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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 _get_best_percentile(self, cluster, counts):
        ''' return all UMIs with counts >1% of the
        median counts in the cluster '''

        if len(cluster) == 1:
            return list(cluster)
        else:
            threshold = np.median(list(counts.values()))/100
            return [read for read in cluster if counts[read] > threshold]