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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 generate_ranks(grid, n):
 """ Takes a grid of phenotypes or resource sets representing as strings representing binary numbers, and an integer indicating the maximum number of clusters to generated. Clusters the data in grid into a maximum of n groups, ranks each group by the complexity and length of its "average" member, and returns a dictionary mapping binary numbers to integers representing the rank of the cluster they're part of. """

    phenotypes = deepcopy(grid)
    if type(phenotypes) is list and type(phenotypes[0]) is list:
        phenotypes = flatten_array(phenotypes)

    # Remove duplicates from types
    types = list(frozenset(phenotypes))
    if len(types) < n:
        ranks = rank_types(types)
    else:
        ranks = cluster_types(types, n)

    return ranks