How to use the x2paddle.op_mapper.tf_op_mapper_nhwc.TFOpMapperNHWC function in x2paddle

print(
                "1.0.0<=tensorflow<2.0.0 is required, and v1.14.0 is recommended"
            )
            return
    except:
        print("Tensorflow is not installed, use \"pip install tensorflow\".")
        return

    from x2paddle.decoder.tf_decoder import TFDecoder
    from x2paddle.op_mapper.tf_op_mapper import TFOpMapper
    from x2paddle.op_mapper.tf_op_mapper_nhwc import TFOpMapperNHWC
    from x2paddle.optimizer.tf_optimizer import TFOptimizer

    print("Now translating model from tensorflow to paddle.")
    model = TFDecoder(model_path, define_input_shape=define_input_shape)
    mapper = TFOpMapperNHWC(model)
    optimizer = TFOptimizer(mapper)
    optimizer.delete_redundance_code()
    optimizer.strip_graph()
    #        optimizer.merge_activation()
    #        optimizer.merge_bias()
    mapper.save_inference_model(save_dir, params_merge)

def __init__(self, decoder):
        super(TFOpMapperNHWC, self).__init__()
        self.decoder = decoder
        self.graph = decoder.tf_graph
        self.weights = dict()
        self.batch_node = None
        self.omit_nodes = list()
        self.used_custom_layers = dict()

        not_placeholder = list()
        for name in self.graph.input_nodes:
            if self.graph.get_node(name).layer_type != "Placeholder":
                not_placeholder.append(name)
        for name in not_placeholder:
            idx = self.graph.input_nodes.index(name)
            del self.graph.input_nodes[idx]

        unsupported_ops = set()