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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 logliks(self, x):
 """Calculate log-likelihood of a feature x for each model Converts all values that are exactly 1 or exactly 0 to 0.999 and 0.001 because they are out of range of the beta distribution. Parameters x : numpy.array-like A single vector to estimate the log-likelihood of the models on Returns ------- logliks : numpy.array Log-likelihood of these data in each member of the model's family """

        x = x.copy()

        # Replace exactly 0 and exactly 1 values with a very small number
        # (machine epsilon, the smallest number that this computer is capable
        # of storing) because 0 and 1 are not in the Beta distribution.
        x[x == 0] = VERY_SMALL_NUMBER
        x[x == 1] = 1 - VERY_SMALL_NUMBER

        return np.array([np.log(prob) + rv.logpdf(x[np.isfinite(x)]).sum()
                         for prob, rv in
                         zip(self.prob_parameters, self.rvs)])