„$ˆH„$àH‰D$0H‰„$ÐHÇ„$ØèH‹\$XH‰ßèH‰ÞL‰ïH‰ÂèH‹\$@H‰ßèH‰ÞL‰ïH‰ÂèH‹D$HL‰ïH‹0èH‹\$`H‰ßèH‰ÞL‰ïH‰ÂèH‹„$°I\$IÇD$I‰$AÆD$H…À„ L‹„$ M…À„ÿL9À‡öH‹Œ$¨1Ò1öL‰çI)ÈèH‹fol$ H‹¼$ÐHƒÀ)¬$€H‰D$pH‹HƒÀhH‰„$ðH;|$0tèH‹H‹|$PHƒÀH‰„$ˆèH‹D$H‹T$L‰ÿH‹L$H‹@èH‰TpI‹FèL‰´$€H‹$H‰Œ€H‹EèH‰l$pH‰TpH‹HÇD$xHƒÀH‰„$ðèHÄL‰à[]A\A]A^A_ÃfDI‰Àéÿÿÿ„H´$ÐL‰çèéüþÿÿH‰ÅëH‰ÅëI‰Äë*I‰ÄëcI‰Äë;I‹<$H9ûtèH‹|$hèH‰ïèH‹EèH‹$H‰l$pH‰LpHÇD$xH‹L‰ÿHƒÀH‰„$ðèL‰çèH‰ßèH‹D$H‹L$H‹@èH‰LpI‹FèH‹L$L‰´$€H‰Œ€ëŒAWAVAUATI‰üUSHìH‹L¼$ðLt$pH‰t$XHƒÀ@L‰ÿH‰L$HfHnÀH‹H‰T$@foÈfoÐL‰D$`HƒÀL‰t$hfHnØH‹flËHƒÀ)L$ fHnàflÔ)T$0èH‹H‹1öHÇ„$ÈfïÀH‹kHƒÀH‹K„$ØH‰„$ð1Àf‰„$Ð„$èH‹EèH‰l$pH‰LpHÇD$xH‹}èH‰$L÷èL‹s H‹C(L¬$€1öI‹~èL‰´$€H‰D$LïH‰èH‹CH‹s0fïÀHœ$ˆfoT$0H‰D$H‹@èH‰t$H‰tpH‹)”$€HƒÀ)„$H‰D$pH‹)„$ HƒÀh)„$°H‰„$ðH„$ÀH‰ÇH‰D$PèH‹H‰ÞL‰ÿÇ„$ÈHƒÀÆ„$àH‰„$ˆH„$àH‰D$0H‰„$ÐHÇ„$ØèH‹\$XH‰ßèH‰ÞL‰ïH‰ÂèH‹\$@H‰ßèH‰ÞL‰ïH‰ÂèH‹D$HL‰ïH‹0èH‹\$`H‰ßèH‰ÞL‰ïH‰ÂèH‹„$°I\$IÇD$I‰$AÆD$H…À„ L‹„$ M…À„ÿL9À‡öH‹Œ$¨1Ò1öL‰çI)ÈèH‹fol$ H‹¼$ÐHƒÀ)¬$€H‰D$pH‹HƒÀhH‰„$ðH;|$0tèH‹H‹|$PHƒÀH‰„$ˆèH‹D$H‹T$L‰ÿH‹L$H‹@èH‰TpI‹FèL‰´$€H‹$H‰Œ€H‹EèH‰l$pH‰TpH‹HÇD$xHƒÀH‰„$ðèHÄL‰à[]A\A]A^A_ÃfDI‰Àéÿÿÿ„H´$ÐL‰çèéüþÿÿH‰ÅëH‰ÅëI‰Äë*I‰ÄëcI‰Äë;I‹<$H9ûtèH‹|$hèH‰ïèH‹EèH‹$H‰l$pH‰LpHÇD$xH‹L‰ÿHƒÀH‰„$ðèL‰çèH‰ßèH‹D$H‹L$H‹@èH‰LpI‹FèH‹L$L‰´$€H‰Œ€ëŒ[ShapeInferenceError] Target=Mismatch between source and target type. Source=Unsupported Source/Target type=AWAVAUATUSHì¸D‹g(D‹n(E9ìu{H‰ûH‰õAƒü„BAƒü„ØAƒü„–Aƒü …˜Aƒý …*L‹e I‹l$AƒL$H…í„Tƒ{( „xH‹H‹XH…Û„uD‹c(D‹m(E9ìt‹¿0Lt$ èL‰÷I‰ÇèHl$0ºH5H‰ïèº0H5H‰ïèD‰æH‰ïèºH5H‰ïèD‰îH‰ïèH‰åHt$8H‰ïèL‰÷èH‰îL‰ÿèH‹H‹<$AÆG IÇGHƒÀI‰IG I‰GHD$H9ÇtèH‹5HL‰ÿè€H‰ïèH‹EÇE( H‰ÇHƒçþ¨…îèI‰ÄH‰E I‹l$AƒL$H…í…¬þÿÿI‹D$H‰ÇHƒçþ¨…ôèƒ{( I‰D$H‰Å…‹þÿÿH‹C H‹XH…Û…‹þÿÿH‹éþÿÿAƒütZH‹C ö@„œL‹`M…ä„OAƒý…H‹] H‹{ƒKH…ÿuSH‹CH‰ÇHƒçþ¨…xèH‰CH‰Çë2H‹C ö@tFL‹`M…ä„éH‰ïèH‹xƒHH‰ÃH…ÿtHÄ¸L‰æ[]A\A]A^A_é„HÄ¸[]A\A]A^A_ÃfDAƒýu2L‹e I‹l$AƒL$H…í„ªƒ{(„ðþÿÿH‹ésýÿÿ@H‰ïèH‹EÇE(H‰ÇHƒçþ¨…èH‰E I‰Äë¢H‰ïèH‹EÇE(H‰ÇHƒçþ¨utèH‰E H‰ÃéÐþÿÿL‹%éÿÿÿ@L‹%é¥þÿÿ@H‹?é þÿÿ„I‹D$H‰ÇHƒçþ¨u/èI‰D$H‰Åé4ÿÿÿH‹?éhÿÿÿH‹?éþÿÿH‹?ë‡H‹?é€þÿÿH‹?ëÌ¿0Lt$ èL‰÷I‰ÅèHl$0ºH5H‰ïèºH5H‰ïèD‰æH‰ïèH‰åHt$8H‰ïèL‰÷èH‰îL‰ïèH‹H‹<$AÆE IÇEHƒÀI‰EIE I‰EHD$H9ÇtèH‹5HL‰ïèH‰Åë H‰ÅëH‰ÅëH‹<$HD$H9ÇtèëL‰÷èL‰ïèH‰ïèH‰Åë H‰ÅëH‰ÅëH‹<$HD$H9ÇtèëL‰÷èL‰ÿèH‰ïèAWAVAUATI‰üUSHìøH‹L´$àL|$`H‰t$PHƒÀ@L‰÷H‰T$@fHnÀH‹L‰|$XfoÈfoÐHƒÀfHnØH‹flËHƒÀ)L$ fHnàflÔ)T$0èH‹H‹1öHÇ„$¸fïÀH‹kHƒÀH‹K„$ÈH‰„$à1Àf‰„$À„$ØH‹EèH‰l$`H‰L`HÇD$hH‹}èH‰$LÿèL‹k H‹C(L|$p1öI‹}èL‰l$pH‰D$LÿH‰èH‹CH‹K0fïÀH\$xfoT$0H‰D$H‹@èH‰L$H‰L`H‹)T$pHƒÀ)„$€H‰D$`H‹)„$HƒÀh)„$ H‰„$àH„$°H‰ÇH‰D$HèH‹H‰ÞL‰÷Ç„$¸HƒÀÆ„$ÐH‰D$xH„$ÐH‰D$0H‰„$ÀHÇ„$ÈèH‹\$PH‰ßèH‰ÞL‰ÿH‰ÂèH‹\$@H‰ßèH‰ÞL‰ÿH‰ÂèH‹„$ I\$IÇD$I‰$AÆD$H…À„L‹„$M…À„óL9À‡êH‹Œ$˜1Ò1öL‰çI)ÈèH‹fol$ H‹¼$ÀHƒÀ)l$pH‰D$`H‹HƒÀhH‰„$àH;|$0tèH‹H‹|$HHƒÀH‰D$xèH‹D$H‹T$L‰÷H‹t$H‹@èH‰T`I‹EèL‰l$pH‹$H‰tpH‹EèH‰l$`H‰T`H‹HÇD$hHƒÀH‰„$àèHÄøL‰à[]A\A]A^A_ÃfDI‰Àéÿÿÿ„H´$ÀL‰çèéÿÿÿH‰ÅëH‰ÅëI‰Äë*I‰ÄëcI‰Äë;I‹<$H9ûtèH‹|$XèH‰ïèH‹EèH‹4$H‰l$`H‰t`HÇD$hH‹L‰÷HƒÀH‰„$àèL‰çèH‰ßèH‹D$H‹t$H‹@èH‰t`I‹EèH‹t$L‰l$pH‰tpë’Attribute expected to have tensor or sparse tensor typeATUHƒì(H‹ÿP‹P(ƒútƒúu%H‹@ H‹@H…Àt HƒÄ(]A\ÃH‹HƒÄ(]A\Ã¿0I‰äèHH5L‰çH‰ÅèL‰æH‰ïèH‹H‹<$HÇEÆE HƒÀH‰EHE H‰EHD$H9ÇtèH‹5HH‰ïèI‰ÄëH‹<$HD$H9ÇtèH‰ïèL‰çèI‰ÄëØ expected to have tensor or sparse type expected to have tensor typeAWAVAUATI‰ôUS‰ÓHìÈL‹-H‹IU@HƒÀfHnÈfHnÂH‹flÁ)$ÿP(H…À„ƒH‰Å‹@(ƒø„”ƒøt/…À…±ƒût'1Àƒût HÄÈ[]A\A]A^A_Ãf„ƒøt[H‰ïèH‹EÇE(H‰ÇHƒçþ¨u]èH‰E H‰ÃƒHH‹@H…Àu©H‹CH‰ÇHƒçþ¨u:èH‰Cëf.„H‰ïèƒHH‰ÃH‹@H…À…iÿÿÿë¾fH‹?ëžH‹?ëÁ¿0Lt$0èL‰÷I‰ÇèHl$@ºH5H‰ïèºH5H‰ïèL‰æH‰ïèºH5H‰ïèH‹D$pLd$ ÆD$ Hl$L‰d$HÇD$H…À…™H´$H‰ïèIEfo$H‹¼$H‰D$0IEhH‰„$°H„$ )\$@H9Ç…ƒH‹H¼$€HƒÀH‰D$HèH‹H¼$°H‹PH‹H0H‹RèH‰L0H‹P H‹H(H‰T$@H‹RèH‰L@H‹PH‹@H‰T$0H‹RèH‰D0H‹HÇD$8HƒÀH‰„$°èH‰îL‰ÿèH‹AÆG IÇGHƒÀI‰IG I‰GH‹|$L9çtèH‹5HL‰ÿè¿0Lt$0èL‰÷I‰ÇèHl$@ºH5H‰ïèºH5H‰ïèL‰æH‰ïèº'H5H‰ïèH‹D$pLd$ ÆD$ Hl$L‰d$HÇD$H…À…H´$H‰ïèIEfo$H‹¼$H‰D$0IEhH‰„$°H„$ )T$@H9ÇtèH‹H¼$€HƒÀH‰D$HèH‹H¼$°H‹PH‹H0H‹RèH‰L0H‹P H‹H(H‰T$@H‹RèH‰L@H‹PH‹@H‰T$0H‹RèH‰D0H‹HÇD$8HƒÀH‰„$°èH‰îL‰ÿèéDþÿÿL‹D$`M…ÀuI‰ÀH‹L$h1Ò1öH‰ïI)ÈèéQýÿÿL9ÀwßëàH‰ÅëwèésýÿÿL‹D$`M…ÀuI‰ÀH‹L$h1Ò1öH‰ïI)ÈèéËþÿÿL9ÀwßëàëÂH‰Åë H‰ÅëH‰ÅëH‹|$L9çuL‰÷èL‰ÿèH‰ïèèëáëËëÎëÂH‹|$L9çtÙèëÒAWAVAUATI‰üUSHìH‹L¼$ðLt$pH‰t$XHƒÀ@L‰ÿH‰L$HfHnÀH‹H‰T$@foÈfoÐL‰D$`HƒÀL‰t$hfHnØH‹flËHƒÀ)L$ fHnàflÔ)T$0èH‹H‹1öHÇ„$ÈfïÀH‹kHƒÀH‹K„$ØH‰„$ð1Àf‰„$Ð„$èH‹EèH‰l$pH‰LpHÇD$xH‹}èH‰$L÷èL‹s H‹C(L¬$€1öI‹~èL‰´$€H‰D$LïH‰èH‹CH‹s0fïÀHœ$ˆfoT$0H‰D$H‹@èH‰t$H‰tpH‹)”$€HƒÀ)„$H‰D$pH‹)„$ HƒÀh)„$°H‰„$ðH„$ÀH‰ÇH‰D$PèH‹H‰ÞL‰ÿÇ„$ÈHƒÀÆ„$àH‰„$ˆH„$àH‰D$0H‰„$ÐHÇ„$ØèH‹\$XH‰ßèH‰ÞL‰ïH‰ÂèH‹\$@H‰ßèH‰ÞL‰ïH‰ÂèH‹D$HL‰ïH‹0èH‹\$`H‰ßèH‰ÞL‰ïH‰ÂèH‹„$°I\$IÇD$I‰$AÆD$H…À„ L‹„$ M…À„ÿL9À‡öH‹Œ$¨1Ò1öL‰çI)ÈèH‹fol$ H‹¼$ÐHƒÀ)¬$€H‰D$pH‹HƒÀhH‰„$ðH;|$0tèH‹H‹|$PHƒÀH‰„$ˆèH‹D$H‹T$L‰ÿH‹L$H‹@èH‰TpI‹FèL‰´$€H‹$H‰Œ€H‹EèH‰l$pH‰TpH‹HÇD$xHƒÀH‰„$ðèHÄL‰à[]A\A]A^A_ÃfDI‰Àéÿÿÿ„H´$ÐL‰çèéüþÿÿH‰ÅëH‰ÅëI‰Äë*I‰ÄëcI‰Äë;I‹<$H9ûtèH‹|$hèH‰ïèH‹EèH‹$H‰l$pH‰LpHÇD$xH‹L‰ÿHƒÀH‰„$ðèL‰çèH‰ßèH‹D$H‹L$H‹@èH‰LpI‹FèH‹L$L‰´$€H‰Œ€ëŒAWAVAUATI‰üUSHìH‹L´$H¬$€H‰t$HƒÀ@L‰÷H‰L$`fHnÀH‹H‰T$foÈfoÐL‰D$HƒÀL‰L$hfHnØH‹H‰l$pflËHƒÀ)L$@fHnàflÔ)T$PèL‹=H‹1öHÇ„$ØfïÀI‹_HƒÀI‹O„$èH‰„$1Àf‰„$à„$øH‹CèH‰œ$€H‰Œ€HÇ„$ˆH‹{èH‰L$ HïèM‹o I‹G(H¬$1öI‹}èL‰¬$H‰D$(HïH‰èI‹GI‹W0L¼$ÐfïÀfoT$PL‰ÿH‰D$0H‹@èH‰T$8H‰”€H‹)”$HƒÀ)„$ H‰„$€H‹)„$°HƒÀh)„$ÀH‰„$èH‹L‰÷Ç„$ØHÇ„$èHƒÀÆ„$ðH‰„$˜H„$ðH‰D$PH‰„$àH„$˜H‰ÆH‰D$xèH‹|$èH‹t$H‰ïH‰ÂèH‹|$èH‹t$H‰ïH‰ÂèH‹D$`H‰ïH‹0èH‹|$èH‹t$H‰ïH‰ÂèH‹D$hH‰ï‹0èH‹„$ÀIl$IÇD$I‰,$AÆD$H…À„,L‹„$°M…À„L9À‡H‹Œ$¸1Ò1öL‰çI)ÈèH‹fol$@H‹¼$àHƒÀ)¬$H‰„$€H‹HƒÀhH‰„$H;|$PtèH‹L‰ÿHƒÀH‰„$˜èH‹D$0H‹L$8L‰÷H‹t$(H‹@èH‰Œ€I‹EèL‰¬$H‰´H‹CèH‰œ$€H‹\$ H‰œ€H‹HÇ„$ˆHƒÀH‰„$èHÄL‰à[]A\A]A^A_Ã@I‰Àéöþÿÿ„H´$àL‰çèéðþÿÿH‰ÃëH‰Åë&H‰Åé±H‰Åë+H‰ÅéI‹<$H9ýtèH‰ÝH‹|$pèH‰ïèH‹|$xèH‹D$0H‹L$8H‹@èH‰Œ€I‹EèH‹L$(L‰¬$H‰ŒH‹CèH‰œ$€H‹\$ H‰œ€HÇ„$ˆH‹L‰÷HƒÀH‰„$èH‰ïèë¶ is null expected to have tensor or sparse tensor type: ATU‰ÕSHƒì0H‹H‰t$‰L$ÿP(H…À„G‹P(H‰Ã‹D$…Òt9Ð… ƒøtƒøt^HƒÄ0[]A\ÃDƒúuH‹C ƒH‰h HƒÄ0[]A\Ã€H‰ßèH‹CÇC(H‰ÇHƒçþ¨u@èH‰C ëÀ€ƒút°H‰ßèH‹CÇC(H‰ÇHƒçþ¨uèH‰C ë‹H‹?ë»H‹?ëë¿0Ld$èHL$L‰çLL$LHH‰ÅH5èL‰æH‰ïèH‹ÆE HÇEHƒÀH‰EHE H‰EH‹|$HD$ H9ÇtèH‹5HH‰ïè¿0Ld$èHL$L‰çLHH5H‰ÅèL‰æH‰ïèéoÿÿÿH‰ÃëH‰ÃëH‹|$HD$ H9ÇtèH‰ïèH‰ßèëÐëÓAWfïÀI‰ÿAVAUATUH‰õSHƒì(H‹^H+HÇG„H¸àÿÿÿÿÿÿH9Ã‡H‰ßèH‰D$H‹D$fHnÀHÃflÀI‰_H‰ÃAL‹uH‹mL9õ„«HD$H‰$ë&„A¶EˆCHƒÅ L‰cHƒÃ BÆ'I9îtzH{L‹eH‰;L‹mL‰èLàt 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[]A\A]A^é[]A\A]A^ÃATUSH‹_H‹/H9ëtI‰üDH‰ïHƒí€èH9ëuïI‹,$H…ít[H‰ï]A\éf[]A\ÃFbRKN10onnx_torch24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEEFvRN10onnx_torch16InferenceContextEEN10onnx_torch14InferenceErrorEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EPFvRN10onnx_torch16InferenceContextEE*ZZN10onnx_torch24MathDocGenerator_opset13EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN10onnx_torch24MathDocGenerator_opset13EPKcEUlRNS_8OpSchemaEE_*ZZN10onnx_torch24MathDocGenerator_opset_7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN10onnx_torch24MathDocGenerator_opset_7EPKcEUlRNS_8OpSchemaEE_*ZZN10onnx_torch34SoftmaxFamilyDocGenerator_opset_11EPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN10onnx_torch34SoftmaxFamilyDocGenerator_opset_11EPKcS1_EUlRNS_8OpSchemaEE_*ZN10onnx_torch11GetOpSchemaINS_14Mod_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_14Pow_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_14Pow_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZZN10onnx_torch37ElementwiseMultiOpDocGenerator_opset8EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN10onnx_torch37ElementwiseMultiOpDocGenerator_opset8EPKcEUlRNS_8OpSchemaEE_*ZN10onnx_torch11GetOpSchemaINS_15Gemm_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_16MatMul_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_16Expand_Onnx_ver8EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch32SoftmaxFamilyDocGenerator_opset1EPKcS1_EUlRNS_8OpSchemaEE_*ZN10onnx_torch20MathDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_*ZN10onnx_torch27MathDocGenerator_old_opset6EPKcEUlRNS_8OpSchemaEE_*ZN10onnx_torch11GetOpSchemaINS_13Pow_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_14Gemm_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_14Gemm_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_14Gemm_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_16MatMul_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_14TopK_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_15TopK_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZZN10onnx_torch34ElementwiseMultiOpDocGenerator_oldEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN10onnx_torch34ElementwiseMultiOpDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_PFbRKN10onnx_torch24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEEThis operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcastipports **unidirectional broadcasConstrain input and output types to high-precision numeric tenso Neg takes one input data (Tensor) and produces one output data (Tensor) where each element flipped sign, y = -x, is applied to the tensor to all numeric Reciprocal takes one input data (Tensor) and produces one output data (Tensor) where the reciprocal is, y = 1/x, is appli Floor takes one input data (Tensor) and produces one output data (Tensor) where the floor is, y = floor(x), is applied to the tensor elem Ceil takes one input data (Tensor) and produces one output data (Tensor) where the ceil is, y = ceil(x), is applied to the tensor element Square root takes one input data (Tensor) and produces one output data (Tensor) where the square root is, y = x^0.5, is applied to the tensor elementwise. If x is negative, then it will Relu takes one where the rectified linear function, y = max(0, x), is applied to the tensor ele Calculates the exponential of the given input tensor, element-wnatural log of thyperbolic tangent of the given input tensor ele Sigmoid takes one input data (Tensor) and produces one output data (Tensor) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the tensor e to numeric tens Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/nump to all tensors. Pow takes input data (Tensor) and exponent Tensor, and produata (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwis Absolute takes one input data (Tensor) and produces one output data (Tensor) where the absolute is, y = abs(x), is applied to the tensor LeakyRelu takessor) and an argument alpha, and produces one output data (Tensor) where the function `f(x) = alpha * x for x < 0`, `f(x) = x for x >= 0`, is applied to the data tensor el Selu takes one where the scaled exponential linear unit function, `y = gamma * (alpha * e^x - alpha) for x <= 0`, `y = gamma * x for x > 0`, is applied to the tensor elementwi Elu takes one ihere the function `f(x) = alpha * (exp(x) - 1.) for x < 0`, `f(x) = x for x >= 0`., is applied to the tensor ele 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 PRelu takes input data (Tensor) and slope tensor as input, a = slope * x for x for x >= 0`., is applied to the data tensor e HardSigmoid tak HardSigmoid function, y = max(0, min(1, alpha * x + beta)), is applied to the t Element-wise max of each of the input tensors. All inputs and outputs must have the same shape Element-wise min of each of the Element-wise sum of each of the Element-wise mean of each of the input tensors. All inputs and outputs must have the same shape and data type. Clip operator limits the given input within an interval. The interval is specified with arguments 'min' and 'max'. They default to numeric_limits::lowest() and numeric_limits::max() respectivied by the inputs 'min' and 'max'. They default to numeric_limits::lowest() and numeric_limits::max(), respectiv€?GCC: (Debian 11.2.0-16) 11.2.0zRxS0DXl€ ”¨¼Ðäø 4H\p„˜¬ÀÔèü$48eBŒD†A ƒv DBMQABp-H†`$ŒGBŒD†K rAB$´GBŒD†K rABÜ8ð898,9@8T8h8|88 ¤nAƒb Eh HzPLRx›P$¥LFŒI †A(ƒG0A (A ABBFZ (A ABBAx0ƒ†ŒL˜³BŽGE ŒH(†A0ƒT (D BBBKh(A EBB<èšBEŒA †D(ƒG0¬ (A ABBK0ìŸBŒD†D ƒG0| AABG8 ²BBŒE †A(ƒD@B (A ABBJ\5H†h4´ÚA†R BŽGFŒEƒã. ¨._ A ìƒ†ŒŽ8ºA†R BŽGFŒEƒ¡. y.h A L,ƒ†ŒŽ8pÅA†R BŽGFŒEƒ¡. y.s A ¬,ƒ†ŒŽLÐkBWŽF B(ŒA0†A8ƒD` 8A0A(B BBBA L`ƒ†ŒŽDDïBŽFB ŒD(†D0ƒD@k 0A(A BBBG Œ)A@ƒ†ŒŽ8°2A†R DŽSŒƒ“. 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NS0_36NegativeLogLikelihoodLoss_Onnx_ver12EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_15Gemm_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_15Gemm_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_14Gemm_Onnx_ver7EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_14Gemm_Onnx_ver7EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_14Gemm_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_14Gemm_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZNSt6vectorIN10onnx_torch18FunctionBodyHelper21AttributeProtoWrapperESaIS2_EEC2ESt16initializer_listIS2_ERKS3_.constprop.0_ZNSt6vectorIN10onnx_torch18FunctionBodyHelper21AttributeProtoWrapperESaIS2_EEC2ESt16initializer_listIS2_ERKS3_.constprop.0.cold_ZNSt6vectorIN10onnx_torch18FunctionBodyHelper21AttributeProtoWrapperESaIS2_EEC2ESt16initializer_listIS2_ERKS3_.constprop.1_ZNSt6vectorIN10onnx_torch18FunctionBodyHelper21AttributeProtoWrapperESaIS2_EEC2ESt16initializer_listIS2_ERKS3_.constprop.1.cold_ZNSt8_Rb_treeINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_N10onnx_torch8OpSchema9AttributeEESt10_Select1stISB_ESt4lessIS5_ESaISB_EE8_M_eraseEPSt13_Rb_tree_nodeISB_E.isra.0_ZNSt8_Rb_treeINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_N10onnx_torch8OpSchema9AttributeEESt10_Select1stISB_ESt4lessIS5_ESaISB_EE7_M_copyILb0ENSH_11_Alloc_nodeEEEPSt13_Rb_tree_nodeISB_ESM_PSt18_Rb_tree_node_baseRT0_.isra.0_ZN10onnx_torch11GetOpSchemaINS_14Add_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Sub_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Mul_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Div_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Add_Onnx_ver7EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Sub_Onnx_ver7EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Mul_Onnx_ver7EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Div_Onnx_ver7EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_18Softmax_Onnx_ver11EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_21LogSoftmax_Onnx_ver11EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_18Hardmax_Onnx_ver11EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Mod_Onnx_ver10EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Neg_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Abs_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_20Reciprocal_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15Floor_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Ceil_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Sqrt_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Relu_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15Relu_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Exp_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Log_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Tanh_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Pow_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Pow_Onnx_ver12EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_17Sigmoid_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Max_Onnx_ver12EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Min_Onnx_ver12EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Sum_Onnx_ver8EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Mean_Onnx_ver8EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15Clip_Onnx_ver12EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15Gemm_Onnx_ver11EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_16MatMul_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_16MatMul_Onnx_ver9EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_16Expand_Onnx_ver8EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_16Expand_Onnx_ver8EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Sign_Onnx_ver9EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Erf_Onnx_ver9EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_17CumSum_Onnx_ver11EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver12EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_34SoftmaxCrossEntropyLoss_Onnx_ver12EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver12EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_17Softmax_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_20LogSoftmax_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_17Hardmax_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Add_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Sub_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Mul_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Div_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Add_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Sub_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Mul_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Div_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Pow_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Pow_Onnx_ver7EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Neg_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Abs_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_20Reciprocal_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15Floor_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Ceil_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Sqrt_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Relu_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_19LeakyRelu_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Selu_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Elu_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Exp_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Log_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Tanh_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15PRelu_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15PRelu_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15PRelu_Onnx_ver7EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_17Sigmoid_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_21HardSigmoid_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Max_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Min_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Sum_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Mean_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Clip_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Gemm_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Gemm_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Gemm_Onnx_ver7EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Gemm_Onnx_ver9EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Max_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Min_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Sum_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Mean_Onnx_ver6EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_16MatMul_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_16MatMul_Onnx_ver1EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_14TopK_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_14TopK_Onnx_ver1EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_15TopK_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_15TopK_Onnx_ver10EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Clip_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15Clip_Onnx_ver11EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Max_Onnx_ver8EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Min_Onnx_ver8EEENS_8OpSchemaEv.cold_ZZN10onnx_torch11GetOpSchemaINS_15TopK_Onnx_ver10EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_15TopK_Onnx_ver10EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_14TopK_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_34SoftmaxCrossEntropyLoss_Onnx_ver12EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_16Expand_Onnx_ver8EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZZN10onnx_torch11GetOpSchemaINS_16MatMul_Onnx_ver1EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_16MatMul_Onnx_ver1EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZN10onnx_torch28matmulShapeInference_opset_9ERNS_16InferenceContextEii.cold_ZN10onnx_torch29ToDimensionOneFloatTensor_oldEf.cold_ZN10onnx_torch24ToDimensionOneTensor_oldEi.cold_ZN10onnx_torch29ToDimensionOneInt64Tensor_oldEl.cold_ZN10onnx_torch29ToDimensionOneInt64Tensor_oldESt6vectorIlSaIlEE.cold_ZN10onnx_torch44BuildContextDependentFunctionBodySCE_opset12ERKNS_24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoE.cold_ZN10onnx_torch41BuildContextDependentFunctionBody_opset12ERKNS_24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoE.cold_ZTSZZN10onnx_torch24MathDocGenerator_opset13EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE__ZTSZN10onnx_torch24MathDocGenerator_opset13EPKcEUlRNS_8OpSchemaEE__ZTSZZN10onnx_torch24MathDocGenerator_opset_7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE__ZTSZN10onnx_torch24MathDocGenerator_opset_7EPKcEUlRNS_8OpSchemaEE__ZTSZZN10onnx_torch34SoftmaxFamilyDocGenerator_opset_11EPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE__ZTSZN10onnx_torch34SoftmaxFamilyDocGenerator_opset_11EPKcS1_EUlRNS_8OpSchemaEE__ZTSZN10onnx_torch11GetOpSchemaINS_14Mod_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_14Pow_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_14Pow_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZZN10onnx_torch37ElementwiseMultiOpDocGenerator_opset8EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE__ZTSZN10onnx_torch37ElementwiseMultiOpDocGenerator_opset8EPKcEUlRNS_8OpSchemaEE__ZTSZN10onnx_torch11GetOpSchemaINS_15Gemm_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_16MatMul_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_16Expand_Onnx_ver8EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch32SoftmaxFamilyDocGenerator_opset1EPKcS1_EUlRNS_8OpSchemaEE__ZTSZN10onnx_torch20MathDocGenerator_oldEPKcEUlRNS_8OpSchemaEE__ZTSZN10onnx_torch27MathDocGenerator_old_opset6EPKcEUlRNS_8OpSchemaEE__ZTSZN10onnx_torch11GetOpSchemaINS_13Pow_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_14Gemm_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_14Gemm_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_14Gemm_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_16MatMul_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_14TopK_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_15TopK_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZZN10onnx_torch34ElementwiseMultiOpDocGenerator_oldEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE__ZTSZN10onnx_torch34ElementwiseMultiOpDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_.LC1.LC3.LC6.LC7.LC8.LC9.LC10.LC11.LC12.LC13.LC14.LC15.LC16.LC17.LC18.LC19.LC20.LC21.LC5.LC4.LC23.LC22.LC25.LC26.LC27.LC28.LC29.LC30.LC31.LC33.LC34.LC38.LC43.LC44.LC45.LC46.LC47.LC48.LC49.LC50.LC39.LC41.LC42.LC40.LC81.LC82.LC83.LC84.LC86.LC87.LC88.LC89.LC55.LC56.LC59.LC60.LC85.LC100.LC99.LC119.LC120.LC121.LC131.LC132.LC0.LC133.LC135.LC136.LC138.LC139.LC141.LC142.LC148.LC149.LC150.LC152.LC153.LC154.LC156.LC157.LC158.LC160.LC161.LC162.LC163.LC164.LC165.LC166.LC174.LC175.LC176.LC177.LC178.LC179.LC180.LC181.LC182.LC168.LC169.LC170.LC171.LC57.LC58.LC172.LC173.LC184.LC186.LC185.LC189.LC190.LC191.LC192.LC193.LC194.LC195.LC196.LC188.LC199.LC200.LC201.LC202.LC203.LC204.LC205.LC206.LC207.LC198.LC210.LC211.LC212.LC213.LC214.LC215.LC216.LC217.LC218.LC209.LC221.LC222.LC223.LC224.LC225.LC226.LC227.LC228.LC229.LC230.LC231.LC232.LC220.LC235.LC236.LC237.LC238.LC239.LC240.LC234.LC245.LC246.LC247.LC248.LC243.LC244.LC252.LC250.LC251.LC256.LC257.LC258.LC254.LC255.LC260.LC261.LC262.LC263.LC264.LC265.LC53.LC54.LC266.LC267.LC271.LC272.LC273.LC274.LC275.LC276.LC277.LC278.LC279.LC280.LC270.LC282.LC284.LC283.LC286.LC287.LC289.LC290.LC292.LC294.LC295.LC296.LC297.LC298.LC299.LC301.LC303.LC302.LC304.LC305.LC306.LC307.LC308.LC309.LC310.LC311.LC312.LC313.LC314.LC315.LC316.LC317.LC319.LC320.LC321.LC323.LC324.LC325.LC326.LC327.LC328.LC329.LC322.LC331.LC332.LC333.LC339.LC338.LC334.LC335.LC336.LC337.LC341.LC342.LC343.LC345.LC346.LC347.LC349.LC350.LC351.LC352.LC353.LC354.LC355.LC356.LC357.LC358.LC359.LC360.LC361.LC362.LC3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This is equivalent to {op_type} with kernel size equal to the spatial dimension of input tensor.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.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.Constrain input and output types to float tensors.p value of the Lp norm used to pool over the input data. 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).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.RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...].RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]). {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.The size of the kernel along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. The convolution operator consumes an input tensor and {filter_desc}, and computes the output.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 ...]. Assuming zero based indices for the shape array, X.shape[1] == (W.shape[1] * group) == C and W.shape[0] mod G == 0. Or in other words FILTER_IN_CHANNEL multiplied by the number of groups should be equal to DATA_CHANNEL and the number of feature maps M should be a multiple of the number of groups G.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.The shape of the convolution kernel. If not present, should be inferred from input W.dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.number of groups input channels and output channels are divided into. 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)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 shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate padsAdditional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.((kernel_spatial_shape[i] - 1) * dilations[i] + 1)Constrain input and output types to float and 8 bit tensors. {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} Whether to use ceil or floor (default) to compute the output shape.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is usedInput tensor must have atleast 2 dimensionsAttribute dilations has incorrect sizeAttribute strides has incorrect sizeAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedAttribute pads has incorrect sizeSecond input tensor has wrong dimensioninputs are expected to have tensor type and output type should not be null.Input shape must have either [C] or [1,C] dimensions where C > 0ngram_indexes must be non-empty with no negative valuesInput tensor must have rank 1 or 2MaxUnpool op must have either two or three inputs.Input tensor X must have atleast 2 dimensions.Attribute pads has incorrect size.Attribute strides has incorrect size.Attribute kernel_shape has incorrect size.Attribute kernel_shape must be specified.'output_shape' must be rank 1 tensor.'output_shape' must have same number of elements as the shape of input tensor X.Input tensor must have at least 2 dimensionsRoIs tensor must have 2 dimensionsAttribute pooled_shape has incorrect lengthAttribute pooled_shape must be specifiedRatio of Dropout must be a scalar.training_mode of Dropout must be a scalar. expected to have tensor or sparse tensor type: inputs are expected to have tensor type.input and zero_point pair is expected to have be same type.weight and zero_point pair is expected to have same type.The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero).Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad./opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/defs/nn/defs.ccThe output of each pooling window is maximum number of elements exclude pad. The storage order of the tensor. 0 is row major, and 1 is column major.Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).Constrain index tensor to int64 MaxUnpool essentially computes the partial inverse of the MaxPool op. The input information to this op is typically the the output information from a MaxPool op. The first input tensor X is the tensor that needs to be unpooled, which is typically the pooled tensor (first output) from MaxPool. The second input tensor, I, contains the indices to the (locally maximal) elements corrsponding to the elements in the first input tensor X. Input tensor I is typically the second output of the MaxPool op. The third (optional) input is a tensor that specifies the output size of the unpooling operation. MaxUnpool is intended to do 'partial' inverse of the MaxPool op. 'Partial' because all the non-maximal values from the original input to MaxPool are set to zero in the output of the MaxUnpool op. Pooling the result of an unpooling operation should give back the original input to the unpooling op. MaxUnpool can produce the same output size for several input sizes, which makes unpooling op ambiguous. The third input argument, output_size, is meant to disambiguate the op and produce output tensor of known/predictable size. In addition to the inputs, MaxUnpool takes three attributes, namely kernel_shape, strides, and pads, which define the exact unpooling op. The attributes typically have the same values as the corrsponding pooling op that the unpooling op is trying to invert. Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored.Output data tensor that contains the result of the unpooling. The convolution operator consumes a quantized input tensor, its scale and zero point, a quantized filter, its scale and zero point, and output's scale and zero point, and computes the quantized output. Each scale and zero-point pair must have same shape. It means they must be either scalars (per tensor) or 1-D tensors (per output channel). Each input or output and its related zero point must have same type. When bias is present it must be quantized using scale = input scale * weight scale and zero point as 0. 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.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. Scale 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).Zero point tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.Optional 1D bias to be added to the convolution, has size of M. Bias must be quantized using scale = x_scale * w_scale and zero_point = 0Constrain input type to 8-bit integer tensor.Constrain filter type to 8-bit integer tensor.Constrain output type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.The shape of the convolution kernel. If not present, should be inferred from input 'w'.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.number of groups input channels and output channels are divided into. default is 1. The integer convolution operator consumes an input tensor, its zero-point, a filter, and its zero-point, and computes the output. The production MUST never overflow. The accumulation may overflow if and only if in 32 bits. 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.Zero point 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.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain output y data type to 32-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.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. 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 five required inputs 'X', 'scale', 'B', 'input_mean' and 'input_var'. Note that 'input_mean' and 'input_var' are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below: Output case #1: Y, running_mean, running_var (training_mode=True) Output case #2: Y (training_mode=False) When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True: ``` running_mean = input_mean * momentum + current_mean * (1 - momentum) running_var = input_var * momentum + current_var * (1 - momentum) Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B where: current_mean = ReduceMean(X, axis=all_except_channel_index) current_var = ReduceVar(X, axis=all_except_channel_index) Notice that ReduceVar refers to the population variance, and it equals to sum(sqrd(x_i - x_avg)) / N where N is the population size (this formula does not use sample size N - 1). ``` The computation of ReduceMean and ReduceVar uses float to avoid overflow for float16 inputs. When training_mode=False: ``` Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B ``` 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. The epsilon value to use to avoid division by zero.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).If set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1running (training) or estimated (testing) mean tensor of shape (C).running (training) or estimated (testing) variance tensor of shape (C).The output tensor of the same shape as XThe running mean after the BatchNormalization operator.The running variance after the BatchNormalization operator. This op uses the population size (N) for calculating variance, and not the sample size N-1.Constrain scale and bias types to float tensors.Constrain mean and variance types to float tensors. Carries out instance normalization as described in the paper https://arxiv.org/abs/1607.08022. y = scale * (x - mean) / sqrt(variance + epsilon) + B, where mean and variance are computed per instance per channel. The input 1-dimensional scale tensor of size C.The input 1-dimensional bias tensor of size C.The output tensor of the same shape as input. Given a matrix, apply Lp-normalization along the provided axis. The axis on which to apply normalization, -1 mean last axis.The order of the normalization, only 1 or 2 are supported. Dropout takes an input floating-point tensor, an optional input ratio (floating-point scalar) and an optional input training_mode (boolean scalar). It produces two tensor outputs, output (floating-point tensor) and mask (optional `Tensor`). If `training_mode` is true then the output Y will be a random dropout; Note that this Dropout scales the masked input data by the following equation, so to convert the trained model into inference mode, the user can simply not pass `training_mode` input or set it to false. ``` output = scale * data * mask, ``` where ``` scale = 1. / (1. - ratio). ``` (Optional) Seed to the random generator, if not specified we will auto generate one.The ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of the scaled input, which is typically the case during training. It is an optional value, if not specified it will default to 0.5.If set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones.Constrain input 'ratio' types to float tensors.Constrain output 'mask' types to boolean tensors. Shrink takes one input data (Tensor) and produces one Tensor output, having same datatype and shape with input. It has two attributes, lambd and bias. The formula of this operator is: If x < -lambd, y = x + bias; If x > lambd, y = x - bias; Otherwise, y = 0. The lambd value for the Shrink formulation. Default is 0.5.The bias value added to output. Default is 0.Constrains input to only numeric types. Flattens the input tensor into a 2D matrix. If input tensor has shape (d_0, d_1, ... d_n) then the output will have shape (d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn). A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.Constrain input and output to all tensor types.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). The number of channels to sum overOutput tensor, which has the shape and type as input tensorConstrain input and output types to float tensors. Local Response Normalization proposed in the [AlexNet paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf). It normalizes over local input regions. The local region is defined across the channels. For an element X[n, c, d1, ..., dk] in a tensor of shape (N x C x D1 x D2, ..., Dk), its region is {X[n, i, d1, ..., dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))}. square_sum[n, c, d1, ..., dk] = sum(X[n, i, d1, ..., dk] ^ 2), where max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2)). Y[n, c, d1, ..., dk] = X[n, c, d1, ..., dk] / (bias + alpha / size * square_sum[n, c, d1, ..., dk] ) ^ beta Input is ether string UTF-8 or int32/int64Maximum 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.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.List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.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.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF) This transform extracts n-grams from the input sequence and save them as a vector. Input can be either a 1-D or 2-D tensor. For 1-D input, output is the n-gram representation of that input. For 2-D input, the output is also a 2-D tensor whose i-th row is the n-gram representation of the i-th input row. More specifically, if input shape is [C], the corresponding output shape would be [max(ngram_indexes) + 1]. If input shape is [N, C], this operator produces a [N, max(ngram_indexes) + 1]-tensor. In contrast to standard n-gram extraction, here, the indexes of extracting an n-gram from the original sequence are not necessarily consecutive numbers. The discontinuity between indexes are controlled by the number of skips. If the number of skips is 2, we should skip two tokens when scanning through the original sequence. Let's consider an example. Assume that input sequence is [94, 17, 36, 12, 28] and the number of skips is 2. The associated 2-grams are [94, 12] and [17, 28] respectively indexed by [0, 3] and [1, 4]. If the number of skips becomes 0, the 2-grams generated are [94, 17], [17, 36], [36, 12], [12, 28] indexed by [0, 1], [1, 2], [2, 3], [3, 4], respectively. The output vector (denoted by Y) stores the count of each n-gram; Y[ngram_indexes[i]] indicates the times that the i-th n-gram is found. The attribute ngram_indexes is used to determine the mapping between index i and the corresponding n-gram's output coordinate. If pool_int64s is [94, 17, 17, 36], ngram_indexes is [1, 0], ngram_counts=[0, 0], then the Y[0] (first element in Y) and Y[1] (second element in Y) are the counts of [17, 36] and [94, 17], respectively. An n-gram which cannot be found in pool_strings/pool_int64s should be ignored and has no effect on the output. Note that we may consider all skips up to S when generating the n-grams. The examples used above are true if mode is "TF". If mode is "IDF", all the counts larger than 1 would be truncated to 1 and the i-th element in weights would be used to scale (by multiplication) the count of the i-th n-gram in pool. If mode is "TFIDF", this operator first computes the counts of all n-grams and then scale them by the associated values in the weights attribute. Only one of pool_strings and pool_int64s can be set. If pool_int64s is set, the input should be an integer tensor. If pool_strings is set, the input must be a string tensor. string enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"Boolean. Whether the identification of stop words in X is case-sensitive. Default is falseList of stop words. If not set, no word would be removed from X.Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation. StringNormalization performs string operations for basic cleaning. This operator has only one input (denoted by X) and only one output (denoted by Y). This operator first examines the elements in the X, and removes elements specified in "stopwords" attribute. After removing stop words, the intermediate result can be further lowercased, uppercased, or just returned depending the "case_change_action" attribute. This operator only accepts [C]- and [1, C]-tensor. If all elements in X are dropped, the output will be the empty value of string tensor with shape [1] if input shape is [C] and shape [1, 1] if input shape is [1, C]. A MeanVarianceNormalization Function: Perform mean variance normalization on the input tensor X using formula:
``` (X-EX)/sqrt(E(X-EX)^2) ``` A 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.Constrain input and output types to all numeric tensors. { Exponent = Constant () Epsilon = Constant () X_RM = ReduceMean (X) EX_squared = Pow (X_RM, Exponent) X_squared = Pow (X, Exponent) E_Xsquared = ReduceMean (X_squared) Variance = Sub (E_Xsquared, EX_squared) STD = Sqrt (Variance) X_variance = Sub (X, X_RM) Processed_STD = Add (STD, Epsilon) Y = Div (X_variance, Processed_STD) } The pads attribute cannot be used simultaneously with auto_pad attributeThis number of op outputs should be 3 when Training_mode = True, but it is not.This number of op outputs should be 1 when Training_mode = False, but it is not.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.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.AUE1íATUH‰õSH‰ûHƒìHƒ?„³H‹mH…í„‘¿èHÇI‰ÄH‹E1ÒHKL‰cI‰D$H÷sH‹H‰ÐH‹mH…íuëTf„H‹mH…ítBI‰Ì¿èHÇH‰ÁH‹E1ÒH‰AH÷sH‹I‰$HÐHƒ8uÁL‰ H‹mH…íu¾HƒÄ[]A\A]Ãf.„H‹WHƒút1H‰ÐHÁè= axis.tensor(string)tensor(complex64)tensor(complex128)FlattensizeScaling parameter.alphaThe exponent.betaLRNInput for n-gram extractionNgram results1-D tensor of floatsmax_gram_lengthmin_gram_lengthmax_skip_countpool_stringspool_int64sngram_countsweightsmodeTfIdfVectorizerUTF-8 strings to normalizeUTF-8 Normalized stringsNONEcase_change_actionis_case_sensitivestopwordslocaleStringNormalizerInput tensorOutput tensorMeanVarianceNormalizationInvalid value() for attribute 'axis'H‹EÐH…Àt ºL‰æL‰çÿÐH‹} L9ïtèH‰ßèH‹EÐH…Àt ºL‰æL‰çÿÐH‹} L9ïtèH‰ßèH‹EÐH…Àt ºL‰æL‰çÿÐH‹} H9ßtèL‰÷èH‹}ÀL9ïtèH‹} H9ßtèL‰÷èH‹EÐH…ÀtáºL‰æL‰çÿÐëÒH‹}ÀL9ïtèH‹} H9ßtèL‰÷èH‹EÐH…ÀtáºL‰æL‰çÿÐëÒH‹}ÀL9ïtèH‹} H9ßtèL‰÷èH‹EÐH…ÀtáºL‰æL‰çÿÐëÒH‰ÅèI‹<$H…ÿtèIƒî I‹>IFH9ÇtèM9îuæH‰ïèèL9ýt}I‹?IGH9ÇtèIƒÇ 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H‹¼$ÀHƒÀ)l$pH‰D$`H‹HƒÀhH‰„$àH;|$0tèH‹H‹|$HHƒÀH‰D$xèH‹D$H‹T$L‰÷H‹t$H‹@èH‰T`I‹EèL‰l$pH‹$H‰tpH‹EèH‰l$`H‰T`H‹HÇD$hHƒÀH‰„$àèHÄøL‰à[]A\A]A^A_ÃfDI‰Àéÿÿÿ„H´$ÀL‰çèéÿÿÿH‰ÅëH‰ÅëI‰Äë*I‰ÄëcI‰Äë;I‹<$H9ûtèH‹|$XèH‰ïèH‹EèH‹4$H‰l$`H‰t`HÇD$hH‹L‰÷HƒÀH‰„$àèL‰çèH‰ßèH‹D$H‹t$H‹@èH‰t`I‹EèH‹t$L‰l$pH‰tpë’Attribute expected to have tensor or sparse tensor type[TypeInferenceError] ATUHƒì(H‹ÿP‹P(ƒútƒúu%H‹@ H‹@H…Àt HƒÄ(]A\ÃH‹HƒÄ(]A\Ã¿0I‰äèHH5L‰çH‰ÅèL‰æH‰ïèH‹H‹<$HÇEÆE HƒÀH‰EHE H‰EHD$H9ÇtèH‹5HH‰ïèI‰ÄëH‹<$HD$H9ÇtèH‰ïèL‰çèI‰ÄëØAWAVM‰ÆAUATI‰üUH‰ÕSH‰óHì¸H‹L|$ H‰L$Ll$0HP@H‹L‰ÿfHnÂHƒÀfHnÈflÁ)$èH‰ßèH‰ÞL‰ïH‰ÂèH‰ïèH‰îL‰ïH‰ÂèH‹D$L‰ïH‹0èL‰÷èL‰öL‰ïH‰ÂèH‹D$`I\$IÇD$I‰$AÆD$H…À„L‹D$PM…À„L9À‡ýH‹L$X1Ò1öL‰çI)ÈèH‹fo$H‹¼$€HƒÀ)T$0H‰D$ H‹HƒÀhH‰„$ H„$H9ÇtèH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èHÄ¸L‰à[]A\A]A^A_ÃfDI‰ÀéûþÿÿH´$€L‰çèéúþÿÿH‰ÅëH‰ÅëI‹<$H9ûtèL‰ÿèH‰ïèInput type was nullInput Element type of input unknownOutput expected to have tensor or sparse tensor type. Got: AWAVAUI‰ÕATI‰ôUH‰ýSHì¸H‹ÿPH…À„6D‹p(Aƒþt Aƒþ…›H‹@ ‹X …Û„…H‹EH‰ïL‰îÿP(D‹`(H‰ÅAƒüt2AƒüttE…ä…ƒAƒþtAƒþt/HÄ¸[]A\A]A^A_ÃDH‰ïèƒH‰X ëØ€H‰ÇèH‹EÇE(H‰ÇHƒçþ¨u%èH‰E ƒH‰X ë¡Aƒüt²H‹@ ƒH‰X ëŽH‹?ëÖ¿0L|$ èL‰ÿI‰ÆèHl$0ºH5H‰ïèºH5H‰ïèL‰îH‰ïèº5H5H‰ïèD‰æH‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰÷èH‹H‹<$AÆF IÇFHƒÀI‰IF I‰FHD$H9ÇtèH‹5HL‰÷è¿0L|$ èL‰ÿI‰ÅèHl$0ºH5H‰ïèºH5H‰ïèL‰æH‰ïèº5H5H‰ïèD‰öH‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰ïèH‹AÆE IÇEHƒÀI‰EIE I‰EH‹<$HD$H9ÇtèH‹5HL‰ïèH‰ÅéàH‰Åé¹H‰Åé™¿0L|$ èL‰ÿI‰ÅèHl$0ºH5H‰ïèºH5H‰ïèL‰æH‰ïèºH5H‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰ïèé ÿÿÿ¿0L|$ èL‰ÿI‰ÄèHl$0ºH5H‰ïèºH5H‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰çèH‹H‹<$AÆD$ IÇD$HƒÀI‰$ID$ I‰D$HD$H9ÇtèH‹5HL‰çèéšþÿÿH‰Åë H‰ÅëH‰ÅëH‹<$HD$H9ÇtèëL‰ÿèL‰çèH‰ïèéaþÿÿH‰Åë%H‰ÅëL‰ÿèL‰÷èH‰ïèL‰ÿèL‰ïèH‰ïèëÄH‰ÅëH‹<$HD$H9ÇtÛèëÔH‹<$HD$H9Çt®èë§AWAVM‰ÆAUATI‰üUH‰ÕSH‰óHì¸H‹L|$ H‰L$Ll$0HP@H‹L‰ÿfHnÂHƒÀfHnÈflÁ)$èH‰ßèH‰ÞL‰ïH‰ÂèH‰ïèH‰îL‰ïH‰ÂèH‹D$L‰ïH‹0èL‰÷èL‰öL‰ïH‰ÂèH‹D$`I\$IÇD$I‰$AÆD$H…À„L‹D$PM…À„L9À‡ýH‹L$X1Ò1öL‰çI)ÈèH‹fo$H‹¼$€HƒÀ)T$0H‰D$ H‹HƒÀhH‰„$ H„$H9ÇtèH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èHÄ¸L‰à[]A\A]A^A_ÃfDI‰ÀéûþÿÿH´$€L‰çèéúþÿÿH‰ÅëH‰ÅëI‹<$H9ûtèL‰ÿèH‰ïèAWAVM‰ÆAUATI‰üUH‰ÕSH‰óHì¸H‹L|$ H‰L$Ll$0HP@H‹L‰ÿfHnÂHƒÀfHnÈflÁ)$èH‰ßèH‰ÞL‰ïH‰ÂèH‰ïèH‰îL‰ïH‰ÂèH‹D$L‰ïH‹0èL‰÷èL‰öL‰ïH‰ÂèH‹D$`I\$IÇD$I‰$AÆD$H…À„L‹D$PM…À„L9À‡ýH‹L$X1Ò1öL‰çI)ÈèH‹fo$H‹¼$€HƒÀ)T$0H‰D$ H‹HƒÀhH‰„$ H„$H9ÇtèH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èHÄ¸L‰à[]A\A]A^A_ÃfDI‰ÀéûþÿÿH´$€L‰çèéúþÿÿH‰ÅëH‰ÅëI‹<$H9ûtèL‰ÿèH‰ïè[ShapeInferenceError] Target=Mismatch between source and target type. Source=Unsupported Source/Target type=AWAVAUATUSHì¸D‹g(D‹n(E9ìu{H‰ûH‰õAƒü„:Aƒü„ØAƒü„ŽAƒü …®Aƒý …*L‹e I‹l$AƒL$H…í„Tƒ{( „xH‹H‹XH…Û„uD‹c(D‹m(E9ìt‹¿0Lt$ èL‰÷I‰ÇèHl$0ºH5H‰ïèº0H5H‰ïèD‰æH‰ïèºH5H‰ïèD‰îH‰ïèH‰åHt$8H‰ïèL‰÷èH‰îL‰ÿèH‹H‹<$AÆG IÇGHƒÀI‰IG I‰GHD$H9ÇtèH‹5HL‰ÿè€H‰ïèH‹EÇE( H‰ÇHƒçþ¨…öèI‰ÄH‰E I‹l$AƒL$H…í…¬þÿÿI‹D$H‰ÇHƒçþ¨…èƒ{( I‰D$H‰Å…‹þÿÿH‹C H‹XH…Û…‹þÿÿH‹éþÿÿAƒütRH‹C ö@„”L‹`M…ä„WAƒý…H‹] H‹{ƒKH…ÿuOH‰ßèH‰ÇèH‰CH‰Çë6H‹C ö@tFL‹`M…ä„ùH‰ïèH‹xƒHH‰ÃH…ÿ„/HÄ¸L‰æ[]A\A]A^A_é@HÄ¸[]A\A]A^A_ÃfDAƒýu2L‹e I‹l$AƒL$H…í„²ƒ{(„øþÿÿH‹é{ýÿÿ@H‰ïèH‹EÇE(H‰ÇHƒçþ¨…˜èH‰E I‰Äë¢„H‰ïèH‹EÇE(H‰ÇHƒçþ¨…‹èH‰E H‰ÃéÌþÿÿDL‹%éûþÿÿ@L‹%éþÿÿ@H‹?éþÿÿI‹D$H‰ÇHƒçþ¨uEèI‰D$H‰Åé,ÿÿÿH‹?é`ÿÿÿH‹@H‰ÇHƒçþ¨„zþÿÿH‹?érþÿÿH‹?ééýÿÿH‹?émÿÿÿH‹?ë¶¿0Lt$ èL‰÷I‰ÅèHl$0ºH5H‰ïèºH5H‰ïèD‰æH‰ïèH‰åHt$8H‰ïèL‰÷èH‰îL‰ïèH‹H‹<$AÆE IÇEHƒÀI‰EIE I‰EHD$H9ÇtèH‹5HL‰ïèH‰Åë H‰ÅëH‰ÅëH‹<$HD$H9ÇtèëL‰÷èL‰ïèH‰ïèH‰Åë H‰ÅëH‰ÅëH‹<$HD$H9ÇtèëL‰÷èL‰ÿèH‰ïèAWAVM‰ÆAUI‰ÕATI‰üUSH‰óHì¸H‹L|$ H‰L$Hl$0HP@H‹L‰ÿL‰L$fHnÂHƒÀfHnÈflÁ)$èH‰ßèH‰ÞH‰ïH‰ÂèL‰ïèL‰îH‰ïH‰ÂèH‹D$H‰ïH‹0èL‰÷èL‰öH‰ïH‰ÂèH‹D$H‰ïH‹0èH‹D$`I\$IÇD$I‰$AÆD$H…À„'L‹D$PM…À„ L9À‡H‹L$X1Ò1öL‰çI)ÈèH‹fo$H‹¼$€HƒÀ)T$0H‰D$ H‹HƒÀhH‰„$ H„$H9ÇtèH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èHÄ¸L‰à[]A\A]A^A_Ãf„I‰Àéøþÿÿ„H´$€L‰çèéïþÿÿH‰ÅëH‰ÅëI‹<$H9ûtèL‰ÿèH‰ïè and Dimension mismatch in unification between ATUHƒì8H‰|$H‰4$H9÷uHƒÄ8]A\Ã¿0Ld$èHL$I‰áL‰çLHH‰ÅH5èL‰æH‰ïèH‹H‹|$HÇEÆE HƒÀH‰EHE H‰EHD$ H9ÇtèH‹5HH‰ïèI‰ÄëH‹|$HD$ H9ÇtèH‰ïèL‰çèI‰Äë× expected to have tensor or sparse type expected to have tensor typeAWAVAUATI‰ôUS‰ÓHìÈL‹-H‹IU@HƒÀfHnÈfHnÂH‹flÁ)$ÿP(H…À„ƒH‰Å‹@(ƒø„Œƒø„ƒ…À…ƒût1ÀƒûtHÄÈ[]A\A]A^A_ÃDH‰ïèH‹EÇE(H‰ÇHƒçþ¨ubèH‰E H‰ÃƒHH‹@H…Àu²H‹CH‰ÇHƒçþ¨u?èH‰Cë˜€H‰ïèƒHH‰ÃH‹@H…À…uÿÿÿëÁf.„H‹?ë™H‹?ë¼¿0Lt$0èL‰÷I‰ÇèHl$@ºH5H‰ïèºH5H‰ïèL‰æH‰ïèºH5H‰ïèH‹D$pLd$ ÆD$ Hl$L‰d$HÇD$H…À…™H´$H‰ïèIEfo$H‹¼$H‰D$0IEhH‰„$°H„$ )\$@H9Ç…ƒH‹H¼$€HƒÀH‰D$HèH‹H¼$°H‹PH‹H0H‹RèH‰L0H‹P H‹H(H‰T$@H‹RèH‰L@H‹PH‹@H‰T$0H‹RèH‰D0H‹HÇD$8HƒÀH‰„$°èH‰îL‰ÿèH‹AÆG IÇGHƒÀI‰IG I‰GH‹|$L9çtèH‹5HL‰ÿè¿0Lt$0èL‰÷I‰ÇèHl$@ºH5H‰ïèºH5H‰ïèL‰æH‰ïèº'H5H‰ïèH‹D$pLd$ ÆD$ Hl$L‰d$HÇD$H…À…H´$H‰ïèIEfo$H‹¼$H‰D$0IEhH‰„$°H„$ )T$@H9ÇtèH‹H¼$€HƒÀH‰D$HèH‹H¼$°H‹PH‹H0H‹RèH‰L0H‹P H‹H(H‰T$@H‹RèH‰L@H‹PH‹@H‰T$0H‹RèH‰D0H‹HÇD$8HƒÀH‰„$°èH‰îL‰ÿèéDþÿÿL‹D$`M…ÀuI‰ÀH‹L$h1Ò1öH‰ïI)ÈèéQýÿÿL9ÀwßëàH‰ÅëwèésýÿÿL‹D$`M…ÀuI‰ÀH‹L$h1Ò1öH‰ïI)ÈèéËþÿÿL9ÀwßëàëÂH‰Åë H‰ÅëH‰ÅëH‹|$L9çuL‰÷èL‰ÿèH‰ïèèëáëËëÎëÂH‹|$L9çtÙèëÒAWAVM‰ÆAUI‰ÕATI‰üUSH‰óHì¸H‹L|$ H‰L$Hl$0HP@H‹L‰ÿL‰L$fHnÂHƒÀfHnÈflÁ)$èH‰ßèH‰ÞH‰ïH‰ÂèL‰ïèL‰îH‰ïH‰ÂèH‹D$H‰ïH‹0èL‰÷èL‰öH‰ïH‰ÂèH‹D$H‰ï‹0èH‹¼$ðèH‹´$ðH‰ïH‰ÂèH‹„$øH‰ï‹0èH‹D$`I\$IÇD$I‰$AÆD$H…À„&L‹D$PM…À„L9À‡ÿH‹L$X1Ò1öL‰çI)ÈèH‹fo$H‹¼$€HƒÀ)T$0H‰D$ H‹HƒÀhH‰„$ H„$H9ÇtèH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èHÄ¸L‰à[]A\A]A^A_Ã„I‰Àéùþÿÿ„H´$€L‰çèéðþÿÿH‰ÅëH‰ÅëI‹<$H9ûtèL‰ÿèH‰ïè expected to have rank but has rank UH‰åATI‰ôSH‰ûHƒì@H‹H‰u¸‰U´ÿPI9Äƒ†H‹L‰æH‰ßÿPH…ÀtuH‹H‰ßL‰æÿP‹P(ƒúuë:f‹P(ƒút0ƒút+ƒútƒú 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H9ÇtèH‰ï]é…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃfH‹H‰ëÖbasic_string::_M_construct null not valid 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)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)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, 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)Constrain input and output types to float tensors.The shape of the convolution kernel. If not present, should be inferred from input W.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.dilation value along each spatial axis of the filter.Stride along each spatial axis.number of groups input channels and output channels are divided into. {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.The size of the kernel along each axis.p value of the Lp norm used to pool over the input data.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. {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} Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used((kernel_spatial_shape[i] - 1) * dilations[i] + 1)Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial 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] - {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} Whether to use ceil or floor (default) to compute the output shape. The convolution operator consumes an input tensor and {filter_desc}, and computes the output.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. Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.Ratio of Dropout must be a scalar.training_mode of Dropout must be a scalar.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. Dropout takes an input floating-point tensor, an optional input ratio (floating-point scalar) and an optional input training_mode (boolean scalar). It produces two tensor outputs, output (floating-point tensor) and mask (optional `Tensor`). If `training_mode` is true then the output Y will be a random dropout; Note that this Dropout scales the masked input data by the following equation, so to convert the trained model into inference mode, the user can simply not pass `training_mode` input or set it to false. ``` output = scale * data * mask, ``` where ``` scale = 1. / (1. - ratio). ``` (Optional) Seed to the random generator, if not specified we will auto generate one.The ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of the scaled input, which is typically the case during training. It is an optional value, if not specified it will default to 0.5.If set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones.Constrain input 'ratio' types to float tensors.Constrain output 'mask' types to boolean tensors./opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/defs/nn/old.cc Flattens the input tensor into a 2D matrix. If input tensor has shape (d_0, d_1, ... d_n) then the output will have shape (d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn). A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.Constrain input and output to all tensor types.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). The number of channels to sum overOutput tensor, which has the shape and type as input tensorConstrain input and output types to float tensors. Local Response Normalization proposed in the [AlexNet paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf). It normalizes over local input regions. The local region is defined across the channels. For an element X[n, c, d1, ..., dk] in a tensor of shape (N x C x D1 x D2, ..., Dk), its region is {X[n, i, d1, ..., dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))}. square_sum[n, c, d1, ..., dk] = sum(X[n, i, d1, ..., dk] ^ 2), where max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2)). Y[n, c, d1, ..., dk] = X[n, c, d1, ..., dk] / (bias + alpha / size * square_sum[n, c, d1, ..., dk] ) ^ beta The output of each pooling window is divided by the number of elements exclude pad.The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero).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.The 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).Constrain index tensor to int64Dilation value along each spatial axis of filter.Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis. MaxUnpool essentially computes the partial inverse of the MaxPool op. The input information to this op is typically the the output information from a MaxPool op. The first input tensor X is the tensor that needs to be unpooled, which is typically the pooled tensor (first output) from MaxPool. The second input tensor, I, contains the indices to the (locally maximal) elements corrsponding to the elements in the first input tensor X. Input tensor I is typically the second output of the MaxPool op. The third (optional) input is a tensor that specifies the output size of the unpooling operation. MaxUnpool is intended to do 'partial' inverse of the MaxPool op. 'Partial' because all the non-maximal values from the original input to MaxPool are set to zero in the output of the MaxUnpool op. Pooling the result of an unpooling operation should give back the original input to the unpooling op. MaxUnpool can produce the same output size for several input sizes, which makes unpooling op ambiguous. The third input argument, output_size, is meant to disambiguate the op and produce output tensor of known/predictable size. In addition to the inputs, MaxUnpool takes three attributes, namely kernel_shape, strides, and pads, which define the exact unpooling op. The attributes typically have the same values as the corrsponding pooling op that the unpooling op is trying to invert. Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored.Output data tensor that contains the result of the unpooling. LpPool consumes an input tensor X and applies Lp pooling across the 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, default is 2.0.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimension are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. GlobalLpPool consumes an input tensor X and applies lp pool pooling across the the values in the same channel. This is equivalent to LpPool with kernel size equal to the spatial dimension of input tensor.Output data tensor from pooling across the input tensor. Dimensions will be N x C x 1 x 1 Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.If set to nonzero, run spatial batch normalization in test mode, default is 0.The epsilon value to use to avoid division by zero, default is 1e-5f.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.legacy optimization attribute.The input 4-dimensional tensor of shape NCHW.The scale as a 1-dimensional tensor of size C to be applied to the output.The bias as a 1-dimensional tensor of size C to be applied to the output.The running mean (training) or the estimated mean (testing) as a 1-dimensional tensor of size C.The running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C.The output 4-dimensional tensor of the same shape as X.The running mean after the BatchNormalization operator. Must be in-place with the input mean. Should not be used for testing.The running variance after the BatchNormalization operator. Must be in-place with the input var. Should not be used for testing.Saved mean used during training to speed up gradient computation. Should not be used for testing.Saved variance used during training to speed up gradient computation. Should not be used for testing. 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. The epsilon value to use to avoid division by zero.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1running (training) or estimated (testing) mean tensor of shape (C).running (training) or estimated (testing) variance tensor of shape (C).The output tensor of the same shape as XThe running mean after the BatchNormalization operator.The running variance after the BatchNormalization operator.Saved mean used during training to speed up gradient computation.Saved variance used during training to speed up gradient computation. 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 five required inputs 'X', 'scale', 'B', 'input_mean' and 'input_var'. Note that 'input_mean' and 'input_var' are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below: Output case #1: Y, running_mean, running_var (training_mode=True) Output case #2: Y (training_mode=False) When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True: ``` running_mean = input_mean * momentum + current_mean * (1 - momentum) running_var = input_var * momentum + current_var * (1 - momentum) Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B where: current_mean = ReduceMean(X, axis=all_except_channel_index) current_var = ReduceVar(X, axis=all_except_channel_index) Notice that ReduceVar refers to the population variance, and it equals to sum(sqrd(x_i - x_avg)) / N where N is the population size (this formula does not use sample size N - 1). ``` When training_mode=False: ``` Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B ``` 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. If set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.The running variance after the BatchNormalization operator. This op uses the population size (N) for calculating variance, and not the sample size N-1.Constrain mean and variance types to float tensors. It allows all float type for U. Carries out instance normalization as described in the paper https://arxiv.org/abs/1607.08022. y = scale * (x - mean) / sqrt(variance + epsilon) + B, where mean and variance are computed per instance per channel. The input 1-dimensional scale tensor of size C.The input 1-dimensional bias tensor of size C.The output 4-dimensional tensor of the same shape as input. Dropout takes one input data (Tensor) and produces two Tensor outputs, output (Tensor) and mask (Tensor). Depending on whether it is in test mode or not, the output Y will either be a random dropout, or a simple copy of the input. Note that our implementation of Dropout does scaling in the training phase, so during testing nothing needs to be done. (float, default 0.5) the ratio of random dropout(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.The output mask. If is_test is nonzero, this output is not filled. 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. Constrain output mask types to boolean tensors. 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.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). Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.If spatial is true, the dimension of scale is (C). If spatial is false, the dimensions of scale are (C x D1 x ... x Dn)If spatial is true, the dimension of bias is (C). If spatial is false, the dimensions of bias are (C x D1 x ... x Dn)If spatial is true, the dimension of the running mean (training) or the estimated mean (testing) is (C). If spatial is false, the dimensions of the running mean (training) or the estimated mean (testing) are (C x D1 x ... x Dn).If spatial is true, the dimension of the running variance(training) or the estimated variance (testing) is (C). If spatial is false, the dimensions of the running variance(training) or the estimated variance (testing) are (C x D1 x ... x Dn). A MeanVarianceNormalization Function: Perform mean variance normalization on the input tensor X using formula:
``` (X-EX)/sqrt(E(X-EX)^2) ``` A 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.Constrain input and output types to all numeric tensors.Input tensor must have atleast 2 dimensionsAttribute dilations has incorrect sizeAttribute strides has incorrect sizeAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedAttribute pads has incorrect sizeSecond input tensor has wrong dimensionMaxUnpool op must have either two or three inputs.Input tensor X must have atleast 2 dimensions.Attribute pads has incorrect size.Attribute strides has incorrect size.Attribute kernel_shape has incorrect size.Attribute kernel_shape must be specified.'output_shape' must be rank 1 tensor.'output_shape' must have same number of elements as the shape of input tensor X.This number of op outputs should be 3 when Training_mode = True, but it is not.This number of op outputs should be 1 when Training_mode = False, but it is not.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute.Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.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.tensor(float16)tensor(float){filter_desc}TXWBYtensor(double)kernel_shapeoutput_shapeoutput_paddingdilationsstridesNOTSETauto_padpadsgroup{name}p{opName}{additionalDescription}kernel_spatial_shape[i]{kernelSpatialShape}ceil_modeseedThe input data as Tensor.dataT1ratioT2training_modeThe output.outputThe output mask.maskDropoutA tensor of rank >= axis.inputaxisFlattensizeScaling parameter.alphaThe exponent.betabiasLRNaverageAveragePoolcount_include_padmaxMaxPoolstorage_orderIIndicesMaxUnpoolStride along each axis.LpPoola filterConvConvTransposeGlobalLpPoolspatialis_testepsilonmomentumconsumed_inputsscalemeanvarsaved_meansaved_varBatchNormalizationScale tensor of shape (C).Bias tensor of shape (C).Uinput_meaninput_varrunning_meanrunning_vartensor(bfloat16)InstanceNormalizationThe ratio of random dropout) for attribute 'axis'Invalid value(ConstantvalueInput tensorOutput 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H‡¸H‰D$H‡0H¿ØH‰‡(ÿÿÿHfHnàH‡àþÿÿflÌ‡ðþÿÿfHnè‡ÿÿÿ‡ÿÿÿfïÀOfHnÊ‡hÿÿÿflÍfïÀL‰¿ˆþÿÿHÇ‡þÿÿÆ‡˜þÿÿÇ‡°þÿÿHÇ‡¸þÿÿHÇ‡ÐþÿÿÆ‡ØþÿÿHÇ‡ ÿÿÿHÇ‡0ÿÿÿHÇ‡8ÿÿÿHÇ‡@ÿÿÿÇ‡Hÿÿÿ€?HÇ‡PÿÿÿHÇ‡XÿÿÿÇ‡`ÿÿÿÆ‡dÿÿÿÇ‡xÿÿÿÆ‡|ÿÿÿG€G O°GÀHÇGÐHÇGØHÇGðHÇGøGàèH‹|$ fïÀHÇƒHÇƒ˜ƒ€L9çtèH‹|$@H9ïtèHƒÄh[]A\A]A^A_ÃH‰D$H‹ƒÈH…ÀtH»¸ºH‰þÿÐH‹ƒ¨H…ÀtH»˜ºH‰þÿÐH‹ƒˆH…ÀtH»xºH‰þÿÐH‹ƒhH…ÀtH»XºH‰þÿÐH»èH»èèH»ÐèH‹|$èH‹»èH‹{`L9ÿtèH‹{@I9þtèH‹{ I9ýtèH‹;H9|$tèH‹|$ L9çtèH‹|$@H9ïtèH‹|$èÿÿÎ¡°Hƒt H‹GÃ@é expected to have type but instead is null expected to have sequence typeElement type of sequence input expected to have optional typeElement type of optional input Region of Interest (RoI) align operation described in the [Mask R-CNN paper](https://arxiv.org/abs/1703.06870). RoiAlign consumes an input tensor X and region of interests (rois) to apply pooling across each RoI; it produces a 4-D tensor of shape (num_rois, C, output_height, output_width). RoiAlign is proposed to avoid the misalignment by removing quantizations while converting from original image into feature map and from feature map into RoI feature; in each ROI bin, the value of the sampled locations are computed directly through bilinear interpolation. Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. default 1; Pooled output Y's height.default 1; Pooled output Y's width.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'.Allowed values are 'half_pixel' and 'output_half_pixel'. Use the value 'half_pixel' to pixel shift the input coordinates by -0.5 (the recommended behavior). Use the value 'output_half_pixel' to omit the pixel shift for the input (use this for a backward-compatible behavior).coordinate_transformation_modeInput 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 [[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.1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.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].Constrain types to float tensors.Constrain types to int tensors./opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/defs/object_detection/defs.ccAn input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.An input tensor with shape [num_batches, num_classes, spatial_dimension]Integer representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.Float representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.Float representing the threshold for deciding when to remove boxes based on score. It is a scalar.selected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].Integer indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models. Filter out boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes with score less than score_threshold are removed. Bounding box format is indicated by attribute center_point_box. Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The selected_indices output is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the Gather or GatherND operation. expected to have rank spatial_scaleoutput_heightoutput_widthsampling_ratiomodeT1XroisT2batch_indicesYtensor(float16)tensor(float)tensor(double)tensor(int64)RoiAlignboxesscoresmax_output_boxes_per_classiou_thresholdscore_thresholdselected_indicescenter_point_boxNonMaxSuppression¿0èH½@þÿÿHþÿÿH‰ÃH…àýÿÿL°ýÿÿH‰½0ýÿÿPHLPHH5èH‹µ0ýÿÿ_H‰ßAXèH‹H‹½@þÿÿHÇCÆC HƒÀH‰HC H‰CH…PþÿÿH9ÇtèH‹5HH‰ßè¿0èH½@þÿÿHH5H‰½0ýÿÿH‰ÃèH‹µ0ýÿÿH‰ßèH‹H‹½@þÿÿHÇCÆC HƒÀH‰HC H‰CH…PþÿÿH9ÇtèH‹5HH‰ßè¿0èH½@þÿÿHþÿÿH‰ÃH…àýÿÿL°ýÿÿH‰½0ýÿÿPHLPHH5èH‹µ0ýÿÿXH‰ßZèH‹H‹½@þÿÿHÇCÆC HƒÀH‰HC H‰CH…PþÿÿH9ÇtèH‹5HH‰ßèH‹½@þÿÿI‰ÄH…PþÿÿH9ÇtèH‰ßèL‰çèI‰ÄëëH‹½@þÿÿI‰ÄH…PþÿÿH9ÇtèH‰ßèL‰çèI‰Äëë¿0èH½@þÿÿHàýÿÿLHH‰ÃH‰½0ýÿÿH5èH‹µ0ýÿÿH‰ßèH‹H‹½@þÿÿHÇCÆC HƒÀH‰HC H‰CH…PþÿÿH9ÇtèH‹5HH‰ßèH‰ÃL‰÷èH‰ßèH‹½@þÿÿI‰ÄH…PþÿÿH9ÇtèH‰ßèL‰çèI‰Äëë¿0èH½@þÿÿHþÿÿH‰ÃH…àýÿÿL°ýÿÿH‰½0ýÿÿPHLPHH5èY^H‰ßH‹µ0ýÿÿèH‹H‹½@þÿÿHÇCÆC HƒÀH‰HC 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H‡¸H‰D$H‡0H¿ØH‰‡(ÿÿÿHfHnàH‡àþÿÿflÌ‡ðþÿÿfHnè‡ÿÿÿ‡ÿÿÿfïÀOfHnÊ‡hÿÿÿflÍfïÀL‰¿ˆþÿÿHÇ‡þÿÿÆ‡˜þÿÿÇ‡°þÿÿHÇ‡¸þÿÿHÇ‡ÐþÿÿÆ‡ØþÿÿHÇ‡ ÿÿÿHÇ‡0ÿÿÿHÇ‡8ÿÿÿHÇ‡@ÿÿÿÇ‡Hÿÿÿ€?HÇ‡PÿÿÿHÇ‡XÿÿÿÇ‡`ÿÿÿÆ‡dÿÿÿÇ‡xÿÿÿÆ‡|ÿÿÿG€G O°GÀHÇGÐHÇGØHÇGðHÇGøGàèH‹|$ fïÀHÇƒHÇƒ˜ƒ€L9çtèH‹|$@H9ïtèHƒÄh[]A\A]A^A_ÃH‰D$H‹ƒÈH…ÀtH»¸ºH‰þÿÐH‹ƒ¨H…ÀtH»˜ºH‰þÿÐH‹ƒˆH…ÀtH»xºH‰þÿÐH‹ƒhH…ÀtH»XºH‰þÿÐH»èH»èèH»ÐèH‹|$èH‹»èH‹{`L9ÿtèH‹{@I9þtèH‹{ I9ýtèH‹;H9|$tèH‹|$ L9çtèH‹|$@H9ïtèH‹|$èÿÿÎ¡°Hƒt H‹GÃ@é expected to have type but instead is null expected to have sequence typeElement type of sequence input expected to have optional typeElement type of optional input expected to have tensor or sparse type Region of Interest (RoI) align operation described in the [Mask R-CNN paper](https://arxiv.org/abs/1703.06870). RoiAlign consumes an input tensor X and region of interests (rois) to apply pooling across each RoI; it produces a 4-D tensor of shape (num_rois, C, output_height, output_width). RoiAlign is proposed to avoid the misalignment by removing quantizations while converting from original image into feature map and from feature map into RoI feature; in each ROI bin, the value of the sampled locations are computed directly through bilinear interpolation. Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. default 1; Pooled output Y's height.default 1; Pooled output Y's width.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'.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 [[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.1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.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].Constrain types to float tensors.Constrain types to int tensors./opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/defs/object_detection/old.ccAn input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.An input tensor with shape [num_batches, num_classes, spatial_dimension]Integer representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.Float representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.Float representing the threshold for deciding when to remove boxes based on score. It is a scalar.selected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].Integer indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models. Filter out boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes with score less than score_threshold are removed. Bounding box format is indicated by attribute center_point_box. Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The selected_indices output is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the Gather or GatherND operation. expected to have rank expected to have tensor typespatial_scaleoutput_heightoutput_widthsampling_ratiomodeT1XroisT2batch_indicesYtensor(float16)tensor(float)tensor(double)tensor(int64)RoiAlignboxesscoresmax_output_boxes_per_classiou_thresholdscore_thresholdselected_indicescenter_point_boxNonMaxSuppression¿0L¥ÿÿÿèHH5L‰çH‰ÃèL‰æH‰ßèH‹H‹½ÿÿÿHÇCÆC HƒÀH‰HC H‰CH… ÿÿÿH9ÇtèH‹5HH‰ßèH‹½ÿÿÿI‰ÄH… ÿÿÿH9ÇtèH‰ßèL‰çèI‰ÄëëHMÐH‰ËH‰…øýÿÿH‰ØHƒè0H‰ÃH‰ÇèL9ãH‹…øýÿÿuÛH‰Ãé#¿0L¥ÿÿÿèH°þÿÿL‰çLHH5H‰ÃèL‰æH‰ßèH‹H‹½ÿÿÿHÇCÆC HƒÀH‰HC H‰CH… ÿÿÿH9ÇtèH‹5HH‰ßè¿0L¥ÿÿÿèH°þÿÿL‰çLHH5I‰ÅèL‰æL‰ïèH‹H‹½ÿÿÿAÆE IÇEHƒÀI‰EIE I‰EH… ÿÿÿH9ÇtèH‹5HL‰ïèH‰ÃëH‹½ÿÿÿH‰ÃH… ÿÿÿH9ÇuL‰ïèL‰÷èH‰ßèH‰ÃëãèëÜH‹½ÿÿÿI‰ÄH… ÿÿÿH9ÇtèH‰ßèL‰çèI‰ÄëëH‰ÃL‰÷èH‰ßè¿0L¥ÿÿÿèH°þÿÿL‰çLHH5I‰ÅèL‰æL‰ïèH‹H‹½ÿÿÿAÆE IÇEHƒÀI‰EIE I‰EH… ÿÿÿH9ÇtèH‹5HL‰ïèH‹½ÿÿÿH‰ÃH… ÿÿÿH9ÇtèL‰ïèé3ÿÿÿH‰Ãëî¿0L¥ÿÿÿèH°þÿÿL‰çLHH5H‰ÃèL‰æH‰ßèH‹H‹½ÿÿÿHÇCÆC HƒÀH‰HC H‰CH… 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certain type specified by the attribute, or a non-empty value containing the input eConstrains input type to all tensor and sequencetensor(complex64tensor(complex12seq(tensor(uint8seq(tensor(uint1seq(tensor(uint3seq(tensor(uint6seq(tensor(int8)seq(tensor(int16seq(tensor(int32seq(tensor(int64seq(tensor(floatseq(tensor(doublseq(tensor(strinseq(tensor(bool)seq(tensor(complConstrains output type to all optional tensor or optional sequen Returns true if the optional-type input contains an element. If it is an empty optional-type, this op returns f type to optional tensor and optional sequence t Outputs the element in the optional-type input. 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Outputs Scale, ZeroPoint and Quantized Input for a given FP32 Input. Scale is calculated as: ``` y_scale = (max(x) - min(x))/(qmax - qmin) * where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8 * data range is adjusted to include 0. ``` Zero point is calculated as: ``` intermediate_zero_point = qmin - min(x)/y_scale y_zero_point = cast(round(saturate(itermediate_zero_point))) * where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8 * for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported. * rounding to nearest ties to even. ``` Data quantization formula is: ``` y = saturate (round (x / y_scale) + y_zero_point) * for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported. * rounding to nearest ties to even. ``` Output scale. It's a scalar, which means a per-tensor/layer quantization.Output zero point. It's a scalar, which means a per-tensor/layer quantization.Constrain 'x' to float tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor. { Q_Min = Constant() Q_Max = Constant() X_Min = ReduceMin (x) X_Min_Adjusted = Min (X_Min, Q_Min) X_Max = ReduceMax (x) X_Max_Adjusted = Max (X_Max, Q_Min) X_Range = Sub (X_Max_Adjusted, X_Min_Adjusted) Scale = Div (X_Range, Q_Max) Min_Scaled = Div (X_Min_Adjusted, Scale) Initial_ZeroPoint_FP = Sub (Q_Min, Min_Scaled) Clipped_ZeroPoint_FP = Clip (Initial_ZeroPoint_FP, Q_Min, Q_Max) Rounded_ZeroPoint_FP = Round (Clipped_ZeroPoint_FP) Zeropoint = Cast (Rounded_ZeroPoint_FP) y_scale = Identity (Scale) y_zero_point = Identity (Zeropoint) y = QuantizeLinear (x, Scale, Zeropoint) } ¿0Hl$èHL$H‰ïLL$LHI‰ÄH5èH‰îL‰çèH‹AÆD$ IÇD$HƒÀI‰$ID$ I‰D$H‹|$HD$ H9ÇtèH‹5HL‰çè¿0Hl$èHL$H‰ïLL$LHI‰ÄH5èH‰îL‰çèéeÿÿÿ¿0Hl$èHL$H‰ïLHH5I‰ÄèH‰îL‰çèé!ÿÿÿH‹|$H‰ÅHD$ 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H‰åL‰ïèºH5H‰ïèH‰îL‰ïèH‰ÇH5èH‰ÇH5èH‰Ç¾èH‰ÇºuH5èH‰ÆL‰çèH‹D$H…Àt ºH‰îH‰ïÿÐL‰ïèHÄÀL‰à]A\A]ÃI‰ÄéI‰Äéf„AUATI‰üUHìÀLl$ H‰åL‰ïèºH5H‰ïèH‰îL‰ïèH‰ÇH5èH‰ÇH5èH‰Ç¾èH‰ÇºzH5èH‰ÆL‰çèH‹D$H…Àt ºH‰îH‰ïÿÐL‰ïèHÄÀL‰à]A\A]ÃI‰ÄéI‰Äéf„AUATI‰üUHìÀLl$ H‰åL‰ïèH5H‰ïèH‰îL‰ïèH‰ÇH5èH‰ÇH5èH‰Ç¾èH‰ÇºÆH5èH‰ÆL‰çèH‹D$H…Àt ºH‰îH‰ïÿÐL‰ïèHÄÀL‰à]A\A]ÃI‰ÄéI‰Äéff.„AUATI‰üUHìÀLl$ H‰åL‰ïèH5H‰ïèH‰îL‰ïèH‰ÇH5èH‰ÇH5èH‰Ç¾èH‰ÇºËH5èH‰ÆL‰çèH‹D$H…Àt ºH‰îH‰ïÿÐL‰ïèHÄÀL‰à]A\A]ÃI‰ÄéI‰Äéff.„AUATI‰üUHìÀLl$ H‰åL‰ïèH5H‰ïèH‰îL‰ïèH‰ÇH5èH‰ÇH5èH‰Ç¾èH‰ÇºH5èH‰ÆL‰çèH‹D$H…Àt ºH‰îH‰ïÿÐL‰ïèHÄÀL‰à]A\A]ÃI‰ÄéI‰Äéff.„AUATI‰üUHìÀLl$ H‰åL‰ïèH5H‰ïèH‰îL‰ïèH‰ÇH5èH‰ÇH5èH‰Ç¾èH‰ÇºH5èH‰ÆL‰çèH‹D$H…Àt ºH‰îH‰ïÿÐL‰ïèHÄÀL‰à]A\A]ÃI‰ÄéI‰Äé¸Ã…ÒtƒúuH‰71ÀÃH‹H‰1ÀÃ¸Ã…ÒtƒúuH‰71ÀÃH‹H‰1ÀÃATI‰üUSH‹_H‹/H9ët$DH‹}HEH9Çt+èHƒÅ H9ëuåI‹,$H…ít$[H‰ï]A\é„HƒÅ H9ëu¿ëØD[]A\ÃH‹UH‰ýHƒÀH‰H‹HE H9ÇtèH‰ï]écannot create std::vector larger than max_size()A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. 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.basic_string::_M_construct null not validKeep the reduced dimension or not, default 1 mean keep reduced dimension. Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulting tensor have the reduced dimension pruned. If select_last_index is True (default False), the index of the last occurrence of the {name} is selected if the {name} appears more than once in the input. Otherwise the index of the first occurrence is selected. The type of the output tensor is integer.The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).Reduced output tensor with integer data type. 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.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 resulting tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulting tensor have the reduced dimension pruned. The type of the output tensor is integer. expected to have tensor or sparse tensor type: 'axis' must be in [-rank(indices), rank(indices)-1]Constrain input and output types to high-precision and 8 bit numeric tensors.Constrain input and output types to high-precision numeric tensors.axis must be in [-rank, rank-1]. input rank was /opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/defs/reduction/old.ccAUE1íATUH‰õSH‰ûHƒìHƒ?„³H‹mH…í„‘¿èHÇI‰ÄH‹E1ÒHKL‰cI‰D$H÷sH‹H‰ÐH‹mH…íuëTf„H‹mH…ítBI‰Ì¿èHÇH‰ÁH‹E1ÒH‰AH÷sH‹I‰$HÐHƒ8uÁL‰ H‹mH…íu¾HƒÄ[]A\A]Ãf.„H‹WHƒút1H‰ÐHÁèHƒÀH9ÇtèL‰ðHƒÀ I‰ÆëÛH‹½ðþÿÿH;½xþÿÿtèL‰çèéNÿÿÿèH‰ÃèH‹½þÿÿH…ÿ„1ÿÿÿèé'ÿÿÿH‰ˆþÿÿèH;ˆþÿÿtH‹;HCH9ÇtèHƒÃ ëàèH‹EÀH…Àt ºL‰þL‰ÿÿÐH‹½pÿÿÿH;½ ÿÿÿtèH‰ßèH‹}H9ßtèL‰ïL‰ãèH‹}°H;½8ÿÿÿt½èë¶èH9(ÿÿÿt"H‹…(ÿÿÿH‹8HƒÀH9ÇtèHƒ…(ÿÿÿ ëÕèH‰ÃèH‹½PÿÿÿH…ÿt£èëœH‹EÀH…Àt ºL‰þL‰ÿÿÐH‹½pÿÿÿH;½ ÿÿÿtèH‰ßèH‹}H9ßtèL‰ïL‰ãèH‹}°H;½8ÿÿÿt½èë¶èH9(ÿÿÿt"H‹…(ÿÿÿH‹8HƒÀH9ÇtèHƒ…(ÿÿÿ ëÕèH‰ÃèH‹½PÿÿÿH…ÿt£èëœH‰ÃèH‹½PÿÿÿH…ÿtèH‹}°H;½8ÿÿÿtèH‹½pÿÿÿH;½ 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ýÿÿH…ÀtH‹µ˜üÿÿºH‰÷ÿÐH‹½ðüÿÿH;½üÿÿtèH‹½°üÿÿH9ßtèH‹½ÐüÿÿL9÷tèL‰ïèHeØL‰à[A\A]A^A_]Ã„H‹½üÿÿL‰É1Ò1öèHýÿÿHPH‰üÿÿH‰ðüÿÿH‹H9Ñ…÷üÿÿóoH)ýÿÿéøüÿÿ€óo@)…ýÿÿéàüÿÿI‰ÇéI‰ÇéH‰ÃéH‰ÃéI‰Äé¸Ã…ÒtƒúuH‰71ÀÃH‹H‰1ÀÃ¸Ã…ÒtƒúuH‰71ÀÃH‹H‰1ÀÃI‰øH‰÷Aÿ H‹UH‰ýHƒÀH‰H‹HE H9ÇtèH‰ï]é…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃfH‹H‰ëÖAUE1íATUH‰õSH‰ûHƒìHƒ?„³H‹mH…í„‘¿èHÇI‰ÄH‹E1ÒHKL‰cI‰D$H÷sH‹H‰ÐH‹mH…íuëTf„H‹mH…ítBI‰Ì¿èHÇH‰ÁH‹E1ÒH‰AH÷sH‹I‰$HÐHƒ8uÁL‰ H‹mH…íu¾HƒÄ[]A\A]Ãf.„H‹WHƒút1H‰ÐHÁè= 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) The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0./opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/defs/rnn/defs.cc 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 A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0 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) 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.The shape format of inputs X, initial_h, initial_c and outputs Y, Y_h, Y_c. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [batch_size, num_directions, hidden_size].Couple the input and forget gates if 1.The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.Optional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.The weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.The last output value of the cell. 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èL‰çèéþÿÿDH‹|$H5è…À„üÿÿH‹|$H5è…À…üÿÿƒ¼$¨…¼HÇ„$ éòûÿÿ€HD$@Ht$81ÒH‰ÇH‰D$èH‰D$@H‰ÇH‹D$8H‰D$PH‹t$(L‰âèL‹d$8H‹D$@é5ûÿÿf„H‹€¸HL$PH‰L$H‰L$@H‹L‹`H‰ÈH‰L$(Là„ÊúÿÿH…É…ÁúÿÿH=èDH‹éØüÿÿL‰ïèÇ„$¨é,ÿÿÿHD$@H‹|$H‰D$é[ÿÿÿH=èH‰ÅééH‰ÅéH‰ÅéH‰ÅéH‰ÅéH‰ÅééH‰ÅéH‰Åé¸Ã…ÒtƒúuH‰71ÀÃH‹H‰1ÀÃ¸Ã…ÒtƒúuH‰71ÀÃH‹H‰1ÀÃI‰øH‰÷Aÿ H‹UH‰ýHƒÀH‰H‹HE H9ÇtèH‰ï]é…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃfH‹H‰ëÖAUE1íATUH‰õSH‰ûHƒìHƒ?„³H‹mH…í„‘¿èHÇI‰ÄH‹E1ÒHKL‰cI‰D$H÷sH‹H‰ÐH‹mH…íuëTf„H‹mH…ítBI‰Ì¿èHÇH‰ÁH‹E1ÒH‰AH÷sH‹I‰$HÐHƒ8uÁL‰ H‹mH…íu¾HƒÄ[]A\A]Ãf.„H‹WHƒút1H‰ÐHÁè= 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 + Wbz + Rbz) - rt = f(Xt*(Wr^T) + Ht-1*Rr + Wbr + Rbr) - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*Rh + Rbh + Wbh) # default, when linear_before_reset = 0 - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*Rh + Rbh) + Wbh) # when linear_before_reset != 0 - Ht = (1 - zt) (.) ht + zt (.) Ht-1 A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0/opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/defs/rnn/old.cc 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 + Wbi + Rbi) The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate. 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 + Pi (.) Ct-1 + Wbi + Rbi) - ft = f(Xt*(Wf^T) + Ht-1*Rf + Pf (.) Ct-1 + Wbf + Rbf) - ct = g(Xt*(Wc^T) + Ht-1*Rc + Wbc + Rbc) - Ct = ft (.) Ct-1 + it (.) ct - ot = f(Xt*(Wo^T) + Ht-1*Ro + Po (.) Ct + Wbo + Rbo) - Ht = ot (.) h(Ct) 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.Couple the input and forget gates if 1, default 0.The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.Optional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.The weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.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. 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) 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 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) Couple the input and forget gates if 1.Attribute expected to have tensor or sparse tensor typeFirst input tensor must have rank 3H‹<$H9ßtèH‰ïèH‹<$H9ßtèH‰ïèH‹D$H…Àt ºL‰æL‰çÿÐH‰ïèH‹<$H9ßtèH‰ïèH‹D$H…Àt ºL‰æL‰çÿÐH‰ïèH‹½ýÿÿH9ßu2L‰óL‰ïèH‰ßèH‹… ýÿÿH…ÀtäH‹µøüÿÿºH‰÷ÿÐëÑèëÇH‹…àüÿÿH…À……M‰æH‹½üÿÿH9ßuhH‹½°üÿÿH;½`üÿÿu_L‰óH‹½HüÿÿèH‹½ýÿÿH;½XüÿÿtèH‹½ðüÿÿL9ïtèL‰ÿèH‰ßèH‹½ðüÿÿL9ïtäèëÝèë‘èëšH‹µhüÿÿºH‰÷ÿÐéeÿÿÿH‰ÃèH‹½püÿÿH…ÿ„{ÿÿÿèéqÿÿÿH‰HüÿÿèH;HüÿÿtH‹;HCH9ÇtèHƒÃ ëàèH‹½ýÿÿH9ßu2L‰óL‰ïèH‰ßèH‹… ýÿÿH…ÀtäH‹µøüÿÿºH‰÷ÿÐëÑèëÇH‹½ýÿÿH9ßu2L‰óL‰ïèH‰ßèH‹… ýÿÿH…ÀtäH‹µøüÿÿºH‰÷ÿÐëÑèëÇH‰øûÿÿèH;øûÿÿ„öH‹;HCH9ÇtèHƒÃ ëÜH‹…àüÿÿH…ÀtH‹µ(üÿÿºH‰÷ÿÐH‹½üÿÿH9ß…žH‹½°üÿÿL9÷…˜L‰ãH‹½øûÿÿèH‹½ýÿÿH;½üÿÿtèH‹½ðüÿÿH;½üÿÿtèH‹½püÿÿH;½üÿÿtèH‹½PüÿÿH;½üÿÿtèL‰ÿèH‰ßèH‰ÃèH‹½0üÿÿH…ÿtˆèëèéXÿÿÿèé^ÿÿÿèH‹… ýÿÿH…Àu8M‰çH‹½ðüÿÿL9÷tèH‹½ÐüÿÿH9ßtèL‰ûL‰ïèH‰ßèH‹µ¸üÿÿºH‰÷ÿÐëµH‹… ýÿÿH…Àu8M‰çH‹½ðüÿÿL9÷tèH‹½ÐüÿÿH9ßtèL‰ûL‰ïèH‰ßèH‹µ¸üÿÿºH‰÷ÿÐëµH‹|$`H;|$ tèH‹¼$@H9ßuZH‹|$èL‰ïèL‰ÿèL‰÷èH‰ïèH‹¼$@H9ßtèH‰ëH‹|$@H;|$tèH‰Ýë«H‰ÃëåèëŸH‹|$(èH‹|$ èL‰çèëÀ¿0èHH5L‰çH‰ÅèL‰æH‰ïèH‹H‹¼$@HÇEÆE HƒÀH‰EHE H‰EH9ßtèH‹5HH‰ïèH¬$Hƒí0H‰ïèL9å„*ÿÿÿëéH‹¼$@I‰ÄH9ßtèL‰ãH‰ïèéÿÿÿH‰Ãëî¿0èHH5L‰çH‰ÅèL‰æH‰ïèH‹H‹¼$@HÇEÆE HƒÀH‰EHE H‰EH9ßtèH‹5HH‰ïèH‰ÅH‹|$@H;|$tèL‰÷èL‰ÿèH‹|$èL‰ïèH‰ïè¿0èHH5L‰çH‰ÅèL‰æH‰ïèH‹H‹¼$@HÇEÆE HƒÀH‰EHE H‰EH9ßtèH‹5HH‰ïèH‹¼$@I‰ÄH9ßtèH‰ïL‰åèé'ÿÿÿI‰ÄëëëÔë÷H‹|$`H;|$ tèH‹¼$@H9ß…ÿÿÿéÿÿÿH‹|$(èH‹|$ èL‰çèéÖþÿÿH‹¼$@H9ß„Åþÿÿèé»þÿÿHœ$Hƒë0H‰ßèL9ã„žþÿÿëéÿÿRó…ãÿÿÿÿU©Ï„±ÿÿ0ÿÿUÏõª×ÿÿ0ÿ›9-¸äÌÚŒßÄÚÐß‹¤}ÿÿ$ò‰šÿè„ƒ úøøõ©Cÿÿ$üÙªÏÔœKÊ¼„ÅùCÿÿaæ²Ò ËSÈ¬ ø’ø«)Àâ¸³å× þSößô¬ÆÌßHÄµ¿ïNÿÿ$üÙªÏÔœKÊ¼„ÅùCÿÿ$üÙªÏÔœKÊ¼„ÅùCÿÿ3 ŽâõûHóÑñ¢¼¿ÕHº«µìÿÿ$ò‰šÿè„ƒ úøøõ©Cÿ›Œ€ÉËû1ßÐÕþß›¾ÛÃ›ÚŒ ÐÌ çŒ ¹Ä ÚÐ ÃÜ ¾è Ð–ç´¹¦• ê´ „ ‰ ¢ ¯ }ÿÿÎ¡°ÿ›ÐÇõ¶/µº+ñâ&ó Ú&» °-ú –-ž-Ó Ð/õ¦/û¦-ÃÊ.Ý¥+¾+ú«- Â.Úbº,™µ,«*î¦*Û¡*‘™*ãÔ&ÕÈ/® Ã/ê!®/¶"º.‚#²+Í#‰$ + $º+ª$ž-¯$º+´$ž-¿$Â.É$ ¥+ß$Â.ë$«-•%Ê.¡%¦-¾%Ã/Ê%¦/Ö%È/ì%°-ø%Ú&—&–-£&·&²+Ã&º.Ï&®/¡)í)ò)‘-¾/¨..µ.â/Ò.×.ÿÿÿÿ/Y¸ÐÈÿµ¸ºKÀÿÿÿ›E:/\± Ó™ Ø¹ „ƒ Ì¶¡ K© øÁ ¼É þÁ ÿ› où«ÿÿ6\äÓô‚ÞäæKìÿÿÿÿ6\þÓŽ‚ÂþÊ†ÿÿÿ›I@,]‚ÞâÂúÄÚÏ¼—²êÓ OÒš òß ´¯òÿ›Ûàÿÿ/\‰Ýº™ÉK‘ÿÿ?ÿÿ/\©Ý¡ºå¹±ÿÿ?ÿÿz‘½;¢¡'Ýz”ã ¢È”ð¢ËÖ˜¨”Üïçê¹_Ùì äç ¶ ¢â þ « ù ä ˆï ƒÿÿn)3ÂEš\’âý¯ ºêï…ªAÄ ýîGÈ²€ !· ªè !Ÿ¢ÈäýÿÿC»ÚÆµ‘sÿÿ\!8¼ JÙ \Ñ âÉ ¯ñ ö èññ ìþ ¨ *ñ è á *ñ Ý é ôÉ ‡ ñ · É ÿÿ$Õ)°ty²ÕÜàÚ«yATI‰üUSH‹_H‹/H9ët$DH‹}HEH9Çt+èHƒÅ H9ëuåI‹,$H…ít$[H‰ï]A\é„HƒÅ H9ëu¿ëØD[]A\ÃUH‰ýSHƒìH‹_H…ÛtfDH‰ßH‹èH…ÛuðH‹EH‹}1öHƒÅ0HÅèH‹}ÐHÇEèHÇEàH9ïtHƒÄ[]éf„HƒÄ[]Ãbasic_string::_M_construct null not validAWAVAUATUSH‰û¿ˆHƒìèH‹+L‹kLpHÇI‰ÄL‰pH‰èLèt H…í„xL‰l$Iƒý‡ìIƒý…Â¶EAˆD$L‰ðM‰l$M|$(Hs BÆ(H‹C(L‰ÿIÇD$(óoC@I‰D$0H‹C8IÇD$8I‰D$@IÇD$XAD$HèH‹kXL‹k`I|$pI‰|$`H‰èLèt H…í„éL‰l$Iƒý‡‘IƒýuC¶EAˆD$pM‰l$hL‰àBÆ/HƒÄ[]A\A]A^A_ÃfDM…í…¬L‰ðé9ÿÿÿ€M…ítÁëdHt$I|$1ÒèI‰D$H‰ÇH‹D$I‰D$L‰êH‰îèL‹l$I‹D$éíþÿÿHt$I|$`1ÒèI‰D$`H‰ÇH‹D$I‰D$pL‰êH‰îèL‹l$I‹|$`éAÿÿÿH=èH=èL‰÷ë‰H‰ÇëH‰ÃëH‰ÃëL‰ÿèI‹|$I9þuH‰ßèL‰çèèH‰ÅëèëßèH‰ïèAUI‰ýATUSHƒìL‹gH‹/I9ì„¢H‹}xH…ˆH9ÇtèH‹}XHEhH9ÇtèH‹]0H…ÛtH‰ßH‹èH…ÛuðH‹E(H‹} 1öHÅèH‹} HEPHÇE8HÇE0H9ÇtèH‹}HEH9Çt:èHÅ¨I9ì…eÿÿÿI‹mH…ít.HƒÄH‰ï[]A\A]éf.„HÅ¨I9ì…0ÿÿÿëÉHƒÄ[]A\A]ÃAVI‰þAUATUSL‹oL‹'M9åt}f„I‹|$8ID$HH9ÇtèI‹\$(I‹l$ H9ët%DH‹}HEH9Çt[èHƒÅ H9ëuåI‹l$ H…ítH‰ïèI‹<$ID$H9Çt=èIƒÄXM9åuM‹&M…ät6[L‰ç]A\A]A^é€HƒÅ H9ëuë¨DIƒÄXM9å…SÿÿÿëÂ[]A\A]A^ÃH‹SH‰ûHƒÀH‰H‹HHCXH9ÇtèH‹H{8HƒÀH‰[éH‹UH‰ýHƒÀH‰H‹HHEXH9ÇtèH‹H}8HƒÀH‰EèH‰ï]éAWAVAUATI‰üUSHìøH‹L´$àL|$`H‰t$PHƒÀ@L‰÷H‰T$@fHnÀH‹L‰|$XfoÈfoÐHƒÀfHnØH‹flËHƒÀ)L$ fHnàflÔ)T$0èH‹H‹1öHÇ„$¸fïÀH‹kHƒÀH‹K„$ÈH‰„$à1Àf‰„$À„$ØH‹EèH‰l$`H‰L`HÇD$hH‹}èH‰$LÿèL‹k H‹C(L|$p1öI‹}èL‰l$pH‰D$LÿH‰èH‹CH‹K0fïÀH\$xfoT$0H‰D$H‹@èH‰L$H‰L`H‹)T$pHƒÀ)„$€H‰D$`H‹)„$HƒÀh)„$ H‰„$àH„$°H‰ÇH‰D$HèH‹H‰ÞL‰÷Ç„$¸HƒÀÆ„$ÐH‰D$xH„$ÐH‰D$0H‰„$ÀHÇ„$ÈèH‹\$PH‰ßèH‰ÞL‰ÿH‰ÂèH‹\$@H‰ßèH‰ÞL‰ÿH‰ÂèH‹„$ I\$IÇD$I‰$AÆD$H…À„L‹„$M…À„óL9À‡êH‹Œ$˜1Ò1öL‰çI)ÈèH‹fol$ H‹¼$ÀHƒÀ)l$pH‰D$`H‹HƒÀhH‰„$àH;|$0tèH‹H‹|$HHƒÀH‰D$xèH‹D$H‹T$L‰÷H‹t$H‹@èH‰T`I‹EèL‰l$pH‹$H‰tpH‹EèH‰l$`H‰T`H‹HÇD$hHƒÀH‰„$àèHÄøL‰à[]A\A]A^A_ÃfDI‰Àéÿÿÿ„H´$ÀL‰çèéÿÿÿH‰ÅëH‰ÅëI‰Äë*I‰ÄëcI‰Äë;I‹<$H9ûtèH‹|$XèH‰ïèH‹EèH‹4$H‰l$`H‰t`HÇD$hH‹L‰÷HƒÀH‰„$àèL‰çèH‰ßèH‹D$H‹t$H‹@èH‰t`I‹EèH‹t$L‰l$pH‰tpë’AWAVAUATI‰üUSHìH‹L¼$ðLt$pH‰t$XHƒÀ@L‰ÿH‰L$HfHnÀH‹H‰T$@foÈfoÐL‰D$`HƒÀL‰t$hfHnØH‹flËHƒÀ)L$ fHnàflÔ)T$0èH‹H‹1öHÇ„$ÈfïÀH‹kHƒÀH‹K„$ØH‰„$ð1Àf‰„$Ð„$èH‹EèH‰l$pH‰LpHÇD$xH‹}èH‰$L÷èL‹s H‹C(L¬$€1öI‹~èL‰´$€H‰D$LïH‰èH‹CH‹s0fïÀHœ$ˆfoT$0H‰D$H‹@èH‰t$H‰tpH‹)”$€HƒÀ)„$H‰D$pH‹)„$ HƒÀh)„$°H‰„$ðH„$ÀH‰ÇH‰D$PèH‹H‰ÞL‰ÿÇ„$ÈHƒÀÆ„$àH‰„$ˆH„$àH‰D$0H‰„$ÐHÇ„$ØèH‹\$XH‰ßèH‰ÞL‰ïH‰ÂèH‹\$@H‰ßèH‰ÞL‰ïH‰ÂèH‹D$HL‰ïH‹0èH‹\$`H‰ßèH‰ÞL‰ïH‰ÂèH‹„$°I\$IÇD$I‰$AÆD$H…À„ L‹„$ M…À„ÿL9À‡öH‹Œ$¨1Ò1öL‰çI)ÈèH‹fol$ H‹¼$ÐHƒÀ)¬$€H‰D$pH‹HƒÀhH‰„$ðH;|$0tèH‹H‹|$PHƒÀH‰„$ˆèH‹D$H‹T$L‰ÿH‹L$H‹@èH‰TpI‹FèL‰´$€H‹$H‰Œ€H‹EèH‰l$pH‰TpH‹HÇD$xHƒÀH‰„$ðèHÄL‰à[]A\A]A^A_ÃfDI‰Àéÿÿÿ„H´$ÐL‰çèéüþÿÿH‰ÅëH‰ÅëI‰Äë*I‰ÄëcI‰Äë;I‹<$H9ûtèH‹|$hèH‰ïèH‹EèH‹$H‰l$pH‰LpHÇD$xH‹L‰ÿHƒÀH‰„$ðèL‰çèH‰ßèH‹D$H‹L$H‹@èH‰LpI‹FèH‹L$L‰´$€H‰Œ€ëŒ[TypeInferenceError] Input type was nullInput Element type of input unknownOutput expected to have tensor or sparse tensor type. 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I‰D$HD$PH9ÇtèH‹5HL‰çèH‹|$@HD$PH9ÇtèL‰çèL‰ïèH‰ïèH‰ÅëÏH‰ÅëÞ¿0Hl$@èHL$H‰ïLHH5I‰ÄèH‰îL‰çèé7þÿÿ¿0Hl$@èHL$H‰ïLHH5I‰ÄèH‰îL‰çèéóýÿÿélþÿÿélþÿÿébþÿÿébþÿÿFvRN10onnx_torch16InferenceContextEEN10onnx_torch14InferenceErrorEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EPFvRN10onnx_torch16InferenceContextEE*ZN10onnx_torch18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_*ZN10onnx_torch16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_*ZN10onnx_torch16RNNDocGenerator2EPKcEUlRNS_8OpSchemaEE_One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. 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H9ÇtèH‰ï]éL‹HƒÇI9øtL‰Çé@Ã…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃf‹‰ëØ…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃf‹‰ëØ…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃf‹‰ëØ…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃf‹‰ëØ…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃf‹‰ëØ…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃf‹‰ëØ…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃf‹‰ëØ…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃf‹‰ëØ…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃf‹‰ëØ…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃf‹‰ëØ…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃf‹‰ëØ…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃf‹‰ëØ…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃf‹‰ëØ…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃf‹‰ëØ…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃf‹‰ëØ…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃf‹‰ëØ…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃf‹‰ëØ…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃf‹‰ëØ…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃf‹‰ëØ…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃf‹‰ëØbasic_string::_M_construct null not valid (no explicit description available)%s: __pos (which is %zu) > this->size() (which is %zu)Extra unparsed input unexpected.' has been deprecated since version is marked single but has an empty string in the graphMismatched attribute type in '' is expected to have field 't'' is expected to have field 'sparse_tensor'' is expected to have field 'g'' is expected to have field 'type_proto'' is expected to have field 'floats'' is expected to have field 'ints'' is expected to have field 'strings'' is expected to have field 'tensors'' is expected to have field 'graphs'' is expected to have field 'type_protos': failed validating the check: Duplicate type constraint nameAttribute specification type mismatch.ATHGUSHƒìH‰H…ötqH‰ûH‰÷I‰ôèH‰D$H‰ÅHƒøvHt$1ÒH‰ßèH‰H‹D$H‰CH‹HƒýuAŠ$ˆëH…ítH‰ÇL‰æH‰éó¤H‹D$H‹H‰CÆHƒÄ[]A\ÃH=èATI‰ôUH‰ýSH‰ÓHƒìH…öuH…ÒtH=èL)ãH‰\$HƒûvHt$1ÒH‰ïèH‰EH‹D$H‰EH‹EHƒûuAŠ$ˆëH…ÛtH‰ÇL‰æH‰Ùó¤H‹D$H‹UH‰EÆHƒÄ[]A\ÃèL‰çèèH‰ÅèH‰ïèH‰ïèL‰çèH‰ïèL‰çèH‹D$H…Àt H‰çºH‰þÿÐH‹|$0èH‰ïèH‹D$H…Àt H‰çºH‰þÿÐH‹|$0èH‰ïèH‹|$H9ßtèH‰ïèH‹|$H9ßtèH‰ïèH‹|$H9ßtèH‰ïèH‹¼$°H9ßtèH‹¼$H;|$tèH‹|$pH;|$tèH‰ïèH‹¼$°H9ßtèH‹¼$H;|$tèH‹|$PH;|$uÁëÄ¿èH5H‰ÇI‰ÄèH‹H‹5L‰çè¿èH‰ïI‰ÄèHl$0H‰ÂH5H‰ïèH‰îL‰çèH‹|$0HD$@H9ÇtèH‹H‹5L‰çèH‰ÅL‹d$M…ätI‹|$ID$H9ÇtèL‰çèH‰ïèH‰ÅL‰çèëÅH‹|$0H‰ÅHD$@H9ÇtäèëÝëØèH‰ßèM…ítH‹;HƒÃ0H9ßuèèëôH‰ÅèH‰ïèH‹¼$ØL9ÿtèH¼$ 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¶„ÄÚ”èþ˜Œ¢þ°ÂüÐâ‚ð‚€™Žž€£‚¨üþ²˜·”¼„Á–ÆˆËŠÐÕ†ÚóßøäŒéúî’¥ÿÿ&BPÿÿB&¶4F¸Tf±t†º¶¢º§±¬¸Çÿÿ C¸wÐ¿È‡Àÿÿ"ÿ›5,}£èËãˆèÞãú‘Œœÿ›5,} åÃê€åàê«°ÐÝÿ›¬—®ð0ñ°‚,îï+ª2÷+¬ ð0áÚ3¾è0ô,½Ê1Ñ²Ï3›¹3ÎÄ3Ù’,¾4Ï3«ð0†©6¸–6¡6•¨,½´,êç+õ‰6× ¨,ý"®.¯#ž.ø$¦.Œ%’Š,¶(¬,ã(ª,î(Ž3Ð)Š,¦*â+È*'Š,‡+'¨,¶+ð0»+¨,Ç+ï+Ó+â+Ø+Ï3Ý+Š,Å,Ê,Â-Æ0ë/Á0Ë0ã0©3„6Ž6{{ÿÿ·ßmÿ›ÐÇõß.µº+ñâ&ó Ú&» ƒ-ú é,ñ,Ó ¼.õ¯.ûù,Ã.Ý¥+¾+úþ, •.Úb,™ˆ,«*î¦*Û¡*‘™*ãÔ&ÕÒ.® ·.ê!ç.¶".‚#²+Í#‰$ + $º+ª$ñ,¯$º+´$ñ,¿$•.É$ ¥+ß$•.ë$þ,•%.¡%ù,¾%·.Ê%¯.Ö%Ò.ì%ƒ-ø%Ú&—&é,£&·&²+Ã&.Ï&ç.¡)í)ò)ä,“/û-€.ˆ.Ú.¥.ª.ÿÿ6Dÿÿ6Dÿÿ6Dÿÿ6Dÿÿ6Dÿÿ6Dÿÿ6Dÿÿ6Dÿÿ6Dÿÿ6Dÿÿ6Dÿÿ6Dÿÿ6Dÿÿ6Dÿÿ6Dÿÿ6Dÿÿ6Dÿÿ6Dÿÿ6Dÿÿ6DAUATI‰ÔUSH‰ûHƒì(H…ö„¿Hƒ~pH‰÷…H‹¾ èHxH‰ÅH‰8M‹,$M‹d$L‰èLàt M…í„çL‰d$Iƒüw9Iƒüu#A¶EˆEL‰eBÆ'H‰+HƒÄ([]A\A]Ã@M…ätàë(f„Ht$1ÒH‰ïèH‰EH‰ÇH‹D$H‰EL‰âL‰îèL‹d$H‹}ë ¿ èM‹,$M‹d$HxH‰ÅH‰8L‰èLàtM…ítML‰d$Iƒü†VÿÿÿHt$1ÒH‰ïèëfDº 1öH‰|$èH‹|$éäþÿÿH=èH=èI‰ÄH‰ïèL‰çèSchema error: ATºI‰üH5UHƒìH‹-H‰ïèI‹<$H‹ÿPH…À„ˆH‰ÇI‰ÄèL‰æH‰ïH‰ÂèH‹EH‹@èL‹¤(ðM…ätuA€|$8t"A¾t$CH‰ïèHƒÄ]H‰ÇA\éDL‰çèI‹$¾ HH‹@0H9ÐtÃL‰çÿÐ¾ðë¹DH‹EH‹xèHï‹w ƒÎèévÿÿÿèUH‰ýSH…ˆHƒìH‹xH9ÇtèH‹}XHEhH9ÇtèH‹]0H…Ût€H‰ßH‹èH…ÛuðH‹E(H‹} 1öHÅèH‹} HEPHÇE8HÇE0H9ÇtèH‹}HƒÅH9ïtHƒÄ[]é€HƒÄ[]ÃATUSH‰ûH‹8HCHH9ÇtèL‹c(H‹k I9ìt)f.„H‹}HEH9Çt;èHƒÅ I9ìuåH‹k H…ítH‰ïèH‹;HƒÃH9ßt []A\é€HƒÅ I9ìu¯ëÈD[]A\ÃAttributes: : Inputs:, Outputs:(no documentation yet)Defined at :basic_string::replaceError parsing function body:, has unsupported type: typestr: has inconsistent type has unsupported type Operator 'Node () has input size not in range [min=, max=]. not in allowed input sizes.) has output size not in allowed output sizes.has output size ) has more inputs () than declared () in op definition.)'s input ) has more outputs ()'s output ' appeared multiple times.Attribute ' for operator Unrecognized attribute: ' has unknown expected type' is missing.Required attribute 'ONNX Schema (inputs_.size() - 1) == i(outputs_.size() - 1) == i!(it.GetName().empty())vector::reserveATI‰üUSH‹_H‹/H9ët$DH‹}HEH9Çt+èHƒÅ H9ëuåI‹,$H…ít$[H‰ï]A\é„HƒÅ 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H‰„$€H‹)„$°HƒÀh)„$ÀH‰„$H„$ÐH‰ÇH‰D$0èH‹H‰ÞL‰÷Ç„$ØHƒÀÆ„$ðH‰„$˜H„$ðH‰D$`H‰„$àHÇ„$èèH‹D$HH‰ïH‹PH‹0èH‹\$hH‰ßèH‰ÞH‰ïH‰ÂèH‹D$PH‰ïH‹PH‹0èH‹\$pH‰ßèH‰ÞH‰ïH‰ÂèH‹D$XH‰ïH‹PH‹0èH‹„$ÀI\$IÇD$I‰$AÆD$H…À„'L‹„$°M…À„L9À‡H‹Œ$¸1Ò1öL‰çI)ÈèH‹fol$ H‹¼$àHƒÀ)¬$H‰„$€H‹HƒÀhH‰„$H;|$`tèH‹H‹|$0HƒÀH‰„$˜èH‹D$H‹L$@L‰÷H‹t$H‹@èH‰Œ€I‹GèL‰¼$H‹L$H‰´I‹EèL‰¬$€H‰Œ€H‹HÇ„$ˆHƒÀH‰„$èHÄL‰à[]A\A]A^A_ÃDI‰ÀéóþÿÿH´$àL‰çèéõþÿÿH‰ÅëH‰ÅëH‰Åë*H‰ÅëmH‰ÅëEI‹<$H9ûtèH‹|$xèH‰ïèI‹EèH‹t$L‰¬$€H‰´€HÇ„$ˆH‹L‰÷HƒÀH‰„$èH‰ïèH‰ßèH‹D$H‹t$@H‹@èH‰´€I‹GèH‹t$L‰¼$H‰´é|ÿÿÿAWAVAUATI‰üUH‰õSHìH‹L´$ðLl$pH‰L$PHƒÀ@L‰÷H‰T$XfHnÀH‹L‰l$`foÈfoÐHƒÀfHnØH‹flËHƒÀ)L$@fHnàflÔ)T$0èL‹=H‹1öHÇ„$ÈfïÀI‹_HƒÀI‹O„$ØH‰„$ð1Àf‰„$Ð„$èH‹CèH‰\$pH‰LpHÇD$xH‹{èH‰L$LïèI‹G L¬$€1öH‹xèH‰„$€H‰D$I‹G(LïH‰H‰D$èI‹GI‹w0L¼$ÀfïÀfoT$0L‰ÿH‰D$ H‹@èH‰t$(H‰tpH‹)”$€HƒÀ)„$H‰D$pH‹)„$ HƒÀh)„$°H‰„$ðèH‹L‰÷Ç„$ÈHÇ„$ØHƒÀÆ„$àH‰„$ˆH„$àH‰D$0H‰„$ÐH„$ˆH‰ÆH‰D$hèH‹UH‹uL‰ïèH‹l$XH‰ïèH‰îL‰ïH‰ÂèH‹D$PL‰ïH‹PH‹0èH‹„$°Il$IÇD$I‰,$AÆD$H…À„#L‹„$ M…À„L9À‡ùH‹Œ$¨1Ò1öL‰çI)ÈèH‹fol$@H‹¼$ÐHƒÀ)¬$€H‰D$pH‹HƒÀhH‰„$ðH;|$0tèH‹L‰ÿHƒÀH‰„$ˆèH‹D$ H‹L$(L‰÷H‹T$H‹@èH‰LpH‹D$H‰„$€H‹@èH‰”€H‹CèH‰\$pH‹\$H‰\pH‹HÇD$xHƒÀH‰„$ðèHÄL‰à[]A\A]A^A_ÃDI‰Àéÿþÿÿ„H´$ÐL‰çèéùþÿÿH‰ÃëH‰Åë#H‰Åé§H‰Åë(H‰ÅëzI‹<$H9ýtèH‰ÝH‹|$`èH‰ïèH‹|$hèH‹D$ H‹L$(H‹@èH‰LpH‹D$H‹L$H‰„$€H‹@èH‰Œ€H‹CèH‰\$pH‹\$H‰\pHÇD$xH‹L‰÷HƒÀH‰„$ðèH‰ïèë¿AWAVAUATI‰üUH‰õSHìH‹L´$ðLl$pH‰L$PHƒÀ@L‰÷H‰T$XfHnÀH‹L‰l$`foÈfoÐHƒÀfHnØH‹flËHƒÀ)L$@fHnàflÔ)T$0èL‹=H‹1öHÇ„$ÈfïÀI‹_HƒÀI‹O„$ØH‰„$ð1Àf‰„$Ð„$èH‹CèH‰\$pH‰LpHÇD$xH‹{èH‰L$LïèI‹G L¬$€1öH‹xèH‰„$€H‰D$I‹G(LïH‰H‰D$èI‹GI‹w0L¼$ÀfïÀfoT$0L‰ÿH‰D$ H‹@èH‰t$(H‰tpH‹)”$€HƒÀ)„$H‰D$pH‹)„$ HƒÀh)„$°H‰„$ðèH‹L‰÷Ç„$ÈHÇ„$ØHƒÀÆ„$àH‰„$ˆH„$àH‰D$0H‰„$ÐH„$ˆH‰ÆH‰D$hèH‹UH‹uL‰ïèH‹l$XH‰ïèH‰îL‰ïH‰ÂèH‹D$PL‰ïH‹PH‹0èH‹„$°Il$IÇD$I‰,$AÆD$H…À„#L‹„$ M…À„L9À‡ùH‹Œ$¨1Ò1öL‰çI)ÈèH‹fol$@H‹¼$ÐHƒÀ)¬$€H‰D$pH‹HƒÀhH‰„$ðH;|$0tèH‹L‰ÿHƒÀH‰„$ˆèH‹D$ H‹L$(L‰÷H‹T$H‹@èH‰LpH‹D$H‰„$€H‹@èH‰”€H‹CèH‰\$pH‹\$H‰\pH‹HÇD$xHƒÀH‰„$ðèHÄL‰à[]A\A]A^A_ÃDI‰Àéÿþÿÿ„H´$ÐL‰çèéùþÿÿH‰ÃëH‰Åë#H‰Åé§H‰Åë(H‰ÅëzI‹<$H9ýtèH‰ÝH‹|$`èH‰ïèH‹|$hèH‹D$ 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_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Mean_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Min_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Mul_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Neg_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Not_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_12Or_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15PRelu_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Pad_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Pow_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13RNN_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_22RandomNormal_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26RandomNormalLike_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_23RandomUniform_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_27RandomUniformLike_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20Reciprocal_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18ReduceL1_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18ReduceL2_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_22ReduceLogSum_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_25ReduceLogSumExp_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19ReduceMax_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ReduceMean_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19ReduceMin_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ReduceProd_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19ReduceSum_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_25ReduceSumSquare_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Relu_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Reshape_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Selu_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Sigmoid_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Softmax_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Softplus_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Softsign_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Split_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Sqrt_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Sub_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Sum_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Tanh_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14TopK_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Upsample_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Xor_Onnx_ver1EEENS_8OpSchemaEv_ZN10onnx_torch15OpSet_Onnx_ver213ForEachSchemaESt8functionIFvONS_8OpSchemaEEE_ZN10onnx_torch11GetOpSchemaINS_22GlobalLpPool_Onnx_ver2EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16LpPool_Onnx_ver2EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Pad_Onnx_ver2EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Split_Onnx_ver2EEENS_8OpSchemaEv_ZN10onnx_torch15OpSet_Onnx_ver313ForEachSchemaESt8functionIFvONS_8OpSchemaEEE_ZN10onnx_torch11GetOpSchemaINS_13GRU_Onnx_ver3EEENS_8OpSchemaEv_ZN10onnx_torch15OpSet_Onnx_ver413ForEachSchemaESt8functionIFvONS_8OpSchemaEEE_ZN10onnx_torch11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEv_ZN10onnx_torch15OpSet_Onnx_ver513ForEachSchemaESt8functionIFvONS_8OpSchemaEEE_ZN10onnx_torch11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEv_ZN10onnx_torch15OpSet_Onnx_ver613ForEachSchemaESt8functionIFvONS_8OpSchemaEEE_ZN10onnx_torch11GetOpSchemaINS_13Abs_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Add_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_28BatchNormalization_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Ceil_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Clip_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Div_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Dropout_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Elu_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Exp_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Floor_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Gemm_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_21HardSigmoid_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_31InstanceNormalization_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19LeakyRelu_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Log_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Max_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Mean_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Min_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Mul_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Neg_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15PRelu_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20Reciprocal_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Relu_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Selu_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Sigmoid_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Sqrt_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Sub_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Sum_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Tanh_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEv_ZN10onnx_torch15OpSet_Onnx_ver713ForEachSchemaESt8functionIFvONS_8OpSchemaEEE_ZN10onnx_torch11GetOpSchemaINS_14Acos_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Add_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13And_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Asin_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Atan_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_21AveragePool_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_28BatchNormalization_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Cos_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Div_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Dropout_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Equal_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Gemm_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Greater_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13GRU_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Less_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14LSTM_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Mul_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_12Or_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Pow_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13RNN_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Sin_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Sub_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Tan_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_21Multinomial_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Xor_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15PRelu_Onnx_ver7EEENS_8OpSchemaEv_ZN10onnx_torch15OpSet_Onnx_ver813ForEachSchemaESt8functionIFvONS_8OpSchemaEEE_ZN10onnx_torch11GetOpSchemaINS_16Expand_Onnx_ver8EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Min_Onnx_ver8EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Max_Onnx_ver8EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Sum_Onnx_ver8EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Mean_Onnx_ver8EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17MaxPool_Onnx_ver8EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Scan_Onnx_ver8EEENS_8OpSchemaEv_ZN10onnx_torch15OpSet_Onnx_ver913ForEachSchemaESt8functionIFvONS_8OpSchemaEEE_ZN10onnx_torch11GetOpSchemaINS_28BatchNormalization_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Compress_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_25ConstantOfShape_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17EyeLike_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Greater_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Less_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19MaxUnpool_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Constant_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16MatMul_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15PRelu_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Gemm_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Flatten_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Sinh_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Cosh_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Asinh_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Acosh_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Atanh_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Shrink_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Sign_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Scan_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13Erf_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_25TfIdfVectorizer_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_35MeanVarianceNormalization_Onnx_ver9EEENS_8OpSchemaEv_ZN10onnx_torch16OpSet_Onnx_ver1013ForEachSchemaESt8functionIFvONS_8OpSchemaEEE_ZN10onnx_torch11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_27StringNormalizer_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15TopK_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18MaxPool_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Mod_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_22AveragePool_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26ThresholdedRelu_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Dropout_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_24MatMulInteger_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_24QLinearMatMul_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_22ConvInteger_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_22QLinearConv_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEv_ZN10onnx_torch16OpSet_Onnx_ver1113ForEachSchemaESt8functionIFvONS_8OpSchemaEEE_ZN10onnx_torch11GetOpSchemaINS_15Loop_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19BitShift_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17CumSum_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Round_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15TopK_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Equal_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19Constant_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_32DynamicQuantizeLinear_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Clip_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Range_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Det_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Flatten_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17ArgMin_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17ArgMax_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19ReduceL1_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19ReduceL2_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_23ReduceLogSum_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26ReduceLogSumExp_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ReduceMax_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_21ReduceMean_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ReduceMin_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_21ReduceProd_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ReduceSum_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26ReduceSumSquare_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Hardmax_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_21LogSoftmax_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Softmax_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Scan_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_22AveragePool_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18MaxPool_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20MaxUnpool_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17LpPool_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Conv_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_24ConvTranspose_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Gemm_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13If_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver11EEENS_8OpSchemaEv_ZN10onnx_torch16OpSet_Onnx_ver1213ForEachSchemaESt8functionIFvONS_8OpSchemaEEE_ZN10onnx_torch11GetOpSchemaINS_17ArgMax_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17ArgMin_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Clip_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Einsum_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18MaxPool_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ReduceMax_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ReduceMin_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Dropout_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19Constant_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Celu_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Max_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Min_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_22LessOrEqual_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_25GreaterOrEqual_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Pow_Onnx_ver12EEENS_8OpSchemaEv_ZN10onnx_torch16OpSet_Onnx_ver1313ForEachSchemaESt8functionIFvONS_8OpSchemaEEE_ZN10onnx_torch11GetOpSchemaINS_19Constant_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Greater_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Less_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Equal_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Add_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Sub_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Mul_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Div_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Softmax_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_21LogSoftmax_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Hardmax_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Mod_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Neg_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Abs_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_21Reciprocal_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Floor_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Ceil_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Sqrt_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Relu_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Exp_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Log_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Tanh_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Pow_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Sigmoid_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Max_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Min_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Sum_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Mean_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Clip_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Gemm_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17MatMul_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Expand_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Sign_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Erf_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Dropout_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Flatten_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14LRN_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_36MeanVarianceNormalization_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ReduceMax_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ReduceMin_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ReduceSum_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26ReduceSumSquare_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_21ReduceMean_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_21ReduceProd_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_23ReduceLogSum_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26ReduceLogSumExp_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19ReduceL1_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19ReduceL2_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17ArgMax_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17ArgMin_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Cast_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Reshape_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Split_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20Transpose_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ScatterND_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26ScatterElements_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_25GatherElements_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_23SpaceToDepth_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_23DepthToSpace_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Tile_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_17Resize_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19Identity_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16IsNaN_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18NonZero_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19GatherND_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Pad_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_25QuantizeLinear_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_27DequantizeLinear_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Loop_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13If_Onnx_ver13EEENS_8OpSchemaEv_ZN10onnx_torch16OpSet_Onnx_ver1413ForEachSchemaESt8functionIFvONS_8OpSchemaEEE_ZN10onnx_torch11GetOpSchemaINS_17CumSum_Onnx_ver14EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Relu_Onnx_ver14EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Reshape_Onnx_ver14EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14GRU_Onnx_ver14EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15LSTM_Onnx_ver14EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14RNN_Onnx_ver14EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Trilu_Onnx_ver14EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_29BatchNormalization_Onnx_ver14EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20HardSwish_Onnx_ver14EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Add_Onnx_ver14EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Sub_Onnx_ver14EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Mul_Onnx_ver14EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Div_Onnx_ver14EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19Identity_Onnx_ver14EEENS_8OpSchemaEv_ZN10onnx_torch16OpSet_Onnx_ver1513ForEachSchemaESt8functionIFvONS_8OpSchemaEEE_ZN10onnx_torch11GetOpSchemaINS_29BatchNormalization_Onnx_ver15EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20Bernoulli_Onnx_ver15EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_14Pow_Onnx_ver15EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19Optional_Onnx_ver15EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_29OptionalHasElement_Onnx_ver15EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_29OptionalGetElement_Onnx_ver15EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19CastLike_Onnx_ver15EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Shape_Onnx_ver15EEENS_8OpSchemaEv_ZN10onnx_torch16OpSet_Onnx_ver1613ForEachSchemaESt8functionIFvONS_8OpSchemaEEE_ZN10onnx_torch11GetOpSchemaINS_19RoiAlign_Onnx_ver16EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_20ScatterND_Onnx_ver16EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26ScatterElements_Onnx_ver16EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_13If_Onnx_ver16EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_15Loop_Onnx_ver16EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19Identity_Onnx_ver16EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_16Where_Onnx_ver16EEENS_8OpSchemaEv_ZN10onnx_torch29RegisterOnnxOperatorSetSchemaEv_ZNSt17_Function_handlerIFvON10onnx_torch8OpSchemaEEZNS0_19RegisterOpSetSchemaINS0_15OpSet_Onnx_ver1EEEviEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFvON10onnx_torch8OpSchemaEEZNS0_19RegisterOpSetSchemaINS0_15OpSet_Onnx_ver2EEEviEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFvON10onnx_torch8OpSchemaEEZNS0_19RegisterOpSetSchemaINS0_15OpSet_Onnx_ver3EEEviEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFvON10onnx_torch8OpSchemaEEZNS0_19RegisterOpSetSchemaINS0_15OpSet_Onnx_ver4EEEviEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFvON10onnx_torch8OpSchemaEEZNS0_19RegisterOpSetSchemaINS0_15OpSet_Onnx_ver5EEEviEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFvON10onnx_torch8OpSchemaEEZNS0_19RegisterOpSetSchemaINS0_15OpSet_Onnx_ver6EEEviEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFvON10onnx_torch8OpSchemaEEZNS0_19RegisterOpSetSchemaINS0_15OpSet_Onnx_ver7EEEviEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFvON10onnx_torch8OpSchemaEEZNS0_19RegisterOpSetSchemaINS0_15OpSet_Onnx_ver8EEEviEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFvON10onnx_torch8OpSchemaEEZNS0_19RegisterOpSetSchemaINS0_15OpSet_Onnx_ver9EEEviEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFvON10onnx_torch8OpSchemaEEZNS0_19RegisterOpSetSchemaINS0_16OpSet_Onnx_ver10EEEviEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFvON10onnx_torch8OpSchemaEEZNS0_19RegisterOpSetSchemaINS0_16OpSet_Onnx_ver11EEEviEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFvON10onnx_torch8OpSchemaEEZNS0_19RegisterOpSetSchemaINS0_16OpSet_Onnx_ver12EEEviEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFvON10onnx_torch8OpSchemaEEZNS0_19RegisterOpSetSchemaINS0_16OpSet_Onnx_ver13EEEviEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFvON10onnx_torch8OpSchemaEEZNS0_19RegisterOpSetSchemaINS0_16OpSet_Onnx_ver14EEEviEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFvON10onnx_torch8OpSchemaEEZNS0_19RegisterOpSetSchemaINS0_16OpSet_Onnx_ver15EEEviEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFvON10onnx_torch8OpSchemaEEZNS0_19RegisterOpSetSchemaINS0_16OpSet_Onnx_ver16EEEviEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch16OpSchemaRegistry21loaded_schema_versionE_ZN10onnx_torch17OpSet_OnnxML_ver113ForEachSchemaESt8functionIFvONS_8OpSchemaEEE_ZN10onnx_torch11GetOpSchemaINS_33ArrayFeatureExtractor_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_21Binarizer_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19CastMap_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26CategoryMapper_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_26DictVectorizer_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_29FeatureVectorizer_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_19Imputer_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_24LabelEncoder_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_28LinearClassifier_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_27LinearRegressor_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_22Normalizer_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_25OneHotEncoder_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_25SVMClassifier_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_24SVMRegressor_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18Scaler_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_34TreeEnsembleClassifier_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_33TreeEnsembleRegressor_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch11GetOpSchemaINS_18ZipMap_OnnxML_ver1EEENS_8OpSchemaEv_ZN10onnx_torch17OpSet_OnnxML_ver213ForEachSchemaESt8functionIFvONS_8OpSchemaEEE_ZN10onnx_torch11GetOpSchemaINS_24LabelEncoder_OnnxML_ver2EEENS_8OpSchemaEv_ZN10onnx_torch22OpSet_OnnxPreview_ver113ForEachSchemaESt8functionIFvONS_8OpSchemaEEE_ZN10onnx_torch11GetOpSchemaINS_25Gradient_OnnxPreview_ve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H‹…ðüÿÿH‹•ýÿÿH‰…ýÿÿÆèH‹½ýÿÿH9ßtèHƒìH‹½0üÿÿE1É1öjLH jHjèHƒÄH‰ÇE1É¾jLH jHjèHƒÄH‰ÇE1É1öjLH jHjèH‰…üÿÿHƒÄ 1ÒL‰þH…ÀüÿÿH½°üÿÿHÇ…ýÿÿH‰½üÿÿH‰…üÿÿH‰…°üÿÿèH‹•ýÿÿfoÆ@.H¾ tensor H‰…°üÿÿH‰•ÀüÿÿÇ@typeH‹•°üÿÿH‰pH‹…ýÿÿH‰…¸üÿÿÆèH‹HfïÀH+H‰ÃHÇ…`üÿÿI‰Î)…Püÿÿ„ÔH¸àÿÿÿÿÿÿH9Á‡áH‰ÏèH‰…üÿÿH‹…üÿÿL‰ñfHnÀHÁflÀH‰`üÿÿ)…PüÿÿL‹sH‹I9Î„¾H‰8üÿÿH‰Ãë/fA¶EˆCHƒ…8üÿÿ H‹…8üÿÿHƒÃ L‰cèBÆ'I9Æ„‹H‹…8üÿÿH{H‰;L‹(L‹`L‰èLàt M…í„gL‰¥ýÿÿIƒüwIƒütšM…ätë!1ÒL‰þH‰ßèH‰H‰ÇH‹…ýÿÿH‰CL‰âL‰îèL‹¥ýÿÿH‹;é`ÿÿÿ„H‰ÃDH… üÿÿºSH‹üÿÿH‹½üÿÿH‰…øûÿÿHµüÿÿH‰…üÿÿH…Püÿÿf‰• üÿÿH‰ÂH‰XüÿÿHÇ…˜üÿÿH‰…ðûÿÿèH‹½(üÿÿH‰…üÿÿ1ÒL‰þH…ýÿÿHÇ…ýÿÿH‰…üÿÿH‰…ðüÿÿèH‹•ýÿÿfoH¾ tensor H‰…ðüÿÿH‰•ýÿÿH‰pÇ@typeÆ@.H‹…ýÿÿH‹•ðüÿÿH‰…øüÿÿÆèH‹HfïÀH+H‰ÃHÇ…€üÿÿI‰Î)…püÿÿ„²H¸àÿÿÿÿÿÿH9Á‡¹H‰ÏèH‰…üÿÿH‹…üÿÿL‰öfHnÀHÆflÀH‰µ€üÿÿ)…püÿÿL‹sH‹I9Ö„¼H‰•8üÿÿH‰Ãë-A¶EˆCHƒ…8üÿÿ H‹…8üÿÿHƒÃ L‰cèBÆ'I9Æ„‹H‹…8üÿÿH{H‰;L‹(L‹`L‰èLàt M…í„+L‰¥ýÿÿIƒüwIƒütšM…ätë!1ÒL‰þH‰ßèH‰H‰ÇH‹…ýÿÿH‰CL‰âL‰îèL‹¥ýÿÿH‹;é`ÿÿÿ„H‰ÃD¸TH‹(üÿÿH‹½üÿÿH‰xüÿÿf‰…àüÿÿH…püÿÿHàüÿÿHµÐüÿÿH‰ÂH‰ÐüÿÿI‰ÆHÇ…ØüÿÿèH5H‰ÇH¹fHnÆfHnÈH•@üÿÿflÁLH5)…@üÿÿèfïÀH‰ÇH 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H9ÇtèH‰ï]éAUE1íATUH‰õSH‰ûHƒìHƒ?„³H‹mH…í„‘¿èHÇI‰ÄH‹E1ÒHKL‰cI‰D$H÷sH‹H‰ÐH‹mH…íuëTf„H‹mH…ítBI‰Ì¿èHÇH‰ÁH‹E1ÒH‰AH÷sH‹I‰$HÐHƒ8uÁL‰ H‹mH…íu¾HƒÄ[]A\A]Ãf.„H‹WHƒút1H‰ÐHÁè= 0. One or more outputs forming a sequence of tensors after splittingConstrain split size to integral tensor.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.Split a tensor into a sequence of tensors, along the specified 'axis'. Lengths of the parts can be specified using argument 'split'. 'split' must contain only positive numbers. 'split' is either a scalar (tensor of empty shape), or a 1-D tensor. If 'split' is a scalar, then 'input' will be split into equally sized chunks(if possible). Last chunk will be smaller if the 'input' size along the given axis 'axis' is not divisible by 'split'. Otherwise, the tensor is split into 'size(split)' chunks, with lengths of the parts on 'axis' specified in 'split'. In this scenario, the sum of entries in 'split' must be equal to the dimension size of input tensor on 'axis'. Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. Insert and concatenate on a new axis or not, default 0 means do not insert new axis. Concatenate a sequence of tensors into a single tensor. All input tensors must have the same shape, except for the dimension size of the axis to concatenate on. By default 'new_axis' is 0, the behavior is similar to numpy.concatenate. When 'new_axis' is 1, the behavior is similar to numpy.stack. 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[]A\A]A^é[]A\A]A^ÃN10onnx_torch14InferenceErrorEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_E*ZN10onnx_torch11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_tensor(complex64tensor(complex12seq(tensor(uint8seq(tensor(uint1seq(tensor(uint3seq(tensor(uint6seq(tensor(int8)seq(tensor(int16seq(tensor(int32seq(tensor(int64seq(tensor(floatseq(tensor(doublseq(tensor(strinseq(tensor(bool)seq(tensor(compl Construct an empty tensor sequence, with given Constrain output types to any te Construct a tensor sequence containing 'inputs' tensors. All tensors in 'inputs' must have the same data type. Constrain input types to any tenConstrain to any Outputs a tensor copy from the tensor at 'position' in 'input_sequence'. Accepted range for 'position' is in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. 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è1öL‰ïèƒ|$8„<Iƒþ„²H¼$`L‰îèL‰ïèº1öH‰ïèH‰ÃLhL‰åë+€QH‹|È‰SH‰îèHƒÅ0H„$H9ÅtdH‹C H…Àt;HcK‹9Ñ|É‹s9òt-ƒÂH‹{‰èH‰ÇHcCH‹S H‰KH‰|Âë§@‹sƒÆL‰ïèH‹C ‹ëÀf.„H‰ÃHƒë0H‰ßèL9ãuïL‰ÿèH‹<$èH‹|$èé¦ýÿÿ@L¯¼$ƒ¼$ÈtL‰ïèÇ„$ÈL‰¼$Àé7þÿÿDH‹D$0H™I÷þƒ¼$ÈH‰ÃtL‰ïèÇ„$ÈH‰œ$Àé—þÿÿf„H‹D$`H™I÷þƒ¼$ÈI‰ÇtL‰ïèÇ„$ÈL‰¼$Àéþÿÿf„H¼$`L‰þèéIþÿÿH‹t$H¼$èé“ýÿÿH‹4$H¼$0èéÌýÿÿèé‰üÿÿH‰ÅéH‰ÅéH‰ÃéH‰ÅéI‰ÄéH‰ÃéH‰ÃéH‰ÃéH‰ÅéH‰ÅéH‰ÅéH‰ÅéH‰÷é¸ûÿÿ…öxLH‹‡ÀH‹—ÈHcöH¹«ªªªªªªªH)ÂHÁúH¯ÑH9Ös!HvHÁâHÐ1Ò‹H(…ÉHDÂÃf.„1ÀÃ¸Ã…ÒtƒúuH‰71ÀÃH‹H‰1ÀÃ¸Ã…ÒtƒúuH‰71ÀÃH‹H‰1ÀÃI‰øH‰÷Aÿ L‹HƒÇI9øtL‰Çé@ÃATI‰üUSH‹_H‹/H9ët$DH‹}HEH9Çt+èHƒÅ H9ëuåI‹,$H…ít$[H‰ï]A\é„HƒÅ H9ëu¿ëØD[]A\ÃH‹UH‰ýHƒÀH‰H‹HE H9ÇtèH‰ï]é…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃfH‹H‰ëÖbasic_string::_M_construct null not valid should be of integer type and specify a type. does not specify a valid type.Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsInput rank for starts and ends should be the same: ( The operator casts the elements of a given input tensor to a data type specified by the 'to' argument and returns an output tensor of the same size in the converted type. The 'to' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations (e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may result 100. There are some string literals reserved for special floating-point values; "+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively. Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly, this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors to string tensors, plain floating-point representation (such as "314.15926") would be used. Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior. Conversion from a numerical type to any numerical type is always allowed. User must be aware of precision loss and value change caused by range difference between two types. For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type. The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoOutput tensor with the same shape as input with type specified by the 'to' argumentConstrain input types. Casting from complex is not supported.Constrain output types. Casting to complex is not supported./opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/defs/tensor/defs.cc The operator casts the elements of a given input tensor (the first input) to the same data type as the elements of the second input tensor. See documentation of the Cast operator for further details. The (first) input tensor will be cast to produce a tensor of the same type as this (second input) tensor.Output tensor produced by casting the first input tensor to have the same type as the second input tensor. Reshape the input tensor similar to numpy.reshape. First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor. At most one dimension of the new shape can be -1. In this case, the value is inferred from the size of the tensor and the remaining dimensions. A dimension could also be 0, in which case the actual dimension value is unchanged (i.e. taken from the input tensor). If 'allowzero' is set, and the new shape includes 0, the dimension will be set explicitly to zero (i.e. not taken from input tensor)(Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy. Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor. Optional attributes start and end can be used to compute a slice of the input tensor's shape. If start axis is omitted, the slice starts from axis 0. The end axis, if specified, is exclusive (and the returned value will not include the size of that axis). If the end axis is omitted, the axes upto the last one will be included. Negative axes indicate counting back from the last axis. Note that axes will be clipped to the range [0, r-1], where r is the rank of the input tensor if they are out-of-range (after adding r in the case of negative axis). Thus, specifying any end value > r is equivalent to specifying an end value of r, and specifying any start value < -r is equivalent to specifying a start value of 0. For example: Input tensor with shape: [2, 3, 4] No attributes specified. Output: [2, 3, 4] Input tensor with shape: [2, 3, 4] start: -1 Output: [4] Input tensor with shape: [2, 3, 4] end: -1 Output: [2, 3] Input tensor with shape: [2, 3, 4] start: 1 end: 2 Output: [3] (Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.(Optional) Ending axis for slicing the shape. Negative value means counting dimensions from the back. If omitted, sizes of all axes upto (including) the last one will be included.Input tensor can be of arbitrary type.Constrain output to int64 tensor. Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor. Total number of elements of the input tensorConstrain output to int64 tensor, which should be a scalar though.Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..Concatenate a list of tensors into a single tensor. All input tensors must have the same shape, except for the dimension size of the axis to concatenate on.List of tensors for concatenationOptional length of each output. Values should be >= 0.Sum of the values must be equal to the dim value at 'axis' specified.One or more outputs forming list of tensors after splittingWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).Split a tensor into a list of tensors, along the specified 'axis'. Lengths of the parts can be specified using input 'split'. Otherwise, the tensor is split to equal sized parts. 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 `starts`, `ends`, `axes` and `steps` inputs to specify the start and end dimension and step 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 represents 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` when sclicing forward and 'INT_MIN' when slicing backward. If a negative value is passed for step, it represents slicing backward. However step value cannot be 0. If `axes` are omitted, they are set to `[0, ..., ndim-1]`. If `steps` are omitted, they are set to `[1, ..., 1]` of length `len(starts)` Example 1: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] steps = [1, 2] result = [ [5, 7], ] Example 2: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000] result = [ [2, 3, 4], ] Tensor of data to extract slices from.1-D tensor of starting indices of corresponding axis in `axes`1-D tensor of ending indices (exclusive) of corresponding axis in `axes`1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).1-D tensor of slice step of corresponding axis in `axes`. Negative value means slicing backward. 'steps' cannot be 0. Defaults to 1.Constrain input and output types to all tensor types.Constrain indices to integer types Transpose the input tensor similar to numpy.transpose. For example, when perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape will be (2, 1, 3). A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given. This operator is deprecated. Please use ScatterElements, which provides the same functionality. Scatter takes three inputs `data`, `updates`, and `indices` of the same rank r >= 1 and an optional attribute axis that identifies an axis of `data` (by default, the outer-most axis, that is axis 0). The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. For each entry in `updates`, the target index in `data` is obtained by combining the corresponding entry in `indices` with the index of the entry itself: the index-value for dimension = axis is obtained from the value of the corresponding entry in `indices` and the index-value for dimension != axis is obtained from the index of the entry itself. For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry is performed as below: ``` output[indices[i][j]][j] = updates[i][j] if axis = 0, output[i][indices[i][j]] = updates[i][j] if axis = 1, ``` This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation. Example 1: ``` data = [ [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], ] indices = [ [1, 0, 2], [0, 2, 1], ] updates = [ [1.0, 1.1, 1.2], [2.0, 2.1, 2.2], ] output = [ [2.0, 1.1, 0.0] [1.0, 0.0, 2.2] [0.0, 2.1, 1.2] ] ``` Example 2: ``` data = [[1.0, 2.0, 3.0, 4.0, 5.0]] indices = [[1, 3]] updates = [[1.1, 2.1]] axis = 1 output = [[1.0, 1.1, 3.0, 2.1, 5.0]] ``` Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of rank r >=1 (same rank and shape as indices)Tensor of rank r >= 1 (same rank as input). ScatterND takes three inputs `data` tensor of rank r >= 1, `indices` tensor of rank q >= 1, and `updates` tensor of rank q + r - indices.shape[-1] - 1. The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. Note that `indices` should not have duplicate entries. That is, two or more `updates` for the same index-location is not supported. `indices` is an integer tensor. Let k denote indices.shape[-1], the last dimension in the shape of `indices`. `indices` is treated as a (q-1)-dimensional tensor of k-tuples, where each k-tuple is a partial-index into `data`. Hence, k can be a value at most the rank of `data`. When k equals rank(data), each update entry specifies an update to a single element of the tensor. When k is less than rank(data) each update entry specifies an update to a slice of the tensor. `updates` is treated as a (q-1)-dimensional tensor of replacement-slice-values. Thus, the first (q-1) dimensions of updates.shape must match the first (q-1) dimensions of indices.shape. The remaining dimensions of `updates` correspond to the dimensions of the replacement-slice-values. Each replacement-slice-value is a (r-k) dimensional tensor, corresponding to the trailing (r-k) dimensions of `data`. Thus, the shape of `updates` must equal indices.shape[0:q-1] ++ data.shape[k:r-1], where ++ denotes the concatenation of shapes. The `output` is calculated via the following equation: output = np.copy(data) update_indices = indices.shape[:-1] for idx in np.ndindex(update_indices): output[indices[idx]] = updates[idx] The order of iteration in the above loop is not specified. In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. This ensures that the output value does not depend on the iteration order. `reduction` allows specification of an optional reduction operation, which is applied to all values in `updates` tensor into `output` at the specified `indices`. In cases where `reduction` is set to "none", indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. This ensures that the output value does not depend on the iteration order. When `reduction` is set to "add", `output` is calculated as follows: output = np.copy(data) update_indices = indices.shape[:-1] for idx in np.ndindex(update_indices): output[indices[idx]] += updates[idx] When `reduction` is set to "mul", `output` is calculated as follows: output = np.copy(data) update_indices = indices.shape[:-1] for idx in np.ndindex(update_indices): output[indices[idx]] *= updates[idx] This operator is the inverse of GatherND. Example 1: ``` data = [1, 2, 3, 4, 5, 6, 7, 8] indices = [[4], [3], [1], [7]] updates = [9, 10, 11, 12] output = [1, 11, 3, 10, 9, 6, 7, 12] ``` Example 2: ``` data = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]] indices = [[0], [2]] updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]] output = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]] ``` Type of reduction to apply: none (default), add, mul. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.Tensor of rank q + r - indices_shape[-1] - 1. ScatterElements takes three inputs `data`, `updates`, and `indices` of the same rank r >= 1 and an optional attribute axis that identifies an axis of `data` (by default, the outer-most axis, that is axis 0). The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. For each entry in `updates`, the target index in `data` is obtained by combining the corresponding entry in `indices` with the index of the entry itself: the index-value for dimension = axis is obtained from the value of the corresponding entry in `indices` and the index-value for dimension != axis is obtained from the index of the entry itself. `reduction` allows specification of an optional reduction operation, which is applied to all values in `updates` tensor into `output` at the specified `indices`. In cases where `reduction` is set to "none", indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry is performed as below: ``` output[indices[i][j]][j] = updates[i][j] if axis = 0, output[i][indices[i][j]] = updates[i][j] if axis = 1, ``` When `reduction` is set to "add", the update corresponding to the [i][j] entry is performed as below: ``` output[indices[i][j]][j] += updates[i][j] if axis = 0, output[i][indices[i][j]] += updates[i][j] if axis = 1, ``` When `reduction` is set to "mul", the update corresponding to the [i][j] entry is performed as below: ``` output[indices[i][j]][j] *= updates[i][j] if axis = 0, output[i][indices[i][j]] *= updates[i][j] if axis = 1, ``` This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation. Example 1: ``` data = [ [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], ] indices = [ [1, 0, 2], [0, 2, 1], ] updates = [ [1.0, 1.1, 1.2], [2.0, 2.1, 2.2], ] output = [ [2.0, 1.1, 0.0] [1.0, 0.0, 2.2] [0.0, 2.1, 1.2] ] ``` Example 2: ``` data = [[1.0, 2.0, 3.0, 4.0, 5.0]] indices = [[1, 3]] updates = [[1.1, 2.1]] axis = 1 output = [[1.0, 1.1, 3.0, 2.1, 5.0]] ``` Input and output types can be of any tensor type. Given `data` tensor of rank r >= 1, and `indices` tensor of rank q, gather entries of the axis dimension of `data` (by default outer-most one as axis=0) indexed by `indices`, and concatenates them in an output tensor of rank q + (r - 1). axis = 0 : Let k = indices[i_{0}, ..., i_{q-1}] Then output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2}] = input[k , j_{0}, ..., j_{r-2}] ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] indices = [ [0, 1], [1, 2], ] output = [ [ [1.0, 1.2], [2.3, 3.4], ], [ [2.3, 3.4], [4.5, 5.7], ], ] ``` axis = 1 : Let k = indices[i_{0}, ..., i_{q-1}] Then output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2}] = input[j_{0}, k, j_{1}, ..., j_{r-2}] ``` data = [ [1.0, 1.2, 1.9], [2.3, 3.4, 3.9], [4.5, 5.7, 5.9], ] indices = [ [0, 2], ] axis = 1, output = [ [[1.0, 1.9]], [[2.3, 3.9]], [[4.5, 5.9]], ] ``` Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Constrain input and output types to any tensor type. GatherElements takes two inputs `data` and `indices` of the same rank r >= 1 and an optional attribute `axis` that identifies an axis of `data` (by default, the outer-most axis, that is axis 0). It is an indexing operation that produces its output by indexing into the input data tensor at index positions determined by elements of the `indices` tensor. Its output shape is the same as the shape of `indices` and consists of one value (gathered from the `data`) for each element in `indices`. For instance, in the 3-D case (r = 3), the output produced is determined by the following equations: ``` out[i][j][k] = input[index[i][j][k]][j][k] if axis = 0, out[i][j][k] = input[i][index[i][j][k]][k] if axis = 1, out[i][j][k] = input[i][j][index[i][j][k]] if axis = 2, ``` This operator is also the inverse of ScatterElements. It is similar to Torch's gather operation. Example 1: ``` data = [ [1, 2], [3, 4], ] indices = [ [0, 0], [1, 0], ] axis = 1 output = [ [1, 1], [4, 3], ] ``` Example 2: ``` data = [ [1, 2, 3], [4, 5, 6], [7, 8, 9], ] indices = [ [1, 2, 0], [2, 0, 0], ] axis = 0 output = [ [4, 8, 3], [7, 2, 3], ] ``` Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of the same shape as indices. Remove single-dimensional entries from the shape of a tensor. Takes an input `axes` with a list of axes to squeeze. If `axes` is not provided, all the single dimensions will be removed from the shape. If an axis is selected with shape entry not equal to one, an error is raised. Tensors with at least max(dims) dimensions.List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Reshaped tensor with same data as input. Insert single-dimensional entries to the shape of an input tensor (`data`). Takes one required input `axes` - which contains a list of dimension indices and this operator will insert a dimension of value `1` into the corresponding index of the output tensor (`expanded`). For example: Given an input tensor (`data`) of shape [3, 4, 5], then Unsqueeze(data, axes=[0, 4]) outputs a tensor (`expanded`) containing same data as `data` but with shape [1, 3, 4, 5, 1]. The input `axes` should not contain any duplicate entries. It is an error if it contains duplicates. The rank of the output tensor (`output_rank`) is the rank of the input tensor (`data`) plus the number of values in `axes`. Each value in `axes` should be within the (inclusive) range [-output_rank , output_rank - 1]. The order of values in `axes` does not matter and can come in any order. List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).Blocks of [blocksize, blocksize] are moved.SpaceToDepth rearranges blocks of spatial data into depth. More specifically, this op outputs a copy of the input tensor where values from the height and width dimensions are moved to the depth dimension. Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.DepthToSpace rearranges (permutes) data from depth into blocks of spatial data. This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of the input tensor where values from the depth dimension are moved in spatial blocks to the height and width dimensions. By default, `mode` = `DCR`. In the DCR mode, elements along the depth dimension from the input tensor are rearranged in the following order: depth, column, and then row. The output y is computed from the input x as below: b, c, h, w = x.shape tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h, w]) tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2]) y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize, w * blocksize]) In the CRD mode, elements along the depth dimension from the input tensor are rearranged in the following order: column, row, and the depth. The output y is computed from the input x as below: b, c, h, w = x.shape tmp = np.reshape(x, [b, c // (blocksize ** 2), blocksize, blocksize, h, w]) tmp = np.transpose(tmp, [0, 1, 4, 2, 5, 3]) y = np.reshape(tmp, [b, c // (blocksize ** 2), h * blocksize, w * blocksize]) Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].Constructs a tensor by tiling a given tensor. This is the same as function `tile` in Numpy, but no broadcast. For example A = [[1, 2], [3, 4]], B = [1, 2], tile(A, B) = [[1, 2, 1, 2], [3, 4, 3, 4]] 1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.Output tensor of the same dimension and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]Constrain repeat's type to int64 tensors.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'. Upsample the input tensor. Each dimension value of the output tensor is: output_dimension = floor(input_dimension * scale). Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).coordinate_transformation_modeFour modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. One of 'scales' and 'sizes' MUST be specified and it is an error if both are specified. If 'sizes' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.The size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified.Constrain input 'X' and output 'Y' to all tensor types.Constrain roi type to float or double. Resize the input tensor. In general, it calculates every value in the output tensor as a weighted average of neighborhood (a.k.a. sampling locations) in the input tensor. Each dimension value of the output tensor is: output_dimension = floor(input_dimension * (roi_end - roi_start) * scale) if input \"sizes\" is not specified. Constrain input and output types to all tensor, sequence, and optional types.optional(seq(tensor(float16)))optional(seq(tensor(complex64)))optional(seq(tensor(complex128))) Selects slices from an input tensor along a given axis where condition evaluates to True for each axis index. In case axis is not provided, input is flattened before elements are selected. Compress behaves like numpy.compress: https://docs.scipy.org/doc/numpy/reference/generated/numpy.compress.html (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.Constrains to boolean tensors. Produces a one-hot tensor based on inputs. The locations represented by the index values in the 'indices' input tensor will have 'on_value' and the other locations will have 'off_value' in the output tensor, where 'on_value' and 'off_value' are specified as part of required input argument 'values', which is a two-element tensor of format [off_value, on_value]. The rank of the output tensor will be one greater than the rank of the input tensor. The additional dimension is for one-hot representation. The additional dimension will be inserted at the position specified by 'axis'. If 'axis' is not specified then then additional dimension will be inserted as the innermost dimension, i.e. axis=-1. The size of the additional dimension is specified by required scalar input 'depth'. The type of the output tensor is the same as the type of the 'values' input. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor. when axis = 0: output[input[i, j, k], i, j, k] = 1 for all i, j, k and 0 otherwise. when axis = -1: output[i, j, k, input[i, j, k]] = 1 for all i, j, k and 0 otherwise. (Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).Input tensor containing indices. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [-depth, depth-1]. In case 'depth' is of non-integer type, it will be casted to int64 before use.Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor. Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used.Constrains input to only numeric types.Constrain input types to float tensors.Constrain output types to boolean tensors.Map infinity to true and other values to false.(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.(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. Return elements, either from X or Y, depending on condition (with Numpy-style broadcasting support). Where behaves like numpy.where with three parameters: https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html **History** - Version 16 adds bfloat16 to the types allowed (for the second and third parameter). When True (nonzero), yield X, otherwise yield Yvalues selected at indices where condition is Truevalues selected at indices where condition is FalseTensor of shape equal to the broadcasted shape of condition, X, and Y. Returns the indices of the elements that are non-zero (in row-major order - by dimension). NonZero behaves similar to numpy.nonzero: https://docs.scipy.org/doc/numpy/reference/generated/numpy.nonzero.html Reverse batch of sequences having different lengths specified by `sequence_lens`. For each slice i iterating on batch axis, the operator reverses the first sequence_lens[i] elements on time axis, and copies elements whose index's beyond sequence_lens[i] to the output. So the output slice i contains reversed sequences on the first sequence_lens[i] elements, then have original values copied for the other elements. Example 1: input = [[0.0, 4.0, 8.0, 12.0], [1.0, 5.0, 9.0, 13.0], [2.0, 6.0, 10.0, 14.0], [3.0, 7.0, 11.0, 15.0]] sequence_lens = [4, 3, 2, 1] time_axis = 0 batch_axis = 1 output = [[3.0, 6.0, 9.0, 12.0], [2.0, 5.0, 8.0, 13.0], [1.0, 4.0, 10.0, 14.0], [0.0, 7.0, 11.0, 15.0]] Example 2: input = [[0.0, 1.0, 2.0, 3.0 ], [4.0, 5.0, 6.0, 7.0 ], [8.0, 9.0, 10.0, 11.0], [12.0, 13.0, 14.0, 15.0]] sequence_lens = [1, 2, 3, 4] time_axis = 1 batch_axis = 0 output = [[0.0, 1.0, 2.0, 3.0 ], [5.0, 4.0, 6.0, 7.0 ], [10.0, 9.0, 8.0, 11.0], [15.0, 14.0, 13.0, 12.0]] (Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.Tensor with same shape of input. Find the unique elements of a tensor. When an optional attribute 'axis' is provided, unique subtensors sliced along the 'axis' are returned. Otherwise the input tensor is flattened and unique values of the flattened tensor are returned. This operator returns the unique values or sliced unique subtensors of the input tensor and three optional outputs. The first output tensor 'Y' contains all unique values or subtensors of the input. The second optional output tensor 'indices' contains indices of 'Y' elements' first occurance in 'X'.. The third optional output tensor 'inverse_indices' contains, for elements of 'X', its corresponding indices in 'Y'. ". The fourth optional output tensor 'counts' contains the count of each element of 'Y' in the input. Outputs are either sorted in ascending order or optionally in the order of the first occurrence of the values in the input. https://docs.scipy.org/doc/numpy/reference/generated/numpy.unique.html Example 1: input_X = [2, 1, 1, 3, 4, 3] attribute_sorted = 0 attribute_axis = None output_Y = [2, 1, 3, 4] output_indices = [0, 1, 3, 4] output_inverse_indices = [0, 1, 1, 2, 3, 2] output_counts = [1, 2, 2, 1] Example 2: input_X = [[1, 3], [2, 3]] attribute_sorted = 1 attribute_axis = None output_Y = [1, 2, 3] output_indices = [0, 2, 1] output_inverse_indices = [0, 2, 1, 2] output_counts = [1, 1, 2] Example 3: input_X = [[1, 0, 0], [1, 0, 0], [2, 3, 4]] attribute_sorted = 1 attribute_axis = 0 output_Y = [[1, 0, 0], [2, 3, 4]] output_indices = [0, 2] output_inverse_indices = [0, 0, 1] output_counts = [2, 1] Example 4: input_x = [[[1., 1.], [0., 1.], [2., 1.], [0., 1.]], [[1., 1.], [0., 1.], [2., 1.], [0., 1.]]] attribute_sorted = 1 attribute_axis = 1 intermediate data are presented below for better understanding: there are 4 subtensors sliced along axis 1 of input_x (shape = (2, 4, 2)): A: [[1, 1], [1, 1]], [[0, 1], [0, 1]], [[2, 1], [2, 1]], [[0, 1], [0, 1]]. there are 3 unique subtensors: [[1, 1], [1, 1]], [[0, 1], [0, 1]], [[2, 1], [2, 1]]. sorted unique subtensors: B: [[0, 1], [0, 1]], [[1, 1], [1, 1]], [[2, 1], [2, 1]]. output_Y is constructed from B: [[[0. 1.], [1. 1.], [2. 1.]], [[0. 1.], [1. 1.], [2. 1.]]] output_indices is to map from B to A: [1, 0, 2] output_inverse_indices is to map from A to B: [1, 0, 2, 0] output_counts = [2 1 1] (Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).A N-D input tensor that is to be processed.A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X'A 1-D INT64 tensor containing indices of 'Y' elements' first occurance in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor. A 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'. A 1-D INT64 tensor containing the count of each element of 'Y' in input 'X'Input can be of any tensor type. Given `data` tensor of rank `r` >= 1, `indices` tensor of rank `q` >= 1, and `batch_dims` integer `b`, this operator gathers slices of `data` into an output tensor of rank `q + r - indices_shape[-1] - 1 - b`. `indices` is an q-dimensional integer tensor, best thought of as a `(q-1)`-dimensional tensor of index-tuples into `data`, where each element defines a slice of `data` `batch_dims` (denoted as `b`) is an integer indicating the number of batch dimensions, i.e the leading `b` number of dimensions of `data` tensor and `indices` are representing the batches, and the gather starts from the `b+1` dimension. Some salient points about the inputs' rank and shape: 1) r >= 1 and q >= 1 are to be honored. There is no dependency condition to be met between ranks `r` and `q` 2) The first `b` dimensions of the shape of `indices` tensor and `data` tensor must be equal. 3) b < min(q, r) is to be honored. 4) The `indices_shape[-1]` should have a value between 1 (inclusive) and rank `r-b` (inclusive) 5) All values in `indices` are expected to be within bounds [-s, s-1] along axis of size `s` (i.e.) `-data_shape[i] <= indices[...,i] <= data_shape[i] - 1`. It is an error if any of the index values are out of bounds. The output is computed as follows: The output tensor is obtained by mapping each index-tuple in the `indices` tensor to the corresponding slice of the input `data`. 1) If `indices_shape[-1] > r-b` => error condition 2) If `indices_shape[-1] == r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensors containing 1-D tensors of dimension `r-b`, where `N` is an integer equals to the product of 1 and all the elements in the batch dimensions of the indices_shape. Let us think of each such `r-b` ranked tensor as `indices_slice`. Each *scalar value* corresponding to `data[0:b-1,indices_slice]` is filled into the corresponding location of the `(q-b-1)`-dimensional tensor to form the `output` tensor (Example 1 below) 3) If `indices_shape[-1] < r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensor containing 1-D tensors of dimension `< r-b`. Let us think of each such tensors as `indices_slice`. Each *tensor slice* corresponding to `data[0:b-1, indices_slice , :]` is filled into the corresponding location of the `(q-b-1)`-dimensional tensor to form the `output` tensor (Examples 2, 3, 4 and 5 below) This operator is the inverse of `ScatterND`. `Example 1` batch_dims = 0 data = [[0,1],[2,3]] # data_shape = [2, 2] indices = [[0,0],[1,1]] # indices_shape = [2, 2] output = [0,3] # output_shape = [2] `Example 2` batch_dims = 0 data = [[0,1],[2,3]] # data_shape = [2, 2] indices = [[1],[0]] # indices_shape = [2, 1] output = [[2,3],[0,1]] # output_shape = [2, 2] `Example 3` batch_dims = 0 data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[0,1],[1,0]] # indices_shape = [2, 2] output = [[2,3],[4,5]] # output_shape = [2, 2] `Example 4` batch_dims = 0 data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[[0,1]],[[1,0]]] # indices_shape = [2, 1, 2] output = [[[2,3]],[[4,5]]] # output_shape = [2, 1, 2] `Example 5` batch_dims = 1 data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[1],[0]] # indices_shape = [2, 1] output = [[2,3],[4,5]] # output_shape = [2, 2] The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Supported modes: `constant`(default), `reflect`, `edge` Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array Example 1 (`constant` mode): 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] mode = 'constant' constant_value = 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], ] Example 2 (`reflect` mode): data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] Example 3 (`edge` mode): data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`.(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0, empty string or False). Given a 2-D matrix or batches of 2-D matrices, returns the upper or lower triangular part of the tensor(s). The attribute "upper" determines whether the upper or lower part is retained. If set to true, the upper triangular matrix is retained. Lower triangular matrix is retained otherwise. Default value for the "upper" attribute is true. Trilu takes one input tensor of shape [*, N, M], where * is zero or more batch dimensions. The upper triangular part consists of the elements on and above the given diagonal (k). The lower triangular part consists of elements on and below the diagonal. All other elements in the matrix are set to zero. If k = 0, the triangular part on and above/below the main diagonal is retained. If upper is set to true, a positive k retains the upper triangular matrix excluding the main diagonal and (k-1) diagonals above it. A negative k value retains the main diagonal and |k| diagonals below it. If upper is set to false, a positive k retains the lower triangular matrix including the main diagonal and k diagonals above it. A negative k value excludes the main diagonal and (|k|-1) diagonals below it. Boolean. Indicates whether upper or lower part of matrix is retained. Default is true.Input tensor of rank 2 or higher.A 0-D tensor containing a single value corresponding to the number diagonals above or below the main diagonal to exclude or include. Default value is 0 if it's not specified.Output tensor of the same type and shape as the input tensor.OneHot node must have three inputs.Input 'depth' must be a scalar or rank 1 tensor.Input 'depth' must have exactly one element.Input 'values' must be rank 1 tensor.Input 'values' must have exactly two elements.Indices tensor must have rank >= 1'axis' must be in [-rank(indices), rank(indices)-1]Target shape may not have multiple -1 dimensions.Invalid Target shape product of 0. Product cannot be 0 in combination with -1Dimension could not be inferred: incompatible shapesAll inputs to Concat must have same rankCan't merge shape info. Both source and target dimension have values but they differ. Source= does not match type of output: expected to have tensor or sparse tensor typedata tensor must have rank >= 1'axes' attribute must not contain any duplicatesvalues in 'axes' are beyond the bounds of the computed output shape'Repeats' input must be 1D tensor of type int64'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.'sequence_lens' must have rank of 1Invalid value for attribute axisBoth `data` and `indices` input tensors in GatherND op need to have rank larger than 0.Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensor'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64Pads has incorrect number of valuesInvalid value of attribute 'axis'. Rank=Mismatch between number of splits (Mismatch between the sum of 'split' () and the split dimension of the input (The input is not evenly splittableSlice op must have either three, four or five inputs.Incorrect or missing input value for starts and endsInput axes has incorrect lengthInput steps has incorrect lengthInput tensor must be 4-dimensionalAUE1íATUH‰õSH‰ûHƒìHƒ?„³H‹mH…í„‘¿èHÇI‰ÄH‹E1ÒHKL‰cI‰D$H÷sH‹H‰ÐH‹mH…íuëTf„H‹mH…ítBI‰Ì¿èHÇH‰ÁH‹E1ÒH‰AH÷sH‹I‰$HÐHƒ8uÁL‰ H‹mH…íu¾HƒÄ[]A\A]Ãf.„H‹WHƒút1H‰ÐHÁè= 1.indicesupdatesScatternonereductionTensor of rank q >= 1.ScatterNDScatterElementsTensor of rank q + (r - 1).GatherGatherElementssqueezedSqueezeOriginal tensorexpandedUnsqueezeblocksizeSpaceToDepthDCRmodeDepthToSpaceInput tensor of any shape.repeatsTilenearestN-D tensorXscalesN-D tensor after resizingYUpsamplecubic_coeff_aexclude_outsidehalf_pixelround_prefer_floornearest_modeextrapolation_valueroisizesResizeIdentity operatorVInput tensorTensor to copy input into.seq(tensor(uint8))seq(tensor(uint16))seq(tensor(uint32))seq(tensor(uint64))seq(tensor(int8))seq(tensor(int16))seq(tensor(int32))seq(tensor(int64))seq(tensor(float16))seq(tensor(float))seq(tensor(double))seq(tensor(string))seq(tensor(bool))seq(tensor(complex64))seq(tensor(complex128))optional(seq(tensor(uint8)))optional(seq(tensor(uint16)))optional(seq(tensor(uint32)))optional(seq(tensor(uint64)))optional(seq(tensor(int8)))optional(seq(tensor(int16)))optional(seq(tensor(int32)))optional(seq(tensor(int64)))optional(seq(tensor(float)))optional(seq(tensor(double)))optional(seq(tensor(string)))optional(seq(tensor(bool)))optional(tensor(uint8))optional(tensor(uint16))optional(tensor(uint32))optional(tensor(uint64))optional(tensor(int8))optional(tensor(int16))optional(tensor(int32))optional(tensor(int64))optional(tensor(float16))optional(tensor(float))optional(tensor(double))optional(tensor(string))optional(tensor(bool))optional(tensor(complex64))optional(tensor(complex128))IdentityconditionCompressdepthT3valuesConstrain to any tensor type.OneHotIsNaNdetect_positivedetect_negativeIsInfBConstrain to boolean tensors.WhereNonZerotime_axisbatch_axisTensor of rank r >= 2.sequence_lensReverseSequencesortedinverse_indicescountsUniquebatch_dimsGatherNDInput tensor.padsconstant_valueTensor after padding.PadupperkTriluInvalid position of 0.Invalid dimension value: Dimension=Invalid attribute perm {}, input shape = {, }Input: type: type: and Output axis must be in [-r, r-1]Dimension of input must be 1 instead of 'input' must have rank >= 2Input rank must be >= 2. 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max_size()AWfïÀI‰ÿHÁâH¸àÿÿÿÿÿÿAVL4AUATUSHƒì(HÇGH9Â‡H‰ÓH…Ò„ÑH‰×I‰ôH‰õèHÃH‰D$I‰I‰_M9ô„ÛH‰ÃHD$H‰$ë'DA¶EˆCHƒÅ L‰cHƒÃ BÆ'I9î„‡L‹mL‹eH{H‰;L‰èLàtM…ít~L‰d$IƒüwIƒüt±M…ät´ë'f.„H‹4$1ÒH‰ßèH‰H‰ÇH‹D$H‰CL‰âL‰îèL‹d$H‹;ésÿÿÿHÇ1ÛHÇGI‰_HƒÄ([]A\A]A^A_ÃH=èH‹\$ëÚH=èH‰ÇëH‰Åë/èH;\$t8H‹D$H‹8HƒÀH9ÇtèHƒD$ ëÛèI‹?H…ÿtèH‰ïèèH‰ÅëÜtensor(uint8)tensor(uint16)tensor(uint32)tensor(uint64)tensor(int8)tensor(int16)tensor(int32)tensor(int64)tensor(bfloat16)tensor(float16)tensor(float)tensor(double)tensor(string)tensor(bool)tensor(complex64)tensor(complex128)AUATUSHìL‹-A¶E„ÀtL‹%HÄL‰à[]A\A]ÃL‰ïè…ÀtÙHl$H5H‰ïèH|$0H5èH|$PH5èH|$pH5èH¼$H5èH¼$°H5èH¼$ÐH5èH¼$ðH5èH¼$H5èH¼$0H5èH¼$PH5èH¼$pH5èH¼$H5èH¼$°H5èH¼$ÐH5èHœ$ðH5H‰ßèL‹%HL$H‰îºL‰çèHL‰æH=èL‰ïèëfDèHCàH9ë„RþÿÿH‰ÃH‹;HCH9ÇußHCàH9ëuèé5þÿÿH‰Åë*H‰ÃL¤$Iƒì I‹<$ID$H9ÇtèI9ìuäH‰ÝL‰ïèH‰ïèAUATUSHìøL‹-A¶E„ÀtL‹%HÄøL‰à[]A\A]ÃL‰ïè…ÀtÙHl$H5H‰ïèH|$0H5èH|$PH5èH|$pH5èH¼$H5èH¼$°H5èH¼$ÐH5èH¼$ðH5èH¼$H5èH¼$0H5èH¼$PH5èH¼$pH5èH¼$H5èH¼$°H5èHœ$ÐH5H‰ßèL‹%HL$H‰îºL‰çèHL‰æH=èL‰ïèëf.„èHCàH9ë„bþÿÿH‰ÃH‹;HCH9ÇußHCàH9ëuèéEþÿÿH‰Åë*H‰ÃL¤$ðIƒì I‹<$ID$H9ÇtèI9ìuäH‰ÝL‰ïèH‰ïèAUATUSHìxH‹-¶E„ÀtL‹%HÄxL‰à[]A\A]Ã@H‰ïè…ÀtØLl$H5L‰ïèH|$0H5èH|$PH5èH|$pH5èH¼$H5èH¼$°H5èH¼$ÐH5èH¼$ðH5èH¼$H5èH¼$0H5èHœ$PH5H‰ßèL‹%HL$L‰îºL‰çèHL‰æH=èH‰ïèëf.„èHCàL9ë„±þÿÿH‰ÃH‹;HCH9ÇußHCàL9ëuèé”þÿÿI‰Äë%I‰ÄHœ$pHƒë H‹;HCH9ÇtèL9ëuæH‰ïèL‰çèH‹SH‰ûHƒÀH‰H‹HHCXH9ÇtèH‹H{8HƒÀH‰[éH‹UH‰ýHƒÀH‰H‹HHEXH9ÇtèH‹H}8HƒÀH‰EèH‰ï]é[TypeInferenceError] Output is null expected to have tensor or sparse tensor type: AWAVAUI‰õATA‰ÌUS‰ÓHìÈL‹5H‹IV@HƒÀfHnÈfHnÂH‹flÁ)$ÿP(H…À„NH‰Å‹@(…ÀtD9àuAƒütAƒüt-HÄÈ[]A\A]A^A_ÃH‰ïèƒH‰X ëÚ€ƒøuH‹E ƒH‰X ëÁfDH‰ïèH‹EÇE(H‰ÇHƒçþ¨uèH‰E ëÈH‹?ëð¿0L|$0èL‰ÿH‰ÃèHl$@ºH5H‰ïèºH5H‰ïèL‰îH‰ïèº0H5H‰ïèD‰æH‰ïèH‹D$pLd$ ÆD$ Hl$L‰d$HÇD$H…À…˜H´$H‰ïèIFfo$H‹¼$H‰D$0IFhH‰„$°H„$ )\$@H9Ç…‚H‹H¼$€HƒÀH‰D$HèH‹H¼$°H‹PH‹H0H‹RèH‰L0H‹P H‹H(H‰T$@H‹RèH‰L@H‹PH‹@H‰T$0H‹RèH‰D0H‹HÇD$8HƒÀH‰„$°èH‰îH‰ßèH‹ÆC HÇCHƒÀH‰HC H‰CH‹|$L9çtèH‹5HH‰ßè¿0L|$0èL‰ÿH‰ÃèHl$@ºH5H‰ïèºH5H‰ïèL‰îH‰ïèºH5H‰ïèH‹D$pLd$ ÆD$ Hl$L‰d$HÇD$H…À…H´$H‰ïèIFfo$H‹¼$H‰D$0IFhH‰„$°H„$ )T$@H9ÇtèH‹H¼$€HƒÀH‰D$HèH‹H¼$°H‹PH‹H0H‹RèH‰L0H‹P H‹H(H‰T$@H‹RèH‰L@H‹PH‹@H‰T$0H‹RèH‰D0H‹HÇD$8HƒÀH‰„$°èH‰îH‰ßèéEþÿÿL‹D$`M…ÀuI‰ÀH‹L$h1Ò1öH‰ïI)ÈèéRýÿÿL9ÀwßëàH‰ÅëwèétýÿÿL‹D$`M…ÀuI‰ÀH‹L$h1Ò1öH‰ïI)ÈèéËþÿÿL9ÀwßëàëÂH‰Åë H‰ÅëH‰ÅëH‹|$L9çuL‰ÿèH‰ßèH‰ïèèëáëËëÎëÂH‹|$L9çtÙèëÒ[ShapeInferenceError] Target=Mismatch between source and target type. Source=Unsupported Source/Target type=AWAVAUATUSHì¸D‹g(D‹n(E9ìu{H‰ûH‰õAƒü„:Aƒü„ØAƒü„†Aƒü …vAƒý …*L‹e I‹l$AƒL$H…í„Tƒ{( „xH‹H‹XH…Û„uD‹c(D‹m(E9ìt‹¿0Lt$ èL‰÷I‰ÇèHl$0ºH5H‰ïèº0H5H‰ïèD‰æH‰ïèºH5H‰ïèD‰îH‰ïèH‰åHt$8H‰ïèL‰÷èH‰îL‰ÿèH‹H‹<$AÆG IÇGHƒÀI‰IG I‰GHD$H9ÇtèH‹5HL‰ÿè€H‰ïèH‹EÇE( H‰ÇHƒçþ¨…ÞèI‰ÄH‰E I‹l$AƒL$H…í…¬þÿÿI‹D$H‰ÇHƒçþ¨…ßèƒ{( I‰D$H‰Å…‹þÿÿH‹C H‹XH…Û…‹þÿÿH‹éþÿÿAƒütRH‹C ö@„ŒL‹`M…ä„?Aƒý…õH‹] H‹{ƒKH…ÿuFH‰ßèH‰ÇèH‰CH‰Çë-H‹C ö@t>L‹`M…ä„áH‰ïèH‰ÇèH‰ÇHÄ¸L‰æ[]A\A]A^A_éDHÄ¸[]A\A]A^A_ÃfDAƒýu2L‹e I‹l$AƒL$H…í„ªƒ{(„ÿÿÿH‹éƒýÿÿ@H‰ïèH‹EÇE(H‰ÇHƒçþ¨…‹èH‰E I‰Äë¢H‰ïèH‹EÇE(H‰ÇHƒçþ¨uoèH‰E H‰ÃéàþÿÿL‹%éÿÿÿ@L‹%éµþÿÿ@H‹?éþÿÿ„L‰çèH‰ÇèI‰D$H‰Åé9ÿÿÿH‹?émÿÿÿH‹?éþÿÿH‹?ëŒ¿0Lt$ èL‰÷I‰ÅèHl$0ºH5H‰ïèºH5H‰ïèD‰æH‰ïèH‰åHt$8H‰ïèL‰÷èH‰îL‰ïèH‹H‹<$AÆE IÇEHƒÀI‰EIE I‰EHD$H9ÇtèH‹5HL‰ïèH‰Åë H‰ÅëH‰ÅëH‹<$HD$H9ÇtèëL‰÷èL‰ÿèH‰ïèH‰Åë H‰ÅëH‰ÅëH‹<$HD$H9ÇtèëL‰÷èL‰ïèH‰ïèAWAVM‰ÆAUATI‰üUH‰ÕSH‰óHì¸H‹L|$ H‰L$Ll$0HP@H‹L‰ÿfHnÂHƒÀfHnÈflÁ)$èH‰ßèH‰ÞL‰ïH‰ÂèH‰ïèH‰îL‰ïH‰ÂèH‹D$L‰ïH‹0èL‰÷èL‰öL‰ïH‰ÂèH‹D$`I\$IÇD$I‰$AÆD$H…À„L‹D$PM…À„L9À‡ýH‹L$X1Ò1öL‰çI)ÈèH‹fo$H‹¼$€HƒÀ)T$0H‰D$ H‹HƒÀhH‰„$ H„$H9ÇtèH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èHÄ¸L‰à[]A\A]A^A_ÃfDI‰ÀéûþÿÿH´$€L‰çèéúþÿÿH‰ÅëH‰ÅëI‹<$H9ûtèL‰ÿèH‰ïèInput type was nullInput Element type of input unknown expected to have tensor or sparse tensor type. 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H‹H(H‰T$ H‹RèH‰L H‹PH‹@H‰T$H‹RèH‰DH‹HÇD$HƒÀH‰„$èHÄ¨L‰à[]A\A]A^A_ÃDI‰Àé ÿÿÿHt$pL‰çèéÿÿÿH‰ÃëH‰ÅëI‹<$H9ýtèH‰ÝL‰ÿèH‰ïèRequired attribute axis is missingaxis must be in [-rank, rank-1].AVA‰öAUATUH‰ýSHƒì H‹I‰åLd$H‹L‰îL‰$$ÇD$axisHÇD$ÆD$ÿÐH‹<$H‰ÃL9çtèH…ÛtSH‹›ðH‹E1öH‰ïA‰ÞÿP8H…ÀtP‹@‰Â÷Ú9ÚÊ9ØŽÂØ…ÛDHðE…ö”ÀHƒÄ []A\A]A^ÃfDE„öt"HƒÄ D‰ð[]A\A]A^ÃHƒÄ 1À[]A\A]A^Ã¿0èHH5L‰ïH‰ÅèL‰îH‰ïèH‹ÆE HÇEHƒÀH‰EHE H‰EH‹<$L9çtèH‹5HH‰ïèH‰Åë0¿0èHH5L‰ïH‰ÅèL‰îH‰ïèë„H‹<$L9çtèH‰ïèH‰ÃëI‰ÄëH‹<$L9çtèI‰ÜH‰ïèL‰çèëÓëÖAWAVAUI‰õATI‰üUH‰ÕSHì¨H‹H‹L|$Lt$ HS@HƒÀL‰ÿfHnÂfHnÈflÁ)$èL‰ïèL‰îL‰÷H‰ÂèH‰ïèH‰îL‰÷H‰ÂèH‹D$PIl$IÇD$I‰,$AÆD$H…À„ L‹D$@M…À„ôL9À‡ëH‹L$H1Ò1öL‰çI)ÈèHCfo$H‹|$pH‰D$HChH‰„$H„$€)T$ H9ÇtèH‹H|$`HƒÀH‰D$(èH‹H¼$H‹PH‹H0H‹RèH‰LH‹P H‹H(H‰T$ H‹RèH‰L H‹PH‹@H‰T$H‹RèH‰DH‹HÇD$HƒÀH‰„$èHÄ¨L‰à[]A\A]A^A_ÃDI‰Àé ÿÿÿHt$pL‰çèéÿÿÿH‰ÃëH‰ÅëI‹<$H9ýtèH‰ÝL‰ÿèH‰ïèAttribute expected to have tensor or sparse tensor typeATUHƒì(H‹ÿP‹P(ƒútƒúu%H‹@ H‹@H…Àt HƒÄ(]A\ÃH‹HƒÄ(]A\Ã¿0I‰äèHH5L‰çH‰ÅèL‰æH‰ïèH‹H‹<$HÇEÆE HƒÀH‰EHE H‰EHD$H9ÇtèH‹5HH‰ïèI‰ÄëH‹<$HD$H9ÇtèH‰ïèL‰çèI‰ÄëØ expected to have tensor or sparse type expected to have tensor typeAWAVAUATUH‰õS‰ÓHìÈL‹%H‹IT$@HƒÀfHnÈfHnÂH‹flÁ)$ÿP(H…À„H‰Ç‹@(ƒøt'ƒøt"…Àu<ƒûtƒûtHÄÈ1À[]A\A]A^A_ÃèHÄÈ[H‰Ç]A\A]A^A_é¿0Ll$0èL‰ïI‰ÇèLt$@ºH5L‰÷èºH5L‰÷èH‰îL‰÷èºH5L‰÷èH‹D$pLt$ ÆD$ Hl$L‰t$HÇD$H…À…H´$H‰ïèID$fo$H‹¼$H‰D$0ID$hH‰„$°H„$ )\$@H9Ç……H‹H¼$€HƒÀH‰D$HèH‹H¼$°H‹PH‹H0H‹RèH‰L0H‹P H‹H(H‰T$@H‹RèH‰L@H‹PH‹@H‰T$0H‹RèH‰D0H‹HÇD$8HƒÀH‰„$°èH‰îL‰ÿèH‹AÆG IÇGHƒÀI‰IG I‰GH‹|$L9÷tèH‹5HL‰ÿè¿0Ll$0èL‰ïI‰ÇèLt$@ºH5L‰÷èºH5L‰÷èH‰îL‰÷èº'H5L‰÷èH‹D$pLt$ ÆD$ Hl$L‰t$HÇD$H…À…!H´$H‰ïèID$fo$H‹¼$H‰D$0ID$hH‰„$°H„$ )T$@H9ÇtèH‹H¼$€HƒÀH‰D$HèH‹H¼$°H‹PH‹H0H‹RèH‰L0H‹P H‹H(H‰T$@H‹RèH‰L@H‹PH‹@H‰T$0H‹RèH‰D0H‹HÇD$8HƒÀH‰„$°èH‰îL‰ÿèéBþÿÿL‹D$`M…ÀuI‰ÀH‹L$h1Ò1öH‰ïI)ÈèéMýÿÿL9ÀwßëàH‰ÅëwèéqýÿÿL‹D$`M…ÀuI‰ÀH‹L$h1Ò1öH‰ïI)ÈèéÉþÿÿL9ÀwßëàëÂH‰Åë H‰ÅëH‰ÅëH‹|$L9÷uL‰ïèL‰ÿèH‰ïèèëáëËëÎëÂH‹|$L9÷tÙèëÒAWfïÀI‰ÿAVAUATUH‰õSHƒì(H‹^H+HÇG„H¸àÿÿÿÿÿÿH9Ã‡H‰ßèH‰D$H‹D$fHnÀHÃflÀI‰_H‰ÃAL‹uH‹mL9õ„«HD$H‰$ë&„A¶EˆCHƒÅ L‰cHƒÃ BÆ'I9îtzH{L‹eH‰;L‹mL‰èLàt M…í„L‰d$IƒüwIƒüt±M…ät´ë'f.„H‹4$1ÒH‰ßèH‰H‰ÇH‹D$H‰CL‰âL‰îèL‹d$H‹;ésÿÿÿI‰_HƒÄ([]A\A]A^A_ÃDHÇD$éÿÿÿH…ÛyèèH=èH‰ÇèH;\$tH‹D$H‹8HƒÀH9ÇtèHƒD$ ëÛèH‰ÅèI‹?H…ÿtèH‰ïèAWAVAUI‰õATI‰üUH‰ÕSHì¨H‹H‹L|$Lt$ HS@HƒÀL‰ÿfHnÂfHnÈflÁ)$èL‰ïèL‰îL‰÷H‰ÂèH‰ïèH‰îL‰÷H‰ÂèH‹D$PIl$IÇD$I‰,$AÆD$H…À„ L‹D$@M…À„ôL9À‡ëH‹L$H1Ò1öL‰çI)ÈèHCfo$H‹|$pH‰D$HChH‰„$H„$€)T$ H9ÇtèH‹H|$`HƒÀH‰D$(èH‹H¼$H‹PH‹H0H‹RèH‰LH‹P H‹H(H‰T$ H‹RèH‰L H‹PH‹@H‰T$H‹RèH‰DH‹HÇD$HƒÀH‰„$èHÄ¨L‰à[]A\A]A^A_ÃDI‰Àé ÿÿÿHt$pL‰çèéÿÿÿH‰ÃëH‰ÅëI‹<$H9ýtèH‰ÝL‰ÿèH‰ïèvector::reserveH‰ðAVHÁèH‹}8HEHH9ÇtèL‹u(L‹m M9îtGI‹}IEH9ÇtèIƒÅ ëãèë°èH‰ÃèI‹¼$èH…ÿ„&ýÿÿèéýÿÿH‹} H…ÿtèH‹}HEH9ÇtèHƒÅXélÿÿÿH‰ÅéGÿÿÿH‰ÃI‹„$hH…À„ÃüÿÿH‹<$ºH‰þÿÐé°üÿÿH‰ÅëH‰Åë#I‹„$ˆH…À„üÿÿºH‰ÞH‰ßÿÐéoüÿÿI‹„$¨H…À„DüÿÿºL‰îL‰ïÿÐé2üÿÿAVAUATUSH‹‡H‰ûH…ÀtH¿€ºH‰þÿÐH»ØèH‹ƒÈH…ÀtH»¸ºH‰þÿÐH‹ƒ¨H…ÀtH»˜ºH‰þÿÐH‹ƒˆH…ÀtH»xºH‰þÿÐH‹ƒhH…ÀtH»XºH‰þÿÐH»èH‹»Hƒ0H9ÇtèL‹³ðL‹£èM9æ„…DI‹|$8ID$HH9ÇtèM‹l$(I‹l$ I9ít)DH‹}HEH9Ç„‡èHƒÅ I9íuáI‹l$ H…ítH‰ïèI‹<$ID$H9Ç„èIƒÄXM9æu‡L‹£èM…ätL‰çèL‹«ØL‹£ÐM9å„º@I‹|$xI„$ˆH9ÇtèI‹|$XID$hH9ÇtèI‹l$0H…ítH‰ïH‹mèH…íuïI‹D$(I‹|$ 1öHÅèI‹|$ ID$PIÇD$8IÇD$0H9ÇtèI‹<$ID$H9Ç„–èIÄ¨M9å…QÿÿÿL‹£ÐM…ätL‰çèL‹«ÀL‹£¸M9å„¼fDI‹|$xI„$ˆH9ÇtèI‹|$XID$hH9ÇtèI‹l$0H…ítH‰ïH‹mèH…íuïI‹D$(I‹|$ 1öHÅèI‹|$ ID$PIÇD$8IÇD$0H9ÇtèI‹<$ID$H9Ç„ÎèIÄ¨M9å…QÿÿÿL‹£¸M…ätL‰çèH‹«H…í„ÒH‹}èH½ˆL‹eèH‹}`HEpH9ÇtèH‹}@HEPH9ÇtèH‹} HE0H9Çt{èH‰ïèM…ätvL‰åëŸ@HƒÅ I9í…[ýÿÿéuýÿÿfDIÄ¨M9å…ÀýÿÿéjþÿÿIÄ¨M9å…ˆþÿÿé2ÿÿÿIƒÄXM9æ…ëüÿÿé_ýÿÿfDH‰ïèM…äuŠH‹{`HCpH9ÇtèH‹{@HCPH9ÇtèH‹{ HC0H9ÇtèH‹;HƒÃH9ßt []A\A]A^é[]A\A]A^Ã expected to have sequence typeElement type of sequence input AVAUATI‰ÔUH‰ýSHƒìPH‹H‰t$ÿPH…À„ƒx(…†Ll$H‹p L‰ïèöD$ „ÙH‹EL‰æH‰ïÿP(ƒx(H‰Åu>H‹X H‹{ƒKH…ÿtdH‹t$(H…ö„èL‰ïèHƒÄP[]A\A]A^ÃDH‰ÇèH‹EÇE(H‰ÇHƒçþ¨uRèH‰ÃH‰E H‹{ƒKH…ÿuœH‹CH‰ÇHƒçþ¨u2èH‰CH‹t$(H‰ÇH…öu…@H‹5éuÿÿÿ@H‹?ë©H‹?ëÉH‰Åé9¿0Lt$0èHL$L‰÷LHH5I‰ÄèL‰öL‰çèH‹H‹|$0IÇD$AÆD$ HƒÀI‰$ID$ I‰D$HD$@H9ÇtèH‹5HL‰çè¿0Lt$0èHL$L‰÷LHH5H‰ÅèL‰öH‰ïèH‹H‹|$0HÇEÆE HƒÀH‰EHE H‰EHD$@H9ÇtèH‹5HH‰ïèH‹|$0HD$@H9ÇtèL‰çèL‰ïèH‰ïèI‰ÄëI‰ÄëH‹|$0HD$@H9ÇtèH‰ïèL‰çèH‰Åë¡H‰Åë° expected to have optional typeElement type of optional input AVAUATI‰ÔUH‰ýSHƒìPH‹H‰t$ÿPH…À„ƒx( …†Ll$H‹p L‰ïèöD$ „ÙH‹EL‰æH‰ïÿP(ƒx( H‰Åu>H‹X H‹{ƒKH…ÿtdH‹t$(H…ö„èL‰ïèHƒÄP[]A\A]A^ÃDH‰ÇèH‹EÇE( H‰ÇHƒçþ¨uRèH‰ÃH‰E H‹{ƒKH…ÿuœH‹CH‰ÇHƒçþ¨u2èH‰CH‹t$(H‰ÇH…öu…@H‹5éuÿÿÿ@H‹?ë©H‹?ëÉH‰Åé9¿0Lt$0èHL$L‰÷LHH5I‰ÄèL‰öL‰çèH‹H‹|$0IÇD$AÆD$ HƒÀI‰$ID$ I‰D$HD$@H9ÇtèH‹5HL‰çè¿0Lt$0èHL$L‰÷LHH5H‰ÅèL‰öH‰ïèH‹H‹|$0HÇEÆE HƒÀH‰EHE H‰EHD$@H9ÇtèH‹5HH‰ïèH‹|$0HD$@H9ÇtèL‰çèL‰ïèH‰ïèI‰ÄëI‰ÄëH‹|$0HD$@H9ÇtèH‰ïèL‰çèH‰Åë¡H‰Åë° expected to have type but instead is nullATI‰ÔUH‰ýHƒì8H‹H‰t$ÿPH…Àt{‹@(ƒøtƒøtƒøtQƒø t,HƒÄ8]A\Ã@H‹t$L‰âH‰ïèHƒÄ8]A\Ã„H‹t$L‰âH‰ïèHƒÄ8]A\Ã„H‹t$L‰âH‰ïèHƒÄ8]A\Ã¿0Ld$èHL$L‰çLHH5H‰ÅèL‰æH‰ïèH‹H‹|$HÇEÆE HƒÀH‰EHE H‰EHD$ H9ÇtèH‹5HH‰ïèI‰ÄëH‹|$HD$ H9ÇtèH‰ïèL‰çèI‰Äë×AT1öUH‰ýHƒìèH‹EH‰ïÿPH…ÀuX]A\ÃH‹E1öH‰ïÿPH…ÀtçH‹E1öH‰ïÿP‹P(ƒúuëA‹P(ƒút8ƒút3ƒútƒú u¹H‹@ ö@t¯H‹@H…ÀuÖH‹‹P(ƒúuÏ€H‹@ ‹@ƒà„Àt‚H‹EH‰ï1öÿP(H‰ï1öI‰ÄH‹EÿPHƒÄL‰æ]H‰ÇA\éFvRN10onnx_torch16InferenceContextEEN10onnx_torch14InferenceErrorEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EPFvRN10onnx_torch16InferenceContextEE*ZN10onnx_torch11GetOpSchemaINS_15Cast_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_15Cast_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN10onnx_torch11GetOpSchemaINS_19CastLike_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_19CastLike_Onnx_ver15EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN10onnx_torch11GetOpSchemaINS_18Reshape_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_16Shape_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_16Shape_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN10onnx_torch11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN10onnx_torch11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN10onnx_torch11GetOpSchemaINS_16Split_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN10onnx_torch11GetOpSchemaINS_20Transpose_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_20ScatterND_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_26ScatterElements_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN10onnx_torch11GetOpSchemaINS_25GatherElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN10onnx_torch11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN10onnx_torch11GetOpSchemaINS_23SpaceToDepth_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_23DepthToSpace_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_15Tile_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_17Resize_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_16IsNaN_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_16Where_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_18NonZero_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_19GatherND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_14Pad_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_16Trilu_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Constrain input and output types to all tensor tConstrain output types to any teInput and output types can be of any tensor type to any tensor t'X' and output 'Y' to all tensorReturns which elements of the inypes (including Constrain to allGCC: (Debian 11.2.0-16) 11.2.0zRxS0DXl€ ”¨¼Ðäø 4H\p„˜¬ÀÔèü$8L`tˆœ°ÄØì(<PdxŒ ´È4ÜeBŒD†A ƒv DBMQAB-H†`0D X8 lnAƒb Eh H@%FHŒK †H(ƒ¥ ABBEO ABB<ÔÐTEŒD †A(ƒG0£(A ABBAÃÆÌÍ0ŸBŒD†D ƒG0| AABG8H²BBŒE †A(ƒD@B (A ABBJzPLRx›<$šBEŒA †D(ƒG0¬ (A ABBKä5H†h€_KŽBB ŒA(†D0ƒN (A BBBGV (A BBBCt(A BBBEÃÆÌÍÎH0ƒ†ŒŽV (A BBBK„ŒA†z EE€$BBŽB B(ŒA0†A8ƒDp¬ 8A0A(B BBBD 8A0A(B BBBI 8A0A(B BBBI ¨±pƒ†ŒŽ4LýA†DƒG H AAJ AAEh&BBŽB B(ŒA0†D8ƒG`t 8A0A(B BBBF 8A0A(B BBBF p1`ƒ†ŒŽD”ÆBŒD†A ƒG0E AABIo AABE\4pnBŒA†D ƒ| DBGH DBJ¨5Qƒ\ C ÄEAƒN Ae Kpè&BBŒA †A(ƒG0Q (A ABBDg (A ABBKm (D ABBJd (K ABBLDÜBŽBB ŒA(†A0ƒD`n 0D(A BBBK0¤ ¢BŒD†G w CBGKAB0Ø wA†DƒD W AANDAA<ŒBBŒA †A(ƒL0– (D ABBAHL ýBEŒA †A(ƒD0» (D ABBOV(A ABBH˜ ØBŽEB ŒA(†A0ƒ (D BBBL`(A BBBLd³BWŽF B(ŒA0†A8ƒD` 8A0A(B BBBA<´.BBŒA †A(ƒGÀ^ (D ABBD<ôBBŒA †A(ƒG ^ (D ABBD<4ÉBBŒA †A(ƒG ] (D ABBEô<HƒoEH†xL¬ÌBBŽB E(ŒD0†A8ƒI€Y 8A0A(B BBBDhüüBBŽB B(ŒA0†A8ƒGðˆ 8D0A(B BBBJG 8A0A(B BBBG0èÛBŒK†L { ABImDE0 ÛBŒK†L { ABImDEDÐYBBŒA †Qð| ABBI¤ DEBEð†ŒP8-BBŽE B(ŒD0†D8ƒJð· 8D0A(B BBBGLŒËBBŽB E(ŒD0†D8ƒGðn 8A0A(B BBBFPÜ-BBŽE B(ŒD0†D8ƒJð· 8D0A(B BBBGP0 -BBŽE B(ŒD0†D8ƒJð· 8D0A(B BBBGP„ íBBŽB E(ŒD0†D8ƒGà{ 8D0A(B BBBFPØ íBBŽB E(ŒD0†D8ƒGà{ 8D0A(B BBBFp, ¾BŽEB ŒA(†D0ƒDP‡ 0A(A BBBGI 0D(A BBBDD 0C(A BBBAP #BFŽB B(ŒD0†A8ƒG 8D0A(B BBBA ô ƒ†ŒŽPíBBŽB E(ŒD0†D8ƒGà{ 8D0A(B BBBF8léBŒA†D@d ABBK ABAd¨†BBŽB B(ŒA0†D8ƒI€] 8C0A(B BBBAL 8A0D(B BBBEL¸BIŽB B(ŒA0†D8ƒD` 8A0A(B BBBFP`íBBŽB E(ŒD0†D8ƒGà{ 8D0A(B BBBF<4´EŽFB ŒA(†A0ƒk (A BBBGPôíBBŽB E(ŒD0†D8ƒGà{ 8D0A(B BBBF H Z«ŒF†D@Pl BBŽB B(ŒD0†A8ƒG°( 8D0A(B BBBGPÀ íBBŽB E(ŒD0†D8ƒGà{ 8D0A(B BBBF0”wA†DƒD W AANDAAPHíBBŽB E(ŒD0†D8ƒGà{ 8D0A(B BBBFPœBBŽB B(ŒD0†A8ƒG°( 8D0A(B BBBGPðBBŽB B(ŒD0†A8ƒG° 8D0A(B BBBBPDBBŽB B(ŒD0†A8ƒG°( 8D0A(B BBBGP˜BBŽB B(ŒD0†A8ƒG°( 8D0A(B BBBGPìBBŽB B(ŒD0†A8ƒG°( 8D0A(B BBBGP@íBBŽB E(ŒD0†D8ƒGà{ 8D0A(B BBBFP”BBŽB B(ŒD0†A8ƒG°( 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handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_18Reshape_Onnx_ver14EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_18Reshape_Onnx_ver14EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_16Shape_Onnx_ver15EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_17Concat_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_16Split_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_16Split_Onnx_ver13EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_16Slice_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_20Transpose_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_20Transpose_Onnx_ver13EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_18Scatter_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_20ScatterND_Onnx_ver16EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_20ScatterND_Onnx_ver16EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_26ScatterElements_Onnx_ver16EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_26ScatterElements_Onnx_ver16EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_17Gather_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_25GatherElements_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_25GatherElements_Onnx_ver13EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_18Squeeze_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_20Unsqueeze_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_23SpaceToDepth_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_23SpaceToDepth_Onnx_ver13EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_23DepthToSpace_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_23DepthToSpace_Onnx_ver13EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_15Tile_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_15Tile_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_17Resize_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_19Identity_Onnx_ver16EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_19Compress_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_17OneHot_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_16IsNaN_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_16Where_Onnx_ver16EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_16Where_Onnx_ver16EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_18NonZero_Onnx_ver13EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_26ReverseSequence_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_17Unique_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_19GatherND_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_19GatherND_Onnx_ver13EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_14Pad_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_14Pad_Onnx_ver13EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_16Trilu_Onnx_ver14EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_16Trilu_Onnx_ver14EEENS_8OpSchemaEv.cold_ZZN10onnx_torch11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZN10onnx_torch34propagateElemTypeFromInputToOutputERNS_16InferenceContextEmm.constprop.0_ZN10onnx_torch34propagateElemTypeFromInputToOutputERNS_16InferenceContextEmm.constprop.0.cold_ZZN10onnx_torch11GetOpSchemaINS_18Reshape_Onnx_ver14EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_18Reshape_Onnx_ver14EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZZN10onnx_torch11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZZN10onnx_torch11GetOpSchemaINS_20Transpose_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_20Transpose_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZZN10onnx_torch11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZZN10onnx_torch11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_20Unsqueeze_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZZN10onnx_torch11GetOpSchemaINS_15Tile_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_15Tile_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_19Compress_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_26ReverseSequence_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_17Unique_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZZN10onnx_torch11GetOpSchemaINS_19GatherND_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_19GatherND_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZZN10onnx_torch11GetOpSchemaINS_14Pad_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_14Pad_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_16Trilu_Onnx_ver14EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_19CastLike_Onnx_ver15EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZZN10onnx_torch11GetOpSchemaINS_19CastLike_Onnx_ver15EEENS_8OpSchemaEvENKUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_clES5_S7_S9_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_19CastLike_Onnx_ver15EEENS_8OpSchemaEvENKUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_clES5_S7_S9_.constprop.0.cold_ZZN10onnx_torch11GetOpSchemaINS_16Split_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_16Split_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_16Where_Onnx_ver16EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZZN10onnx_torch11GetOpSchemaINS_23DepthToSpace_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_23DepthToSpace_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZZN10onnx_torch11GetOpSchemaINS_23SpaceToDepth_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_23SpaceToDepth_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZTSZN10onnx_torch11GetOpSchemaINS_15Cast_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_15Cast_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0__ZTSZN10onnx_torch11GetOpSchemaINS_19CastLike_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_19CastLike_Onnx_ver15EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0__ZTSZN10onnx_torch11GetOpSchemaINS_18Reshape_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_16Shape_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_16Shape_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0__ZTSZN10onnx_torch11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0__ZTSZN10onnx_torch11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0__ZTSZN10onnx_torch11GetOpSchemaINS_16Split_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0__ZTSZN10onnx_torch11GetOpSchemaINS_20Transpose_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_20ScatterND_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_26ScatterElements_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0__ZTSZN10onnx_torch11GetOpSchemaINS_25GatherElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0__ZTSZN10onnx_torch11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0__ZTSZN10onnx_torch11GetOpSchemaINS_23SpaceToDepth_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_23DepthToSpace_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_15Tile_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_17Resize_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_16IsNaN_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_16Where_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_18NonZero_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_19GatherND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_14Pad_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_16Trilu_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_.LC0.LC1.LC3.LC4.LC31.LC69.LC70.LC71.LC72.L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L‹HƒÇI9øtL‰Çé@ÃATI‰üUSH‹_H‹/H9ët$DH‹}HEH9Çt+èHƒÅ H9ëuåI‹,$H…ít$[H‰ï]A\é„HƒÅ H9ëu¿ëØD[]A\ÃH‹UH‰ýHƒÀH‰H‹HE H9ÇtèH‰ï]é…Òtƒútƒút"1ÀÃ€H‹H‰1ÀÃH‰71ÀÃfH‹H‰ëÖbasic_string::_M_construct null not valid should be of integer type and specify a type. does not specify a valid type.Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/steps The operator casts the elements of a given input tensor to a data type specified by the 'to' argument and returns an output tensor of the same size in the converted type. The 'to' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations (e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may result 100. There are some string literals reserved for special floating-point values; "+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively. Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly, this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors to string tensors, plain floating-point representation (such as "314.15926") would be used. Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior. Conversion from a numerical type to any numerical type is always allowed. User must be aware of precision loss and value change caused by range difference between two types. For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type. The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoOutput tensor with the same shape as input with type specified by the 'to' argumentConstrain input types. Casting from complex is not supported.Constrain output types. Casting to complex is not supported./opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/defs/tensor/old.cc Reshape the input tensor similar to numpy.reshape. First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor. At most one dimension of the new shape can be -1. In this case, the value is inferred from the size of the tensor and the remaining dimensions. A dimension could also be 0, in which case the actual dimension value is unchanged (i.e. taken from the input tensor).Constrain input and output types to all tensor types. Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor. Input tensor can be of arbitrary type.Constrain output to int64 tensor. Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor. Total number of elements of the input tensorConstrain output to int64 tensor, which should be a scalar though.Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..Concatenate a list of tensors into a single tensor. All input tensors must have the same shape, except for the dimension size of the axis to concatenate on.List of tensors for concatenationOne or more outputs forming list of tensors after splittingWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).length of each output. Values should be >= 0.Split a tensor into a list of tensors, along the specified 'axis'. Lengths of the parts can be specified using argument 'split'. Otherwise, the tensor is split to equal sized parts. 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 `starts`, `ends`, `axes` and `steps` inputs to specify the start and end dimension and step 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 represents 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` when sclicing forward and 'INT_MIN' when slicing backward. If a negative value is passed for step, it represents slicing backward. However step value cannot be 0. If `axes` are omitted, they are set to `[0, ..., ndim-1]`. If `steps` are omitted, they are set to `[1, ..., 1]` of length `len(starts)` Example 1: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] steps = [1, 2] result = [ [5, 7], ] Example 2: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000] result = [ [2, 3, 4], ] Tensor of data to extract slices from.1-D tensor of starting indices of corresponding axis in `axes`1-D tensor of ending indices (exclusive) of corresponding axis in `axes`1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).1-D tensor of slice step of corresponding axis in `axes`. Negative value means slicing backward. 'steps' cannot be 0. Defaults to 1.Constrain indices to integer types Transpose the input tensor similar to numpy.transpose. For example, when perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape will be (2, 1, 3). A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given. ScatterND takes three inputs `data` tensor of rank r >= 1, `indices` tensor of rank q >= 1, and `updates` tensor of rank q + r - indices.shape[-1] - 1. The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. Note that `indices` should not have duplicate entries. That is, two or more `updates` for the same index-location is not supported. `indices` is an integer tensor. Let k denote indices.shape[-1], the last dimension in the shape of `indices`. `indices` is treated as a (q-1)-dimensional tensor of k-tuples, where each k-tuple is a partial-index into `data`. Hence, k can be a value at most the rank of `data`. When k equals rank(data), each update entry specifies an update to a single element of the tensor. When k is less than rank(data) each update entry specifies an update to a slice of the tensor. `updates` is treated as a (q-1)-dimensional tensor of replacement-slice-values. Thus, the first (q-1) dimensions of updates.shape must match the first (q-1) dimensions of indices.shape. The remaining dimensions of `updates` correspond to the dimensions of the replacement-slice-values. Each replacement-slice-value is a (r-k) dimensional tensor, corresponding to the trailing (r-k) dimensions of `data`. Thus, the shape of `updates` must equal indices.shape[0:q-1] ++ data.shape[k:r-1], where ++ denotes the concatenation of shapes. The `output` is calculated via the following equation: output = np.copy(data) update_indices = indices.shape[:-1] for idx in np.ndindex(update_indices): output[indices[idx]] = updates[idx] The order of iteration in the above loop is not specified. In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. This ensures that the output value does not depend on the iteration order. This operator is the inverse of GatherND. Example 1: ``` data = [1, 2, 3, 4, 5, 6, 7, 8] indices = [[4], [3], [1], [7]] updates = [9, 10, 11, 12] output = [1, 11, 3, 10, 9, 6, 7, 12] ``` Example 2: ``` data = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]] indices = [[0], [2]] updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]] output = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]] ``` Tensor of rank q + r - indices_shape[-1] - 1.Constrain input and output types to any tensor type. ScatterElements takes three inputs `data`, `updates`, and `indices` of the same rank r >= 1 and an optional attribute axis that identifies an axis of `data` (by default, the outer-most axis, that is axis 0). The output of the operation is produced by creating a copy of the input `data`, and then updating its value to values specified by `updates` at specific index positions specified by `indices`. Its output shape is the same as the shape of `data`. For each entry in `updates`, the target index in `data` is obtained by combining the corresponding entry in `indices` with the index of the entry itself: the index-value for dimension = axis is obtained from the value of the corresponding entry in `indices` and the index-value for dimension != axis is obtained from the index of the entry itself. For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry is performed as below: ``` output[indices[i][j]][j] = updates[i][j] if axis = 0, output[i][indices[i][j]] = updates[i][j] if axis = 1, ``` This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation. Example 1: ``` data = [ [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], ] indices = [ [1, 0, 2], [0, 2, 1], ] updates = [ [1.0, 1.1, 1.2], [2.0, 2.1, 2.2], ] output = [ [2.0, 1.1, 0.0] [1.0, 0.0, 2.2] [0.0, 2.1, 1.2] ] ``` Example 2: ``` data = [[1.0, 2.0, 3.0, 4.0, 5.0]] indices = [[1, 3]] updates = [[1.1, 2.1]] axis = 1 output = [[1.0, 1.1, 3.0, 2.1, 5.0]] ``` Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of rank r >=1 (same rank and shape as indices)Tensor of rank r >= 1 (same rank as input).Input and output types can be of any tensor type. Given `data` tensor of rank r >= 1, and `indices` tensor of rank q, gather entries of the axis dimension of `data` (by default outer-most one as axis=0) indexed by `indices`, and concatenates them in an output tensor of rank q + (r - 1). axis = 0 : Let k = indices[i_{0}, ..., i_{q-1}] Then output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2}] = input[k , j_{0}, ..., j_{r-2}] ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] indices = [ [0, 1], [1, 2], ] output = [ [ [1.0, 1.2], [2.3, 3.4], ], [ [2.3, 3.4], [4.5, 5.7], ], ] ``` axis = 1 : Let k = indices[i_{0}, ..., i_{q-1}] Then output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2}] = input[j_{0}, k, j_{1}, ..., j_{r-2}] ``` data = [ [1.0, 1.2, 1.9], [2.3, 3.4, 3.9], [4.5, 5.7, 5.9], ] indices = [ [0, 2], ] axis = 1, output = [ [ [1.0, 1.9], [2.3, 3.9], [4.5, 5.9], ], ] ``` Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds. GatherElements takes two inputs `data` and `indices` of the same rank r >= 1 and an optional attribute `axis` that identifies an axis of `data` (by default, the outer-most axis, that is axis 0). It is an indexing operation that produces its output by indexing into the input data tensor at index positions determined by elements of the `indices` tensor. Its output shape is the same as the shape of `indices` and consists of one value (gathered from the `data`) for each element in `indices`. For instance, in the 3-D case (r = 3), the output produced is determined by the following equations: ``` out[i][j][k] = input[index[i][j][k]][j][k] if axis = 0, out[i][j][k] = input[i][index[i][j][k]][k] if axis = 1, out[i][j][k] = input[i][j][index[i][j][k]] if axis = 2, ``` This operator is also the inverse of ScatterElements. It is similar to Torch's gather operation. Example 1: ``` data = [ [1, 2], [3, 4], ] indices = [ [0, 0], [1, 0], ] axis = 1 output = [ [ [1, 1], [4, 3], ], ] ``` Example 2: ``` data = [ [1, 2, 3], [4, 5, 6], [7, 8, 9], ] indices = [ [1, 2, 0], [2, 0, 0], ] axis = 0 output = [ [ [4, 8, 3], [7, 2, 3], ], ] ``` Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of the same shape as indices.List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Remove single-dimensional entries from the shape of a tensor. Takes a parameter `axes` with a list of axes to squeeze. If `axes` is not provided, all the single dimensions will be removed from the shape. If an axis is selected with shape entry not equal to one, an error is raised. Tensors with at least max(dims) dimensions.Reshaped tensor with same data as input.List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded). Insert single-dimensional entries to the shape of an input tensor (`data`). Takes one required argument `axes` - which contains a list of dimension indices and this operator will insert a dimension of value `1` into the corresponding index of the output tensor (`expanded`). For example: Given an input tensor (`data`) of shape [3, 4, 5], then Unsqueeze(data, axes=[0, 4]) outputs a tensor (`expanded`) containing same data as `data` but with shape [1, 3, 4, 5, 1]. The attribute `axes` should not contain any duplicate entries. It is an error if it contains duplicates. The rank of the output tensor (`output_rank`) is the rank of the input tensor (`data`) plus the number of values in `axes`. Each value in `axes` should be within the (inclusive) range [-output_rank , output_rank - 1]. The order of values in `axes` does not matter and can come in any order. Blocks of [blocksize, blocksize] are moved.SpaceToDepth rearranges blocks of spatial data into depth. More specifically, this op outputs a copy of the input tensor where values from the height and width dimensions are moved to the depth dimension. Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.DepthToSpace rearranges (permutes) data from depth into blocks of spatial data. This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of the input tensor where values from the depth dimension are moved in spatial blocks to the height and width dimensions. By default, `mode` = `DCR`. In the DCR mode, elements along the depth dimension from the input tensor are rearranged in the following order: depth, column, and then row. The output y is computed from the input x as below: b, c, h, w = x.shape tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h, w]) tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2]) y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize, w * blocksize]) In the CRD mode, elements along the depth dimension from the input tensor are rearranged in the following order: column, row, and the depth. The output y is computed from the input x as below: b, c, h, w = x.shape tmp = np.reshape(x, [b, c // (blocksize ** 2), blocksize, blocksize, h, w]) tmp = np.transpose(tmp, [0, 1, 4, 2, 5, 3]) y = np.reshape(tmp, [b, c // (blocksize ** 2), h * blocksize, w * blocksize]) Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].Constructs a tensor by tiling a given tensor. This is the same as function `tile` in Numpy, but no broadcast. For example A = [[1, 2], [3, 4]], B = [1, 2], tile(A, B) = [[1, 2, 1, 2], [3, 4, 3, 4]] 1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.Output tensor of the same dimension and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]Constrain repeat's type to int64 tensors.Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_half_pixel_for_nn",
x_original = (x_resized + 0.5) / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).coordinate_transformation_modeFour modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified. If 'size' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.The size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified.Constrain input 'X' and output 'Y' to all tensor types.Constrain roi type to float or double. Resize the input tensor. In general, it calculates every value in the output tensor as a weighted average of neighborhood (a.k.a. sampling locations) in the input tensor. Each dimension value of the output tensor is: output_dimension = floor(input_dimension * (roi_end - roi_start) * scale) if input \"sizes\" is not specified. Constrain input types to float tensors.Constrain output types to boolean tensors. Returns the indices of the elements that are non-zero (in row-major order - by dimension). NonZero behaves similar to numpy.nonzero: https://docs.scipy.org/doc/numpy/reference/generated/numpy.nonzero.html Given `data` tensor of rank `r` >= 1, `indices` tensor of rank `q` >= 1, and `batch_dims` integer `b`, this operator gathers slices of `data` into an output tensor of rank `q + r - indices_shape[-1] - 1 - b`. `indices` is an q-dimensional integer tensor, best thought of as a `(q-1)`-dimensional tensor of index-tuples into `data`, where each element defines a slice of `data` `batch_dims` (denoted as `b`) is an integer indicating the number of batch dimensions, i.e the leading `b` number of dimensions of `data` tensor and `indices` are representing the batches, and the gather starts from the `b+1` dimension. Some salient points about the inputs' rank and shape: 1) r >= 1 and q >= 1 are to be honored. There is no dependency condition to be met between ranks `r` and `q` 2) The first `b` dimensions of the shape of `indices` tensor and `data` tensor must be equal. 3) b < min(q, r) is to be honored. 4) The `indices_shape[-1]` should have a value between 1 (inclusive) and rank `r-b` (inclusive) 5) All values in `indices` are expected to be within bounds [-s, s-1] along axis of size `s` (i.e.) `-data_shape[i] <= indices[...,i] <= data_shape[i] - 1`. It is an error if any of the index values are out of bounds. The output is computed as follows: The output tensor is obtained by mapping each index-tuple in the `indices` tensor to the corresponding slice of the input `data`. 1) If `indices_shape[-1] > r-b` => error condition 2) If `indices_shape[-1] == r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensors containing 1-D tensors of dimension `r-b`, where `N` is an integer equals to the product of 1 and all the elements in the batch dimensions of the indices_shape. Let us think of each such `r-b` ranked tensor as `indices_slice`. Each *scalar value* corresponding to `data[0:b-1,indices_slice]` is filled into the corresponding location of the `(q-b-1)`-dimensional tensor to form the `output` tensor (Example 1 below) 3) If `indices_shape[-1] < r-b`, since the rank of `indices` is `q`, `indices` can be thought of as `N` `(q-b-1)`-dimensional tensor containing 1-D tensors of dimension `< r-b`. Let us think of each such tensors as `indices_slice`. Each *tensor slice* corresponding to `data[0:b-1, indices_slice , :]` is filled into the corresponding location of the `(q-b-1)`-dimensional tensor to form the `output` tensor (Examples 2, 3, 4 and 5 below) This operator is the inverse of `ScatterND`. `Example 1` batch_dims = 0 data = [[0,1],[2,3]] # data_shape = [2, 2] indices = [[0,0],[1,1]] # indices_shape = [2, 2] output = [0,3] # output_shape = [2] `Example 2` batch_dims = 0 data = [[0,1],[2,3]] # data_shape = [2, 2] indices = [[1],[0]] # indices_shape = [2, 1] output = [[2,3],[0,1]] # output_shape = [2, 2] `Example 3` batch_dims = 0 data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[0,1],[1,0]] # indices_shape = [2, 2] output = [[2,3],[4,5]] # output_shape = [2, 2] `Example 4` batch_dims = 0 data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[[0,1]],[[1,0]]] # indices_shape = [2, 1, 2] output = [[[2,3]],[[4,5]]] # output_shape = [2, 1, 2] `Example 5` batch_dims = 1 data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[1],[0]] # indices_shape = [2, 1] output = [[2,3],[4,5]] # output_shape = [2, 2] The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Supported modes: `constant`(default), `reflect`, `edge` Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array Example 1 (`constant` mode): 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] mode = 'constant' constant_value = 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], ] Example 2 (`reflect` mode): data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] Example 3 (`edge` mode): data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`.(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0).Constrains input and output to only numeric types. The operator casts the elements of a given input tensor to a data type specified by the 'to' argument and returns an output tensor of the same size in the converted type. The 'to' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. NOTE: Casting to and from strings is not supported yet. Constrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported.Which axis to concat on. Default value is 1.Concatenate a list of tensors into a single tensorConstrain output types to float tensors.Optional list of output lengths (see also arg 'split')Split a tensor into a list of tensors, along the specified 'axis'. The lengths of the split can be specified using argument 'axis' or optional second input blob to the operator. Otherwise, the tensor is split to equal sized parts. List of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.Three modes: constant(default), reflect, edgeOne float, indicates the value to be filled, default is 0 Given `data` tensor, paddings, mode, and value. Example: Insert 0 paddings to the beginning of the second dimension. data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] paddings = [0, 0, 2, 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], ], ] Constrain input and output types to float tensors. Reshape the input tensor similar to numpy.reshape. It takes a tensor as input and an argument `shape`. It outputs the reshaped tensor. At most one dimension of the new shape can be -1. In this case, the value is inferred from the size of the tensor and the remaining dimensions. A dimension could also be 0, in which case the actual dimension value is unchanged (i.e. taken from the input tensor).legacy optimization attribute.Repeat the elements of a tensor along an axis.Number of repeated copies to make of the input tensor.Output tensor of same shape and type as input.Constrain tiles and axis's type to int64 tensors.The scale along width dimension. It takes value greater than or equal to 1.The scale along height dimension. It takes value greater than or equal to 1.Two interpolation modes: nearest(default), bilinear4-D tensor after resizing, [N,C,H,W]Constrain output types to bool, int32, int64, float16, float, double tensors. Upsample the input tensor. The width and height of the output tensor are: output_width = floor(input_width * width_scale), output_height = floor(input_height * height_scale). Example: Given `data` tensor, width_scale, height_scale, mode, Upsample the input 4-D tensor in nearest mode: data = [[[ [1, 2], [3, 4] ]]] width_scale = 2 height_scale = 2 mode = "nearest" output = [[[ [1, 1, 2, 2], [1, 1, 2, 2], [3, 3, 4, 4], [3, 3, 4, 4] ]]] The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc) Upsample the input tensor. Each dimension value of the output tensor is: output_dimension = floor(input_dimension * scale). 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'. Resize the input tensor. Each dimension value of the output tensor is: output_dimension = floor(input_dimension * scale). 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` attributes 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], ] Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Starting indices of corresponding axis in `axes`Ending indices (exclusive) of corresponding axis in axes` 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 `starts`, `ends`, `axes` and `steps` inputs to specify the start and end dimension and step 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 a negative value is passed for step, it represents slicing backward. If `axes` are omitted, they are set to `[0, ..., ndim-1]`. If `steps` are omitted, they are set to `[1, ..., 1]` of length `len(starts)` Example 1: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] steps = [1, 2] result = [ [5, 7], ] Example 2: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000] result = [ [2, 3, 4], ] 1-D tensor of axes that `starts` and `ends` apply to.1-D tensor of slice step of corresponding axis in `axes`. Default to 1. Given `data`, `updates` and `indices` input tensors of rank r >= 1, write the values provided by `updates` into the first input, `data`, along `axis` dimension of `data` (by default outer-most one as axis=0) at corresponding `indices`. For each entry in `updates`, the target index in `data` is specified by corresponding entry in `indices` for dimension = axis, and index in source for dimension != axis. For instance, in a 2-D tensor case, data[indices[i][j]][j] = updates[i][j] if axis = 0, or data[i][indices[i][j]] = updates[i][j] if axis = 1, where i and j are loop counters from 0 up to the respective size in `updates` - 1. Example 1: data = [ [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], ] indices = [ [1, 0, 2], [0, 2, 1], ] updates = [ [1.0, 1.1, 1.2], [2.0, 2.1, 2.2], ] output = [ [2.0, 1.1, 0.0] [1.0, 0.0, 2.2] [0.0, 2.1, 1.2] ] Example 2: data = [[1.0, 2.0, 3.0, 4.0, 5.0]] indices = [[1, 3]] updates = [[1.1, 2.1]] axis = 1 output = [[1.0, 1.1, 3.0, 2.1, 5.0]] Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of r >= 1 (same rank as input).DepthToSpace rearranges (permutes) data from depth into blocks of spatial data. This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of the input tensor where values from the depth dimension are moved in spatial blocks to the height and width dimensions. Given `data` tensor of rank r >= 1, and `indices` tensor of rank q, gather entries of the axis dimension of `data` (by default outer-most one as axis=0) indexed by `indices`, and concatenates them in an output tensor of rank q + (r - 1). Example 1: ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] indices = [ [0, 1], [1, 2], ] output = [ [ [1.0, 1.2], [2.3, 3.4], ], [ [2.3, 3.4], [4.5, 5.7], ], ] ``` Example 2: ``` data = [ [1.0, 1.2, 1.9], [2.3, 3.4, 3.9], [4.5, 5.7, 5.9], ] indices = [ [0, 2], ] axis = 1, output = [ [ [1.0, 1.9], [2.3, 3.9], [4.5, 5.9], ], ] ``` Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. It is an error if any of the index values are out of bounds.List of non-negative integers, indicate the dimensions to squeeze.List of non-negative integers, indicate the dimensions to be inserted Insert single-dimensional entries to the shape of a tensor. Takes one required argument `axes`, a list of dimensions that will be inserted. Dimension indices in `axes` are as seen in the output tensor. For example: Given a tensor such that tensor with shape [3, 4, 5], then Unsqueeze(tensor, axes=[0, 4]) has shape [1, 3, 4, 5, 1] Produces a one-hot tensor based on inputs. The locations represented by the index values in the 'indices' input tensor will have 'on_value' and the other locations will have 'off_value' in the output tensor, where 'on_value' and 'off_value' are specified as part of required input argument 'values', which is a two-element tensor of format [off_value, on_value]. The rank of the output tensor will be one greater than the rank of the input tensor. The additional dimension is for one-hot representation. The additional dimension will be inserted at the position specified by 'axis'. If 'axis' is not specified then then additional dimension will be inserted as the innermost dimension, i.e. axis=-1. The size of the additional dimension is specified by required scalar input 'depth'. The type of the output tensor is the same as the type of the 'values' input. 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. (Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor.Input tensor containing indices. The values must be non-negative integers. Any entries in the 'indices' input tensor with values outside the range [0, depth) will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [0, depth). In case 'depth' is of non-integer type, it will be casted to int64 before use.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.Constrains input to only numeric types. Selects slices from an input tensor along a given axis where condition evaluates to True for each axis index. In case axis is not provided, input is flattened before elements are selected. Compress behaves like numpy.compress: https://docs.scipy.org/doc/numpy/reference/generated/numpy.compress.html (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length alone the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.Constrains to boolean tensors.List of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.One float, indicates the value to be filled. 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], ], ] Given `data` tensor of rank `r` >= 1, and `indices` tensor of rank `q` >= 1, this operator gathers slices of `data` into an output tensor of rank `q + r - indices_shape[-1] - 1`. `indices` is an q-dimensional integer tensor, best thought of as a `(q-1)`-dimensional tensor of index-tuples into `data`, where each element defines a slice of `data` Some salient points about the inputs' rank and shape: 1) r >= 1 and q >= 1 are to be honored. There is no dependency condition to be met between ranks `r` and `q` 2) The `indices_shape[-1]` should have a value between 1 (inclusive) and rank `r` (inclusive) 3) All values in `indices` are expected to be within bounds [-s, s-1] along axis of size `s` (i.e.) `-data_shape[i] <= indices[...,i] <= data_shape[i] - 1`. It is an error if any of the index values are out of bounds. The output is computed as follows: The output tensor is obtained by mapping each index-tuple in the `indices` tensor to the corresponding slice of the input `data`. 1) If `indices_shape[-1] > r` => error condition 2) If `indices_shape[-1] == r`, since the rank of `indices` is `q`, `indices` can be thought of as a `(q-1)`-dimensional tensor containing 1-D tensors of dimension `r`. Let us think of each such `r` ranked tensor as `indices_slice`. Each *scalar value* corresponding to `data[indices_slice]` is filled into the corresponding location of the `(q-1)`-dimensional tensor to form the `output` tensor (Example 1 below) 3) If `indices_shape[-1] < r`, since the rank of `indices` is `q`, `indices` can be thought of as a `(q-1)`-dimensional tensor containing 1-D tensors of dimension `< r`. Let us think of each such tensors as `indices_slice`. Each *tensor slice* corresponding to `data[indices_slice , :]` is filled into the corresponding location of the `(q-1)`-dimensional tensor to form the `output` tensor (Examples 2, 3, and 4 below) This operator is the inverse of `ScatterND`. `Example 1` data = [[0,1],[2,3]] # data_shape = [2, 2] indices = [[0,0],[1,1]] # indices_shape = [2, 2] output = [0,3] # output_shape = [2] `Example 2` data = [[0,1],[2,3]] # data_shape = [2, 2] indices = [[1],[0]] # indices_shape = [2, 1] output = [[2,3],[0,1]] # output_shape = [2, 2] `Example 3` data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[0,1],[1,0]] # indices_shape = [2, 2] output = [[2,3],[4,5]] # output_shape = [2, 2] `Example 4` data = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2] indices = [[[0,1]],[[1,0]]] # indices_shape = [2, 1, 2] output = [[[2,3]],[[4,5]]] # output_shape = [2, 1, 2] Constrain input and output types to all tensor and sequence types. Return elements, either from X or Y, depending on condition (with Numpy-style broadcasting support). Where behaves like numpy.where with three parameters: https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html When True (nonzero), yield X, otherwise yield Yvalues selected at indices where condition is Truevalues selected at indices where condition is FalseTensor of shape equal to the broadcasted shape of condition, X, and Y. expected to have type but instead is nullTarget shape may not have multiple -1 dimensionsInvalid Target shape product of 0Dimension could not be inferred: incompatible shapesRequired attribute axis is missingaxis must be in [-rank, rank-1].All inputs to Concat must have same rankCan't merge shape info. Both source and target dimension have values but they differ. Source= does not match type of output: expected to have tensor or sparse tensor typedata tensor must have rank >= 1values in 'axes' are beyond the bounds of the computed output shape'axes' attribute must not contain any duplicates'Repeats' input must be 1D tensor of type int64'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0.Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensor'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64Pads has incorrect number of valuesrank must be greater than axis) is not equal to the existing rank value (Number of elements of attribute 'scales' must be same as rank of input 'X'Attribute 'scales' must have floats type.Attribute 'scales' is required.Incorrect or missing attribute value for starts and endsAttribute axes has incorrect lengthAttribute value for pads is requiredAttribute pads has incorrect lengthOneHot node must have three inputs.Input 'depth' must be a scalar or rank 1 tensor.Input 'depth' must have exactly one element.Input 'values' must be rank 1 tensor.Input 'values' must have exactly two elements.Indices tensor must have rank >= 1'axis' must be in [-rank(indices)-1, rank(indices)]Invalid value of attribute 'axis'. Rank=Mismatch between number of splits (Mismatch between the sum of 'split' () and the split dimension of the input (The input is not evenly splittableSlice op must have either three, four or five inputs.Incorrect or missing input value for starts and endsInput axes has incorrect lengthInput steps has incorrect lengthInput tensor must be 4-dimensionalAUE1íATUH‰õSH‰ûHƒìHƒ?„³H‹mH…í„‘¿èHÇI‰ÄH‹E1ÒHKL‰cI‰D$H÷sH‹H‰ÐH‹mH…íuëTf„H‹mH…ítBI‰Ì¿èHÇH‰ÁH‹E1ÒH‰AH÷sH‹I‰$HÐHƒ8uÁL‰ H‹mH…íu¾HƒÄ[]A\A]Ãf.„H‹WHƒút1H‰ÐHÁèL‹`M…ä„éH‰ïèH‰ÇèH‰ÇHÄ¸L‰æ[]A\A]A^A_éDHÄ¸[]A\A]A^A_ÃfDAƒýu2L‹e I‹l$AƒL$H…í„²ƒ{(„øþÿÿH‹é{ýÿÿ@H‰ïèH‹EÇE(H‰ÇHƒçþ¨…˜èH‰E I‰Äë¢„H‰ïèH‹EÇE(H‰ÇHƒçþ¨utèH‰E H‰ÃéÐþÿÿL‹%éÿÿÿ@L‹%é¥þÿÿ@H‹?é þÿÿ„I‹D$H‰ÇHƒçþ¨u'èI‰D$H‰Åé,ÿÿÿH‹?é`ÿÿÿH‹?éþÿÿH‹?ë‡H‹?ëÔH‹?é{þÿÿ¿0Lt$ èL‰÷I‰ÅèHl$0ºH5H‰ïèºH5H‰ïèD‰æH‰ïèH‰åHt$8H‰ïèL‰÷èH‰îL‰ïèH‹H‹<$AÆE IÇEHƒÀI‰EIE I‰EHD$H9ÇtèH‹5HL‰ïèH‰Åë H‰ÅëH‰ÅëH‹<$HD$H9ÇtèëL‰÷èL‰ïèH‰ïèH‰Åë H‰ÅëH‰ÅëH‹<$HD$H9ÇtèëL‰÷èL‰ÿèH‰ïèÿÿ/®ì ÇSç ¨â ôÁù° ’4« Ô¦ ¡ }to not specifiedValue of attribute Attribute vector::_M_range_insertT1Input tensor to be cast.inputT2outputCastTAn input tensor.dataSpecified shape for output.shapeReshaped data.reshapedReshapeShape of the input tensorShapesizeSizeaxisinputsConcatenated tensorconcat_resultConcatThe tensor to splitoutputssplitSplitTindstartsendsaxesstepsSliced data tensor.SlicepermTransposed output.transposedTransposeTensor of rank r >= 1.Tensor of rank q >= 1.indicesupdatesScatterNDScatterElementsTensor of rank q + (r - 1).GatherGatherElementssqueezedSqueezeOriginal tensorexpandedUnsqueezeblocksizeSpaceToDepthDCRmodeDepthToSpaceInput tensor of any shape.repeatsTilenearestcubic_coeff_aexclude_outsidehalf_pixelround_prefer_floornearest_modeextrapolation_valueN-D tensorXroiscalessizesN-D tensor after resizingYResizeIdentity operatorInput tensorTensor to copy input into.IdentityIsNaNNonZerobatch_dimsGatherNDconstantInput tensor.padsconstant_valueTensor after padding.PadWhich axis to concat onoutputs...Which axis to split onlength of each outputpaddingsvalueNew shapeconsumed_inputstilesAxis along which to repeat.width_scaleheight_scale4-D tensor, [N,C,H,W]UpsampleScatterdepthT3valuesConstrain to any tensor type.OneHotconditionCompressWhich axis to split on. Vseq(tensor(uint8))seq(tensor(uint16))seq(tensor(uint32))seq(tensor(uint64))seq(tensor(int8))seq(tensor(int16))seq(tensor(int32))seq(tensor(int64))seq(tensor(float16))seq(tensor(float))seq(tensor(double))seq(tensor(string))seq(tensor(bool))seq(tensor(complex64))seq(tensor(complex128))BConstrain to boolean tensors.WhereInvalid position of 0Invalid dimension value: Dimension=Invalid attribute perm {}, input shape = {, }Input: type: type: and Output axis must be in [-r, r-1] must be 1 instead of Dimension of input Ranks inferred (). 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H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èHÄ¸L‰à[]A\A]A^A_ÃfDI‰ÀéûþÿÿH´$€L‰çèéúþÿÿH‰ÅëH‰ÅëI‹<$H9ûtèL‰ÿèH‰ïèAWAVM‰ÆAUATI‰üUH‰ÕSH‰óHì¸H‹L|$ H‰L$Ll$0HP@H‹L‰ÿfHnÂHƒÀfHnÈflÁ)$èH‰ßèH‰ÞL‰ïH‰ÂèH‰ïèH‰îL‰ïH‰ÂèH‹D$L‰ïH‹0èL‰÷èL‰öL‰ïH‰ÂèH‹D$`I\$IÇD$I‰$AÆD$H…À„L‹D$PM…À„L9À‡ýH‹L$X1Ò1öL‰çI)ÈèH‹fo$H‹¼$€HƒÀ)T$0H‰D$ H‹HƒÀhH‰„$ H„$H9ÇtèH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èHÄ¸L‰à[]A\A]A^A_ÃfDI‰ÀéûþÿÿH´$€L‰çèéúþÿÿH‰ÅëH‰ÅëI‹<$H9ûtèL‰ÿèH‰ïèAWAVAUI‰õATI‰üUH‰ÕSHì¨H‹H‹L|$Lt$ HS@HƒÀL‰ÿfHnÂfHnÈflÁ)$èL‰ïèL‰îL‰÷H‰ÂèH‰ïèH‰îL‰÷H‰ÂèH‹D$PIl$IÇD$I‰,$AÆD$H…À„ L‹D$@M…À„ôL9À‡ëH‹L$H1Ò1öL‰çI)ÈèHCfo$H‹|$pH‰D$HChH‰„$H„$€)T$ H9ÇtèH‹H|$`HƒÀH‰D$(èH‹H¼$H‹PH‹H0H‹RèH‰LH‹P H‹H(H‰T$ H‹RèH‰L H‹PH‹@H‰T$H‹RèH‰DH‹HÇD$HƒÀH‰„$èHÄ¨L‰à[]A\A]A^A_ÃDI‰Àé ÿÿÿHt$pL‰çèéÿÿÿH‰ÃëH‰ÅëI‹<$H9ýtèH‰ÝL‰ÿèH‰ïèAttribute expected to have tensor or sparse tensor typeATUHƒì(H‹ÿP‹P(ƒútƒúu%H‹@ H‹@H…Àt HƒÄ(]A\ÃH‹HƒÄ(]A\Ã¿0I‰äèHH5L‰çH‰ÅèL‰æH‰ïèH‹H‹<$HÇEÆE HƒÀH‰EHE H‰EHD$H9ÇtèH‹5HH‰ïèI‰ÄëH‹<$HD$H9ÇtèH‰ïèL‰çèI‰ÄëØ expected to have tensor or sparse type expected to have tensor typeAWAVAUATUH‰õS‰ÓHìÈL‹%H‹IT$@HƒÀfHnÈfHnÂH‹flÁ)$ÿP(H…À„H‰Ç‹@(ƒøt'ƒøt"…Àu<ƒûtƒûtHÄÈ1À[]A\A]A^A_ÃèHÄÈ[H‰Ç]A\A]A^A_é¿0Ll$0èL‰ïI‰ÇèLt$@ºH5L‰÷èºH5L‰÷èH‰îL‰÷èºH5L‰÷èH‹D$pLt$ ÆD$ Hl$L‰t$HÇD$H…À…H´$H‰ïèID$fo$H‹¼$H‰D$0ID$hH‰„$°H„$ )\$@H9Ç……H‹H¼$€HƒÀH‰D$HèH‹H¼$°H‹PH‹H0H‹RèH‰L0H‹P H‹H(H‰T$@H‹RèH‰L@H‹PH‹@H‰T$0H‹RèH‰D0H‹HÇD$8HƒÀH‰„$°èH‰îL‰ÿèH‹AÆG IÇGHƒÀI‰IG I‰GH‹|$L9÷tèH‹5HL‰ÿè¿0Ll$0èL‰ïI‰ÇèLt$@ºH5L‰÷èºH5L‰÷èH‰îL‰÷èº'H5L‰÷èH‹D$pLt$ ÆD$ Hl$L‰t$HÇD$H…À…!H´$H‰ïèID$fo$H‹¼$H‰D$0ID$hH‰„$°H„$ )T$@H9ÇtèH‹H¼$€HƒÀH‰D$HèH‹H¼$°H‹PH‹H0H‹RèH‰L0H‹P H‹H(H‰T$@H‹RèH‰L@H‹PH‹@H‰T$0H‹RèH‰D0H‹HÇD$8HƒÀH‰„$°èH‰îL‰ÿèéBþÿÿL‹D$`M…ÀuI‰ÀH‹L$h1Ò1öH‰ïI)ÈèéMýÿÿL9ÀwßëàH‰ÅëwèéqýÿÿL‹D$`M…ÀuI‰ÀH‹L$h1Ò1öH‰ïI)ÈèéÉþÿÿL9ÀwßëàëÂH‰Åë H‰ÅëH‰ÅëH‹|$L9÷uL‰ïèL‰ÿèH‰ïèèëáëËëÎëÂH‹|$L9÷tÙèëÒAWAVM‰ÆAUI‰ÕATI‰üUSH‰óHì¸H‹L|$ H‰L$Hl$0HP@H‹L‰ÿL‰L$fHnÂHƒÀfHnÈflÁ)$èH‰ßèH‰ÞH‰ïH‰ÂèL‰ïèL‰îH‰ïH‰ÂèH‹D$H‰ïH‹0èL‰÷èL‰öH‰ïH‰ÂèH‹D$H‰ïH‹0èH‹¼$ðèH‹´$ðH‰ïH‰ÂèH‹„$øH‰ï‹0èH‹D$`I\$IÇD$I‰$AÆD$H…À„%L‹D$PM…À„L9À‡þH‹L$X1Ò1öL‰çI)ÈèH‹fo$H‹¼$€HƒÀ)T$0H‰D$ H‹HƒÀhH‰„$ H„$H9ÇtèH‹H|$pHƒÀH‰D$8èH‹H¼$ H‹PH‹H0H‹RèH‰L H‹P H‹H(H‰T$0H‹RèH‰L0H‹PH‹@H‰T$ H‹RèH‰D H‹HÇD$(HƒÀH‰„$ èHÄ¸L‰à[]A\A]A^A_Ã€I‰Àéúþÿÿ„H´$€L‰çèéñþÿÿH‰ÅëH‰ÅëI‹<$H9ûtèL‰ÿèH‰ïèAWfïÀI‰ÿAVAUATUH‰õSHƒì(H‹^H+HÇG„H¸àÿÿÿÿÿÿH9Ã‡H‰ßèH‰D$H‹D$fHnÀHÃflÀI‰_H‰ÃAL‹uH‹mL9õ„«HD$H‰$ë&„A¶EˆCHƒÅ L‰cHƒÃ BÆ'I9îtzH{L‹eH‰;L‹mL‰èLàt M…í„L‰d$IƒüwIƒüt±M…ät´ë'f.„H‹4$1ÒH‰ßèH‰H‰ÇH‹D$H‰CL‰âL‰îèL‹d$H‹;ésÿÿÿI‰_HƒÄ([]A\A]A^A_ÃDHÇD$éÿÿÿH…ÛyèèH=èH‰ÇèH;\$tH‹D$H‹8HƒÀH9ÇtèHƒD$ ëÛèH‰ÅèI‹?H…ÿtèH‰ïèAWAVAUI‰õATI‰üUH‰ÕSHì¨H‹H‹L|$Lt$ HS@HƒÀL‰ÿfHnÂfHnÈflÁ)$èL‰ïèL‰îL‰÷H‰ÂèH‰ïèH‰îL‰÷H‰ÂèH‹D$PIl$IÇD$I‰,$AÆD$H…À„ L‹D$@M…À„ôL9À‡ëH‹L$H1Ò1öL‰çI)ÈèHCfo$H‹|$pH‰D$HChH‰„$H„$€)T$ H9ÇtèH‹H|$`HƒÀH‰D$(èH‹H¼$H‹PH‹H0H‹RèH‰LH‹P H‹H(H‰T$ H‹RèH‰L H‹PH‹@H‰T$H‹RèH‰DH‹HÇD$HƒÀH‰„$èHÄ¨L‰à[]A\A]A^A_ÃDI‰Àé 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ÿÿÿHt$pL‰çèéÿÿÿH‰ÃëH‰ÅëI‹<$H9ýtèH‰ÝL‰ÿèH‰ïèAWAVAUI‰õATI‰üUH‰ÕSHì¨H‹H‹L|$Lt$ HS@HƒÀL‰ÿfHnÂfHnÈflÁ)$èL‰ïèL‰îL‰÷H‰ÂèH‰ïèH‰îL‰÷H‰ÂèH‹D$PIl$IÇD$I‰,$AÆD$H…À„ L‹D$@M…À„ôL9À‡ëH‹L$H1Ò1öL‰çI)ÈèHCfo$H‹|$pH‰D$HChH‰„$H„$€)T$ H9ÇtèH‹H|$`HƒÀH‰D$(èH‹H¼$H‹PH‹H0H‹RèH‰LH‹P H‹H(H‰T$ H‹RèH‰L H‹PH‹@H‰T$H‹RèH‰DH‹HÇD$HƒÀH‰„$èHÄ¨L‰à[]A\A]A^A_ÃDI‰Àé ÿÿÿHt$pL‰çèéÿÿÿH‰ÃëH‰ÅëI‹<$H9ýtèH‰ÝL‰ÿèH‰ïèAWAVAUI‰õATI‰üUH‰ÕSHì¨H‹H‹L|$Lt$ HS@HƒÀL‰ÿfHnÂfHnÈflÁ)$èL‰ïèL‰îL‰÷H‰ÂèH‰ïèH‰îL‰÷H‰ÂèH‹D$PIl$IÇD$I‰,$AÆD$H…À„ L‹D$@M…À„ôL9À‡ëH‹L$H1Ò1öL‰çI)ÈèHCfo$H‹|$pH‰D$HChH‰„$H„$€)T$ H9ÇtèH‹H|$`HƒÀH‰D$(èH‹H¼$H‹PH‹H0H‹RèH‰LH‹P H‹H(H‰T$ H‹RèH‰L H‹PH‹@H‰T$H‹RèH‰DH‹HÇD$HƒÀH‰„$èHÄ¨L‰à[]A\A]A^A_ÃDI‰Àé ÿÿÿHt$pL‰çèéÿÿÿH‰ÃëH‰ÅëI‹<$H9ýtèH‰ÝL‰ÿèH‰ïècannot create std::vector larger than max_size()AUATUSH‰ÓHƒìH‹ÿH…À„…Lc`(L‹h0H¸øÿÿÿÿÿÿIÁäI9ÄwwM…ätUL‰çèL‰âL‰îH‰ÇJ, èH‰ÁH‹;fHnÁfHnÍH‰kflÁH…ÿtèHƒÄ¸[]A\A]ÃfD1í1ÉëÂf.„HƒÄ1À[]A\A]ÃH=èAWAVAUI‰õATI‰üUH‰ÕSHì¨H‹H‹L|$Lt$ HS@HƒÀL‰ÿfHnÂfHnÈflÁ)$èL‰ïèL‰îL‰÷H‰ÂèH‰ïèH‰îL‰÷H‰ÂèH‹D$PIl$IÇD$I‰,$AÆD$H…À„ 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expected to have sequence typeElement type of sequence input AVAUATI‰ÔUH‰ýSHƒìPH‹H‰t$ÿPH…À„ƒx(…†Ll$H‹p L‰ïèöD$ „ÙH‹EL‰æH‰ïÿP(ƒx(H‰Åu>H‹X H‹{ƒKH…ÿtdH‹t$(H…ö„èL‰ïèHƒÄP[]A\A]A^ÃDH‰ÇèH‹EÇE(H‰ÇHƒçþ¨uRèH‰ÃH‰E H‹{ƒKH…ÿuœH‹CH‰ÇHƒçþ¨u2èH‰CH‹t$(H‰ÇH…öu…@H‹5éuÿÿÿ@H‹?ë©H‹?ëÉH‰Åé9¿0Lt$0èHL$L‰÷LHH5I‰ÄèL‰öL‰çèH‹H‹|$0IÇD$AÆD$ HƒÀI‰$ID$ I‰D$HD$@H9ÇtèH‹5HL‰çè¿0Lt$0èHL$L‰÷LHH5H‰ÅèL‰öH‰ïèH‹H‹|$0HÇEÆE HƒÀH‰EHE H‰EHD$@H9ÇtèH‹5HH‰ïèH‹|$0HD$@H9ÇtèL‰çèL‰ïèH‰ïèI‰ÄëI‰ÄëH‹|$0HD$@H9ÇtèH‰ïèL‰çèH‰Åë¡H‰Åë° expected to have optional typeElement type of optional input AVAUATI‰ÔUH‰ýSHƒìPH‹H‰t$ÿPH…À„ƒx( …†Ll$H‹p L‰ïèöD$ „ÙH‹EL‰æH‰ïÿP(ƒx( H‰Åu>H‹X H‹{ƒKH…ÿtdH‹t$(H…ö„èL‰ïèHƒÄP[]A\A]A^ÃDH‰ÇèH‹EÇE( H‰ÇHƒçþ¨uRèH‰ÃH‰E H‹{ƒKH…ÿuœH‹CH‰ÇHƒçþ¨u2èH‰CH‹t$(H‰ÇH…öu…@H‹5éuÿÿÿ@H‹?ë©H‹?ëÉH‰Åé9¿0Lt$0èHL$L‰÷LHH5I‰ÄèL‰öL‰çèH‹H‹|$0IÇD$AÆD$ HƒÀI‰$ID$ I‰D$HD$@H9ÇtèH‹5HL‰çè¿0Lt$0èHL$L‰÷LHH5H‰ÅèL‰öH‰ïèH‹H‹|$0HÇEÆE HƒÀH‰EHE H‰EHD$@H9ÇtèH‹5HH‰ïèH‹|$0HD$@H9ÇtèL‰çèL‰ïèH‰ïèI‰ÄëI‰ÄëH‹|$0HD$@H9ÇtèH‰ïèL‰çèH‰Åë¡H‰Åë°AT1öUH‰ýHƒìèH‹EH‰ïÿPH…ÀuX]A\ÃH‹E1öH‰ïÿPH…ÀtçH‹E1öH‰ïÿP‹P(ƒúuëA‹P(ƒút8ƒút3ƒútƒú u¹H‹@ ö@t¯H‹@H…ÀuÖH‹‹P(ƒúuÏ€H‹@ ‹@ƒà„Àt‚H‹EH‰ï1öÿP(H‰ï1öI‰ÄH‹EÿPHƒÄL‰æ]H‰ÇA\éFvRN10onnx_torch16InferenceContextEEN10onnx_torch14InferenceErrorEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_EPFvRN10onnx_torch16InferenceContextEE*ZN10onnx_torch11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_18Reshape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN10onnx_torch11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_20ScatterND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_26ScatterElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_15Split_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_13Pad_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Constrain input and output types to all tensor tConstrain output types to any te to any tensor tReturns which elements of the inConstrain to allGCC: (Debian 11.2.0-16) 11.2.0zRx0DXl €”¨¼Ðäø 4H\p„˜¬ÀÔèü$8L`tˆœ°ÄØì(<PdxŒ ´ÈÜð4eBŒD†A ƒv DBMQABP-H†`l €”8 ¨nAƒb Eh H@Ì%FHŒK †H(ƒ¥ ABBEO ABBD±KŽEB ŒD(†A0ƒz (A BBBHPÃÆÌÍÎ<XÐTEŒD †A(ƒG0£(A ABBAÃÆÌÍ˜0¬ŸBŒD†D ƒG0| AABG8à²BBŒE †A(ƒD@B (A ABBJzPLRx›<$šBEŒA †D(ƒG0¬ (A ABBK|5H†h˜ŒA†z EE4¸ýA†DƒG H AAJ AAE8ðSBŒA†D ƒt ABBæ ABG8,SBŒA†D ƒt ABBæ ABGDPÆBŒD†A ƒG0E AABIo AABE4°nBŒA†D ƒ| DBGH DBJè5Qƒ\ C EAƒN Ae KDBŽBB ŒA(†A0ƒD`n 0D(A BBBKLX{BBŽB B(ŒA0†A8ƒG°^ 8D0A(B BBBDL¨ËBBŽB B(ŒA0†A8ƒG°^ 8D0A(B BBBDLøãBBŽB B(ŒA0†A8ƒGÐ^ 8D0A(B BBBD0` ¢BŒD†G w CBGKAB0” wA†DƒD W AANDAA<°BBŒA †A(ƒL0– (D ABBAH ýBEŒA †A(ƒD0» (D ABBOV(A ABBHT ØBŽEB ŒA(†A0ƒ (D BBBL`(A BBBPˆ/BEŽE B(ŒD0†D8ƒJð³ 8D0A(B BBBHPÜ/BEŽE B(ŒD0†D8ƒJð³ 8D0A(B BBBHP0/BEŽE B(ŒD0†D8ƒJð³ 8D0A(B BBBHL„ÌBBŽB E(ŒD0†A8ƒI€Y 8A0A(B BBBDhÔBBŽB B(ŒA0†A8ƒGð 8D0A(B BBBJG 8A0A(B BBBG0XÛBŒK†L { ABImDEDtFBBŒA †N`y ABBG™ DEBE¼ù`†ŒDÜFBBŒA †N`y ABBG™ DEBE$ù`†ŒPD-BBŽE B(ŒD0†D8ƒJð· 8D0A(B BBBGL˜ËBBŽB E(ŒD0†D8ƒGðn 8A0A(B BBBFPè-BBŽE B(ŒD0†D8ƒJð· 8D0A(B BBBGP<-BBŽE B(ŒD0†D8ƒJð· 8D0A(B BBBGP#BFŽB B(ŒD0†A8ƒG 8D0A(B BBBA äƒ†ŒŽP íBBŽB E(ŒD0†D8ƒGà{ 8D0A(B BBBF8\ éBŒA†D@d ABBK ABAd˜ †BBŽB B(ŒA0†D8ƒI€] 8C0A(B BBBAL 8A0D(B BBBEP }BBŽE E(ŒD0†A8ƒJðþ 8D0A(B BBBHLT ¸BIŽB B(ŒA0†D8ƒD` 8A0A(B BBBFP¤ íBBŽB E(ŒD0†D8ƒGà{ 8D0A(B BBBFPø íBBŽB E(ŒD0†D8ƒGà{ 8D0A(B BBBFPLíBBŽB E(ŒD0†D8ƒGà{ 8D0A(B BBBFP íBBŽB E(ŒD0†D8ƒGà{ 8D0A(B BBBFPôíBBŽB E(ŒD0†D8ƒGà{ 8D0A(B BBBFPHíBBŽB E(ŒD0†D8ƒGà{ 8D0A(B BBBFL´¹BBŒA †A(ƒG0q (F ABBGT (C ABBAPìíBBŽB E(ŒD0†D8ƒGà{ 8D0A(B BBBFP@ íBBŽB E(ŒD0†D8ƒGà{ 8D0A(B BBBF0¬wA†DƒD W AANDAAPÈ íBBŽB E(ŒD0†D8ƒGà{ 8D0A(B BBBFPíBBŽB E(ŒD0†D8ƒGà{ 8D0A(B BBBFPpêBBŽB B(ŒD0†D8ƒJàt 8D0A(B BBBEPÄMBBŽE E(ŒD0†A8ƒJðË 8D0A(B BBBKPíBBŽB E(ŒD0†D8ƒGà{ 8D0A(B BBBFPlíBBŽB E(ŒD0†D8ƒGà{ 8D0A(B BBBFPÀíBBŽB E(ŒD0†D8ƒGà{ 8D0A(B BBBFPíBBŽB E(ŒD0†D8ƒGà{ 8D0A(B BBBF4€ïBEŒA †A(ƒD0Û(A ABB¸"AƒV IALØ#BBŒA †A(ƒG@] (A ABBHp (A ABBBH(GBOŽB B(ŒA0†A8ƒDP• 8A0A(B BBBFt8Aƒv0¯A†DƒK Š AALDAA<¬-BBŒA †D(ƒG0œ (A ABBFDìABŽGB ŒA(†A0ƒG@Â 0C(C EBBK 4@ƒ†ŒŽp'A†ZHŒëBBŽB B(ŒA0†A8ƒD`Ò8A0A(B BBBLÀÈBBŽB B(ŒA0†D8ƒLP® 8D0A(B BBBAPïBIŽB B(ŒA0†A8ƒOˆ 8A0A(B BBBF|Px† BIŽB B(ŒD0†A8ƒD¹ 8A0A(B BBBEPÌmBŽBB ŒA(†A0ƒP (A BBBEA(A BBB4 ,A†C KŒJƒf. \.@ AX)ƒ†Œ8x¡A†C FŽIŒNƒ{. |. 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_EELb1EEEEE16_M_allocate_nodeIJRKSJ_EEEPSK_DpOT_.isra.0_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_15IsNaN_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_14Cast_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_14Cast_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_14Cast_Onnx_ver6EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_14Cast_Onnx_ver6EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZZZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_ENKUlPKNS_11TensorProtoEE_clES8_.constprop.0_ZZZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_ENKUlPKNS_11TensorProtoEE_clES8_.constprop.0.cold_ZNSt10_HashtableIllSaIlENSt8__detail9_IdentityESt8equal_toIlESt4hashIlENS1_18_Mod_range_hashingENS1_20_Default_ranged_hashENS1_20_Prime_rehash_policyENS1_17_Hashtable_traitsILb0ELb1ELb1EEEE9_M_insertIRKlNS1_10_AllocNodeISaINS1_10_Hash_nodeIlLb0EEEEEEEESt4pairINS1_14_Node_iteratorIlLb1ELb0EEEbEOT_RKT0_St17integral_constantIbLb1EE.isra.0_ZNSt10_HashtableIllSaIlENSt8__detail9_IdentityESt8equal_toIlESt4hashIlENS1_18_Mod_range_hashingENS1_20_Default_ranged_hashENS1_20_Prime_rehash_policyENS1_17_Hashtable_traitsILb0ELb1ELb1EEEE9_M_insertIRKlNS1_10_AllocNodeISaINS1_10_Hash_nodeIlLb0EEEEEEEESt4pairINS1_14_Node_iteratorIlLb1ELb0EEEbEOT_RKT0_St17integral_constantIbLb1EE.isra.0.cold_GLOBAL__sub_I_old.cc_ZStL8__ioinit_ZNSt8_Rb_treeINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_N10onnx_torch8OpSchema9AttributeEESt10_Select1stISB_ESt4lessIS5_ESaISB_EE8_M_eraseEPSt13_Rb_tree_nodeISB_E.isra.0_ZNSt8_Rb_treeINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4pairIKS5_N10onnx_torch8OpSchema9AttributeEESt10_Select1stISB_ESt4lessIS5_ESaISB_EE7_M_copyILb0ENSH_11_Alloc_nodeEEEPSt13_Rb_tree_nodeISB_ESM_PSt18_Rb_tree_node_baseRT0_.isra.0_ZN10onnx_torch11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_18Reshape_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_18Reshape_Onnx_ver13EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_17Reshape_Onnx_ver5EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch22DataPropagationContextEEZNS0_11GetOpSchemaINS0_16Shape_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E0_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_17Concat_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_16Split_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_16Slice_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_19Transpose_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_20ScatterND_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_20ScatterND_Onnx_ver13EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_20ScatterND_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_26ScatterElements_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_26ScatterElements_Onnx_ver13EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_26ScatterElements_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_17Gather_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_25GatherElements_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_18Squeeze_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_20Unsqueeze_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_22SpaceToDepth_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_23DepthToSpace_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_14Tile_Onnx_ver6EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_19Identity_Onnx_ver13EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_18Identity_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_17NonZero_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_19GatherND_Onnx_ver12EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_14Pad_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Cast_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_16Concat_Onnx_ver1EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_16Concat_Onnx_ver4EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_15Split_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_13Pad_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_17Reshape_Onnx_ver1EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_14Tile_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_18Upsample_Onnx_ver1EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_18Upsample_Onnx_ver7EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_15Slice_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_16Slice_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_17Scatter_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_22DepthToSpace_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_16Gather_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_17Squeeze_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_19Unsqueeze_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_16OneHot_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_18Compress_Onnx_ver9EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_15Split_Onnx_ver2EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_15Split_Onnx_ver2EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_13Pad_Onnx_ver2EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_13Pad_Onnx_ver2EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_19GatherND_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEv.cold_ZN10onnx_torch11GetOpSchemaINS_19Identity_Onnx_ver14EEENS_8OpSchemaEv.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_15Where_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2__ZN10onnx_torch11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEv.cold_ZN10onnx_torch34propagateElemTypeFromInputToOutputERNS_16InferenceContextEmm.constprop.0_ZN10onnx_torch34propagateElemTypeFromInputToOutputERNS_16InferenceContextEmm.constprop.0.cold_ZZN10onnx_torch11GetOpSchemaINS_18Reshape_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_18Reshape_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_17Concat_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZZN10onnx_torch11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZZN10onnx_torch11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_18Squeeze_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_20Unsqueeze_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZZN10onnx_torch11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_19GatherND_Onnx_ver12EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZZN10onnx_torch11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZZN10onnx_torch11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZZN10onnx_torch11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_15Slice_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_17Squeeze_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_19Unsqueeze_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZZN10onnx_torch11GetOpSchemaINS_13Pad_Onnx_ver2EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_13Pad_Onnx_ver2EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_19GatherND_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZZN10onnx_torch11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZNSt17_Function_handlerIFvRN10onnx_torch22DataPropagationContextEEZNS0_11GetOpSchemaINS0_16Shape_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E0_E9_M_invokeERKSt9_Any_dataS2_.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_17NonZero_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZZN10onnx_torch11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZNSt17_Function_handlerIFvRN10onnx_torch16InferenceContextEEZNS0_11GetOpSchemaINS0_15Where_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_.cold_ZZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZZN10onnx_torch11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZZN10onnx_torch11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0_ZZN10onnx_torch11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.constprop.0.cold_ZTSZN10onnx_torch11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_18Reshape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0__ZTSZN10onnx_torch11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_20ScatterND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_26ScatterElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE__ZTSZN10onnx_torch11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8O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Got: AWAVAUI‰ÕATI‰ôUH‰ýSHì¸H‹ÿPH…À„ÔD‹p(Aƒþt Aƒþ…“H‹@ ‹X …Û„eH‹EH‰ïL‰îÿP(D‹`(H‰ÅAƒüt2Aƒüt,E…ä…¨AƒþteAƒþt/HÄ¸[]A\A]A^A_ÃDH‹U ‹B‰Z ƒÈ‰Bë×fDH‰ÇèH‹EÇE(H‰ÇHƒçþ¨uEèH‰E ƒH‰X ë¡H‰ÇèH‹EÇE(H‰ÇHƒçþ¨uèH‰E ƒH‰X énÿÿÿH‹?ë¶H‹?ëá¿0L|$ èL‰ÿI‰ÆèHl$0ºH5H‰ïèºH5H‰ïèL‰îH‰ïèº5H5H‰ïèD‰æH‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰÷èH‹H‹<$AÆF IÇFHƒÀI‰IF I‰FHD$H9ÇtèH‹5HL‰÷è¿0L|$ èL‰ÿI‰ÄèHl$0ºH5H‰ïèºH5H‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰çèH‹H‹<$AÆD$ IÇD$HƒÀI‰$ID$ I‰D$HD$H9ÇtèH‹5HL‰çèH‰Åé½H‰Åé!H‰ÅéÕ¿0L|$ èL‰ÿI‰ÅèHl$0ºH5H‰ïèºH5H‰ïèL‰æH‰ïèº5H5H‰ïèD‰öH‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰ïèH‹AÆE IÇEHƒÀI‰EIE I‰EH‹<$HD$H9ÇtèH‹5HL‰ïè¿0L|$ èL‰ÿI‰ÅèHl$0ºH5H‰ïèºH5H‰ïèL‰æH‰ïèºH5H‰ïèH‰åHt$8H‰ïèL‰ÿèH‰îL‰ïèé%ÿÿÿH‰Åë H‰ÅéH‰ÅëH‹<$HD$H9ÇtèL‰ïèH‰ïèëÉëÌH‰ÅëH‹<$HD$H9ÇtèL‰çèH‰ïèH‰ÅëL‰ÿèL‰÷èH‰ïèH‹<$HD$H9ÇtâèëÛH‰ÅëÖépÿÿÿL‰ÿèé{ÿÿÿL‰ÿèëÿÿaæ²Ò ËSÈ¬ ø’ø«)Àâ¸³å× þSößô¬ÆÌßHÄµ¿ïNAWAVAUATI‰üUSHìH‹L¼$ðLt$pH‰t$XHƒÀ@L‰ÿH‰L$HfHnÀH‹H‰T$@foÈfoÐL‰D$`HƒÀL‰t$hfHnØH‹flËHƒÀ)L$ fHnàflÔ)T$0èH‹H‹1öHÇ„$ÈfïÀH‹kHƒÀH‹K„$ØH‰„$ð1Àf‰„$Ð„$èH‹EèH‰l$pH‰LpHÇD$xH‹}èH‰$L÷èL‹s H‹C(L¬$€1öI‹~èL‰´$€H‰D$LïH‰èH‹CH‹s0fïÀHœ$ˆfoT$0H‰D$H‹@èH‰t$H‰tpH‹)”$€HƒÀ)„$H‰D$pH‹)„$ HƒÀh)„$°H‰„$ðH„$ÀH‰ÇH‰D$PèH‹H‰ÞL‰ÿÇ„$ÈHƒÀÆ„$àH‰„$ˆH„$àH‰D$0H‰„$ÐHÇ„$ØèH‹\$XH‰ßèH‰ÞL‰ïH‰ÂèH‹\$@H‰ßèH‰ÞL‰ïH‰ÂèH‹D$HL‰ïH‹0èH‹\$`H‰ßèH‰ÞL‰ïH‰ÂèH‹„$°I\$IÇD$I‰$AÆD$H…À„ L‹„$ M…À„ÿL9À‡öH‹Œ$¨1Ò1öL‰çI)ÈèH‹fol$ H‹¼$ÐHƒÀ)¬$€H‰D$pH‹HƒÀhH‰„$ðH;|$0tèH‹H‹|$PHƒÀH‰„$ˆèH‹D$H‹T$L‰ÿH‹L$H‹@èH‰TpI‹FèL‰´$€H‹$H‰Œ€H‹EèH‰l$pH‰TpH‹HÇD$xHƒÀH‰„$ðèHÄL‰à[]A\A]A^A_ÃfDI‰Àéÿÿÿ„H´$ÐL‰çèéüþÿÿH‰ÅëH‰ÅëI‰Äë*I‰ÄëcI‰Äë;I‹<$H9ûtèH‹|$hèH‰ïèH‹EèH‹$H‰l$pH‰LpHÇD$xH‹L‰ÿHƒÀH‰„$ðèL‰çèH‰ßèH‹D$H‹L$H‹@èH‰LpI‹FèH‹L$L‰´$€H‰Œ€ëŒÿÿ$üÙªÏÔœKÊ¼„ÅùCAWAVAUATI‰üUSHìH‹L¼$ðLt$pH‰t$XHƒÀ@L‰ÿH‰L$HfHnÀH‹H‰T$@foÈfoÐL‰D$`HƒÀL‰t$hfHnØH‹flËHƒÀ)L$ fHnàflÔ)T$0èH‹H‹1öHÇ„$ÈfïÀH‹kHƒÀH‹K„$ØH‰„$ð1Àf‰„$Ð„$èH‹EèH‰l$pH‰LpHÇD$xH‹}èH‰$L÷èL‹s H‹C(L¬$€1öI‹~èL‰´$€H‰D$LïH‰èH‹CH‹s0fïÀHœ$ˆfoT$0H‰D$H‹@èH‰t$H‰tpH‹)”$€HƒÀ)„$H‰D$pH‹)„$ HƒÀh)„$°H‰„$ðH„$ÀH‰ÇH‰D$PèH‹H‰ÞL‰ÿÇ„$ÈHƒÀÆ„$àH‰„$ˆH„$àH‰D$0H‰„$ÐHÇ„$ØèH‹\$XH‰ßèH‰ÞL‰ïH‰ÂèH‹\$@H‰ßèH‰ÞL‰ïH‰ÂèH‹D$HL‰ïH‹0èH‹\$`H‰ßèH‰ÞL‰ïH‰ÂèH‹„$°I\$IÇD$I‰$AÆD$H…À„ L‹„$ M…À„ÿL9À‡öH‹Œ$¨1Ò1öL‰çI)ÈèH‹fol$ H‹¼$ÐHƒÀ)¬$€H‰D$pH‹HƒÀhH‰„$ðH;|$0tèH‹H‹|$PHƒÀH‰„$ˆèH‹D$H‹T$L‰ÿH‹L$H‹@èH‰TpI‹FèL‰´$€H‹$H‰Œ€H‹EèH‰l$pH‰TpH‹HÇD$xHƒÀH‰„$ðèHÄL‰à[]A\A]A^A_ÃfDI‰Àéÿÿÿ„H´$ÐL‰çèéüþÿÿH‰ÅëH‰ÅëI‰Äë*I‰ÄëcI‰Äë;I‹<$H9ûtèH‹|$hèH‰ïèH‹EèH‹$H‰l$pH‰LpHÇD$xH‹L‰ÿHƒÀH‰„$ðèL‰çèH‰ßèH‹D$H‹L$H‹@èH‰LpI‹FèH‹L$L‰´$€H‰Œ€ëŒÿÿ$üÙªÏÔœKÊ¼„ÅùCAWAVAUATI‰üUSHìøH‹L´$àL|$`H‰t$PHƒÀ@L‰÷H‰T$@fHnÀH‹L‰|$XfoÈfoÐHƒÀfHnØH‹flËHƒÀ)L$ fHnàflÔ)T$0èH‹H‹1öHÇ„$¸fïÀH‹kHƒÀH‹K„$ÈH‰„$à1Àf‰„$À„$ØH‹EèH‰l$`H‰L`HÇD$hH‹}èH‰$LÿèL‹k H‹C(L|$p1öI‹}èL‰l$pH‰D$LÿH‰èH‹CH‹K0fïÀH\$xfoT$0H‰D$H‹@èH‰L$H‰L`H‹)T$pHƒÀ)„$€H‰D$`H‹)„$HƒÀh)„$ H‰„$àH„$°H‰ÇH‰D$HèH‹H‰ÞL‰÷Ç„$¸HƒÀÆ„$ÐH‰D$xH„$ÐH‰D$0H‰„$ÀHÇ„$ÈèH‹\$PH‰ßèH‰ÞL‰ÿH‰ÂèH‹\$@H‰ßèH‰ÞL‰ÿH‰ÂèH‹„$ I\$IÇD$I‰$AÆD$H…À„L‹„$M…À„óL9À‡êH‹Œ$˜1Ò1öL‰çI)ÈèH‹fol$ H‹¼$ÀHƒÀ)l$pH‰D$`H‹HƒÀhH‰„$àH;|$0tèH‹H‹|$HHƒÀH‰D$xèH‹D$H‹T$L‰÷H‹t$H‹@èH‰T`I‹EèL‰l$pH‹$H‰tpH‹EèH‰l$`H‰T`H‹HÇD$hHƒÀH‰„$àèHÄøL‰à[]A\A]A^A_ÃfDI‰Àéÿÿÿ„H´$ÀL‰çèéÿÿÿH‰ÅëH‰ÅëI‰Äë*I‰ÄëcI‰Äë;I‹<$H9ûtèH‹|$XèH‰ïèH‹EèH‹4$H‰l$`H‰t`HÇD$hH‹L‰÷HƒÀH‰„$àèL‰çèH‰ßèH‹D$H‹t$H‹@èH‰t`I‹EèH‹t$L‰l$pH‰tpë’ÿÿ$ò‰šÿè„ƒ úøøõ©C expected to have tensor or sparse type expected to have tensor typeAVAUATI‰ôU‰ÕSHì°H‹ÿP(H…À„äH‰Ã‹@(ƒø„Žƒøt)…À…ƒýt!1ÀƒýtHÄ°[]A\A]A^ÃDƒøt[H‰ßèH‹CÇC(H‰ÇHƒçþ¨uuèH‰C H‰ÅƒHH‹@H…Àu¯H‹EH‰ÇHƒçþ¨uRèH‰Eë•f.„H‹[ H‹CƒKH…À…vÿÿÿH‹CH‰ÇHƒçþ¨uèH‰CéYÿÿÿfDH‹?ë†H‹?ë©H‹?ëÝ¿0Ll$ èL‰ïI‰ÆèHl$0ºH5H‰ïèºH5H‰ïèL‰æH‰ïèº'H5H‰ïèH‰åHt$8H‰ïèL‰ïèH‰îL‰÷èH‹AÆF IÇFHƒÀI‰IF I‰FH‹<$HD$H9ÇtèH‹5HL‰÷è¿0Ll$ èL‰ïI‰ÆèHl$0ºH5H‰ïèºH5H‰ïèL‰æH‰ïèºH5H‰ïèH‰åHt$8H‰ïèL‰ïèH‰îL‰÷èé&ÿÿÿH‰Åë H‰ÅëH‰ÅëH‹<$HD$H9ÇtèëL‰ïèL‰÷èH‰ïèëÂëÅëÈÿÿ3ÒœªµH¨‹¦×ñôŠHïàê¡[ShapeInferenceError] Dimension value inferred ().Ranks inferred () is not equal to the existing dim value (Attribute expected to have tensor or sparse tensor type) is not equal to the existing rank value (Number of elements of input 'sizes' must be same as rank of input 'X'Input 'sizes' must have int64 element type.Number of elements of input 'scales' must be same as rank of input 'X'Input 'scales' must have float element type.¿0èƒ}(I‰Ä…ðL‹} Ll$ L‰ïèHl$0ºH5H‰ïèºH5H‰ïèL‰öH‰ïèº*H5H‰ïèL‰þH‰ïèºH5H‰ïèH‰åHt$8H‰ïèL‰ïèH‰îL‰çèH‹H‹<$AÆD$ IÇD$HƒÀI‰$ID$ I‰D$HD$H9ÇtèH‹5HL‰çèE1ÿéÿÿÿH‰Åë#H‹<$H‰ÅHD$H9ÇtèëL‰ïH‰ÅèL‰çèH‰ïè¿0èH}°HH5I‰ÄH‰ûèH‰ÞL‰çèH‹H‹}°AÆD$ IÇD$HƒÀI‰$ID$ I‰D$HEÀH9ÇtèH‹5HL‰çè¿0èH}°HH5I‰ÄH‰ûèH‰ÞL‰çèH‹H‹}°AÆD$ IÇD$HƒÀI‰$ID$ I‰D$HEÀH9ÇtèH‹5HL‰çèH‹}°H‰ÃHEÀH9ÇtèL‰çèH‰ßèH‰ÃëëH‹}°H‰ÃHEÀH9ÇtèL‰çèH‰ßèH‰Ãëë¿0èH}°HMŒI‰ÆA‹D$LMLHH‰û‰EA‹EVH5‰EŒHPèXH‰ÞZL‰÷èH‹H‹}°AÆF IÇFHƒÀI‰IF I‰FHEÀH9ÇtèH‹5HL‰÷è¿0èH}°HH5I‰ÄH‰ûèH‰ÞL‰çèH‹H‹}°AÆD$ IÇD$HƒÀI‰$ID$ I‰D$HEÀH9ÇtèH‹5HL‰çèH‹}°H‰ÃHEÀH9ÇtèL‰÷èH‰ßèH‰Ãëë¿0èH}°HH5I‰ÄH‰ûèH‰ÞL‰çèH‹H‹}°AÆD$ IÇD$HƒÀI‰$ID$ I‰D$HEÀH9ÇtèH‹5HL‰çèH‰ÃH‹}H…ÿtèH‰ßèH‹}°H‰ÃHEÀH9ÇtèL‰çèH‰ßèH‰ÃëëH‹}°H‰ÃHEÀH9ÇtèL‰çèë¡H‰Ãëñ¿0èH}°HH5I‰ÄH‰ûèH‰ÞL‰çèH‹H‹}°AÆD$ IÇD$HƒÀI‰$ID$ 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H‹¼$ÐHƒÀ)¬$€H‰D$pH‹HƒÀhH‰„$ðH;|$0tèH‹H‹|$XHƒÀH‰„$ˆèH‹D$H‹L$L‰ÿH‹$H‹@èH‰LpI‹FèH‹L$L‰´$€H‰Œ€H‹EèH‰l$pH‰TpH‹HÇD$xHƒÀH‰„$ðèHÄL‰à[]A\A]A^A_ÃfI‰Àéÿÿÿ„H´$ÐL‰çèéÿÿÿH‰ÅëH‰ÅëI‰Äë*I‰ÄëcI‰Äë;I‹<$H9ûtèH‹|$hèH‰ïèH‹EèH‹$H‰l$pH‰LpHÇD$xH‹L‰ÿHƒÀH‰„$ðèL‰çèH‰ßèH‹D$H‹L$H‹@èH‰LpI‹FèH‹L$L‰´$€H‰Œ€ëŒÿÿ$üÙªÏÔœOÊÀ€ÅùCAWAVAUATI‰üUSHìH‹L¼$L´$€H‰t$hHƒÀ@L‰ÿH‰L$PfHnÀH‹H‰T$HfoÈfoÐL‰D$pHƒÀL‰L$XfHnØH‹L‰t$xflËHƒÀ)L$ fHnàflÔ)T$0èH‹H‹1öHÇ„$ØfïÀL‹kHƒÀH‹K„$èH‰„$1Àf‰„$à„$øI‹EèL‰¬$€H‰Œ€HÇ„$ˆI‹}èH‰L$L÷èL‹s H‹C(H¬$1öI‹~èL‰´$H‰D$HïH‰èH‹CH‹[0fïÀfoT$0H‰D$H‹@èH‰\$@H‰œ€H‹Hœ$˜)”$HƒÀ)„$ H‰„$€H‹)„$°HƒÀh)„$ÀH‰„$H„$ÐH‰ÇH‰D$`èH‹H‰ÞL‰ÿÇ„$ØHƒÀÆ„$ðH‰„$˜H„$ðH‰D$0H‰„$àHÇ„$èèH‹\$hH‰ßèH‰ÞH‰ïH‰ÂèH‹\$HH‰ßèH‰ÞH‰ïH‰ÂèH‹D$PH‰ïH‹PH‹0èH‹\$pH‰ßèH‰ÞH‰ïH‰ÂèH‹D$XH‰ïH‹PH‹0èH‹¼$PèH‹´$PH‰ïH‰ÂèH‹„$XH‰ïH‹PH‹0èH‹„$ÀI\$IÇD$I‰$AÆD$H…À„)L‹„$°M…À„L9À‡H‹Œ$¸1Ò1öL‰çI)ÈèH‹fol$ H‹¼$àHƒÀ)¬$H‰„$€H‹HƒÀhH‰„$H;|$0tèH‹H‹|$`HƒÀH‰„$˜èH‹D$H‹L$@L‰ÿH‹t$H‹@èH‰Œ€I‹FèL‰´$H‹L$H‰´I‹EèL‰¬$€H‰Œ€H‹HÇ„$ˆHƒÀH‰„$èHÄL‰à[]A\A]A^A_Ã€I‰ÀéñþÿÿH´$àL‰çèéóþÿÿH‰ÅëH‰ÅëH‰Åë*H‰ÅëmH‰ÅëEI‹<$H9ûtèH‹|$xèH‰ïèI‹EèH‹t$L‰¬$€H‰´€HÇ„$ˆH‹L‰ÿHƒÀH‰„$èH‰ïèH‰ßèH‹D$H‹t$@H‹L$H‹@èH‰´€I‹FèH‹t$L‰´$H‰´I‹EèL‰¬$€H‰Œ€HÇ„$ˆé|ÿÿÿÿÿ%ŽÉ¼¿™Ä´šº£µéMAWAVAUATI‰üUSHìH‹L¼$L´$€H‰t$hHƒÀ@L‰ÿH‰L$PfHnÀH‹H‰T$HfoÈfoÐL‰D$pHƒÀL‰L$XfHnØH‹L‰t$xflËHƒÀ)L$ fHnàflÔ)T$0èH‹H‹1öHÇ„$ØfïÀL‹kHƒÀH‹K„$èH‰„$1Àf‰„$à„$øI‹EèL‰¬$€H‰Œ€HÇ„$ˆI‹}èH‰L$L÷èL‹s H‹C(H¬$1öI‹~èL‰´$H‰D$HïH‰èH‹CH‹[0fïÀfoT$0H‰D$H‹@èH‰\$@H‰œ€H‹Hœ$˜)”$HƒÀ)„$ H‰„$€H‹)„$°HƒÀh)„$ÀH‰„$H„$ÐH‰ÇH‰D$`èH‹H‰ÞL‰ÿÇ„$ØHƒÀÆ„$ðH‰„$˜H„$ðH‰D$0H‰„$àHÇ„$èèH‹\$hH‰ßèH‰ÞH‰ïH‰ÂèH‹\$HH‰ßèH‰ÞH‰ïH‰ÂèH‹D$PH‰ïH‹PH‹0èH‹\$pH‰ßèH‰ÞH‰ïH‰ÂèH‹D$XH‰ï‹0èH‹¼$PèH‹´$PH‰ïH‰ÂèH‹„$XH‰ï‹0èH‹„$ÀI\$IÇD$I‰$AÆD$H…À„3L‹„$°M…À„L9À‡ H‹Œ$¸1Ò1öL‰çI)ÈèH‹fol$ H‹¼$àHƒÀ)¬$H‰„$€H‹HƒÀhH‰„$H;|$0tèH‹H‹|$`HƒÀH‰„$˜èH‹D$H‹L$@L‰ÿH‹t$H‹@èH‰Œ€I‹FèL‰´$H‹L$H‰´I‹EèL‰¬$€H‰Œ€H‹HÇ„$ˆHƒÀH‰„$èHÄL‰à[]A\A]A^A_Ãf„I‰Àéïþÿÿ„H´$àL‰çèééþÿÿH‰ÅëH‰ÅëH‰Åë*H‰ÅëmH‰ÅëEI‹<$H9ûtèH‹|$xèH‰ïèI‹EèH‹t$L‰¬$€H‰´€HÇ„$ˆH‹L‰ÿHƒÀH‰„$èH‰ïèH‰ßèH‹D$H‹t$@H‹L$H‹@èH‰´€I‹FèH‹t$L‰´$H‰´I‹EèL‰¬$€H‰Œ€HÇ„$ˆé|ÿÿÿÿÿ%ŽÉ¼¿™Ä´º™—µéMvector::_M_default_appendH…ö„?H¸ÿÿÿÿÿÿÿAWAVAUATI‰üUSH‰óHƒìH‹WL‹H‰ÕL)ÅI‰íIÁýL)èH‰ÁH‹GH)ÐHÁøH9Æ†«H9ñ‚YL9îL‰èHCÆLè‚:H…À…ùI‰éE1öE1ÿLýH‰ÚÇEHƒêt!H}HÁâ1öL‰L$L‰$èL‹$L‹L$M…ÉM…À…LëfInÇM‰t$IŸfHnÈflÁA$HƒÄ[]A\A]A^A_Ãf„ÇHJHƒëtH™H‰Ï1öH)ÐHXüH‰ÚèH‰ÁHÙI‰L$HƒÄ[]A\A]A^A_ÃfDÃ€L‰ÆL‰ÊL‰ÿL‰$èL‹$L‰ÇèéVÿÿÿHºÿÿÿÿÿÿÿH9ÐHGÂL4…L‰÷èM‹$M‹L$I‰ÇIÆM)ÁéØþÿÿI¾üÿÿÿÿÿÿëÕH=è is undefined so it cannot be parsed.ParseData type mismatch for tensor: load external data into raw data for tensor: Cannot parse data from external tensors. Please does not match the actual sizebasic_string::_M_construct null not validThe type of tensor: [ShapeInferenceError] . Expected: Actual:vector::_M_range_insert expected size Data size mismatch. 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H{H…É„HcS‹9Â9BH‹|Ñ‰CL‰öèL‰÷èL‰îL‰÷èH„$H‹t$H‰ÇH‰D$èH‹E¾H‰ïHÇD$8ÿP(H‰ÃH…À„‹@(ƒø„ƒø„ô…À…H‰ßèH‹CÇC(H‰ÇHƒçþ¨…_èH‰C H‹XH‰ÅƒHH…Û„ËL{L‰õë(QH‹|È‰SH‰îèHƒÅ0H„$@H9Å„¨H‹C H…À„kHcK‹9Ñ|Á‹s9ò„YƒÂH‹{‰èH‰ÇHcCH‹S H‰KH‰|Âë›H‹¼$àH;|$„YE1ä1ÛE1ÿèépýÿÿH‹E1öH‰ïÿPH…À„ãýÿÿH‹E1öH‰ïÿP€‹P(ƒút@ƒút;ƒút ƒú …µýÿÿH‹@ ö@„§ýÿÿH‹@H…ÀuÎH‹‹P(ƒúuÇ€H‹@ ‹@ƒà„À„výÿÿH‹E1öH‰ïÿPƒx(„H‹H‹@H…À„ý‹Pƒú„¼ƒú…H‹@ L‰ïH‹pèé"ýÿÿf‹s9ðtIƒÀH‹{‰èH‰ÇHcCH‹S H‰KH‰|Âé¢ýÿÿ‹sƒÆL‰ÿèH‹C ‹é‘þÿÿ‹s@ƒÆèH‹K ‹ë§H‹|$èL‰÷èH‹|$èL‰ïèM…ätL‰çèH‹D$H…ÀtH‰ÇèL‹d$@H‹l$HL‰ãL9åtH‹;HCH9Çt4èHƒÃ H9ëuæM…ätL‰çèHÄH[]A\A]A^A_Ã€HƒÃ H9Ýu·ëÏDH‹¼$àH;|$tèH‹D$H‹t$1ÒL‰÷HÇ„$°H‰„$àèH‹”$°foL‰öH‰ïH‰„$àH‰”$ðH‹„$°H‹”$àH‰„$èÆH‹EÿH‰D$H…À„ÚLc`(L‹h0H¸øÿÿÿÿÿÿIÁäI9Ä‡CM…ä„4L‰çèH‰D$H‰ÇL‰âJ L‰îH‰D$ èé’fH‰ÇèH‹CÇC(H‰ÇHƒçþ¨…§èH‰C é‚ùÿÿH‰ÇèH‹CÇC(H‰ÇHƒçþ¨…oèH‰C é€ùÿÿH‹¼$àH;|$tèE1ÿéTúÿÿE1äE1ÿéIúÿÿHÇD$ H‹¼$àH;|$tèE1íééøÿÿƒ¼$¨ujHÇ„$ éiúÿÿf„H‹@ H‹@H…À…ýÿÿH‹é÷üÿÿH‹D$ H+D$Hƒø¸LDøéÇùÿÿHÇD$ HÇD$épÿÿÿL‰ïèÇ„$¨HÇ„$ éìùÿÿƒø…ûÿÿH‹k H‹]ƒMH…Û…5ûÿÿH‹EH‰ÇHƒçþ¨uuèH‰EH‰Ãéûÿÿƒø…îùÿÿL‹{ I‹_AƒOH…Û…úÿÿI‹GH‰ÇHƒçþ¨u1èI‰GH‰Ãé÷ùÿÿH‹?é‰þÿÿH‹?éQþÿÿH‹?éÂùÿÿH‹?é™úÿÿH‹?ëÊH‹?ë†H=èH=èH=èH=èH‰ÅééH‰ÅéH‰ÇéH‰ÅéH‰ÅéH‰ÅéééH‰ÅéH‰Åéë¯@H‰÷éÈôÿÿ¸Ã…ÒtƒúuH‰71ÀÃH‹H‰1ÀÃ¸Ã…ÒtƒúuH‰71ÀÃH‹H‰1ÀÃH‹UH‰ýHƒÀH‰H‹HE H9ÇtèH‰ï]éAUE1íATUH‰õSH‰ûHƒìHƒ?„³H‹mH…í„‘¿èHÇI‰ÄH‹E1ÒHKL‰cI‰D$H÷sH‹H‰ÐH‹mH…íuëTf„H‹mH…ítBI‰Ì¿èHÇH‰ÁH‹E1ÒH‰AH÷sH‹I‰$HÐHƒ8uÁL‰ H‹mH…íu¾HƒÄ[]A\A]Ãf.„H‹WHƒút1H‰ÐHÁè…¾@¢Àôº¤•°ý¼¿ÈÔªà…ì¢ø²ÿ›Œ€ ¡-œy‰¦à³³†Z…’’ïÞ‰´¹·ÐÄ¦‘Æ ‡š ”• ¾Ø ËÓ —‰ü ‰ ‹ Õ ‰Î Ú ÿÿ$ò‰šÿè„ƒ úøøõ©Cÿ›Œ€ÉËû1ßÐÕþß›¾ÛÃ›ÚŒ ÐÌ çŒ ¹Ä ÚÐ ÃÜ ¾è Ð–ç´¹¦• ê´ „ ‰ ¢ ¯ }ÿÿÎ¡°ÿ›ÐÇõ¶/µº+ñâ&ó Ú&» °-ú –-ž-Ó Ð/õ¦/û¦-ÃÊ.Ý¥+¾+ú«- Â.Úbº,™µ,«*î¦*Û¡*‘™*ãÔ&ÕÈ/® Ã/ê!®/¶"º.‚#²+Í#‰$ + $º+ª$ž-¯$º+´$ž-¿$Â.É$ ¥+ß$Â.ë$«-•%Ê.¡%¦-¾%Ã/Ê%¦/Ö%È/ì%°-ø%Ú&—&–-£&·&²+Ã&º.Ï&®/¡)í)ò)‘-¾/¨..µ.â/Ò.×.ÿÿÿÿ0Sú…‚Â úÿÿÿÿ9XîŒöÉ‹îÿÿÿÿ(6\‹Ó«Í‹«›ú2£çO“ÿÿÿÿ"6UÆÌÞ‰îÆÍ2Ö³OÎÿÿÿÿ/X¢Ï²Œà¢ Oªÿÿÿÿ0S£Ê«Š£ÿÿÿÿ9X™Ï¡Œó™ÿÿÿÿ"6\‹Ó£ù‹ß^›øO“ÿÿÿÿ"6U¯±Çî²¯ã¿œO·ÿÿÿÿ9XÄÏÔŒ™Äá¸Ìÿÿÿÿ9XäÏôŒ™äêOìÿÿÿÿ9XüÏ„ŒÖüÿÿÿÿ0S×ãß Ÿ×ÿÿÿÿ)6\ Ä¯ Š ÞÎŸ ïA§ ëO— ÿÿÿÿ#=\·èÏ©·•¹ÇŠm¿ÿÿ ÿÿ"6U‚ Ìš ‰Ì‚ ˜#’ ïOŠ ÿÿÿÿ"=\ Ó§ ”Á ˜Ÿ âm— ÿÿ ÿÿ6UñÌ‰²ñïOùÿÿÿ›¤˜dÛ¿_€ÊðÞ1øõB¢è¬š¾=•¸ ì ÌãÈà •‰ã£•ªÝ@ˆÃTøã±ãð•²¢¾ðÊ€Öˆÿ›mb‡)ãv¢æëðÊŸÕû¢§õÈ€¤Í·ž‘©Œö·üÞ‡ÙÔ§ATI‰üUSH‹_H‹/H9ët$DH‹}HEH9Çt+èHƒÅ H9ëuåI‹,$H…ít$[H‰ï]A\é„HƒÅ H9ëu¿ëØD[]A\Ãcannot create std::vector larger than max_size()basic_string::_M_construct null not validLabel encoder has only one input.Label encoder has only one output.Only one of keys_*'s can be set in label encoder.Input type is not string tensor but key_strings is setInput type is not int64 tensor but keys_int64s is setInput type is not float tensor but keys_floats is setOnly one of values_*'s can be set in label encoder.The indices, based on 0 as the first index of any dimension.Selected output data as an arrayThe input must be a tensor of a numeric type or string. The output will be of the same tensor type./opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/defs/traditionalml/defs.ccThe input must be a tensor of a numeric type. The output will be of the same tensor type.Values greater than this are mapped to 1, others to 0. Converts a map to a tensor.
The map key must be an int64 and the values will be ordered in ascending order based on this key.
The operator supports dense packing or sparse packing. If using sparse packing, the key cannot exceed the max_map-1 value. The input map that is to be cast to a tensorA tensor representing the same data as the input map, ordered by their keysThe input must be an integer map to either string or float.The output is a 1-D tensor of string, float, or integer.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).
One of 'DENSE', 'SPARSE'.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor. Converts strings to integers and vice versa.
Two sequences of equal length are used to map between integers and strings, with strings and integers at the same index detailing the mapping.
Each operator converts either integers to strings or strings to integers, depending on which default value attribute is provided. Only one default value attribute should be defined.
If the string default value is set, it will convert integers to strings. If the int default value is set, it will convert strings to integers. Output data. If strings are input, the output values are integers, and vice versa.The input must be a tensor of strings or integers, either [N,C] or [C].The output is a tensor of strings or integers. Its shape will be the same as the input shape.The strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceThe integers of the map. This sequence must be the same length as the 'cats_strings' sequence.A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.An integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined. Uses an index mapping to convert a dictionary to an array.
Given a dictionary, each key is looked up in the vocabulary attribute corresponding to the key type. The index into the vocabulary array at which the key is found is then used to index the output 1-D tensor 'Y' and insert into it the value found in the dictionary 'X'.
The key type of the input map must correspond to the element type of the defined vocabulary attribute. Therefore, the output array will be equal in length to the index mapping vector parameter. All keys in the input dictionary must be present in the index mapping vector. For each item in the input dictionary, insert its value in the output array. Any keys not present in the input dictionary, will be zero in the output array.
For example: if the ``string_vocabulary`` parameter is set to ``["a", "c", "b", "z"]``, then an input of ``{"a": 4, "c": 8}`` will produce an output of ``[4, 8, 0, 0]``. A 1-D tensor holding values from the input dictionary.The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.A string vocabulary array.
One and only one of the vocabularies must be defined.An integer vocabulary array.
One and only one of the vocabularies must be defined. Concatenates input tensors into one continuous output.
All input shapes are 2-D and are concatenated along the second dimention. 1-D tensors are treated as [1,C]. Inputs are copied to the output maintaining the order of the input arguments.
All inputs must be integers or floats, while the output will be all floating point values. An ordered collection of tensors, all with the same element type.The output array, elements ordered as the inputs.The input type must be a tensor of a numeric type.The size of each input in the input list Replaces inputs that equal one value with another, leaving all other elements alone.
This operator is typically used to replace missing values in situations where they have a canonical representation, such as -1, 0, NaN, or some extreme value.
One and only one of imputed_value_floats or imputed_value_int64s should be defined -- floats if the input tensor holds floats, integers if the input tensor holds integers. The imputed values must all fit within the width of the tensor element type. One and only one of the replaced_value_float or replaced_value_int64 should be defined, which one depends on whether floats or integers are being processed.
The imputed_value attribute length can be 1 element, or it can have one element per input feature.
In other words, if the input tensor has the shape [*,F], then the length of the attribute array may be 1 or F. If it is 1, then it is broadcast along the last dimension and applied to each feature. 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. Maps each element in the input tensor to another value.
The mapping is determined by the two parallel attributes, 'keys_*' and 'values_*' attribute. The i-th value in the specified 'keys_*' attribute would be mapped to the i-th value in the specified 'values_*' attribute. It implies that input's element type and the element type of the specified 'keys_*' should be identical while the output type is identical to the specified 'values_*' attribute. If an input element can not be found in the specified 'keys_*' attribute, the 'default_*' that matches the specified 'values_*' attribute may be used as its output value.
Let's consider an example which maps a string tensor to an integer tensor. Assume and 'keys_strings' is ["Amy", "Sally"], 'values_int64s' is [5, 6], and 'default_int64' is '-1'. The input ["Dori", "Amy", "Amy", "Sally", "Sally"] would be mapped to [-1, 5, 5, 6, 6].
Since this operator is an one-to-one mapping, its input and output shapes are the same. Notice that only one of 'keys_*'/'values_*' can be set.
For key look-up, bit-wise comparison is used so even a float NaN can be mapped to a value in 'values_*' attribute.
Input data. It can be either tensor or scalar.The input type is a tensor of any shape.Output type is determined by the specified 'values_*' attribute.A list of strings. One and only one of 'keys_*'s should be set.A list of strings. One and only one of 'value_*'s should be set.Classification outputs (one class per example).Classification scores ([N,E] - one score for each class and exampleThe input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]The output will be a tensor of strings or integers.A collection of weights of the model(s).Indicates whether to do OvR or multinomial (0=OvR is the default).Class labels when using string labels. One and only one 'classlabels' attribute must be defined.Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' Generalized linear regression evaluation.
If targets is set to 1 (default) then univariate regression is performed.
If targets is set to M then M sets of coefficients must be passed in as a sequence and M results will be output for each input n in N.
The coefficients array is of length n, and the coefficients for each target are contiguous. Intercepts are optional but if provided must match the number of targets. Regression outputs (one per target, per example).The input must be a tensor of a numeric type.Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Weights of the intercepts, if used.The total number of regression targets, 1 if not defined. Normalize the input. There are three normalization modes, which have the corresponding formulas, defined using element-wise infix operators '/' and '^' and tensor-wide functions 'max' and 'sum':

Max: Y = X / max(X)
L1: Y = X / sum(X)
L2: Y = sqrt(X^2 / sum(X^2)}
In all modes, if the divisor is zero, Y == X.
For batches, that is, [N,C] tensors, normalization is done along the C axis. In other words, each row of the batch is normalized independently. Data to be encoded, a tensor of shape [N,C] or [C] Replace each input element with an array of ones and zeros, where a single one is placed at the index of the category that was passed in. The total category count will determine the size of the extra dimension of the output array Y.
For example, if we pass a tensor with a single value of 4, and a category count of 8, the output will be a tensor with ``[0,0,0,0,1,0,0,0]``.
This operator assumes every input feature is from the same set of categories.
If the input is a tensor of float, int32, or double, the data will be cast to integers and the cats_int64s category list will be used for the lookups. Encoded output data, having one more dimension than X.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.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.First, offset by this.
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'Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores.The input must be a tensor of a numeric type, either [C] or [N,C].The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Its size will match the bactch size of the input.The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.First set of probability coefficients.Second set of probability coefficients. This array must be same size as prob_a.
If these are provided then output Z are probability estimates, otherwise they are raw scores.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.Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.Regression outputs (one score per target per example).The input type must be a tensor of a numeric type, either [C] or [N,C].Flag indicating whether the regression is a one-class SVM or not.The number of support vectors.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.' Tree Ensemble classifier. Returns the top class for each of N inputs.
The attributes named 'nodes_X' form a sequence of tuples, associated by index into the sequences, which must all be of equal length. These tuples define the nodes.
Similarly, all fields prefixed with 'class_' are tuples of votes at the leaves. A leaf may have multiple votes, where each vote is weighted by the associated class_weights index.
One and only one of classlabels_strings or classlabels_int64s will be defined. The class_ids are indices into this list. 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.Node id for each node. Ids may restart at zero for each tree, but it not required to.Thresholds to do the splitting on for each node.Popularity of each node, used for performance and may be omitted.The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'Child node if expression is true.Child node if expression is false.For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the defalt value is false (0) for all nodes.nodes_missing_value_tracks_trueThe id of the tree that this node is in.node id that this weight is for.The index of the class list that each weight is for.The weight for the class in class_id.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0) Tree Ensemble regressor. Returns the regressed values for each input in N.
All args with nodes_ are fields of a tuple of tree nodes, and it is assumed they are the same length, and an index i will decode the tuple across these inputs. Each node id can appear only once for each tree id.
All fields prefixed with target_ are tuples of votes at the leaves.
A leaf may have multiple votes, where each vote is weighted by the associated target_weights index.
All trees must have their node ids start at 0 and increment by 1.
Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF Node id for each node. Node ids must restart at zero for each tree and increase sequentially.Child node if expression is trueChild node if expression is falseFor each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the defalt value is false (0) for all nodes.The id of the tree that each node is in.The index of the target that each weight is forDefines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.' Creates a map from the input and the attributes.
The values are provided by the input tensor, while the keys are specified by the attributes. Must provide keys in either classlabels_strings or classlabels_int64s (but not both).
The columns of the tensor correspond one-by-one to the keys specified by the attributes. There must be as many columns as keys.
The output will be a sequence of string or integer maps to float.The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.Input's shape should be 1D or 2D expected to have tensor or sparse typeUH‰ýSHƒìH‹_H…ÛtfDH‰ßH‹èH…ÛuðH‹EH‹}1öHƒÅ0HÅèH‹}ÐHÇEèHÇEàH9ïtHƒÄ[]éf„HƒÄ[]ÃAWAVAUATUSH‰û¿ˆHƒìèH‹+L‹kLpHÇI‰ÄL‰pH‰èLèt H…í„xL‰l$Iƒý‡ìIƒý…Â¶EAˆD$L‰ðM‰l$M|$(Hs BÆ(H‹C(L‰ÿIÇD$(óoC@I‰D$0H‹C8IÇD$8I‰D$@IÇD$XAD$HèH‹kXL‹k`I|$pI‰|$`H‰èLèt H…í„éL‰l$Iƒý‡‘IƒýuC¶EAˆD$pM‰l$hL‰àBÆ/HƒÄ[]A\A]A^A_ÃfDM…í…¬L‰ðé9ÿÿÿ€M…ítÁëdHt$I|$1ÒèI‰D$H‰ÇH‹D$I‰D$L‰êH‰îèL‹l$I‹D$éíþÿÿHt$I|$`1ÒèI‰D$`H‰ÇH‹D$I‰D$pL‰êH‰îèL‹l$I‹|$`éAÿÿÿH=èH=èL‰÷ë‰H‰ÇëH‰ÃëH‰ÃëL‰ÿèI‹|$I9þuH‰ßèL‰çèèH‰ÅëèëßèH‰ïèAUI‰ýATUSHƒìL‹gH‹/I9ì„¢H‹}xH…ˆH9ÇtèH‹}XHEhH9ÇtèH‹]0H…ÛtH‰ßH‹èH…ÛuðH‹E(H‹} 1öHÅèH‹} HEPHÇE8HÇE0H9ÇtèH‹}HEH9Çt:èHÅ¨I9ì…eÿÿÿI‹mH…ít.HƒÄH‰ï[]A\A]éf.„HÅ¨I9ì…0ÿÿÿëÉHƒÄ[]A\A]ÃAVI‰þAUATUSL‹oL‹'M9åt}f„I‹|$8ID$HH9ÇtèI‹\$(I‹l$ H9ët%DH‹}HEH9Çt[èHƒÅ H9ëuåI‹l$ H…ítH‰ïèI‹<$ID$H9Çt=èIƒÄXM9åuM‹&M…ät6[L‰ç]A\A]A^é€HƒÅ H9ëuë¨DIƒÄXM9å…SÿÿÿëÂ[]A\A]A^ÃH‹SH‰ûHƒÀH‰H‹HHCXH9ÇtèH‹H{8HƒÀH‰[éH‹UH‰ýHƒÀH‰H‹HHEXH9ÇtèH‹H}8HƒÀH‰EèH‰ï]é[ShapeInferenceError] Target=Mismatch between source and target type. 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H‹GÃ@éN10onnx_torch14InferenceErrorEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_E*ZN10onnx_torch11GetOpSchemaINS_19CastMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_26CategoryMapper_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_26DictVectorizer_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_24LabelEncoder_OnnxML_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_28LinearClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_25SVMClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_34TreeEnsembleClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_18ZipMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_classlabels_striclasslabels_int6 Select elements of the input tensor based on the indices passed.
The indices are applied to the last axes of the te Maps the values of the input tensor to either 0 or 1, element-wise, based on the outcome of a comparison against a threshol Linear clas Rescale input data, for example to standardize features by removing the mean and scaling to unit variance. 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H…ítH‰ïèI‹<$ID$H9Ç„èIƒÄXM9æu‡L‹£èM…ätL‰çèL‹«ØL‹£ÐM9å„º@I‹|$xI„$ˆH9ÇtèI‹|$XID$hH9ÇtèI‹l$0H…ítH‰ïH‹mèH…íuïI‹D$(I‹|$ 1öHÅèI‹|$ ID$PIÇD$8IÇD$0H9ÇtèI‹<$ID$H9Ç„–èIÄ¨M9å…QÿÿÿL‹£ÐM…ätL‰çèL‹«ÀL‹£¸M9å„¼fDI‹|$xI„$ˆH9ÇtèI‹|$XID$hH9ÇtèI‹l$0H…ítH‰ïH‹mèH…íuïI‹D$(I‹|$ 1öHÅèI‹|$ ID$PIÇD$8IÇD$0H9ÇtèI‹<$ID$H9Ç„ÎèIÄ¨M9å…QÿÿÿL‹£¸M…ätL‰çèH‹«H…í„ÒH‹}èH½ˆL‹eèH‹}`HEpH9ÇtèH‹}@HEPH9ÇtèH‹} HE0H9Çt{èH‰ïèM…ätvL‰åëŸ@HƒÅ I9í…[ýÿÿéuýÿÿfDIÄ¨M9å…ÀýÿÿéjþÿÿIÄ¨M9å…ˆþÿÿé2ÿÿÿIƒÄXM9æ…ëüÿÿé_ýÿÿfDH‰ïèM…äuŠH‹{`HCpH9ÇtèH‹{@HCPH9ÇtèH‹{ HC0H9ÇtèH‹;HƒÃH9ßt []A\A]A^é[]A\A]A^Ãÿÿ Converts strings to integers and vice versa.
If the string default value is set, it will convert integers to strings. If the int default value is set, it will convert strings to integers.
Each operator converts either integers to strings or strings to integers, depending on which default value attribute is provided. Only one default value attribute should be defined.
When converting from integers to strings, the string is fetched from the 'classes_strings' list, by simple indexing.
When converting from strings to integers, the string is looked up in the list and the index at which it is found is used as the converted value. Output data. If strings are input, the output values are integers, and vice versa.The input type must be a tensor of integers or strings, of any shape.The output type will be a tensor of strings or integers, and will have the same shape as the input.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.A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined./opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/defs/traditionalml/old.ccT1Input data.XT2Ytensor(string)tensor(int64)A list of labels.classes_stringsdefault_int64default_stringLabelEncoderai.onnx.mlH‹…øþÿÿH…ÀtH½èþÿÿºH‰þÿÐH‹…ØþÿÿH…ÀtH½ÈþÿÿºH‰þÿÐH‹…¸þÿÿH…ÀtH½¨þÿÿºH‰þÿÐH‹…˜þÿÿH…ÀtH½ˆþÿÿºH‰þÿÐH½0þÿÿèH½þÿÿèH½þÿÿèH½èýÿÿèH‹½ÀýÿÿèH‹½ýÿÿL9ÿtèH‹½pýÿÿL9÷tèH‹½PýÿÿL9ïtèH‹½0ýÿÿH;½ üÿÿtèH‹½ðüÿÿH;½¸üÿÿtèH‹½ýÿÿH9ßtèL‰çèH‹… ýÿÿH…Àt ºL‰îL‰ïÿÐH‹½ðüÿÿH;½¸üÿÿtèL‰ÿèH‰ßèH‹½ýÿÿH9ßtèL‰óL½0ýÿÿëÓÿÿ%»‹«¤›èý«» £ô O“ÿÿ ¡ßN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_E*ZN10onnx_torch11GetOpSchemaINS_24LabelEncoder_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_GCC: (Debian 11.2.0-16) 11.2.0zRx0DXl@€ÅA†DƒG C AAGO AAG{ AACzPLRx›<$šBEŒA †D(ƒG0¬ (A ABBK0$wA†DƒD W AANDAAL˜)BBŽB B(ŒA0†A8ƒLPù 8A0A(B BBBGH¨ýBEŒA †A(ƒD0» (D ABBOV(A ABBHôØBŽEB ŒA(†A0ƒ (D BBBL`(A BBB4@ïBEŒA †A(ƒD0Û(A 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H…ítH‰ïèI‹<$ID$H9Ç„èIƒÄXM9æu‡L‹£èM…ätL‰çèL‹«ØL‹£ÐM9å„º@I‹|$xI„$ˆH9ÇtèI‹|$XID$hH9ÇtèI‹l$0H…ítH‰ïH‹mèH…íuïI‹D$(I‹|$ 1öHÅèI‹|$ ID$PIÇD$8IÇD$0H9ÇtèI‹<$ID$H9Ç„–èIÄ¨M9å…QÿÿÿL‹£ÐM…ätL‰çèL‹«ÀL‹£¸M9å„¼fDI‹|$xI„$ˆH9ÇtèI‹|$XID$hH9ÇtèI‹l$0H…ítH‰ïH‹mèH…íuïI‹D$(I‹|$ 1öHÅèI‹|$ ID$PIÇD$8IÇD$0H9ÇtèI‹<$ID$H9Ç„ÎèIÄ¨M9å…QÿÿÿL‹£¸M…ätL‰çèH‹«H…í„ÒH‹}èH½ˆL‹eèH‹}`HEpH9ÇtèH‹}@HEPH9ÇtèH‹} HE0H9Çt{èH‰ïèM…ätvL‰åëŸ@HƒÅ I9í…[ýÿÿéuýÿÿfDIÄ¨M9å…ÀýÿÿéjþÿÿIÄ¨M9å…ˆþÿÿé2ÿÿÿIƒÄXM9æ…ëüÿÿé_ýÿÿfDH‰ïèM…äuŠH‹{`HCpH9ÇtèH‹{@HCPH9ÇtèH‹{ HC0H9ÇtèH‹;HƒÃH9ßt []A\A]A^é[]A\A]A^Ãÿÿ Gradient operator computes the partial derivatives of a specific tensor w.r.t. some other tensors. This operator is widely used in gradient-based training algorithms. To illustrate its use, let's consider a computation graph, ``` X -----. | v W --> Conv --> H --> Gemm --> Y ^ | Z ``` , where W and Z are trainable tensors. Note that operators' attributes are omitted for the sake of simplicity. Let dY/dW (dY/dZ) be the gradient of Y with respect to W (Z). The user can compute gradient by inserting Gradient operator to form another graph shown below. ``` W --> Conv --> H --> Gemm --> Y | ^ ^ | | | | X Z | | | | | .----------' | | | (W/Z/X is the 1st/2nd/3rd input of Gradient as shown in | | | "xs" followed by "zs") | v v '---> Gradient(xs=["W", "Z"], zs=["X"], y="Y") | | | '-----------------------------------> dY/dW (1st output of Gradient) | '---------------------------------------> dY/dZ (2nd output of Gradient) ``` By definition, the tensor "y" is a function of independent variables in "xs" and "zs". Since we only compute the gradient of "y" w.r.t. the differentiable variables in "xs", this Gradient only outputs dY/dW and dY/dZ. Note that "H" cannot appear in "xs" and "zs". The reason is that "H" can be determined by tensors "W" and "X" and therefore "H" is not an independent variable. All outputs are optional. If needed, for example, user can assign an empty string to the 1st output name of that Gradient to skip the generation of dY/dW. Note that the concept of optional outputs can also be found in ONNX's RNN, GRU, and LSTM. Gradient operator can compute derivative against intermediate tensors. For example, the gradient of Y with respect to H can be done via ``` W --> Conv --> H --> Gemm --> Y ^ | ^ | | | X | Z .-------' | | .----------' | | (H/Z is the 1st/2nd input of Gradient as shown in "xs") v v Gradient(xs=["H", "Z"], y="Y") | | | '-----------------------------------> dY/dH (1st output of Gradient) | '---------------------------------------> dY/dZ (2nd output of Gradient) ``` It is possible to represent high-order differentiation using Gradient operators. For example, given the following linear model: ``` W --> Gemm --> Y --> Loss --> O ^ ^ | | X L ``` To compute the 2nd order derivative of O with respect to W (denoted by d^2O/dW^2), one can do ``` W --> Gemm --> Y --> Loss --> O | ^ ^ | | | | X .------------L | | | | | | | v +------+-+> Gradient(xs=["X", "W"], zs=["L"], y="O") ---> dO/dX (1st output of Gradient) | | | | | | | '---> dO/dW (2nd output of Gradient) | v v '---> Gradient(xs=["X", "W"], zs=["L"], y="dO/dW") ---> d(dO/dW)dX (1st output of | Gradient) | | '---> d^2O/dW^2 (2nd output of Gradient) ``` The tensors named in attributes "xs", "zs", and "y" define the differentiated computation graph, and the inputs to Gradient node define the values at which the gradient is computed. We can feed different tensors to the identified graph. For example, one can compute the gradient of Y with respect to H at a specific value of H, H_1, by providing that value as an input to the Gradient node. ``` W --> Conv --> H --> Gemm --> Y ^ ^ | | X Z Z_1 (2nd input of Gradient) | v H_1 --> Gradient(xs=["H", "Z"], y="Y") ---> dY/dH when H = H_1 and Y = Y_1. | '------------------------------> dY/dZ (2nd output of Gradient) ``` When the inputs of Gradient are the tensors named in "xs" and "zs", the computation can be optimized. More specifically, intermediate variables in forward pass can be reused if the gradient is computed via reverse-mode auto-differentiation. The values fed into graph identified by the attributes. The i-th input is the value of the i-th tensor specified in the concatenated list of the attribute "xs" and the attribute "zs". For example, if xs=["A", "B"] and zs=["C"], the first input is used as the value of symbol "A" and the 3rd input is substituted for all the occurrences of "C".The gradient of the tensor specified by the attribute "y" with respect to each of tensors specified in the attribute "xs". The i-th output is the gradient of "y" with respect to the i-th tensor specified in the attribute "xs".Input tensor names of the differentiated sub-graph. It contains only the necessary differentiated inputs of a (sub-)graph. Variables (usually called intermediate variables) that can be generated from inputs cannot be included in this attribute.Input tensor names of the differentiated sub-graph. It contains only the necessary non-differentiated inputs of a (sub-)graph. Variables (usually called intermediate variables) that can be generated from inputs cannot be included in this attribute.The targeted tensor. It can be viewed as the output of the differentiated function. The attribute "xs" and attribute "zs" are the minimal independent variable set that determines the value of "y".Allow inputs to be any kind of floating-point tensor./opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/defs/training/defs.cc Compute one iteration of ADAGRAD, a stochastic gradient based optimization algorithm. This operator can conduct the optimization of multiple tensor variables. Let's define the behavior of this operator. As you can imagine, ADAGRAD requires some parameters: - The initial learning-rate "R". - The update count "T". That is, the number of training iterations conducted. - A L2-norm regularization coefficient "norm_coefficient". - A learning-rate decay factor "decay_factor". - A small constant "epsilon" to avoid dividing-by-zero. At each ADAGRAD iteration, the optimized tensors are moved along a direction computed based on their estimated gradient and accumulated squared gradient. Assume that only a single tensor "X" is updated by this operator. We need the value of "X", its gradient "G", and its accumulated squared gradient "H". Therefore, variables in this operator's input list are sequentially "R", "T", "X", "G", and "H". Other parameters are given as attributes because they are usually constants. Also, the corresponding output tensors are the new value of "X" (called "X_new"), and then the new accumulated squared gradient (called "H_new"). Those outputs are computed from the given inputs following the pseudo code below. Let "+", "-", "*", and "/" are all element-wise arithmetic operations with numpy-style broadcasting support. The pseudo code to compute those outputs is: // Compute a scalar learning-rate factor. At the first update of X, T is generally // 0 (0-based update index) or 1 (1-based update index). r = R / (1 + T * decay_factor); // Add gradient of 0.5 * norm_coefficient * ||X||_2^2, where ||X||_2 is the 2-norm. G_regularized = norm_coefficient * X + G; // Compute new accumulated squared gradient. H_new = H + G_regularized * G_regularized; // Compute the adaptive part of per-coordinate learning rate. Note that Sqrt(...) // computes element-wise square-root. H_adaptive = Sqrt(H_new) + epsilon // Compute the new value of "X". X_new = X - r * G_regularized / H_adaptive; If one assign this operators to optimize multiple inputs, for example, "X_1" and "X_2", the same pseudo code may be extended to handle all tensors jointly. More specifically, we can view "X" as a concatenation of "X_1" and "X_2" (of course, their gradient and accumulate gradient should be concatenated too) and then just reuse the entire pseudo code. Note that ADAGRAD was first proposed in http://jmlr.org/papers/volume12/duchi11a/duchi11a.pdf. In that reference paper, this operator is a special case of the Figure 1's composite mirror descent update. The update count of "X". It should be a scalar.The current values of optimized tensors, followed by their respective gradients, followed by their respective accumulated squared gradients.For example, if two tensor "X_1" and "X_2" are optimized, The input list would be ["X_1", "X_2", gradient of "X_1", gradient of "X_2", accumulated squared gradient of "X_1", accumulated squared gradient of "X_2"].Updated values of optimized tensors, followed by their updated values of accumulated squared gradients. For example, if two tensor "X_1" and "X_2" are optimized, the output list would be [new value of "X_1," new value of "X_2" new accumulated squared gradient of "X_1", new accumulated squared gradient of "X_2"].Small scalar to avoid dividing by zero.The decay factor of learning rate after one update.The effective learning rate is computed by r = R / (1 + T * decay_factor). Default to 0 so that increasing update counts doesn't reduce the learning rate.Regularization coefficient in 0.5 * norm_coefficient * ||X||_2^2. Default to 0, which means no regularization.Constrain input types to float scalars.Constrain input types to 64-bit integer scalars.Constrain input and output types to float tensors. Compute one iteration of stochastic gradient update with momentum. This operator can conduct the optimization of multiple tensor variables. Let's define the behavior of this operator. As you can imagine, SG with momentum requires several parameters: - The learning-rate "R". - The update count "T". That is, the number of conducted training iterations. It should be zero in the first training iteration. - A L2-norm regularization coefficient "norm_coefficient". - A decay coefficient of previous accumulated gradient (i.e., momentum) "alpha". - The scaling coefficient of current gradient "beta". - An attribute to choose either standard momentum or Nesterov's momentum "mode" should be used. For the sake of simplicity, assume that there is only one tensor (called "X") to be optimized. Other necessary inputs are "X"'s gradient (called "G") and "X"'s momentum (called "V"). This Momentum operator maps all these inputs to the new value of "X" (called "X_new") and its new momentum (called "V_new"). This operator supports two different momentum algorithms. Set the attribute "mode" to "nesterov" if Nesterov's momentum is desired. Otherwise, set the attribute "model" to "standard" to use standard momentum. Computation details are described subsequently. Let "+", "-", "*", and "/" are all element-wise operations with numpy-style broadcasting. Pseudo code for SG with standard momentum: // Add gradient of 0.5 * norm_coefficient * ||X||^2, where ||X|| is the sum of squared // values of all elements in X. G_regularized = norm_coefficient * X + G // In the first training iteration, beta should always be 1. beta_adjusted = T > 0 ? beta : 1 // Compute the current momentum based on previous momentum and the current gradient. V_new = alpha * V + beta_adjusted * G_regularized // Update X. X_new = X - R * V_new Pseudo code for SG with Nesterov's momentum: // Add gradient of 0.5 * norm_coefficient * ||X||^2, where ||X|| is the sum of squared // values of all elements in X. G_regularized = norm_coefficient * X + G; // In the first training iteration, beta should always be 1. beta_adjusted = T > 0 ? beta : 1 // Compute the current momentum based on previous momentum and the current gradient. V_new = alpha * V + beta_adjusted * G_regularized; // Compute final update direction and then update X. X_new = X - R * (G_regularized + alpha * V_new) If one assign this operators to optimize multiple inputs, for example, "X_1" and "X_2". The same pseudo code would be extended to handle all tensors jointly. More specifically, we can view "X" as a concatenation of "X_1" and "X_2" (of course, their gradient and accumulate gradient should be concatenated too) and then our pseudo code becomes applicable. Update count of "X". It should be a scalar.It sequentially contains the current values of optimized tensors, then their gradient tensors, and finally their momentum tensors. For example, if two tensors "X_1" and "X_2" are optimized, The expected input list would be ["X_1", "X_2", gradient of "X_1", gradient of "X_2", momentum of "X_1", momentum of "X_2"].It sequentially contains the new values of optimized tensors and then the new values of their momentum tensors. For example, if two tensors "X_1" and "X_2" are optimized, the output list would be [new value of "X_1," new value of "X_2" new momentum of "X_1", new momentum of "X_2"].The decay factor of momentum. It should be a scalar.The coefficient of gradient in computing new momentum. It should be a scalar.Coefficient of 0.5 * norm_coefficient * ||X||^2.Its value should be either "nesterov" or "standard". The value "nesterov" leads to the use of Nesterov's momentum while "standard" invokes stochastic gradient method using standard momentumConstrain input types to float tensors. Compute one iteration of Adam, a stochastic gradient based optimization algorithm. This operator can conduct the optimization of multiple tensor variables. Let's define the behavior of this operator. First of all, Adam requires some parameters: - The learning-rate "R". - The update count "T". That is, the number of training iterations conducted. - A L2-norm regularization coefficient "norm_coefficient". - A small constant "epsilon" to avoid dividing-by-zero. - Two coefficients, "alpha" and "beta". At each Adam iteration, the optimized tensors are moved along a direction computed based on their exponentially-averaged historical gradient and exponentially-averaged historical squared gradient. Assume that only a tensor "X" is being optimized. The rest of required information is - the value of "X", - "X"'s gradient (denoted by "G"), - "X"'s exponentially-averaged historical gradient (denoted by "V"), and - "X"'s exponentially-averaged historical squared gradient (denoted by "H"). Some of those parameters are passed into this operator as input tensors and others are stored as this operator's attributes. Specifically, this operator's input tensor list is ["R", "T", "X", "G", "V", "H"]. That is, "R" is the first input, "T" is the second input, and so on. Other parameters are given as attributes because they are constants. Moreover, the corresponding output tensors are - the new value of "X" (called "X_new"), - the new exponentially-averaged historical gradient (denoted by "V_new"), and - the new exponentially-averaged historical squared gradient (denoted by "H_new"). Those outputs are computed following the pseudo code below. Let "+", "-", "*", and "/" are all element-wise arithmetic operations with numpy-style broadcasting support. The pseudo code to compute those outputs is: // Add gradient of 0.5 * norm_coefficient * ||X||_2^2, where ||X||_2 is the 2-norm. G_regularized = norm_coefficient * X + G // Update exponentially-averaged historical gradient. V_new = alpha * V + (1 - alpha) * G_regularized // Update exponentially-averaged historical squared gradient. H_new = beta * H + (1 - beta) * G_regularized * G_regularized // Compute the element-wise square-root of H_new. V_new will be element-wisely // divided by H_sqrt for a better update direction. H_sqrt = Sqrt(H_new) + epsilon // Compute learning-rate. Note that "alpha**T"/"beta**T" is alpha's/beta's T-th power. R_adjusted = T > 0 ? R * Sqrt(1 - beta**T) / (1 - alpha**T) : R // Compute new value of "X". X_new = X - R_adjusted * V_new / H_sqrt // Post-update regularization. X_final = (1 - norm_coefficient_post) * X_new If there are multiple inputs to be optimized, the pseudo code will be applied independently to each of them. The tensors to be optimized, followed by their respective gradients, followed by their respective accumulated gradients (aka momentum), followed by their respective accumulated squared gradients. For example, to optimize tensors "X_1" and "X_2,", the input list would be ["X_1", "X_2", gradient of "X_1", gradient of "X_2", accumulated gradient of "X_1", accumulated gradient of "X_2", accumulated squared gradient of "X_1", accumulated squared gradient of "X_2"].New values of optimized tensors, followed by their respective new accumulated gradients, followed by their respective new accumulated squared gradients. For example, if two tensors "X_1" and "X_2" are optimized, the outputs list would be [new value of "X_1", new value of "X_2", new accumulated gradient of "X_1", new accumulated gradient of "X_2", new accumulated squared gradient of "X_1", new accumulated squared gradient of "X_2"].Coefficient of previously accumulated gradient in running average. Default to 0.9.Coefficient of previously accumulated squared-gradient in running average. Default to 0.999.Regularization coefficient of 0.5 * norm_coefficient * ||X||_2^2. Default to 0, which means no regularization.accumulated squared-gradient tensor count should be a multiple of 4 in the The sum of optimized tensor count, gradient tensor count, momentum tensor count, should be a multiple of 2 in the input list of Momentum operatorThe sum of optimized tensor count and momentum tensor count T1InputsT2Outputsxszsytensor(float16)tensor(float)tensor(double)Gradientai.onnx.preview.trainingThe initial learning rate.RTT3inputsoutputsepsilondecay_factornorm_coefficienttensor(int64)AdagradThe learning rate.alphabetamodeMomentumnorm_coefficient_postAdam"inputs" of Adam operator.èL‹µ¨úÿÿI9Þ„ùL‰ðI‹>HƒÀH9ÇtèL‰ðHƒÀ I‰Æë×H‰ÃèI‹<$H…ÿtèL‹µàúÿÿL‰ðHƒè H‹8I‰ÆHƒÀH9ÇtèM9þuàH‹=èH‹½0ûÿÿH;½¸úÿÿtèH‹½àúÿÿèH‰ßèèL‹µÀúÿÿI9Þt[L‰ðI‹>HƒÀH9ÇtèL‰ðHƒÀ I‰ÆëÛH‹½PûÿÿH;½ØúÿÿtèL‰÷èë‡H‹½PûÿÿH;½ØúÿÿtŒèë…èèH‰ÃèH‹½ðúÿÿH…ÿ„NÿÿÿèéDÿÿÿH‹½ýÿÿH9ßu.L‰óL‰ïèH‰ßèH‹… ýÿÿH…ÀtäºL‰þL‰ÿÿÐëÕèëËH‹½ýÿÿH9ßu.L‰óL‰ïèH‰ßèH‹… ýÿÿH…ÀtäºL‰þL‰ÿÿÐëÕèëËH‹½ýÿÿH9ßu2L‰óL‰ïèH‰ßèH‹… ýÿÿH…ÀtäH‹µÈüÿÿºH‰÷ÿÐëÑèëÇ¿0Ld$èLL‰çH HH5H‰ÅèL‰æH‰ïèH‹H‹|$HÇEÆE HƒÀH‰EHE H‰EHD$ H9ÇtèH‹5HH‰ïèI‰ÄH‰ïèL‰çèH‹|$I‰ÄHD$ H9ÇtÞèë×¿0I‰äèH HL‰çH5H‰ÅèL‰æH‰ïèH‹H‹<$HÇEÆE HƒÀH‰EHE H‰EHD$H9ÇtèH‹5HH‰ïèI‰ÄH‰ïèL‰çèH‹<$I‰ÄHD$H9ÇtßèëØÿ›]S:nÕåí©ÐÕ±ÝøÍ‰¦åølÅÄ½éÝüµ˜Ý¤Í°µÿ›¢Ž5“˜ÿÿ6U…Ì•‰Æ…ƒOÿÿÿÿ6U…Ì•‰Æ…ƒOÿÿÿÿ6\êÓúêèOòÿÿÿÿz‘½;¢¡'Ýz”ã ¢È”ð¢ËÖ˜¨”Üïçê¹_Ùì äç ¶ ¢â þ « ù ä ˆï ƒÿÿöÿÿ1Ž<¡‰ÿÿºÿÿ(„3—Hƒt H‹GÃ@é expected to have type but instead is null expected to have sequence typeElement type of sequence input expected to have optional typeElement type of optional input AUATI‰ÔUH‰ýHƒì`H‹H‰t$ÿPH…À„‹@(ƒøt%ƒøt ƒø„×ƒø t2HƒÄ`]A\A]Ã„H‹t$L‰âH‰ïèHƒÄ`]A\A]ÃfDH‹t$H‹EH‰ïH‰t$ÿPH…À„dƒx( …ZLl$ H‹p L‰ïèöD$0„wH‹EL‰æH‰ïÿP(ƒx( H‰Å…L‹` I‹|$AƒL$H…ÿ„{H‹t$8H…ö„5èL‰ïèHƒÄ`]A\A]ÃfDH‹t$H‹EH‰ïH‰t$ÿPH…À„ƒx(…öLl$ H‹p L‰ïèöD$0„IH‹EL‰æH‰ïÿP(ƒx(H‰ÅuGL‹` I‹|$AƒL$H…ÿ„·H‹t$8H…ö„‰èL‰ïèHƒÄ`]A\A]Ãf.„H‰ÇèH‹EÇE(H‰ÇHƒçþ¨…°èH‰E I‰ÄëH‰ÇèH‹EÇE( H‰ÇHƒçþ¨uèH‰E I‰ÄéÂþÿÿH‹?ëê@H‹5ékÿÿÿ@H‹5é¿þÿÿ@I‹D$H‰ÇHƒçþ¨uBèI‰D$H‰Çé'ÿÿÿfDI‹D$H‰ÇHƒçþ¨uèI‰D$H‰ÇécþÿÿH‹?éHÿÿÿH‹?ë¹H‹?ëÜH‰Åé>¿0Hl$@èHL$H‰ïLHH5I‰ÄèH‰îL‰çèH‹H‹|$@IÇD$AÆD$ HƒÀI‰$ID$ I‰D$HD$PH9ÇtèH‹5HL‰çè¿0Hl$@èHL$H‰ïLHH5I‰ÄèH‰îL‰çèH‹AÆD$ IÇD$HƒÀI‰$ID$ I‰D$H‹|$@HD$PH9ÇtèH‹5HL‰çèH‹|$@HD$PH9ÇtèL‰çèL‰ïèH‰ïèH‰ÅëH‰ÅëH‹|$@HD$PH9ÇtèL‰çèH‰ïèH‰Åë¡H‰Åë°H‰Åé¿0Hl$@èHL$H‰ïLHH5I‰ÄèH‰îL‰çèH‹H‹|$@IÇD$AÆD$ HƒÀI‰$ID$ I‰D$HD$PH9ÇtèH‹5HL‰çèH‹|$@HD$PH9ÇtèL‰çèL‰ïèH‰ïèH‰ÅëÏH‰ÅëÞ¿0Hl$@èHL$H‰ïLHH5I‰ÄèH‰îL‰çèé7þÿÿ¿0Hl$@èHL$H‰ïLHH5I‰ÄèH‰îL‰çèéóýÿÿélþÿÿélþÿÿébþÿÿébþÿÿN10onnx_torch14InferenceErrorEN10onnx_torch8OpSchema19num_inputs_allowed_MUliE_EN10onnx_torch8OpSchema20num_outputs_allowed_MUliE_E*ZN10onnx_torch11GetOpSchemaINS_24Adagrad_OnnxPreview_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_25Momentum_OnnxPreview_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN10onnx_torch11GetOpSchemaINS_21Adam_OnnxPreview_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_Allow outputs to be any kind of tensor(complex64tensor(complex12GCC: (Debian 11.2.0-16) 11.2.0zRx0DXl€”4¨eBŒD†A ƒv DBMQABà-H†`zPLRx›<$šBEŒA †D(ƒG0¬ (A ABBK\5H†h0xwA†DƒD W AANDAAL´)BBŽB B(ŒA0†A8ƒLPù 8A0A(B BBBGHüýBEŒA †A(ƒD0» (D ABBOV(A ABBHHØBŽEB ŒA(†A0ƒ (D BBBL`(A BBB”<Hƒo°EH†xPÔrBBŽB B(ŒD0†A8ƒGÀx 8D0A(B BBBGL(óBBŽB E(ŒD0†D8ƒGðn 8A0A(B BBBFPxrBBŽB B(ŒD0†A8ƒGÀx 8D0A(B BBBGPÌrBBŽB B(ŒD0†A8ƒGÀx 8D0A(B BBBGh ÑBBŽB B(ŒA0†A8ƒGðö 8D0A(B BBBLW 8A0A(B BBBGPŒcBBŽB B(ŒD0†A8ƒGÀe 8D0A(B BBBJPàrBBŽB B(ŒD0†A8ƒGÀƒ 8D0A(B BBBD4,ïBEŒA †A(ƒD0Û(A ABBd"AƒV IA0„¯A†DƒK Š AALDAA¸'A†ZHÔëBBŽB B(ŒA0†A8ƒD`Ò8A0A(B BBBL(¹BBŽB B(ŒA0†A8ƒLp 8D0A(B BBBKPxµBPŽO O(ŒA0†A8ƒG ] 8A0A(B BBBAPÌBFŽB B(ŒA0†A8ƒGÐt 8A0A(B BBBEP mBŽBB ŒA(†A0ƒP (A BBBEA(A BBB8tuA†C KŽLŒƒ. 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unsupported. existing=Container for generated shape data cannot be nullptr when enable_data_propagation option is set.cannot create std::vector larger than max_size()Warning: Shape inference does not support models with experimental operators: Warning: Unsupported operator . No schema registered for this operator.. Please check if it is a valid file.Unable to parse model from file:. Please check if it is a valid protobuf file of model.Unable to save inferred model to the target path:Cannot use the same name as both a subgraph initializer and subgraph input: The number of graph input cannot be smaller than the number of node inputGraph initializer names must appear after the actual inputs: ATHGUSHƒìH‰H…ötqH‰ûH‰÷I‰ôèH‰D$H‰ÅHƒøvHt$1ÒH‰ßèH‰H‹D$H‰CH‹HƒýuAŠ$ˆëH…ítH‰ÇL‰æH‰éó¤H‹D$H‹H‰CÆHƒÄ[]A\ÃH=èèH‰ïèèèL‰ïèèH‰ÃèH‰ßèI‹l$H…ít0H‹}HEH‹]H9ÇtèH‰ïH‰ÝèëØH‰ÃèH‰ßëÁI‹D$I‹<$1öHÅèIÇD$IÇD$M…öt I‹<$IƒÄ0L9çuèH‰ÅèH‰ïèèëäèH‰ïèèèL‰ïèèH‰ÃèH‰ßèI‹l$H…ít0H‹}HEH‹]H9ÇtèH‰ïH‰ÝèëØH‰ÃèH‰ßëÁI‹D$I‹<$1öHÅèIÇD$IÇD$M…öt I‹<$IƒÄ0L9çuèH‰ÅèH‰ïèèëäATI‰üƒþ wtH‰ðHc‚HÐÿàH5èºýÿÿë\H5è¬ýÿÿëNH5èžýÿÿë@H5èýÿÿë2H5è‚ýÿÿë$H5ètýÿÿëH5èfýÿÿëL‰çèL‰àA\Ã¿0Hl$ èHL$HH‰ïH5I‰ÄèH‰îL‰çèH‹H‹|$ IÇD$AÆD$ HƒÀI‰$ID$ I‰D$HD$0H9ÇtèH‹5HL‰çèH‰ÅL‰çèH‰ïèH‹|$ H‰ÅHD$0H9ÇtÞèë×¿0Hl$`èH‰ïI‰ÇèLt$pºH5L‰÷èº H5L‰÷èD‰îL‰÷èº H5L‰÷èD‰æL‰÷èLl$@Ht$xL‰ïèH‰ïèL‰îL‰ÿèH‹H‹|$@AÆG IÇGHƒÀI‰IG I‰GHD$PH9ÇtèH‹5HL‰ÿè¿0è‹s(H|$ 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version_range.second/opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/version_converter/convert.h%s:%u: %s: Assertion `%s` failed: Warning: invalid version (must be between %s and %s)Warning: skipping schema search for experimental op 'ConstantFill' and keeping the op as is. Please be advised the converted model may not be working properly if target runtime does not support this experimental op./opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/version_converter/BaseConverter.h%s:%u: %s: Assertion `%s` failed: No Adapter To Version %s for %s%s:%u: %s: Assertion `%s` failed: No Adapter From Version %s for %s%s:%u: %s: Assertion `%s` failed: No Adapter For %s(initial_domain == "" || initial_domain == "ai.onnx") && (target_domain == "" || target_domain == "ai.onnx")%s:%u: %s: Assertion `%s` failed: Warning: default onnx version converter can only convert between default domain opset versions ('' or 'ai.onnx') initial_version.version() == it->version()/opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/version_converter/convert.cc%s:%u: %s: Assertion `%s` failed: initial_version does not reflect current state of modelFinished conversion; returning modelinitial_domain == target_domain%s:%u: %s: Assertion `%s` failed: initial_version and target_version must have the same domainsAUATI‰ü¿0USH‰óHƒìD‹nèH‹sH‹SH‰ÅD‰hH‹H}HòHƒÀH‰EHE H‰EèI‰,$HƒÄL‰à[]A\A]ÃI‰ÄH‰ïèL‰çèÿÿKbpfindinGraphListinsertAftern->inGraphList()insertBeforereplaceInputgraph_ == node->owningGraph()addInputassertInVersionRangecurr_version: , next_version: ConstantFillUndefinedCapturedadapter_lookupApplying adapterai.onnxassertDefaultDomainconvert_versionH‹|$HD$ H9ÇtèH‰ïèH‹<$HD$H9ÇtèH‰ïèH‹<$HD$H9ÇtèH‰ïèH‹<$HD$H9ÇtèH‰ïèH‹<$HD$H9ÇtèH‰ïèH‹<$HD$H9ÇtèH‰ïèH‹|$HD$ H9ÇtèH‰ïèH‹|$HD$ H9ÇtèH‰ïèH‹|$HD$ H9ÇtèH‰ïèH‹|$ HD$0H9ÇtèH‰ïèH‹½PÿÿÿH;½ÀþÿÿtèH‹½0ÿÿÿH;½@þÿÿtèH‹½ÿÿÿH;½XþÿÿtèH‹½ðþÿÿH…ÿÿÿH9ÇtèH‹} H;½°þÿÿtèH‹½pÿÿÿH;½ÈþÿÿtèH‹½ÐþÿÿH;½¨þÿÿtèH‰ßèH‹½XÿÿÿH…ÿt°èë©H‹½8ÿÿÿH…ÿtèM…ít“L‰ïèë‰H‹} H;½°þÿÿtèH‹½PÿÿÿH;½ÀþÿÿtèH‹½pÿÿÿH;½ÈþÿÿtèH‰ßèH‹} H;½°þÿÿtèH‹½PÿÿÿH;½ÀþÿÿtèH‹½0ÿÿÿH;½@þÿÿtèH‹½ÿÿÿH;½XþÿÿtèH‹½pÿÿÿH;½ÈþÿÿtèH‰ßèH‹½PÿÿÿH…ÿ„Çþÿÿèé½þÿÿH‹}¨H…ÿtèH‰ßèH‹} HE°H9ÇtèH‰ßèH‹} HE°H9ÇtèH‰ßèH‹D$H…Àt 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std::end(unallowed_types_)/opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/version_converter/adapters/type_restriction.h%s:%u: %s: Assertion `%s` failed: DataType of Input or Output of Add is of an unallowed type for Opset Version %d.isUnallowedUI‰øH‰åAVATI‰ÔSHƒì(H‹^I‰ÞH…ÛtH‹€8„îƒCI‹t$8M‹\$@L9Þt}M‹ˆ€M‹PxL‰ÏL)×H‰ûHÁÿHÁûfH‹‹P`H…ÿŽ°H‰ùL‰Ðë2f„;P„ç;P„6;P„=HƒÀHƒé„?;uÓI9Á…ÇHƒÆI9óu¡I‹t$PM‹\$XL9Þ„}M‹ˆ€M‹PxL‰ÏL)×H‰ûHÁÿHÁûfH‹‹P`H…ÿŽ+H‰ùL‰Ðë2f„;P„;P„f;P„mHƒÀHƒé„o;uÓL9È…÷HƒÆI9óu¡M…ötL‰÷èHeèL‰à[A\A^]ÃfDHƒÀI9Á„9ÿÿÿHLeÀ¹(PL L‰ç1ÀAÿppHLH5èYL‰ç^èfDHƒÀéÜþÿÿ€HƒÀéÌþÿÿ€L‰ÉH)ÁHÁùHƒù„åHƒù„ÏHƒù…¦þÿÿ;…žþÿÿéþÿÿðƒCéþÿÿf„HƒÀL9È„ ÿÿÿHLeÀ¹(PL L‰ç1ÀAÿppHLH5èXL‰çZèfDHƒÀé¬þÿÿ€HƒÀéœþÿÿ€L‰ÉH)ÁHÁùHƒùtNHƒùti(kcount_include_pad) == 0/opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/version_converter/adapters/averagepool_7_6.h%s:%u: %s: Assertion `%s` failed: AveragePool in Opset Version 6 does not support including padadapt_averagepool_7_6UH‰åAUATI‰ÔSHƒì(H‹NH‰ËH…ÉtH‹€8t`ƒAM‹T$LmÀI‹|$ÇEÀŠL‰êL‰ÖèI9ÂtI|$¾ŠèH‹Hƒxu&H…ÛtH‰ßèHeèL‰à[A\A]]ÃðƒAëHHƒìL‰ï¹PL 1ÀLHH5èXL‰ïZèI‰ÄëI‰ÄëH‹}ÀHEÐH9ÇtèH…ÛtH‰ßèL‰çèATI‰ÔUSH‹nH…í„”H‹€;t0I‹L$8‹UH‹ B‰Eƒy`u~‰Uƒøt;L‰à[]A\Ãf„HUðƒEI‹D$8H‹ƒx`uN€;uq¸ÿÿÿÿðÁƒøuÅH‹EH‰ïÿP€;tg‹EPÿ‰Uƒøu¨H‹EH‰ïÿPëœDH‹B8H‹ƒx`tŠI|$¾˜èH…í„rÿÿÿH‹HU€;t‹EPÿéOÿÿÿ„¸ÿÿÿÿðÁEë–I‰ÄH…ítH‰ïèL‰çènode->i(kis_test) != 0adapt_batch_normalization_6_7/opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/version_converter/adapters/batch_normalization_6_7.h%s:%u: %s: Assertion `%s` failed: ONNX currently only supports inference, not training.UH‰åAVAUATI‰ÔSHƒì H‹NH‰ËH…ÉtH‹€8tvƒAM‹T$LmÀI‹|$ÇEÀ.L‰êL‰ÖèI9Ât)Mt$¾.L‰÷èH‹HƒxtA¾.L‰÷èH…ÛtH‰ßèHeàL‰à[A\A]A^]Ã€ðƒAë‡f„HHƒìL‰ï¹PL 1ÀLHH5èXL‰ïZèI‰ÄëI‰ÄëH‹}ÀHEÐH9ÇtèH…ÛtH‰ßèL‰çènode->i(kis_test) == 1adapt_dropout_6_7/opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/version_converter/adapters/dropout_6_7.h%s:%u: %s: Assertion `%s` failed: Training is not supported with Dropout OpUH‰åAVAUATI‰ÔSHƒì H‹NH‰ËH…ÉtH‹€8tvƒAM‹T$LmÀI‹|$ÇEÀ.L‰êL‰ÖèI9Ât)Mt$¾.L‰÷èH‹HƒxuA¾.L‰÷èH…ÛtH‰ßèHeàL‰à[A\A]A^]Ã€ðƒAë‡f„HHƒìL‰ï¹PL 1ÀLHH5èXL‰ïZèI‰ÄëI‰ÄëH‹}ÀHEÐH9ÇtèH…ÛtH‰ßèL‰çèoutputs.size() != 2adapt_maxpool_8_7/opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/version_converter/adapters/maxpool_8_7.h%s:%u: %s: Assertion `%s` failed: Opset version 7 of MaxPool cannot include Indices outputUH‰åATI‰ÔSHƒì H‹NH‰ËH…ÉtH‹€8tbƒAI‹D$XI+D$PHƒøtcM‹T$I‹|$HUÐÇEÐ‹L‰ÖèI9ÂtI|$¾‹èH…ÛtH‰ßèHeðL‰à[A\]Ã@ðƒAI‹D$XI+D$PHƒøuHHƒìLeÐ¹PL 1ÀLHH5L‰çèXL‰çZèI‰ÄëI‰ÄëH‹}ÐHEàH9ÇtèH…ÛtH‰ßèL‰çèAUATI‰ÔUSHƒì8H‹nH…ítH‹€8„²ƒELl$H\$ ¸iaÇD$ spatH|$L‰îH‰\$f‰D$$ÆD$&lHÇD$ÆD$'èH‹|$H9ßtèM‹T$D‹\$L‰êI‹|$L‰ÖD‰\$èI9ÂtMl$D‰ÞL‰ïèH‹Hƒxt0H…ítH‰ïèHƒÄ8L‰à[]A\A]ÃDðƒEéHÿÿÿfD‹t$L‰ïèëÂI‰ÄëI‰ÄH‹|$H9ßtèH…ítH‰ïèL‰çèAUI‰ýATUSHƒìH‹oH…í„£fDH‹}`HEpL‹eH9ÇtèH‹]8H…ÛtH‰ßH‹èH…ÛuðH‹E0H‹}(1öHÅèH‹}(HEXHÇE@HÇE8H9ÇtèH‹}HEH9ÇtèH‰ïèM…ätL‰åépÿÿÿH‰ïèM…äuëI‹EI‹}1öHÅèIÇEIÇEHƒÄ[]A\A]ÃH¹«ªªªªªªªAWAVAUATUSHƒì(H‹oL‹7H‰èL)ðHÁøH¯ÁH¹ªªªªªªªH9È„nH…À¹I‰õI‰ÿHEÈH‰óHÈ’ÁH‰D$M)õ¶ÉH…É…áH…À…E1ä·MåH‹rI}fA‰EH‹BI‰EIE H‰D$I‰EH‹BHðH‰ÂèL9ó„ÅL‰ðL‰âë&DH‰JH‹H H‰J H‹HHƒÀ0HƒÂ0H‰JèH9Ãt7·Hp f‰ H‹HH‰JHJ H‰JH‹HH9ñu¼óoH J ë½f„Hº«ªªªªªª HCÐL)ðHÁèH¯ÂHºÿÿÿÿÿÿÿH!ÐH|@HÁçLçHƒÇ0H9ë„ƒH‰ØH‰ú·Hp f‰ H‹HH‰JHJ H‰JH‹HH9ñ„™H‰JH‹H HƒÀ0HƒÂ0H‰JðH‹HèH‰JèH9Åu¸Hº«ªªªªªª H)ÝHEÐHÁèH¯ÂHºÿÿÿÿÿÿÿH!ÐHD@HÁàHÇfInÄfHnßflÃM…ötL‰÷)D$èfoD$Ld$AM‰gHƒÄ([]A\A]A^A_Ã@óoP H‹HHƒÀ0HƒÂ0RðH‰JèH9è…ÿÿÿéaÿÿÿH¸àÿÿÿÿÿÿH‰D$H‰ÇH‰T$èH‹T$I‰Äéþÿÿ€L‰çéÊþÿÿH‰ÇH¸ªªªªªªªH9ÇHFÇH@HÁàH‰D$H‰Çë´H=èH‰ÇèM…ät L‰çèèI‹}H;|$tðèëéH‰ÅèH‰ïèUH‰òH‰ýH‹wH;wt7·H~f‰H‹BH‰FHF H‰FH‹BH‹rHðH‰ÂèHƒE0]Ã]écheck_numpy_unibroadcastable_and_require_broadcast(MN, C_shape) != -1/opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/version_converter/adapters/gemm_6_7.h%s:%u: %s: Assertion `%s` failed: Gemm being converted from 6 to 7 does not have broadcastable inputs.adapt_gemm_6_7UH‰åAWAVAUATI‰ÔSHƒìhL‹nM…ítH‹€8„3AƒEI‹T$8I‹D$@H‹wH}€H)ÐH‰U€ºHÁøH‰EˆèH‹E€M\$M‹T$fïÀ¾LL‰ßL‰xÿÿÿH‹L‹xH‹XHÇE IƒÇhH‰pÿÿÿ)EèI9Â„ê¾LL‰ßLuèH‹H‹pÿÿÿHƒxH‹Qh„èH‹E˜H‹u H9ð„¼· HxLuf‰H‹JH‰HHH H‰HH‹BH‹rHðH‰ÂèHƒE˜0H‹½xÿÿÿM‹T$¾MèI9Â„2H‹½xÿÿÿ¾MèH‹L‹ChHƒx„wH‹u˜H;u „A·@0H~f‰I‹@8H‰FHF I‹PHH‰FI‹p@HòèHƒE˜0L‰þL‰÷èƒøÿ„<H‹xÿÿÿM‹T$¾AH‰ßèI9Ât ¾AH‰ßèH‹EH‹]˜H‰ÁH9Ãt&H‹yHA I‰ÏH9ÇtHèIƒÇ0L‰ùL9ûußH‹MH…ÉtH‰ÏèM…ítL‰ïèHeØL‰à[A\A]A^A_]Ã€H‰ÈHƒÀ0H‰ÁH9Ãu™ë¸€ðAƒEéÇýÿÿDL‹ChH‹u˜H;u …íþÿÿIP0L‰÷èé ÿÿÿDH‹…pÿÿÿ1öH‹PhLuL‰÷èéiþÿÿf.„Hr0L‰÷èéNþÿÿ€L‰ÆL‰÷èé¸þÿÿ„H HƒìHE°QH‰ÃH‰ÇL L¹$H1ÀH5èXH‰ßZèH‰Ãë H‰ÃëH‰ÃëH‹}°HEÀH9ÇtèL‰÷èM…ítL‰ïèH‰ßèAWI‰×HºÿÿÿÿÿÿÿAVAUATUSHƒìL‹gL‹7L‰àL)ðHÁøH9Ð„H…ÀºH‰ýI‰õHEÐ1ÉHÐH‰ò’ÁL)òH…É…šH…À…¹1Û1ÿfïÀM)ìfHnÏóA*L|K'fHnÐflÊó)$H…Ò,M…ä?M…öu/fo$H‰]]HƒÄ[]A\A]A^A_ÃfDL‰öèM…äL‰÷èëÇL‰âL‰îL‰ÿèM…öt³ëàH»üÿÿÿÿÿÿH‰ßH‰$èH‹$H‰ÇHÃéNÿÿÿH¹ÿÿÿÿÿÿÿH9ÈHGÁH…ëÇH=èAWI‰ÿAVAUATUSHƒìL‹oM…í„ßfI‹m8M‹uH…ít`H‹]8L‹eH…ÛtH‹{èH‰ßH‹[èH…ÛuæH‹}HEH9ÇtèH‰ïèM…ätL‰åëµDH‰ïèM…äuéI‹E0I‹}(1öHÅèI‹}(IEXIÇE@IÇE8H9ÇtèI‹}IEH9ÇtèL‰ïèM…ötM‰õé3ÿÿÿL‰ïèM…öuèI‹GI‹?1öHÅèIÇGIÇGHƒÄ[]A\A]A^A_ÃSH‰ûHƒÃ0èH‹{ÐH9ßt 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„A·@0H~f‰I‹@8H‰FHF I‹PHH‰FI‹p@HòèHƒE˜0L‰þL‰÷èƒøÿ„,ƒø„‹H‹]˜L‹mL9ët%fDI‹}IE H9ÇtKèIƒÅ0L9ëuåL‹mM…ítL‰ïèHƒ½xÿÿÿtH‹½xÿÿÿèHeØL‰à[A\A]A^A_]Ã@IƒÅ0L9ëuŸë¸DðƒAéñýÿÿfDHU°¾AL‰ïHÇE°èéWÿÿÿfH‹…pÿÿÿH‹u˜L‹@hH;u …îþÿÿIP0L‰÷èéÿÿÿfDH‹…hÿÿÿ1öH‹PhLuL‰÷èéjþÿÿfL‰ÆL‰÷èéØþÿÿHr0L‰÷èéGþÿÿ€HLe°¹'PL 1ÀLÿspHH5L‰çÿsHÿsèHƒÄ L‰çèI‰Äë I‰ÄëI‰ÄëH‹}°HEÀH9Çu&L‰÷èHƒ½xÿÿÿtH‹½xÿÿÿèL‰çèèëÓadapt_broadcast_backward_compatibility/opt/logicmoo_workspace/packs_xtra/pytorch/third_party/onnx/onnx/version_converter/adapters/broadcast_backward_compatibility.hUH‰åAVATI‰ÔSH‰ûHƒì8H‹NI‰ÎH…ÉtH‹€8tuƒAI‹T$8I‹D$@H}°H‹sH)ÐH‰U°ºHÁøH‰E¸èH‹E°H‹PH‹8HrhHƒÇhèƒøÿtYƒøt4M…ötL‰÷èHeèL‰à[A\A^]Ãf„ðƒAëˆf„HUÀI|$¾AHÇEÀèë¯HLeÀ¹ PL 1ÀLÿspHH5L‰çÿsHÿsèHƒÄ L‰çèI‰ÄëI‰ÄëH‹}ÀHEÐH9ÇtèM…ötL‰÷èL‰çèATI‰Ô¾ŸI|$HƒìHÇD$HT$èHƒÄL‰àA\ÃAVAUI‰ÕATI‰üUS‰óHƒìH‹OH‹/H‰ÊH)êH‰ÐHÁúHÁøH…ÒŽ]HÁâHêë6fH‹E9X„£H‹E9X„¦H‹E9X„©HƒÅ H9Õ„H‹E9XuÃ¿(èI‹ufïÀ‰XI‰ÆH‹IÇF HƒÀAFI‰I‹EH‰ÂH)ò„µH¸øÿÿÿÿÿÿH‰ÓH9Â‡H‰×èH‰ÁI‹EI‹uI‰ÅI)õfHnÁHËflÀI‰^ 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foundadapt_reshape_5_4size_from_dimnode->hasAttribute(kshape)UH‰åAWAVAUATSHìˆH‹B8H‰µPÿÿÿH‰•pÿÿÿH‰…xÿÿÿH‹@L‹(H‰…`ÿÿÿAƒ}0„6H‹H‹pÿÿÿL‹€H‹€˜HƒÃH‰XÿÿÿH‰E€L9À„HH}°H]ÀM‰ÇH‹…`ÿÿÿI¼Ãõ(\Âõ(H‰}ˆI½KY†8ÖÅm4ëUfDH‹PHH‹}ˆH‰]°H‹p@HòèI‹W(L‹M°H;U¸„XI9Ù…IÇL9}€„ÒH‹…xÿÿÿH‹@€x8u«L‹pIƒþ †Iƒþc†¢Iþç†£Iþ'†¤H‹}ˆL‰ò¾ë*Hù?B†ëHù–˜†Hùÿàõ† H‰ÐH‰ÑI÷å‰ðƒÆHÁêHùŸ†w¾H‰}ˆ1ÒH‰]°è‹E¸H‹}°pÿL‹ L‹]ˆf.„L‰òM‰ðHÁêH‰ÐI÷äH‰ÑHƒâüHÁéHÊH’L‰òI‰ÎHÁàI)À‰ðC¶LAˆC¶AFÿƒîˆHú'w¯L‰]ˆHúç‡AN0ˆI‹W(L‹M°H;U¸…¨þÿÿI‹ H…ÒtL‰ÎL‰hÿÿÿèL‹hÿÿÿ…À…þÿÿM‰þL‹eˆM‰ÏI9ÙtL‰ÿèH‹½XÿÿÿI–¨¾HèH‹½pÿÿÿ¾èH‹µ`ÿÿÿL‰çèH‹]°L‹}¸H…ÛtH‰ßèI9ß„[H‹…pÿÿÿH‹½Xÿÿÿ¾HL‹PèI9Â„oHeØ[A\A]A^A_]ÃfDL‰ÏIÇèL9}€…Ûýÿÿë«DL‹ C¶DqˆGC¶qˆé‹ýÿÿH‰}ˆp1ÒH‰]°èH‹}°IƒþcvÄ‹E¸pÿé<þÿÿH‰}ˆpéþÿÿ@H‰}ˆpé þÿÿMe¾)L‰çèH‹H‹˜ÀH9˜¸„H‹pÿÿÿH¸¾HH{H‰½XÿÿÿèHE°I‰ÄH‹½pÿÿÿ¾èH‹µ`ÿÿÿL‰çèH‹]°L‹}¸H…ÛtH‰ßèI9ß…²þÿÿL‰ïèé¥þÿÿH‹…PÿÿÿH‹µ`ÿÿÿH‹8èéŠþÿÿ¾)L‰çH] èH}H‹H‰]H‹°H‹HòèH‹E˜H…À„n¨…fH‹…pÿÿÿ¾)L‰çL‹uHƒÀH‰…XÿÿÿèH‹A¼L‰öH‹P`H‹@XH9Â„•L¯ 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18version_conversion9AddLayoutD2Ev_ZN10onnx_torch18version_conversion9AddLayoutD1Ev_ZN10onnx_torch18version_conversion24BatchNormalization_13_14D2Ev_ZN10onnx_torch18version_conversion24BatchNormalization_13_14D1Ev_ZN10onnx_torch18version_conversion12RemoveLayoutD2Ev_ZN10onnx_torch18version_conversion12RemoveLayoutD1Ev_ZN10onnx_torch18version_conversion20RemoveConsumedInputsD2Ev_ZN10onnx_torch18version_conversion20RemoveConsumedInputsD1Ev_ZN10onnx_torch18version_conversion11Reshape_4_5D2Ev_ZN10onnx_torch18version_conversion11Reshape_4_5D1Ev_ZN10onnx_torch18version_conversion11Dropout_6_7D2Ev_ZN10onnx_torch18version_conversion11Dropout_6_7D1Ev_ZN10onnx_torch18version_conversion12Upsample_6_7D2Ev_ZN10onnx_torch18version_conversion12Upsample_6_7D1Ev_ZN10onnx_torch18version_conversion30BroadcastBackwardCompatibilityD2Ev_ZN10onnx_torch18version_conversion30BroadcastBackwardCompatibilityD1Ev_ZN10onnx_torch18version_conversion9SetIsTestD2Ev_ZN10onnx_torch18version_conversion9SetIsTestD1Ev_ZN10onnx_torch18version_conversion8Gemm_7_6D2Ev_ZN10onnx_torch18version_conversion8Gemm_7_6D1Ev_ZN10onnx_torch18version_conversion15AveragePool_7_6D2Ev_ZN10onnx_torch18version_conversion15AveragePool_7_6D1Ev_ZN10onnx_torch18version_conversion7Sum_8_7D2Ev_ZN10onnx_torch18version_conversion7Sum_8_7D1Ev_ZN10onnx_torch18version_conversion11MaxPool_8_7D2Ev_ZN10onnx_torch18version_conversion11MaxPool_8_7D1Ev_ZN10onnx_torch18version_conversion22BatchNormalization_8_9D2Ev_ZN10onnx_torch18version_conversion22BatchNormalization_8_9D1Ev_ZN10onnx_torch18version_conversion8Scan_8_9D2Ev_ZN10onnx_torch18version_conversion8Scan_8_9D1Ev_ZN10onnx_torch18version_conversion12Upsample_8_9D2Ev_ZN10onnx_torch18version_conversion12Upsample_8_9D1Ev_ZN10onnx_torch18version_conversion20ExtendSupportedTypesD2Ev_ZN10onnx_torch18version_conversion20ExtendSupportedTypesD1Ev_ZN10onnx_torch18version_conversion8Cast_9_8D2Ev_ZN10onnx_torch18version_conversion8Cast_9_8D1Ev_ZN10onnx_torch18version_conversion8Scan_9_8D2Ev_ZN10onnx_torch18version_conversion8Scan_9_8D1Ev_ZN10onnx_torch18version_conversion12Upsample_9_8D2Ev_ZN10onnx_torch18version_conversion12Upsample_9_8D1Ev_ZN10onnx_torch18version_conversion10Slice_9_10D2Ev_ZN10onnx_torch18version_conversion10Slice_9_10D1Ev_ZN10onnx_torch18version_conversion14RoiAlign_15_16D2Ev_ZN10onnx_torch18version_conversion14RoiAlign_15_16D1Ev_ZN10onnx_torch18version_conversion11Reshape_5_4D2Ev_ZN10onnx_torch18version_conversion11Reshape_5_4D1Ev_ZN10onnx_torch18version_conversion17CompatibleAdapterD2Ev_ZN10onnx_torch18version_conversion17CompatibleAdapterD1Ev_ZN10onnx_torch18version_conversion22BatchNormalization_6_5D2Ev_ZN10onnx_torch18version_conversion22BatchNormalization_6_5D1Ev_ZN10onnx_torch18version_conversion29BroadcastForwardCompatibilityD2Ev_ZN10onnx_torch18version_conversion29BroadcastForwardCompatibilityD1Ev_ZN10onnx_torch18version_conversion8Gemm_6_7D2Ev_ZN10onnx_torch18version_conversion8Gemm_6_7D1Ev_ZN10onnx_torch18version_conversion22BatchNormalization_6_7D2Ev_ZN10onnx_torch18version_conversion22BatchNormalization_6_7D1Ev_ZN10onnx_torch18version_conversion24NoPreviousVersionAdapterD2Ev_ZN10onnx_torch18version_conversion24NoPreviousVersionAdapterD1Ev_ZN10onnx_torch18version_conversion11MaxPool_8_7D0Ev_ZN10onnx_torch18version_conversion20ExtendSupportedTypesD0Ev_ZN10onnx_torch18version_conversion8Cast_9_8D0Ev_ZN10onnx_torch18version_conversion8Scan_8_9D0Ev_ZN10onnx_torch18version_conversion12Upsample_8_9D0Ev_ZN10onnx_torch18version_conversion15AveragePool_7_6D0Ev_ZN10onnx_torch18version_conversion7Sum_8_7D0Ev_ZN10onnx_torch18version_conversion22BatchNormalization_8_9D0Ev_ZN10onnx_torch18version_conversion9AddLayoutD0Ev_ZN10onnx_torch18version_conversion24BatchNormalization_13_14D0Ev_ZN10onnx_torch18version_conversion20AxesInputToAttributeD0Ev_ZN10onnx_torch18version_conversion11Split_13_12D0Ev_ZN10onnx_torch18version_conversion20RemoveConsumedInputsD0Ev_ZN10onnx_torch18version_conversion13Upsample_9_10D0Ev_ZN10onnx_torch18version_conversion10Clip_10_11D0Ev_ZN10onnx_torch18version_conversion10Slice_9_10D0Ev_ZN10onnx_torch18version_conversion9TopK_9_10D0Ev_ZN10onnx_torch18version_conversion8Gemm_6_7D0Ev_ZN10onnx_torch18version_conversion24NoPreviousVersionAdapterD0Ev_ZN10onnx_torch18version_conversion22BatchNormalization_6_7D0Ev_ZN10onnx_torch18version_conversion11Dropout_6_7D0Ev_ZN10onnx_torch18version_conversion9SetIsTestD0Ev_ZN10onnx_torch18version_conversion8Gemm_7_6D0Ev_ZN10onnx_torch18version_conversion12Upsample_6_7D0Ev_ZN10onnx_torch18version_conversion30BroadcastBackwardCompatibilityD0Ev_ZN10onnx_torch18version_conversion11Split_12_13D0Ev_ZN10onnx_torch18version_conversion13Softmax_12_13D0Ev_ZN10onnx_torch18version_conversion18ArgMaxArgMin_12_11D0Ev_ZN10onnx_torch18version_conversion20AxesAttributeToInputD0Ev_ZN10onnx_torch18version_conversion13Scatter_10_11D0Ev_ZN10onnx_torch18version_conversion13Dropout_11_12D0Ev_ZN10onnx_torch18version_conversion9Pad_10_11D0Ev_ZN10onnx_torch18version_conversion12Resize_10_1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S6_IS7_SF_EEESH_SJ_SaIS6_IS7_SM_EEEESaISQ_ENSt8__detail10_Select1stESJ_SH_NSS_18_Mod_range_hashingENSS_20_Default_ranged_hashENSS_20_Prime_rehash_policyENSS_17_Hashtable_traitsILb1ELb0ELb1EEEED1Ev_ZN10onnx_torch18version_conversion23DefaultVersionConverterD2Ev_ZTVN10onnx_torch18version_conversion20BaseVersionConverterE_ZN10onnx_torch18version_conversion23DefaultVersionConverterD1Ev_ZN10onnx_torch18version_conversion23DefaultVersionConverterD0Ev_ZNSt8ios_base4InitC1Ev_ZNSt8ios_base4InitD1Ev__dso_handle__cxa_atexit_ZN10onnx_torch14AttributeProtoD1Ev_ZN10onnx_torch14AttributeProtoC1ERKS0__ZN10onnx_torch8OpSchemaC2ERKS0__ZN10onnx_torch13FunctionProtoC1ERKS0__ZN10onnx_torch13FunctionProtoD1Ev_ZN10onnx_torch8OpSchemaC1ERKS0__ZN10onnx_torch8OpSchemaD2Ev_ZN10onnx_torch8OpSchemaD1Ev_ZNSt6vectorIN10onnx_torch8OpSchemaESaIS1_EE17_M_realloc_insertIJRS1_EEEvN9__gnu_cxx17__normal_iteratorIPS1_S3_EEDpOT__ZN10onnx_torch13FunctionProtoC1EOS0__ZNSt6vectorIN10onnx_torch8OpSchemaESaIS1_EED2Ev_ZNSt6vectorIN10onnx_torch8OpSchemaESaIS1_EED1Ev_ZN10onnx_torch18version_conversion23DefaultVersionConverterC2Ev_ZTVN10onnx_torch18version_conversion23DefaultVersionConverterE_ZN10onnx_torch16OpSchemaRegistry20DomainToVersionRange8InstanceEv_ZN10onnx_torch16OpSchemaRegistry3mapB5cxx11Ev_ZSt29_Rb_tree_insert_and_rebalancebPSt18_Rb_tree_node_baseS0_RS__ZTVN10onnx_torch18version_conversion9SetIsTestE_ZTVN10onnx_torch18version_conversion24NoPreviousVersionAdapterE_ZN10onnx_torch18version_conversion23DefaultVersionConverterC1Ev_ZNK10onnx_torch18version_conversion23DefaultVersionConverter15convert_versionERKNS_10ModelProtoERKNS_7OpSetIDES7__ZN10onnx_torch16ImportModelProtoERKNS_10ModelProtoE_ZTVSt19_Sp_counted_deleterIPN10onnx_torch5GraphESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZN10onnx_torch13PrepareOutputERKNS_10ModelProtoE_ZN10onnx_torch16ExportModelProtoEPNS_10ModelProtoERKSt10shared_ptrINS_5GraphEE_ZN10onnx_torch10ModelProtoD1Ev_ZN10onnx_torch18version_conversion14ConvertVersionERKNS_10ModelProtoEi_ZTSSt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EE_ZTISt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EE_ZTVN10__cxxabiv117__class_type_infoE_ZTSN10onnx_torch10AttributesINS_4NodeEEE_ZTIN10onnx_torch10AttributesINS_4NodeEEE_ZTSSt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EE_ZTISt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EE_ZTVN10__cxxabiv120__si_class_type_infoE_ZTSN10onnx_torch14AttributeValueE_ZTIN10onnx_torch14AttributeValueE_ZTSN10onnx_torch20VectorAttributeValueIdLNS_13AttributeKindE1EEE_ZTIN10onnx_torch20VectorAttributeValueIdLNS_13AttributeKindE1EEE_ZTSN10onnx_torch20ScalarAttributeValueINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEELNS_13AttributeKindE4EEE_ZTIN10onnx_torch20ScalarAttributeValueINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEELNS_13AttributeKindE4EEE_ZTSN10onnx_torch20ScalarAttributeValueIlLNS_13AttributeKindE2EEE_ZTIN10onnx_torch20ScalarAttributeValueIlLNS_13AttributeKindE2EEE_ZTSN10onnx_torch20VectorAttributeValueIlLNS_13AttributeKindE3EEE_ZTIN10onnx_torch20VectorAttributeValueIlLNS_13AttributeKindE3EEE_ZTSN10onnx_torch20ScalarAttributeValueINS_6TensorELNS_13AttributeKindE6EEE_ZTIN10onnx_torch20ScalarAttributeValueINS_6TensorELNS_13AttributeKindE6EEE_ZTSN10onnx_torch4NodeE_ZTIN10onnx_torch4NodeE_ZTVN10__cxxabiv121__vmi_class_type_infoE_ZTSN10onnx_torch18version_conversion7AdapterE_ZTIN10onnx_torch18version_conversion7AdapterE_ZTSN10onnx_torch18version_conversion20BaseVersionConverterE_ZTIN10onnx_torch18version_conversion20BaseVersionConverterE_ZTSN10onnx_torch18version_conversion18ArgMaxArgMin_12_11E_ZTIN10onnx_torch18version_conversion18ArgMaxArgMin_12_11E_ZTSN10onnx_torch18version_conversion15AveragePool_7_6E_ZTIN10onnx_torch18version_conversion15AveragePool_7_6E_ZTSN10onnx_torch18version_conversion20AxesAttributeToInputE_ZTIN10onnx_torch18version_conversion20AxesAttributeToInputE_ZTSN10onnx_torch18version_conversion20AxesInputToAttributeE_ZTIN10onnx_torch18version_conversion20AxesInputToAttributeE_ZTSN10onnx_torch18version_conversion22BatchNormalization_6_5E_ZTIN10onnx_torch18version_conversion22BatchNormalization_6_5E_ZTSN10onnx_torch18version_conversion22BatchNormalization_6_7E_ZTIN10onnx_torch18version_conversion22BatchNormalization_6_7E_ZTSN10onnx_torch18version_conversion22BatchNormalization_8_9E_ZTIN10onnx_torch18version_conversion22BatchNormalization_8_9E_ZTSN10onnx_torch18version_conversion30BroadcastBackwardCompatibilityE_ZTIN10onnx_torch18version_conversion30BroadcastBackwardCompatibilityE_ZTSN10onnx_torch18version_conversion29BroadcastForwardCompatibilityE_ZTIN10onnx_torch18version_conversion29BroadcastForwardCompatibilityE_ZTSN10onnx_torch18version_conversion8Cast_9_8E_ZTIN10onnx_torch18version_conversion8Cast_9_8E_ZTSN10onnx_torch18version_conversion10Clip_10_11E_ZTIN10onnx_torch18version_conversion10Clip_10_11E_ZTSN10onnx_torch18version_conversion17CompatibleAdapterE_ZTIN10onnx_torch18version_conversion17CompatibleAdapterE_ZTSN10onnx_torch18version_conversion10Concat_3_4E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