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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 precision_recall_curve(y_true, scores, distances=False):
 """Precision-recall curve Parameters y_true : (n_samples, ) array-like Boolean reference. scores : (n_samples, ) array-like Predicted score. distances : boolean, optional When True, indicate that `scores` are actually `distances` Returns ------- precision : numpy array Precision recall : numpy array Recall thresholds : numpy array Corresponding thresholds auc : float Area under curve """


    if distances:
        scores = -scores

    precision, recall, thresholds = sklearn.metrics.precision_recall_curve(
        y_true, scores, pos_label=True)

    if distances:
        thresholds = -thresholds

    auc = sklearn.metrics.auc(precision, recall, reorder=True)

    return precision, recall, thresholds, auc