How to use the x2paddle.op_mapper.caffe_op_mapper.CaffeOpMapper function in x2paddle

def caffe2paddle(proto, weight, save_dir, caffe_proto, params_merge=False):
    from x2paddle.decoder.caffe_decoder import CaffeDecoder
    from x2paddle.op_mapper.caffe_op_mapper import CaffeOpMapper
    from x2paddle.optimizer.caffe_optimizer import CaffeOptimizer
    import google.protobuf as gpb
    ver_part = gpb.__version__.split('.')
    version_satisfy = False
    if (int(ver_part[0]) == 3 and int(ver_part[1]) >= 6) \
        or (int(ver_part[0]) > 3):
        version_satisfy = True
    assert version_satisfy, 'google.protobuf >= 3.6.0 is required'
    print("Now translating model from caffe to paddle.")
    model = CaffeDecoder(proto, weight, caffe_proto)
    mapper = CaffeOpMapper(model)
    optimizer = CaffeOptimizer(mapper)
    optimizer.merge_bn_scale()
    optimizer.merge_op_activation()
    mapper.save_inference_model(save_dir, params_merge)

def __init__(self, decoder):
        super(CaffeOpMapper, self).__init__()
        self.graph = decoder.caffe_graph
        self.weights = dict()
        resolver = decoder.resolver
        self.used_custom_layers = {}

        print("Total nodes: {}".format(len(self.graph.topo_sort)))
        for node_name in self.graph.topo_sort:
            node = self.graph.get_node(node_name)
            if node.layer_type == 'DepthwiseConvolution':
                node.layer_type = 'ConvolutionDepthwise'
            op = node.layer_type
            if hasattr(self, op):
                self.set_node_shape(node)
                func = getattr(self, op)
                func(node)
            elif op in custom_layers: