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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 trainer_results(trainer, mean=0, std=1, title='', show=True, save=True):
 """Plot the performance of the Network and SupervisedDataSet in a pybrain Trainer DataSet target and output values are denormalized before plotting with: output * std + mean Which inverses the normalization (output - mean) / std Args: trainer (Trainer):
 a pybrain Trainer instance containing a valid Network and DataSet ds (DataSet):
 a pybrain DataSet to override the one contained in `trainer`. Required if trainer is a Network instance rather than a Trainer instance. mean (float):
 mean of the denormalized dataset (default: 0) Only affects the scale of the plot std (float):
 std (standard deviation) of the denormalized dataset (default: 1) title (str):
 title to display on the plot. Returns: 3-tuple: (trainer, mean, std), A trainer/dataset along with denormalization info """

    return plot_network_results(network=trainer.module, ds=trainer.ds, mean=mean, std=std, title=title,
                                show=show, save=save)