code: def forward(self, h):
      for i, layer in enumerate(self.layers):
          h = layer(h)
          if i != self.num_layers - 1:
              h = F.relu(h)
      return h
  
imports: from typing import List
  import dgl.function
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn import AvgPooling, SumPooling
  from ogb.graphproppred.mol_encoder import AtomEncoder
label_desc: Use image node_id 2 for calling the MLP obj's underlying member method code with example usage: obj.forward(h) and returns: h