code: def __init__(
      self, hidden_channels, out_channels, num_layers=3, dropout=0.5
  ):
      super().__init__()
  
      self.dropout = dropout
  
      self.atom_encoder = AtomEncoder(hidden_channels)
      self.bond_encoder = BondEncoder(hidden_channels)
  
      self.convs = torch.nn.ModuleList()
      for _ in range(num_layers):
          nn = Sequential(
              Linear(hidden_channels, 2 * hidden_channels),
              BatchNorm(2 * hidden_channels),
              ReLU(),
              Linear(2 * hidden_channels, hidden_channels),
              BatchNorm(hidden_channels),
              ReLU(),
          )
          self.convs.append(GINEConv(nn, train_eps=True))
  
      self.lin = Linear(hidden_channels, out_channels)
  
imports: import os
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn.functional
  from ogb.graphproppred import Evaluator
  from ogb.graphproppred import PygGraphPropPredDataset
  from ogb.graphproppred.mol_encoder import AtomEncoder, BondEncoder
  from torch.nn import BatchNorm1d
  from torch.nn import Linear, ReLU, Sequential
  from torch.nn.parallel import DistributedDataParallel
  from torch.utils.data.distributed import DistributedSampler
  import torch_geometric.transforms
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import GINEConv, global_mean_pool
  from torch_geometric.typing import WITH_TORCH_SPARSE
label_desc: Use image node_id 1 to create a new GIN object from inherited base classes: torch.nn with example: obj = GIN(hidden_channels, out_channels, num_layers, dropout)