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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 from_pcount(nevents):
 """We assume a Poisson process. nevents is the number of events in some interval. The distribution of values is the distribution of the Poisson rate parameter given this observed number of events, where the "rate" is in units of events per interval of the same duration. The max-likelihood value is nevents, but the mean value is nevents + 1. The gamma distribution is obtained by assuming an improper, uniform prior for the rate between 0 and infinity."""

        if nevents < 0:
            raise ValueError('Poisson parameter `nevents` must be nonnegative')
        return Uval(np.random.gamma(nevents + 1, size=uval_nsamples))