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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 mad(a, axis=None, c=1.4826, return_med=False):
 """Compute normalized median absolute difference Can also return median array, as this can be expensive, and often we want both med and nmad Note: 1.4826 = 1/0.6745 """

    a = checkma(a)
    #return np.ma.median(np.fabs(a - np.ma.median(a))) / c
    if a.count() > 0:
        if axis is None:
            med = fast_median(a)
            out = fast_median(np.fabs(a - med)) * c
        else:
            med = np.ma.median(a, axis=axis)
            #This is necessary for broadcasting
            med = np.expand_dims(med, axis=axis)
            out = np.ma.median(np.ma.fabs(a - med), axis=axis) * c
    else:
        out = np.ma.masked
    if return_med:
        out = (out, med)
    return out