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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 cost_normalization(C, norm=None):
 """ Apply normalization to the loss matrix Parameters C : np.array (n1, n2) The cost matrix to normalize. norm : str type of normalization from 'median','max','log','loglog'. Any other value do not normalize. Returns ------- C : np.array (n1, n2) The input cost matrix normalized according to given norm. """


    if norm == "median":
        C /= float(np.median(C))
    elif norm == "max":
        C /= float(np.max(C))
    elif norm == "log":
        C = np.log(1 + C)
    elif norm == "loglog":
        C = np.log(1 + np.log(1 + C))

    return C