text: from sklearn.neural_network import MLPRegressor
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import StandardScaler
  
  from models import models_util
  
  dataset = models_util.load_metadata_dataset()
  # Difficulty and BPM input features.
  X_train = dataset[:,0:2]
  y = dataset[:,3]
  
  model = make_pipeline(StandardScaler(), MLPRegressor(hidden_layer_sizes=(6,)))
  model.fit(X_train, y)
  
  models_util.save_model(model, models_util.MetadataPredictor.CS)
  print("Trained model saved.")