code: def forward(self, gw, feature, drop_rate):
      for i in range(self.num_layers):
          final = i == (self.num_layers - 1)
          feature = self.SageConv(
              gw,
              feature,
              self.name + "_" + str(i),
              self.hidden_size,
              None if final else self.act,
          )
          if not final:
              feature = self.bn_drop(feature, 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 5 for calling the GraphSageModel obj's underlying member method code with example usage: obj.forward(gw, feature, drop_rate) and returns: feature