runtime/test/generated/spec_V1_2/vts_V1_2/sub_quantized_different_scales.model.cpp

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

namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

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

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

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

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

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

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

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

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_2() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_2(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_2() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_2(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_2() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_2(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_2() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_2(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_3() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_3(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_3() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_3(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_3() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_3(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_3() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_3(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_4() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_4(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_4() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_4(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_4() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_4(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_4() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_4(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_5() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_5(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_5() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_5(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_5() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_5(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_5() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_5(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_6() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_6(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_6() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_6(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_6() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_6(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_6() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_6(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_7() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_7(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_7() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_7(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_7() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_7(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_7() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_7(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_8() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_8(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_8() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_8(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_8() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_8(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_8() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_8(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_9() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_9(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_9() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_9(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_9() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_9(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_9() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_9(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_10() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_10(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_10() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_10(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_10() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_10(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_10() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_10(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_11() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_11(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_11() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_11(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_11() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_11(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_11() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_11(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_12() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_12(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_12() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_12(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_12() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_12(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_12() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_12(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_13() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_13(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_13() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_13(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_13() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_13(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_13() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_13(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_14() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_14(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_14() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_14(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_14() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_14(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_14() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_14(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_15() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_15(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_15() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_15(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_15() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_15(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_15() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_15(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_16() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_16(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_16() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_16(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_16() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_16(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_16() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_16(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_17() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_17(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_17() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_17(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_17() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_17(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_17() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_17(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_18() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_18(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_18() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_18(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_18() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_18(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_18() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_18(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_19() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_19(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_19() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_19(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_19() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_19(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_19() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_19(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_20() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_20(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_20() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_20(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_20() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_20(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_20() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_20(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_21() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_21(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_21() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_21(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_21() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_21(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_21() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_21(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_22() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_22(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_22() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_22(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_22() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_22(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_22() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_22(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_23() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_23(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_23() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_23(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_23() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_23(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_23() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_23(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_24() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_24(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_24() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_24(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_24() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_24(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_24() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_24(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_25() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_25(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_25() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_25(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_25() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_25(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_25() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_25(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_26() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_26(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_26() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_26(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_26() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_26(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_26() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_26(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_27() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_27(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_27() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_27(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_27() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_27(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_27() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_27(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_28() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_28(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_28() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_28(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_28() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_28(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_28() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_28(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_29() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_29(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_29() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_29(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_29() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_29(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_29() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_29(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_30() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_30(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_30() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_30(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_30() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_30(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_30() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_30(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_31() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_31(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_31() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_31(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_31() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_31(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_31() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_31(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_32() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_32(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_32() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_32(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_32() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_32(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_32() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_32(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_33() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_33(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_33() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_33(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_33() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_33(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_33() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_33(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_34() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_34(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_34() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_34(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_34() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_34(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_34() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_34(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_35() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_35(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_35() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_35(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_35() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_35(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_35() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_35(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_36() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_36(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_36() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_36(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_36() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_36(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_36() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_36(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_37() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_37(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_37() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_37(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_37() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_37(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_37() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_37(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_38() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_38(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_38() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_38(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_38() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_38(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_38() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_38(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_39() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_39(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_39() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_39(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_39() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_39(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_39() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_39(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_40() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_40(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_40() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_40(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_40() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_40(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_40() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_40(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_41() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_41(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_41() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_41(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_41() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_41(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_41() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_41(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_42() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_42(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_42() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_42(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_42() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_42(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_42() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_42(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_43() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_43(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_43() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_43(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_43() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_43(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_43() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_43(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_44() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_44(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_44() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_44(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_44() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_44(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_44() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_44(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_45() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_45(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_45() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_45(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_45() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_45(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_45() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_45(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_46() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_46(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_46() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_46(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_46() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_46(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_46() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_46(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_47() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_47(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_47() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_47(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_47() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_47(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_47() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_47(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_48() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_48(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_48() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_48(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_48() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_48(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_48() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_48(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_49() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_49(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_49() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_49(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_49() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_49(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_49() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_49(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_50() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_50(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_50() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_50(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_50() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_50(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_50() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_50(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_51() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_51(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_51() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_51(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_51() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_51(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_51() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_51(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_52() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_52(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_52() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_52(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_52() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_52(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_52() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_52(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_53() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_53(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_53() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_53(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_53() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_53(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_53() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_53(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_54() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_54(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_54() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_54(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_54() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_54(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_54() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_54(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_55() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_55(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_55() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_55(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_55() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_55(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_55() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_55(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_56() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_56(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_56() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_56(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_56() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_56(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_56() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 1, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_56(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_57() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_57(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_57() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_57(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_57() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_57(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_57() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_57(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_58() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_58(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_58() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_58(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_58() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_58(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_58() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_58(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_59() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_59(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_59() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_59(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_59() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_59(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_59() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_59(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_60() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_60(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_60() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_60(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_60() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_60(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_60() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_60(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_61() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_61(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_61() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_61(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_61() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_61(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_61() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_61(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_62() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_62(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_62() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_62(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_62() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_62(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_62() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 1.0f,
            .zeroPoint = 1,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_62(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_63() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_63(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_63() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_63(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_63() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_63(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_63() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 0.01f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_63(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_64() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_64(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_dynamic_output_shape_64() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0, 1};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape_64(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_64() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_64(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales
namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales {

Model createTestModel_all_inputs_as_internal_dynamic_output_shape_64() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {0},
            .numberOfConsumers = 0,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 5, .length = 4},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {144},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_QUANT8_ASYMM,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 10.0f,
            .zeroPoint = 120,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 9, .length = 1},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 10, .length = 4},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::ADD,
            .inputs = {4, 5, 6},
            .outputs = {0},
        },
        {
            .type = OperationType::ADD,
            .inputs = {7, 8, 9},
            .outputs = {1},
        },
        {
            .type = OperationType::SUB,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {4, 7};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 120, 0, 0, 0, 0, 120, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_all_inputs_as_internal_dynamic_output_shape_64(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::sub_quantized_different_scales