code: def forward(self, gw, feature):
      for i in range(self.num_layers):
          feature = gin(
              gw,
              feature,
              self.hidden_size,
              self.act,
              self.name + "_" + str(i),
          )
          feature = fluid.layers.layer_norm(
              feature,
              begin_norm_axis=1,
              param_attr=fluid.ParamAttr(
                  name="norm_scale_%s" % (i),
                  initializer=fluid.initializer.Constant(1.0),
              ),
              bias_attr=fluid.ParamAttr(
                  name="norm_bias_%s" % (i),
                  initializer=fluid.initializer.Constant(0.0),
              ),
          )
  
          feature = fluid.layers.relu(feature)
      return feature
  
imports: import os
  import re
  import time
  from random import random
  from functools import reduce, partial
  import numpy
  import multiprocessing
  import paddle
  import paddle.fluid
  import paddle.fluid
  import paddle.fluid.layers
  from pgl.graph_wrapper import GraphWrapper
  from pgl.layers.conv import gcn, gat
  from pgl.utils import paddle_helper
label_desc: Use image node_id 2 for calling the GinModel obj's underlying member method code with example usage: obj.forward(gw, feature) and returns: feature