code: def pinsage_mean(
      gw, feature, hidden_size, act, initializer, learning_rate, name
  ):
      """doc"""
      msg = gw.send(
          weighted_copy_send,
          nfeat_list=[("h", feature)],
          efeat_list=["weight"],
      )
      neigh_feature = gw.recv(msg, mean_recv)
      self_feature = feature
      self_feature = L.fc(
          self_feature,
          hidden_size,
          act=act,
          param_attr=fluid.ParamAttr(
              name=name + "_l.w_0",
              initializer=initializer,
              learning_rate=learning_rate,
          ),
          bias_attr=name + "_l.b_0",
      )
      neigh_feature = L.fc(
          neigh_feature,
          hidden_size,
          act=act,
          param_attr=fluid.ParamAttr(
              name=name + "_r.w_0",
              initializer=initializer,
              learning_rate=learning_rate,
          ),
          bias_attr=name + "_r.b_0",
      )
      output = L.concat([self_feature, neigh_feature], axis=1)
      output = L.l2_normalize(output, axis=1)
      return output
  
file_path: PGL/legacy/pgl/message_passing.py
imports: import numpy
  import paddle
  import paddle.fluid
  import paddle.fluid.layers
  from pgl.utils import paddle_helper
label_desc: Use image node_id 9 for calling a global function with example usage: pinsage_mean(gw, feature, hidden_size, act, initializer, learning_rate, name) and returns: output