code: def train_model(config):
      score = config["model_id"]
  
      # Import model libraries, etc...
      # Load data and train model code here...
  
      # Return final stats. You can also return intermediate progress
      # using ray.train.report() if needed.
      # To return your model, you could write it to storage and return its
      # URI in this dict, or return it as a Tune Checkpoint:
      # https://docs.ray.io/en/latest/tune/tutorials/tune-checkpoints.html
      return {"score": score, "other_data": ...}
  
label_desc: Use image node_id 1 for calling a global function with example usage: train_model(config) and returns: dict