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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 gev_like(x, xi, mu=0, sigma=1):
 R""" Generalized Extreme Value log-likelihood .. math:: pdf(x \mid \xi,\mu,\sigma) = \frac{1}{\sigma}(1 + \xi \left[\frac{x-\mu}{\sigma}\right])^{-1/\xi-1}\exp{-(1+\xi \left[\frac{x-\mu}{\sigma}\right])^{-1/\xi}} .. math:: \sigma & > 0,\\ x & > \mu-\sigma/\xi \text{ if } \xi > 0,\\ x & < \mu-\sigma/\xi \text{ if } \xi < 0\\ x & \in [-\infty,\infty] \text{ if } \xi = 0 """


    return flib.gev(x, xi, mu, sigma)