code: def get_trainer_args(
      num_features=5,
      num_samples=20,
      bsz=4,
      lr=0.1,
      n_epochs=5,
      device=None,
  ):
      ds = DataSet(num_samples=num_samples, num_features=num_features)
      dl = DataLoader(ds, batch_size=bsz)
      model = Model(num_features=num_features)
  
      optimizer = torch.optim.SGD(model.parameters(), lr=lr)
  
      kwargs = {
          "model": model,
          "driver": "torch",
          "device": device,
          "optimizers": optimizer,
          "train_dataloader": dl,
          "evaluate_dataloaders": dl,
          "metrics": {"acc": Accuracy()},
          "n_epochs": n_epochs,
      }
  
      return kwargs
  
label_desc: Use image node_id 1 for calling a global function with example usage: get_trainer_args(num_features, num_samples, bsz, lr, n_epochs, device) and returns: kwargs