How to use the x2paddle.core.util.string function in x2paddle

val_var = self.graph.get_input_node(node, idx=4, copy=True)

        self.omit_nodes.append(val_scale.layer_name)
        self.omit_nodes.append(val_b.layer_name)
        self.omit_nodes.append(val_mean.layer_name)
        self.omit_nodes.append(val_var.layer_name)

        momentum = node.get_attr('momentum', .9)
        epsilon = node.get_attr('epsilon', 1e-5)

        # Attribute: spatial is used in BatchNormalization-1,6,7
        spatial = bool(node.get_attr('spatial'))
        attr = {
            "momentum": momentum,
            "epsilon": epsilon,
            "data_layout": string('NCHW'),
            "is_test": True,
            "param_attr": string(val_scale.layer_name),
            "bias_attr": string(val_b.layer_name),
            "moving_mean_name": string(val_mean.layer_name),
            "moving_variance_name": string(val_var.layer_name),
            "use_global_stats": spatial,
            "name": string(node.layer_name)
        }
        node.fluid_code.add_layer("batch_norm",
                                  inputs=val_x,
                                  output=node,
                                  param_attr=attr)

if auto_pad == "SAME_UPPER" or auto_pad == "SAME_LOWER":
            input_shape = val_x.out_shapes[0]
            pad_h = get_same_padding(input_shape[2], kernel_shape[0],
                                     strides[0])
            pad_w = get_same_padding(input_shape[3], kernel_shape[1],
                                     strides[1])
            attr = {"paddings": pad_h + pad_w, "pad_value": 0.0}

        attr = {
            "pool_size": kernel_shape,
            "pool_type": string("max"),
            "pool_stride": strides,
            "pool_padding": paddings,
            "ceil_mode": ceil_mode,
            "name": string(node.layer_name),
            "exclusive": False
        }
        node.fluid_code.add_layer(fluid_op,
                                  inputs=val_x,
                                  output=node,
                                  param_attr=attr)

def create_parameter(self, node, parameter=None):
        if parameter is not None:
            node = parameter
        dtype = node.dtype
        shape = node.out_shapes[0]

        self.weights[node.layer_name] = node.weight
        attr = {
            'dtype': string(dtype),
            'shape': shape,
            'name': string(node.layer_name),
            'attr': string(node.layer_name),
            'default_initializer': 'Constant(0.0)'
        }
        node.fluid_code.add_layer("create_parameter",
                                  inputs=None,
                                  output=node,
                                  param_attr=attr)

def place_holder(self, node):
        self.input_shapes.append(node.out_shapes[0])
        attr = {
            "dtype": string(node.dtype),
            "shape": node.out_shapes[0],
            "name": string(node.layer_name),
            "append_batch_size": 'False'
        }

        node.fluid_code.add_layer("data",
                                  inputs=None,
                                  output=node,
                                  param_attr=attr)

elif len(pads) == 8:
            paddings = np.array(pads).reshape(
                (-1, 4)).transpose().flatten().tolist()  # SSEE -> SESE
            if sum(paddings[:4]) == 0:
                fluid_op = 'pad2d'
                paddings = paddings[4:]
                attr['mode'] = string(mode)
        attr['paddings'] = paddings
        if op_independent:
            attr['name'] = string(node.layer_name)
            node.fluid_code.add_layer(fluid_op,
                                      inputs=val_x,
                                      output=node,
                                      param_attr=attr)
        else:
            attr['name'] = string(node.layer_name + '_paded')
            node.fluid_code.add_layer(fluid_op,
                                      inputs=val_x,
                                      output=node.layer_name + '_paded',
                                      param_attr=attr)
            return node.layer_name + '_paded'

if '_' in mapped_attrs:
            mapped_attrs.pop('_')
        fluid_attrs = default_attrs.copy()
        fluid_attrs.update(mapped_attrs)
        inputs = inputs if input_perm is None else list(
            map(lambda i: inputs[i], input_perm))
        val_inps = []
        for idx, ipt in enumerate(inputs):
            val_inps.append(self.graph.get_input_node(node, idx=idx, copy=True))

        val_outs = outputs if output_perm is None else list(
            map(lambda i: outputs[i], output_perm))
        attr = fluid_attrs
        assert len(val_inps) == 1, 'directly_map error with multi inputs'
        if fluid_op not in ['shape']:
            attr['name'] = string(node.layer_name)
        node.fluid_code.add_layer(fluid_op,
                                  inputs=val_inps[0],
                                  output=val_outs[0],
                                  param_attr=attr)

if auto_pad == "SAME_UPPER" or auto_pad == "SAME_LOWER":
            pad_h = get_same_padding(input_shape[2], kernel_shape[0],
                                     strides[0])
            pad_w = get_same_padding(input_shape[3], kernel_shape[1],
                                     strides[1])
            attr = {"paddings": pad_h + pad_w, "pad_value": 0.0}

        attr = {
            "num_filters": num_out_channels,
            "filter_size": kernel_shape,
            "stride": strides,
            "padding": paddings,
            "dilation": dilations,
            "groups": num_groups,
            'param_attr': string(val_w.layer_name),
            "name": string(node.layer_name)
        }
        if has_bias:
            attr["bias_attr"] = string(val_b.layer_name)
        else:
            attr["bias_attr"] = False
        node.fluid_code.add_layer(fluid_op,
                                  inputs=val_x,
                                  output=node,
                                  param_attr=attr)

def GlobalAveragePool(self, node):
        val_x = self.graph.get_input_node(node, idx=0, copy=True)
        val_y = self.graph.get_node(node.layer.output[0], copy=True)
        input_shape = val_x.out_shapes[0]
        output_shape = val_y.out_shapes[0]
        assert input_shape is not None or output_shape is not None, 'poolnd not inferred'  # N
        if input_shape:
            poolnd = len(input_shape) - 2  # NC...
        elif output_shape:
            poolnd = len(output_shape) - 2  # NC...
        assert 2 <= poolnd <= 3, 'only pool2d and pool3d is supported'
        fluid_op = 'pool{}d'.format(poolnd)
        attr = {
            "pool_type": string("avg"),
            "global_pooling": True,
            "name": string(node.layer_name)
        }
        node.fluid_code.add_layer(fluid_op,
                                  inputs=val_x,
                                  output=node,
                                  param_attr=attr)

pads = node.get_attr('pads')
        mode = node.get_attr('mode', 'constant')
        value = node.get_attr('value', 0.)
        data_shape = val_x.out_shapes[0]
        output_shape = node.out_shapes[0]
        assume_pad2d = False
        attr = {}
        if len(pads) == 4:
            assume_pad2d |= mode != 'constant'
            if data_shape:
                assume_pad2d |= data_shape and len(data_shape) == 4  # NCHW
            if output_shape:
                assume_pad2d |= output_shape and len(output_shape) == 4  # NCHW
        if assume_pad2d:
            fluid_op = 'pad2d'
            attr['data_format'] = string('NCHW')
            attr['mode'] = string(mode)
        else:
            attr = {'pad_value': value}
            fluid_op = 'pad'
        if len(pads) == 4:
            paddings = np.array(pads).reshape(
                (-1, 2)).transpose().flatten().tolist()  # SSEE -> SESE
        elif len(pads) == 8:
            paddings = np.array(pads).reshape(
                (-1, 4)).transpose().flatten().tolist()  # SSEE -> SESE
            if sum(paddings[:4]) == 0:
                fluid_op = 'pad2d'
                paddings = paddings[4:]
                attr['mode'] = string(mode)
        attr['paddings'] = paddings
        if op_independent: