code: def __init__(
      self, in_channels, hidden_channels, out_channels, num_layers
  ):
      super().__init__()
  
      self.convs = torch.nn.ModuleList()
      self.batch_norms = torch.nn.ModuleList()
  
      for i in range(num_layers):
          mlp = Sequential(
              Linear(in_channels, 2 * hidden_channels),
              BatchNorm(2 * hidden_channels),
              ReLU(),
              Linear(2 * hidden_channels, hidden_channels),
          )
          conv = GINConv(mlp, train_eps=True)
  
          self.convs.append(conv)
          self.batch_norms.append(BatchNorm(hidden_channels))
  
          in_channels = hidden_channels
  
      self.lin1 = Linear(hidden_channels, hidden_channels)
      self.batch_norm1 = BatchNorm(hidden_channels)
      self.lin2 = Linear(hidden_channels, out_channels)
  
file_path: pytorch_geometric/examples/jit/gin.py
imports: import os.path
  import torch
  import torch.nn.functional
  from torch.nn import BatchNorm1d
  from torch.nn import Linear, ReLU, Sequential
  from torch_geometric.datasets import TUDataset
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import GINConv, global_add_pool
  from torch_geometric.transforms import OneHotDegree
label_desc: Use image node_id 1 to create a new GIN object from inherited base classes: torch.nn with example: obj = GIN(in_channels, hidden_channels, out_channels, num_layers)