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,
      )
  
      self.train_acc = Accuracy(
          task="multiclass", num_classes=out_channels
      )
      self.val_acc = Accuracy(
          task="multiclass", num_classes=out_channels
      )
      self.test_acc = Accuracy(
          task="multiclass", num_classes=out_channels
      )
  
file_path: pytorch_geometric/examples/pytorch_lightning/gin.py
imports: import os.path
  import pytorch_lightning
  import torch
  import torch.nn.functional
  from torchmetrics import Accuracy
  import torch_geometric.transforms
  from torch_geometric.data.lightning import LightningDataset
  from torch_geometric.datasets import TUDataset
  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: pl with example: obj = Model(in_channels, out_channels, hidden_channels, num_layers, dropout)