TMVAFastInferencePrototype/Linear_8.hxx at master · sitongan/TMVAFastInferencePrototype · GitHub

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//Code generated automatically by TMVA for Inference of Model file [Linear_8.onnx] at [Sun May 16 14:39:47 2021]
#include<algorithm>
#include<vector>
#include <Eigen/Dense>
namespace TMVA_SOFIE_Linear_8{
namespace BLAS{
	extern "C" void sgemm_(const char * transa, const char * transb, const int * m, const int * n, const int * k,
	                       const float * alpha, const float * A, const int * lda, const float * B, const int * ldb,
	                       const float * beta, float * C, const int * ldc);
}//BLAS
float tensor_6bias[400] = {-0.0865255222, -0.13473624, 0.0880954936, -0.0702161193, 0.082276389, 0.0991556644, 0.167038172, -0.0247640088, 0.0380980372, -0.0766585246, 0.0116074467, -0.0370487794, 0.0372571573, -0.0579243414, 0.115155771, 0.010439422, -0.0795015693, -0.00658304011, -0.00314732338, 0.112015776, -0.00897108298, 0.00179712963, 0.0743352398, -0.0433578938, -0.0605837144, 0.00744644366, 0.11539112, -0.12165805, -0.104283817, 0.0323160142, -0.0873822793, -0.0054093292, 0.0614236258, 0.122468069, -0.0643428639, 0.0600572303, 0.0452834778, 0.162645683, 0.149409562, -0.106560692, -0.0770278573, 0.145776168, -0.101439163, -0.044440262, 0.0943290666, 0.0310331732, 0.029994417, 0.181150377, 0.116438918, 0.134423569, -0.0865255222, -0.13473624, 0.0880954936, -0.0702161193, 0.082276389, 0.0991556644, 0.167038172, -0.0247640088, 0.0380980372, -0.0766585246, 0.0116074467, -0.0370487794, 0.0372571573, -0.0579243414, 0.115155771, 0.010439422, -0.0795015693, -0.00658304011, -0.00314732338, 0.112015776, -0.00897108298, 0.00179712963, 0.0743352398, -0.0433578938, -0.0605837144, 0.00744644366, 0.11539112, -0.12165805, -0.104283817, 0.0323160142, -0.0873822793, -0.0054093292, 0.0614236258, 0.122468069, -0.0643428639, 0.0600572303, 0.0452834778, 0.162645683, 0.149409562, -0.106560692, -0.0770278573, 0.145776168, -0.101439163, -0.044440262, 0.0943290666, 0.0310331732, 0.029994417, 0.181150377, 0.116438918, 0.134423569, -0.0865255222, -0.13473624, 0.0880954936, -0.0702161193, 0.082276389, 0.0991556644, 0.167038172, -0.0247640088, 0.0380980372, -0.0766585246, 0.0116074467, -0.0370487794, 0.0372571573, -0.0579243414, 0.115155771, 0.010439422, -0.0795015693, -0.00658304011, -0.00314732338, 0.112015776, -0.00897108298, 0.00179712963, 0.0743352398, -0.0433578938, -0.0605837144, 0.00744644366, 0.11539112, -0.12165805, -0.104283817, 0.0323160142, -0.0873822793, -0.0054093292, 0.0614236258, 0.122468069, -0.0643428639, 0.0600572303, 0.0452834778, 0.162645683, 0.149409562, -0.106560692, -0.0770278573, 0.145776168, -0.101439163, -0.044440262, 0.0943290666, 0.0310331732, 0.029994417, 0.181150377, 0.116438918, 0.134423569, -0.0865255222, -0.13473624, 0.0880954936, -0.0702161193, 0.082276389, 0.0991556644, 0.167038172, -0.0247640088, 0.0380980372, -0.0766585246, 0.0116074467, -0.0370487794, 0.0372571573, -0.0579243414, 0.115155771, 0.010439422, -0.0795015693, -0.00658304011, -0.00314732338, 0.112015776, -0.00897108298, 0.00179712963, 0.0743352398, -0.0433578938, -0.0605837144, 0.00744644366, 0.11539112, -0.12165805, -0.104283817, 0.0323160142, -0.0873822793, -0.0054093292, 0.0614236258, 0.122468069, -0.0643428639, 0.0600572303, 0.0452834778, 0.162645683, 0.149409562, -0.106560692, -0.0770278573, 0.145776168, -0.101439163, -0.044440262, 0.0943290666, 0.0310331732, 0.029994417, 0.181150377, 0.116438918, 0.134423569, -0.0865255222, -0.13473624, 0.0880954936, -0.0702161193, 0.082276389, 0.0991556644, 0.167038172, -0.0247640088, 0.0380980372, -0.0766585246, 0.0116074467, -0.0370487794, 0.0372571573, -0.0579243414, 0.115155771, 0.010439422, -0.0795015693, -0.00658304011, -0.00314732338, 0.112015776, -0.00897108298, 0.00179712963, 0.0743352398, -0.0433578938, -0.0605837144, 0.00744644366, 0.11539112, -0.12165805, -0.104283817, 0.0323160142, -0.0873822793, -0.0054093292, 0.0614236258, 0.122468069, -0.0643428639, 0.0600572303, 0.0452834778, 0.162645683, 0.149409562, -0.106560692, -0.0770278573, 0.145776168, -0.101439163, -0.044440262, 0.0943290666, 0.0310331732, 0.029994417, 0.181150377, 0.116438918, 0.134423569, -0.0865255222, -0.13473624, 0.0880954936, -0.0702161193, 0.082276389, 0.0991556644, 0.167038172, -0.0247640088, 0.0380980372, -0.0766585246, 0.0116074467, -0.0370487794, 0.0372571573, -0.0579243414, 0.115155771, 0.010439422, -0.0795015693, -0.00658304011, -0.00314732338, 0.112015776, -0.00897108298, 0.00179712963, 0.0743352398, -0.0433578938, -0.0605837144, 0.00744644366, 0.11539112, -0.12165805, -0.104283817, 0.0323160142, -0.0873822793, -0.0054093292, 0.0614236258, 0.122468069, -0.0643428639, 0.0600572303, 0.0452834778, 0.162645683, 0.149409562, -0.106560692, -0.0770278573, 0.145776168, -0.101439163, -0.044440262, 0.0943290666, 0.0310331732, 0.029994417, 0.181150377, 0.116438918, 0.134423569, -0.0865255222, -0.13473624, 0.0880954936, -0.0702161193, 0.082276389, 0.0991556644, 0.167038172, -0.0247640088, 0.0380980372, -0.0766585246, 0.0116074467, -0.0370487794, 0.0372571573, -0.0579243414, 0.115155771, 0.010439422, -0.0795015693, -0.00658304011, -0.00314732338, 0.112015776, -0.00897108298, 0.00179712963, 0.0743352398, -0.0433578938, -0.0605837144, 0.00744644366, 0.11539112, -0.12165805, -0.104283817, 0.0323160142, -0.0873822793, -0.0054093292, 0.0614236258, 0.122468069, -0.0643428639, 0.0600572303, 0.0452834778, 0.162645683, 0.149409562, -0.106560692, -0.0770278573, 0.145776168, -0.101439163, -0.044440262, 0.0943290666, 0.0310331732, 0.029994417, 0.181150377, 0.116438918, 0.134423569, -0.0865255222, -0.13473624, 0.0880954936, -0.0702161193, 0.082276389, 0.0991556644, 0.167038172, -0.0247640088, 0.0380980372, -0.0766585246, 0.0116074467, -0.0370487794, 0.0372571573, -0.0579243414, 0.115155771, 0.010439422, -0.0795015693, -0.00658304011, -0.00314732338, 0.112015776, -0.00897108298, 0.00179712963, 0.0743352398, -0.0433578938, -0.0605837144, 0.00744644366, 0.11539112, -0.12165805, -0.104283817, 0.0323160142, -0.0873822793, -0.0054093292, 0.0614236258, 0.122468069, -0.0643428639, 0.0600572303, 0.0452834778, 0.162645683, 0.149409562, -0.106560692, -0.0770278573, 0.145776168, -0.101439163, -0.044440262, 0.0943290666, 0.0310331732, 0.029994417, 0.181150377, 0.116438918, 0.134423569};
float tensor_4weight[2500] = {0.153046712, 0.0541287139, 0.0445186049, 0.128236383, 0.0151499752, 0.0772123784, 0.0958652049, -0.121294208, 0.045770254, -0.0958474949, 0.0600436665, 0.095616363, -0.0551988855, 0.133688182, -0.0729776546, -0.0118133305, 0.0440124422, -0.0325559154, 0.125734434, -0.041554004, -0.0674553812, -0.0406705774, 0.122080237, -0.0971305966, 0.151852012, -0.00464068586, -0.049293343, -0.132459611, 0.0657416359, 0.0142357768, -0.0282614976, 0.0719706416, 0.0766567141, -0.105188027, 0.184419245, -0.02146996, -0.0512494855, -0.0606206656, 0.0248709079, 0.0821560994, -0.00925347023, 0.182340339, 0.179146692, 0.149073407, 0.0555239096, -0.103368543, 0.0798476413, 0.0540262237, -0.108557113, 0.0776808858, 0.111955538, 0.203195795, 0.103608914, 0.0567243993, -0.0988262221, -0.0297720637, 0.012285457, 0.0509501919, -0.0253470745, 0.00101265137, 0.00827431306, -0.146829128, -0.0802807361, -0.111262895, -0.107141495, -0.0687769428, -0.066101484, 7.2618277e-05, 0.00554781593, 0.0524461865, 0.12928842, 0.104864448, -0.012226549, 0.0133734643, 0.175566599, -0.028944362, -0.0458253734, 0.0505064987, 0.131106153, -0.108606607, 0.00298315915, 0.109454267, 0.0296211522, 0.105820753, -0.118430115, 0.14837569, 0.00619457429, -0.00363949593, 0.0501893573, 0.0222900566, -0.106138982, -0.127301604, -0.0650469214, 0.0895064548, -0.0546363816, -0.0738953203, -0.0136217186, -0.049773965, 0.0846839696, 0.140841201, 0.138808116, 0.0151533401, 0.0206870362, 0.139534891, -0.0713992342, 0.174571529, 0.151029482, -0.0376801714, 0.147825927, 0.092633076, -0.0645904168, 0.117283396, 0.0199252367, 0.176261544, -0.00354275294, 0.0396990478, 0.0478373654, -0.0880818814, -0.0308060609, -0.094038792, 0.10496337, 0.115083478, -0.122336559, 0.00347908377, 0.163675651, -0.0877024531, 0.00118289515, -0.00533543481, 0.114974581, -0.0113934567, 0.0644664392, -0.00899290666, 0.10140831, -0.118346445, -0.0848536938, 0.11980629, 0.144289792, 0.120312057, -0.11954128, 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float tensor_18bias[80] = {0.0580350012, 0.0598478466, -0.0330895409, 0.0418219753, 0.012042217, -0.14448829, 0.110721789, 0.018284658, 0.00344521319, 0.0974587947, 0.0580350012, 0.0598478466, -0.0330895409, 0.0418219753, 0.012042217, -0.14448829, 0.110721789, 0.018284658, 0.00344521319, 0.0974587947, 0.0580350012, 0.0598478466, -0.0330895409, 0.0418219753, 0.012042217, -0.14448829, 0.110721789, 0.018284658, 0.00344521319, 0.0974587947, 0.0580350012, 0.0598478466, -0.0330895409, 0.0418219753, 0.012042217, -0.14448829, 0.110721789, 0.018284658, 0.00344521319, 0.0974587947, 0.0580350012, 0.0598478466, -0.0330895409, 0.0418219753, 0.012042217, -0.14448829, 0.110721789, 0.018284658, 0.00344521319, 0.0974587947, 0.0580350012, 0.0598478466, -0.0330895409, 0.0418219753, 0.012042217, -0.14448829, 0.110721789, 0.018284658, 0.00344521319, 0.0974587947, 0.0580350012, 0.0598478466, -0.0330895409, 0.0418219753, 0.012042217, -0.14448829, 0.110721789, 0.018284658, 0.00344521319, 0.0974587947, 0.0580350012, 0.0598478466, -0.0330895409, 0.0418219753, 0.012042217, -0.14448829, 0.110721789, 0.018284658, 0.00344521319, 0.0974587947};
float tensor_16bias[400] = {-0.0249088928, -0.0551015362, -0.0158167537, -0.0292323064, 0.0926068127, 0.0121522723, 0.0583137386, -0.136182129, -0.115019836, -0.0362252742, -0.161076948, 0.0887485445, -0.128120869, -0.128887415, 0.0115277311, 0.146457747, -0.120889731, -0.0756880865, 0.0389402993, -0.0496587083, 0.117353901, 0.162885323, -0.123118781, 0.101868883, 0.00541673787, 0.108104087, 0.0195515044, 0.0408362411, -0.0276273955, 0.0233217273, 0.146649286, -0.0774809346, 0.0496090166, 0.0855228901, 0.134886578, 0.130352378, 0.0870538428, 0.0745023042, 0.0376017652, 0.129035175, 0.0851973742, 0.0895722732, 0.0610439032, 0.00338048325, 0.0193772037, 0.00643290114, -0.0945113823, 0.0262184739, -0.0826716498, 0.0766651258, -0.0249088928, -0.0551015362, -0.0158167537, -0.0292323064, 0.0926068127, 0.0121522723, 0.0583137386, -0.136182129, -0.115019836, -0.0362252742, -0.161076948, 0.0887485445, -0.128120869, -0.128887415, 0.0115277311, 0.146457747, -0.120889731, -0.0756880865, 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float tensor_8bias[400] = {0.0985282063, 0.0882865041, 0.00711160246, 0.102775298, 0.158064172, 0.0463122427, 0.110826045, 0.0358141214, 0.0966012925, 0.10756243, -0.0784189329, 0.0366433598, 0.107032776, -0.00972931087, -0.141101301, -0.134936839, -0.086105369, -0.0178510118, 0.00805269741, -0.0324496739, -0.103503779, 0.14614442, 0.0686945021, 0.116558976, 0.077228643, -0.0467637293, -0.0787744746, 0.0967559665, -0.0923270881, 0.0939930528, -0.0974683166, 0.0268885624, 0.04916339, -0.121635072, -0.0918837339, -0.0125830909, 0.00458387332, 0.144989952, 0.0911227018, -0.0422558561, -0.0640833676, -0.084523648, -0.0213913564, -0.081313163, 0.000571266282, 0.112365097, 0.0509226844, 0.0906473324, 0.0128586255, -0.0608058646, 0.0985282063, 0.0882865041, 0.00711160246, 0.102775298, 0.158064172, 0.0463122427, 0.110826045, 0.0358141214, 0.0966012925, 0.10756243, -0.0784189329, 0.0366433598, 0.107032776, -0.00972931087, -0.141101301, -0.134936839, -0.086105369, -0.0178510118, 0.00805269741, 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float tensor_0bias[400] = {0.115543447, 0.0252122935, -0.0109798582, 0.13175714, -0.00898314267, 0.152876139, -0.0128141586, 0.112128057, 0.0305474754, 0.126865074, 0.142794579, 0.00335078058, 0.0476873405, -0.0892651901, 0.0187907349, 0.122951388, 0.0542392172, -0.00441618497, 0.0182345398, 0.0531022064, -0.0796347633, -0.0825375095, -0.0247144867, 0.041062478, 0.0648297742, -0.0342504606, 0.165592089, -0.0206753761, 0.06749136, 0.123591982, 0.0560114011, 0.146573484, 0.115727276, 0.0737257004, 0.147394419, 0.13681519, 0.138669997, 0.0676513985, 0.0150277652, 0.103848055, 0.139042646, 0.004427162, -0.0269598775, -0.0058525987, 0.0180224869, 0.130175799, 0.119096905, 0.0303068627, 0.0760233626, 0.0858778208, 0.115543447, 0.0252122935, -0.0109798582, 0.13175714, -0.00898314267, 0.152876139, -0.0128141586, 0.112128057, 0.0305474754, 0.126865074, 0.142794579, 0.00335078058, 0.0476873405, -0.0892651901, 0.0187907349, 0.122951388, 0.0542392172, -0.00441618497, 0.0182345398, 0.0531022064, 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float tensor_12bias[400] = {0.103633195, -0.159113452, 0.0428821072, 0.0293371677, 0.118340842, 0.100854471, -0.0321903415, 0.0875298753, 0.104554303, -0.043526344, -0.0851469114, 0.146725997, -0.107539192, -0.123961456, -0.0161444135, -0.124704204, 0.0682420433, 0.118932702, -0.0045294133, 0.0102368584, 0.126839831, -0.0968320891, 0.0250099488, 0.130616471, -0.0883112773, 0.0668042749, 0.153620586, 0.0694143102, 0.139408275, 0.0794798285, 0.138951734, -0.014447513, -0.11221756, -0.0944432169, 0.135357827, 0.0322046764, 0.0767789707, -0.00613953592, 0.00248281821, -0.0622172728, 0.0804678798, 0.155320436, -0.100424111, 0.139072686, 0.0487745292, -0.0770259574, 0.114749216, 0.105324626, -0.111226916, 0.112556688, 0.103633195, -0.159113452, 0.0428821072, 0.0293371677, 0.118340842, 0.100854471, -0.0321903415, 0.0875298753, 0.104554303, -0.043526344, -0.0851469114, 0.146725997, -0.107539192, -0.123961456, -0.0161444135, -0.124704204, 0.0682420433, 0.118932702, -0.0045294133, 0.0102368584, 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0.0345313884, -0.107750244};
float tensor_35[400];
float tensor_34[400];
float tensor_31[400];
float tensor_29[400];
float tensor_28[400];
float tensor_27[400];
float tensor_26[400];
float tensor_36[400];
float tensor_33[400];
float tensor_30[400];
float tensor_32[400];
float tensor_23[400];
float tensor_25[400];
float tensor_37[400];
float tensor_24[400];
float tensor_39[80];
float tensor_38[400];
float tensor_22[400];
float tensor_21[400];
std::vector<float> infer(float* tensor_input1){
	Eigen::Map<Eigen::Matrix<float,8,100,Eigen::RowMajor>> em_input1(tensor_input1);
	Eigen::Map<Eigen::Matrix<float,50,100,Eigen::RowMajor>> em_0weight(tensor_0weight);
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_21(tensor_21);
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_0bias(tensor_0bias);
	em_21 = em_input1 * em_0weight.transpose()+ em_0bias ;
	for (int id = 0; id < 400 ; id++){
		tensor_22[id] = ((tensor_21[id] > 0 )? tensor_21[id] : 0);
	}
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_22(tensor_22);
	Eigen::Map<Eigen::Matrix<float,50,50,Eigen::RowMajor>> em_2weight(tensor_2weight);
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_23(tensor_23);
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_2bias(tensor_2bias);
	em_23 = em_22 * em_2weight.transpose()+ em_2bias ;
	for (int id = 0; id < 400 ; id++){
		tensor_24[id] = ((tensor_23[id] > 0 )? tensor_23[id] : 0);
	}
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_24(tensor_24);
	Eigen::Map<Eigen::Matrix<float,50,50,Eigen::RowMajor>> em_4weight(tensor_4weight);
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_25(tensor_25);
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_4bias(tensor_4bias);
	em_25 = em_24 * em_4weight.transpose()+ em_4bias ;
	for (int id = 0; id < 400 ; id++){
		tensor_26[id] = ((tensor_25[id] > 0 )? tensor_25[id] : 0);
	}
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_26(tensor_26);
	Eigen::Map<Eigen::Matrix<float,50,50,Eigen::RowMajor>> em_6weight(tensor_6weight);
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_27(tensor_27);
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_6bias(tensor_6bias);
	em_27 = em_26 * em_6weight.transpose()+ em_6bias ;
	for (int id = 0; id < 400 ; id++){
		tensor_28[id] = ((tensor_27[id] > 0 )? tensor_27[id] : 0);
	}
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_28(tensor_28);
	Eigen::Map<Eigen::Matrix<float,50,50,Eigen::RowMajor>> em_8weight(tensor_8weight);
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_29(tensor_29);
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_8bias(tensor_8bias);
	em_29 = em_28 * em_8weight.transpose()+ em_8bias ;
	for (int id = 0; id < 400 ; id++){
		tensor_30[id] = ((tensor_29[id] > 0 )? tensor_29[id] : 0);
	}
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_30(tensor_30);
	Eigen::Map<Eigen::Matrix<float,50,50,Eigen::RowMajor>> em_10weight(tensor_10weight);
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_31(tensor_31);
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_10bias(tensor_10bias);
	em_31 = em_30 * em_10weight.transpose()+ em_10bias ;
	for (int id = 0; id < 400 ; id++){
		tensor_32[id] = ((tensor_31[id] > 0 )? tensor_31[id] : 0);
	}
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_32(tensor_32);
	Eigen::Map<Eigen::Matrix<float,50,50,Eigen::RowMajor>> em_12weight(tensor_12weight);
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_33(tensor_33);
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_12bias(tensor_12bias);
	em_33 = em_32 * em_12weight.transpose()+ em_12bias ;
	for (int id = 0; id < 400 ; id++){
		tensor_34[id] = ((tensor_33[id] > 0 )? tensor_33[id] : 0);
	}
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_34(tensor_34);
	Eigen::Map<Eigen::Matrix<float,50,50,Eigen::RowMajor>> em_14weight(tensor_14weight);
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_35(tensor_35);
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_14bias(tensor_14bias);
	em_35 = em_34 * em_14weight.transpose()+ em_14bias ;
	for (int id = 0; id < 400 ; id++){
		tensor_36[id] = ((tensor_35[id] > 0 )? tensor_35[id] : 0);
	}
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_36(tensor_36);
	Eigen::Map<Eigen::Matrix<float,50,50,Eigen::RowMajor>> em_16weight(tensor_16weight);
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_37(tensor_37);
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_16bias(tensor_16bias);
	em_37 = em_36 * em_16weight.transpose()+ em_16bias ;
	for (int id = 0; id < 400 ; id++){
		tensor_38[id] = ((tensor_37[id] > 0 )? tensor_37[id] : 0);
	}
	Eigen::Map<Eigen::Matrix<float,8,50,Eigen::RowMajor>> em_38(tensor_38);
	Eigen::Map<Eigen::Matrix<float,10,50,Eigen::RowMajor>> em_18weight(tensor_18weight);
	Eigen::Map<Eigen::Matrix<float,8,10,Eigen::RowMajor>> em_39(tensor_39);
	Eigen::Map<Eigen::Matrix<float,8,10,Eigen::RowMajor>> em_18bias(tensor_18bias);
	em_39 = em_38 * em_18weight.transpose()+ em_18bias ;
	std::vector<float> ret (tensor_39, tensor_39 + sizeof(tensor_39) / sizeof(tensor_39[0]));
	return ret;
}
} //TMVA_SOFIE_Linear_8