runtime/test/generated/spec_V1_2/random_multinomial_float16.example.cpp

// Generated from random_multinomial_float16.mod.py
// DO NOT EDIT
// clang-format off
#include "TestHarness.h"

using namespace test_helper;

namespace generated_tests::random_multinomial_float16 {

const TestModel& get_test_model() {
    static TestModel model = {
        .expectedMultinomialDistributionTolerance = 0.025f,
        .inputIndexes = {0},
        .isRelaxed = false,
        .operands = {{
                .channelQuant = {},
                .data = TestBuffer::createFromVector<_Float16>({0.18163621f, 0.3327348f, 0.50951556f, 0.08877703f, 0.51185218f, 0.1673424f, 0.63870587f, 0.56538613f, 0.96808477f, 0.56056615f, 0.02934992f, 0.03258404f, 0.72106282f, 0.54496657f, 0.22183018f, 0.14761066f, 0.38261428f, 0.73637053f, 0.78623964f, 0.00538754f, 0.17758578f, 0.21533023f, 0.83887367f, 0.71035332f, 0.28841254f, 0.24017199f, 0.11599192f, 0.55738622f, 0.35368502f, 0.09133554f, 0.81038602f, 0.03550774f, 0.88201054f, 0.07141711f, 0.20562562f, 0.9998214f, 0.36866199f, 0.84549652f, 0.68196711f, 0.60800431f, 0.04897644f, 0.79349817f, 0.34294643f, 0.31649012f, 0.61759858f, 0.67423009f, 0.14606593f, 0.82548304f, 0.14200278f, 0.58680437f, 0.41861224f, 0.86453261f, 0.80694849f, 0.23185477f, 0.81298628f, 0.1415089f, 0.16687062f, 0.03645446f, 0.50345389f, 0.82756624f, 0.84377461f, 0.46665451f, 0.91611352f, 0.29161655f, 0.86669246f, 0.21630808f, 0.98051105f, 0.7158922f, 0.54117114f, 0.37597655f, 0.61811207f, 0.39746145f, 0.19850883f, 0.10498004f, 0.45398218f, 0.84427816f, 0.44244307f, 0.97186493f, 0.02943687f, 0.05995579f, 0.91047162f, 0.84049659f, 0.29692092f, 0.37859579f, 0.68067858f, 0.53199727f, 0.30587859f, 0.96904311f, 0.53895138f, 0.8035052f, 0.56936886f, 0.90789923f, 0.48829865f, 0.74295622f, 0.07730283f, 0.35745998f, 0.63879813f, 0.8858171f, 0.78119555f, 0.34389121f, 0.00664631f, 0.43921788f, 0.38457199f, 0.95430791f, 0.92432083f, 0.29473732f, 0.25805162f, 0.94078243f, 0.63156303f, 0.49350546f, 0.60020588f, 0.1699311f, 0.78426143f, 0.31030305f, 0.74027296f, 0.86801874f, 0.71064432f, 0.9804212f, 0.26738505f, 0.22304029f, 0.9747488f, 0.93888746f, 0.02851034f, 0.22468271f, 0.67886092f, 0.79198019f, 0.56653174f, 0.17544579f, 0.89579936f, 0.9627906f, 0.04028579f, 0.586905f, 0.72510547f, 0.60705131f, 0.4332557f, 0.15888959f, 0.3452346f, 0.56390766f, 0.31160624f, 0.14503308f, 0.19243339f, 0.26307077f, 0.53711017f, 0.90120554f, 0.20798102f, 0.44519176f, 0.83232068f, 0.82690943f, 0.23728192f, 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0.08313659f, 0.31389776f, 0.26683639f, 0.1740869f, 0.19860426f, 0.54852056f, 0.45089482f, 0.29739356f, 0.06490634f, 0.94512628f, 0.75476861f, 0.79302202f, 0.91709407f, 0.44093711f, 0.42285809f, 0.87353064f, 0.05154859f, 0.05673061f, 0.03360053f, 0.47314265f, 0.14352713f, 0.86919501f, 0.89407749f, 0.71384359f, 0.43505102f, 0.76105734f, 0.480729f, 0.26590561f, 0.23636561f, 0.53526685f, 0.43621137f, 0.70461497f, 0.04695302f, 0.29312615f, 0.47657411f, 0.47776949f, 0.67893515f, 0.74761854f, 0.1964709f, 0.90858326f, 0.65050969f, 0.78866488f, 0.56645663f, 0.28301728f, 0.21439215f, 0.23534408f, 0.99123621f, 0.33798052f, 0.57132079f, 0.13509136f, 0.23913264f, 0.9882279f, 0.12259069f, 0.59413715f, 0.98916346f, 0.15804781f, 0.53868433f, 0.82989573f, 0.31032958f, 0.52338512f, 0.43014882f, 0.80809309f, 0.5810279f, 0.41232677f, 0.7232558f, 0.15152519f, 0.61332742f, 0.69908457f, 0.68902723f, 0.4086777f, 0.56938072f, 0.30977628f, 0.75155389f, 0.77055871f, 0.24496359f, 0.00515177f, 0.68565391f, 0.04478322f, 0.74595021f, 0.44720965f, 0.25587623f, 0.42443591f, 0.05974449f, 0.20046287f, 0.3034349f, 0.9062242f, 0.64120083f, 0.52238185f, 0.11133412f, 0.43655075f, 0.76620214f, 0.36598683f, 0.67400905f, 0.89241105f, 0.69407209f, 0.64427034f, 0.18430072f, 0.92961135f, 0.37992458f, 0.41103806f, 0.99307206f, 0.62659181f, 0.44814843f, 0.07694981f}),
                .dimensions = {1, 1024},
                .isIgnored = false,
                .lifetime = TestOperandLifeTime::MODEL_INPUT,
                .numberOfConsumers = 1,
                .scale = 0.0f,
                .type = TestOperandType::TENSOR_FLOAT16,
                .zeroPoint = 0
            }, {
                .channelQuant = {},
                .data = TestBuffer::createFromVector<int32_t>({128}),
                .dimensions = {},
                .isIgnored = false,
                .lifetime = TestOperandLifeTime::CONSTANT_COPY,
                .numberOfConsumers = 1,
                .scale = 0.0f,
                .type = TestOperandType::INT32,
                .zeroPoint = 0
            }, {
                .channelQuant = {},
                .data = TestBuffer::createFromVector<int32_t>({37, 42}),
                .dimensions = {2},
                .isIgnored = false,
                .lifetime = TestOperandLifeTime::CONSTANT_COPY,
                .numberOfConsumers = 1,
                .scale = 0.0f,
                .type = TestOperandType::TENSOR_INT32,
                .zeroPoint = 0
            }, {
                .channelQuant = {},
                .data = TestBuffer::createFromVector<int32_t>({0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}),
                .dimensions = {1, 128},
                .isIgnored = true,
                .lifetime = TestOperandLifeTime::MODEL_OUTPUT,
                .numberOfConsumers = 0,
                .scale = 0.0f,
                .type = TestOperandType::TENSOR_INT32,
                .zeroPoint = 0
            }},
        .operations = {{
                .inputs = {0, 1, 2},
                .outputs = {3},
                .type = TestOperationType::RANDOM_MULTINOMIAL
            }},
        .outputIndexes = {3}
    };
    return model;
}

}  // namespace generated_tests::random_multinomial_float16