code: def train(hparams, *args):
      """Train your awesome model.
  
      :param hparams: The arguments to run the model with.
      """
      # Initialize experiments and track all the hyperparameters
      exp = Experiment(
          name=hparams.test_tube_exp_name,
          # Location to save the metrics.
          save_dir=hparams.log_path,
          # The experiment version is optional, but using the one
          # from SLURM means the exp will not collide with other
          # versions if SLURM runs multiple at once.
          version=hparams.hpc_exp_number,
          autosave=False,
      )
      exp.argparse(hparams)
  
      # Pretend to train.
      x = hparams.x_val
      for train_step in range(0, 100):
          y = hparams.y_val
          out = x * y
          exp.log({"fake_err": out.item()})  # Log metrics.
  
      # Save exp when done.
      exp.save()
  
label_desc: Use image node_id 1 for calling a global function with example usage: train(hparams) without return types