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diff --git a/.venv/lib/python3.12/site-packages/networkx/algorithms/traversal/breadth_first_search.py b/.venv/lib/python3.12/site-packages/networkx/algorithms/traversal/breadth_first_search.py
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+"""Basic algorithms for breadth-first searching the nodes of a graph."""
+
+from collections import deque
+
+import networkx as nx
+
+__all__ = [
+    "bfs_edges",
+    "bfs_tree",
+    "bfs_predecessors",
+    "bfs_successors",
+    "descendants_at_distance",
+    "bfs_layers",
+    "bfs_labeled_edges",
+    "generic_bfs_edges",
+]
+
+
+@nx._dispatchable
+def generic_bfs_edges(G, source, neighbors=None, depth_limit=None):
+    """Iterate over edges in a breadth-first search.
+
+    The breadth-first search begins at `source` and enqueues the
+    neighbors of newly visited nodes specified by the `neighbors`
+    function.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+        Starting node for the breadth-first search; this function
+        iterates over only those edges in the component reachable from
+        this node.
+
+    neighbors : function
+        A function that takes a newly visited node of the graph as input
+        and returns an *iterator* (not just a list) of nodes that are
+        neighbors of that node with custom ordering. If not specified, this is
+        just the ``G.neighbors`` method, but in general it can be any function
+        that returns an iterator over some or all of the neighbors of a
+        given node, in any order.
+
+    depth_limit : int, optional(default=len(G))
+        Specify the maximum search depth.
+
+    Yields
+    ------
+    edge
+        Edges in the breadth-first search starting from `source`.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(7)
+    >>> list(nx.generic_bfs_edges(G, source=0))
+    [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]
+    >>> list(nx.generic_bfs_edges(G, source=2))
+    [(2, 1), (2, 3), (1, 0), (3, 4), (4, 5), (5, 6)]
+    >>> list(nx.generic_bfs_edges(G, source=2, depth_limit=2))
+    [(2, 1), (2, 3), (1, 0), (3, 4)]
+
+    The `neighbors` param can be used to specify the visitation order of each
+    node's neighbors generically. In the following example, we modify the default
+    neighbor to return *odd* nodes first:
+
+    >>> def odd_first(n):
+    ...     return sorted(G.neighbors(n), key=lambda x: x % 2, reverse=True)
+
+    >>> G = nx.star_graph(5)
+    >>> list(nx.generic_bfs_edges(G, source=0))  # Default neighbor ordering
+    [(0, 1), (0, 2), (0, 3), (0, 4), (0, 5)]
+    >>> list(nx.generic_bfs_edges(G, source=0, neighbors=odd_first))
+    [(0, 1), (0, 3), (0, 5), (0, 2), (0, 4)]
+
+    Notes
+    -----
+    This implementation is from `PADS`_, which was in the public domain
+    when it was first accessed in July, 2004.  The modifications
+    to allow depth limits are based on the Wikipedia article
+    "`Depth-limited-search`_".
+
+    .. _PADS: http://www.ics.uci.edu/~eppstein/PADS/BFS.py
+    .. _Depth-limited-search: https://en.wikipedia.org/wiki/Depth-limited_search
+    """
+    if neighbors is None:
+        neighbors = G.neighbors
+    if depth_limit is None:
+        depth_limit = len(G)
+
+    seen = {source}
+    n = len(G)
+    depth = 0
+    next_parents_children = [(source, neighbors(source))]
+    while next_parents_children and depth < depth_limit:
+        this_parents_children = next_parents_children
+        next_parents_children = []
+        for parent, children in this_parents_children:
+            for child in children:
+                if child not in seen:
+                    seen.add(child)
+                    next_parents_children.append((child, neighbors(child)))
+                    yield parent, child
+            if len(seen) == n:
+                return
+        depth += 1
+
+
+@nx._dispatchable
+def bfs_edges(G, source, reverse=False, depth_limit=None, sort_neighbors=None):
+    """Iterate over edges in a breadth-first-search starting at source.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+       Specify starting node for breadth-first search; this function
+       iterates over only those edges in the component reachable from
+       this node.
+
+    reverse : bool, optional
+       If True traverse a directed graph in the reverse direction
+
+    depth_limit : int, optional(default=len(G))
+        Specify the maximum search depth
+
+    sort_neighbors : function (default=None)
+        A function that takes an iterator over nodes as the input, and
+        returns an iterable of the same nodes with a custom ordering.
+        For example, `sorted` will sort the nodes in increasing order.
+
+    Yields
+    ------
+    edge: 2-tuple of nodes
+       Yields edges resulting from the breadth-first search.
+
+    Examples
+    --------
+    To get the edges in a breadth-first search::
+
+        >>> G = nx.path_graph(3)
+        >>> list(nx.bfs_edges(G, 0))
+        [(0, 1), (1, 2)]
+        >>> list(nx.bfs_edges(G, source=0, depth_limit=1))
+        [(0, 1)]
+
+    To get the nodes in a breadth-first search order::
+
+        >>> G = nx.path_graph(3)
+        >>> root = 2
+        >>> edges = nx.bfs_edges(G, root)
+        >>> nodes = [root] + [v for u, v in edges]
+        >>> nodes
+        [2, 1, 0]
+
+    Notes
+    -----
+    The naming of this function is very similar to
+    :func:`~networkx.algorithms.traversal.edgebfs.edge_bfs`. The difference
+    is that ``edge_bfs`` yields edges even if they extend back to an already
+    explored node while this generator yields the edges of the tree that results
+    from a breadth-first-search (BFS) so no edges are reported if they extend
+    to already explored nodes. That means ``edge_bfs`` reports all edges while
+    ``bfs_edges`` only reports those traversed by a node-based BFS. Yet another
+    description is that ``bfs_edges`` reports the edges traversed during BFS
+    while ``edge_bfs`` reports all edges in the order they are explored.
+
+    Based on the breadth-first search implementation in PADS [1]_
+    by D. Eppstein, July 2004; with modifications to allow depth limits
+    as described in [2]_.
+
+    References
+    ----------
+    .. [1] http://www.ics.uci.edu/~eppstein/PADS/BFS.py.
+    .. [2] https://en.wikipedia.org/wiki/Depth-limited_search
+
+    See Also
+    --------
+    bfs_tree
+    :func:`~networkx.algorithms.traversal.depth_first_search.dfs_edges`
+    :func:`~networkx.algorithms.traversal.edgebfs.edge_bfs`
+
+    """
+    if reverse and G.is_directed():
+        successors = G.predecessors
+    else:
+        successors = G.neighbors
+
+    if sort_neighbors is not None:
+        yield from generic_bfs_edges(
+            G, source, lambda node: iter(sort_neighbors(successors(node))), depth_limit
+        )
+    else:
+        yield from generic_bfs_edges(G, source, successors, depth_limit)
+
+
+@nx._dispatchable(returns_graph=True)
+def bfs_tree(G, source, reverse=False, depth_limit=None, sort_neighbors=None):
+    """Returns an oriented tree constructed from of a breadth-first-search
+    starting at source.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+       Specify starting node for breadth-first search
+
+    reverse : bool, optional
+       If True traverse a directed graph in the reverse direction
+
+    depth_limit : int, optional(default=len(G))
+        Specify the maximum search depth
+
+    sort_neighbors : function (default=None)
+        A function that takes an iterator over nodes as the input, and
+        returns an iterable of the same nodes with a custom ordering.
+        For example, `sorted` will sort the nodes in increasing order.
+
+    Returns
+    -------
+    T: NetworkX DiGraph
+       An oriented tree
+
+    Examples
+    --------
+    >>> G = nx.path_graph(3)
+    >>> list(nx.bfs_tree(G, 1).edges())
+    [(1, 0), (1, 2)]
+    >>> H = nx.Graph()
+    >>> nx.add_path(H, [0, 1, 2, 3, 4, 5, 6])
+    >>> nx.add_path(H, [2, 7, 8, 9, 10])
+    >>> sorted(list(nx.bfs_tree(H, source=3, depth_limit=3).edges()))
+    [(1, 0), (2, 1), (2, 7), (3, 2), (3, 4), (4, 5), (5, 6), (7, 8)]
+
+
+    Notes
+    -----
+    Based on http://www.ics.uci.edu/~eppstein/PADS/BFS.py
+    by D. Eppstein, July 2004. The modifications
+    to allow depth limits based on the Wikipedia article
+    "`Depth-limited-search`_".
+
+    .. _Depth-limited-search: https://en.wikipedia.org/wiki/Depth-limited_search
+
+    See Also
+    --------
+    dfs_tree
+    bfs_edges
+    edge_bfs
+    """
+    T = nx.DiGraph()
+    T.add_node(source)
+    edges_gen = bfs_edges(
+        G,
+        source,
+        reverse=reverse,
+        depth_limit=depth_limit,
+        sort_neighbors=sort_neighbors,
+    )
+    T.add_edges_from(edges_gen)
+    return T
+
+
+@nx._dispatchable
+def bfs_predecessors(G, source, depth_limit=None, sort_neighbors=None):
+    """Returns an iterator of predecessors in breadth-first-search from source.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+       Specify starting node for breadth-first search
+
+    depth_limit : int, optional(default=len(G))
+        Specify the maximum search depth
+
+    sort_neighbors : function (default=None)
+        A function that takes an iterator over nodes as the input, and
+        returns an iterable of the same nodes with a custom ordering.
+        For example, `sorted` will sort the nodes in increasing order.
+
+    Returns
+    -------
+    pred: iterator
+        (node, predecessor) iterator where `predecessor` is the predecessor of
+        `node` in a breadth first search starting from `source`.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(3)
+    >>> dict(nx.bfs_predecessors(G, 0))
+    {1: 0, 2: 1}
+    >>> H = nx.Graph()
+    >>> H.add_edges_from([(0, 1), (0, 2), (1, 3), (1, 4), (2, 5), (2, 6)])
+    >>> dict(nx.bfs_predecessors(H, 0))
+    {1: 0, 2: 0, 3: 1, 4: 1, 5: 2, 6: 2}
+    >>> M = nx.Graph()
+    >>> nx.add_path(M, [0, 1, 2, 3, 4, 5, 6])
+    >>> nx.add_path(M, [2, 7, 8, 9, 10])
+    >>> sorted(nx.bfs_predecessors(M, source=1, depth_limit=3))
+    [(0, 1), (2, 1), (3, 2), (4, 3), (7, 2), (8, 7)]
+    >>> N = nx.DiGraph()
+    >>> nx.add_path(N, [0, 1, 2, 3, 4, 7])
+    >>> nx.add_path(N, [3, 5, 6, 7])
+    >>> sorted(nx.bfs_predecessors(N, source=2))
+    [(3, 2), (4, 3), (5, 3), (6, 5), (7, 4)]
+
+    Notes
+    -----
+    Based on http://www.ics.uci.edu/~eppstein/PADS/BFS.py
+    by D. Eppstein, July 2004. The modifications
+    to allow depth limits based on the Wikipedia article
+    "`Depth-limited-search`_".
+
+    .. _Depth-limited-search: https://en.wikipedia.org/wiki/Depth-limited_search
+
+    See Also
+    --------
+    bfs_tree
+    bfs_edges
+    edge_bfs
+    """
+    for s, t in bfs_edges(
+        G, source, depth_limit=depth_limit, sort_neighbors=sort_neighbors
+    ):
+        yield (t, s)
+
+
+@nx._dispatchable
+def bfs_successors(G, source, depth_limit=None, sort_neighbors=None):
+    """Returns an iterator of successors in breadth-first-search from source.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+       Specify starting node for breadth-first search
+
+    depth_limit : int, optional(default=len(G))
+        Specify the maximum search depth
+
+    sort_neighbors : function (default=None)
+        A function that takes an iterator over nodes as the input, and
+        returns an iterable of the same nodes with a custom ordering.
+        For example, `sorted` will sort the nodes in increasing order.
+
+    Returns
+    -------
+    succ: iterator
+       (node, successors) iterator where `successors` is the non-empty list of
+       successors of `node` in a breadth first search from `source`.
+       To appear in the iterator, `node` must have successors.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(3)
+    >>> dict(nx.bfs_successors(G, 0))
+    {0: [1], 1: [2]}
+    >>> H = nx.Graph()
+    >>> H.add_edges_from([(0, 1), (0, 2), (1, 3), (1, 4), (2, 5), (2, 6)])
+    >>> dict(nx.bfs_successors(H, 0))
+    {0: [1, 2], 1: [3, 4], 2: [5, 6]}
+    >>> G = nx.Graph()
+    >>> nx.add_path(G, [0, 1, 2, 3, 4, 5, 6])
+    >>> nx.add_path(G, [2, 7, 8, 9, 10])
+    >>> dict(nx.bfs_successors(G, source=1, depth_limit=3))
+    {1: [0, 2], 2: [3, 7], 3: [4], 7: [8]}
+    >>> G = nx.DiGraph()
+    >>> nx.add_path(G, [0, 1, 2, 3, 4, 5])
+    >>> dict(nx.bfs_successors(G, source=3))
+    {3: [4], 4: [5]}
+
+    Notes
+    -----
+    Based on http://www.ics.uci.edu/~eppstein/PADS/BFS.py
+    by D. Eppstein, July 2004.The modifications
+    to allow depth limits based on the Wikipedia article
+    "`Depth-limited-search`_".
+
+    .. _Depth-limited-search: https://en.wikipedia.org/wiki/Depth-limited_search
+
+    See Also
+    --------
+    bfs_tree
+    bfs_edges
+    edge_bfs
+    """
+    parent = source
+    children = []
+    for p, c in bfs_edges(
+        G, source, depth_limit=depth_limit, sort_neighbors=sort_neighbors
+    ):
+        if p == parent:
+            children.append(c)
+            continue
+        yield (parent, children)
+        children = [c]
+        parent = p
+    yield (parent, children)
+
+
+@nx._dispatchable
+def bfs_layers(G, sources):
+    """Returns an iterator of all the layers in breadth-first search traversal.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A graph over which to find the layers using breadth-first search.
+
+    sources : node in `G` or list of nodes in `G`
+        Specify starting nodes for single source or multiple sources breadth-first search
+
+    Yields
+    ------
+    layer: list of nodes
+        Yields list of nodes at the same distance from sources
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> dict(enumerate(nx.bfs_layers(G, [0, 4])))
+    {0: [0, 4], 1: [1, 3], 2: [2]}
+    >>> H = nx.Graph()
+    >>> H.add_edges_from([(0, 1), (0, 2), (1, 3), (1, 4), (2, 5), (2, 6)])
+    >>> dict(enumerate(nx.bfs_layers(H, [1])))
+    {0: [1], 1: [0, 3, 4], 2: [2], 3: [5, 6]}
+    >>> dict(enumerate(nx.bfs_layers(H, [1, 6])))
+    {0: [1, 6], 1: [0, 3, 4, 2], 2: [5]}
+    """
+    if sources in G:
+        sources = [sources]
+
+    current_layer = list(sources)
+    visited = set(sources)
+
+    for source in current_layer:
+        if source not in G:
+            raise nx.NetworkXError(f"The node {source} is not in the graph.")
+
+    # this is basically BFS, except that the current layer only stores the nodes at
+    # same distance from sources at each iteration
+    while current_layer:
+        yield current_layer
+        next_layer = []
+        for node in current_layer:
+            for child in G[node]:
+                if child not in visited:
+                    visited.add(child)
+                    next_layer.append(child)
+        current_layer = next_layer
+
+
+REVERSE_EDGE = "reverse"
+TREE_EDGE = "tree"
+FORWARD_EDGE = "forward"
+LEVEL_EDGE = "level"
+
+
+@nx._dispatchable
+def bfs_labeled_edges(G, sources):
+    """Iterate over edges in a breadth-first search (BFS) labeled by type.
+
+    We generate triple of the form (*u*, *v*, *d*), where (*u*, *v*) is the
+    edge being explored in the breadth-first search and *d* is one of the
+    strings 'tree', 'forward', 'level', or 'reverse'.  A 'tree' edge is one in
+    which *v* is first discovered and placed into the layer below *u*.  A
+    'forward' edge is one in which *u* is on the layer above *v* and *v* has
+    already been discovered.  A 'level' edge is one in which both *u* and *v*
+    occur on the same layer.  A 'reverse' edge is one in which *u* is on a layer
+    below *v*.
+
+    We emit each edge exactly once.  In an undirected graph, 'reverse' edges do
+    not occur, because each is discovered either as a 'tree' or 'forward' edge.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A graph over which to find the layers using breadth-first search.
+
+    sources : node in `G` or list of nodes in `G`
+        Starting nodes for single source or multiple sources breadth-first search
+
+    Yields
+    ------
+    edges: generator
+       A generator of triples (*u*, *v*, *d*) where (*u*, *v*) is the edge being
+       explored and *d* is described above.
+
+    Examples
+    --------
+    >>> G = nx.cycle_graph(4, create_using=nx.DiGraph)
+    >>> list(nx.bfs_labeled_edges(G, 0))
+    [(0, 1, 'tree'), (1, 2, 'tree'), (2, 3, 'tree'), (3, 0, 'reverse')]
+    >>> G = nx.complete_graph(3)
+    >>> list(nx.bfs_labeled_edges(G, 0))
+    [(0, 1, 'tree'), (0, 2, 'tree'), (1, 2, 'level')]
+    >>> list(nx.bfs_labeled_edges(G, [0, 1]))
+    [(0, 1, 'level'), (0, 2, 'tree'), (1, 2, 'forward')]
+    """
+    if sources in G:
+        sources = [sources]
+
+    neighbors = G._adj
+    directed = G.is_directed()
+    visited = set()
+    visit = visited.discard if directed else visited.add
+    # We use visited in a negative sense, so the visited set stays empty for the
+    # directed case and level edges are reported on their first occurrence in
+    # the undirected case.  Note our use of visited.discard -- this is built-in
+    # thus somewhat faster than a python-defined def nop(x): pass
+    depth = {s: 0 for s in sources}
+    queue = deque(depth.items())
+    push = queue.append
+    pop = queue.popleft
+    while queue:
+        u, du = pop()
+        for v in neighbors[u]:
+            if v not in depth:
+                depth[v] = dv = du + 1
+                push((v, dv))
+                yield u, v, TREE_EDGE
+            else:
+                dv = depth[v]
+                if du == dv:
+                    if v not in visited:
+                        yield u, v, LEVEL_EDGE
+                elif du < dv:
+                    yield u, v, FORWARD_EDGE
+                elif directed:
+                    yield u, v, REVERSE_EDGE
+        visit(u)
+
+
+@nx._dispatchable
+def descendants_at_distance(G, source, distance):
+    """Returns all nodes at a fixed `distance` from `source` in `G`.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A graph
+    source : node in `G`
+    distance : the distance of the wanted nodes from `source`
+
+    Returns
+    -------
+    set()
+        The descendants of `source` in `G` at the given `distance` from `source`
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> nx.descendants_at_distance(G, 2, 2)
+    {0, 4}
+    >>> H = nx.DiGraph()
+    >>> H.add_edges_from([(0, 1), (0, 2), (1, 3), (1, 4), (2, 5), (2, 6)])
+    >>> nx.descendants_at_distance(H, 0, 2)
+    {3, 4, 5, 6}
+    >>> nx.descendants_at_distance(H, 5, 0)
+    {5}
+    >>> nx.descendants_at_distance(H, 5, 1)
+    set()
+    """
+    if source not in G:
+        raise nx.NetworkXError(f"The node {source} is not in the graph.")
+
+    bfs_generator = nx.bfs_layers(G, source)
+    for i, layer in enumerate(bfs_generator):
+        if i == distance:
+            return set(layer)
+    return set()