1e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang// Generated file (from: local_response_norm_float_1_relaxed.mod.py). Do not edit
2e8e5d34c4159532eb324df393c2c752a508bced1Miao Wangvoid CreateModel(Model *model) {
3e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  OperandType type2(Type::FLOAT32, {});
4e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  OperandType type1(Type::INT32, {});
5e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  OperandType type0(Type::TENSOR_FLOAT32, {1, 1, 1, 6});
6e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  // Phase 1, operands
7e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  auto input = model->addOperand(&type0);
8e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  auto radius = model->addOperand(&type1);
9e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  auto bias = model->addOperand(&type2);
10e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  auto alpha = model->addOperand(&type2);
11e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  auto beta = model->addOperand(&type2);
12e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  auto output = model->addOperand(&type0);
13e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  // Phase 2, operations
14e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  static int32_t radius_init[] = {20};
15e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  model->setOperandValue(radius, radius_init, sizeof(int32_t) * 1);
16e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  static float bias_init[] = {9.0f};
17e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  model->setOperandValue(bias, bias_init, sizeof(float) * 1);
18e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  static float alpha_init[] = {4.0f};
19e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  model->setOperandValue(alpha, alpha_init, sizeof(float) * 1);
20e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  static float beta_init[] = {0.5f};
21e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  model->setOperandValue(beta, beta_init, sizeof(float) * 1);
22e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output});
23e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  // Phase 3, inputs and outputs
24e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  model->identifyInputsAndOutputs(
25e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang    {input},
26e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang    {output});
27e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  // Phase 4: set relaxed execution
28e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  model->relaxComputationFloat32toFloat16(true);
29e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  assert(model->isValid());
30e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang}
31e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang
32e8e5d34c4159532eb324df393c2c752a508bced1Miao Wangbool is_ignored(int i) {
33e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  static std::set<int> ignore = {};
34e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  return ignore.find(i) != ignore.end();
35e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang}
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