code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      hidden_channels: int = 64,
      num_layers: int = 3,
      dropout: float = 0.5,
  ):
      super().__init__()
  
      self.gnn = GIN(
          in_channels,
          hidden_channels,
          num_layers,
          dropout=dropout,
          jk="cat",
      )
  
      self.classifier = MLP(
          [hidden_channels, hidden_channels, out_channels],
          norm="batch_norm",
          dropout=dropout,
      )
  
file_path: pytorch_geometric/examples/pytorch_ignite/gin.py
imports: import os.path
  import ignite
  import ignite.contrib.handlers.tensorboard_logger
  import ignite.contrib.handlers.tqdm_logger
  import torch
  import torch.nn.functional
  import torch_geometric.transforms
  from torch_geometric import seed_everything
  from torch_geometric.datasets import TUDataset
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import GIN, MLP, global_add_pool
label_desc: Use image node_id 1 to create a new Model object from inherited base classes: torch.nn with example: obj = Model(in_channels, out_channels, hidden_channels, num_layers, dropout)