code: def forward(self, gw, feature, drop_rate):
      for i in range(self.num_layers):
          feature = self.GaANConv(gw, feature, self.name + "_" + str(i))
          feature = fluid.layers.dropout(
              feature, dropout_prob=drop_rate
          )
      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 3 for calling the GaANModel obj's underlying member method code with example usage: obj.forward(gw, feature, drop_rate) and returns: feature