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

	#include <xnnpack/cache.h>
	#include <xnnpack/common.h>
	#include <xnnpack/memory.h>
	#include <xnnpack/models.h>

	namespace models {

	ExecutionPlan FP32MobileNetV2(bool use_jit, pthreadpool_t threadpool) {
	alignas(16) static std::array<float, 150528 + XNN_EXTRA_BYTES / sizeof(float)> v0;
	alignas(16) static std::array<float, 401408 + XNN_EXTRA_BYTES / sizeof(float)> v1;
	alignas(16) static std::array<float, 401408 + XNN_EXTRA_BYTES / sizeof(float)> v2;
	alignas(16) static std::array<float, 200704 + XNN_EXTRA_BYTES / sizeof(float)> v3;
	alignas(16) static std::array<float, 1204224 + XNN_EXTRA_BYTES / sizeof(float)> v4;
	alignas(16) static std::array<float, 301056 + XNN_EXTRA_BYTES / sizeof(float)> v5;
	alignas(16) static std::array<float, 75264 + XNN_EXTRA_BYTES / sizeof(float)> v6;
	alignas(16) static std::array<float, 451584 + XNN_EXTRA_BYTES / sizeof(float)> v7;
	alignas(16) static std::array<float, 451584 + XNN_EXTRA_BYTES / sizeof(float)> v8;
	alignas(16) static std::array<float, 75264 + XNN_EXTRA_BYTES / sizeof(float)> v9;
	alignas(16) static std::array<float, 75264 + XNN_EXTRA_BYTES / sizeof(float)> v10;
	alignas(16) static std::array<float, 451584 + XNN_EXTRA_BYTES / sizeof(float)> v11;
	alignas(16) static std::array<float, 112896 + XNN_EXTRA_BYTES / sizeof(float)> v12;
	alignas(16) static std::array<float, 25088 + XNN_EXTRA_BYTES / sizeof(float)> v13;
	alignas(16) static std::array<float, 150528 + XNN_EXTRA_BYTES / sizeof(float)> v14;
	alignas(16) static std::array<float, 150528 + XNN_EXTRA_BYTES / sizeof(float)> v15;
	alignas(16) static std::array<float, 25088 + XNN_EXTRA_BYTES / sizeof(float)> v16;
	alignas(16) static std::array<float, 25088 + XNN_EXTRA_BYTES / sizeof(float)> v17;
	alignas(16) static std::array<float, 150528 + XNN_EXTRA_BYTES / sizeof(float)> v18;
	alignas(16) static std::array<float, 150528 + XNN_EXTRA_BYTES / sizeof(float)> v19;
	alignas(16) static std::array<float, 25088 + XNN_EXTRA_BYTES / sizeof(float)> v20;
	alignas(16) static std::array<float, 25088 + XNN_EXTRA_BYTES / sizeof(float)> v21;
	alignas(16) static std::array<float, 150528 + XNN_EXTRA_BYTES / sizeof(float)> v22;
	alignas(16) static std::array<float, 37632 + XNN_EXTRA_BYTES / sizeof(float)> v23;
	alignas(16) static std::array<float, 12544 + XNN_EXTRA_BYTES / sizeof(float)> v24;
	alignas(16) static std::array<float, 75264 + XNN_EXTRA_BYTES / sizeof(float)> v25;
	alignas(16) static std::array<float, 75264 + XNN_EXTRA_BYTES / sizeof(float)> v26;
	alignas(16) static std::array<float, 12544 + XNN_EXTRA_BYTES / sizeof(float)> v27;
	alignas(16) static std::array<float, 12544 + XNN_EXTRA_BYTES / sizeof(float)> v28;
	alignas(16) static std::array<float, 75264 + XNN_EXTRA_BYTES / sizeof(float)> v29;
	alignas(16) static std::array<float, 75264 + XNN_EXTRA_BYTES / sizeof(float)> v30;
	alignas(16) static std::array<float, 12544 + XNN_EXTRA_BYTES / sizeof(float)> v31;
	alignas(16) static std::array<float, 12544 + XNN_EXTRA_BYTES / sizeof(float)> v32;
	alignas(16) static std::array<float, 75264 + XNN_EXTRA_BYTES / sizeof(float)> v33;
	alignas(16) static std::array<float, 75264 + XNN_EXTRA_BYTES / sizeof(float)> v34;
	alignas(16) static std::array<float, 12544 + XNN_EXTRA_BYTES / sizeof(float)> v35;
	alignas(16) static std::array<float, 12544 + XNN_EXTRA_BYTES / sizeof(float)> v36;
	alignas(16) static std::array<float, 75264 + XNN_EXTRA_BYTES / sizeof(float)> v37;
	alignas(16) static std::array<float, 75264 + XNN_EXTRA_BYTES / sizeof(float)> v38;
	alignas(16) static std::array<float, 18816 + XNN_EXTRA_BYTES / sizeof(float)> v39;
	alignas(16) static std::array<float, 112896 + XNN_EXTRA_BYTES / sizeof(float)> v40;
	alignas(16) static std::array<float, 112896 + XNN_EXTRA_BYTES / sizeof(float)> v41;
	alignas(16) static std::array<float, 18816 + XNN_EXTRA_BYTES / sizeof(float)> v42;
	alignas(16) static std::array<float, 18816 + XNN_EXTRA_BYTES / sizeof(float)> v43;
	alignas(16) static std::array<float, 112896 + XNN_EXTRA_BYTES / sizeof(float)> v44;
	alignas(16) static std::array<float, 112896 + XNN_EXTRA_BYTES / sizeof(float)> v45;
	alignas(16) static std::array<float, 18816 + XNN_EXTRA_BYTES / sizeof(float)> v46;
	alignas(16) static std::array<float, 18816 + XNN_EXTRA_BYTES / sizeof(float)> v47;
	alignas(16) static std::array<float, 112896 + XNN_EXTRA_BYTES / sizeof(float)> v48;
	alignas(16) static std::array<float, 28224 + XNN_EXTRA_BYTES / sizeof(float)> v49;
	alignas(16) static std::array<float, 7840 + XNN_EXTRA_BYTES / sizeof(float)> v50;
	alignas(16) static std::array<float, 47040 + XNN_EXTRA_BYTES / sizeof(float)> v51;
	alignas(16) static std::array<float, 47040 + XNN_EXTRA_BYTES / sizeof(float)> v52;
	alignas(16) static std::array<float, 7840 + XNN_EXTRA_BYTES / sizeof(float)> v53;
	alignas(16) static std::array<float, 7840 + XNN_EXTRA_BYTES / sizeof(float)> v54;
	alignas(16) static std::array<float, 47040 + XNN_EXTRA_BYTES / sizeof(float)> v55;
	alignas(16) static std::array<float, 47040 + XNN_EXTRA_BYTES / sizeof(float)> v56;
	alignas(16) static std::array<float, 7840 + XNN_EXTRA_BYTES / sizeof(float)> v57;
	alignas(16) static std::array<float, 7840 + XNN_EXTRA_BYTES / sizeof(float)> v58;
	alignas(16) static std::array<float, 47040 + XNN_EXTRA_BYTES / sizeof(float)> v59;
	alignas(16) static std::array<float, 47040 + XNN_EXTRA_BYTES / sizeof(float)> v60;
	alignas(16) static std::array<float, 15680 + XNN_EXTRA_BYTES / sizeof(float)> v61;
	alignas(16) static std::array<float, 62720 + XNN_EXTRA_BYTES / sizeof(float)> v62;
	alignas(16) static std::array<float, 1280 + XNN_EXTRA_BYTES / sizeof(float)> v63;
	alignas(16) static std::array<float, 1001 + XNN_EXTRA_BYTES / sizeof(float)> v64;
	alignas(16) static std::array<float, 864 + XNN_EXTRA_BYTES / sizeof(float)> w65;
	alignas(16) static std::array<float, 32 + XNN_EXTRA_BYTES / sizeof(float)> w66;
	alignas(16) static std::array<float, 288 + XNN_EXTRA_BYTES / sizeof(float)> w67;
	alignas(16) static std::array<float, 32 + XNN_EXTRA_BYTES / sizeof(float)> w68;
	alignas(16) static std::array<float, 512 + XNN_EXTRA_BYTES / sizeof(float)> w69;
	alignas(16) static std::array<float, 16 + XNN_EXTRA_BYTES / sizeof(float)> w70;
	alignas(16) static std::array<float, 1536 + XNN_EXTRA_BYTES / sizeof(float)> w71;
	alignas(16) static std::array<float, 96 + XNN_EXTRA_BYTES / sizeof(float)> w72;
	alignas(16) static std::array<float, 864 + XNN_EXTRA_BYTES / sizeof(float)> w73;
	alignas(16) static std::array<float, 96 + XNN_EXTRA_BYTES / sizeof(float)> w74;
	alignas(16) static std::array<float, 2304 + XNN_EXTRA_BYTES / sizeof(float)> w75;
	alignas(16) static std::array<float, 24 + XNN_EXTRA_BYTES / sizeof(float)> w76;
	alignas(16) static std::array<float, 3456 + XNN_EXTRA_BYTES / sizeof(float)> w77;
	alignas(16) static std::array<float, 144 + XNN_EXTRA_BYTES / sizeof(float)> w78;
	alignas(16) static std::array<float, 1296 + XNN_EXTRA_BYTES / sizeof(float)> w79;
	alignas(16) static std::array<float, 144 + XNN_EXTRA_BYTES / sizeof(float)> w80;
	alignas(16) static std::array<float, 3456 + XNN_EXTRA_BYTES / sizeof(float)> w81;
	alignas(16) static std::array<float, 24 + XNN_EXTRA_BYTES / sizeof(float)> w82;
	alignas(16) static std::array<float, 3456 + XNN_EXTRA_BYTES / sizeof(float)> w83;
	alignas(16) static std::array<float, 144 + XNN_EXTRA_BYTES / sizeof(float)> w84;
	alignas(16) static std::array<float, 1296 + XNN_EXTRA_BYTES / sizeof(float)> w85;
	alignas(16) static std::array<float, 144 + XNN_EXTRA_BYTES / sizeof(float)> w86;
	alignas(16) static std::array<float, 4608 + XNN_EXTRA_BYTES / sizeof(float)> w87;
	alignas(16) static std::array<float, 32 + XNN_EXTRA_BYTES / sizeof(float)> w88;
	alignas(16) static std::array<float, 6144 + XNN_EXTRA_BYTES / sizeof(float)> w89;
	alignas(16) static std::array<float, 192 + XNN_EXTRA_BYTES / sizeof(float)> w90;
	alignas(16) static std::array<float, 1728 + XNN_EXTRA_BYTES / sizeof(float)> w91;
	alignas(16) static std::array<float, 192 + XNN_EXTRA_BYTES / sizeof(float)> w92;
	alignas(16) static std::array<float, 6144 + XNN_EXTRA_BYTES / sizeof(float)> w93;
	alignas(16) static std::array<float, 32 + XNN_EXTRA_BYTES / sizeof(float)> w94;
	alignas(16) static std::array<float, 6144 + XNN_EXTRA_BYTES / sizeof(float)> w95;
	alignas(16) static std::array<float, 192 + XNN_EXTRA_BYTES / sizeof(float)> w96;
	alignas(16) static std::array<float, 1728 + XNN_EXTRA_BYTES / sizeof(float)> w97;
	alignas(16) static std::array<float, 192 + XNN_EXTRA_BYTES / sizeof(float)> w98;
	alignas(16) static std::array<float, 6144 + XNN_EXTRA_BYTES / sizeof(float)> w99;
	alignas(16) static std::array<float, 32 + XNN_EXTRA_BYTES / sizeof(float)> w100;
	alignas(16) static std::array<float, 6144 + XNN_EXTRA_BYTES / sizeof(float)> w101;
	alignas(16) static std::array<float, 192 + XNN_EXTRA_BYTES / sizeof(float)> w102;
	alignas(16) static std::array<float, 1728 + XNN_EXTRA_BYTES / sizeof(float)> w103;
	alignas(16) static std::array<float, 192 + XNN_EXTRA_BYTES / sizeof(float)> w104;
	alignas(16) static std::array<float, 12288 + XNN_EXTRA_BYTES / sizeof(float)> w105;
	alignas(16) static std::array<float, 64 + XNN_EXTRA_BYTES / sizeof(float)> w106;
	alignas(16) static std::array<float, 24576 + XNN_EXTRA_BYTES / sizeof(float)> w107;
	alignas(16) static std::array<float, 384 + XNN_EXTRA_BYTES / sizeof(float)> w108;
	alignas(16) static std::array<float, 3456 + XNN_EXTRA_BYTES / sizeof(float)> w109;
	alignas(16) static std::array<float, 384 + XNN_EXTRA_BYTES / sizeof(float)> w110;
	alignas(16) static std::array<float, 24576 + XNN_EXTRA_BYTES / sizeof(float)> w111;
	alignas(16) static std::array<float, 64 + XNN_EXTRA_BYTES / sizeof(float)> w112;
	alignas(16) static std::array<float, 24576 + XNN_EXTRA_BYTES / sizeof(float)> w113;
	alignas(16) static std::array<float, 384 + XNN_EXTRA_BYTES / sizeof(float)> w114;
	alignas(16) static std::array<float, 3456 + XNN_EXTRA_BYTES / sizeof(float)> w115;
	alignas(16) static std::array<float, 384 + XNN_EXTRA_BYTES / sizeof(float)> w116;
	alignas(16) static std::array<float, 24576 + XNN_EXTRA_BYTES / sizeof(float)> w117;
	alignas(16) static std::array<float, 64 + XNN_EXTRA_BYTES / sizeof(float)> w118;
	alignas(16) static std::array<float, 24576 + XNN_EXTRA_BYTES / sizeof(float)> w119;
	alignas(16) static std::array<float, 384 + XNN_EXTRA_BYTES / sizeof(float)> w120;
	alignas(16) static std::array<float, 3456 + XNN_EXTRA_BYTES / sizeof(float)> w121;
	alignas(16) static std::array<float, 384 + XNN_EXTRA_BYTES / sizeof(float)> w122;
	alignas(16) static std::array<float, 24576 + XNN_EXTRA_BYTES / sizeof(float)> w123;
	alignas(16) static std::array<float, 64 + XNN_EXTRA_BYTES / sizeof(float)> w124;
	alignas(16) static std::array<float, 24576 + XNN_EXTRA_BYTES / sizeof(float)> w125;
	alignas(16) static std::array<float, 384 + XNN_EXTRA_BYTES / sizeof(float)> w126;
	alignas(16) static std::array<float, 3456 + XNN_EXTRA_BYTES / sizeof(float)> w127;
	alignas(16) static std::array<float, 384 + XNN_EXTRA_BYTES / sizeof(float)> w128;
	alignas(16) static std::array<float, 36864 + XNN_EXTRA_BYTES / sizeof(float)> w129;
	alignas(16) static std::array<float, 96 + XNN_EXTRA_BYTES / sizeof(float)> w130;
	alignas(16) static std::array<float, 55296 + XNN_EXTRA_BYTES / sizeof(float)> w131;
	alignas(16) static std::array<float, 576 + XNN_EXTRA_BYTES / sizeof(float)> w132;
	alignas(16) static std::array<float, 5184 + XNN_EXTRA_BYTES / sizeof(float)> w133;
	alignas(16) static std::array<float, 576 + XNN_EXTRA_BYTES / sizeof(float)> w134;
	alignas(16) static std::array<float, 55296 + XNN_EXTRA_BYTES / sizeof(float)> w135;
	alignas(16) static std::array<float, 96 + XNN_EXTRA_BYTES / sizeof(float)> w136;
	alignas(16) static std::array<float, 55296 + XNN_EXTRA_BYTES / sizeof(float)> w137;
	alignas(16) static std::array<float, 576 + XNN_EXTRA_BYTES / sizeof(float)> w138;
	alignas(16) static std::array<float, 5184 + XNN_EXTRA_BYTES / sizeof(float)> w139;
	alignas(16) static std::array<float, 576 + XNN_EXTRA_BYTES / sizeof(float)> w140;
	alignas(16) static std::array<float, 55296 + XNN_EXTRA_BYTES / sizeof(float)> w141;
	alignas(16) static std::array<float, 96 + XNN_EXTRA_BYTES / sizeof(float)> w142;
	alignas(16) static std::array<float, 55296 + XNN_EXTRA_BYTES / sizeof(float)> w143;
	alignas(16) static std::array<float, 576 + XNN_EXTRA_BYTES / sizeof(float)> w144;
	alignas(16) static std::array<float, 5184 + XNN_EXTRA_BYTES / sizeof(float)> w145;
	alignas(16) static std::array<float, 576 + XNN_EXTRA_BYTES / sizeof(float)> w146;
	alignas(16) static std::array<float, 92160 + XNN_EXTRA_BYTES / sizeof(float)> w147;
	alignas(16) static std::array<float, 160 + XNN_EXTRA_BYTES / sizeof(float)> w148;
	alignas(16) static std::array<float, 153600 + XNN_EXTRA_BYTES / sizeof(float)> w149;
	alignas(16) static std::array<float, 960 + XNN_EXTRA_BYTES / sizeof(float)> w150;
	alignas(16) static std::array<float, 8640 + XNN_EXTRA_BYTES / sizeof(float)> w151;
	alignas(16) static std::array<float, 960 + XNN_EXTRA_BYTES / sizeof(float)> w152;
	alignas(16) static std::array<float, 153600 + XNN_EXTRA_BYTES / sizeof(float)> w153;
	alignas(16) static std::array<float, 160 + XNN_EXTRA_BYTES / sizeof(float)> w154;
	alignas(16) static std::array<float, 153600 + XNN_EXTRA_BYTES / sizeof(float)> w155;
	alignas(16) static std::array<float, 960 + XNN_EXTRA_BYTES / sizeof(float)> w156;
	alignas(16) static std::array<float, 8640 + XNN_EXTRA_BYTES / sizeof(float)> w157;
	alignas(16) static std::array<float, 960 + XNN_EXTRA_BYTES / sizeof(float)> w158;
	alignas(16) static std::array<float, 153600 + XNN_EXTRA_BYTES / sizeof(float)> w159;
	alignas(16) static std::array<float, 160 + XNN_EXTRA_BYTES / sizeof(float)> w160;
	alignas(16) static std::array<float, 153600 + XNN_EXTRA_BYTES / sizeof(float)> w161;
	alignas(16) static std::array<float, 960 + XNN_EXTRA_BYTES / sizeof(float)> w162;
	alignas(16) static std::array<float, 8640 + XNN_EXTRA_BYTES / sizeof(float)> w163;
	alignas(16) static std::array<float, 960 + XNN_EXTRA_BYTES / sizeof(float)> w164;
	alignas(16) static std::array<float, 307200 + XNN_EXTRA_BYTES / sizeof(float)> w165;
	alignas(16) static std::array<float, 320 + XNN_EXTRA_BYTES / sizeof(float)> w166;
	alignas(16) static std::array<float, 409600 + XNN_EXTRA_BYTES / sizeof(float)> w167;
	alignas(16) static std::array<float, 1280 + XNN_EXTRA_BYTES / sizeof(float)> w168;
	alignas(16) static std::array<float, 1281280 + XNN_EXTRA_BYTES / sizeof(float)> w169;
	alignas(16) static std::array<float, 1001 + XNN_EXTRA_BYTES / sizeof(float)> w170;

	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));
	std::generate(v0.begin(), v0.end(), std::ref(f32rng));
	std::generate(v1.begin(), v1.end(), std::ref(f32rng));
	std::generate(v2.begin(), v2.end(), std::ref(f32rng));
	std::generate(v3.begin(), v3.end(), std::ref(f32rng));
	std::generate(v4.begin(), v4.end(), std::ref(f32rng));
	std::generate(v5.begin(), v5.end(), std::ref(f32rng));
	std::generate(v6.begin(), v6.end(), std::ref(f32rng));
	std::generate(v7.begin(), v7.end(), std::ref(f32rng));
	std::generate(v8.begin(), v8.end(), std::ref(f32rng));
	std::generate(v9.begin(), v9.end(), std::ref(f32rng));
	std::generate(v10.begin(), v10.end(), std::ref(f32rng));
	std::generate(v11.begin(), v11.end(), std::ref(f32rng));
	std::generate(v12.begin(), v12.end(), std::ref(f32rng));
	std::generate(v13.begin(), v13.end(), std::ref(f32rng));
	std::generate(v14.begin(), v14.end(), std::ref(f32rng));
	std::generate(v15.begin(), v15.end(), std::ref(f32rng));
	std::generate(v16.begin(), v16.end(), std::ref(f32rng));
	std::generate(v17.begin(), v17.end(), std::ref(f32rng));
	std::generate(v18.begin(), v18.end(), std::ref(f32rng));
	std::generate(v19.begin(), v19.end(), std::ref(f32rng));
	std::generate(v20.begin(), v20.end(), std::ref(f32rng));
	std::generate(v21.begin(), v21.end(), std::ref(f32rng));
	std::generate(v22.begin(), v22.end(), std::ref(f32rng));
	std::generate(v23.begin(), v23.end(), std::ref(f32rng));
	std::generate(v24.begin(), v24.end(), std::ref(f32rng));
	std::generate(v25.begin(), v25.end(), std::ref(f32rng));
	std::generate(v26.begin(), v26.end(), std::ref(f32rng));
	std::generate(v27.begin(), v27.end(), std::ref(f32rng));
	std::generate(v28.begin(), v28.end(), std::ref(f32rng));
	std::generate(v29.begin(), v29.end(), std::ref(f32rng));
	std::generate(v30.begin(), v30.end(), std::ref(f32rng));
	std::generate(v31.begin(), v31.end(), std::ref(f32rng));
	std::generate(v32.begin(), v32.end(), std::ref(f32rng));
	std::generate(v33.begin(), v33.end(), std::ref(f32rng));
	std::generate(v34.begin(), v34.end(), std::ref(f32rng));
	std::generate(v35.begin(), v35.end(), std::ref(f32rng));
	std::generate(v36.begin(), v36.end(), std::ref(f32rng));
	std::generate(v37.begin(), v37.end(), std::ref(f32rng));
	std::generate(v38.begin(), v38.end(), std::ref(f32rng));
	std::generate(v39.begin(), v39.end(), std::ref(f32rng));
	std::generate(v40.begin(), v40.end(), std::ref(f32rng));
	std::generate(v41.begin(), v41.end(), std::ref(f32rng));
	std::generate(v42.begin(), v42.end(), std::ref(f32rng));
	std::generate(v43.begin(), v43.end(), std::ref(f32rng));
	std::generate(v44.begin(), v44.end(), std::ref(f32rng));
	std::generate(v45.begin(), v45.end(), std::ref(f32rng));
	std::generate(v46.begin(), v46.end(), std::ref(f32rng));
	std::generate(v47.begin(), v47.end(), std::ref(f32rng));
	std::generate(v48.begin(), v48.end(), std::ref(f32rng));
	std::generate(v49.begin(), v49.end(), std::ref(f32rng));
	std::generate(v50.begin(), v50.end(), std::ref(f32rng));
	std::generate(v51.begin(), v51.end(), std::ref(f32rng));
	std::generate(v52.begin(), v52.end(), std::ref(f32rng));
	std::generate(v53.begin(), v53.end(), std::ref(f32rng));
	std::generate(v54.begin(), v54.end(), std::ref(f32rng));
	std::generate(v55.begin(), v55.end(), std::ref(f32rng));
	std::generate(v56.begin(), v56.end(), std::ref(f32rng));
	std::generate(v57.begin(), v57.end(), std::ref(f32rng));
	std::generate(v58.begin(), v58.end(), std::ref(f32rng));
	std::generate(v59.begin(), v59.end(), std::ref(f32rng));
	std::generate(v60.begin(), v60.end(), std::ref(f32rng));
	std::generate(v61.begin(), v61.end(), std::ref(f32rng));
	std::generate(v62.begin(), v62.end(), std::ref(f32rng));
	std::generate(v63.begin(), v63.end(), std::ref(f32rng));
	std::generate(v64.begin(), v64.end(), std::ref(f32rng));
	std::generate(w65.begin(), w65.end(), std::ref(f32rng));
	std::generate(w66.begin(), w66.end(), std::ref(f32rng));
	std::generate(w67.begin(), w67.end(), std::ref(f32rng));
	std::generate(w68.begin(), w68.end(), std::ref(f32rng));
	std::generate(w69.begin(), w69.end(), std::ref(f32rng));
	std::generate(w70.begin(), w70.end(), std::ref(f32rng));
	std::generate(w71.begin(), w71.end(), std::ref(f32rng));
	std::generate(w72.begin(), w72.end(), std::ref(f32rng));
	std::generate(w73.begin(), w73.end(), std::ref(f32rng));
	std::generate(w74.begin(), w74.end(), std::ref(f32rng));
	std::generate(w75.begin(), w75.end(), std::ref(f32rng));
	std::generate(w76.begin(), w76.end(), std::ref(f32rng));
	std::generate(w77.begin(), w77.end(), std::ref(f32rng));
	std::generate(w78.begin(), w78.end(), std::ref(f32rng));
	std::generate(w79.begin(), w79.end(), std::ref(f32rng));
	std::generate(w80.begin(), w80.end(), std::ref(f32rng));
	std::generate(w81.begin(), w81.end(), std::ref(f32rng));
	std::generate(w82.begin(), w82.end(), std::ref(f32rng));
	std::generate(w83.begin(), w83.end(), std::ref(f32rng));
	std::generate(w84.begin(), w84.end(), std::ref(f32rng));
	std::generate(w85.begin(), w85.end(), std::ref(f32rng));
	std::generate(w86.begin(), w86.end(), std::ref(f32rng));
	std::generate(w87.begin(), w87.end(), std::ref(f32rng));
	std::generate(w88.begin(), w88.end(), std::ref(f32rng));
	std::generate(w89.begin(), w89.end(), std::ref(f32rng));
	std::generate(w90.begin(), w90.end(), std::ref(f32rng));
	std::generate(w91.begin(), w91.end(), std::ref(f32rng));
	std::generate(w92.begin(), w92.end(), std::ref(f32rng));
	std::generate(w93.begin(), w93.end(), std::ref(f32rng));
	std::generate(w94.begin(), w94.end(), std::ref(f32rng));
	std::generate(w95.begin(), w95.end(), std::ref(f32rng));
	std::generate(w96.begin(), w96.end(), std::ref(f32rng));
	std::generate(w97.begin(), w97.end(), std::ref(f32rng));
	std::generate(w98.begin(), w98.end(), std::ref(f32rng));
	std::generate(w99.begin(), w99.end(), std::ref(f32rng));
	std::generate(w100.begin(), w100.end(), std::ref(f32rng));
	std::generate(w101.begin(), w101.end(), std::ref(f32rng));
	std::generate(w102.begin(), w102.end(), std::ref(f32rng));
	std::generate(w103.begin(), w103.end(), std::ref(f32rng));
	std::generate(w104.begin(), w104.end(), std::ref(f32rng));
	std::generate(w105.begin(), w105.end(), std::ref(f32rng));
	std::generate(w106.begin(), w106.end(), std::ref(f32rng));
	std::generate(w107.begin(), w107.end(), std::ref(f32rng));
	std::generate(w108.begin(), w108.end(), std::ref(f32rng));
	std::generate(w109.begin(), w109.end(), std::ref(f32rng));
	std::generate(w110.begin(), w110.end(), std::ref(f32rng));
	std::generate(w111.begin(), w111.end(), std::ref(f32rng));
	std::generate(w112.begin(), w112.end(), std::ref(f32rng));
	std::generate(w113.begin(), w113.end(), std::ref(f32rng));
	std::generate(w114.begin(), w114.end(), std::ref(f32rng));
	std::generate(w115.begin(), w115.end(), std::ref(f32rng));
	std::generate(w116.begin(), w116.end(), std::ref(f32rng));
	std::generate(w117.begin(), w117.end(), std::ref(f32rng));
	std::generate(w118.begin(), w118.end(), std::ref(f32rng));
	std::generate(w119.begin(), w119.end(), std::ref(f32rng));
	std::generate(w120.begin(), w120.end(), std::ref(f32rng));
	std::generate(w121.begin(), w121.end(), std::ref(f32rng));
	std::generate(w122.begin(), w122.end(), std::ref(f32rng));
	std::generate(w123.begin(), w123.end(), std::ref(f32rng));
	std::generate(w124.begin(), w124.end(), std::ref(f32rng));
	std::generate(w125.begin(), w125.end(), std::ref(f32rng));
	std::generate(w126.begin(), w126.end(), std::ref(f32rng));
	std::generate(w127.begin(), w127.end(), std::ref(f32rng));
	std::generate(w128.begin(), w128.end(), std::ref(f32rng));
	std::generate(w129.begin(), w129.end(), std::ref(f32rng));
	std::generate(w130.begin(), w130.end(), std::ref(f32rng));
	std::generate(w131.begin(), w131.end(), std::ref(f32rng));
	std::generate(w132.begin(), w132.end(), std::ref(f32rng));
	std::generate(w133.begin(), w133.end(), std::ref(f32rng));
	std::generate(w134.begin(), w134.end(), std::ref(f32rng));
	std::generate(w135.begin(), w135.end(), std::ref(f32rng));
	std::generate(w136.begin(), w136.end(), std::ref(f32rng));
	std::generate(w137.begin(), w137.end(), std::ref(f32rng));
	std::generate(w138.begin(), w138.end(), std::ref(f32rng));
	std::generate(w139.begin(), w139.end(), std::ref(f32rng));
	std::generate(w140.begin(), w140.end(), std::ref(f32rng));
	std::generate(w141.begin(), w141.end(), std::ref(f32rng));
	std::generate(w142.begin(), w142.end(), std::ref(f32rng));
	std::generate(w143.begin(), w143.end(), std::ref(f32rng));
	std::generate(w144.begin(), w144.end(), std::ref(f32rng));
	std::generate(w145.begin(), w145.end(), std::ref(f32rng));
	std::generate(w146.begin(), w146.end(), std::ref(f32rng));
	std::generate(w147.begin(), w147.end(), std::ref(f32rng));
	std::generate(w148.begin(), w148.end(), std::ref(f32rng));
	std::generate(w149.begin(), w149.end(), std::ref(f32rng));
	std::generate(w150.begin(), w150.end(), std::ref(f32rng));
	std::generate(w151.begin(), w151.end(), std::ref(f32rng));
	std::generate(w152.begin(), w152.end(), std::ref(f32rng));
	std::generate(w153.begin(), w153.end(), std::ref(f32rng));
	std::generate(w154.begin(), w154.end(), std::ref(f32rng));
	std::generate(w155.begin(), w155.end(), std::ref(f32rng));
	std::generate(w156.begin(), w156.end(), std::ref(f32rng));
	std::generate(w157.begin(), w157.end(), std::ref(f32rng));
	std::generate(w158.begin(), w158.end(), std::ref(f32rng));
	std::generate(w159.begin(), w159.end(), std::ref(f32rng));
	std::generate(w160.begin(), w160.end(), std::ref(f32rng));
	std::generate(w161.begin(), w161.end(), std::ref(f32rng));
	std::generate(w162.begin(), w162.end(), std::ref(f32rng));
	std::generate(w163.begin(), w163.end(), std::ref(f32rng));
	std::generate(w164.begin(), w164.end(), std::ref(f32rng));
	std::generate(w165.begin(), w165.end(), std::ref(f32rng));
	std::generate(w166.begin(), w166.end(), std::ref(f32rng));
	std::generate(w167.begin(), w167.end(), std::ref(f32rng));
	std::generate(w168.begin(), w168.end(), std::ref(f32rng));
	std::generate(w169.begin(), w169.end(), std::ref(f32rng));
	std::generate(w170.begin(), w170.end(), std::ref(f32rng));

	ExecutionPlan operators;
	xnn_status status;
	xnn_code_cache* code_cache_ptr = nullptr;
	#if XNN_PLATFORM_JIT
	xnn_code_cache code_cache;
	if (use_jit) {
	status = xnn_init_code_cache(&code_cache);
	if (status != xnn_status_success) {
	std::cerr << "failed to initailize code cache" << std::endl;
	return ExecutionPlan();
	}
	code_cache_ptr = &code_cache;
	}
	#endif // XNN_PLATFORM_JIT

	xnn_operator_t op0 = nullptr;
	status = xnn_create_convolution2d_nhwc_f32(
	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 */,
	32 /* output_channels_per_group */,
	3 /* input pixel stride */,
	32 /* output pixel stride */,
	w65.data(), w66.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_convolution2d_nhwc_f32(
	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 */,
	32 /* groups */,
	1 /* input channels per group */,
	1 /* output_channels_per_group */,
	32 /* input pixel stride */,
	32 /* output pixel stride */,
	w67.data(), w68.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	16 /* output_channels_per_group */,
	32 /* input pixel stride */,
	16 /* output pixel stride */,
	w69.data(), w70.data(),
	-std::numeric_limits<float>::infinity() /* output min /, std::numeric_limits<float>::infinity() / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	96 /* output_channels_per_group */,
	16 /* input pixel stride */,
	96 /* output pixel stride */,
	w71.data(), w72.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_convolution2d_nhwc_f32(
	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 */,
	96 /* groups */,
	1 /* input channels per group */,
	1 /* output_channels_per_group */,
	96 /* input pixel stride */,
	96 /* output pixel stride */,
	w73.data(), w74.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	96 /* input channels per group */,
	24 /* output_channels_per_group */,
	96 /* input pixel stride */,
	24 /* output pixel stride */,
	w75.data(), w76.data(),
	-std::numeric_limits<float>::infinity() /* output min /, std::numeric_limits<float>::infinity() / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	144 /* output_channels_per_group */,
	24 /* input pixel stride */,
	144 /* output pixel stride */,
	w77.data(), w78.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	144 /* groups */,
	1 /* input channels per group */,
	1 /* output_channels_per_group */,
	144 /* input pixel stride */,
	144 /* output pixel stride */,
	w79.data(), w80.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	144 /* input channels per group */,
	24 /* output_channels_per_group */,
	144 /* input pixel stride */,
	24 /* output pixel stride */,
	w81.data(), w82.data(),
	-std::numeric_limits<float>::infinity() /* output min /, std::numeric_limits<float>::infinity() / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_add_nd_f32(
	-std::numeric_limits<float>::infinity() /* 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_f32(
	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 */,
	144 /* output_channels_per_group */,
	24 /* input pixel stride */,
	144 /* output pixel stride */,
	w83.data(), w84.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_convolution2d_nhwc_f32(
	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 */,
	144 /* groups */,
	1 /* input channels per group */,
	1 /* output_channels_per_group */,
	144 /* input pixel stride */,
	144 /* output pixel stride */,
	w85.data(), w86.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	144 /* input channels per group */,
	32 /* output_channels_per_group */,
	144 /* input pixel stride */,
	32 /* output pixel stride */,
	w87.data(), w88.data(),
	-std::numeric_limits<float>::infinity() /* output min /, std::numeric_limits<float>::infinity() / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	192 /* output_channels_per_group */,
	32 /* input pixel stride */,
	192 /* output pixel stride */,
	w89.data(), w90.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_convolution2d_nhwc_f32(
	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 */,
	192 /* groups */,
	1 /* input channels per group */,
	1 /* output_channels_per_group */,
	192 /* input pixel stride */,
	192 /* output pixel stride */,
	w91.data(), w92.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	192 /* input channels per group */,
	32 /* output_channels_per_group */,
	192 /* input pixel stride */,
	32 /* output pixel stride */,
	w93.data(), w94.data(),
	-std::numeric_limits<float>::infinity() /* output min /, std::numeric_limits<float>::infinity() / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_add_nd_f32(
	-std::numeric_limits<float>::infinity() /* output min /, std::numeric_limits<float>::infinity() / 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_convolution2d_nhwc_f32(
	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 */,
	192 /* output_channels_per_group */,
	32 /* input pixel stride */,
	192 /* output pixel stride */,
	w95.data(), w96.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	192 /* groups */,
	1 /* input channels per group */,
	1 /* output_channels_per_group */,
	192 /* input pixel stride */,
	192 /* output pixel stride */,
	w97.data(), w98.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	192 /* input channels per group */,
	32 /* output_channels_per_group */,
	192 /* input pixel stride */,
	32 /* output pixel stride */,
	w99.data(), w100.data(),
	-std::numeric_limits<float>::infinity() /* output min /, std::numeric_limits<float>::infinity() / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_add_nd_f32(
	-std::numeric_limits<float>::infinity() /* 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_convolution2d_nhwc_f32(
	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 */,
	192 /* output_channels_per_group */,
	32 /* input pixel stride */,
	192 /* output pixel stride */,
	w101.data(), w102.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	192 /* groups */,
	1 /* input channels per group */,
	1 /* output_channels_per_group */,
	192 /* input pixel stride */,
	192 /* output pixel stride */,
	w103.data(), w104.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	192 /* input channels per group */,
	64 /* output_channels_per_group */,
	192 /* input pixel stride */,
	64 /* output pixel stride */,
	w105.data(), w106.data(),
	-std::numeric_limits<float>::infinity() /* output min /, std::numeric_limits<float>::infinity() / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_convolution2d_nhwc_f32(
	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 */,
	384 /* output_channels_per_group */,
	64 /* input pixel stride */,
	384 /* output pixel stride */,
	w107.data(), w108.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	384 /* groups */,
	1 /* input channels per group */,
	1 /* output_channels_per_group */,
	384 /* input pixel stride */,
	384 /* output pixel stride */,
	w109.data(), w110.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_convolution2d_nhwc_f32(
	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 */,
	384 /* input channels per group */,
	64 /* output_channels_per_group */,
	384 /* input pixel stride */,
	64 /* output pixel stride */,
	w111.data(), w112.data(),
	-std::numeric_limits<float>::infinity() /* output min /, std::numeric_limits<float>::infinity() / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_add_nd_f32(
	-std::numeric_limits<float>::infinity() /* 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_f32(
	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 */,
	384 /* output_channels_per_group */,
	64 /* input pixel stride */,
	384 /* output pixel stride */,
	w113.data(), w114.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_convolution2d_nhwc_f32(
	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 */,
	384 /* groups */,
	1 /* input channels per group */,
	1 /* output_channels_per_group */,
	384 /* input pixel stride */,
	384 /* output pixel stride */,
	w115.data(), w116.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	384 /* input channels per group */,
	64 /* output_channels_per_group */,
	384 /* input pixel stride */,
	64 /* output pixel stride */,
	w117.data(), w118.data(),
	-std::numeric_limits<float>::infinity() /* output min /, std::numeric_limits<float>::infinity() / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_add_nd_f32(
	-std::numeric_limits<float>::infinity() /* output min /, std::numeric_limits<float>::infinity() / 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_convolution2d_nhwc_f32(
	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 */,
	384 /* output_channels_per_group */,
	64 /* input pixel stride */,
	384 /* output pixel stride */,
	w119.data(), w120.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	384 /* groups */,
	1 /* input channels per group */,
	1 /* output_channels_per_group */,
	384 /* input pixel stride */,
	384 /* output pixel stride */,
	w121.data(), w122.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_convolution2d_nhwc_f32(
	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 */,
	384 /* input channels per group */,
	64 /* output_channels_per_group */,
	384 /* input pixel stride */,
	64 /* output pixel stride */,
	w123.data(), w124.data(),
	-std::numeric_limits<float>::infinity() /* output min /, std::numeric_limits<float>::infinity() / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_add_nd_f32(
	-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_convolution2d_nhwc_f32(
	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 */,
	384 /* output_channels_per_group */,
	64 /* input pixel stride */,
	384 /* output pixel stride */,
	w125.data(), w126.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	384 /* groups */,
	1 /* input channels per group */,
	1 /* output_channels_per_group */,
	384 /* input pixel stride */,
	384 /* output pixel stride */,
	w127.data(), w128.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_convolution2d_nhwc_f32(
	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 */,
	384 /* input channels per group */,
	96 /* output_channels_per_group */,
	384 /* input pixel stride */,
	96 /* output pixel stride */,
	w129.data(), w130.data(),
	-std::numeric_limits<float>::infinity() /* output min /, std::numeric_limits<float>::infinity() / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	96 /* input channels per group */,
	576 /* output_channels_per_group */,
	96 /* input pixel stride */,
	576 /* output pixel stride */,
	w131.data(), w132.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	576 /* groups */,
	1 /* input channels per group */,
	1 /* output_channels_per_group */,
	576 /* input pixel stride */,
	576 /* output pixel stride */,
	w133.data(), w134.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_convolution2d_nhwc_f32(
	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 */,
	576 /* input channels per group */,
	96 /* output_channels_per_group */,
	576 /* input pixel stride */,
	96 /* output pixel stride */,
	w135.data(), w136.data(),
	-std::numeric_limits<float>::infinity() /* output min /, std::numeric_limits<float>::infinity() / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_add_nd_f32(
	-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_convolution2d_nhwc_f32(
	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 */,
	96 /* input channels per group */,
	576 /* output_channels_per_group */,
	96 /* input pixel stride */,
	576 /* output pixel stride */,
	w137.data(), w138.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	576 /* groups */,
	1 /* input channels per group */,
	1 /* output_channels_per_group */,
	576 /* input pixel stride */,
	576 /* output pixel stride */,
	w139.data(), w140.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_convolution2d_nhwc_f32(
	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 */,
	576 /* input channels per group */,
	96 /* output_channels_per_group */,
	576 /* input pixel stride */,
	96 /* output pixel stride */,
	w141.data(), w142.data(),
	-std::numeric_limits<float>::infinity() /* output min /, std::numeric_limits<float>::infinity() / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_add_nd_f32(
	-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_convolution2d_nhwc_f32(
	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 */,
	96 /* input channels per group */,
	576 /* output_channels_per_group */,
	96 /* input pixel stride */,
	576 /* output pixel stride */,
	w143.data(), w144.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	576 /* groups */,
	1 /* input channels per group */,
	1 /* output_channels_per_group */,
	576 /* input pixel stride */,
	576 /* output pixel stride */,
	w145.data(), w146.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_convolution2d_nhwc_f32(
	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 */,
	576 /* input channels per group */,
	160 /* output_channels_per_group */,
	576 /* input pixel stride */,
	160 /* output pixel stride */,
	w147.data(), w148.data(),
	-std::numeric_limits<float>::infinity() /* output min /, std::numeric_limits<float>::infinity() / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	w149.data(), w150.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_convolution2d_nhwc_f32(
	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 */,
	960 /* groups */,
	1 /* input channels per group */,
	1 /* output_channels_per_group */,
	960 /* input pixel stride */,
	960 /* output pixel stride */,
	w151.data(), w152.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	w153.data(), w154.data(),
	-std::numeric_limits<float>::infinity() /* output min /, std::numeric_limits<float>::infinity() / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_add_nd_f32(
	-std::numeric_limits<float>::infinity() /* output min /, std::numeric_limits<float>::infinity() / output max */,
	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_f32(
	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 */,
	w155.data(), w156.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_convolution2d_nhwc_f32(
	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 */,
	960 /* groups */,
	1 /* input channels per group */,
	1 /* output_channels_per_group */,
	960 /* input pixel stride */,
	960 /* output pixel stride */,
	w157.data(), w158.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	w159.data(), w160.data(),
	-std::numeric_limits<float>::infinity() /* output min /, std::numeric_limits<float>::infinity() / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	-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_f32(
	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 */,
	w161.data(), w162.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_convolution2d_nhwc_f32(
	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 */,
	960 /* groups */,
	1 /* input channels per group */,
	1 /* output_channels_per_group */,
	960 /* input pixel stride */,
	960 /* output pixel stride */,
	w163.data(), w164.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	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 */,
	320 /* output_channels_per_group */,
	960 /* input pixel stride */,
	320 /* output pixel stride */,
	w165.data(), w166.data(),
	-std::numeric_limits<float>::infinity() /* output min /, std::numeric_limits<float>::infinity() / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_convolution2d_nhwc_f32(
	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 */,
	320 /* input channels per group */,
	1280 /* output_channels_per_group */,
	320 /* input pixel stride */,
	1280 /* output pixel stride */,
	w167.data(), w168.data(),
	0.0f /* output min /, 6.0f / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&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_f32(
	1280 /* channels /, 1280 / input stride /, 1280 / 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_f32(
	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 */,
	w169.data(), w170.data(),
	-std::numeric_limits<float>::infinity() /* output min /, std::numeric_limits<float>::infinity() / output max */,
	0 /* flags */,
	code_cache_ptr,
	nullptr,
	&op63);
	if (status != xnn_status_success) {
	std::cerr << "failed to create operation #63" << std::endl;
	return ExecutionPlan();
	}
	operators.emplace_back(op63, xnn_delete_operator);

	#if XNN_PLATFORM_JIT
	if (use_jit) {
	xnn_finalize_code_memory(&code_cache.cache.code);
	}
	#endif // XNN_PLATFORM_JIT

	status = xnn_reshape_convolution2d_nhwc_f32(
	op0,
	/batch_size=/1, /input_height=/224, /input_width=/224,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #0" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op1,
	/batch_size=/1, /input_height=/112, /input_width=/112,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #1" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op2,
	/batch_size=/1, /input_height=/112, /input_width=/112,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #2" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op3,
	/batch_size=/1, /input_height=/112, /input_width=/112,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #3" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op4,
	/batch_size=/1, /input_height=/112, /input_width=/112,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #4" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op5,
	/batch_size=/1, /input_height=/56, /input_width=/56,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #5" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op6,
	/batch_size=/1, /input_height=/56, /input_width=/56,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #6" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op7,
	/batch_size=/1, /input_height=/56, /input_width=/56,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #7" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op8,
	/batch_size=/1, /input_height=/56, /input_width=/56,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #8" << std::endl;
	return ExecutionPlan();
	}

	{
	const size_t a_shape[] = { 1, 56, 56, 24 };
	const size_t b_shape[] = { 1, 56, 56, 24 };
	status = xnn_reshape_add_nd_f32(
	op9,
	4, a_shape, 4, b_shape,
	/threadpool=/threadpool);
	}
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #9" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op10,
	/batch_size=/1, /input_height=/56, /input_width=/56,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #10" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op11,
	/batch_size=/1, /input_height=/56, /input_width=/56,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #11" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op12,
	/batch_size=/1, /input_height=/28, /input_width=/28,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #12" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op13,
	/batch_size=/1, /input_height=/28, /input_width=/28,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #13" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op14,
	/batch_size=/1, /input_height=/28, /input_width=/28,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #14" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op15,
	/batch_size=/1, /input_height=/28, /input_width=/28,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #15" << std::endl;
	return ExecutionPlan();
	}

	{
	const size_t a_shape[] = { 1, 28, 28, 32 };
	const size_t b_shape[] = { 1, 28, 28, 32 };
	status = xnn_reshape_add_nd_f32(
	op16,
	4, a_shape, 4, b_shape,
	/threadpool=/threadpool);
	}
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #16" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op17,
	/batch_size=/1, /input_height=/28, /input_width=/28,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #17" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op18,
	/batch_size=/1, /input_height=/28, /input_width=/28,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #18" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op19,
	/batch_size=/1, /input_height=/28, /input_width=/28,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #19" << std::endl;
	return ExecutionPlan();
	}

	{
	const size_t a_shape[] = { 1, 28, 28, 32 };
	const size_t b_shape[] = { 1, 28, 28, 32 };
	status = xnn_reshape_add_nd_f32(
	op20,
	4, a_shape, 4, b_shape,
	/threadpool=/threadpool);
	}
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #20" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op21,
	/batch_size=/1, /input_height=/28, /input_width=/28,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #21" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op22,
	/batch_size=/1, /input_height=/28, /input_width=/28,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #22" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op23,
	/batch_size=/1, /input_height=/14, /input_width=/14,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #23" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op24,
	/batch_size=/1, /input_height=/14, /input_width=/14,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #24" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op25,
	/batch_size=/1, /input_height=/14, /input_width=/14,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #25" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op26,
	/batch_size=/1, /input_height=/14, /input_width=/14,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #26" << std::endl;
	return ExecutionPlan();
	}

	{
	const size_t a_shape[] = { 1, 14, 14, 64 };
	const size_t b_shape[] = { 1, 14, 14, 64 };
	status = xnn_reshape_add_nd_f32(
	op27,
	4, a_shape, 4, b_shape,
	/threadpool=/threadpool);
	}
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #27" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op28,
	/batch_size=/1, /input_height=/14, /input_width=/14,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #28" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op29,
	/batch_size=/1, /input_height=/14, /input_width=/14,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #29" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op30,
	/batch_size=/1, /input_height=/14, /input_width=/14,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #30" << std::endl;
	return ExecutionPlan();
	}

	{
	const size_t a_shape[] = { 1, 14, 14, 64 };
	const size_t b_shape[] = { 1, 14, 14, 64 };
	status = xnn_reshape_add_nd_f32(
	op31,
	4, a_shape, 4, b_shape,
	/threadpool=/threadpool);
	}
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #31" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op32,
	/batch_size=/1, /input_height=/14, /input_width=/14,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #32" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op33,
	/batch_size=/1, /input_height=/14, /input_width=/14,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #33" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op34,
	/batch_size=/1, /input_height=/14, /input_width=/14,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #34" << std::endl;
	return ExecutionPlan();
	}

	{
	const size_t a_shape[] = { 1, 14, 14, 64 };
	const size_t b_shape[] = { 1, 14, 14, 64 };
	status = xnn_reshape_add_nd_f32(
	op35,
	4, a_shape, 4, b_shape,
	/threadpool=/threadpool);
	}
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #35" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op36,
	/batch_size=/1, /input_height=/14, /input_width=/14,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #36" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op37,
	/batch_size=/1, /input_height=/14, /input_width=/14,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #37" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op38,
	/batch_size=/1, /input_height=/14, /input_width=/14,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #38" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op39,
	/batch_size=/1, /input_height=/14, /input_width=/14,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #39" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op40,
	/batch_size=/1, /input_height=/14, /input_width=/14,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #40" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op41,
	/batch_size=/1, /input_height=/14, /input_width=/14,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #41" << std::endl;
	return ExecutionPlan();
	}

	{
	const size_t a_shape[] = { 1, 14, 14, 96 };
	const size_t b_shape[] = { 1, 14, 14, 96 };
	status = xnn_reshape_add_nd_f32(
	op42,
	4, a_shape, 4, b_shape,
	/threadpool=/threadpool);
	}
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #42" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op43,
	/batch_size=/1, /input_height=/14, /input_width=/14,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #43" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op44,
	/batch_size=/1, /input_height=/14, /input_width=/14,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #44" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op45,
	/batch_size=/1, /input_height=/14, /input_width=/14,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #45" << std::endl;
	return ExecutionPlan();
	}

	{
	const size_t a_shape[] = { 1, 14, 14, 96 };
	const size_t b_shape[] = { 1, 14, 14, 96 };
	status = xnn_reshape_add_nd_f32(
	op46,
	4, a_shape, 4, b_shape,
	/threadpool=/threadpool);
	}
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #46" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op47,
	/batch_size=/1, /input_height=/14, /input_width=/14,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #47" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op48,
	/batch_size=/1, /input_height=/14, /input_width=/14,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #48" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op49,
	/batch_size=/1, /input_height=/7, /input_width=/7,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #49" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op50,
	/batch_size=/1, /input_height=/7, /input_width=/7,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #50" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op51,
	/batch_size=/1, /input_height=/7, /input_width=/7,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #51" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op52,
	/batch_size=/1, /input_height=/7, /input_width=/7,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #52" << std::endl;
	return ExecutionPlan();
	}

	{
	const size_t a_shape[] = { 1, 7, 7, 160 };
	const size_t b_shape[] = { 1, 7, 7, 160 };
	status = xnn_reshape_add_nd_f32(
	op53,
	4, a_shape, 4, b_shape,
	/threadpool=/threadpool);
	}
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #53" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op54,
	/batch_size=/1, /input_height=/7, /input_width=/7,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #54" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op55,
	/batch_size=/1, /input_height=/7, /input_width=/7,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #55" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op56,
	/batch_size=/1, /input_height=/7, /input_width=/7,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #56" << std::endl;
	return ExecutionPlan();
	}

	{
	const size_t a_shape[] = { 1, 7, 7, 160 };
	const size_t b_shape[] = { 1, 7, 7, 160 };
	status = xnn_reshape_add_nd_f32(
	op57,
	4, a_shape, 4, b_shape,
	/threadpool=/threadpool);
	}
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #57" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op58,
	/batch_size=/1, /input_height=/7, /input_width=/7,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #58" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op59,
	/batch_size=/1, /input_height=/7, /input_width=/7,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #59" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op60,
	/batch_size=/1, /input_height=/7, /input_width=/7,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #60" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op61,
	/batch_size=/1, /input_height=/7, /input_width=/7,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #61" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_global_average_pooling_nwc_f32(
	op62,
	/batch_size=/1, /width=/49,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #62" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_reshape_convolution2d_nhwc_f32(
	op63,
	/batch_size=/1, /input_height=/1, /input_width=/1,
	/output_height_out=/nullptr, /output_width_out=/nullptr,
	/threadpool=/threadpool);
	if (status != xnn_status_success) {
	std::cerr << "failed to reshape operation #63" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op0,
	/input=/v0.data(), /output=/v1.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #0" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op1,
	/input=/v1.data(), /output=/v2.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #1" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op2,
	/input=/v2.data(), /output=/v3.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #2" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op3,
	/input=/v3.data(), /output=/v4.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #3" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op4,
	/input=/v4.data(), /output=/v5.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #4" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op5,
	/input=/v5.data(), /output=/v6.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #5" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op6,
	/input=/v6.data(), /output=/v7.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #6" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op7,
	/input=/v7.data(), /output=/v8.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #7" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op8,
	/input=/v8.data(), /output=/v9.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #8" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_add_nd_f32(
	op9,
	v9.data() /* a /, v6.data() / b /, /output=*/v10.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #9" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op10,
	/input=/v10.data(), /output=/v11.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #10" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op11,
	/input=/v11.data(), /output=/v12.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #11" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op12,
	/input=/v12.data(), /output=/v13.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #12" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op13,
	/input=/v13.data(), /output=/v14.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #13" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op14,
	/input=/v14.data(), /output=/v15.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #14" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op15,
	/input=/v15.data(), /output=/v16.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #15" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_add_nd_f32(
	op16,
	v16.data() /* a /, v13.data() / b /, /output=*/v17.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #16" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op17,
	/input=/v17.data(), /output=/v18.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #17" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op18,
	/input=/v18.data(), /output=/v19.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #18" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op19,
	/input=/v19.data(), /output=/v20.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #19" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_add_nd_f32(
	op20,
	v20.data() /* a /, v17.data() / b /, /output=*/v21.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #20" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op21,
	/input=/v21.data(), /output=/v22.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #21" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op22,
	/input=/v22.data(), /output=/v23.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #22" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op23,
	/input=/v23.data(), /output=/v24.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #23" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op24,
	/input=/v24.data(), /output=/v25.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #24" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op25,
	/input=/v25.data(), /output=/v26.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #25" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op26,
	/input=/v26.data(), /output=/v27.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #26" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_add_nd_f32(
	op27,
	v27.data() /* a /, v24.data() / b /, /output=*/v28.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #27" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op28,
	/input=/v28.data(), /output=/v29.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #28" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op29,
	/input=/v29.data(), /output=/v30.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #29" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op30,
	/input=/v30.data(), /output=/v31.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #30" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_add_nd_f32(
	op31,
	v31.data() /* a /, v28.data() / b /, /output=*/v32.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #31" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op32,
	/input=/v32.data(), /output=/v33.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #32" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op33,
	/input=/v33.data(), /output=/v34.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #33" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op34,
	/input=/v34.data(), /output=/v35.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #34" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_add_nd_f32(
	op35,
	v35.data() /* a /, v32.data() / b /, /output=*/v36.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #35" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op36,
	/input=/v36.data(), /output=/v37.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #36" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op37,
	/input=/v37.data(), /output=/v38.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #37" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op38,
	/input=/v38.data(), /output=/v39.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #38" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op39,
	/input=/v39.data(), /output=/v40.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #39" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op40,
	/input=/v40.data(), /output=/v41.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #40" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op41,
	/input=/v41.data(), /output=/v42.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #41" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_add_nd_f32(
	op42,
	v42.data() /* a /, v39.data() / b /, /output=*/v43.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #42" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op43,
	/input=/v43.data(), /output=/v44.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #43" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op44,
	/input=/v44.data(), /output=/v45.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #44" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op45,
	/input=/v45.data(), /output=/v46.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #45" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_add_nd_f32(
	op46,
	v46.data() /* a /, v43.data() / b /, /output=*/v47.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #46" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op47,
	/input=/v47.data(), /output=/v48.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #47" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op48,
	/input=/v48.data(), /output=/v49.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #48" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op49,
	/input=/v49.data(), /output=/v50.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #49" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op50,
	/input=/v50.data(), /output=/v51.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #50" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op51,
	/input=/v51.data(), /output=/v52.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #51" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op52,
	/input=/v52.data(), /output=/v53.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #52" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_add_nd_f32(
	op53,
	v53.data() /* a /, v50.data() / b /, /output=*/v54.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #53" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op54,
	/input=/v54.data(), /output=/v55.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #54" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op55,
	/input=/v55.data(), /output=/v56.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #55" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op56,
	/input=/v56.data(), /output=/v57.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #56" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_add_nd_f32(
	op57,
	v57.data() /* a /, v54.data() / b /, /output=*/v58.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #57" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op58,
	/input=/v58.data(), /output=/v59.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #58" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op59,
	/input=/v59.data(), /output=/v60.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #59" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op60,
	/input=/v60.data(), /output=/v61.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #60" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op61,
	/input=/v61.data(), /output=/v62.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #61" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_global_average_pooling_nwc_f32(
	op62,
	/input=/v62.data(), /output=/v63.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #62" << std::endl;
	return ExecutionPlan();
	}

	status = xnn_setup_convolution2d_nhwc_f32(
	op63,
	/input=/v63.data(), /output=/v64.data());
	if (status != xnn_status_success) {
	std::cerr << "failed to setup operation #63" << std::endl;
	return ExecutionPlan();
	}

	XNN_PRAGMA_CLANG("clang diagnostic push")
	XNN_PRAGMA_CLANG("clang diagnostic ignored \"-Wpessimizing-move\"")
	return operators;
	XNN_PRAGMA_CLANG("clang diagnostic pop")
	}

	ExecutionPlan FP32MobileNetV2(pthreadpool_t threadpool) {
	return FP32MobileNetV2(/use_jit=/false, threadpool);
	}

	ExecutionPlan FP32MobileNetV2Jit(pthreadpool_t threadpool) {
	return FP32MobileNetV2(/use_jit=/true, threadpool);
	}

	} // namespace models