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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def pipeline(n_samples=100, output_file=None, **kwargs):
 """Train an MLP classifier in a pipeline on synthetic data. The pipeline scales the input data before passing it to the net. Note: This docstring is used to create the help for the CLI. Parameters 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=True)
    # important: wrap the model with the parsed arguments
    parsed = parse_args(kwargs, defaults=DEFAULTS_PIPE)
    model = parsed(model)

    X, y = get_data(n_samples=n_samples)
    print("Training MLP classifier in a pipeline")
    model.fit(X, y)

    save_model(model, output_file)