code: def __init__(self, in_dim, out_dim, dropout, aggregators):
      super().__init__()
  
      self.dropout = dropout
  
      self.aggregators = aggregators
  
      self.batchnorm_h = nn.BatchNorm1d(out_dim)
      self.pretrans = MLP(in_size=2 * in_dim, out_size=in_dim)
      self.posttrans = MLP(
          in_size=(len(aggregators) * 1 + 1) * in_dim, out_size=out_dim
      )
  
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 1 to create a new DGNLayer object from inherited base classes: nn with example: obj = DGNLayer(in_dim, out_dim, dropout, aggregators)