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def annot_min(x,y, ax=None):
    xmax = x[np.argmin(y)]
    ymax = np.array(y).min()
    text= "x={:.3f}, y={:.3f}".format(xmax, ymax)
    if not ax:
        ax=plt.gca()
    bbox_props = dict(boxstyle="square,pad=0.3", fc="w", ec="k", lw=0.72)
    arrowprops=dict(arrowstyle="->",connectionstyle="angle,angleA=0,angleB=60", color='red')
    kw = dict(xycoords='data',textcoords="axes fraction",
              arrowprops=arrowprops, bbox=bbox_props, ha="right", va="top")

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def plot_learning_curve(log_df,
                        metric_name='loss',
                        rolling=False,
                        window_size=5,
                        ylim=(None, None), **kwargs):
    '''
    A simple function for plotting a learning curve of the model

    Args:
        log_df: input pandas Dataframe