runtime/test/generated/spec_V1_2/cts/sub_v1_2.model.cpp

// Generated from sub_v1_2.mod.py
// DO NOT EDIT
// clang-format off
#include "TestGenerated.h"

namespace generated_tests::sub_v1_2 {

void CreateModel_none(Model *model) {
  OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1});
  OperandType type1(Type::INT32, {});
  // Phase 1, operands
  auto input0 = model->addOperand(&type0);
  auto input1 = model->addOperand(&type0);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type0);
  // Phase 2, operations
  static int32_t act_init[] = {0};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0, input1},
    {output0});
  assert(model->isValid());
}

bool is_ignored_none(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_none_dynamic_output_shape(Model *model) {
  OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1});
  OperandType type1(Type::INT32, {});
  OperandType type13(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
  // Phase 1, operands
  auto input0 = model->addOperand(&type0);
  auto input1 = model->addOperand(&type0);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type13);
  // Phase 2, operations
  static int32_t act_init[] = {0};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0, input1},
    {output0});
  assert(model->isValid());
}

bool is_ignored_none_dynamic_output_shape(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_none_all_inputs_as_internal(Model *model) {
  OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1});
  OperandType type1(Type::INT32, {});
  OperandType type14(Type::TENSOR_FLOAT32, {1});
  // Phase 1, operands
  auto input0 = model->addOperand(&type0);
  auto input1 = model->addOperand(&type0);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type0);
  auto input0_tmp = model->addOperand(&type0);
  auto dummy = model->addOperand(&type14);
  auto param15 = model->addOperand(&type1);
  auto input1_tmp = model->addOperand(&type0);
  auto dummy1 = model->addOperand(&type14);
  auto param16 = model->addOperand(&type1);
  // Phase 2, operations
  static int32_t act_init[] = {0};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  static float dummy_init[] = {0.0f};
  model->setOperandValue(dummy, dummy_init, sizeof(float) * 1);
  static int32_t param15_init[] = {0};
  model->setOperandValue(param15, param15_init, sizeof(int32_t) * 1);
  static float dummy1_init[] = {0.0f};
  model->setOperandValue(dummy1, dummy1_init, sizeof(float) * 1);
  static int32_t param16_init[] = {0};
  model->setOperandValue(param16, param16_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy, param15}, {input0});
  model->addOperation(ANEURALNETWORKS_ADD, {input1_tmp, dummy1, param16}, {input1});
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0_tmp, input1_tmp},
    {output0});
  assert(model->isValid());
}

bool is_ignored_none_all_inputs_as_internal(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_none_all_inputs_as_internal_dynamic_output_shape(Model *model) {
  OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1});
  OperandType type1(Type::INT32, {});
  OperandType type13(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
  OperandType type14(Type::TENSOR_FLOAT32, {1});
  // Phase 1, operands
  auto input0 = model->addOperand(&type0);
  auto input1 = model->addOperand(&type0);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type13);
  auto input0_tmp = model->addOperand(&type0);
  auto dummy2 = model->addOperand(&type14);
  auto param17 = model->addOperand(&type1);
  auto input1_tmp = model->addOperand(&type0);
  auto dummy3 = model->addOperand(&type14);
  auto param18 = model->addOperand(&type1);
  // Phase 2, operations
  static int32_t act_init[] = {0};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  static float dummy2_init[] = {0.0f};
  model->setOperandValue(dummy2, dummy2_init, sizeof(float) * 1);
  static int32_t param17_init[] = {0};
  model->setOperandValue(param17, param17_init, sizeof(int32_t) * 1);
  static float dummy3_init[] = {0.0f};
  model->setOperandValue(dummy3, dummy3_init, sizeof(float) * 1);
  static int32_t param18_init[] = {0};
  model->setOperandValue(param18, param18_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy2, param17}, {input0});
  model->addOperation(ANEURALNETWORKS_ADD, {input1_tmp, dummy3, param18}, {input1});
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0_tmp, input1_tmp},
    {output0});
  assert(model->isValid());
}

bool is_ignored_none_all_inputs_as_internal_dynamic_output_shape(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_relu(Model *model) {
  OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1});
  OperandType type1(Type::INT32, {});
  // Phase 1, operands
  auto input0 = model->addOperand(&type0);
  auto input1 = model->addOperand(&type0);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type0);
  // Phase 2, operations
  static int32_t act_init[] = {1};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0, input1},
    {output0});
  assert(model->isValid());
}

bool is_ignored_relu(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_relu_dynamic_output_shape(Model *model) {
  OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1});
  OperandType type1(Type::INT32, {});
  OperandType type13(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
  // Phase 1, operands
  auto input0 = model->addOperand(&type0);
  auto input1 = model->addOperand(&type0);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type13);
  // Phase 2, operations
  static int32_t act_init[] = {1};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0, input1},
    {output0});
  assert(model->isValid());
}

bool is_ignored_relu_dynamic_output_shape(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_relu_all_inputs_as_internal(Model *model) {
  OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1});
  OperandType type1(Type::INT32, {});
  OperandType type14(Type::TENSOR_FLOAT32, {1});
  // Phase 1, operands
  auto input0 = model->addOperand(&type0);
  auto input1 = model->addOperand(&type0);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type0);
  auto input0_tmp = model->addOperand(&type0);
  auto dummy4 = model->addOperand(&type14);
  auto param19 = model->addOperand(&type1);
  auto input1_tmp = model->addOperand(&type0);
  auto dummy5 = model->addOperand(&type14);
  auto param20 = model->addOperand(&type1);
  // Phase 2, operations
  static int32_t act_init[] = {1};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  static float dummy4_init[] = {0.0f};
  model->setOperandValue(dummy4, dummy4_init, sizeof(float) * 1);
  static int32_t param19_init[] = {0};
  model->setOperandValue(param19, param19_init, sizeof(int32_t) * 1);
  static float dummy5_init[] = {0.0f};
  model->setOperandValue(dummy5, dummy5_init, sizeof(float) * 1);
  static int32_t param20_init[] = {0};
  model->setOperandValue(param20, param20_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy4, param19}, {input0});
  model->addOperation(ANEURALNETWORKS_ADD, {input1_tmp, dummy5, param20}, {input1});
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0_tmp, input1_tmp},
    {output0});
  assert(model->isValid());
}

bool is_ignored_relu_all_inputs_as_internal(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_relu_all_inputs_as_internal_dynamic_output_shape(Model *model) {
  OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1});
  OperandType type1(Type::INT32, {});
  OperandType type13(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
  OperandType type14(Type::TENSOR_FLOAT32, {1});
  // Phase 1, operands
  auto input0 = model->addOperand(&type0);
  auto input1 = model->addOperand(&type0);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type13);
  auto input0_tmp = model->addOperand(&type0);
  auto dummy6 = model->addOperand(&type14);
  auto param21 = model->addOperand(&type1);
  auto input1_tmp = model->addOperand(&type0);
  auto dummy7 = model->addOperand(&type14);
  auto param22 = model->addOperand(&type1);
  // Phase 2, operations
  static int32_t act_init[] = {1};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  static float dummy6_init[] = {0.0f};
  model->setOperandValue(dummy6, dummy6_init, sizeof(float) * 1);
  static int32_t param21_init[] = {0};
  model->setOperandValue(param21, param21_init, sizeof(int32_t) * 1);
  static float dummy7_init[] = {0.0f};
  model->setOperandValue(dummy7, dummy7_init, sizeof(float) * 1);
  static int32_t param22_init[] = {0};
  model->setOperandValue(param22, param22_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy6, param21}, {input0});
  model->addOperation(ANEURALNETWORKS_ADD, {input1_tmp, dummy7, param22}, {input1});
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0_tmp, input1_tmp},
    {output0});
  assert(model->isValid());
}

bool is_ignored_relu_all_inputs_as_internal_dynamic_output_shape(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_relu1(Model *model) {
  OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1});
  OperandType type1(Type::INT32, {});
  // Phase 1, operands
  auto input0 = model->addOperand(&type0);
  auto input1 = model->addOperand(&type0);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type0);
  // Phase 2, operations
  static int32_t act_init[] = {2};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0, input1},
    {output0});
  assert(model->isValid());
}

bool is_ignored_relu1(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_relu1_dynamic_output_shape(Model *model) {
  OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1});
  OperandType type1(Type::INT32, {});
  OperandType type13(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
  // Phase 1, operands
  auto input0 = model->addOperand(&type0);
  auto input1 = model->addOperand(&type0);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type13);
  // Phase 2, operations
  static int32_t act_init[] = {2};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0, input1},
    {output0});
  assert(model->isValid());
}

bool is_ignored_relu1_dynamic_output_shape(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_relu1_all_inputs_as_internal(Model *model) {
  OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1});
  OperandType type1(Type::INT32, {});
  OperandType type14(Type::TENSOR_FLOAT32, {1});
  // Phase 1, operands
  auto input0 = model->addOperand(&type0);
  auto input1 = model->addOperand(&type0);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type0);
  auto input0_tmp = model->addOperand(&type0);
  auto dummy8 = model->addOperand(&type14);
  auto param23 = model->addOperand(&type1);
  auto input1_tmp = model->addOperand(&type0);
  auto dummy9 = model->addOperand(&type14);
  auto param24 = model->addOperand(&type1);
  // Phase 2, operations
  static int32_t act_init[] = {2};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  static float dummy8_init[] = {0.0f};
  model->setOperandValue(dummy8, dummy8_init, sizeof(float) * 1);
  static int32_t param23_init[] = {0};
  model->setOperandValue(param23, param23_init, sizeof(int32_t) * 1);
  static float dummy9_init[] = {0.0f};
  model->setOperandValue(dummy9, dummy9_init, sizeof(float) * 1);
  static int32_t param24_init[] = {0};
  model->setOperandValue(param24, param24_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy8, param23}, {input0});
  model->addOperation(ANEURALNETWORKS_ADD, {input1_tmp, dummy9, param24}, {input1});
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0_tmp, input1_tmp},
    {output0});
  assert(model->isValid());
}

bool is_ignored_relu1_all_inputs_as_internal(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_relu1_all_inputs_as_internal_dynamic_output_shape(Model *model) {
  OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1});
  OperandType type1(Type::INT32, {});
  OperandType type13(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
  OperandType type14(Type::TENSOR_FLOAT32, {1});
  // Phase 1, operands
  auto input0 = model->addOperand(&type0);
  auto input1 = model->addOperand(&type0);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type13);
  auto input0_tmp = model->addOperand(&type0);
  auto dummy10 = model->addOperand(&type14);
  auto param25 = model->addOperand(&type1);
  auto input1_tmp = model->addOperand(&type0);
  auto dummy11 = model->addOperand(&type14);
  auto param26 = model->addOperand(&type1);
  // Phase 2, operations
  static int32_t act_init[] = {2};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  static float dummy10_init[] = {0.0f};
  model->setOperandValue(dummy10, dummy10_init, sizeof(float) * 1);
  static int32_t param25_init[] = {0};
  model->setOperandValue(param25, param25_init, sizeof(int32_t) * 1);
  static float dummy11_init[] = {0.0f};
  model->setOperandValue(dummy11, dummy11_init, sizeof(float) * 1);
  static int32_t param26_init[] = {0};
  model->setOperandValue(param26, param26_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy10, param25}, {input0});
  model->addOperation(ANEURALNETWORKS_ADD, {input1_tmp, dummy11, param26}, {input1});
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0_tmp, input1_tmp},
    {output0});
  assert(model->isValid());
}

bool is_ignored_relu1_all_inputs_as_internal_dynamic_output_shape(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_relu6(Model *model) {
  OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1});
  OperandType type1(Type::INT32, {});
  // Phase 1, operands
  auto input0 = model->addOperand(&type0);
  auto input1 = model->addOperand(&type0);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type0);
  // Phase 2, operations
  static int32_t act_init[] = {3};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0, input1},
    {output0});
  assert(model->isValid());
}

bool is_ignored_relu6(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_relu6_dynamic_output_shape(Model *model) {
  OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1});
  OperandType type1(Type::INT32, {});
  OperandType type13(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
  // Phase 1, operands
  auto input0 = model->addOperand(&type0);
  auto input1 = model->addOperand(&type0);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type13);
  // Phase 2, operations
  static int32_t act_init[] = {3};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0, input1},
    {output0});
  assert(model->isValid());
}

bool is_ignored_relu6_dynamic_output_shape(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_relu6_all_inputs_as_internal(Model *model) {
  OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1});
  OperandType type1(Type::INT32, {});
  OperandType type14(Type::TENSOR_FLOAT32, {1});
  // Phase 1, operands
  auto input0 = model->addOperand(&type0);
  auto input1 = model->addOperand(&type0);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type0);
  auto input0_tmp = model->addOperand(&type0);
  auto dummy12 = model->addOperand(&type14);
  auto param27 = model->addOperand(&type1);
  auto input1_tmp = model->addOperand(&type0);
  auto dummy13 = model->addOperand(&type14);
  auto param28 = model->addOperand(&type1);
  // Phase 2, operations
  static int32_t act_init[] = {3};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  static float dummy12_init[] = {0.0f};
  model->setOperandValue(dummy12, dummy12_init, sizeof(float) * 1);
  static int32_t param27_init[] = {0};
  model->setOperandValue(param27, param27_init, sizeof(int32_t) * 1);
  static float dummy13_init[] = {0.0f};
  model->setOperandValue(dummy13, dummy13_init, sizeof(float) * 1);
  static int32_t param28_init[] = {0};
  model->setOperandValue(param28, param28_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy12, param27}, {input0});
  model->addOperation(ANEURALNETWORKS_ADD, {input1_tmp, dummy13, param28}, {input1});
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0_tmp, input1_tmp},
    {output0});
  assert(model->isValid());
}

bool is_ignored_relu6_all_inputs_as_internal(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_relu6_all_inputs_as_internal_dynamic_output_shape(Model *model) {
  OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1});
  OperandType type1(Type::INT32, {});
  OperandType type13(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
  OperandType type14(Type::TENSOR_FLOAT32, {1});
  // Phase 1, operands
  auto input0 = model->addOperand(&type0);
  auto input1 = model->addOperand(&type0);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type13);
  auto input0_tmp = model->addOperand(&type0);
  auto dummy14 = model->addOperand(&type14);
  auto param29 = model->addOperand(&type1);
  auto input1_tmp = model->addOperand(&type0);
  auto dummy15 = model->addOperand(&type14);
  auto param30 = model->addOperand(&type1);
  // Phase 2, operations
  static int32_t act_init[] = {3};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  static float dummy14_init[] = {0.0f};
  model->setOperandValue(dummy14, dummy14_init, sizeof(float) * 1);
  static int32_t param29_init[] = {0};
  model->setOperandValue(param29, param29_init, sizeof(int32_t) * 1);
  static float dummy15_init[] = {0.0f};
  model->setOperandValue(dummy15, dummy15_init, sizeof(float) * 1);
  static int32_t param30_init[] = {0};
  model->setOperandValue(param30, param30_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy14, param29}, {input0});
  model->addOperation(ANEURALNETWORKS_ADD, {input1_tmp, dummy15, param30}, {input1});
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0_tmp, input1_tmp},
    {output0});
  assert(model->isValid());
}

bool is_ignored_relu6_all_inputs_as_internal_dynamic_output_shape(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_float16_none(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type15(Type::TENSOR_FLOAT16, {1, 2, 2, 1});
  // Phase 1, operands
  auto input0 = model->addOperand(&type15);
  auto input1 = model->addOperand(&type15);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type15);
  // Phase 2, operations
  static int32_t act_init[] = {0};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0, input1},
    {output0});
  assert(model->isValid());
}

bool is_ignored_float16_none(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_float16_none_dynamic_output_shape(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type15(Type::TENSOR_FLOAT16, {1, 2, 2, 1});
  OperandType type16(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
  // Phase 1, operands
  auto input0 = model->addOperand(&type15);
  auto input1 = model->addOperand(&type15);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type16);
  // Phase 2, operations
  static int32_t act_init[] = {0};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0, input1},
    {output0});
  assert(model->isValid());
}

bool is_ignored_float16_none_dynamic_output_shape(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_float16_none_all_inputs_as_internal(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type15(Type::TENSOR_FLOAT16, {1, 2, 2, 1});
  OperandType type17(Type::TENSOR_FLOAT16, {1, 2, 2, 1});
  OperandType type18(Type::TENSOR_FLOAT16, {1});
  // Phase 1, operands
  auto input0 = model->addOperand(&type17);
  auto input1 = model->addOperand(&type17);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type15);
  auto input0_tmp = model->addOperand(&type17);
  auto dummy16 = model->addOperand(&type18);
  auto param31 = model->addOperand(&type1);
  auto input1_tmp = model->addOperand(&type17);
  auto dummy17 = model->addOperand(&type18);
  auto param32 = model->addOperand(&type1);
  // Phase 2, operations
  static int32_t act_init[] = {0};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  static _Float16 dummy16_init[] = {0.0f};
  model->setOperandValue(dummy16, dummy16_init, sizeof(_Float16) * 1);
  static int32_t param31_init[] = {0};
  model->setOperandValue(param31, param31_init, sizeof(int32_t) * 1);
  static _Float16 dummy17_init[] = {0.0f};
  model->setOperandValue(dummy17, dummy17_init, sizeof(_Float16) * 1);
  static int32_t param32_init[] = {0};
  model->setOperandValue(param32, param32_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy16, param31}, {input0});
  model->addOperation(ANEURALNETWORKS_ADD, {input1_tmp, dummy17, param32}, {input1});
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0_tmp, input1_tmp},
    {output0});
  assert(model->isValid());
}

bool is_ignored_float16_none_all_inputs_as_internal(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_float16_none_all_inputs_as_internal_dynamic_output_shape(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type16(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
  OperandType type17(Type::TENSOR_FLOAT16, {1, 2, 2, 1});
  OperandType type18(Type::TENSOR_FLOAT16, {1});
  // Phase 1, operands
  auto input0 = model->addOperand(&type17);
  auto input1 = model->addOperand(&type17);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type16);
  auto input0_tmp = model->addOperand(&type17);
  auto dummy18 = model->addOperand(&type18);
  auto param33 = model->addOperand(&type1);
  auto input1_tmp = model->addOperand(&type17);
  auto dummy19 = model->addOperand(&type18);
  auto param34 = model->addOperand(&type1);
  // Phase 2, operations
  static int32_t act_init[] = {0};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  static _Float16 dummy18_init[] = {0.0f};
  model->setOperandValue(dummy18, dummy18_init, sizeof(_Float16) * 1);
  static int32_t param33_init[] = {0};
  model->setOperandValue(param33, param33_init, sizeof(int32_t) * 1);
  static _Float16 dummy19_init[] = {0.0f};
  model->setOperandValue(dummy19, dummy19_init, sizeof(_Float16) * 1);
  static int32_t param34_init[] = {0};
  model->setOperandValue(param34, param34_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy18, param33}, {input0});
  model->addOperation(ANEURALNETWORKS_ADD, {input1_tmp, dummy19, param34}, {input1});
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0_tmp, input1_tmp},
    {output0});
  assert(model->isValid());
}

bool is_ignored_float16_none_all_inputs_as_internal_dynamic_output_shape(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_float16_relu(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type15(Type::TENSOR_FLOAT16, {1, 2, 2, 1});
  // Phase 1, operands
  auto input0 = model->addOperand(&type15);
  auto input1 = model->addOperand(&type15);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type15);
  // Phase 2, operations
  static int32_t act_init[] = {1};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0, input1},
    {output0});
  assert(model->isValid());
}

bool is_ignored_float16_relu(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_float16_relu_dynamic_output_shape(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type15(Type::TENSOR_FLOAT16, {1, 2, 2, 1});
  OperandType type16(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
  // Phase 1, operands
  auto input0 = model->addOperand(&type15);
  auto input1 = model->addOperand(&type15);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type16);
  // Phase 2, operations
  static int32_t act_init[] = {1};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0, input1},
    {output0});
  assert(model->isValid());
}

bool is_ignored_float16_relu_dynamic_output_shape(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_float16_relu_all_inputs_as_internal(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type15(Type::TENSOR_FLOAT16, {1, 2, 2, 1});
  OperandType type17(Type::TENSOR_FLOAT16, {1, 2, 2, 1});
  OperandType type18(Type::TENSOR_FLOAT16, {1});
  // Phase 1, operands
  auto input0 = model->addOperand(&type17);
  auto input1 = model->addOperand(&type17);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type15);
  auto input0_tmp = model->addOperand(&type17);
  auto dummy20 = model->addOperand(&type18);
  auto param35 = model->addOperand(&type1);
  auto input1_tmp = model->addOperand(&type17);
  auto dummy21 = model->addOperand(&type18);
  auto param36 = model->addOperand(&type1);
  // Phase 2, operations
  static int32_t act_init[] = {1};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  static _Float16 dummy20_init[] = {0.0f};
  model->setOperandValue(dummy20, dummy20_init, sizeof(_Float16) * 1);
  static int32_t param35_init[] = {0};
  model->setOperandValue(param35, param35_init, sizeof(int32_t) * 1);
  static _Float16 dummy21_init[] = {0.0f};
  model->setOperandValue(dummy21, dummy21_init, sizeof(_Float16) * 1);
  static int32_t param36_init[] = {0};
  model->setOperandValue(param36, param36_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy20, param35}, {input0});
  model->addOperation(ANEURALNETWORKS_ADD, {input1_tmp, dummy21, param36}, {input1});
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0_tmp, input1_tmp},
    {output0});
  assert(model->isValid());
}

bool is_ignored_float16_relu_all_inputs_as_internal(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_float16_relu_all_inputs_as_internal_dynamic_output_shape(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type16(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
  OperandType type17(Type::TENSOR_FLOAT16, {1, 2, 2, 1});
  OperandType type18(Type::TENSOR_FLOAT16, {1});
  // Phase 1, operands
  auto input0 = model->addOperand(&type17);
  auto input1 = model->addOperand(&type17);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type16);
  auto input0_tmp = model->addOperand(&type17);
  auto dummy22 = model->addOperand(&type18);
  auto param37 = model->addOperand(&type1);
  auto input1_tmp = model->addOperand(&type17);
  auto dummy23 = model->addOperand(&type18);
  auto param38 = model->addOperand(&type1);
  // Phase 2, operations
  static int32_t act_init[] = {1};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  static _Float16 dummy22_init[] = {0.0f};
  model->setOperandValue(dummy22, dummy22_init, sizeof(_Float16) * 1);
  static int32_t param37_init[] = {0};
  model->setOperandValue(param37, param37_init, sizeof(int32_t) * 1);
  static _Float16 dummy23_init[] = {0.0f};
  model->setOperandValue(dummy23, dummy23_init, sizeof(_Float16) * 1);
  static int32_t param38_init[] = {0};
  model->setOperandValue(param38, param38_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy22, param37}, {input0});
  model->addOperation(ANEURALNETWORKS_ADD, {input1_tmp, dummy23, param38}, {input1});
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0_tmp, input1_tmp},
    {output0});
  assert(model->isValid());
}

bool is_ignored_float16_relu_all_inputs_as_internal_dynamic_output_shape(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_float16_relu1(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type15(Type::TENSOR_FLOAT16, {1, 2, 2, 1});
  // Phase 1, operands
  auto input0 = model->addOperand(&type15);
  auto input1 = model->addOperand(&type15);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type15);
  // Phase 2, operations
  static int32_t act_init[] = {2};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0, input1},
    {output0});
  assert(model->isValid());
}

bool is_ignored_float16_relu1(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_float16_relu1_dynamic_output_shape(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type15(Type::TENSOR_FLOAT16, {1, 2, 2, 1});
  OperandType type16(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
  // Phase 1, operands
  auto input0 = model->addOperand(&type15);
  auto input1 = model->addOperand(&type15);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type16);
  // Phase 2, operations
  static int32_t act_init[] = {2};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0, input1},
    {output0});
  assert(model->isValid());
}

bool is_ignored_float16_relu1_dynamic_output_shape(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_float16_relu1_all_inputs_as_internal(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type15(Type::TENSOR_FLOAT16, {1, 2, 2, 1});
  OperandType type17(Type::TENSOR_FLOAT16, {1, 2, 2, 1});
  OperandType type18(Type::TENSOR_FLOAT16, {1});
  // Phase 1, operands
  auto input0 = model->addOperand(&type17);
  auto input1 = model->addOperand(&type17);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type15);
  auto input0_tmp = model->addOperand(&type17);
  auto dummy24 = model->addOperand(&type18);
  auto param39 = model->addOperand(&type1);
  auto input1_tmp = model->addOperand(&type17);
  auto dummy25 = model->addOperand(&type18);
  auto param40 = model->addOperand(&type1);
  // Phase 2, operations
  static int32_t act_init[] = {2};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  static _Float16 dummy24_init[] = {0.0f};
  model->setOperandValue(dummy24, dummy24_init, sizeof(_Float16) * 1);
  static int32_t param39_init[] = {0};
  model->setOperandValue(param39, param39_init, sizeof(int32_t) * 1);
  static _Float16 dummy25_init[] = {0.0f};
  model->setOperandValue(dummy25, dummy25_init, sizeof(_Float16) * 1);
  static int32_t param40_init[] = {0};
  model->setOperandValue(param40, param40_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy24, param39}, {input0});
  model->addOperation(ANEURALNETWORKS_ADD, {input1_tmp, dummy25, param40}, {input1});
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0_tmp, input1_tmp},
    {output0});
  assert(model->isValid());
}

bool is_ignored_float16_relu1_all_inputs_as_internal(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_float16_relu1_all_inputs_as_internal_dynamic_output_shape(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type16(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
  OperandType type17(Type::TENSOR_FLOAT16, {1, 2, 2, 1});
  OperandType type18(Type::TENSOR_FLOAT16, {1});
  // Phase 1, operands
  auto input0 = model->addOperand(&type17);
  auto input1 = model->addOperand(&type17);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type16);
  auto input0_tmp = model->addOperand(&type17);
  auto dummy26 = model->addOperand(&type18);
  auto param41 = model->addOperand(&type1);
  auto input1_tmp = model->addOperand(&type17);
  auto dummy27 = model->addOperand(&type18);
  auto param42 = model->addOperand(&type1);
  // Phase 2, operations
  static int32_t act_init[] = {2};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  static _Float16 dummy26_init[] = {0.0f};
  model->setOperandValue(dummy26, dummy26_init, sizeof(_Float16) * 1);
  static int32_t param41_init[] = {0};
  model->setOperandValue(param41, param41_init, sizeof(int32_t) * 1);
  static _Float16 dummy27_init[] = {0.0f};
  model->setOperandValue(dummy27, dummy27_init, sizeof(_Float16) * 1);
  static int32_t param42_init[] = {0};
  model->setOperandValue(param42, param42_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy26, param41}, {input0});
  model->addOperation(ANEURALNETWORKS_ADD, {input1_tmp, dummy27, param42}, {input1});
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0_tmp, input1_tmp},
    {output0});
  assert(model->isValid());
}

bool is_ignored_float16_relu1_all_inputs_as_internal_dynamic_output_shape(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_float16_relu6(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type15(Type::TENSOR_FLOAT16, {1, 2, 2, 1});
  // Phase 1, operands
  auto input0 = model->addOperand(&type15);
  auto input1 = model->addOperand(&type15);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type15);
  // Phase 2, operations
  static int32_t act_init[] = {3};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0, input1},
    {output0});
  assert(model->isValid());
}

bool is_ignored_float16_relu6(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_float16_relu6_dynamic_output_shape(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type15(Type::TENSOR_FLOAT16, {1, 2, 2, 1});
  OperandType type16(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
  // Phase 1, operands
  auto input0 = model->addOperand(&type15);
  auto input1 = model->addOperand(&type15);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type16);
  // Phase 2, operations
  static int32_t act_init[] = {3};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0, input1},
    {output0});
  assert(model->isValid());
}

bool is_ignored_float16_relu6_dynamic_output_shape(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_float16_relu6_all_inputs_as_internal(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type15(Type::TENSOR_FLOAT16, {1, 2, 2, 1});
  OperandType type17(Type::TENSOR_FLOAT16, {1, 2, 2, 1});
  OperandType type18(Type::TENSOR_FLOAT16, {1});
  // Phase 1, operands
  auto input0 = model->addOperand(&type17);
  auto input1 = model->addOperand(&type17);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type15);
  auto input0_tmp = model->addOperand(&type17);
  auto dummy28 = model->addOperand(&type18);
  auto param43 = model->addOperand(&type1);
  auto input1_tmp = model->addOperand(&type17);
  auto dummy29 = model->addOperand(&type18);
  auto param44 = model->addOperand(&type1);
  // Phase 2, operations
  static int32_t act_init[] = {3};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  static _Float16 dummy28_init[] = {0.0f};
  model->setOperandValue(dummy28, dummy28_init, sizeof(_Float16) * 1);
  static int32_t param43_init[] = {0};
  model->setOperandValue(param43, param43_init, sizeof(int32_t) * 1);
  static _Float16 dummy29_init[] = {0.0f};
  model->setOperandValue(dummy29, dummy29_init, sizeof(_Float16) * 1);
  static int32_t param44_init[] = {0};
  model->setOperandValue(param44, param44_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy28, param43}, {input0});
  model->addOperation(ANEURALNETWORKS_ADD, {input1_tmp, dummy29, param44}, {input1});
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0_tmp, input1_tmp},
    {output0});
  assert(model->isValid());
}

bool is_ignored_float16_relu6_all_inputs_as_internal(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_float16_relu6_all_inputs_as_internal_dynamic_output_shape(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type16(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
  OperandType type17(Type::TENSOR_FLOAT16, {1, 2, 2, 1});
  OperandType type18(Type::TENSOR_FLOAT16, {1});
  // Phase 1, operands
  auto input0 = model->addOperand(&type17);
  auto input1 = model->addOperand(&type17);
  auto act = model->addOperand(&type1);
  auto output0 = model->addOperand(&type16);
  auto input0_tmp = model->addOperand(&type17);
  auto dummy30 = model->addOperand(&type18);
  auto param45 = model->addOperand(&type1);
  auto input1_tmp = model->addOperand(&type17);
  auto dummy31 = model->addOperand(&type18);
  auto param46 = model->addOperand(&type1);
  // Phase 2, operations
  static int32_t act_init[] = {3};
  model->setOperandValue(act, act_init, sizeof(int32_t) * 1);
  static _Float16 dummy30_init[] = {0.0f};
  model->setOperandValue(dummy30, dummy30_init, sizeof(_Float16) * 1);
  static int32_t param45_init[] = {0};
  model->setOperandValue(param45, param45_init, sizeof(int32_t) * 1);
  static _Float16 dummy31_init[] = {0.0f};
  model->setOperandValue(dummy31, dummy31_init, sizeof(_Float16) * 1);
  static int32_t param46_init[] = {0};
  model->setOperandValue(param46, param46_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_ADD, {input0_tmp, dummy30, param45}, {input0});
  model->addOperation(ANEURALNETWORKS_ADD, {input1_tmp, dummy31, param46}, {input1});
  model->addOperation(ANEURALNETWORKS_SUB, {input0, input1, act}, {output0});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input0_tmp, input1_tmp},
    {output0});
  assert(model->isValid());
}

bool is_ignored_float16_relu6_all_inputs_as_internal_dynamic_output_shape(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_quant8(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type2(Type::TENSOR_QUANT8_ASYMM, {2, 4, 16, 2}, 0.5f, 0);
  // Phase 1, operands
  auto input01 = model->addOperand(&type2);
  auto input11 = model->addOperand(&type2);
  auto param = model->addOperand(&type1);
  auto output01 = model->addOperand(&type2);
  // Phase 2, operations
  static int32_t param_init[] = {0};
  model->setOperandValue(param, param_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_SUB, {input01, input11, param}, {output01});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input01, input11},
    {output01});
  assert(model->isValid());
}

bool is_ignored_quant8(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_quant8_dynamic_output_shape(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type19(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.5f, 0);
  OperandType type2(Type::TENSOR_QUANT8_ASYMM, {2, 4, 16, 2}, 0.5f, 0);
  // Phase 1, operands
  auto input01 = model->addOperand(&type2);
  auto input11 = model->addOperand(&type2);
  auto param = model->addOperand(&type1);
  auto output01 = model->addOperand(&type19);
  // Phase 2, operations
  static int32_t param_init[] = {0};
  model->setOperandValue(param, param_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_SUB, {input01, input11, param}, {output01});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input01, input11},
    {output01});
  assert(model->isValid());
}

bool is_ignored_quant8_dynamic_output_shape(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_quant8_all_inputs_as_internal(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type2(Type::TENSOR_QUANT8_ASYMM, {2, 4, 16, 2}, 0.5f, 0);
  OperandType type20(Type::TENSOR_QUANT8_ASYMM, {1}, 0.5f, 0);
  // Phase 1, operands
  auto input01 = model->addOperand(&type2);
  auto input11 = model->addOperand(&type2);
  auto param = model->addOperand(&type1);
  auto output01 = model->addOperand(&type2);
  auto input01_tmp = model->addOperand(&type2);
  auto dummy32 = model->addOperand(&type20);
  auto param47 = model->addOperand(&type1);
  auto input11_tmp = model->addOperand(&type2);
  auto dummy33 = model->addOperand(&type20);
  auto param48 = model->addOperand(&type1);
  // Phase 2, operations
  static int32_t param_init[] = {0};
  model->setOperandValue(param, param_init, sizeof(int32_t) * 1);
  static uint8_t dummy32_init[] = {0};
  model->setOperandValue(dummy32, dummy32_init, sizeof(uint8_t) * 1);
  static int32_t param47_init[] = {0};
  model->setOperandValue(param47, param47_init, sizeof(int32_t) * 1);
  static uint8_t dummy33_init[] = {0};
  model->setOperandValue(dummy33, dummy33_init, sizeof(uint8_t) * 1);
  static int32_t param48_init[] = {0};
  model->setOperandValue(param48, param48_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_ADD, {input01_tmp, dummy32, param47}, {input01});
  model->addOperation(ANEURALNETWORKS_ADD, {input11_tmp, dummy33, param48}, {input11});
  model->addOperation(ANEURALNETWORKS_SUB, {input01, input11, param}, {output01});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input01_tmp, input11_tmp},
    {output01});
  assert(model->isValid());
}

bool is_ignored_quant8_all_inputs_as_internal(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_quant8_all_inputs_as_internal_dynamic_output_shape(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type19(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.5f, 0);
  OperandType type2(Type::TENSOR_QUANT8_ASYMM, {2, 4, 16, 2}, 0.5f, 0);
  OperandType type20(Type::TENSOR_QUANT8_ASYMM, {1}, 0.5f, 0);
  // Phase 1, operands
  auto input01 = model->addOperand(&type2);
  auto input11 = model->addOperand(&type2);
  auto param = model->addOperand(&type1);
  auto output01 = model->addOperand(&type19);
  auto input01_tmp = model->addOperand(&type2);
  auto dummy34 = model->addOperand(&type20);
  auto param49 = model->addOperand(&type1);
  auto input11_tmp = model->addOperand(&type2);
  auto dummy35 = model->addOperand(&type20);
  auto param50 = model->addOperand(&type1);
  // Phase 2, operations
  static int32_t param_init[] = {0};
  model->setOperandValue(param, param_init, sizeof(int32_t) * 1);
  static uint8_t dummy34_init[] = {0};
  model->setOperandValue(dummy34, dummy34_init, sizeof(uint8_t) * 1);
  static int32_t param49_init[] = {0};
  model->setOperandValue(param49, param49_init, sizeof(int32_t) * 1);
  static uint8_t dummy35_init[] = {0};
  model->setOperandValue(dummy35, dummy35_init, sizeof(uint8_t) * 1);
  static int32_t param50_init[] = {0};
  model->setOperandValue(param50, param50_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_ADD, {input01_tmp, dummy34, param49}, {input01});
  model->addOperation(ANEURALNETWORKS_ADD, {input11_tmp, dummy35, param50}, {input11});
  model->addOperation(ANEURALNETWORKS_SUB, {input01, input11, param}, {output01});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {input01_tmp, input11_tmp},
    {output01});
  assert(model->isValid());
}

bool is_ignored_quant8_all_inputs_as_internal_dynamic_output_shape(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_zero_sized(Model *model) {
  OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1});
  OperandType type1(Type::INT32, {});
  OperandType type10(Type::BOOL, {});
  OperandType type11(Type::TENSOR_FLOAT32, {1, 1, 1, 2});
  OperandType type12(Type::TENSOR_FLOAT32, {0, 2, 2, 2});
  OperandType type3(Type::TENSOR_FLOAT32, {1, 2});
  OperandType type4(Type::TENSOR_FLOAT32, {1, 8});
  OperandType type5(Type::TENSOR_FLOAT32, {0});
  OperandType type6(Type::TENSOR_INT32, {0});
  OperandType type7(Type::TENSOR_FLOAT32, {0, 4});
  OperandType type8(Type::TENSOR_INT32, {1});
  OperandType type9(Type::FLOAT32, {});
  // Phase 1, operands
  auto scores = model->addOperand(&type3);
  auto roi = model->addOperand(&type4);
  auto param1 = model->addOperand(&type8);
  auto param2 = model->addOperand(&type9);
  auto param3 = model->addOperand(&type1);
  auto param4 = model->addOperand(&type1);
  auto param5 = model->addOperand(&type9);
  auto param6 = model->addOperand(&type9);
  auto param7 = model->addOperand(&type9);
  auto scoresOut = model->addOperand(&type5);
  auto roiOut = model->addOperand(&type7);
  auto classesOut = model->addOperand(&type6);
  auto batchSplitOut = model->addOperand(&type6);
  auto in = model->addOperand(&type11);
  auto param8 = model->addOperand(&type1);
  auto param9 = model->addOperand(&type1);
  auto param10 = model->addOperand(&type9);
  auto param11 = model->addOperand(&type9);
  auto param12 = model->addOperand(&type1);
  auto param13 = model->addOperand(&type1);
  auto layout = model->addOperand(&type10);
  auto featureMap = model->addOperand(&type12);
  auto op = model->addOperand(&type0);
  auto param14 = model->addOperand(&type1);
  auto out = model->addOperand(&type12);
  // Phase 2, operations
  static float scores_init[] = {0.9f, 0.1f};
  model->setOperandValue(scores, scores_init, sizeof(float) * 2);
  static float roi_init[] = {1.0f, 1.0f, 10.0f, 10.0f, 0.0f, 0.0f, 10.0f, 10.0f};
  model->setOperandValue(roi, roi_init, sizeof(float) * 8);
  static int32_t param1_init[] = {0};
  model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1);
  static float param2_init[] = {0.3f};
  model->setOperandValue(param2, param2_init, sizeof(float) * 1);
  static int32_t param3_init[] = {-1};
  model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1);
  static int32_t param4_init[] = {0};
  model->setOperandValue(param4, param4_init, sizeof(int32_t) * 1);
  static float param5_init[] = {0.4f};
  model->setOperandValue(param5, param5_init, sizeof(float) * 1);
  static float param6_init[] = {1.0f};
  model->setOperandValue(param6, param6_init, sizeof(float) * 1);
  static float param7_init[] = {0.3f};
  model->setOperandValue(param7, param7_init, sizeof(float) * 1);
  static int32_t param8_init[] = {2};
  model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1);
  static int32_t param9_init[] = {2};
  model->setOperandValue(param9, param9_init, sizeof(int32_t) * 1);
  static float param10_init[] = {2.0f};
  model->setOperandValue(param10, param10_init, sizeof(float) * 1);
  static float param11_init[] = {2.0f};
  model->setOperandValue(param11, param11_init, sizeof(float) * 1);
  static int32_t param12_init[] = {4};
  model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1);
  static int32_t param13_init[] = {4};
  model->setOperandValue(param13, param13_init, sizeof(int32_t) * 1);
  static bool8 layout_init[] = {false};
  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
  static float op_init[] = {1.0f, 2.0f, 3.0f, 4.0f};
  model->setOperandValue(op, op_init, sizeof(float) * 4);
  static int32_t param14_init[] = {0};
  model->setOperandValue(param14, param14_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param1, param2, param3, param4, param5, param6, param7}, {scoresOut, roiOut, classesOut, batchSplitOut});
  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param8, param9, param10, param11, param12, param13, layout}, {featureMap});
  model->addOperation(ANEURALNETWORKS_SUB, {featureMap, op, param14}, {out});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {in},
    {scoresOut, classesOut, out});
  assert(model->isValid());
}

bool is_ignored_zero_sized(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_zero_sized_dynamic_output_shape(Model *model) {
  OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1});
  OperandType type1(Type::INT32, {});
  OperandType type10(Type::BOOL, {});
  OperandType type11(Type::TENSOR_FLOAT32, {1, 1, 1, 2});
  OperandType type12(Type::TENSOR_FLOAT32, {0, 2, 2, 2});
  OperandType type13(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
  OperandType type3(Type::TENSOR_FLOAT32, {1, 2});
  OperandType type4(Type::TENSOR_FLOAT32, {1, 8});
  OperandType type5(Type::TENSOR_FLOAT32, {0});
  OperandType type6(Type::TENSOR_INT32, {0});
  OperandType type7(Type::TENSOR_FLOAT32, {0, 4});
  OperandType type8(Type::TENSOR_INT32, {1});
  OperandType type9(Type::FLOAT32, {});
  // Phase 1, operands
  auto scores = model->addOperand(&type3);
  auto roi = model->addOperand(&type4);
  auto param1 = model->addOperand(&type8);
  auto param2 = model->addOperand(&type9);
  auto param3 = model->addOperand(&type1);
  auto param4 = model->addOperand(&type1);
  auto param5 = model->addOperand(&type9);
  auto param6 = model->addOperand(&type9);
  auto param7 = model->addOperand(&type9);
  auto scoresOut = model->addOperand(&type5);
  auto roiOut = model->addOperand(&type7);
  auto classesOut = model->addOperand(&type6);
  auto batchSplitOut = model->addOperand(&type6);
  auto in = model->addOperand(&type11);
  auto param8 = model->addOperand(&type1);
  auto param9 = model->addOperand(&type1);
  auto param10 = model->addOperand(&type9);
  auto param11 = model->addOperand(&type9);
  auto param12 = model->addOperand(&type1);
  auto param13 = model->addOperand(&type1);
  auto layout = model->addOperand(&type10);
  auto featureMap = model->addOperand(&type12);
  auto op = model->addOperand(&type0);
  auto param14 = model->addOperand(&type1);
  auto out = model->addOperand(&type13);
  // Phase 2, operations
  static float scores_init[] = {0.9f, 0.1f};
  model->setOperandValue(scores, scores_init, sizeof(float) * 2);
  static float roi_init[] = {1.0f, 1.0f, 10.0f, 10.0f, 0.0f, 0.0f, 10.0f, 10.0f};
  model->setOperandValue(roi, roi_init, sizeof(float) * 8);
  static int32_t param1_init[] = {0};
  model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1);
  static float param2_init[] = {0.3f};
  model->setOperandValue(param2, param2_init, sizeof(float) * 1);
  static int32_t param3_init[] = {-1};
  model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1);
  static int32_t param4_init[] = {0};
  model->setOperandValue(param4, param4_init, sizeof(int32_t) * 1);
  static float param5_init[] = {0.4f};
  model->setOperandValue(param5, param5_init, sizeof(float) * 1);
  static float param6_init[] = {1.0f};
  model->setOperandValue(param6, param6_init, sizeof(float) * 1);
  static float param7_init[] = {0.3f};
  model->setOperandValue(param7, param7_init, sizeof(float) * 1);
  static int32_t param8_init[] = {2};
  model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1);
  static int32_t param9_init[] = {2};
  model->setOperandValue(param9, param9_init, sizeof(int32_t) * 1);
  static float param10_init[] = {2.0f};
  model->setOperandValue(param10, param10_init, sizeof(float) * 1);
  static float param11_init[] = {2.0f};
  model->setOperandValue(param11, param11_init, sizeof(float) * 1);
  static int32_t param12_init[] = {4};
  model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1);
  static int32_t param13_init[] = {4};
  model->setOperandValue(param13, param13_init, sizeof(int32_t) * 1);
  static bool8 layout_init[] = {false};
  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
  static float op_init[] = {1.0f, 2.0f, 3.0f, 4.0f};
  model->setOperandValue(op, op_init, sizeof(float) * 4);
  static int32_t param14_init[] = {0};
  model->setOperandValue(param14, param14_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param1, param2, param3, param4, param5, param6, param7}, {scoresOut, roiOut, classesOut, batchSplitOut});
  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param8, param9, param10, param11, param12, param13, layout}, {featureMap});
  model->addOperation(ANEURALNETWORKS_SUB, {featureMap, op, param14}, {out});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {in},
    {scoresOut, classesOut, out});
  assert(model->isValid());
}

bool is_ignored_zero_sized_dynamic_output_shape(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_zero_sized_relaxed(Model *model) {
  OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1});
  OperandType type1(Type::INT32, {});
  OperandType type10(Type::BOOL, {});
  OperandType type11(Type::TENSOR_FLOAT32, {1, 1, 1, 2});
  OperandType type12(Type::TENSOR_FLOAT32, {0, 2, 2, 2});
  OperandType type3(Type::TENSOR_FLOAT32, {1, 2});
  OperandType type4(Type::TENSOR_FLOAT32, {1, 8});
  OperandType type5(Type::TENSOR_FLOAT32, {0});
  OperandType type6(Type::TENSOR_INT32, {0});
  OperandType type7(Type::TENSOR_FLOAT32, {0, 4});
  OperandType type8(Type::TENSOR_INT32, {1});
  OperandType type9(Type::FLOAT32, {});
  // Phase 1, operands
  auto scores = model->addOperand(&type3);
  auto roi = model->addOperand(&type4);
  auto param1 = model->addOperand(&type8);
  auto param2 = model->addOperand(&type9);
  auto param3 = model->addOperand(&type1);
  auto param4 = model->addOperand(&type1);
  auto param5 = model->addOperand(&type9);
  auto param6 = model->addOperand(&type9);
  auto param7 = model->addOperand(&type9);
  auto scoresOut = model->addOperand(&type5);
  auto roiOut = model->addOperand(&type7);
  auto classesOut = model->addOperand(&type6);
  auto batchSplitOut = model->addOperand(&type6);
  auto in = model->addOperand(&type11);
  auto param8 = model->addOperand(&type1);
  auto param9 = model->addOperand(&type1);
  auto param10 = model->addOperand(&type9);
  auto param11 = model->addOperand(&type9);
  auto param12 = model->addOperand(&type1);
  auto param13 = model->addOperand(&type1);
  auto layout = model->addOperand(&type10);
  auto featureMap = model->addOperand(&type12);
  auto op = model->addOperand(&type0);
  auto param14 = model->addOperand(&type1);
  auto out = model->addOperand(&type12);
  // Phase 2, operations
  static float scores_init[] = {0.9f, 0.1f};
  model->setOperandValue(scores, scores_init, sizeof(float) * 2);
  static float roi_init[] = {1.0f, 1.0f, 10.0f, 10.0f, 0.0f, 0.0f, 10.0f, 10.0f};
  model->setOperandValue(roi, roi_init, sizeof(float) * 8);
  static int32_t param1_init[] = {0};
  model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1);
  static float param2_init[] = {0.3f};
  model->setOperandValue(param2, param2_init, sizeof(float) * 1);
  static int32_t param3_init[] = {-1};
  model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1);
  static int32_t param4_init[] = {0};
  model->setOperandValue(param4, param4_init, sizeof(int32_t) * 1);
  static float param5_init[] = {0.4f};
  model->setOperandValue(param5, param5_init, sizeof(float) * 1);
  static float param6_init[] = {1.0f};
  model->setOperandValue(param6, param6_init, sizeof(float) * 1);
  static float param7_init[] = {0.3f};
  model->setOperandValue(param7, param7_init, sizeof(float) * 1);
  static int32_t param8_init[] = {2};
  model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1);
  static int32_t param9_init[] = {2};
  model->setOperandValue(param9, param9_init, sizeof(int32_t) * 1);
  static float param10_init[] = {2.0f};
  model->setOperandValue(param10, param10_init, sizeof(float) * 1);
  static float param11_init[] = {2.0f};
  model->setOperandValue(param11, param11_init, sizeof(float) * 1);
  static int32_t param12_init[] = {4};
  model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1);
  static int32_t param13_init[] = {4};
  model->setOperandValue(param13, param13_init, sizeof(int32_t) * 1);
  static bool8 layout_init[] = {false};
  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
  static float op_init[] = {1.0f, 2.0f, 3.0f, 4.0f};
  model->setOperandValue(op, op_init, sizeof(float) * 4);
  static int32_t param14_init[] = {0};
  model->setOperandValue(param14, param14_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param1, param2, param3, param4, param5, param6, param7}, {scoresOut, roiOut, classesOut, batchSplitOut});
  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param8, param9, param10, param11, param12, param13, layout}, {featureMap});
  model->addOperation(ANEURALNETWORKS_SUB, {featureMap, op, param14}, {out});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {in},
    {scoresOut, classesOut, out});
  // Phase 4: set relaxed execution
  model->relaxComputationFloat32toFloat16(true);
  assert(model->isValid());
}

bool is_ignored_zero_sized_relaxed(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_zero_sized_relaxed_dynamic_output_shape(Model *model) {
  OperandType type0(Type::TENSOR_FLOAT32, {1, 2, 2, 1});
  OperandType type1(Type::INT32, {});
  OperandType type10(Type::BOOL, {});
  OperandType type11(Type::TENSOR_FLOAT32, {1, 1, 1, 2});
  OperandType type12(Type::TENSOR_FLOAT32, {0, 2, 2, 2});
  OperandType type13(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
  OperandType type3(Type::TENSOR_FLOAT32, {1, 2});
  OperandType type4(Type::TENSOR_FLOAT32, {1, 8});
  OperandType type5(Type::TENSOR_FLOAT32, {0});
  OperandType type6(Type::TENSOR_INT32, {0});
  OperandType type7(Type::TENSOR_FLOAT32, {0, 4});
  OperandType type8(Type::TENSOR_INT32, {1});
  OperandType type9(Type::FLOAT32, {});
  // Phase 1, operands
  auto scores = model->addOperand(&type3);
  auto roi = model->addOperand(&type4);
  auto param1 = model->addOperand(&type8);
  auto param2 = model->addOperand(&type9);
  auto param3 = model->addOperand(&type1);
  auto param4 = model->addOperand(&type1);
  auto param5 = model->addOperand(&type9);
  auto param6 = model->addOperand(&type9);
  auto param7 = model->addOperand(&type9);
  auto scoresOut = model->addOperand(&type5);
  auto roiOut = model->addOperand(&type7);
  auto classesOut = model->addOperand(&type6);
  auto batchSplitOut = model->addOperand(&type6);
  auto in = model->addOperand(&type11);
  auto param8 = model->addOperand(&type1);
  auto param9 = model->addOperand(&type1);
  auto param10 = model->addOperand(&type9);
  auto param11 = model->addOperand(&type9);
  auto param12 = model->addOperand(&type1);
  auto param13 = model->addOperand(&type1);
  auto layout = model->addOperand(&type10);
  auto featureMap = model->addOperand(&type12);
  auto op = model->addOperand(&type0);
  auto param14 = model->addOperand(&type1);
  auto out = model->addOperand(&type13);
  // Phase 2, operations
  static float scores_init[] = {0.9f, 0.1f};
  model->setOperandValue(scores, scores_init, sizeof(float) * 2);
  static float roi_init[] = {1.0f, 1.0f, 10.0f, 10.0f, 0.0f, 0.0f, 10.0f, 10.0f};
  model->setOperandValue(roi, roi_init, sizeof(float) * 8);
  static int32_t param1_init[] = {0};
  model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1);
  static float param2_init[] = {0.3f};
  model->setOperandValue(param2, param2_init, sizeof(float) * 1);
  static int32_t param3_init[] = {-1};
  model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1);
  static int32_t param4_init[] = {0};
  model->setOperandValue(param4, param4_init, sizeof(int32_t) * 1);
  static float param5_init[] = {0.4f};
  model->setOperandValue(param5, param5_init, sizeof(float) * 1);
  static float param6_init[] = {1.0f};
  model->setOperandValue(param6, param6_init, sizeof(float) * 1);
  static float param7_init[] = {0.3f};
  model->setOperandValue(param7, param7_init, sizeof(float) * 1);
  static int32_t param8_init[] = {2};
  model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1);
  static int32_t param9_init[] = {2};
  model->setOperandValue(param9, param9_init, sizeof(int32_t) * 1);
  static float param10_init[] = {2.0f};
  model->setOperandValue(param10, param10_init, sizeof(float) * 1);
  static float param11_init[] = {2.0f};
  model->setOperandValue(param11, param11_init, sizeof(float) * 1);
  static int32_t param12_init[] = {4};
  model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1);
  static int32_t param13_init[] = {4};
  model->setOperandValue(param13, param13_init, sizeof(int32_t) * 1);
  static bool8 layout_init[] = {false};
  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
  static float op_init[] = {1.0f, 2.0f, 3.0f, 4.0f};
  model->setOperandValue(op, op_init, sizeof(float) * 4);
  static int32_t param14_init[] = {0};
  model->setOperandValue(param14, param14_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param1, param2, param3, param4, param5, param6, param7}, {scoresOut, roiOut, classesOut, batchSplitOut});
  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param8, param9, param10, param11, param12, param13, layout}, {featureMap});
  model->addOperation(ANEURALNETWORKS_SUB, {featureMap, op, param14}, {out});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {in},
    {scoresOut, classesOut, out});
  // Phase 4: set relaxed execution
  model->relaxComputationFloat32toFloat16(true);
  assert(model->isValid());
}

bool is_ignored_zero_sized_relaxed_dynamic_output_shape(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_zero_sized_quant8(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type10(Type::BOOL, {});
  OperandType type21(Type::TENSOR_QUANT8_ASYMM, {0, 2, 2, 2}, 0.1f, 128);
  OperandType type22(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 2}, 0.1f, 128);
  OperandType type23(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 0.1f, 128);
  OperandType type24(Type::TENSOR_QUANT16_ASYMM, {1, 8}, 0.125f, 0);
  OperandType type25(Type::TENSOR_QUANT16_ASYMM, {0, 4}, 0.125f, 0);
  OperandType type26(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 0.1f, 128);
  OperandType type27(Type::TENSOR_QUANT8_ASYMM, {0}, 0.1f, 128);
  OperandType type6(Type::TENSOR_INT32, {0});
  OperandType type8(Type::TENSOR_INT32, {1});
  OperandType type9(Type::FLOAT32, {});
  // Phase 1, operands
  auto scores = model->addOperand(&type26);
  auto roi = model->addOperand(&type24);
  auto param1 = model->addOperand(&type8);
  auto param2 = model->addOperand(&type9);
  auto param3 = model->addOperand(&type1);
  auto param4 = model->addOperand(&type1);
  auto param5 = model->addOperand(&type9);
  auto param6 = model->addOperand(&type9);
  auto param7 = model->addOperand(&type9);
  auto scoresOut = model->addOperand(&type27);
  auto roiOut = model->addOperand(&type25);
  auto classesOut = model->addOperand(&type6);
  auto batchSplitOut = model->addOperand(&type6);
  auto in = model->addOperand(&type22);
  auto param8 = model->addOperand(&type1);
  auto param9 = model->addOperand(&type1);
  auto param10 = model->addOperand(&type9);
  auto param11 = model->addOperand(&type9);
  auto param12 = model->addOperand(&type1);
  auto param13 = model->addOperand(&type1);
  auto layout = model->addOperand(&type10);
  auto featureMap = model->addOperand(&type21);
  auto op = model->addOperand(&type23);
  auto param14 = model->addOperand(&type1);
  auto out = model->addOperand(&type21);
  // Phase 2, operations
  static uint8_t scores_init[] = {137, 129};
  model->setOperandValue(scores, scores_init, sizeof(uint8_t) * 2);
  static uint16_t roi_init[] = {8, 8, 80, 80, 0, 0, 80, 80};
  model->setOperandValue(roi, roi_init, sizeof(uint16_t) * 8);
  static int32_t param1_init[] = {0};
  model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1);
  static float param2_init[] = {0.3f};
  model->setOperandValue(param2, param2_init, sizeof(float) * 1);
  static int32_t param3_init[] = {-1};
  model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1);
  static int32_t param4_init[] = {0};
  model->setOperandValue(param4, param4_init, sizeof(int32_t) * 1);
  static float param5_init[] = {0.4f};
  model->setOperandValue(param5, param5_init, sizeof(float) * 1);
  static float param6_init[] = {1.0f};
  model->setOperandValue(param6, param6_init, sizeof(float) * 1);
  static float param7_init[] = {0.3f};
  model->setOperandValue(param7, param7_init, sizeof(float) * 1);
  static int32_t param8_init[] = {2};
  model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1);
  static int32_t param9_init[] = {2};
  model->setOperandValue(param9, param9_init, sizeof(int32_t) * 1);
  static float param10_init[] = {2.0f};
  model->setOperandValue(param10, param10_init, sizeof(float) * 1);
  static float param11_init[] = {2.0f};
  model->setOperandValue(param11, param11_init, sizeof(float) * 1);
  static int32_t param12_init[] = {4};
  model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1);
  static int32_t param13_init[] = {4};
  model->setOperandValue(param13, param13_init, sizeof(int32_t) * 1);
  static bool8 layout_init[] = {false};
  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
  static uint8_t op_init[] = {138, 148, 158, 168};
  model->setOperandValue(op, op_init, sizeof(uint8_t) * 4);
  static int32_t param14_init[] = {0};
  model->setOperandValue(param14, param14_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param1, param2, param3, param4, param5, param6, param7}, {scoresOut, roiOut, classesOut, batchSplitOut});
  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param8, param9, param10, param11, param12, param13, layout}, {featureMap});
  model->addOperation(ANEURALNETWORKS_SUB, {featureMap, op, param14}, {out});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {in},
    {scoresOut, classesOut, out});
  assert(model->isValid());
}

bool is_ignored_zero_sized_quant8(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_zero_sized_quant8_dynamic_output_shape(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type10(Type::BOOL, {});
  OperandType type21(Type::TENSOR_QUANT8_ASYMM, {0, 2, 2, 2}, 0.1f, 128);
  OperandType type22(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 2}, 0.1f, 128);
  OperandType type23(Type::TENSOR_QUANT8_ASYMM, {1, 2, 2, 1}, 0.1f, 128);
  OperandType type24(Type::TENSOR_QUANT16_ASYMM, {1, 8}, 0.125f, 0);
  OperandType type25(Type::TENSOR_QUANT16_ASYMM, {0, 4}, 0.125f, 0);
  OperandType type26(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 0.1f, 128);
  OperandType type27(Type::TENSOR_QUANT8_ASYMM, {0}, 0.1f, 128);
  OperandType type28(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.1f, 128);
  OperandType type6(Type::TENSOR_INT32, {0});
  OperandType type8(Type::TENSOR_INT32, {1});
  OperandType type9(Type::FLOAT32, {});
  // Phase 1, operands
  auto scores = model->addOperand(&type26);
  auto roi = model->addOperand(&type24);
  auto param1 = model->addOperand(&type8);
  auto param2 = model->addOperand(&type9);
  auto param3 = model->addOperand(&type1);
  auto param4 = model->addOperand(&type1);
  auto param5 = model->addOperand(&type9);
  auto param6 = model->addOperand(&type9);
  auto param7 = model->addOperand(&type9);
  auto scoresOut = model->addOperand(&type27);
  auto roiOut = model->addOperand(&type25);
  auto classesOut = model->addOperand(&type6);
  auto batchSplitOut = model->addOperand(&type6);
  auto in = model->addOperand(&type22);
  auto param8 = model->addOperand(&type1);
  auto param9 = model->addOperand(&type1);
  auto param10 = model->addOperand(&type9);
  auto param11 = model->addOperand(&type9);
  auto param12 = model->addOperand(&type1);
  auto param13 = model->addOperand(&type1);
  auto layout = model->addOperand(&type10);
  auto featureMap = model->addOperand(&type21);
  auto op = model->addOperand(&type23);
  auto param14 = model->addOperand(&type1);
  auto out = model->addOperand(&type28);
  // Phase 2, operations
  static uint8_t scores_init[] = {137, 129};
  model->setOperandValue(scores, scores_init, sizeof(uint8_t) * 2);
  static uint16_t roi_init[] = {8, 8, 80, 80, 0, 0, 80, 80};
  model->setOperandValue(roi, roi_init, sizeof(uint16_t) * 8);
  static int32_t param1_init[] = {0};
  model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1);
  static float param2_init[] = {0.3f};
  model->setOperandValue(param2, param2_init, sizeof(float) * 1);
  static int32_t param3_init[] = {-1};
  model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1);
  static int32_t param4_init[] = {0};
  model->setOperandValue(param4, param4_init, sizeof(int32_t) * 1);
  static float param5_init[] = {0.4f};
  model->setOperandValue(param5, param5_init, sizeof(float) * 1);
  static float param6_init[] = {1.0f};
  model->setOperandValue(param6, param6_init, sizeof(float) * 1);
  static float param7_init[] = {0.3f};
  model->setOperandValue(param7, param7_init, sizeof(float) * 1);
  static int32_t param8_init[] = {2};
  model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1);
  static int32_t param9_init[] = {2};
  model->setOperandValue(param9, param9_init, sizeof(int32_t) * 1);
  static float param10_init[] = {2.0f};
  model->setOperandValue(param10, param10_init, sizeof(float) * 1);
  static float param11_init[] = {2.0f};
  model->setOperandValue(param11, param11_init, sizeof(float) * 1);
  static int32_t param12_init[] = {4};
  model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1);
  static int32_t param13_init[] = {4};
  model->setOperandValue(param13, param13_init, sizeof(int32_t) * 1);
  static bool8 layout_init[] = {false};
  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
  static uint8_t op_init[] = {138, 148, 158, 168};
  model->setOperandValue(op, op_init, sizeof(uint8_t) * 4);
  static int32_t param14_init[] = {0};
  model->setOperandValue(param14, param14_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param1, param2, param3, param4, param5, param6, param7}, {scoresOut, roiOut, classesOut, batchSplitOut});
  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param8, param9, param10, param11, param12, param13, layout}, {featureMap});
  model->addOperation(ANEURALNETWORKS_SUB, {featureMap, op, param14}, {out});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {in},
    {scoresOut, classesOut, out});
  assert(model->isValid());
}

bool is_ignored_zero_sized_quant8_dynamic_output_shape(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_zero_sized_float16(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type10(Type::BOOL, {});
  OperandType type15(Type::TENSOR_FLOAT16, {1, 2, 2, 1});
  OperandType type29(Type::TENSOR_FLOAT16, {0, 2, 2, 2});
  OperandType type30(Type::TENSOR_FLOAT16, {1, 1, 1, 2});
  OperandType type31(Type::FLOAT16, {});
  OperandType type32(Type::TENSOR_FLOAT16, {1, 8});
  OperandType type33(Type::TENSOR_FLOAT16, {0, 4});
  OperandType type34(Type::TENSOR_FLOAT16, {1, 2});
  OperandType type35(Type::TENSOR_FLOAT16, {0});
  OperandType type6(Type::TENSOR_INT32, {0});
  OperandType type8(Type::TENSOR_INT32, {1});
  // Phase 1, operands
  auto scores = model->addOperand(&type34);
  auto roi = model->addOperand(&type32);
  auto param1 = model->addOperand(&type8);
  auto param2 = model->addOperand(&type31);
  auto param3 = model->addOperand(&type1);
  auto param4 = model->addOperand(&type1);
  auto param5 = model->addOperand(&type31);
  auto param6 = model->addOperand(&type31);
  auto param7 = model->addOperand(&type31);
  auto scoresOut = model->addOperand(&type35);
  auto roiOut = model->addOperand(&type33);
  auto classesOut = model->addOperand(&type6);
  auto batchSplitOut = model->addOperand(&type6);
  auto in = model->addOperand(&type30);
  auto param8 = model->addOperand(&type1);
  auto param9 = model->addOperand(&type1);
  auto param10 = model->addOperand(&type31);
  auto param11 = model->addOperand(&type31);
  auto param12 = model->addOperand(&type1);
  auto param13 = model->addOperand(&type1);
  auto layout = model->addOperand(&type10);
  auto featureMap = model->addOperand(&type29);
  auto op = model->addOperand(&type15);
  auto param14 = model->addOperand(&type1);
  auto out = model->addOperand(&type29);
  // Phase 2, operations
  static _Float16 scores_init[] = {0.8999999761581421f, 0.10000000149011612f};
  model->setOperandValue(scores, scores_init, sizeof(_Float16) * 2);
  static _Float16 roi_init[] = {1.0f, 1.0f, 10.0f, 10.0f, 0.0f, 0.0f, 10.0f, 10.0f};
  model->setOperandValue(roi, roi_init, sizeof(_Float16) * 8);
  static int32_t param1_init[] = {0};
  model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1);
  static _Float16 param2_init[] = {0.30000001192092896f};
  model->setOperandValue(param2, param2_init, sizeof(_Float16) * 1);
  static int32_t param3_init[] = {-1};
  model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1);
  static int32_t param4_init[] = {0};
  model->setOperandValue(param4, param4_init, sizeof(int32_t) * 1);
  static _Float16 param5_init[] = {0.4000000059604645f};
  model->setOperandValue(param5, param5_init, sizeof(_Float16) * 1);
  static _Float16 param6_init[] = {1.0f};
  model->setOperandValue(param6, param6_init, sizeof(_Float16) * 1);
  static _Float16 param7_init[] = {0.30000001192092896f};
  model->setOperandValue(param7, param7_init, sizeof(_Float16) * 1);
  static int32_t param8_init[] = {2};
  model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1);
  static int32_t param9_init[] = {2};
  model->setOperandValue(param9, param9_init, sizeof(int32_t) * 1);
  static _Float16 param10_init[] = {2.0f};
  model->setOperandValue(param10, param10_init, sizeof(_Float16) * 1);
  static _Float16 param11_init[] = {2.0f};
  model->setOperandValue(param11, param11_init, sizeof(_Float16) * 1);
  static int32_t param12_init[] = {4};
  model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1);
  static int32_t param13_init[] = {4};
  model->setOperandValue(param13, param13_init, sizeof(int32_t) * 1);
  static bool8 layout_init[] = {false};
  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
  static _Float16 op_init[] = {1.0f, 2.0f, 3.0f, 4.0f};
  model->setOperandValue(op, op_init, sizeof(_Float16) * 4);
  static int32_t param14_init[] = {0};
  model->setOperandValue(param14, param14_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param1, param2, param3, param4, param5, param6, param7}, {scoresOut, roiOut, classesOut, batchSplitOut});
  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param8, param9, param10, param11, param12, param13, layout}, {featureMap});
  model->addOperation(ANEURALNETWORKS_SUB, {featureMap, op, param14}, {out});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {in},
    {scoresOut, classesOut, out});
  assert(model->isValid());
}

bool is_ignored_zero_sized_float16(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2
namespace generated_tests::sub_v1_2 {

void CreateModel_zero_sized_float16_dynamic_output_shape(Model *model) {
  OperandType type1(Type::INT32, {});
  OperandType type10(Type::BOOL, {});
  OperandType type15(Type::TENSOR_FLOAT16, {1, 2, 2, 1});
  OperandType type16(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
  OperandType type29(Type::TENSOR_FLOAT16, {0, 2, 2, 2});
  OperandType type30(Type::TENSOR_FLOAT16, {1, 1, 1, 2});
  OperandType type31(Type::FLOAT16, {});
  OperandType type32(Type::TENSOR_FLOAT16, {1, 8});
  OperandType type33(Type::TENSOR_FLOAT16, {0, 4});
  OperandType type34(Type::TENSOR_FLOAT16, {1, 2});
  OperandType type36(Type::TENSOR_FLOAT16, {0});
  OperandType type6(Type::TENSOR_INT32, {0});
  OperandType type8(Type::TENSOR_INT32, {1});
  // Phase 1, operands
  auto scores = model->addOperand(&type34);
  auto roi = model->addOperand(&type32);
  auto param1 = model->addOperand(&type8);
  auto param2 = model->addOperand(&type31);
  auto param3 = model->addOperand(&type1);
  auto param4 = model->addOperand(&type1);
  auto param5 = model->addOperand(&type31);
  auto param6 = model->addOperand(&type31);
  auto param7 = model->addOperand(&type31);
  auto scoresOut = model->addOperand(&type36);
  auto roiOut = model->addOperand(&type33);
  auto classesOut = model->addOperand(&type6);
  auto batchSplitOut = model->addOperand(&type6);
  auto in = model->addOperand(&type30);
  auto param8 = model->addOperand(&type1);
  auto param9 = model->addOperand(&type1);
  auto param10 = model->addOperand(&type31);
  auto param11 = model->addOperand(&type31);
  auto param12 = model->addOperand(&type1);
  auto param13 = model->addOperand(&type1);
  auto layout = model->addOperand(&type10);
  auto featureMap = model->addOperand(&type29);
  auto op = model->addOperand(&type15);
  auto param14 = model->addOperand(&type1);
  auto out = model->addOperand(&type16);
  // Phase 2, operations
  static _Float16 scores_init[] = {0.8999999761581421f, 0.10000000149011612f};
  model->setOperandValue(scores, scores_init, sizeof(_Float16) * 2);
  static _Float16 roi_init[] = {1.0f, 1.0f, 10.0f, 10.0f, 0.0f, 0.0f, 10.0f, 10.0f};
  model->setOperandValue(roi, roi_init, sizeof(_Float16) * 8);
  static int32_t param1_init[] = {0};
  model->setOperandValue(param1, param1_init, sizeof(int32_t) * 1);
  static _Float16 param2_init[] = {0.30000001192092896f};
  model->setOperandValue(param2, param2_init, sizeof(_Float16) * 1);
  static int32_t param3_init[] = {-1};
  model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1);
  static int32_t param4_init[] = {0};
  model->setOperandValue(param4, param4_init, sizeof(int32_t) * 1);
  static _Float16 param5_init[] = {0.4000000059604645f};
  model->setOperandValue(param5, param5_init, sizeof(_Float16) * 1);
  static _Float16 param6_init[] = {1.0f};
  model->setOperandValue(param6, param6_init, sizeof(_Float16) * 1);
  static _Float16 param7_init[] = {0.30000001192092896f};
  model->setOperandValue(param7, param7_init, sizeof(_Float16) * 1);
  static int32_t param8_init[] = {2};
  model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1);
  static int32_t param9_init[] = {2};
  model->setOperandValue(param9, param9_init, sizeof(int32_t) * 1);
  static _Float16 param10_init[] = {2.0f};
  model->setOperandValue(param10, param10_init, sizeof(_Float16) * 1);
  static _Float16 param11_init[] = {2.0f};
  model->setOperandValue(param11, param11_init, sizeof(_Float16) * 1);
  static int32_t param12_init[] = {4};
  model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1);
  static int32_t param13_init[] = {4};
  model->setOperandValue(param13, param13_init, sizeof(int32_t) * 1);
  static bool8 layout_init[] = {false};
  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
  static _Float16 op_init[] = {1.0f, 2.0f, 3.0f, 4.0f};
  model->setOperandValue(op, op_init, sizeof(_Float16) * 4);
  static int32_t param14_init[] = {0};
  model->setOperandValue(param14, param14_init, sizeof(int32_t) * 1);
  model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param1, param2, param3, param4, param5, param6, param7}, {scoresOut, roiOut, classesOut, batchSplitOut});
  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param8, param9, param10, param11, param12, param13, layout}, {featureMap});
  model->addOperation(ANEURALNETWORKS_SUB, {featureMap, op, param14}, {out});
  // Phase 3, inputs and outputs
  model->identifyInputsAndOutputs(
    {in},
    {scoresOut, classesOut, out});
  assert(model->isValid());
}

bool is_ignored_zero_sized_float16_dynamic_output_shape(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace generated_tests::sub_v1_2