models/fp16-mobilenet-v3-large.cc

// Copyright 2020 Google LLC
//
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree.

#include <xnnpack.h>

#include <algorithm>
#include <functional>
#include <iostream>
#include <limits>
#include <random>

#include "models/models.h"

namespace models {

ExecutionPlan FP16MobileNetV3Large(pthreadpool_t threadpool) {
  alignas(16) static uint16_t v0[150528];
  alignas(16) static uint16_t v1[200704];
  alignas(16) static uint16_t v2[200704];
  alignas(16) static uint16_t v3[200704];
  alignas(16) static uint16_t v4[200704];
  alignas(16) static uint16_t v5[200704];
  alignas(16) static uint16_t v6[802816];
  alignas(16) static uint16_t v7[200704];
  alignas(16) static uint16_t v8[75264];
  alignas(16) static uint16_t v9[225792];
  alignas(16) static uint16_t v10[225792];
  alignas(16) static uint16_t v11[75264];
  alignas(16) static uint16_t v12[75264];
  alignas(16) static uint16_t v13[225792];
  alignas(16) static uint16_t v14[56448];
  alignas(16) static uint16_t v15[72];
  alignas(16) static uint16_t v16[24];
  alignas(16) static uint16_t v17[72];
  alignas(16) static uint16_t v18[56448];
  alignas(16) static uint16_t v19[31360];
  alignas(16) static uint16_t v20[94080];
  alignas(16) static uint16_t v21[94080];
  alignas(16) static uint16_t v22[120];
  alignas(16) static uint16_t v23[32];
  alignas(16) static uint16_t v24[120];
  alignas(16) static uint16_t v25[94080];
  alignas(16) static uint16_t v26[31360];
  alignas(16) static uint16_t v27[31360];
  alignas(16) static uint16_t v28[94080];
  alignas(16) static uint16_t v29[94080];
  alignas(16) static uint16_t v30[120];
  alignas(16) static uint16_t v31[32];
  alignas(16) static uint16_t v32[120];
  alignas(16) static uint16_t v33[94080];
  alignas(16) static uint16_t v34[31360];
  alignas(16) static uint16_t v35[31360];
  alignas(16) static uint16_t v36[188160];
  alignas(16) static uint16_t v37[188160];
  alignas(16) static uint16_t v38[47040];
  alignas(16) static uint16_t v39[47040];
  alignas(16) static uint16_t v40[15680];
  alignas(16) static uint16_t v41[39200];
  alignas(16) static uint16_t v42[39200];
  alignas(16) static uint16_t v43[39200];
  alignas(16) static uint16_t v44[39200];
  alignas(16) static uint16_t v45[15680];
  alignas(16) static uint16_t v46[15680];
  alignas(16) static uint16_t v47[36064];
  alignas(16) static uint16_t v48[36064];
  alignas(16) static uint16_t v49[36064];
  alignas(16) static uint16_t v50[36064];
  alignas(16) static uint16_t v51[15680];
  alignas(16) static uint16_t v52[15680];
  alignas(16) static uint16_t v53[36064];
  alignas(16) static uint16_t v54[36064];
  alignas(16) static uint16_t v55[36064];
  alignas(16) static uint16_t v56[36064];
  alignas(16) static uint16_t v57[15680];
  alignas(16) static uint16_t v58[15680];
  alignas(16) static uint16_t v59[94080];
  alignas(16) static uint16_t v60[94080];
  alignas(16) static uint16_t v61[94080];
  alignas(16) static uint16_t v62[94080];
  alignas(16) static uint16_t v63[480];
  alignas(16) static uint16_t v64[120];
  alignas(16) static uint16_t v65[480];
  alignas(16) static uint16_t v66[94080];
  alignas(16) static uint16_t v67[21952];
  alignas(16) static uint16_t v68[131712];
  alignas(16) static uint16_t v69[131712];
  alignas(16) static uint16_t v70[131712];
  alignas(16) static uint16_t v71[131712];
  alignas(16) static uint16_t v72[672];
  alignas(16) static uint16_t v73[168];
  alignas(16) static uint16_t v74[672];
  alignas(16) static uint16_t v75[131712];
  alignas(16) static uint16_t v76[21952];
  alignas(16) static uint16_t v77[21952];
  alignas(16) static uint16_t v78[131712];
  alignas(16) static uint16_t v79[131712];
  alignas(16) static uint16_t v80[32928];
  alignas(16) static uint16_t v81[32928];
  alignas(16) static uint16_t v82[672];
  alignas(16) static uint16_t v83[168];
  alignas(16) static uint16_t v84[672];
  alignas(16) static uint16_t v85[32928];
  alignas(16) static uint16_t v86[7840];
  alignas(16) static uint16_t v87[47040];
  alignas(16) static uint16_t v88[47040];
  alignas(16) static uint16_t v89[47040];
  alignas(16) static uint16_t v90[47040];
  alignas(16) static uint16_t v91[960];
  alignas(16) static uint16_t v92[240];
  alignas(16) static uint16_t v93[960];
  alignas(16) static uint16_t v94[47040];
  alignas(16) static uint16_t v95[7840];
  alignas(16) static uint16_t v96[7840];
  alignas(16) static uint16_t v97[47040];
  alignas(16) static uint16_t v98[47040];
  alignas(16) static uint16_t v99[47040];
  alignas(16) static uint16_t v100[47040];
  alignas(16) static uint16_t v101[960];
  alignas(16) static uint16_t v102[240];
  alignas(16) static uint16_t v103[960];
  alignas(16) static uint16_t v104[47040];
  alignas(16) static uint16_t v105[7840];
  alignas(16) static uint16_t v106[7840];
  alignas(16) static uint16_t v107[47040];
  alignas(16) static uint16_t v108[47040];
  alignas(16) static uint16_t v109[960];
  alignas(16) static uint16_t v110[1280];
  alignas(16) static uint16_t v111[1280];
  alignas(16) static uint16_t v112[1280];
  alignas(16) static uint16_t v113[1001];
  alignas(16) static uint16_t w114[432];
  alignas(16) static uint16_t w115[16];
  alignas(16) static uint16_t w116[144];
  alignas(16) static uint16_t w117[16];
  alignas(16) static uint16_t w118[256];
  alignas(16) static uint16_t w119[16];
  alignas(16) static uint16_t w120[1024];
  alignas(16) static uint16_t w121[64];
  alignas(16) static uint16_t w122[576];
  alignas(16) static uint16_t w123[64];
  alignas(16) static uint16_t w124[1536];
  alignas(16) static uint16_t w125[24];
  alignas(16) static uint16_t w126[1728];
  alignas(16) static uint16_t w127[72];
  alignas(16) static uint16_t w128[648];
  alignas(16) static uint16_t w129[72];
  alignas(16) static uint16_t w130[1728];
  alignas(16) static uint16_t w131[24];
  alignas(16) static uint16_t w132[1728];
  alignas(16) static uint16_t w133[72];
  alignas(16) static uint16_t w134[1800];
  alignas(16) static uint16_t w135[72];
  alignas(16) static uint16_t w136[1728];
  alignas(16) static uint16_t w137[24];
  alignas(16) static uint16_t w138[1728];
  alignas(16) static uint16_t w139[72];
  alignas(16) static uint16_t w140[2880];
  alignas(16) static uint16_t w141[40];
  alignas(16) static uint16_t w142[4800];
  alignas(16) static uint16_t w143[120];
  alignas(16) static uint16_t w144[3000];
  alignas(16) static uint16_t w145[120];
  alignas(16) static uint16_t w146[3840];
  alignas(16) static uint16_t w147[32];
  alignas(16) static uint16_t w148[3840];
  alignas(16) static uint16_t w149[120];
  alignas(16) static uint16_t w150[4800];
  alignas(16) static uint16_t w151[40];
  alignas(16) static uint16_t w152[4800];
  alignas(16) static uint16_t w153[120];
  alignas(16) static uint16_t w154[3000];
  alignas(16) static uint16_t w155[120];
  alignas(16) static uint16_t w156[3840];
  alignas(16) static uint16_t w157[32];
  alignas(16) static uint16_t w158[3840];
  alignas(16) static uint16_t w159[120];
  alignas(16) static uint16_t w160[4800];
  alignas(16) static uint16_t w161[40];
  alignas(16) static uint16_t w162[9600];
  alignas(16) static uint16_t w163[240];
  alignas(16) static uint16_t w164[2160];
  alignas(16) static uint16_t w165[240];
  alignas(16) static uint16_t w166[19200];
  alignas(16) static uint16_t w167[80];
  alignas(16) static uint16_t w168[16000];
  alignas(16) static uint16_t w169[200];
  alignas(16) static uint16_t w170[1800];
  alignas(16) static uint16_t w171[200];
  alignas(16) static uint16_t w172[16000];
  alignas(16) static uint16_t w173[80];
  alignas(16) static uint16_t w174[14720];
  alignas(16) static uint16_t w175[184];
  alignas(16) static uint16_t w176[1656];
  alignas(16) static uint16_t w177[184];
  alignas(16) static uint16_t w178[14720];
  alignas(16) static uint16_t w179[80];
  alignas(16) static uint16_t w180[14720];
  alignas(16) static uint16_t w181[184];
  alignas(16) static uint16_t w182[1656];
  alignas(16) static uint16_t w183[184];
  alignas(16) static uint16_t w184[14720];
  alignas(16) static uint16_t w185[80];
  alignas(16) static uint16_t w186[38400];
  alignas(16) static uint16_t w187[480];
  alignas(16) static uint16_t w188[4320];
  alignas(16) static uint16_t w189[480];
  alignas(16) static uint16_t w190[57600];
  alignas(16) static uint16_t w191[120];
  alignas(16) static uint16_t w192[57600];
  alignas(16) static uint16_t w193[480];
  alignas(16) static uint16_t w194[53760];
  alignas(16) static uint16_t w195[112];
  alignas(16) static uint16_t w196[75264];
  alignas(16) static uint16_t w197[672];
  alignas(16) static uint16_t w198[6048];
  alignas(16) static uint16_t w199[672];
  alignas(16) static uint16_t w200[112896];
  alignas(16) static uint16_t w201[168];
  alignas(16) static uint16_t w202[112896];
  alignas(16) static uint16_t w203[672];
  alignas(16) static uint16_t w204[75264];
  alignas(16) static uint16_t w205[112];
  alignas(16) static uint16_t w206[75264];
  alignas(16) static uint16_t w207[672];
  alignas(16) static uint16_t w208[16800];
  alignas(16) static uint16_t w209[672];
  alignas(16) static uint16_t w210[112896];
  alignas(16) static uint16_t w211[168];
  alignas(16) static uint16_t w212[112896];
  alignas(16) static uint16_t w213[672];
  alignas(16) static uint16_t w214[107520];
  alignas(16) static uint16_t w215[160];
  alignas(16) static uint16_t w216[153600];
  alignas(16) static uint16_t w217[960];
  alignas(16) static uint16_t w218[24000];
  alignas(16) static uint16_t w219[960];
  alignas(16) static uint16_t w220[230400];
  alignas(16) static uint16_t w221[240];
  alignas(16) static uint16_t w222[230400];
  alignas(16) static uint16_t w223[960];
  alignas(16) static uint16_t w224[153600];
  alignas(16) static uint16_t w225[160];
  alignas(16) static uint16_t w226[153600];
  alignas(16) static uint16_t w227[960];
  alignas(16) static uint16_t w228[24000];
  alignas(16) static uint16_t w229[960];
  alignas(16) static uint16_t w230[230400];
  alignas(16) static uint16_t w231[240];
  alignas(16) static uint16_t w232[230400];
  alignas(16) static uint16_t w233[960];
  alignas(16) static uint16_t w234[153600];
  alignas(16) static uint16_t w235[160];
  alignas(16) static uint16_t w236[153600];
  alignas(16) static uint16_t w237[960];
  alignas(16) static uint16_t w238[1228800];
  alignas(16) static uint16_t w239[1280];
  alignas(16) static uint16_t w240[1281280];
  alignas(16) static uint16_t w241[1001];

  std::random_device random_device;
  auto rng = std::mt19937(random_device());
  auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, +1.0f), std::ref(rng));
  auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);
  std::generate(v0, v0 + 150528, std::ref(f16rng));
  std::generate(v1, v1 + 200704, std::ref(f16rng));
  std::generate(v2, v2 + 200704, std::ref(f16rng));
  std::generate(v3, v3 + 200704, std::ref(f16rng));
  std::generate(v4, v4 + 200704, std::ref(f16rng));
  std::generate(v5, v5 + 200704, std::ref(f16rng));
  std::generate(v6, v6 + 802816, std::ref(f16rng));
  std::generate(v7, v7 + 200704, std::ref(f16rng));
  std::generate(v8, v8 + 75264, std::ref(f16rng));
  std::generate(v9, v9 + 225792, std::ref(f16rng));
  std::generate(v10, v10 + 225792, std::ref(f16rng));
  std::generate(v11, v11 + 75264, std::ref(f16rng));
  std::generate(v12, v12 + 75264, std::ref(f16rng));
  std::generate(v13, v13 + 225792, std::ref(f16rng));
  std::generate(v14, v14 + 56448, std::ref(f16rng));
  std::generate(v15, v15 + 72, std::ref(f16rng));
  std::generate(v16, v16 + 24, std::ref(f16rng));
  std::generate(v17, v17 + 72, std::ref(f16rng));
  std::generate(v18, v18 + 56448, std::ref(f16rng));
  std::generate(v19, v19 + 31360, std::ref(f16rng));
  std::generate(v20, v20 + 94080, std::ref(f16rng));
  std::generate(v21, v21 + 94080, std::ref(f16rng));
  std::generate(v22, v22 + 120, std::ref(f16rng));
  std::generate(v23, v23 + 32, std::ref(f16rng));
  std::generate(v24, v24 + 120, std::ref(f16rng));
  std::generate(v25, v25 + 94080, std::ref(f16rng));
  std::generate(v26, v26 + 31360, std::ref(f16rng));
  std::generate(v27, v27 + 31360, std::ref(f16rng));
  std::generate(v28, v28 + 94080, std::ref(f16rng));
  std::generate(v29, v29 + 94080, std::ref(f16rng));
  std::generate(v30, v30 + 120, std::ref(f16rng));
  std::generate(v31, v31 + 32, std::ref(f16rng));
  std::generate(v32, v32 + 120, std::ref(f16rng));
  std::generate(v33, v33 + 94080, std::ref(f16rng));
  std::generate(v34, v34 + 31360, std::ref(f16rng));
  std::generate(v35, v35 + 31360, std::ref(f16rng));
  std::generate(v36, v36 + 188160, std::ref(f16rng));
  std::generate(v37, v37 + 188160, std::ref(f16rng));
  std::generate(v38, v38 + 47040, std::ref(f16rng));
  std::generate(v39, v39 + 47040, std::ref(f16rng));
  std::generate(v40, v40 + 15680, std::ref(f16rng));
  std::generate(v41, v41 + 39200, std::ref(f16rng));
  std::generate(v42, v42 + 39200, std::ref(f16rng));
  std::generate(v43, v43 + 39200, std::ref(f16rng));
  std::generate(v44, v44 + 39200, std::ref(f16rng));
  std::generate(v45, v45 + 15680, std::ref(f16rng));
  std::generate(v46, v46 + 15680, std::ref(f16rng));
  std::generate(v47, v47 + 36064, std::ref(f16rng));
  std::generate(v48, v48 + 36064, std::ref(f16rng));
  std::generate(v49, v49 + 36064, std::ref(f16rng));
  std::generate(v50, v50 + 36064, std::ref(f16rng));
  std::generate(v51, v51 + 15680, std::ref(f16rng));
  std::generate(v52, v52 + 15680, std::ref(f16rng));
  std::generate(v53, v53 + 36064, std::ref(f16rng));
  std::generate(v54, v54 + 36064, std::ref(f16rng));
  std::generate(v55, v55 + 36064, std::ref(f16rng));
  std::generate(v56, v56 + 36064, std::ref(f16rng));
  std::generate(v57, v57 + 15680, std::ref(f16rng));
  std::generate(v58, v58 + 15680, std::ref(f16rng));
  std::generate(v59, v59 + 94080, std::ref(f16rng));
  std::generate(v60, v60 + 94080, std::ref(f16rng));
  std::generate(v61, v61 + 94080, std::ref(f16rng));
  std::generate(v62, v62 + 94080, std::ref(f16rng));
  std::generate(v63, v63 + 480, std::ref(f16rng));
  std::generate(v64, v64 + 120, std::ref(f16rng));
  std::generate(v65, v65 + 480, std::ref(f16rng));
  std::generate(v66, v66 + 94080, std::ref(f16rng));
  std::generate(v67, v67 + 21952, std::ref(f16rng));
  std::generate(v68, v68 + 131712, std::ref(f16rng));
  std::generate(v69, v69 + 131712, std::ref(f16rng));
  std::generate(v70, v70 + 131712, std::ref(f16rng));
  std::generate(v71, v71 + 131712, std::ref(f16rng));
  std::generate(v72, v72 + 672, std::ref(f16rng));
  std::generate(v73, v73 + 168, std::ref(f16rng));
  std::generate(v74, v74 + 672, std::ref(f16rng));
  std::generate(v75, v75 + 131712, std::ref(f16rng));
  std::generate(v76, v76 + 21952, std::ref(f16rng));
  std::generate(v77, v77 + 21952, std::ref(f16rng));
  std::generate(v78, v78 + 131712, std::ref(f16rng));
  std::generate(v79, v79 + 131712, std::ref(f16rng));
  std::generate(v80, v80 + 32928, std::ref(f16rng));
  std::generate(v81, v81 + 32928, std::ref(f16rng));
  std::generate(v82, v82 + 672, std::ref(f16rng));
  std::generate(v83, v83 + 168, std::ref(f16rng));
  std::generate(v84, v84 + 672, std::ref(f16rng));
  std::generate(v85, v85 + 32928, std::ref(f16rng));
  std::generate(v86, v86 + 7840, std::ref(f16rng));
  std::generate(v87, v87 + 47040, std::ref(f16rng));
  std::generate(v88, v88 + 47040, std::ref(f16rng));
  std::generate(v89, v89 + 47040, std::ref(f16rng));
  std::generate(v90, v90 + 47040, std::ref(f16rng));
  std::generate(v91, v91 + 960, std::ref(f16rng));
  std::generate(v92, v92 + 240, std::ref(f16rng));
  std::generate(v93, v93 + 960, std::ref(f16rng));
  std::generate(v94, v94 + 47040, std::ref(f16rng));
  std::generate(v95, v95 + 7840, std::ref(f16rng));
  std::generate(v96, v96 + 7840, std::ref(f16rng));
  std::generate(v97, v97 + 47040, std::ref(f16rng));
  std::generate(v98, v98 + 47040, std::ref(f16rng));
  std::generate(v99, v99 + 47040, std::ref(f16rng));
  std::generate(v100, v100 + 47040, std::ref(f16rng));
  std::generate(v101, v101 + 960, std::ref(f16rng));
  std::generate(v102, v102 + 240, std::ref(f16rng));
  std::generate(v103, v103 + 960, std::ref(f16rng));
  std::generate(v104, v104 + 47040, std::ref(f16rng));
  std::generate(v105, v105 + 7840, std::ref(f16rng));
  std::generate(v106, v106 + 7840, std::ref(f16rng));
  std::generate(v107, v107 + 47040, std::ref(f16rng));
  std::generate(v108, v108 + 47040, std::ref(f16rng));
  std::generate(v109, v109 + 960, std::ref(f16rng));
  std::generate(v110, v110 + 1280, std::ref(f16rng));
  std::generate(v111, v111 + 1280, std::ref(f16rng));
  std::generate(v112, v112 + 1280, std::ref(f16rng));
  std::generate(v113, v113 + 1001, std::ref(f16rng));
  std::generate(w114, w114 + 432, std::ref(f16rng));
  std::generate(w115, w115 + 16, std::ref(f16rng));
  std::generate(w116, w116 + 144, std::ref(f16rng));
  std::generate(w117, w117 + 16, std::ref(f16rng));
  std::generate(w118, w118 + 256, std::ref(f16rng));
  std::generate(w119, w119 + 16, std::ref(f16rng));
  std::generate(w120, w120 + 1024, std::ref(f16rng));
  std::generate(w121, w121 + 64, std::ref(f16rng));
  std::generate(w122, w122 + 576, std::ref(f16rng));
  std::generate(w123, w123 + 64, std::ref(f16rng));
  std::generate(w124, w124 + 1536, std::ref(f16rng));
  std::generate(w125, w125 + 24, std::ref(f16rng));
  std::generate(w126, w126 + 1728, std::ref(f16rng));
  std::generate(w127, w127 + 72, std::ref(f16rng));
  std::generate(w128, w128 + 648, std::ref(f16rng));
  std::generate(w129, w129 + 72, std::ref(f16rng));
  std::generate(w130, w130 + 1728, std::ref(f16rng));
  std::generate(w131, w131 + 24, std::ref(f16rng));
  std::generate(w132, w132 + 1728, std::ref(f16rng));
  std::generate(w133, w133 + 72, std::ref(f16rng));
  std::generate(w134, w134 + 1800, std::ref(f16rng));
  std::generate(w135, w135 + 72, std::ref(f16rng));
  std::generate(w136, w136 + 1728, std::ref(f16rng));
  std::generate(w137, w137 + 24, std::ref(f16rng));
  std::generate(w138, w138 + 1728, std::ref(f16rng));
  std::generate(w139, w139 + 72, std::ref(f16rng));
  std::generate(w140, w140 + 2880, std::ref(f16rng));
  std::generate(w141, w141 + 40, std::ref(f16rng));
  std::generate(w142, w142 + 4800, std::ref(f16rng));
  std::generate(w143, w143 + 120, std::ref(f16rng));
  std::generate(w144, w144 + 3000, std::ref(f16rng));
  std::generate(w145, w145 + 120, std::ref(f16rng));
  std::generate(w146, w146 + 3840, std::ref(f16rng));
  std::generate(w147, w147 + 32, std::ref(f16rng));
  std::generate(w148, w148 + 3840, std::ref(f16rng));
  std::generate(w149, w149 + 120, std::ref(f16rng));
  std::generate(w150, w150 + 4800, std::ref(f16rng));
  std::generate(w151, w151 + 40, std::ref(f16rng));
  std::generate(w152, w152 + 4800, std::ref(f16rng));
  std::generate(w153, w153 + 120, std::ref(f16rng));
  std::generate(w154, w154 + 3000, std::ref(f16rng));
  std::generate(w155, w155 + 120, std::ref(f16rng));
  std::generate(w156, w156 + 3840, std::ref(f16rng));
  std::generate(w157, w157 + 32, std::ref(f16rng));
  std::generate(w158, w158 + 3840, std::ref(f16rng));
  std::generate(w159, w159 + 120, std::ref(f16rng));
  std::generate(w160, w160 + 4800, std::ref(f16rng));
  std::generate(w161, w161 + 40, std::ref(f16rng));
  std::generate(w162, w162 + 9600, std::ref(f16rng));
  std::generate(w163, w163 + 240, std::ref(f16rng));
  std::generate(w164, w164 + 2160, std::ref(f16rng));
  std::generate(w165, w165 + 240, std::ref(f16rng));
  std::generate(w166, w166 + 19200, std::ref(f16rng));
  std::generate(w167, w167 + 80, std::ref(f16rng));
  std::generate(w168, w168 + 16000, std::ref(f16rng));
  std::generate(w169, w169 + 200, std::ref(f16rng));
  std::generate(w170, w170 + 1800, std::ref(f16rng));
  std::generate(w171, w171 + 200, std::ref(f16rng));
  std::generate(w172, w172 + 16000, std::ref(f16rng));
  std::generate(w173, w173 + 80, std::ref(f16rng));
  std::generate(w174, w174 + 14720, std::ref(f16rng));
  std::generate(w175, w175 + 184, std::ref(f16rng));
  std::generate(w176, w176 + 1656, std::ref(f16rng));
  std::generate(w177, w177 + 184, std::ref(f16rng));
  std::generate(w178, w178 + 14720, std::ref(f16rng));
  std::generate(w179, w179 + 80, std::ref(f16rng));
  std::generate(w180, w180 + 14720, std::ref(f16rng));
  std::generate(w181, w181 + 184, std::ref(f16rng));
  std::generate(w182, w182 + 1656, std::ref(f16rng));
  std::generate(w183, w183 + 184, std::ref(f16rng));
  std::generate(w184, w184 + 14720, std::ref(f16rng));
  std::generate(w185, w185 + 80, std::ref(f16rng));
  std::generate(w186, w186 + 38400, std::ref(f16rng));
  std::generate(w187, w187 + 480, std::ref(f16rng));
  std::generate(w188, w188 + 4320, std::ref(f16rng));
  std::generate(w189, w189 + 480, std::ref(f16rng));
  std::generate(w190, w190 + 57600, std::ref(f16rng));
  std::generate(w191, w191 + 120, std::ref(f16rng));
  std::generate(w192, w192 + 57600, std::ref(f16rng));
  std::generate(w193, w193 + 480, std::ref(f16rng));
  std::generate(w194, w194 + 53760, std::ref(f16rng));
  std::generate(w195, w195 + 112, std::ref(f16rng));
  std::generate(w196, w196 + 75264, std::ref(f16rng));
  std::generate(w197, w197 + 672, std::ref(f16rng));
  std::generate(w198, w198 + 6048, std::ref(f16rng));
  std::generate(w199, w199 + 672, std::ref(f16rng));
  std::generate(w200, w200 + 112896, std::ref(f16rng));
  std::generate(w201, w201 + 168, std::ref(f16rng));
  std::generate(w202, w202 + 112896, std::ref(f16rng));
  std::generate(w203, w203 + 672, std::ref(f16rng));
  std::generate(w204, w204 + 75264, std::ref(f16rng));
  std::generate(w205, w205 + 112, std::ref(f16rng));
  std::generate(w206, w206 + 75264, std::ref(f16rng));
  std::generate(w207, w207 + 672, std::ref(f16rng));
  std::generate(w208, w208 + 16800, std::ref(f16rng));
  std::generate(w209, w209 + 672, std::ref(f16rng));
  std::generate(w210, w210 + 112896, std::ref(f16rng));
  std::generate(w211, w211 + 168, std::ref(f16rng));
  std::generate(w212, w212 + 112896, std::ref(f16rng));
  std::generate(w213, w213 + 672, std::ref(f16rng));
  std::generate(w214, w214 + 107520, std::ref(f16rng));
  std::generate(w215, w215 + 160, std::ref(f16rng));
  std::generate(w216, w216 + 153600, std::ref(f16rng));
  std::generate(w217, w217 + 960, std::ref(f16rng));
  std::generate(w218, w218 + 24000, std::ref(f16rng));
  std::generate(w219, w219 + 960, std::ref(f16rng));
  std::generate(w220, w220 + 230400, std::ref(f16rng));
  std::generate(w221, w221 + 240, std::ref(f16rng));
  std::generate(w222, w222 + 230400, std::ref(f16rng));
  std::generate(w223, w223 + 960, std::ref(f16rng));
  std::generate(w224, w224 + 153600, std::ref(f16rng));
  std::generate(w225, w225 + 160, std::ref(f16rng));
  std::generate(w226, w226 + 153600, std::ref(f16rng));
  std::generate(w227, w227 + 960, std::ref(f16rng));
  std::generate(w228, w228 + 24000, std::ref(f16rng));
  std::generate(w229, w229 + 960, std::ref(f16rng));
  std::generate(w230, w230 + 230400, std::ref(f16rng));
  std::generate(w231, w231 + 240, std::ref(f16rng));
  std::generate(w232, w232 + 230400, std::ref(f16rng));
  std::generate(w233, w233 + 960, std::ref(f16rng));
  std::generate(w234, w234 + 153600, std::ref(f16rng));
  std::generate(w235, w235 + 160, std::ref(f16rng));
  std::generate(w236, w236 + 153600, std::ref(f16rng));
  std::generate(w237, w237 + 960, std::ref(f16rng));
  std::generate(w238, w238 + 1228800, std::ref(f16rng));
  std::generate(w239, w239 + 1280, std::ref(f16rng));
  std::generate(w240, w240 + 1281280, std::ref(f16rng));
  std::generate(w241, w241 + 1001, std::ref(f16rng));

  ExecutionPlan operators;
  xnn_status status;

  xnn_operator_t op0 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 1 /* right padding */,
    1 /* bottom padding */, 0 /* left padding */,
    3 /* kernel height */, 3 /* kernel width */,
    2 /* subsampling height */, 2 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    3 /* input channels per group */,
    16 /* output_channels_per_group */,
    3 /* input pixel stride */,
    16 /* output pixel stride */,
    w114, w115,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op0);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #0" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op0, xnn_delete_operator);

  xnn_operator_t op1 = nullptr;
  status = xnn_create_hardswish_nc_f16(
    16 /* channels */,
    16 /* input stride */,
    16 /* output stride */,
    0 /* flags */,
    &op1);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #1" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op1, xnn_delete_operator);

  xnn_operator_t op2 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    1 /* top padding */, 1 /* right padding */,
    1 /* bottom padding */, 1 /* left padding */,
    3 /* kernel height */, 3 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    16 /* groups */,
    1 /* input channels per group */,
    1 /* output_channels_per_group */,
    16 /* input pixel stride */,
    16 /* output pixel stride */,
    w116, w117,
    0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op2);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #2" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op2, xnn_delete_operator);

  xnn_operator_t op3 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    16 /* input channels per group */,
    16 /* output_channels_per_group */,
    16 /* input pixel stride */,
    16 /* output pixel stride */,
    w118, w119,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op3);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #3" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op3, xnn_delete_operator);

  xnn_operator_t op4 = nullptr;
  status = xnn_create_add_nd_f16(
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op4);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #4" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op4, xnn_delete_operator);

  xnn_operator_t op5 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    16 /* input channels per group */,
    64 /* output_channels_per_group */,
    16 /* input pixel stride */,
    64 /* output pixel stride */,
    w120, w121,
    0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op5);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #5" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op5, xnn_delete_operator);

  xnn_operator_t op6 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 1 /* right padding */,
    1 /* bottom padding */, 0 /* left padding */,
    3 /* kernel height */, 3 /* kernel width */,
    2 /* subsampling height */, 2 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    64 /* groups */,
    1 /* input channels per group */,
    1 /* output_channels_per_group */,
    64 /* input pixel stride */,
    64 /* output pixel stride */,
    w122, w123,
    0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op6);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #6" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op6, xnn_delete_operator);

  xnn_operator_t op7 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    64 /* input channels per group */,
    24 /* output_channels_per_group */,
    64 /* input pixel stride */,
    24 /* output pixel stride */,
    w124, w125,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op7);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #7" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op7, xnn_delete_operator);

  xnn_operator_t op8 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    24 /* input channels per group */,
    72 /* output_channels_per_group */,
    24 /* input pixel stride */,
    72 /* output pixel stride */,
    w126, w127,
    0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op8);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #8" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op8, xnn_delete_operator);

  xnn_operator_t op9 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    1 /* top padding */, 1 /* right padding */,
    1 /* bottom padding */, 1 /* left padding */,
    3 /* kernel height */, 3 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    72 /* groups */,
    1 /* input channels per group */,
    1 /* output_channels_per_group */,
    72 /* input pixel stride */,
    72 /* output pixel stride */,
    w128, w129,
    0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op9);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #9" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op9, xnn_delete_operator);

  xnn_operator_t op10 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    72 /* input channels per group */,
    24 /* output_channels_per_group */,
    72 /* input pixel stride */,
    24 /* output pixel stride */,
    w130, w131,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op10);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #10" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op10, xnn_delete_operator);

  xnn_operator_t op11 = nullptr;
  status = xnn_create_add_nd_f16(
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op11);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #11" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op11, xnn_delete_operator);

  xnn_operator_t op12 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    24 /* input channels per group */,
    72 /* output_channels_per_group */,
    24 /* input pixel stride */,
    72 /* output pixel stride */,
    w132, w133,
    0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op12);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #12" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op12, xnn_delete_operator);

  xnn_operator_t op13 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    1 /* top padding */, 2 /* right padding */,
    2 /* bottom padding */, 1 /* left padding */,
    5 /* kernel height */, 5 /* kernel width */,
    2 /* subsampling height */, 2 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    72 /* groups */,
    1 /* input channels per group */,
    1 /* output_channels_per_group */,
    72 /* input pixel stride */,
    72 /* output pixel stride */,
    w134, w135,
    0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op13);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #13" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op13, xnn_delete_operator);

  xnn_operator_t op14 = nullptr;
  status = xnn_create_global_average_pooling_nwc_f16(
    72 /* channels */, 72 /* input stride */, 72 /* output stride */,
    -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
    0 /* flags */,
    &op14);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #14" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op14, xnn_delete_operator);

  xnn_operator_t op15 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    72 /* input channels per group */,
    24 /* output_channels_per_group */,
    72 /* input pixel stride */,
    24 /* output pixel stride */,
    w136, w137,
    0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op15);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #15" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op15, xnn_delete_operator);

  xnn_operator_t op16 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    24 /* input channels per group */,
    72 /* output_channels_per_group */,
    24 /* input pixel stride */,
    72 /* output pixel stride */,
    w138, w139,
    0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
    0 /* flags */,
    &op16);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #16" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op16, xnn_delete_operator);

  xnn_operator_t op17 = nullptr;
  status = xnn_create_multiply_nd_f16(
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op17);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #17" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op17, xnn_delete_operator);

  xnn_operator_t op18 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    72 /* input channels per group */,
    40 /* output_channels_per_group */,
    72 /* input pixel stride */,
    40 /* output pixel stride */,
    w140, w141,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op18);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #18" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op18, xnn_delete_operator);

  xnn_operator_t op19 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    40 /* input channels per group */,
    120 /* output_channels_per_group */,
    40 /* input pixel stride */,
    120 /* output pixel stride */,
    w142, w143,
    0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op19);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #19" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op19, xnn_delete_operator);

  xnn_operator_t op20 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    2 /* top padding */, 2 /* right padding */,
    2 /* bottom padding */, 2 /* left padding */,
    5 /* kernel height */, 5 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    120 /* groups */,
    1 /* input channels per group */,
    1 /* output_channels_per_group */,
    120 /* input pixel stride */,
    120 /* output pixel stride */,
    w144, w145,
    0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op20);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #20" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op20, xnn_delete_operator);

  xnn_operator_t op21 = nullptr;
  status = xnn_create_global_average_pooling_nwc_f16(
    120 /* channels */, 120 /* input stride */, 120 /* output stride */,
    -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
    0 /* flags */,
    &op21);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #21" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op21, xnn_delete_operator);

  xnn_operator_t op22 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    120 /* input channels per group */,
    32 /* output_channels_per_group */,
    120 /* input pixel stride */,
    32 /* output pixel stride */,
    w146, w147,
    0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op22);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #22" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op22, xnn_delete_operator);

  xnn_operator_t op23 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    32 /* input channels per group */,
    120 /* output_channels_per_group */,
    32 /* input pixel stride */,
    120 /* output pixel stride */,
    w148, w149,
    0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
    0 /* flags */,
    &op23);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #23" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op23, xnn_delete_operator);

  xnn_operator_t op24 = nullptr;
  status = xnn_create_multiply_nd_f16(
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op24);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #24" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op24, xnn_delete_operator);

  xnn_operator_t op25 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    120 /* input channels per group */,
    40 /* output_channels_per_group */,
    120 /* input pixel stride */,
    40 /* output pixel stride */,
    w150, w151,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op25);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #25" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op25, xnn_delete_operator);

  xnn_operator_t op26 = nullptr;
  status = xnn_create_add_nd_f16(
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op26);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #26" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op26, xnn_delete_operator);

  xnn_operator_t op27 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    40 /* input channels per group */,
    120 /* output_channels_per_group */,
    40 /* input pixel stride */,
    120 /* output pixel stride */,
    w152, w153,
    0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op27);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #27" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op27, xnn_delete_operator);

  xnn_operator_t op28 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    2 /* top padding */, 2 /* right padding */,
    2 /* bottom padding */, 2 /* left padding */,
    5 /* kernel height */, 5 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    120 /* groups */,
    1 /* input channels per group */,
    1 /* output_channels_per_group */,
    120 /* input pixel stride */,
    120 /* output pixel stride */,
    w154, w155,
    0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op28);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #28" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op28, xnn_delete_operator);

  xnn_operator_t op29 = nullptr;
  status = xnn_create_global_average_pooling_nwc_f16(
    120 /* channels */, 120 /* input stride */, 120 /* output stride */,
    -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
    0 /* flags */,
    &op29);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #29" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op29, xnn_delete_operator);

  xnn_operator_t op30 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    120 /* input channels per group */,
    32 /* output_channels_per_group */,
    120 /* input pixel stride */,
    32 /* output pixel stride */,
    w156, w157,
    0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op30);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #30" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op30, xnn_delete_operator);

  xnn_operator_t op31 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    32 /* input channels per group */,
    120 /* output_channels_per_group */,
    32 /* input pixel stride */,
    120 /* output pixel stride */,
    w158, w159,
    0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
    0 /* flags */,
    &op31);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #31" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op31, xnn_delete_operator);

  xnn_operator_t op32 = nullptr;
  status = xnn_create_multiply_nd_f16(
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op32);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #32" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op32, xnn_delete_operator);

  xnn_operator_t op33 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    120 /* input channels per group */,
    40 /* output_channels_per_group */,
    120 /* input pixel stride */,
    40 /* output pixel stride */,
    w160, w161,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op33);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #33" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op33, xnn_delete_operator);

  xnn_operator_t op34 = nullptr;
  status = xnn_create_add_nd_f16(
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op34);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #34" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op34, xnn_delete_operator);

  xnn_operator_t op35 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    40 /* input channels per group */,
    240 /* output_channels_per_group */,
    40 /* input pixel stride */,
    240 /* output pixel stride */,
    w162, w163,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op35);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #35" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op35, xnn_delete_operator);

  xnn_operator_t op36 = nullptr;
  status = xnn_create_hardswish_nc_f16(
    240 /* channels */,
    240 /* input stride */,
    240 /* output stride */,
    0 /* flags */,
    &op36);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #36" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op36, xnn_delete_operator);

  xnn_operator_t op37 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 1 /* right padding */,
    1 /* bottom padding */, 0 /* left padding */,
    3 /* kernel height */, 3 /* kernel width */,
    2 /* subsampling height */, 2 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    240 /* groups */,
    1 /* input channels per group */,
    1 /* output_channels_per_group */,
    240 /* input pixel stride */,
    240 /* output pixel stride */,
    w164, w165,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op37);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #37" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op37, xnn_delete_operator);

  xnn_operator_t op38 = nullptr;
  status = xnn_create_hardswish_nc_f16(
    240 /* channels */,
    240 /* input stride */,
    240 /* output stride */,
    0 /* flags */,
    &op38);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #38" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op38, xnn_delete_operator);

  xnn_operator_t op39 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    240 /* input channels per group */,
    80 /* output_channels_per_group */,
    240 /* input pixel stride */,
    80 /* output pixel stride */,
    w166, w167,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op39);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #39" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op39, xnn_delete_operator);

  xnn_operator_t op40 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    80 /* input channels per group */,
    200 /* output_channels_per_group */,
    80 /* input pixel stride */,
    200 /* output pixel stride */,
    w168, w169,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op40);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #40" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op40, xnn_delete_operator);

  xnn_operator_t op41 = nullptr;
  status = xnn_create_hardswish_nc_f16(
    200 /* channels */,
    200 /* input stride */,
    200 /* output stride */,
    0 /* flags */,
    &op41);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #41" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op41, xnn_delete_operator);

  xnn_operator_t op42 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    1 /* top padding */, 1 /* right padding */,
    1 /* bottom padding */, 1 /* left padding */,
    3 /* kernel height */, 3 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    200 /* groups */,
    1 /* input channels per group */,
    1 /* output_channels_per_group */,
    200 /* input pixel stride */,
    200 /* output pixel stride */,
    w170, w171,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op42);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #42" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op42, xnn_delete_operator);

  xnn_operator_t op43 = nullptr;
  status = xnn_create_hardswish_nc_f16(
    200 /* channels */,
    200 /* input stride */,
    200 /* output stride */,
    0 /* flags */,
    &op43);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #43" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op43, xnn_delete_operator);

  xnn_operator_t op44 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    200 /* input channels per group */,
    80 /* output_channels_per_group */,
    200 /* input pixel stride */,
    80 /* output pixel stride */,
    w172, w173,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op44);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #44" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op44, xnn_delete_operator);

  xnn_operator_t op45 = nullptr;
  status = xnn_create_add_nd_f16(
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op45);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #45" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op45, xnn_delete_operator);

  xnn_operator_t op46 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    80 /* input channels per group */,
    184 /* output_channels_per_group */,
    80 /* input pixel stride */,
    184 /* output pixel stride */,
    w174, w175,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op46);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #46" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op46, xnn_delete_operator);

  xnn_operator_t op47 = nullptr;
  status = xnn_create_hardswish_nc_f16(
    184 /* channels */,
    184 /* input stride */,
    184 /* output stride */,
    0 /* flags */,
    &op47);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #47" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op47, xnn_delete_operator);

  xnn_operator_t op48 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    1 /* top padding */, 1 /* right padding */,
    1 /* bottom padding */, 1 /* left padding */,
    3 /* kernel height */, 3 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    184 /* groups */,
    1 /* input channels per group */,
    1 /* output_channels_per_group */,
    184 /* input pixel stride */,
    184 /* output pixel stride */,
    w176, w177,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op48);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #48" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op48, xnn_delete_operator);

  xnn_operator_t op49 = nullptr;
  status = xnn_create_hardswish_nc_f16(
    184 /* channels */,
    184 /* input stride */,
    184 /* output stride */,
    0 /* flags */,
    &op49);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #49" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op49, xnn_delete_operator);

  xnn_operator_t op50 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    184 /* input channels per group */,
    80 /* output_channels_per_group */,
    184 /* input pixel stride */,
    80 /* output pixel stride */,
    w178, w179,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op50);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #50" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op50, xnn_delete_operator);

  xnn_operator_t op51 = nullptr;
  status = xnn_create_add_nd_f16(
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op51);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #51" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op51, xnn_delete_operator);

  xnn_operator_t op52 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    80 /* input channels per group */,
    184 /* output_channels_per_group */,
    80 /* input pixel stride */,
    184 /* output pixel stride */,
    w180, w181,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op52);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #52" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op52, xnn_delete_operator);

  xnn_operator_t op53 = nullptr;
  status = xnn_create_hardswish_nc_f16(
    184 /* channels */,
    184 /* input stride */,
    184 /* output stride */,
    0 /* flags */,
    &op53);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #53" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op53, xnn_delete_operator);

  xnn_operator_t op54 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    1 /* top padding */, 1 /* right padding */,
    1 /* bottom padding */, 1 /* left padding */,
    3 /* kernel height */, 3 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    184 /* groups */,
    1 /* input channels per group */,
    1 /* output_channels_per_group */,
    184 /* input pixel stride */,
    184 /* output pixel stride */,
    w182, w183,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op54);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #54" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op54, xnn_delete_operator);

  xnn_operator_t op55 = nullptr;
  status = xnn_create_hardswish_nc_f16(
    184 /* channels */,
    184 /* input stride */,
    184 /* output stride */,
    0 /* flags */,
    &op55);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #55" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op55, xnn_delete_operator);

  xnn_operator_t op56 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    184 /* input channels per group */,
    80 /* output_channels_per_group */,
    184 /* input pixel stride */,
    80 /* output pixel stride */,
    w184, w185,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op56);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #56" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op56, xnn_delete_operator);

  xnn_operator_t op57 = nullptr;
  status = xnn_create_add_nd_f16(
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op57);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #57" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op57, xnn_delete_operator);

  xnn_operator_t op58 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    80 /* input channels per group */,
    480 /* output_channels_per_group */,
    80 /* input pixel stride */,
    480 /* output pixel stride */,
    w186, w187,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op58);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #58" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op58, xnn_delete_operator);

  xnn_operator_t op59 = nullptr;
  status = xnn_create_hardswish_nc_f16(
    480 /* channels */,
    480 /* input stride */,
    480 /* output stride */,
    0 /* flags */,
    &op59);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #59" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op59, xnn_delete_operator);

  xnn_operator_t op60 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    1 /* top padding */, 1 /* right padding */,
    1 /* bottom padding */, 1 /* left padding */,
    3 /* kernel height */, 3 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    480 /* groups */,
    1 /* input channels per group */,
    1 /* output_channels_per_group */,
    480 /* input pixel stride */,
    480 /* output pixel stride */,
    w188, w189,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op60);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #60" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op60, xnn_delete_operator);

  xnn_operator_t op61 = nullptr;
  status = xnn_create_hardswish_nc_f16(
    480 /* channels */,
    480 /* input stride */,
    480 /* output stride */,
    0 /* flags */,
    &op61);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #61" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op61, xnn_delete_operator);

  xnn_operator_t op62 = nullptr;
  status = xnn_create_global_average_pooling_nwc_f16(
    480 /* channels */, 480 /* input stride */, 480 /* output stride */,
    -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
    0 /* flags */,
    &op62);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #62" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op62, xnn_delete_operator);

  xnn_operator_t op63 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    480 /* input channels per group */,
    120 /* output_channels_per_group */,
    480 /* input pixel stride */,
    120 /* output pixel stride */,
    w190, w191,
    0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op63);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #63" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op63, xnn_delete_operator);

  xnn_operator_t op64 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    120 /* input channels per group */,
    480 /* output_channels_per_group */,
    120 /* input pixel stride */,
    480 /* output pixel stride */,
    w192, w193,
    0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
    0 /* flags */,
    &op64);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #64" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op64, xnn_delete_operator);

  xnn_operator_t op65 = nullptr;
  status = xnn_create_multiply_nd_f16(
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op65);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #65" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op65, xnn_delete_operator);

  xnn_operator_t op66 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    480 /* input channels per group */,
    112 /* output_channels_per_group */,
    480 /* input pixel stride */,
    112 /* output pixel stride */,
    w194, w195,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op66);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #66" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op66, xnn_delete_operator);

  xnn_operator_t op67 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    112 /* input channels per group */,
    672 /* output_channels_per_group */,
    112 /* input pixel stride */,
    672 /* output pixel stride */,
    w196, w197,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op67);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #67" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op67, xnn_delete_operator);

  xnn_operator_t op68 = nullptr;
  status = xnn_create_hardswish_nc_f16(
    672 /* channels */,
    672 /* input stride */,
    672 /* output stride */,
    0 /* flags */,
    &op68);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #68" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op68, xnn_delete_operator);

  xnn_operator_t op69 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    1 /* top padding */, 1 /* right padding */,
    1 /* bottom padding */, 1 /* left padding */,
    3 /* kernel height */, 3 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    672 /* groups */,
    1 /* input channels per group */,
    1 /* output_channels_per_group */,
    672 /* input pixel stride */,
    672 /* output pixel stride */,
    w198, w199,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op69);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #69" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op69, xnn_delete_operator);

  xnn_operator_t op70 = nullptr;
  status = xnn_create_hardswish_nc_f16(
    672 /* channels */,
    672 /* input stride */,
    672 /* output stride */,
    0 /* flags */,
    &op70);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #70" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op70, xnn_delete_operator);

  xnn_operator_t op71 = nullptr;
  status = xnn_create_global_average_pooling_nwc_f16(
    672 /* channels */, 672 /* input stride */, 672 /* output stride */,
    -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
    0 /* flags */,
    &op71);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #71" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op71, xnn_delete_operator);

  xnn_operator_t op72 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    672 /* input channels per group */,
    168 /* output_channels_per_group */,
    672 /* input pixel stride */,
    168 /* output pixel stride */,
    w200, w201,
    0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op72);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #72" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op72, xnn_delete_operator);

  xnn_operator_t op73 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    168 /* input channels per group */,
    672 /* output_channels_per_group */,
    168 /* input pixel stride */,
    672 /* output pixel stride */,
    w202, w203,
    0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
    0 /* flags */,
    &op73);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #73" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op73, xnn_delete_operator);

  xnn_operator_t op74 = nullptr;
  status = xnn_create_multiply_nd_f16(
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op74);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #74" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op74, xnn_delete_operator);

  xnn_operator_t op75 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    672 /* input channels per group */,
    112 /* output_channels_per_group */,
    672 /* input pixel stride */,
    112 /* output pixel stride */,
    w204, w205,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op75);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #75" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op75, xnn_delete_operator);

  xnn_operator_t op76 = nullptr;
  status = xnn_create_add_nd_f16(
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op76);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #76" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op76, xnn_delete_operator);

  xnn_operator_t op77 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    112 /* input channels per group */,
    672 /* output_channels_per_group */,
    112 /* input pixel stride */,
    672 /* output pixel stride */,
    w206, w207,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op77);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #77" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op77, xnn_delete_operator);

  xnn_operator_t op78 = nullptr;
  status = xnn_create_hardswish_nc_f16(
    672 /* channels */,
    672 /* input stride */,
    672 /* output stride */,
    0 /* flags */,
    &op78);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #78" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op78, xnn_delete_operator);

  xnn_operator_t op79 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    1 /* top padding */, 2 /* right padding */,
    2 /* bottom padding */, 1 /* left padding */,
    5 /* kernel height */, 5 /* kernel width */,
    2 /* subsampling height */, 2 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    672 /* groups */,
    1 /* input channels per group */,
    1 /* output_channels_per_group */,
    672 /* input pixel stride */,
    672 /* output pixel stride */,
    w208, w209,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op79);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #79" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op79, xnn_delete_operator);

  xnn_operator_t op80 = nullptr;
  status = xnn_create_hardswish_nc_f16(
    672 /* channels */,
    672 /* input stride */,
    672 /* output stride */,
    0 /* flags */,
    &op80);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #80" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op80, xnn_delete_operator);

  xnn_operator_t op81 = nullptr;
  status = xnn_create_global_average_pooling_nwc_f16(
    672 /* channels */, 672 /* input stride */, 672 /* output stride */,
    -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
    0 /* flags */,
    &op81);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #81" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op81, xnn_delete_operator);

  xnn_operator_t op82 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    672 /* input channels per group */,
    168 /* output_channels_per_group */,
    672 /* input pixel stride */,
    168 /* output pixel stride */,
    w210, w211,
    0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op82);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #82" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op82, xnn_delete_operator);

  xnn_operator_t op83 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    168 /* input channels per group */,
    672 /* output_channels_per_group */,
    168 /* input pixel stride */,
    672 /* output pixel stride */,
    w212, w213,
    0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
    0 /* flags */,
    &op83);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #83" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op83, xnn_delete_operator);

  xnn_operator_t op84 = nullptr;
  status = xnn_create_multiply_nd_f16(
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op84);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #84" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op84, xnn_delete_operator);

  xnn_operator_t op85 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    672 /* input channels per group */,
    160 /* output_channels_per_group */,
    672 /* input pixel stride */,
    160 /* output pixel stride */,
    w214, w215,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op85);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #85" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op85, xnn_delete_operator);

  xnn_operator_t op86 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    160 /* input channels per group */,
    960 /* output_channels_per_group */,
    160 /* input pixel stride */,
    960 /* output pixel stride */,
    w216, w217,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op86);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #86" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op86, xnn_delete_operator);

  xnn_operator_t op87 = nullptr;
  status = xnn_create_hardswish_nc_f16(
    960 /* channels */,
    960 /* input stride */,
    960 /* output stride */,
    0 /* flags */,
    &op87);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #87" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op87, xnn_delete_operator);

  xnn_operator_t op88 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    2 /* top padding */, 2 /* right padding */,
    2 /* bottom padding */, 2 /* left padding */,
    5 /* kernel height */, 5 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    960 /* groups */,
    1 /* input channels per group */,
    1 /* output_channels_per_group */,
    960 /* input pixel stride */,
    960 /* output pixel stride */,
    w218, w219,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op88);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #88" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op88, xnn_delete_operator);

  xnn_operator_t op89 = nullptr;
  status = xnn_create_hardswish_nc_f16(
    960 /* channels */,
    960 /* input stride */,
    960 /* output stride */,
    0 /* flags */,
    &op89);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #89" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op89, xnn_delete_operator);

  xnn_operator_t op90 = nullptr;
  status = xnn_create_global_average_pooling_nwc_f16(
    960 /* channels */, 960 /* input stride */, 960 /* output stride */,
    -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
    0 /* flags */,
    &op90);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #90" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op90, xnn_delete_operator);

  xnn_operator_t op91 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    960 /* input channels per group */,
    240 /* output_channels_per_group */,
    960 /* input pixel stride */,
    240 /* output pixel stride */,
    w220, w221,
    0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op91);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #91" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op91, xnn_delete_operator);

  xnn_operator_t op92 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    240 /* input channels per group */,
    960 /* output_channels_per_group */,
    240 /* input pixel stride */,
    960 /* output pixel stride */,
    w222, w223,
    0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
    0 /* flags */,
    &op92);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #92" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op92, xnn_delete_operator);

  xnn_operator_t op93 = nullptr;
  status = xnn_create_multiply_nd_f16(
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op93);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #93" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op93, xnn_delete_operator);

  xnn_operator_t op94 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    960 /* input channels per group */,
    160 /* output_channels_per_group */,
    960 /* input pixel stride */,
    160 /* output pixel stride */,
    w224, w225,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op94);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #94" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op94, xnn_delete_operator);

  xnn_operator_t op95 = nullptr;
  status = xnn_create_add_nd_f16(
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op95);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #95" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op95, xnn_delete_operator);

  xnn_operator_t op96 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    160 /* input channels per group */,
    960 /* output_channels_per_group */,
    160 /* input pixel stride */,
    960 /* output pixel stride */,
    w226, w227,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op96);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #96" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op96, xnn_delete_operator);

  xnn_operator_t op97 = nullptr;
  status = xnn_create_hardswish_nc_f16(
    960 /* channels */,
    960 /* input stride */,
    960 /* output stride */,
    0 /* flags */,
    &op97);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #97" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op97, xnn_delete_operator);

  xnn_operator_t op98 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    2 /* top padding */, 2 /* right padding */,
    2 /* bottom padding */, 2 /* left padding */,
    5 /* kernel height */, 5 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    960 /* groups */,
    1 /* input channels per group */,
    1 /* output_channels_per_group */,
    960 /* input pixel stride */,
    960 /* output pixel stride */,
    w228, w229,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op98);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #98" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op98, xnn_delete_operator);

  xnn_operator_t op99 = nullptr;
  status = xnn_create_hardswish_nc_f16(
    960 /* channels */,
    960 /* input stride */,
    960 /* output stride */,
    0 /* flags */,
    &op99);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #99" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op99, xnn_delete_operator);

  xnn_operator_t op100 = nullptr;
  status = xnn_create_global_average_pooling_nwc_f16(
    960 /* channels */, 960 /* input stride */, 960 /* output stride */,
    -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
    0 /* flags */,
    &op100);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #100" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op100, xnn_delete_operator);

  xnn_operator_t op101 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    960 /* input channels per group */,
    240 /* output_channels_per_group */,
    960 /* input pixel stride */,
    240 /* output pixel stride */,
    w230, w231,
    0.0f /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op101);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #101" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op101, xnn_delete_operator);

  xnn_operator_t op102 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    240 /* input channels per group */,
    960 /* output_channels_per_group */,
    240 /* input pixel stride */,
    960 /* output pixel stride */,
    w232, w233,
    0.0f /* output min */, +0x1.00014Fp+0 /* output max */,
    0 /* flags */,
    &op102);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #102" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op102, xnn_delete_operator);

  xnn_operator_t op103 = nullptr;
  status = xnn_create_multiply_nd_f16(
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op103);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #103" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op103, xnn_delete_operator);

  xnn_operator_t op104 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    960 /* input channels per group */,
    160 /* output_channels_per_group */,
    960 /* input pixel stride */,
    160 /* output pixel stride */,
    w234, w235,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op104);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #104" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op104, xnn_delete_operator);

  xnn_operator_t op105 = nullptr;
  status = xnn_create_add_nd_f16(
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op105);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #105" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op105, xnn_delete_operator);

  xnn_operator_t op106 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    160 /* input channels per group */,
    960 /* output_channels_per_group */,
    160 /* input pixel stride */,
    960 /* output pixel stride */,
    w236, w237,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op106);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #106" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op106, xnn_delete_operator);

  xnn_operator_t op107 = nullptr;
  status = xnn_create_hardswish_nc_f16(
    960 /* channels */,
    960 /* input stride */,
    960 /* output stride */,
    0 /* flags */,
    &op107);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #107" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op107, xnn_delete_operator);

  xnn_operator_t op108 = nullptr;
  status = xnn_create_global_average_pooling_nwc_f16(
    960 /* channels */, 960 /* input stride */, 960 /* output stride */,
    -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
    0 /* flags */,
    &op108);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #108" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op108, xnn_delete_operator);

  xnn_operator_t op109 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    960 /* input channels per group */,
    1280 /* output_channels_per_group */,
    960 /* input pixel stride */,
    1280 /* output pixel stride */,
    w238, w239,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op109);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #109" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op109, xnn_delete_operator);

  xnn_operator_t op110 = nullptr;
  status = xnn_create_hardswish_nc_f16(
    1280 /* channels */,
    1280 /* input stride */,
    1280 /* output stride */,
    0 /* flags */,
    &op110);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #110" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op110, xnn_delete_operator);

  xnn_operator_t op111 = nullptr;
  status = xnn_create_global_average_pooling_nwc_f16(
    1280 /* channels */, 1280 /* input stride */, 1280 /* output stride */,
    -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
    0 /* flags */,
    &op111);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #111" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op111, xnn_delete_operator);

  xnn_operator_t op112 = nullptr;
  status = xnn_create_convolution2d_nhwc_f16(
    0 /* top padding */, 0 /* right padding */,
    0 /* bottom padding */, 0 /* left padding */,
    1 /* kernel height */, 1 /* kernel width */,
    1 /* subsampling height */, 1 /* subsampling width */,
    1 /* dilation_height */, 1 /* dilation_width */,
    1 /* groups */,
    1280 /* input channels per group */,
    1001 /* output_channels_per_group */,
    1280 /* input pixel stride */,
    1001 /* output pixel stride */,
    w240, w241,
    -std::numeric_limits<float>::infinity() /* output min */, std::numeric_limits<float>::infinity() /* output max */,
    0 /* flags */,
    &op112);
  if (status != xnn_status_success) {
    std::cerr << "failed to create operation #112" << std::endl;
    return ExecutionPlan();
  }
  operators.emplace_back(op112, xnn_delete_operator);



  status = xnn_setup_convolution2d_nhwc_f16(
    op0,
    1 /* batch size */, 224 /* input height */, 224 /* input width */,
    v0 /* input */, v1 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #0" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_hardswish_nc_f16(
    op1,
    12544 /* batch size */,
    v1 /* input */, v2 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #1" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op2,
    1 /* batch size */, 112 /* input height */, 112 /* input width */,
    v2 /* input */, v3 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #2" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op3,
    1 /* batch size */, 112 /* input height */, 112 /* input width */,
    v3 /* input */, v4 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #3" << std::endl;
    return ExecutionPlan();
  }

  {
    const size_t a_shape[] = { 1, 112, 112, 16 };
    const size_t b_shape[] = { 1, 112, 112, 16 };
    status = xnn_setup_add_nd_f16(
      op4,
      4, a_shape, 4, b_shape,
      v4 /* a */, v2 /* b */, v5 /* output */,
      threadpool /* threadpool */);
  }
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #4" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op5,
    1 /* batch size */, 112 /* input height */, 112 /* input width */,
    v5 /* input */, v6 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #5" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op6,
    1 /* batch size */, 112 /* input height */, 112 /* input width */,
    v6 /* input */, v7 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #6" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op7,
    1 /* batch size */, 56 /* input height */, 56 /* input width */,
    v7 /* input */, v8 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #7" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op8,
    1 /* batch size */, 56 /* input height */, 56 /* input width */,
    v8 /* input */, v9 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #8" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op9,
    1 /* batch size */, 56 /* input height */, 56 /* input width */,
    v9 /* input */, v10 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #9" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op10,
    1 /* batch size */, 56 /* input height */, 56 /* input width */,
    v10 /* input */, v11 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #10" << std::endl;
    return ExecutionPlan();
  }

  {
    const size_t a_shape[] = { 1, 56, 56, 24 };
    const size_t b_shape[] = { 1, 56, 56, 24 };
    status = xnn_setup_add_nd_f16(
      op11,
      4, a_shape, 4, b_shape,
      v11 /* a */, v8 /* b */, v12 /* output */,
      threadpool /* threadpool */);
  }
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #11" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op12,
    1 /* batch size */, 56 /* input height */, 56 /* input width */,
    v12 /* input */, v13 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #12" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op13,
    1 /* batch size */, 56 /* input height */, 56 /* input width */,
    v13 /* input */, v14 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #13" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_global_average_pooling_nwc_f16(
    op14,
    1 /* batch size */, 784 /* width */,
    v14 /* input */, v15 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #14" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op15,
    1 /* batch size */, 1 /* input height */, 1 /* input width */,
    v15 /* input */, v16 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #15" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op16,
    1 /* batch size */, 1 /* input height */, 1 /* input width */,
    v16 /* input */, v17 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #16" << std::endl;
    return ExecutionPlan();
  }

  {
    const size_t a_shape[] = { 1, 28, 28, 72 };
    const size_t b_shape[] = { 1, 1, 1, 72 };
    status = xnn_setup_multiply_nd_f16(
      op17,
      4, a_shape, 4, b_shape,
      v14 /* a */, v17 /* b */, v18 /* output */,
      threadpool /* threadpool */);
  }
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #17" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op18,
    1 /* batch size */, 28 /* input height */, 28 /* input width */,
    v18 /* input */, v19 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #18" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op19,
    1 /* batch size */, 28 /* input height */, 28 /* input width */,
    v19 /* input */, v20 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #19" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op20,
    1 /* batch size */, 28 /* input height */, 28 /* input width */,
    v20 /* input */, v21 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #20" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_global_average_pooling_nwc_f16(
    op21,
    1 /* batch size */, 784 /* width */,
    v21 /* input */, v22 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #21" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op22,
    1 /* batch size */, 1 /* input height */, 1 /* input width */,
    v22 /* input */, v23 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #22" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op23,
    1 /* batch size */, 1 /* input height */, 1 /* input width */,
    v23 /* input */, v24 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #23" << std::endl;
    return ExecutionPlan();
  }

  {
    const size_t a_shape[] = { 1, 28, 28, 120 };
    const size_t b_shape[] = { 1, 1, 1, 120 };
    status = xnn_setup_multiply_nd_f16(
      op24,
      4, a_shape, 4, b_shape,
      v21 /* a */, v24 /* b */, v25 /* output */,
      threadpool /* threadpool */);
  }
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #24" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op25,
    1 /* batch size */, 28 /* input height */, 28 /* input width */,
    v25 /* input */, v26 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #25" << std::endl;
    return ExecutionPlan();
  }

  {
    const size_t a_shape[] = { 1, 28, 28, 40 };
    const size_t b_shape[] = { 1, 28, 28, 40 };
    status = xnn_setup_add_nd_f16(
      op26,
      4, a_shape, 4, b_shape,
      v26 /* a */, v19 /* b */, v27 /* output */,
      threadpool /* threadpool */);
  }
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #26" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op27,
    1 /* batch size */, 28 /* input height */, 28 /* input width */,
    v27 /* input */, v28 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #27" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op28,
    1 /* batch size */, 28 /* input height */, 28 /* input width */,
    v28 /* input */, v29 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #28" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_global_average_pooling_nwc_f16(
    op29,
    1 /* batch size */, 784 /* width */,
    v29 /* input */, v30 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #29" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op30,
    1 /* batch size */, 1 /* input height */, 1 /* input width */,
    v30 /* input */, v31 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #30" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op31,
    1 /* batch size */, 1 /* input height */, 1 /* input width */,
    v31 /* input */, v32 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #31" << std::endl;
    return ExecutionPlan();
  }

  {
    const size_t a_shape[] = { 1, 28, 28, 120 };
    const size_t b_shape[] = { 1, 1, 1, 120 };
    status = xnn_setup_multiply_nd_f16(
      op32,
      4, a_shape, 4, b_shape,
      v29 /* a */, v32 /* b */, v33 /* output */,
      threadpool /* threadpool */);
  }
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #32" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op33,
    1 /* batch size */, 28 /* input height */, 28 /* input width */,
    v33 /* input */, v34 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #33" << std::endl;
    return ExecutionPlan();
  }

  {
    const size_t a_shape[] = { 1, 28, 28, 40 };
    const size_t b_shape[] = { 1, 28, 28, 40 };
    status = xnn_setup_add_nd_f16(
      op34,
      4, a_shape, 4, b_shape,
      v34 /* a */, v27 /* b */, v35 /* output */,
      threadpool /* threadpool */);
  }
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #34" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op35,
    1 /* batch size */, 28 /* input height */, 28 /* input width */,
    v35 /* input */, v36 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #35" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_hardswish_nc_f16(
    op36,
    784 /* batch size */,
    v36 /* input */, v37 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #36" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op37,
    1 /* batch size */, 28 /* input height */, 28 /* input width */,
    v37 /* input */, v38 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #37" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_hardswish_nc_f16(
    op38,
    196 /* batch size */,
    v38 /* input */, v39 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #38" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op39,
    1 /* batch size */, 14 /* input height */, 14 /* input width */,
    v39 /* input */, v40 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #39" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op40,
    1 /* batch size */, 14 /* input height */, 14 /* input width */,
    v40 /* input */, v41 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #40" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_hardswish_nc_f16(
    op41,
    196 /* batch size */,
    v41 /* input */, v42 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #41" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op42,
    1 /* batch size */, 14 /* input height */, 14 /* input width */,
    v42 /* input */, v43 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #42" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_hardswish_nc_f16(
    op43,
    196 /* batch size */,
    v43 /* input */, v44 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #43" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op44,
    1 /* batch size */, 14 /* input height */, 14 /* input width */,
    v44 /* input */, v45 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #44" << std::endl;
    return ExecutionPlan();
  }

  {
    const size_t a_shape[] = { 1, 14, 14, 80 };
    const size_t b_shape[] = { 1, 14, 14, 80 };
    status = xnn_setup_add_nd_f16(
      op45,
      4, a_shape, 4, b_shape,
      v45 /* a */, v40 /* b */, v46 /* output */,
      threadpool /* threadpool */);
  }
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #45" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op46,
    1 /* batch size */, 14 /* input height */, 14 /* input width */,
    v46 /* input */, v47 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #46" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_hardswish_nc_f16(
    op47,
    196 /* batch size */,
    v47 /* input */, v48 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #47" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op48,
    1 /* batch size */, 14 /* input height */, 14 /* input width */,
    v48 /* input */, v49 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #48" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_hardswish_nc_f16(
    op49,
    196 /* batch size */,
    v49 /* input */, v50 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #49" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op50,
    1 /* batch size */, 14 /* input height */, 14 /* input width */,
    v50 /* input */, v51 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #50" << std::endl;
    return ExecutionPlan();
  }

  {
    const size_t a_shape[] = { 1, 14, 14, 80 };
    const size_t b_shape[] = { 1, 14, 14, 80 };
    status = xnn_setup_add_nd_f16(
      op51,
      4, a_shape, 4, b_shape,
      v51 /* a */, v46 /* b */, v52 /* output */,
      threadpool /* threadpool */);
  }
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #51" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op52,
    1 /* batch size */, 14 /* input height */, 14 /* input width */,
    v52 /* input */, v53 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #52" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_hardswish_nc_f16(
    op53,
    196 /* batch size */,
    v53 /* input */, v54 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #53" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op54,
    1 /* batch size */, 14 /* input height */, 14 /* input width */,
    v54 /* input */, v55 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #54" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_hardswish_nc_f16(
    op55,
    196 /* batch size */,
    v55 /* input */, v56 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #55" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op56,
    1 /* batch size */, 14 /* input height */, 14 /* input width */,
    v56 /* input */, v57 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #56" << std::endl;
    return ExecutionPlan();
  }

  {
    const size_t a_shape[] = { 1, 14, 14, 80 };
    const size_t b_shape[] = { 1, 14, 14, 80 };
    status = xnn_setup_add_nd_f16(
      op57,
      4, a_shape, 4, b_shape,
      v57 /* a */, v52 /* b */, v58 /* output */,
      threadpool /* threadpool */);
  }
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #57" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op58,
    1 /* batch size */, 14 /* input height */, 14 /* input width */,
    v58 /* input */, v59 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #58" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_hardswish_nc_f16(
    op59,
    196 /* batch size */,
    v59 /* input */, v60 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #59" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op60,
    1 /* batch size */, 14 /* input height */, 14 /* input width */,
    v60 /* input */, v61 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #60" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_hardswish_nc_f16(
    op61,
    196 /* batch size */,
    v61 /* input */, v62 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #61" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_global_average_pooling_nwc_f16(
    op62,
    1 /* batch size */, 196 /* width */,
    v62 /* input */, v63 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #62" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op63,
    1 /* batch size */, 1 /* input height */, 1 /* input width */,
    v63 /* input */, v64 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #63" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op64,
    1 /* batch size */, 1 /* input height */, 1 /* input width */,
    v64 /* input */, v65 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #64" << std::endl;
    return ExecutionPlan();
  }

  {
    const size_t a_shape[] = { 1, 14, 14, 480 };
    const size_t b_shape[] = { 1, 1, 1, 480 };
    status = xnn_setup_multiply_nd_f16(
      op65,
      4, a_shape, 4, b_shape,
      v62 /* a */, v65 /* b */, v66 /* output */,
      threadpool /* threadpool */);
  }
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #65" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op66,
    1 /* batch size */, 14 /* input height */, 14 /* input width */,
    v66 /* input */, v67 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #66" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op67,
    1 /* batch size */, 14 /* input height */, 14 /* input width */,
    v67 /* input */, v68 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #67" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_hardswish_nc_f16(
    op68,
    196 /* batch size */,
    v68 /* input */, v69 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #68" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op69,
    1 /* batch size */, 14 /* input height */, 14 /* input width */,
    v69 /* input */, v70 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #69" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_hardswish_nc_f16(
    op70,
    196 /* batch size */,
    v70 /* input */, v71 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #70" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_global_average_pooling_nwc_f16(
    op71,
    1 /* batch size */, 196 /* width */,
    v71 /* input */, v72 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #71" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op72,
    1 /* batch size */, 1 /* input height */, 1 /* input width */,
    v72 /* input */, v73 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #72" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op73,
    1 /* batch size */, 1 /* input height */, 1 /* input width */,
    v73 /* input */, v74 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #73" << std::endl;
    return ExecutionPlan();
  }

  {
    const size_t a_shape[] = { 1, 14, 14, 672 };
    const size_t b_shape[] = { 1, 1, 1, 672 };
    status = xnn_setup_multiply_nd_f16(
      op74,
      4, a_shape, 4, b_shape,
      v71 /* a */, v74 /* b */, v75 /* output */,
      threadpool /* threadpool */);
  }
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #74" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op75,
    1 /* batch size */, 14 /* input height */, 14 /* input width */,
    v75 /* input */, v76 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #75" << std::endl;
    return ExecutionPlan();
  }

  {
    const size_t a_shape[] = { 1, 14, 14, 112 };
    const size_t b_shape[] = { 1, 14, 14, 112 };
    status = xnn_setup_add_nd_f16(
      op76,
      4, a_shape, 4, b_shape,
      v76 /* a */, v67 /* b */, v77 /* output */,
      threadpool /* threadpool */);
  }
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #76" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op77,
    1 /* batch size */, 14 /* input height */, 14 /* input width */,
    v77 /* input */, v78 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #77" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_hardswish_nc_f16(
    op78,
    196 /* batch size */,
    v78 /* input */, v79 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #78" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op79,
    1 /* batch size */, 14 /* input height */, 14 /* input width */,
    v79 /* input */, v80 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #79" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_hardswish_nc_f16(
    op80,
    49 /* batch size */,
    v80 /* input */, v81 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #80" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_global_average_pooling_nwc_f16(
    op81,
    1 /* batch size */, 49 /* width */,
    v81 /* input */, v82 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #81" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op82,
    1 /* batch size */, 1 /* input height */, 1 /* input width */,
    v82 /* input */, v83 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #82" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op83,
    1 /* batch size */, 1 /* input height */, 1 /* input width */,
    v83 /* input */, v84 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #83" << std::endl;
    return ExecutionPlan();
  }

  {
    const size_t a_shape[] = { 1, 7, 7, 672 };
    const size_t b_shape[] = { 1, 1, 1, 672 };
    status = xnn_setup_multiply_nd_f16(
      op84,
      4, a_shape, 4, b_shape,
      v81 /* a */, v84 /* b */, v85 /* output */,
      threadpool /* threadpool */);
  }
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #84" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op85,
    1 /* batch size */, 7 /* input height */, 7 /* input width */,
    v85 /* input */, v86 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #85" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op86,
    1 /* batch size */, 7 /* input height */, 7 /* input width */,
    v86 /* input */, v87 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #86" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_hardswish_nc_f16(
    op87,
    49 /* batch size */,
    v87 /* input */, v88 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #87" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op88,
    1 /* batch size */, 7 /* input height */, 7 /* input width */,
    v88 /* input */, v89 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #88" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_hardswish_nc_f16(
    op89,
    49 /* batch size */,
    v89 /* input */, v90 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #89" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_global_average_pooling_nwc_f16(
    op90,
    1 /* batch size */, 49 /* width */,
    v90 /* input */, v91 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #90" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op91,
    1 /* batch size */, 1 /* input height */, 1 /* input width */,
    v91 /* input */, v92 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #91" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op92,
    1 /* batch size */, 1 /* input height */, 1 /* input width */,
    v92 /* input */, v93 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #92" << std::endl;
    return ExecutionPlan();
  }

  {
    const size_t a_shape[] = { 1, 7, 7, 960 };
    const size_t b_shape[] = { 1, 1, 1, 960 };
    status = xnn_setup_multiply_nd_f16(
      op93,
      4, a_shape, 4, b_shape,
      v90 /* a */, v93 /* b */, v94 /* output */,
      threadpool /* threadpool */);
  }
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #93" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op94,
    1 /* batch size */, 7 /* input height */, 7 /* input width */,
    v94 /* input */, v95 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #94" << std::endl;
    return ExecutionPlan();
  }

  {
    const size_t a_shape[] = { 1, 7, 7, 160 };
    const size_t b_shape[] = { 1, 7, 7, 160 };
    status = xnn_setup_add_nd_f16(
      op95,
      4, a_shape, 4, b_shape,
      v95 /* a */, v86 /* b */, v96 /* output */,
      threadpool /* threadpool */);
  }
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #95" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op96,
    1 /* batch size */, 7 /* input height */, 7 /* input width */,
    v96 /* input */, v97 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #96" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_hardswish_nc_f16(
    op97,
    49 /* batch size */,
    v97 /* input */, v98 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #97" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op98,
    1 /* batch size */, 7 /* input height */, 7 /* input width */,
    v98 /* input */, v99 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #98" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_hardswish_nc_f16(
    op99,
    49 /* batch size */,
    v99 /* input */, v100 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #99" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_global_average_pooling_nwc_f16(
    op100,
    1 /* batch size */, 49 /* width */,
    v100 /* input */, v101 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #100" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op101,
    1 /* batch size */, 1 /* input height */, 1 /* input width */,
    v101 /* input */, v102 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #101" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op102,
    1 /* batch size */, 1 /* input height */, 1 /* input width */,
    v102 /* input */, v103 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #102" << std::endl;
    return ExecutionPlan();
  }

  {
    const size_t a_shape[] = { 1, 7, 7, 960 };
    const size_t b_shape[] = { 1, 1, 1, 960 };
    status = xnn_setup_multiply_nd_f16(
      op103,
      4, a_shape, 4, b_shape,
      v100 /* a */, v103 /* b */, v104 /* output */,
      threadpool /* threadpool */);
  }
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #103" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op104,
    1 /* batch size */, 7 /* input height */, 7 /* input width */,
    v104 /* input */, v105 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #104" << std::endl;
    return ExecutionPlan();
  }

  {
    const size_t a_shape[] = { 1, 7, 7, 160 };
    const size_t b_shape[] = { 1, 7, 7, 160 };
    status = xnn_setup_add_nd_f16(
      op105,
      4, a_shape, 4, b_shape,
      v105 /* a */, v96 /* b */, v106 /* output */,
      threadpool /* threadpool */);
  }
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #105" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op106,
    1 /* batch size */, 7 /* input height */, 7 /* input width */,
    v106 /* input */, v107 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #106" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_hardswish_nc_f16(
    op107,
    49 /* batch size */,
    v107 /* input */, v108 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #107" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_global_average_pooling_nwc_f16(
    op108,
    1 /* batch size */, 49 /* width */,
    v108 /* input */, v109 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #108" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op109,
    1 /* batch size */, 1 /* input height */, 1 /* input width */,
    v109 /* input */, v110 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #109" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_hardswish_nc_f16(
    op110,
    1 /* batch size */,
    v110 /* input */, v111 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #110" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_global_average_pooling_nwc_f16(
    op111,
    1 /* batch size */, 1 /* width */,
    v111 /* input */, v112 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #111" << std::endl;
    return ExecutionPlan();
  }

  status = xnn_setup_convolution2d_nhwc_f16(
    op112,
    1 /* batch size */, 1 /* input height */, 1 /* input width */,
    v112 /* input */, v113 /* output */,
    threadpool /* threadpool */);
  if (status != xnn_status_success) {
    std::cerr << "failed to setup operation #112" << std::endl;
    return ExecutionPlan();
  }

  #pragma clang diagnostic push
  #pragma clang diagnostic ignored "-Wpessimizing-move"
  return operators;
  #pragma clang diagnostic pop
}

}  // namespace models