code: def forward(self, feed_dict):
      g = feed_dict["graph"]
      nfeat = g.node_feat["feat"]
      h = self.atom_encoder(nfeat)
      h = self.mlp(h)
      h_graph = self.pool(g, h)
      output = self.graph_pred_linear(h_graph)
      return output
  
imports: import os
  import sys
  import math
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import pgl.nn
  from pgl.utils.logger import log
  import models.conv
  import models.mol_encoder
  import models.layers
label_desc: Use image node_id 2 for calling the MLP obj's underlying member method code with example usage: obj.forward(feed_dict) and returns: output