1e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang// Generated file (from: lsh_projection_2_relaxed.mod.py). Do not edit
2e8e5d34c4159532eb324df393c2c752a508bced1Miao Wangvoid CreateModel(Model *model) {
3e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  OperandType type3(Type::INT32, {});
4e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  OperandType type2(Type::TENSOR_FLOAT32, {3});
5e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  OperandType type0(Type::TENSOR_FLOAT32, {4, 2});
6e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  OperandType type1(Type::TENSOR_INT32, {3, 2});
7e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  OperandType type4(Type::TENSOR_INT32, {4});
8e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  // Phase 1, operands
9e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  auto hash = model->addOperand(&type0);
10e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  auto lookup = model->addOperand(&type1);
11e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  auto weight = model->addOperand(&type2);
12e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  auto type_param = model->addOperand(&type3);
13e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  auto output = model->addOperand(&type4);
14e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  // Phase 2, operations
15e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  static float hash_init[] = {0.123f, 0.456f, -0.321f, -0.654f, 1.234f, 5.678f, -4.321f, -8.765f};
16e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  model->setOperandValue(hash, hash_init, sizeof(float) * 8);
17e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  static int32_t type_param_init[] = {1};
18e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  model->setOperandValue(type_param, type_param_init, sizeof(int32_t) * 1);
19e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  model->addOperation(ANEURALNETWORKS_LSH_PROJECTION, {hash, lookup, weight, type_param}, {output});
20e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  // Phase 3, inputs and outputs
21e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  model->identifyInputsAndOutputs(
22e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang    {lookup, weight},
23e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang    {output});
24e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  // Phase 4: set relaxed execution
25e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  model->relaxComputationFloat32toFloat16(true);
26e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  assert(model->isValid());
27e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang}
28e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang
29e8e5d34c4159532eb324df393c2c752a508bced1Miao Wangbool is_ignored(int i) {
30e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  static std::set<int> ignore = {};
31e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang  return ignore.find(i) != ignore.end();
32e8e5d34c4159532eb324df393c2c752a508bced1Miao Wang}
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