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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 coupling(corpus_or_featureset, featureset_name=None, min_weight=1, filter=lambda f, v, c, dc: True, node_attrs=[]):
 """ A network of papers linked by their joint posession of features. """


    featureset = _get_featureset(corpus_or_featureset, featureset_name)

    c = lambda f: featureset.count(f)           # Overall count.
    dc = lambda f: featureset.documentCount(f)  # Document count.
    f = lambda elem: featureset.index[elem]
    v = lambda p, f: featureset.features[p].value(f)

    select = lambda p, elem: filter(f(elem), v(p, f(elem)), c(f(elem)), dc(f(elem)))

    pairs = defaultdict(list)
    for elem, papers in featureset.with_feature.iteritems():
        selected = [p for p in papers if select(p, elem)]
        for combo in combinations(selected, 2):
            combo = tuple(sorted(combo))
            pairs[combo].append(featureset.index[elem])

    graph = nx.Graph()
    for combo, features in pairs.iteritems():
        count = len(features)
        if count >= min_weight:
            graph.add_edge(combo[0], combo[1], features=features, weight=count)

    # Add node attributes.
    for attr in node_attrs:
        for node in graph.nodes():
            value = ''
            if node in corpus_or_featureset:
                paper = corpus_or_featureset[node]
                if hasattr(paper, attr):
                    value = getattr(paper, attr)
                    if value is None:
                        value = ''
                    elif callable(value):
                        value = value()
            graph.node[node][attr] = value

    return graph