code: def forward(self, g, h, snorm_n):
      h = self.embedding_h(h)
  
      for i, conv in enumerate(self.layers):
          h_t = conv(g, h, snorm_n)
          h = h_t
  
      g.ndata["h"] = h
  
      hg = dgl.mean_nodes(g, "h")
  
      return self.MLP_layer(hg)
  
imports: import argparse
  import random
  import dgl
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from dgl.dataloading import GraphDataLoader
  from ogb.graphproppred import Evaluator
  from ogb.graphproppred.mol_encoder import AtomEncoder
  from preprocessing import prepare_dataset
  from torch.utils.data import Dataset
  from tqdm import tqdm
label_desc: Use image node_id 2 for calling the DGNNet obj's underlying member method code with example usage: obj.forward(g, h, snorm_n) and returns: self