def METHOD_NAME(target, predicted_labels, predicted_probas):
    figures = []
    try:
        #
        fig = plt.figure(figsize=(10, 7))
        ax1 = fig.add_subplot(1, 1, 1)
        _ = skplt.metrics.plot_confusion_matrix(
            target, predicted_labels, normalize=False, ax=ax1
        )
        figures += [
            {
                "title": "Confusion Matrix",
                "fname": "confusion_matrix.png",
                "figure": fig,
            }
        ]
        #
        fig = plt.figure(figsize=(10, 7))
        ax1 = fig.add_subplot(1, 1, 1)
        _ = skplt.metrics.plot_confusion_matrix(
            target, predicted_labels, normalize=True, ax=ax1
        )
        figures += [
            {
                "title": "Normalized Confusion Matrix",
                "fname": "confusion_matrix_normalized.png",
                "figure": fig,
            }
        ]
        #
        fig = plt.figure(figsize=(10, 7))
        ax1 = fig.add_subplot(1, 1, 1)
        _ = skplt.metrics.plot_roc(target, predicted_probas, ax=ax1)
        figures += [{"title": "ROC Curve", "fname": "roc_curve.png", "figure": fig}]
        #
        fig = plt.figure(figsize=(10, 7))
        ax1 = fig.add_subplot(1, 1, 1)
        _ = skplt.metrics.plot_precision_recall(target, predicted_probas, ax=ax1)
        figures += [
            {
                "title": "Precision Recall Curve",
                "fname": "precision_recall_curve.png",
                "figure": fig,
            }
        ]
        plt.close("all")
    except Exception as e:
        print(str(e))
    return figures