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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 cluster(dupes, threshold=.5, max_components=30000):
    '''
    Takes in a list of duplicate pairs and clusters them in to a
    list records that all refer to the same entity based on a given
    threshold

    Keyword arguments:
    threshold -- number betweent 0 and 1 (default is .5). lowering the
                 number will increase precision, raising it will increase
                 recall
    '''
    distance_threshold = 1 - threshold
    dupe_sub_graphs = connected_components(dupes, max_components)

    for sub_graph in dupe_sub_graphs:
        if len(sub_graph) > 1:

            i_to_id, condensed_distances, N = condensedDistance(sub_graph)

            linkage = fastcluster.linkage(condensed_distances,
                                          method='centroid',
                                          preserve_input=True)

            partition = hcluster.fcluster(linkage,
                                          distance_threshold,
                                          criterion='distance')

            clusters = defaultdict(list)

            for i, cluster_id in enumerate(partition):
                clusters[cluster_id].append(i)

            for cluster in viewvalues(clusters):
                if len(cluster) > 1:
                    scores = confidences(cluster, condensed_distances, N)
                    yield tuple(i_to_id[i] for i in cluster), scores

        else:
            (ids, score), = sub_graph
            if score > threshold:
                yield tuple(ids), (score,) * 2