code: def net(n_samples=100, output_file=None, **kwargs):
      
  
      model = get_model(with_pipeline=False)
      # important: wrap the model with the parsed arguments
      parsed = parse_args(kwargs, defaults=DEFAULTS_NET)
      model = parsed(model)
  
      X, y = get_data(n_samples=n_samples)
      print("Training MLP classifier")
      model.fit(X, y)
  
      save_model(model, output_file)
docs: Train an MLP classifier on synthetic data.
  
      n_samples : int (default=100)
        Number of training samples
  
      output_file : str (default=None)
        If not None, file name used to save the model.
  
      kwargs : dict
        Additional model parameters.