code: def forward(self, g, h, snorm_n):
      g.ndata["h"] = h
  
      # pretransformation
      g.apply_edges(self.pretrans_edges)
  
      # aggregation
      g.update_all(self.message_func, self.reduce_func)
      h = torch.cat([h, g.ndata["h"]], dim=1)
  
      # posttransformation
      h = self.posttrans(h)
  
      # graph and batch normalization
      h = h * snorm_n
      h = self.batchnorm_h(h)
      h = F.relu(h)
  
      h = F.dropout(h, self.dropout, training=self.training)
  
      return h
  
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 5 for calling the DGNLayer obj's underlying member method code with example usage: obj.forward(g, h, snorm_n) and returns: h