def build_residual_network(G, capacity):
    """Build a residual network and initialize a zero flow.

    The residual network :samp:`R` from an input graph :samp:`G` has the
    same nodes as :samp:`G`. :samp:`R` is a DiGraph that contains a pair
    of edges :samp:`(u, v)` and :samp:`(v, u)` iff :samp:`(u, v)` is not a
    self-loop, and at least one of :samp:`(u, v)` and :samp:`(v, u)` exists
    in :samp:`G`.

    For each edge :samp:`(u, v)` in :samp:`R`, :samp:`R[u][v]['capacity']`
    is equal to the capacity of :samp:`(u, v)` in :samp:`G` if it exists
    in :samp:`G` or zero otherwise. If the capacity is infinite,
    :samp:`R[u][v]['capacity']` will have a high arbitrary finite value
    that does not affect the solution of the problem. This value is stored in
    :samp:`R.graph['inf']`. For each edge :samp:`(u, v)` in :samp:`R`,
    :samp:`R[u][v]['flow']` represents the flow function of :samp:`(u, v)` and
    satisfies :samp:`R[u][v]['flow'] == -R[v][u]['flow']`.

    The flow value, defined as the total flow into :samp:`t`, the sink, is
    stored in :samp:`R.graph['flow_value']`. If :samp:`cutoff` is not
    specified, reachability to :samp:`t` using only edges :samp:`(u, v)` such
    that :samp:`R[u][v]['flow'] < R[u][v]['capacity']` induces a minimum
    :samp:`s`-:samp:`t` cut.

    """
    if G.is_multigraph():
        raise nx.NetworkXError(
            'MultiGraph and MultiDiGraph not supported (yet).')

    R = nx.DiGraph()
    R.add_nodes_from(G)

    inf = float('inf')
    # Extract edges with positive capacities. Self loops excluded.
    edge_list = [(u, v, attr) for u, v, attr in G.edges(data=True)
                 if u != v and attr.get(capacity, inf) > 0]
    # Simulate infinity with three times the sum of the finite edge capacities
    # or any positive value if the sum is zero. This allows the
    # infinite-capacity edges to be distinguished for unboundedness detection
    # and directly participate in residual capacity calculation. If the maximum
    # flow is finite, these edges cannot appear in the minimum cut and thus
    # guarantee correctness. Since the residual capacity of an
    # infinite-capacity edge is always at least 2/3 of inf, while that of an
    # finite-capacity edge is at most 1/3 of inf, if an operation moves more
    # than 1/3 of inf units of flow to t, there must be an infinite-capacity
    # s-t path in G.
    inf = 3 * sum(attr[capacity] for u, v, attr in edge_list
                  if attr in capacity and attr[capacity] != inf) or 1
    if G.is_directed():
        for u, v, attr in edge_list:
            r = min(attr.get(capacity, inf), inf)
            if not R.has_edge(u, v):
                # Both (u, v) and (v, u) must be present in the residual
                # network.
                R.add_edge(u, v, capacity=r)
                R.add_edge(v, u, capacity=0)
            else:
                # The edge (u, v) was added when (v, u) was visited.
                R[u][v]['capacity'] = r
    else:
        for u, v, attr in edge_list:
            # Add a pair of edges with equal residual capacities.
            r = min(attr.get(capacity, inf), inf)
            R.add_edge(u, v, capacity=r)
            R.add_edge(v, u, capacity=r)

    # Record the value simulating infinity.
    R.graph['inf'] = inf

    return R
dataset/ETHPy150Open networkx/networkx/networkx/algorithms/flow/utils.py/build_residual_network/swapped operands of `in`