Генерируем код из свёрточной нейросети

#include <math.h>
#include <stdbool.h>
#include <stdio.h>

// Helper functions
float relu(float x) { return x > 0 ? x : 0; }

// Weights and biases as static const arrays
static const float conv_weight_1[8][3][3][3] = {0.18587972f, 0.121047735f, 0.17288074f, 0.08455727f, 0.0485634f, 0.17696865f, 0.23123726f, 0.009449424f, -0.13741924f, 0.037898965f, -0.18485852f, -0.11933022f, 0.18557934f, -0.025339043f, -0.20868859f, 0.23971556f, -0.11025837f, -0.121831715f, 0.10065536f, -0.06220964f, -0.14702433f, 0.10525637f, -0.04338868f, -0.17699996f, -0.0018675479f, 0.036309663f, 0.057767697f, -0.21331704f, -0.12620851f, 0.064701356f, -0.13833307f, -0.0472311f, 0.20548113f, -0.14090912f, 0.029706385f, -0.21022002f, -0.14372696f, 0.0063392776f, -0.06921412f, -0.2791596f, 0.1764991f, -0.18104845f, 0.11674259f, -0.16609037f, 0.1697487f, -0.14898174f, 0.10151631f, 0.073107496f, 0.25148767f, 0.11875583f, 0.03553228f, -0.043522116f, -0.14276616f, 0.008376238f, 0.08770262f, 0.1391882f, -0.10047718f, -0.15711795f, -0.117568515f, -0.07665795f, 0.06161963f, 0.15164651f, 0.028194599f, 0.22096808f, 0.2431096f, 0.03576198f, -0.009671796f, 0.012078963f, -0.070114166f, 0.12780929f, -0.106363885f, 0.20263496f, 0.2120262f, -0.10372441f, -0.1132339f, -0.16479634f, -0.25613025f, -0.18334556f, 0.05688063f, -0.034994174f, 0.0074980077f, -0.15407342f, -0.05180002f, 0.07300109f, -0.12925492f, 0.11309288f, -0.1066956f, 0.0006347491f, -0.102805644f, -0.17888196f, -0.21910636f, -0.19949521f, -0.101234324f, -0.1480992f, 0.019629637f, 0.220031f, -0.1895895f, -0.09270225f, -0.05160002f, 0.09028402f, 0.04654272f, -0.05983804f, 0.2325804f, 0.15463609f, 0.20519464f, 0.0015799687f, -0.05571402f, -0.06328881f, 0.07991943f, 0.20522158f, 0.10235304f, -0.10682472f, -0.14876384f, 0.055760477f, 0.1278167f, 0.1413231f, 0.15905875f, -0.009081793f, -0.04256893f, 0.17434955f, -0.13484013f, -0.2243677f, -0.09236428f, 0.0464507f, 0.04232938f, -0.07280475f, -0.16625403f, -0.08719382f, -0.11597104f, -0.0069498788f, 0.23863281f, 0.18005827f, 0.06037078f, 0.0059162714f, -0.20164676f, 0.083544165f, -0.080685474f, -0.2193284f, -0.11991214f, 0.17391151f, -0.1218012f, -0.14624813f, 0.14416482f, -0.09316614f, -0.06794647f, -0.0053348253f, 0.1767148f, -0.0922832f, 0.028185792f, 0.06822536f, -0.011330995f, -0.12797001f, -0.17424917f, -0.13697718f, -0.02097442f, -0.14353067f, -0.014437698f, 0.06559853f, 0.050830856f, -0.11536993f, -0.13294452f, -0.010713228f, 0.10396988f, -0.2236162f, 0.028499635f, -0.056220997f, -0.23829699f, 0.12911959f, -0.16551167f, -0.17241694f, -0.17532152f, -0.1830276f, -0.07733551f, 0.12940213f, 0.1107603f, -0.24203625f, 0.06869265f, -0.1062812f, 0.17401338f, -0.18734984f, -0.16918914f, -0.12681346f, 0.12253324f, 0.112866014f, 0.088412955f, 0.13669038f, 0.20461999f, -0.106537044f, 0.066308446f, 0.09695877f, 0.18126749f, -0.021619802f, -0.08979456f, -0.06822988f, 0.19300121f, -0.12601602f, -0.1638609f, -0.22723255f, 0.16028236f, -0.07325995f, -0.02953431f, -0.07375873f, -0.10595172f, 0.12711321f, 0.21269503f, -0.05770088f, 0.17617175f, 0.05954884f, -0.23088637f, -0.031167213f, 0.19944976f, 0.14972858f, -0.18328778f, -0.2738161f, -0.2282324f, -0.14132747};
static const float conv_bias_1[8] = {-0.00203097f, -0.010076913f, 0.013481268f, 0.007829253f, -0.0186532f, 0.011523341f, 0.00530615f, 0.01692545};

// BatchNorm Layer 2 parameters
static const float bn_gamma_2[8] = {0.98168665f, 1.0019723f, 0.9917244f, 1.0100024f, 1.0451597f, 0.98673356f, 0.9868369f, 0.97035754};
static const float bn_beta_2[8] = {-0.050274678f, -0.02993278f, -0.017009627f, 0.04422035f, 0.022492304f, 0.048113592f, 0.013216179f, 0.03941332};
static const float bn_mean_2[8] = {0.37913904f, -0.6056627f, 0.1063048f, -0.6519152f, 0.15887979f, -0.8966578f, -0.6657587f, -0.46499717};
static const float bn_var_2[8] = {0.07922225f, 0.06290531f, 0.03129498f, 0.079476185f, 0.018167619f, 0.10999437f, 0.09218159f, 0.058106475};
static const float bn_epsilon_2 = 1.0e-5f;

static const float conv_weight_5[16][3][3][8] = {-0.032921623f, -0.10940024f, 0.16191663f, -0.069271564f, -0.041521292f, -0.124647625f, 0.08016453f, 0.00871154f, -0.042515177f, 0.12534514f, -0.10745969f, -0.022505233f, 0.10584046f, 0.1386311f, -0.10150835f, -0.14044157f, -0.15495038f, -0.17127715f, 0.11427639f, -0.0138859125f, 0.092083305f, -0.1763202f, -0.17500323f, -0.15450633f, -0.10620337f, 0.18433082f, -0.16328703f, -0.11173595f, 0.14889485f, -0.15831311f, -0.04649333f, 0.037205f, 0.055708345f, -0.007888299f, 0.16473189f, -0.1509607f, -0.018000863f, -0.15380456f, -0.083087526f, 0.11785507f, 0.09230249f, 0.06641538f, -0.157055f, -0.06941546f, -0.08303712f, 0.143003f, -0.08312522f, -0.07389153f, 0.059727896f, -0.1624715f, -0.08661648f, -0.121932834f, -0.0845204f, -0.023913676f, -0.14496797f, 0.032577954f, -0.030440416f, 0.083259396f, 0.0478976f, -0.056674477f, 0.07930681f, -0.06211178f, 0.10858849f, 0.15475237f, 0.13957772f, -0.017746255f, -0.0011891541f, -0.104658164f, 0.03579825f, -0.085428365f, 0.14062774f, -0.044331424f, -0.13944416f, -0.15666506f, 0.033049043f, 0.14430767f, -0.09740738f, 0.13338658f, -0.018282678f, -0.1222347f, 0.07064747f, 0.07815654f, -0.06624203f, 0.017755773f, -0.112984575f, -0.019077664f, -0.025341736f, 0.15520224f, -0.04911464f, -0.08501078f, -0.05050606f, 0.09212136f, -0.041969262f, 0.10541266f, 0.1525624f, 0.11110815f, -0.10709718f, -0.09107055f, -0.0045625567f, 0.024054363f, 0.10872079f, -0.12622324f, 0.0798516f, -0.08466377f, 0.03405213f, 0.052285556f, 0.059396073f, -0.020291582f, 0.13270181f, 0.14442311f, 0.06973366f, -0.13554053f, 0.17050008f, 0.009219963f, 0.04165541f, 0.12986818f, 0.1804796f, 0.026551858f, -0.09652964f, -0.122968994f, 0.14837356f, -0.0076785632f, 0.014346782f, -0.0736109f, -0.028747391f, 0.1441928f, 0.088926904f, 0.07355117f, -0.14709945f, 0.058273964f, 0.060640868f, 0.043683365f, 0.113986015f, 0.027661432f, 0.031936944f, -0.055947922f, 0.0023716334f, 0.0143922735f, 0.12698726f, -0.09706419f, 0.031016462f, -0.14047599f, 0.119724356f, -0.063508675f, 0.122740746f, -0.029598214f, 0.09476029f, 0.13921918f, -0.113042586f, 0.0025941627f, -0.021147054f, -0.002771141f, 0.13666838f, -0.13063428f, 0.12808213f, -0.017647384f, -0.05765373f, 0.058853664f, 0.11621439f, -0.118551046f, -0.093635894f, -0.12073656f, -0.09523678f, -0.11203925f, -0.114207886f, -0.06304875f, -0.103139326f, -0.09855859f, 0.057472214f, -0.11781305f, 0.09060761f, 0.07464975f, 0.07828852f, 0.019741984f, -0.10694203f, 0.04977464f, -0.1344205f, 0.1437716f, -0.086144775f, 0.13096195f, 0.06717016f, 0.01817719f, 0.14874567f, 0.122467756f, -0.12373957f, -0.09900395f, -0.13663545f, -0.11504395f, 0.05682162f, -0.08302901f, -0.09266137f, -0.03654032f, 0.17914487f, -0.01603123f, -0.033542126f, 0.15354382f, 0.08001873f, 0.16781895f, 0.16591813f, -0.15616514f, 0.16971578f, 0.043518674f, 0.027255919f, 0.0824188f, 0.013278276f, -0.15167385f, 0.020788705f, -0.14987731f, 0.173393f, -0.045372095f, -0.11483041f, 0.025331056f, 0.11649729f, 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0.13898517f, -0.109365985f, 0.14749068f, -0.047583625f, 0.1116952f, -0.049751457f, -0.004482609f, 0.053652056f, -0.106106475f, -0.054106552f, -0.110164605f, -0.1544714f, -0.099411406f, -0.07344631f, -0.0953466f, -0.12221039f, 0.05488131f, -0.021240564f, -0.13188587f, -0.093117476f, 0.106150135f, 0.07411079f, -0.015821967f, 0.13603835f, 0.12331678f, 0.015897382f, 0.1521543f, 0.13011706f, -0.0711123f, -0.18211515f, -0.05799291f, 0.01832248f, -0.070773624f, -0.00024035371f, -0.110039465f, 0.074715056f, 0.053124566f, -0.15792386f, 0.01858957f, -0.17564054f, -0.017082391f, -0.015387269f, -0.16218713f, -0.1623316f, 0.049591105f, -0.1613508f, -0.12059379f, 0.0149598215f, -0.041587196f, 0.13713504f, -0.09244943f, 0.1353631f, -0.08673819f, 0.06554621f, 0.067954496f, -0.14818624f, 0.099827684f, -0.16151817f, 0.024889885f, 0.053001054f, -0.08943675f, 0.057837307f, 0.0713115f, 0.19459166f, 0.13036554f, 0.07030857f, 0.04675356f, -0.06858545f, 0.12111921f, 0.08752249f, -0.13620032f, 0.06827583f, 0.05279535f, 0.15004288f, -0.16576444f, -0.15205155f, -0.099241816f, -0.09444495f, 0.11274064f, -0.042995464f, 0.100666754f, 0.058389544f, -0.03178451f, -0.04923604f, 0.029453432f, 0.03008575f, -0.13797271f, -0.07073313f, 0.057328857f, 0.103207305f, 0.006165897f, -0.09860096f, 0.12020302f, 0.040816043f, -0.16348544f, 0.07382618f, -0.09850644f, -0.004558952f, -0.19513294f, -0.026560908f, -0.05009867f, 0.14265257f, -0.12755798f, -0.008363784f, 0.10406049f, -0.08670214f, -0.022192797f, -0.045518726f, 0.10056809f, 0.0072727157f, -0.12164004f, -0.15989242f, 0.1103027f, -0.1392871f, 0.05777928f, 0.12767753f, -0.07000203f, -0.10936205f, 0.111951895f, -0.047387682f, 0.14115508f, 0.11640959f, -0.09910793f, 0.11817767f, -0.11107367f, -0.062363952f, -0.13970424f, -0.0057171667f, -0.0029816919f, -0.08639152f, -0.104494795f, -0.010309849f, -0.1747143f, 0.1451486f, 0.04764593f, 0.09462862f, -0.13419881f, 0.09006426f, -0.018118802f, 0.06578858f, -0.11685841f, -0.016883595f, 0.041302875f, -0.078286305f, 0.080241665f, -0.03984811f, 0.08625191f, -0.07640103f, -0.04401475f, 0.13183844f, 0.14061193f, 0.12993106f, -0.08380193f, 0.12941301f, 0.049253408f, 0.07291028f, -0.050643805f, 0.047413126f, -0.12858091f, -0.09592657f, -0.149293f, 0.10248002f, -0.1372588f, 0.057795573f, -0.13348922f, -0.027215064f, 0.017416365f, -0.17348571f, 0.14907445f, 0.026208686f, -0.12820971f, 0.08696437f, -0.105140515f, 0.13007428f, -0.08933484f, -0.11749256f, 0.13076465f, 0.008975257f, 0.15341958f, -0.111283205f, 0.08113391f, -0.044624653f, -0.058102284f, -0.013786059f, -0.03545194f, 0.027740316f, 0.025746398f, 0.051712226f, -0.015193969f, 0.10219375f, -0.1413207f, -0.017936954f, -0.14799748f, 0.15205887f, -0.028456496f, -0.12538007f, 0.119064875f, -0.13574892f, -0.010538462f, 0.09826524f, 0.078034885f, -0.0064881514f, 0.11932294f, 0.1314965f, -0.054080255f, 0.14674774f, 0.018013274f, -0.099693656f, -0.10946194f, 0.18964513f, -0.03656911f, 0.012945877f, 0.11716457f, -0.08293339f, 0.0602363f, 0.07459894f, 0.084843956f, -0.07552533f, 0.08028165f, 0.1117001f, -0.1683126f, 0.12983313f, 0.08635235f, -0.09481935f, -0.16143952f, -0.0051517286f, 0.037628535f, -0.1567192f, -0.12518084f, 0.06554116f, 0.16599506f, -0.014981962f, 0.121077165f, -0.03905693f, 0.09897784f, -0.020809807f, 0.08412495f, -0.111136675f, 0.14713581f, -0.07472463f, -0.037196387f, -0.13008556f, -0.0409618f, 0.12336648f, 0.19708091f, 0.08335954f, 0.15462688f, 0.012329818f, 0.009264616f, -0.1462149f, 0.122586995f, -0.13280495f, 0.15833955f, 0.13615756f, -0.099087045f, -0.089531094f, -0.1051237f, 0.00405704f, -0.120871894f, -0.011182233f, 0.011605045f, 0.17648281f, 0.09284108f, -0.13378264f, -0.065633684f, 0.16186938f, 0.089165516f, 0.04659324f, -0.030847376f, 0.0029359497f, -0.021037806f, 0.11169356f, -0.06739304f, -0.160798f, 0.115304284f, 0.13946196f, 0.12504584f, 0.08090405f, -0.14644076f, 0.0031151425f, -0.02458636f, -0.08293406f, 0.03702806f, 0.0739293f, -0.100792095f, 0.09745129f, -0.08193276f, -0.1002472f, 0.1485017f, 0.038496647f, 0.09052367f, 0.039009146f, -0.030962009f, 0.042268354f, -0.19048661f, 0.003430234f, -0.19182207f, -0.12262295f, -0.075450435f, -0.06798172f, 0.10382398f, -0.14264844f, -0.103586935f, 0.13268313f, 0.06589533f, -0.12893903f, 0.055980198f, 0.053069264f, -0.0036996375f, 0.12770201f, -0.08605869f, -0.10339874f, -0.05534512f, -0.07960149f, 0.119950436f, -0.1713563f, -0.11403044f, -0.065252244f, -0.06096683f, -0.08688706f, -0.15069771f, 0.15032803f, 0.13762522f, -0.08409314f, -0.051052485f, 0.097476564f, -0.15687352f, -0.093663186f, -0.009175874f, -0.081020184f, 0.009840328f, 0.047986433f, -0.06651307f, -0.11119223f, 0.05922704f, 0.108607516f, -0.02579354f, 0.14305086f, 0.11745637f, 0.114354104f, 0.122877896f, -0.12256314f, 0.15088156f, -0.047389183f, -0.07868761f, -0.0014914856f, -0.023512201f, -0.09688163f, -0.15046377f, 0.04475044f, 0.030239932f, -0.0394072f, 0.114664376f, 0.061201062f, -0.090506025f, 0.0026744066f, 0.13799408f, -0.1093762f, 0.0076772654f, -0.085716166f, -0.083466955f, 0.093924604f, -0.09964515f, 0.025250932f, 0.14668733f, -0.05157583f, -0.018613717f, 0.05313001f, 0.00086469494f, 0.13368407f, 0.0454782f, 0.017457953f, -0.11611501f, 0.071808785f, 0.08648791f, -0.1457547f, 0.18803349f, -0.11002053f, 0.13918297f, 0.09081318f, 0.094014935f, 0.11476589f, -0.15610051f, -0.006708683f, 0.051463317f, -0.009894858f, -0.046019614f, -0.14438799f, -0.027091756f, -0.041622914f, -0.112474196f, 0.108520135f, 0.13311079f, 0.17880943f, -0.16496797f, -0.09749159f, -0.10198664f, -0.06238624f, -0.06889405f, -0.1023355f, -0.12016803f, 0.16328931f, 0.08912886f, 0.07563671f, 0.14636125f, -0.15415531f, -0.0016903864f, 0.1417462f, -0.060953464f, 0.12137554f, -0.02554388f, 0.018063406f, -0.056473926f, 0.061444845f, -0.15722468f, 0.08293354f, 0.065895766f, 0.09508206f, -0.049204793f, -0.07153849f, 0.017635543f, -0.04998676f, -0.036625918f, 0.025911884f, -0.16629377f, 0.15053555f, -0.098026976f, 0.113513544f, -0.11295276f, 0.07267059f, -0.13988389f, -0.11747412f, 0.14212748f, 0.1470299f, 0.037804775f, 0.074026935f, 0.035776433};
static const float conv_bias_5[16] = {0.008974173f, 0.025272723f, 0.016067615f, 0.012045786f, 0.0013063726f, -0.006902565f, -0.01590336f, -0.0019097797f, -0.003770476f, 0.01883083f, 0.004808998f, -0.002449425f, 0.0051636747f, -0.010356479f, -0.021960584f, 0.02524823};

// BatchNorm Layer 6 parameters
static const float bn_gamma_6[16] = {0.9970602f, 0.99411815f, 1.0175468f, 0.9735324f, 0.9825919f, 1.0711048f, 1.0154266f, 0.9946459f, 0.98268074f, 0.98620737f, 1.0366497f, 1.033742f, 1.0328511f, 0.99571776f, 0.98329604f, 0.99553585};
static const float bn_beta_6[16] = {0.018273586f, 0.027951801f, 0.019440271f, -0.04174148f, -0.025660608f, 0.009486955f, -0.0058508725f, -0.0047886316f, 0.006596671f, 0.041918516f, 0.01475671f, 0.0126463175f, -0.011013145f, 0.04897816f, -0.015491389f, -0.021619609};
static const float bn_mean_6[16] = {-0.53654706f, 0.41737816f, 0.0015398671f, 0.14438444f, 0.18740515f, -0.0025442853f, 0.025991563f, 0.09008155f, -0.062362846f, 0.42262065f, -0.3457238f, -0.28381133f, 0.12840614f, 0.23063773f, -0.34756568f, 0.22335944};
static const float bn_var_6[16] = {1.5773301f, 0.34731704f, 0.44441396f, 0.21671796f, 0.42032093f, 0.2007031f, 0.16204634f, 0.26503283f, 0.16014051f, 0.84953725f, 0.6475874f, 0.33527154f, 0.30592126f, 0.5216121f, 0.57215f, 0.24342401};
static const float bn_epsilon_6 = 1.0e-5f;

static const float conv_weight_9[32][3][3][16] = {-0.055055447f, -0.061948217f, -0.03918481f, -0.039639983f, -0.077895544f, 0.019756086f, 0.057310294f, -0.0017441676f, -0.0443523f, 0.057133228f, -0.07266437f, 0.0304754f, 0.06657164f, -0.0143321995f, -0.12016775f, 0.026387151f, -0.08658278f, -0.029470775f, 0.05611512f, 0.029876798f, 0.057317216f, -0.071932f, 0.026328936f, 0.033159424f, -0.05241771f, -0.07785983f, -0.036275975f, -0.050009243f, -0.008863725f, 0.019402687f, 0.017385485f, 0.12870434f, 0.027674934f, 0.05597259f, -0.0024583514f, 0.055855285f, 0.034729682f, -9.773443e-5f, -0.026122905f, 0.031376135f, -0.030597202f, -0.111677654f, 0.1258648f, 0.02548981f, -0.053605705f, 0.08820574f, 0.09258804f, 0.07375393f, -0.052094802f, 0.074402034f, 0.061841786f, 0.12047232f, -0.03636069f, 0.099530354f, 0.011962476f, 0.019765113f, 0.10072135f, 0.048057225f, 0.034215175f, -0.13891777f, 0.004854343f, -0.014763275f, -0.11981327f, -0.04818768f, -0.005019221f, 0.05603398f, -0.14477617f, -0.11191803f, -0.017211339f, 0.106614076f, -0.02497007f, -0.021554952f, 0.08482855f, -0.012244279f, 0.04701823f, -0.049307536f, 0.0064133024f, -0.11980049f, -0.09783599f, 0.0806154f, -0.054736078f, -0.019101424f, 0.07580026f, -0.080741264f, -0.06499605f, 0.04812402f, 0.005159053f, -0.057510994f, 0.01428005f, -0.034307197f, 0.08756979f, -0.04555581f, -0.05956618f, -0.08043583f, -0.13000001f, -0.009638911f, 0.09347264f, -0.11011109f, -0.11088536f, -0.07942186f, 0.020948773f, 0.0490211f, -0.056803577f, 0.0029511657f, 0.04625788f, -0.006627793f, 0.11063067f, 0.090678856f, -0.1416624f, -0.11465399f, 0.058604456f, 0.067637004f, -0.100913316f, 0.059991855f, -0.08338867f, 0.10212215f, 0.013737224f, 0.023505501f, 0.079180226f, 0.08577815f, -0.10630817f, -0.08470573f, 0.014888016f, 0.058668397f, -0.053459212f, 0.066784054f, 0.03279432f, 0.037761066f, 0.081241176f, -0.074316785f, 0.035669837f, 0.046522412f, -0.0565033f, 0.010923501f, -0.009030712f, -0.006958616f, 0.046834208f, 0.034852136f, 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0.06370446f, 0.08029955f, -0.04548731f, 0.06419634f, -0.032415025f, -0.034925673f, 0.046499748f, 0.047861617f, 0.096857294f, 0.04437293f, 0.041177414f, -0.022540478f, -0.13982496f, 0.08662404f, 0.016518934f, 0.06775572f, -0.11710131f, -0.07789375f, -0.1015912f, -0.0765061f, -0.09938961f, -0.09834413f, -0.009171303f, -0.042072274f, 0.12103027f, 0.060579058f, -0.1295065f, -0.047268458f, -0.00071408635f, 0.051901508f, -0.020374032f, 0.0517913f, 0.11246328f, -0.08108078f, 0.040276565f, -0.049816925f, 0.08335835f, -0.006681052f, 0.07376462f, 0.009953177f, -0.05168933f, -0.021615805f, 0.11613863f, 0.074702285f, -0.06710167f, 0.010333648f, 0.098601714f, -0.028687987f, 0.09739791f, 0.030357942f, 0.023704488f, -0.0668192f, 0.10172327f, -0.101498485f, 0.024498692f, 0.0072265253f, 0.03280301f, 0.05598161f, -0.08570277f, -0.065320455f, 0.089211985f, -0.106974915f, -0.06409194f, -0.009598454f, 0.04631071f, 0.01930022f, 0.107956275f, 0.060778372f, -0.033273123f, -0.03412344f, 0.07142621f, 0.08136456f, -0.017232206f, -0.037395854f, -0.015804606f, -0.047880843f, 0.028375879f, -0.028520828};
static const float conv_bias_9[32] = {0.007040083f, 0.010110945f, 0.0020507576f, 0.010653843f, -0.013022381f, 0.004711954f, 0.011034204f, 0.0060508405f, 0.005829514f, 0.007880951f, -0.0072228564f, -0.0035681673f, -0.0079564955f, 0.00015195333f, -0.00604497f, 0.0019471187f, -0.013752448f, 0.005455345f, -0.0057776463f, 0.0017645599f, -0.0045746914f, 0.010626799f, -0.0064475248f, 0.00041438703f, -0.017797416f, 0.0060747815f, -0.016685087f, 0.0020739331f, -0.016356546f, -0.005977066f, 0.01511046f, -0.008845729};

// BatchNorm Layer 10 parameters
static const float bn_gamma_10[32] = {0.99419194f, 0.9949303f, 0.96498024f, 0.99060327f, 0.9710039f, 0.9687332f, 1.0377338f, 0.98083025f, 1.0178404f, 1.0035532f, 0.9901397f, 0.9763715f, 1.0161848f, 1.0451049f, 0.99049056f, 0.97619313f, 0.9532403f, 0.9834566f, 1.0755836f, 1.0227544f, 0.9566068f, 1.0058588f, 0.98802084f, 1.0698819f, 1.0382202f, 0.96943724f, 0.9728987f, 0.99332535f, 0.9987585f, 0.9586074f, 1.0096283f, 0.9730518};
static const float bn_beta_10[32] = {-0.041649472f, 0.046545945f, -0.04020455f, -0.040806193f, -0.08142576f, -0.046871334f, -0.030287048f, -0.033591248f, 0.04143502f, -0.011334697f, -0.035980064f, -0.024090635f, -0.017023087f, 0.028561203f, -0.02882035f, -0.028182084f, -0.04678938f, -0.02551839f, -0.038637344f, -0.032412067f, 0.003973097f, 0.017737871f, -0.0615464f, 0.02206577f, -0.0153774945f, -0.010527649f, 0.025763586f, 0.004641873f, -0.016201692f, -0.020155445f, -0.01850363f, -0.019480491};
static const float bn_mean_10[32] = {-0.06773546f, -0.5726219f, -0.34025624f, 0.7495847f, 0.507042f, -0.5811398f, -0.09629567f, -0.35786775f, -0.19147927f, -0.24484938f, -0.17559373f, -0.42349383f, -0.0458028f, -0.40064487f, -0.4618655f, -0.5602557f, 0.41097894f, -0.24413052f, -0.17531377f, 0.34102824f, -0.74120545f, -0.5508152f, 0.423056f, -0.6425426f, 0.19547282f, 0.12270049f, -0.27433506f, -0.2705173f, 0.18379682f, -0.060307346f, 0.51621306f, -0.19426127};
static const float bn_var_10[32] = {0.19119042f, 0.6752974f, 0.518854f, 0.9798632f, 0.7718481f, 1.3807673f, 0.15258485f, 0.3383186f, 0.39480808f, 0.35360938f, 0.24545349f, 0.7024803f, 0.22738734f, 0.30039456f, 0.37344906f, 0.30790332f, 0.82198334f, 0.7560703f, 0.13406433f, 0.37200144f, 0.6639502f, 0.31142962f, 0.52014595f, 0.30027223f, 0.24528652f, 0.38823754f, 0.51358956f, 1.4016906f, 0.16081083f, 1.0806209f, 0.19386603f, 0.51370066};
static const float bn_epsilon_10 = 1.0e-5f;

static const float conv_weight_13[32][3][3][32] = {0.096229695f, -0.019581573f, -0.052228205f, -0.049869496f, -0.07079462f, 0.0456422f, -0.09566267f, 0.07780636f, 0.040976238f, -0.09006781f, 0.005277797f, 0.08703714f, -0.09516224f, -0.061035566f, 0.013495019f, -0.1189197f, -0.010388801f, -0.017728671f, -0.0074216365f, -0.086534224f, 0.01775394f, -0.037985373f, -0.09201616f, -0.028593019f, -0.05677823f, 0.09706298f, -0.079751305f, -0.059052363f, 0.06748546f, 0.02278108f, -0.117204964f, 0.0047254795f, 0.088998236f, -0.11865055f, -0.07042303f, 0.070717044f, 0.056208204f, -0.009285088f, 0.032246377f, -0.06451038f, -0.08955015f, 0.08314404f, 0.06154808f, 0.036065806f, -0.088950895f, 0.008395785f, 0.052564204f, 0.01374224f, -0.08491432f, 0.023810316f, 0.025276316f, -0.049900588f, 0.045110304f, 0.07879097f, 0.055016693f, -0.058360495f, -0.07878472f, 0.056105427f, 0.09712867f, 0.057063542f, -0.025622472f, -0.026469938f, 0.059427127f, 0.035619088f, 0.07344474f, -0.03372806f, -0.057528146f, -0.052479964f, 0.04405655f, 0.07053304f, -0.022239655f, -0.020582395f, -0.05768981f, 0.021427503f, 0.044213932f, -0.07361715f, 0.013981419f, 0.029907111f, -0.012627486f, -0.016648661f, -0.015484168f, 9.0413974e-5f, 0.025648896f, 0.0077042985f, 0.09267333f, 0.027128458f, 0.010460306f, 0.08063695f, 0.10172126f, 0.063538276f, -0.011762739f, 0.05874128f, -0.08604903f, -0.023545075f, -0.067544565f, 0.08588349f, 0.084928684f, -0.12099423f, -0.012326552f, -0.087576985f, -0.014948996f, 0.08312222f, 0.002402677f, -0.048441924f, -0.113919586f, 0.02176334f, 0.04540255f, 0.09886788f, 0.055843256f, -0.07276854f, -0.059563953f, -0.040320367f, 0.023040984f, 0.023332357f, 0.095852606f, -0.07761987f, -0.06627403f, -0.07909659f, -0.0808342f, -0.043424405f, -0.048372604f, 0.010954625f, -0.017390141f, 0.0270048f, -0.04266908f, -0.03677339f, 0.03626792f, -0.045240633f, -0.0802856f, 0.03250536f, -0.08061403f, 0.0034783548f, 0.03629325f, -0.026787559f, -0.045207642f, -0.09106288f, -0.020825654f, 0.09373123f, -0.027632736f, 0.041450344f, -0.11287741f, -0.09862376f, -0.057015672f, -0.06530136f, 0.022209784f, 0.10464482f, 0.029332882f, -0.003194419f, -0.01929705f, -0.0287575f, -0.056620788f, 0.073408864f, 0.071015716f, -0.059250318f, -0.077360585f, -0.07323186f, 0.08070402f, -0.0025151342f, 0.08390869f, 0.08281148f, 0.08075425f, 0.06791143f, 0.09445235f, 0.023520302f, -0.055385318f, -0.036951426f, -0.015140904f, -0.03455435f, 0.07980386f, -0.023071285f, 0.045116723f, -0.022326576f, 0.05613915f, -0.10338308f, 0.038001686f, 0.052006703f, -0.0020092882f, 0.08451271f, -0.07595352f, -0.062774435f, 0.083210334f, 0.099993244f, -0.06694985f, -0.08182749f, 0.04549175f, -0.06808945f, 0.087280676f, -0.098581284f, -0.01207242f, 0.08622528f, 0.005889317f, 0.08071847f, 0.04174406f, 0.0008826911f, 0.08058236f, 0.030410951f, -0.027206305f, -0.04221282f, -0.02995503f, 0.08725428f, -0.05748474f, 0.090054035f, -0.07523419f, 0.036304608f, -0.034808204f, 0.07546886f, 0.042017125f, 0.05744616f, -0.0131510915f, -0.062472153f, -0.003836011f, -0.032905374f, 0.056984354f, -0.05148625f, 0.056231547f, 0.0027243528f, 0.083402574f, -0.038244225f, -0.09792848f, -0.029414676f, -0.03667046f, -0.046237238f, -0.0772059f, -0.05840805f, -0.062210433f, -0.108018935f, 0.08872317f, -0.07048456f, 0.006436974f, 0.0299972f, 0.027132366f, -0.031273257f, -0.09099739f, 0.094455466f, -0.05793695f, 0.0019008619f, -0.0833344f, 0.06043014f, 0.01544141f, -0.0520485f, 0.05286733f, -0.042549547f, -0.0062212176f, -0.093311474f, -0.0768233f, 0.014818725f, 0.044993162f, -0.09154078f, -0.012431397f, -0.03721115f, -0.06375718f, 0.053197905f, 0.08270748f, -0.0011181425f, -0.07037852f, -0.069260396f, -0.038687512f, -0.019697467f, 0.07261147f, 0.08574757f, 0.0055850036f, -0.016815973f, -0.07971956f, -0.070275165f, -0.042437978f, 0.08565419f, -0.0070344787f, 0.021820331f, -0.04466796f, -0.027023066f, 0.004457634f, -0.04674031f, 0.04490321f, -0.06800713f, -0.06655912f, -0.10486102f, -0.07597774f, 0.01385719f, -0.08846822f, 0.0063141277f, -0.05344885f, 0.050396625f, 0.020745464f, 0.07980125f, 0.07973672f, -0.020253612f, 0.055547517f, -0.017163917f, -0.022755751f, -0.084827304f, -0.08956699f, 0.0668066f, 0.028077923f, -0.10576784f, -0.09215782f, 0.019337116f, 0.06843187f, -0.088223346f, 0.03354519f, 0.048722133f, 0.07147435f, -0.04480159f, -0.0011433087f, -0.075590365f, 0.010140585f, -0.05461234f, -0.0005532763f, 0.022488916f, 0.045639753f, 0.014717057f, 0.021114029f, 0.059162173f, 0.035473112f, -0.062589586f, 0.1055959f, -0.017950421f, 0.0152645325f, -0.027333386f, -0.06804242f, -0.027653763f, -0.035991017f, 0.016849019f, -0.07027703f, -0.10073761f, 0.015894901f, 0.038956843f, -0.09471599f, 0.10203733f, -0.046976943f, -0.0466275f, -0.0027901244f, 0.036537163f, -0.0003906224f, 0.06717982f, -0.026751904f, 0.089622825f, 0.028965848f, -0.006294001f, -0.015514683f, -0.066488385f, -0.046082154f, 0.037776556f, -0.013084309f, -0.04205633f, -0.083223f, 0.05705536f, -0.076967075f, 0.06691185f, 0.023253344f, 0.10594597f, -0.08712139f, 0.0039701653f, 0.03603263f, -0.028402733f, 0.05135053f, -0.05394864f, -0.044620108f, -0.03544139f, -0.013907338f, 0.024890764f, -0.008109453f, 0.07287442f, 0.02169836f, -1.34485745e-5f, 0.06432225f, 0.13129923f, 0.058526143f, -0.060416646f, -0.031076564f, 0.056696896f, -0.051976975f, -0.0561826f, 0.019261157f, 0.009131019f, -0.08734673f, 0.045119874f, 0.027915705f, 0.084032096f, 0.006029248f, -0.02265723f, -0.0790379f, 0.06500217f, 0.060300305f, -0.0709023f, -0.0047304844f, 0.082948364f, 0.0060551283f, -0.017770033f, 0.0043364107f, -0.014015322f, -0.014703432f, -0.102621734f, 0.009208117f, 0.0016227094f, -0.093122326f, 0.0058992417f, 0.053465266f, -0.029085368f, -0.059451383f, 0.080896616f, 0.089871205f, 0.041768916f, -0.06064589f, -0.079929136f, 0.022245785f, -0.06260237f, 0.06064506f, -0.071218394f, -0.12552936f, -0.02332484f, 0.0046217456f, 0.083485216f, 0.057411175f, 0.005519909f, 0.06549271f, 0.08887977f, -0.0105010355f, 0.03971297f, -0.07819008f, 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0.002925959f, -0.045211982f, -0.091353625f, -0.08377349f, -0.03233975f, 0.07674517f, 0.08067381f, 0.041903153f, -0.09175595f, 0.095887884f, 0.027250899f, 0.12419165f, 0.0693312f, 0.008006805f, 0.11395376f, 0.005442312f, 0.06391355f, -0.054389004f, -0.04707038f, 0.075274974f, -0.030240197f, -0.031219864f, -0.019031666f, -0.06415527f, -0.0061617126f, -0.01502315f, 0.0027335691f, 0.084407695f, 0.060947523f, 0.076823406f, -0.022362595f, -0.07184079f, 0.03647003f, 0.05059862f, 0.04298482f, -0.05105361f, -0.1198929f, 0.06314838f, 0.061456446f, 0.07967657f, -0.018894512f, 0.025730763f, -0.060947195f, -0.0021727036f, -0.044061244f, 0.0407353f, 0.05995962f, -0.017394634f, -0.06971815f, 0.032448195f, -0.1333233f, 0.0274211f, -0.09072119f, -0.03139085f, -0.100838706f, 0.032948602f, -0.06126701f, 0.055512115f, -0.115141496f, -0.0019766474f, -0.016771343f, 0.0037118485f, -0.009979621f, -0.040927332f, -0.020483416f, 0.05007313f, 0.06830484f, 0.017088303f, -0.11148418f, 0.03262775f, 0.017960003f, 0.052041918f, 0.07518856f, 0.09213892f, -0.037956636f, 0.099729344f, 0.0046931854f, -0.08477043f, -0.029026125f, -0.029546998f, -0.066882f, -0.06828069f, -0.01779507f, -0.006292778f, 0.064561225f, 0.0032764953f, 0.012297545f, -0.024903506f, -0.06340083f, -0.022373084f, 0.030493654f, 0.08396112f, -0.1091902f, -0.05355874f, -0.028784966f, -0.013943671f, 0.030829977f, 0.029249586f, -0.059777137f, -0.025524773f, -0.08343132f, 0.021081006f, -0.05381967f, -0.06809463f, -0.045231774f, -0.010952179f, -0.04810793f, 0.045741666f, -0.02206041f, -0.08679462f, 0.07577625f, 0.058303703f, 0.051377226f, -0.05638589f, -0.10179665f, 0.047461312f, 0.028559867f, 0.065339245f, 0.04536496f, 0.039246768f, -0.029629514f, -0.10163681f, -0.007157992f, -0.036101796f, 0.04975718f, -0.035399616f, 0.056288145f, -0.053043064f, 0.02739983f, -0.014204625f, -0.11077774f, 0.02292363f, 0.034786757f, 0.109427504f, -0.047179483f, 0.047589984f, 0.059782777f, -0.033519506f, 0.07057441f, 0.07467664f, -0.059681773f, -0.013926624f, 0.07628189f, 0.0513407f, 0.072196275f, -0.009399882f, -0.11046698f, -0.01721849f, -0.05785028f, 0.007547982f, -0.058333f, -0.08265134f, 0.09531846f, 0.06536639f, -0.097319186f, 0.12673192f, -0.03354493f, 0.03991809f, -0.064283475f, 0.058342952f, -0.007846933f, 0.026008671f, -0.09529943f, 0.053125322f, 0.045313526f, 0.06372903f, 0.00826898f, 0.036566414f, -0.10882423f, -0.006771426f, 0.0034270578f, 0.062505715f, 0.038322315f, -0.03707282f, 0.09841689f, -0.038210716f, 0.06890913f, -0.041329604f, 0.011299501f, 0.03404033f, 0.088165276f, 0.0016130401f, 0.018682424f, -0.10010453f, -0.05591719f, 0.036665834f, -0.04335322f, 0.038166136f, -0.06305965f, -0.017326271f, -0.0030378923};
static const float conv_bias_13[32] = {0.0016281478f, 0.00026510525f, 0.004175839f, 0.005631324f, 0.00042296216f, 0.009622805f, 0.006131644f, 0.0017807547f, -0.00939767f, 0.0028879587f, 0.0020589463f, -0.0013957405f, -0.0048263134f, -0.0029211596f, 0.00084796536f, -0.0037440462f, -0.0035404211f, 0.00011058235f, 0.010500487f, 0.0036833705f, -0.0026435484f, 0.004274206f, 0.009668496f, -0.0011543353f, 0.010637808f, -0.007672646f, 0.0012038709f, -0.0063222083f, -0.00503111f, -0.008872649f, 0.004586279f, -0.0028198967};

// BatchNorm Layer 14 parameters
static const float bn_gamma_14[32] = {0.9563748f, 0.9868319f, 1.0266442f, 0.9504581f, 0.9865843f, 0.99618614f, 1.0336552f, 1.0756035f, 1.0219843f, 0.9772946f, 1.0154797f, 1.02012f, 1.0349392f, 1.0583279f, 1.0525901f, 0.99019784f, 1.0324804f, 0.97275585f, 0.9760747f, 1.064788f, 1.00276f, 1.0372043f, 1.0011387f, 1.0117155f, 1.0224965f, 0.9982087f, 0.99353653f, 1.0061864f, 1.0793535f, 1.0322009f, 1.0163096f, 1.0613135};
static const float bn_beta_14[32] = {-0.0471098f, -0.020299504f, -0.008405963f, -0.05477565f, -0.022333728f, -0.036476407f, -0.00194196f, 0.03463617f, -0.012925061f, -0.04551779f, -0.013730809f, -0.010417456f, -0.018730879f, 0.028865276f, 0.020467035f, -0.03682603f, -0.016295195f, -0.06327474f, -0.057454143f, 0.027681695f, -0.01995454f, -0.0024487826f, -0.021104168f, -0.02563947f, -0.03653094f, -0.043042276f, -0.02202871f, -0.025299693f, -0.020798996f, -0.009789378f, -0.009135884f, -0.03166201};
static const float bn_mean_14[32] = {-0.7366319f, -0.19687442f, 0.523612f, -1.2872694f, 0.04292986f, 0.5102207f, 0.3618754f, 0.28941137f, 0.7502799f, 0.057289384f, 0.34542513f, 0.3486771f, 0.0951047f, -0.09612797f, 0.48688373f, 0.56933254f, 0.25865453f, 0.30439973f, 0.5229329f, 0.3295833f, 0.2854349f, 0.026448153f, -0.08765908f, 0.30475876f, 0.5502438f, 0.44672418f, -0.4085896f, -0.11019294f, 0.46753395f, 0.50143415f, -0.15094805f, 0.1278162};
static const float bn_var_14[32] = {1.738271f, 0.69746494f, 0.62115806f, 1.6748234f, 0.6375297f, 0.76045173f, 0.3602371f, 0.46984938f, 0.7545463f, 0.24433136f, 0.5923f, 0.5668501f, 0.63297945f, 0.522514f, 0.487214f, 0.40315595f, 0.5706326f, 0.672863f, 0.6657244f, 0.7355857f, 0.5075803f, 0.5721368f, 0.38543224f, 0.92862266f, 0.58121604f, 0.87510216f, 0.82485735f, 0.59170985f, 0.5823804f, 0.63780236f, 0.6481246f, 0.27962714};
static const float bn_epsilon_14 = 1.0e-5f;

static const float dense_weight_20[16][32] = {0.16109988f, -0.2373991f, 0.1217683f, -0.02951442f, -0.25348315f, -0.00020209355f, -0.110520944f, -0.19292745f, -0.091564424f, 0.028825128f, 0.25756338f, -0.036176857f, -0.025901023f, 0.077678695f, -0.3476414f, -0.01824972f, -0.30061415f, 0.19625795f, -0.294149f, 0.10314192f, -0.26120645f, -0.15204099f, 0.29590875f, -0.17578724f, -0.048023526f, -0.10703777f, 0.06818228f, -0.30772915f, 0.09290686f, -0.099080265f, -0.37040213f, 0.31223363f, -0.3435868f, -0.009690915f, 0.24062666f, 0.13195553f, 0.2679479f, 0.07127536f, -0.25229874f, -0.019541938f, 0.1563363f, 0.23281102f, 0.2916562f, 0.1994248f, 0.3092145f, 0.14481509f, -0.0069551915f, 0.15262893f, 0.088298015f, -0.07801413f, 0.20453447f, -0.1853366f, 0.08971482f, -0.117826834f, -0.1240507f, 0.20628394f, -0.18493776f, 0.047956098f, -0.09865069f, -0.11736088f, -0.28945205f, -0.16673471f, 0.15666051f, -0.16415924f, 0.27248648f, 0.049818117f, -0.12787533f, -0.09620818f, -0.18387975f, -0.0012276011f, 0.2340325f, 0.17651673f, -0.00088368997f, 0.30701455f, -0.16198075f, 0.29375687f, -0.25408873f, 0.06245884f, 0.081211895f, -0.18953697f, 0.24328938f, 0.30332497f, -0.13161579f, -0.25131172f, -0.10556457f, -0.011034961f, -0.018094996f, -0.23098826f, 0.22933966f, -0.08574003f, -0.17012903f, 0.005259886f, 0.32670754f, 0.17911609f, -0.1407857f, -0.030219467f, -0.1397539f, 0.26637608f, 0.0831323f, -0.2714044f, 0.1500887f, -0.2584463f, -0.25432524f, -0.119554125f, 0.105640225f, 0.23952481f, 0.061464265f, -0.3278501f, 0.25255558f, 0.2890134f, -0.07812356f, 0.047197398f, -0.19162121f, 0.10464178f, -0.35993567f, 0.12516165f, 0.23876658f, -0.2884212f, -0.15379873f, -0.24174093f, 0.22990587f, -0.24328803f, -0.24696992f, -0.21586154f, 0.06117899f, 0.16005246f, -0.024553802f, -0.07247148f, -0.35687754f, 0.11050374f, 0.0075247437f, 0.09951824f, -0.2700575f, -0.3101669f, -0.015744964f, 0.16771357f, 0.08835753f, -0.20040107f, -0.07114534f, -0.18542312f, -0.12848303f, -0.020790797f, 0.018136332f, 0.3333345f, 0.17674883f, -0.013486184f, 0.06360757f, -0.052450728f, 0.22137827f, -0.34087273f, -0.018673176f, -0.3125332f, -0.3447238f, -0.33113605f, 0.12508732f, -0.2740995f, -0.23087575f, 0.18962114f, 0.26966423f, 0.008090658f, 0.11277256f, 0.2733058f, -0.25923893f, 0.30148643f, 0.04840211f, -0.110948734f, 0.13408796f, 0.34659073f, 0.29691985f, 0.27504122f, -0.19047493f, -0.04387129f, -0.32653838f, 0.0671182f, -0.00077877066f, -0.061801568f, 0.0046638856f, 0.20593226f, -0.2820845f, -0.030669179f, -0.22539203f, 0.2598947f, 0.3950924f, -0.10547682f, -0.21953386f, -0.028169869f, -0.12085811f, -0.14004134f, -0.1400156f, 0.11410862f, 0.11651496f, 0.38276556f, -0.11810443f, -0.115631446f, -0.01669345f, -0.28333154f, -0.35797292f, -0.023019845f, 0.18866481f, -0.22093032f, -0.07096585f, -0.3008375f, 0.14999254f, -0.123193316f, 0.043191254f, -0.31510288f, -0.3579907f, -0.18614346f, -0.27397177f, 0.1331199f, -0.3522773f, -0.11853428f, 0.0069366796f, 0.13839002f, -0.24617273f, -0.18032745f, -0.15875487f, -0.21235925f, 0.18593808f, 0.1563894f, -0.3078746f, 0.29398215f, -0.043374836f, 0.255703f, 0.16619712f, -0.12717356f, 0.32259008f, -0.22615759f, 0.36382094f, -0.0492351f, 0.038449578f, 0.28749946f, 0.041713964f, -0.17890169f, 0.29141814f, -0.12370582f, 0.10912168f, 0.3709036f, 0.05891729f, -0.15551041f, -0.024015127f, -0.09288898f, -0.11407798f, -0.011665043f, 0.15445216f, 0.06192446f, 0.16242611f, 0.08661549f, -0.29440823f, -0.16891374f, 0.20139429f, -0.14002669f, -0.111811f, 0.028665246f, 0.015459098f, 0.29008088f, 0.22901697f, 0.09484562f, 0.06822012f, -0.030012628f, 0.23512776f, -0.095714755f, -0.2900909f, -0.28888297f, 0.015557656f, 0.29269397f, 0.4225599f, -0.04524602f, -0.0029534078f, 0.33017573f, -0.096742265f, 0.29316428f, 0.14635402f, -0.14497575f, -0.09064538f, -0.15672362f, 0.12945682f, 0.21636029f, -0.013301143f, 0.16654465f, -0.107857116f, 0.09641305f, -0.30932495f, 0.07568194f, -0.23041546f, 0.083106175f, 0.2392423f, -0.21856074f, 0.28166977f, -0.051612794f, -0.21060604f, -0.054655813f, 0.2274121f, -0.0600764f, -0.3384488f, 0.20600505f, 0.2029925f, 0.21863262f, 0.1678565f, -0.17444345f, -0.30686417f, -0.27991045f, -0.16044016f, 0.22284679f, -0.18534486f, 0.30141106f, 0.29558152f, -0.057825666f, -0.2297967f, -0.40179577f, 0.2596386f, 0.052589003f, 0.12579207f, 0.2935832f, -0.061553665f, 0.19943798f, 0.32919404f, 0.25254795f, -0.045953322f, -0.3312127f, -0.17569786f, 0.27098247f, -0.24861908f, -0.23431833f, 0.10086403f, 0.025109913f, 0.10910459f, 0.10927979f, 0.2880737f, 0.07142238f, -0.18507354f, -0.28846803f, 0.30000526f, 0.38561797f, 0.14380862f, 0.19814283f, 0.10314505f, -0.16441566f, 0.02620362f, -0.23230593f, 0.21757361f, -0.118790776f, 0.16624387f, 0.3941641f, 0.06405486f, -0.3455727f, 0.2650505f, 0.21347706f, -0.18652731f, -0.23650202f, 0.0022709637f, 0.069618285f, -0.21206082f, -0.08961218f, -0.059418354f, -0.10677188f, 0.10597327f, -0.18328267f, 0.30539095f, 0.072703354f, 0.39725706f, -0.056211866f, -0.1951449f, 0.26450908f, -0.20051855f, -0.076631755f, 0.18648161f, 0.15196964f, -0.24869257f, -0.17876397f, -0.1753892f, 0.37167045f, 0.047364246f, -0.16911715f, 0.09235566f, -0.1956549f, 0.27124488f, 0.41579625f, -0.12802345f, -0.031545218f, 0.4179148f, -0.2517093f, -0.13827059f, -0.25993127f, -0.21162015f, -0.009690995f, -0.29216522f, -0.043685477f, 0.1018316f, -0.22566687f, 0.103697695f, 0.2791512f, 0.410304f, 0.17149231f, -0.14034398f, -0.07589943f, -0.13970843f, 0.08702434f, -0.14045645f, -0.28299117f, -0.16277066f, 0.36881313f, 0.25873134f, -0.09835898f, -0.28650275f, -0.18479683f, -0.29278278f, -0.009717744f, -0.033532146f, 0.11087059f, -0.2740209f, -0.2419268f, 0.097458854f, -0.19898537f, 0.24828002f, -0.040894073f, -0.1458045f, -0.08926367f, 0.24650016f, 0.10435977f, 0.10337486f, 0.122954935f, -0.08509374f, -0.32886368f, 0.29444098f, 0.11959655f, -0.2025788f, -0.2548167f, -0.3470019f, 0.022327472f, 0.055613957f, -0.17495538f, -0.30035022f, -0.24004187f, -0.325276f, -0.010491951f, 0.2743823f, 0.31717622f, 0.19330469f, 0.10674557f, -0.021753842f, 0.16606772f, 0.0013272896f, -0.3366355f, -0.3493264f, -0.21319123f, -0.08194182f, 0.19533648f, -0.1885086f, -0.017024467f, -0.16963683f, 0.1664131f, -0.21903832f, 0.105386965f, 0.11641766f, 0.23184305f, 0.11715482f, -0.15824305f, 0.2626595f, 0.34418672f, -0.09908385f, 0.14408511f, -0.21405686f, 0.18107696f, -0.22482774f, -0.10641612f, 0.3345295f, 0.11879974f, 0.3224928f, -0.2282257f, 0.3928494f, -0.2565941f, 0.09082311f, 0.32383212f, 0.015686095f, 0.028123694f, -0.059095502f, 0.1738644f, 0.010870926f, 0.3258825f, 0.3318772f, 0.05984188f, -0.11824927f, 0.25166887f, -0.30654404f, 0.31232488f, -0.047939207f, -0.15186775f, -0.35163796f, -0.14002277f, 0.2566637f, -0.2837982f, 0.31819114f, 0.2516665f, 0.18021917f, -0.092518345f, 0.27289554f, 0.25223622f, -0.19618551f, -0.27024522f, -0.02382794f, 0.29647532f, 0.12840103f, -0.25064135f, 0.06563795f, 0.24887846f, -0.049036685f, 0.3647784f, -0.14410335f, -0.3450019f, 0.03652655f, 0.018491028};
static const float dense_bias_20[16] = {-0.040367942f, -0.013407245f, 0.07973389f, -0.013689038f, -0.020774312f, 0.07533835f, -0.024551049f, 0.082157664f, 0.037945513f, -0.044755224f, 0.008997095f, 0.08639919f, 0.028870115f, -0.05502578f, 0.03460033f, -0.008230461};

static const float dense_weight_22[3][16] = {0.51020753f, 0.55349916f, -0.049355466f, 0.053037405f, -0.1797136f, -0.39531076f, 0.14960219f, 0.20782551f, -0.021829039f, -0.12166548f, 0.24777623f, -0.66602015f, 0.28577563f, 0.005845588f, -0.4212268f, 0.46160778f, 0.047018427f, -0.44757316f, 0.22899078f, -0.34011096f, -0.52380484f, 0.15326568f, -0.21372889f, 0.40697756f, -0.44121504f, -0.44167534f, 0.00053473946f, 0.03994124f, -0.45101336f, -0.43224716f, 0.473408f, -0.45670527f, -0.086724f, -0.12890399f, 0.37285057f, -0.28706253f, 0.4446035f, -0.13623033f, 0.23755132f, -0.44146776f, -0.54994565f, 0.4999899f, -0.21519753f, 0.56769145f, 0.42023405f, 0.007990325f, 0.18044694f, -0.46905872};
static const float dense_bias_22[3] = {-0.060926273f, 0.080041386f, -0.04634488};

void neural_net(float* output, const float* input) {
    // Input dimensions: 64 x 64 x 3
    // Input is flat array in HWC format: index = (h * width + w) * channels + c


    // Conv Layer 1: 3->8, kernel=3 x 3
    float conv_out_1[8][64][64];
    for(int oc = 0; oc < 8; oc++) {
        for(int oh = 0; oh < 64; oh++) {
            for(int ow = 0; ow < 64; ow++) {
                float sum = conv_bias_1[oc];
                for(int ic = 0; ic < 3; ic++) {
                    for(int kh = 0; kh < 3; kh++) {
                        for(int kw = 0; kw < 3; kw++) {
                            int ih = oh * 1 - 1 + kh * 1;
                            int iw = ow * 1 - 1 + kw * 1;
                            if(ih >= 0 && ih < 64 && iw >= 0 && iw < 64) {
                                float input_val = input[(ih * 64 + iw) * 3 + ic];
                                sum += conv_weight_1[oc][kh][kw][ic] * input_val;
                            }
                        }
                    }
                }
                conv_out_1[oc][oh][ow] = sum;
            }
        }
    }
    #ifdef DEBUG
    printf("conv_weight_1[0][0][0][0] = %.6f\n", conv_weight_1[0][0][0][0]);
    printf("conv_weight_1[0][0][0][1] = %.6f\n", conv_weight_1[0][0][0][1]);
    printf("conv_weight_1[0][0][1][0] = %.6f\n", conv_weight_1[0][0][1][0]);
    printf("After Conv 1: output[0][0][0]=%.4f\n", conv_out_1[0][0][0]);
    #endif

    // BatchNorm Layer 2
    for(int c = 0; c < 8; c++) {
        float gamma = bn_gamma_2[c];
        float beta = bn_beta_2[c];
        float mean = bn_mean_2[c];
        float var = bn_var_2[c];
        float inv_std = 1.0f / sqrt(var + bn_epsilon_2);
        for(int h = 0; h < 64; h++) {
            for(int w = 0; w < 64; w++) {
                float val = conv_out_1[c][h][w];
                float normalized = (val - mean) * inv_std;
                conv_out_1[c][h][w] = gamma * normalized + beta;
            }
        }
    }

    // ReLU activation
    for(int c = 0; c < 8; c++) {
        for(int h = 0; h < 64; h++) {
            for(int w = 0; w < 64; w++) {
                conv_out_1[c][h][w] = relu(conv_out_1[c][h][w]);
            }
        }
    }

    // MaxPool Layer 4: kernel=2 x 2
    float pool_out_4[8][32][32];
    for(int c = 0; c < 8; c++) {
        for(int oh = 0; oh < 32; oh++) {
            for(int ow = 0; ow < 32; ow++) {
                float max_val = -1e38f;
                for(int kh = 0; kh < 2; kh++) {
                    for(int kw = 0; kw < 2; kw++) {
                        int ih = oh * 2 + kh - 0;
                        int iw = ow * 2 + kw - 0;
                        if(ih >= 0 && ih < 64 && iw >= 0 && iw < 64) {
                            float val = conv_out_1[c][ih][iw];
                            if(val > max_val) max_val = val;
                        }
                    }
                }
                pool_out_4[c][oh][ow] = max_val;
            }
        }
    }

    // Conv Layer 5: 8->16, kernel=3 x 3
    float conv_out_5[16][32][32];
    for(int oc = 0; oc < 16; oc++) {
        for(int oh = 0; oh < 32; oh++) {
            for(int ow = 0; ow < 32; ow++) {
                float sum = conv_bias_5[oc];
                for(int ic = 0; ic < 8; ic++) {
                    for(int kh = 0; kh < 3; kh++) {
                        for(int kw = 0; kw < 3; kw++) {
                            int ih = oh * 1 - 1 + kh * 1;
                            int iw = ow * 1 - 1 + kw * 1;
                            if(ih >= 0 && ih < 32 && iw >= 0 && iw < 32) {
                                float input_val = pool_out_4[ic][ih][iw];
                                sum += conv_weight_5[oc][kh][kw][ic] * input_val;
                            }
                        }
                    }
                }
                conv_out_5[oc][oh][ow] = sum;
            }
        }
    }
    #ifdef DEBUG
    printf("After Conv 5: output[0][0][0]=%.4f\n", conv_out_5[0][0][0]);
    #endif

    // BatchNorm Layer 6
    for(int c = 0; c < 16; c++) {
        float gamma = bn_gamma_6[c];
        float beta = bn_beta_6[c];
        float mean = bn_mean_6[c];
        float var = bn_var_6[c];
        float inv_std = 1.0f / sqrt(var + bn_epsilon_6);
        for(int h = 0; h < 32; h++) {
            for(int w = 0; w < 32; w++) {
                float val = conv_out_5[c][h][w];
                float normalized = (val - mean) * inv_std;
                conv_out_5[c][h][w] = gamma * normalized + beta;
            }
        }
    }

    // ReLU activation
    for(int c = 0; c < 16; c++) {
        for(int h = 0; h < 32; h++) {
            for(int w = 0; w < 32; w++) {
                conv_out_5[c][h][w] = relu(conv_out_5[c][h][w]);
            }
        }
    }

    // MaxPool Layer 8: kernel=2 x 2
    float pool_out_8[16][16][16];
    for(int c = 0; c < 16; c++) {
        for(int oh = 0; oh < 16; oh++) {
            for(int ow = 0; ow < 16; ow++) {
                float max_val = -1e38f;
                for(int kh = 0; kh < 2; kh++) {
                    for(int kw = 0; kw < 2; kw++) {
                        int ih = oh * 2 + kh - 0;
                        int iw = ow * 2 + kw - 0;
                        if(ih >= 0 && ih < 32 && iw >= 0 && iw < 32) {
                            float val = conv_out_5[c][ih][iw];
                            if(val > max_val) max_val = val;
                        }
                    }
                }
                pool_out_8[c][oh][ow] = max_val;
            }
        }
    }

    // Conv Layer 9: 16->32, kernel=3 x 3
    float conv_out_9[32][16][16];
    for(int oc = 0; oc < 32; oc++) {
        for(int oh = 0; oh < 16; oh++) {
            for(int ow = 0; ow < 16; ow++) {
                float sum = conv_bias_9[oc];
                for(int ic = 0; ic < 16; ic++) {
                    for(int kh = 0; kh < 3; kh++) {
                        for(int kw = 0; kw < 3; kw++) {
                            int ih = oh * 1 - 1 + kh * 1;
                            int iw = ow * 1 - 1 + kw * 1;
                            if(ih >= 0 && ih < 16 && iw >= 0 && iw < 16) {
                                float input_val = pool_out_8[ic][ih][iw];
                                sum += conv_weight_9[oc][kh][kw][ic] * input_val;
                            }
                        }
                    }
                }
                conv_out_9[oc][oh][ow] = sum;
            }
        }
    }
    #ifdef DEBUG
    printf("After Conv 9: output[0][0][0]=%.4f\n", conv_out_9[0][0][0]);
    #endif

    // BatchNorm Layer 10
    for(int c = 0; c < 32; c++) {
        float gamma = bn_gamma_10[c];
        float beta = bn_beta_10[c];
        float mean = bn_mean_10[c];
        float var = bn_var_10[c];
        float inv_std = 1.0f / sqrt(var + bn_epsilon_10);
        for(int h = 0; h < 16; h++) {
            for(int w = 0; w < 16; w++) {
                float val = conv_out_9[c][h][w];
                float normalized = (val - mean) * inv_std;
                conv_out_9[c][h][w] = gamma * normalized + beta;
            }
        }
    }

    // ReLU activation
    for(int c = 0; c < 32; c++) {
        for(int h = 0; h < 16; h++) {
            for(int w = 0; w < 16; w++) {
                conv_out_9[c][h][w] = relu(conv_out_9[c][h][w]);
            }
        }
    }

    // MaxPool Layer 12: kernel=2 x 2
    float pool_out_12[32][8][8];
    for(int c = 0; c < 32; c++) {
        for(int oh = 0; oh < 8; oh++) {
            for(int ow = 0; ow < 8; ow++) {
                float max_val = -1e38f;
                for(int kh = 0; kh < 2; kh++) {
                    for(int kw = 0; kw < 2; kw++) {
                        int ih = oh * 2 + kh - 0;
                        int iw = ow * 2 + kw - 0;
                        if(ih >= 0 && ih < 16 && iw >= 0 && iw < 16) {
                            float val = conv_out_9[c][ih][iw];
                            if(val > max_val) max_val = val;
                        }
                    }
                }
                pool_out_12[c][oh][ow] = max_val;
            }
        }
    }

    // Conv Layer 13: 32->32, kernel=3 x 3
    float conv_out_13[32][8][8];
    for(int oc = 0; oc < 32; oc++) {
        for(int oh = 0; oh < 8; oh++) {
            for(int ow = 0; ow < 8; ow++) {
                float sum = conv_bias_13[oc];
                for(int ic = 0; ic < 32; ic++) {
                    for(int kh = 0; kh < 3; kh++) {
                        for(int kw = 0; kw < 3; kw++) {
                            int ih = oh * 1 - 1 + kh * 1;
                            int iw = ow * 1 - 1 + kw * 1;
                            if(ih >= 0 && ih < 8 && iw >= 0 && iw < 8) {
                                float input_val = pool_out_12[ic][ih][iw];
                                sum += conv_weight_13[oc][kh][kw][ic] * input_val;
                            }
                        }
                    }
                }
                conv_out_13[oc][oh][ow] = sum;
            }
        }
    }
    #ifdef DEBUG
    printf("After Conv 13: output[0][0][0]=%.4f\n", conv_out_13[0][0][0]);
    #endif

    // BatchNorm Layer 14
    for(int c = 0; c < 32; c++) {
        float gamma = bn_gamma_14[c];
        float beta = bn_beta_14[c];
        float mean = bn_mean_14[c];
        float var = bn_var_14[c];
        float inv_std = 1.0f / sqrt(var + bn_epsilon_14);
        for(int h = 0; h < 8; h++) {
            for(int w = 0; w < 8; w++) {
                float val = conv_out_13[c][h][w];
                float normalized = (val - mean) * inv_std;
                conv_out_13[c][h][w] = gamma * normalized + beta;
            }
        }
    }

    // ReLU activation
    for(int c = 0; c < 32; c++) {
        for(int h = 0; h < 8; h++) {
            for(int w = 0; w < 8; w++) {
                conv_out_13[c][h][w] = relu(conv_out_13[c][h][w]);
            }
        }
    }

    // MaxPool Layer 16: kernel=2 x 2
    float pool_out_16[32][4][4];
    for(int c = 0; c < 32; c++) {
        for(int oh = 0; oh < 4; oh++) {
            for(int ow = 0; ow < 4; ow++) {
                float max_val = -1e38f;
                for(int kh = 0; kh < 2; kh++) {
                    for(int kw = 0; kw < 2; kw++) {
                        int ih = oh * 2 + kh - 0;
                        int iw = ow * 2 + kw - 0;
                        if(ih >= 0 && ih < 8 && iw >= 0 && iw < 8) {
                            float val = conv_out_13[c][ih][iw];
                            if(val > max_val) max_val = val;
                        }
                    }
                }
                pool_out_16[c][oh][ow] = max_val;
            }
        }
    }

    // AdaptiveMeanPool: 1x1
    float adaptpool_out_17[32][1][1];
    for(int c = 0; c < 32; c++) {
        for(int oh = 0; oh < 1; oh++) {
            for(int ow = 0; ow < 1; ow++) {
                int h_start = oh * 4 / 1;
                int h_end = (oh+1) * 4 / 1;
                int w_start = ow * 4 / 1;
                int w_end = (ow+1) * 4 / 1;
                float sum = 0.0f;
                int count = 0;
                for(int h = h_start; h < h_end; h++) {
                    for(int w = w_start; w < w_end; w++) {
                        sum += pool_out_16[c][h][w];
                        count++;
                    }
                }
                adaptpool_out_17[c][oh][ow] = sum / count;
            }
        }
    }

    // Flatten
    float flatten_out_18[32];
    int idx = 0;
    for(int c = 0; c < 32; c++) {
        for(int h = 0; h < 1; h++) {
            for(int w = 0; w < 1; w++) {
                flatten_out_18[idx++] = adaptpool_out_17[c][h][w];
            }
        }
    }
    // Dropout (skipped during inference)

    // Dense Layer 20: 32 -> 16
    float dense_out_20[16];
    for(int j = 0; j < 16; j++) {
        dense_out_20[j] = dense_bias_20[j];
        for(int k = 0; k < 32; k++) {
            dense_out_20[j] += dense_weight_20[j][k] * flatten_out_18[k];
        }
    }
    #ifdef DEBUG
    printf("After Dense 20: outputs: ");
    for(int j = 0; j < 16; j++) printf("%.4f ", dense_out_20[j]);
    printf("\n");
    #endif
    // Dropout (skipped during inference)

    // Dense Layer 22: 16 -> 3
    float dense_out_22[3];
    for(int j = 0; j < 3; j++) {
        dense_out_22[j] = dense_bias_22[j];
        for(int k = 0; k < 16; k++) {
            dense_out_22[j] += dense_weight_22[j][k] * dense_out_20[k];
        }
    }
    #ifdef DEBUG
    printf("After Dense 22: outputs: ");
    for(int j = 0; j < 3; j++) printf("%.4f ", dense_out_22[j]);
    printf("\n");
    #endif

    // Copy output
    for(int i = 0; i < 3; i++) {
        output[i] = dense_out_22[i];
    }
}