How to use the onnxruntime.core.providers.nuphar.scripts.model_quantizer.QuantizeConfig function in onnxruntime

if not in_node.doc_string:
        return None
    from model_editor_internal import parse_custom_attributes
    custom_qcfg = parse_custom_attributes(in_node)
    if custom_qcfg:
        assert custom_qcfg['IntermediateBit'] == 32
        assert custom_qcfg['PerRowQuantization']
        assert custom_qcfg['QuantizeBitOfVector'] == custom_qcfg['QuantizeBitOfMatrix']
        qbits = custom_qcfg['QuantizeBitOfVector']
        assert ("Asymmetric" in custom_qcfg['VectorQuantizationType']) == ("Asymmetric" in custom_qcfg['MatrixQuantizationType'])
        symmetric = 0 if "Asymmetric" in custom_qcfg['VectorQuantizationType'] else 1
        x_signed = 0 if "Unsigned" in custom_qcfg['VectorQuantizationType'] else 1
        w_signed = 0 if "Unsigned" in custom_qcfg['MatrixQuantizationType'] else 1
        x_reserved_bits = custom_qcfg['ReservedBitOfVector']
        w_reserved_bits = custom_qcfg['ReservedBitOfMatrix']
        return {'W' : dict(QuantizeConfig(signed=w_signed, reserved_bits=w_reserved_bits, type_bits=qbits)),
                'X' : dict(QuantizeConfig(signed=x_signed, reserved_bits=x_reserved_bits, type_bits=qbits)),
                'Symmetric' : symmetric}
    return None

def convert_matmul_model(input_model, output_model, only_for_scan=False, share_input_quantization=False, preset_str='asymm8_param0_input1', qcfg_json=None, export_qcfg_json=None):
    preset_qcfgs = {'asymm8_param0_input1' : {'W' : dict(QuantizeConfig(signed=1, reserved_bits=0, type_bits=8)),
                                              'X' : dict(QuantizeConfig(signed=0, reserved_bits=1, type_bits=8)),
                                              'Symmetric' : 0},
                    'symm16_param3_input3' : {'W' : dict(QuantizeConfig(signed=1, reserved_bits=3, type_bits=16)),
                                              'X' : dict(QuantizeConfig(signed=1, reserved_bits=3, type_bits=16)),
                                              'Symmetric' : 1}}
    default_qcfg = preset_qcfgs[preset_str]
    in_mp = onnx.load(input_model)

    qcfg_dict = {}
    if qcfg_json and not export_qcfg_json:
        with open(qcfg_json, 'r') as f:
            qcfg_dict = json.load(f)

    out_mp = onnx.ModelProto()
    out_mp.CopyFrom(in_mp)
    out_mp.ir_version = 5 # update ir version to avoid requirement of initializer in graph input

def from_dict(qcfg_dict):
        return QuantizeConfig(1 if qcfg_dict['QuantizationType'] == 'Signed' else 0,
                              qcfg_dict['ReservedBit'],
                              qcfg_dict['QuantizeBit'])