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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 tip_fdr(a, alpha=0.05):
 """ Returns adjusted TIP p-values for a particular `alpha`. (see :func:`tip_zscores` for more info) :param a: NumPy array, where each row is the signal for a feature :param alpha: False discovery rate """

    zscores = tip_zscores(a)
    pvals = stats.norm.pdf(zscores)
    rejected, fdrs = fdrcorrection(pvals)
    return fdrs