code: def main():
      parser = argparse.ArgumentParser()
      parser.add_argument("--max_epochs", type=int, default=10)
      args = parser.parse_args()
  
      class RandomDataset(Dataset):
          def __init__(self, size, length):
              self.len = length
              self.data = torch.randn(length, size)
  
          def __getitem__(self, index):
              return self.data[index]
  
          def __len__(self):
              return self.len
  
      class BoringDataModule(LightningDataModule):
          def train_dataloader(self):
              return DataLoader(RandomDataset(32, 64), batch_size=2)
  
          def val_dataloader(self):
              return DataLoader(RandomDataset(32, 64), batch_size=2)
  
          def test_dataloader(self):
              return DataLoader(RandomDataset(32, 64), batch_size=2)
  
          def predict_dataloader(self):
              return DataLoader(RandomDataset(32, 64), batch_size=2)
  
      class BoringModel(LightningModule):
          def __init__(self):
              super().__init__()
              self.layer = torch.nn.Linear(32, 2)
  
          def forward(self, x):
              return self.layer(x)
  
          def training_step(self, batch, batch_idx):
              loss = self(batch).sum()
              self.log("train_loss", loss)
              return {"loss": loss}
  
          def validation_step(self, batch, batch_idx):
              loss = self(batch).sum()
              self.log("valid_loss", loss)
  
          def test_step(self, batch, batch_idx):
              loss = self(batch).sum()
              self.log("test_loss", loss)
  
          def configure_optimizers(self):
              return torch.optim.SGD(self.layer.parameters(), lr=0.1)
  
      model = BoringModel()
      datamodule = BoringDataModule()
      trainer = pl.Trainer(**vars(args))
      trainer.fit(model, datamodule)
  
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: self, self, DataLoader, DataLoader, DataLoader, DataLoader, self, dict, torch