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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 roc_curve_plot(data, value_column, outcome_column, bootstrap_samples=100, ax=None):
 """Create a ROC curve and compute the bootstrap AUC for the given variable and outcome Parameters data : Pandas dataframe Dataframe to retrieve information from value_column : str Column to retrieve the values from outcome_column : str Column to use as the outcome variable bootstrap_samples : int, optional Number of bootstrap samples to use to compute the AUC ax : Axes, default None Axes to plot on Returns ------- (mean_bootstrap_auc, roc_plot) : (float, matplotlib plot) Mean AUC for the given number of bootstrap samples and the plot """

    scores = bootstrap_auc(df=data,
                           col=value_column,
                           pred_col=outcome_column,
                           n_bootstrap=bootstrap_samples)
    mean_bootstrap_auc = scores.mean()
    print("{}, Bootstrap (samples = {}) AUC:{}, std={}".format(
        value_column, bootstrap_samples, mean_bootstrap_auc, scores.std()))

    outcome = data[outcome_column].astype(int)
    values = data[value_column]
    fpr, tpr, thresholds = roc_curve(outcome, values)

    if ax is None:
        ax = plt.gca()

    roc_plot = ax.plot(fpr, tpr, lw=1, label=value_column)

    ax.set_xlim([-0.05, 1.05])
    ax.set_ylim([-0.05, 1.05])
    ax.set_xlabel('False Positive Rate')
    ax.set_ylabel('True Positive Rate')
    ax.legend(loc=2, borderaxespad=0.)
    ax.set_title('{} ROC Curve (n={})'.format(value_column, len(values)))

    return (mean_bootstrap_auc, roc_plot)