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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 _diversity_metric(solution, population):
 """Return diversity value for solution compared to given population. Metric is sum of distance between solution and each solution in population, normalized to [0.0, 1.0]. """

    # Edge case for empty population
    # If there are no other solutions, the given solution has maximum diversity
    if population == []:
        return 1.0

    return (
        sum([_manhattan_distance(solution, other) for other in population])
        # Normalize (assuming each value in solution is in range [0.0, 1.0])
        # NOTE: len(solution) is maximum manhattan distance
        / (len(population) * len(solution)))