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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 half_cauchy_like(x, alpha, beta):
 R""" Half-Cauchy log-likelihood. Simply the absolute value of Cauchy. .. math:: f(x \mid \alpha, \beta) = \frac{2}{\pi \beta [1 + (\frac{x-\alpha}{\beta})^2]} :Parameters: - `alpha` : Location parameter. - `beta` : Scale parameter (beta > 0). .. note:: - x must be non-negative. """


    x = np.atleast_1d(x)
    if sum(x.ravel() < 0):
        return -inf
    return flib.cauchy(x, alpha, beta) + len(x) * np.log(2)