def METHOD_NAME():
    # `input_model_path` is the path to the original, unquantized ONNX model.
    input_model_path = "models/resnet_trained_for_cifar10.onnx"
    # `output_model_path` is the path where the quantized model will be saved.
    output_model_path = "models/resnet.qdq.U8S8.onnx"
    # `calibration_dataset_path` is the path to the dataset used for calibration during quantization.
    calibration_dataset_path = "data/"
    # `dr` (Data Reader) is an instance of ResNet50DataReader, which is a utility class that 
    # reads the calibration dataset and prepares it for the quantization process.
    dr = resnet_calibration_reader(calibration_dataset_path)
    # `quantize_static` is a function that applies static quantization to the model.
    # The parameters of this function are:
    # - `input_model_path`: the path to the original, unquantized model.
    # - `output_model_path`: the path where the quantized model will be saved.
    # - `dr`: an instance of a data reader utility, which provides data for model calibration.
    # - `quant_format`: the format of quantization operators. Need to set to QDQ or QOperator.
    # - `activation_type`: the data type of activation tensors after quantization. In this case, it's QUInt8 (Quantized Unsigned Int 8).
    # - `weight_type`: the data type of weight tensors after quantization. In this case, it's QInt8 (Quantized Int 8).
    vai_q_onnx.quantize_static(
        input_model_path,
        output_model_path,
        dr,
        quant_format=QuantFormat.QDQ,
        calibrate_method=vai_q_onnx.PowerOfTwoMethod.MinMSE,
        activation_type=QuantType.QUInt8,
        weight_type=QuantType.QInt8,
    )
    print('Calibrated and quantized model saved at:', output_model_path)