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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 skew_normal_like(x, mu, tau, alpha):
 R""" Azzalini's skew-normal log-likelihood .. math:: f(x \mid \mu, \tau, \alpha) = 2 \Phi((x-\mu)\sqrt{\tau}\alpha) \phi(x,\mu,\tau) where :math:\Phi is the normal CDF and :math: \phi is the normal PDF. :Parameters: - `x` : Input data. - `mu` : Mean of the distribution. - `tau` : Precision of the distribution (> 0). - `alpha` : Shape parameter of the distribution. .. note:: See http://azzalini.stat.unipd.it/SN/ """

    return flib.sn_like(x, mu, tau, alpha)