code: def forward(self, graph, feature):
      def _send_func(src_feat, dst_feat, edge_feat):
          return {"msg": src_feat["h"]}
  
      def _recv_func(message):
          return getattr(message, self.aggr_func)(message["msg"])
  
      msg = graph.send(_send_func, src_feat={"h": feature})
      neigh_feature = graph.recv(reduce_func=_recv_func, msg=msg)
  
      self_feature = self.self_linear(feature)
      neigh_feature = self.neigh_linear(neigh_feature)
      output = self_feature + neigh_feature
  
      if self.activation is not None:
          output = self.activation(output)
  
      return output
  
imports: import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import pgl
  from pgl.nn import functional
label_desc: Use image node_id 2 for calling the GraphSageMeanConv obj's underlying member method code with example usage: obj.forward(graph, feature) and returns: output, dict, getattr