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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 terms(model, threshold=0.01, **kwargs):
 """ Two terms are coupled if the posterior probability for both terms is greather than ``threshold`` for the same topic. Parameters model : :class:`.LDAModel` threshold : float Default: 0.01 kwargs : kwargs Passed on to :func:`.cooccurrence`\. Returns ------- :ref:`networkx.Graph <networkx:graph>` """


    select = lambda f, v, c, dc: v > threshold
    graph = cooccurrence(model.phi, filter=select, **kwargs)

    # Only include labels for terms that are actually in the graph.
    label_map = {k: v for k, v in model.vocabulary.items()
                 if k in graph.nodes()}
    graph.name = ''
    return networkx.relabel_nodes(graph, label_map)