code: def neighbor_aggregator(self, feature):
      for i in range(3):
          feature = L.fc(
              feature,
              size=self.hidden_size,
              name="simple_mlp_{}".format(i),
          )
          # feature = L.batch_norm(feature)
          feature = L.relu(feature)
          feature = L.dropout(feature, dropout_prob=self.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 2 for calling the MLPModel obj's underlying member method code with example usage: obj.neighbor_aggregator(feature) and returns: feature