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diff --git a/.venv/lib/python3.12/site-packages/networkx/algorithms/flow/gomory_hu.py b/.venv/lib/python3.12/site-packages/networkx/algorithms/flow/gomory_hu.py
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+"""
+Gomory-Hu tree of undirected Graphs.
+"""
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+from .edmondskarp import edmonds_karp
+from .utils import build_residual_network
+
+default_flow_func = edmonds_karp
+
+__all__ = ["gomory_hu_tree"]
+
+
+@not_implemented_for("directed")
+@nx._dispatchable(edge_attrs={"capacity": float("inf")}, returns_graph=True)
+def gomory_hu_tree(G, capacity="capacity", flow_func=None):
+    r"""Returns the Gomory-Hu tree of an undirected graph G.
+
+    A Gomory-Hu tree of an undirected graph with capacities is a
+    weighted tree that represents the minimum s-t cuts for all s-t
+    pairs in the graph.
+
+    It only requires `n-1` minimum cut computations instead of the
+    obvious `n(n-1)/2`. The tree represents all s-t cuts as the
+    minimum cut value among any pair of nodes is the minimum edge
+    weight in the shortest path between the two nodes in the
+    Gomory-Hu tree.
+
+    The Gomory-Hu tree also has the property that removing the
+    edge with the minimum weight in the shortest path between
+    any two nodes leaves two connected components that form
+    a partition of the nodes in G that defines the minimum s-t
+    cut.
+
+    See Examples section below for details.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Undirected graph
+
+    capacity : string
+        Edges of the graph G are expected to have an attribute capacity
+        that indicates how much flow the edge can support. If this
+        attribute is not present, the edge is considered to have
+        infinite capacity. Default value: 'capacity'.
+
+    flow_func : function
+        Function to perform the underlying flow computations. Default value
+        :func:`edmonds_karp`. This function performs better in sparse graphs
+        with right tailed degree distributions.
+        :func:`shortest_augmenting_path` will perform better in denser
+        graphs.
+
+    Returns
+    -------
+    Tree : NetworkX graph
+        A NetworkX graph representing the Gomory-Hu tree of the input graph.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        Raised if the input graph is directed.
+
+    NetworkXError
+        Raised if the input graph is an empty Graph.
+
+    Examples
+    --------
+    >>> G = nx.karate_club_graph()
+    >>> nx.set_edge_attributes(G, 1, "capacity")
+    >>> T = nx.gomory_hu_tree(G)
+    >>> # The value of the minimum cut between any pair
+    ... # of nodes in G is the minimum edge weight in the
+    ... # shortest path between the two nodes in the
+    ... # Gomory-Hu tree.
+    ... def minimum_edge_weight_in_shortest_path(T, u, v):
+    ...     path = nx.shortest_path(T, u, v, weight="weight")
+    ...     return min((T[u][v]["weight"], (u, v)) for (u, v) in zip(path, path[1:]))
+    >>> u, v = 0, 33
+    >>> cut_value, edge = minimum_edge_weight_in_shortest_path(T, u, v)
+    >>> cut_value
+    10
+    >>> nx.minimum_cut_value(G, u, v)
+    10
+    >>> # The Gomory-Hu tree also has the property that removing the
+    ... # edge with the minimum weight in the shortest path between
+    ... # any two nodes leaves two connected components that form
+    ... # a partition of the nodes in G that defines the minimum s-t
+    ... # cut.
+    ... cut_value, edge = minimum_edge_weight_in_shortest_path(T, u, v)
+    >>> T.remove_edge(*edge)
+    >>> U, V = list(nx.connected_components(T))
+    >>> # Thus U and V form a partition that defines a minimum cut
+    ... # between u and v in G. You can compute the edge cut set,
+    ... # that is, the set of edges that if removed from G will
+    ... # disconnect u from v in G, with this information:
+    ... cutset = set()
+    >>> for x, nbrs in ((n, G[n]) for n in U):
+    ...     cutset.update((x, y) for y in nbrs if y in V)
+    >>> # Because we have set the capacities of all edges to 1
+    ... # the cutset contains ten edges
+    ... len(cutset)
+    10
+    >>> # You can use any maximum flow algorithm for the underlying
+    ... # flow computations using the argument flow_func
+    ... from networkx.algorithms import flow
+    >>> T = nx.gomory_hu_tree(G, flow_func=flow.boykov_kolmogorov)
+    >>> cut_value, edge = minimum_edge_weight_in_shortest_path(T, u, v)
+    >>> cut_value
+    10
+    >>> nx.minimum_cut_value(G, u, v, flow_func=flow.boykov_kolmogorov)
+    10
+
+    Notes
+    -----
+    This implementation is based on Gusfield approach [1]_ to compute
+    Gomory-Hu trees, which does not require node contractions and has
+    the same computational complexity than the original method.
+
+    See also
+    --------
+    :func:`minimum_cut`
+    :func:`maximum_flow`
+
+    References
+    ----------
+    .. [1] Gusfield D: Very simple methods for all pairs network flow analysis.
+           SIAM J Comput 19(1):143-155, 1990.
+
+    """
+    if flow_func is None:
+        flow_func = default_flow_func
+
+    if len(G) == 0:  # empty graph
+        msg = "Empty Graph does not have a Gomory-Hu tree representation"
+        raise nx.NetworkXError(msg)
+
+    # Start the tree as a star graph with an arbitrary node at the center
+    tree = {}
+    labels = {}
+    iter_nodes = iter(G)
+    root = next(iter_nodes)
+    for n in iter_nodes:
+        tree[n] = root
+
+    # Reuse residual network
+    R = build_residual_network(G, capacity)
+
+    # For all the leaves in the star graph tree (that is n-1 nodes).
+    for source in tree:
+        # Find neighbor in the tree
+        target = tree[source]
+        # compute minimum cut
+        cut_value, partition = nx.minimum_cut(
+            G, source, target, capacity=capacity, flow_func=flow_func, residual=R
+        )
+        labels[(source, target)] = cut_value
+        # Update the tree
+        # Source will always be in partition[0] and target in partition[1]
+        for node in partition[0]:
+            if node != source and node in tree and tree[node] == target:
+                tree[node] = source
+                labels[node, source] = labels.get((node, target), cut_value)
+        #
+        if target != root and tree[target] in partition[0]:
+            labels[source, tree[target]] = labels[target, tree[target]]
+            labels[target, source] = cut_value
+            tree[source] = tree[target]
+            tree[target] = source
+
+    # Build the tree
+    T = nx.Graph()
+    T.add_nodes_from(G)
+    T.add_weighted_edges_from(((u, v, labels[u, v]) for u, v in tree.items()))
+    return T