code: def graphsage_sum(feature, gw, hidden_size, name, act):
      msg = gw.send(lambda s, d, e: s["h"], nfeat_list=[("h", feature)])
      neigh_feature = gw.recv(
          msg, lambda feat: L.sequence_pool(feat, pool_type="sum")
      )
  
      hidden_size = hidden_size
      self_feature = linear(feature, hidden_size, name + "_l", act)
      neigh_feature = linear(
          neigh_feature, hidden_size, name + "_r", act
      )
      output = L.concat([self_feature, neigh_feature], axis=1)
      output = L.l2_normalize(output, axis=1)
      return output
  
imports: import os
  import json
  import numpy
  import pgl
  import paddle.fluid
  import paddle.fluid.layers
  from ernie import ErnieModel
  from pgl.utils.logger import log
label_desc: Use image node_id 2 for calling a global function with example usage: graphsage_sum(feature, gw, hidden_size, name, act) and returns: output