def net(n_samples=100, output_file=None, **kwargs):
    """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.

    """

    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)