code: def __init__(self, n_hidden, n_output, n_layers=5):
      super().__init__()
      self.node_encoder = AtomEncoder(n_hidden)
      self.edge_encoders = nn.ModuleList(
          [BondEncoder(n_hidden) for _ in range(n_layers)]
      )
  
      self.pool = dglnn.AvgPooling()
      self.dropout = nn.Dropout(0.5)
      self.layers = nn.ModuleList()
      for _ in range(n_layers):
          self.layers.append(
              dglnn.GINEConv(MLP(n_hidden), learn_eps=True)
          )
      self.predictor = nn.Linear(n_hidden, n_output)
  
      # add virtual node
      self.virtual_emb = nn.Embedding(1, n_hidden)
      nn.init.constant_(self.virtual_emb.weight.data, 0)
      self.virtual_layers = nn.ModuleList()
      for _ in range(n_layers - 1):
          self.virtual_layers.append(MLP(n_hidden))
      self.virtual_pool = dglnn.SumPooling()
  
imports: import argparse
  import dgl
  import dgl.nn
  import torch
  import torch.distributed
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from dgl.data import AsGraphPredDataset
  from dgl.dataloading import GraphDataLoader
  from ogb.graphproppred import DglGraphPropPredDataset, Evaluator
  from ogb.graphproppred.mol_encoder import AtomEncoder, BondEncoder
  from tqdm import tqdm
label_desc: Use image node_id 1 to create a new GIN object from inherited base classes: nn with example: obj = GIN(n_hidden, n_output, n_layers)