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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 get_undirected(G):
 """ Convert a directed graph to an undirected graph that maintains parallel edges if geometries differ. Parameters G : networkx multidigraph Returns ------- networkx multigraph """


    start_time = time.time()

    # set from/to nodes before making graph undirected
    G = G.copy()
    for u, v, k, data in G.edges(keys=True, data=True):
        G.edges[u, v, k]['from'] = u
        G.edges[u, v, k]['to'] = v
        
        # add geometry if it doesn't already exist, to retain parallel
        # edges' distinct geometries
        if 'geometry' not in data:
            point_u = Point((G.nodes[u]['x'], G.nodes[u]['y']))
            point_v = Point((G.nodes[v]['x'], G.nodes[v]['y']))
            data['geometry'] = LineString([point_u, point_v])

    # update edge keys so we don't retain only one edge of sets of parallel edges
    # when we convert from a multidigraph to a multigraph
    G = update_edge_keys(G)

    # now convert multidigraph to a multigraph, retaining all edges in both
    # directions for now, as well as all graph attributes
    H = nx.MultiGraph()
    H.add_nodes_from(G.nodes(data=True))
    H.add_edges_from(G.edges(keys=True, data=True))
    H.graph = G.graph
    H.name = G.name

    # the previous operation added all directed edges from G as undirected
    # edges in H. this means we have duplicate edges for every bi-directional
    # street. so, look through the edges and remove any duplicates
    duplicate_edges = []
    for u, v, key, data in H.edges(keys=True, data=True):

        # if we haven't already flagged this edge as a duplicate
        if not (u, v, key) in duplicate_edges:

            # look at every other edge between u and v, one at a time
            for key_other in H[u][v]:

                # don't compare this edge to itself
                if not key_other == key:

                    # compare the first edge's data to the second's to see if
                    # they are duplicates
                    data_other = H.edges[u, v, key_other]
                    if is_duplicate_edge(data, data_other):

                        # if they match up, flag the duplicate for removal
                        duplicate_edges.append((u, v, key_other))

    H.remove_edges_from(duplicate_edges)
    log('Made undirected graph in {:,.2f} seconds'.format(time.time() - start_time))

    return H