Bump networkx to 3.3 (#11727)

stubs/networkx/@tests/stubtest_allowlist.txt

@@ -11,16 +11,25 @@ networkx\.(convert_matrix\.)?from_pandas_edgelist
 networkx\.(generators\.)?(random_clustered\.)?random_clustered_graph
 networkx\.(relabel\.)?relabel_nodes
 
-# Stubtest doesn't understand aliases of class-decorated methods (possibly https://github.com/python/mypy/issues/6700 )
+# Stubtest doesn't understand aliases of class-decorated functions (possibly https://github.com/python/mypy/issues/6700 )
 networkx\.(algorithms\.)?(centrality\.)?(current_flow_closeness\.)?information_centrality
 networkx\.(generators\.)?(random_graphs\.)?binomial_graph
 networkx\.(generators\.)?(random_graphs\.)?erdos_renyi_graph
+networkx\.(algorithms\.(minors\.(contraction\.)?)?)?identified_nodes
+networkx\.algorithms\.isomorphism\.isomorph\.faster_graph_could_be_isomorphic
+networkx\.algorithms\.isomorphism\.isomorph\.fast_graph_could_be_isomorphic
+networkx\.algorithms\.isomorphism\.isomorph\.graph_could_be_isomorphic
+networkx\.algorithms\.flow\.maxflow\.default_flow_func
+networkx\.algorithms\.[a-z_\.]+\.default_flow_func
+networkx\.(algorithms\.(centrality\.(load\.)?)?)?load_centrality
+networkx\.algorithms\.bipartite\.(matching\.)?maximum_matching
+networkx\.algorithms\.bipartite\.(cluster\.)?clustering
 
 # Stubtest says: "runtime argument "backend" has a default value of type None, which is
 #   incompatible with stub argument type builtins.str. This is often caused by overloads
 #   failing to account for explicitly passing in the default value."
 # Which is true, but would require some way of concatenating `backend` to ParamSpec.kwargs
-networkx\.(utils\.backends\.)?_dispatch\.__call__
+networkx\.(utils\.)?(backends\.)?_dispatchable\.__call__
 
 # TODO: stubtest does not like @cached_property https://github.com/python/mypy/issues/17625
 # "is inconsistent, cannot reconcile @property on stub with runtime object"

stubs/networkx/@tests/test_cases/check_dispatch_decorator.py

@@ -1,9 +1,9 @@
 from typing_extensions import assert_type
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
 
-@_dispatch
+@_dispatchable
 def some_method(int_p: int, str_p: str) -> float:
     return 0.0
 

stubs/networkx/METADATA.toml

@@ -1,4 +1,4 @@
-version = "3.2.1"
+version = "3.3"
 upstream_repository = "https://github.com/networkx/networkx"
 # requires a version of numpy with a `py.typed` file
 # see https://github.com/python/typeshed/issues/12551

stubs/networkx/networkx/__init__.pyi

@@ -10,7 +10,7 @@ from networkx.lazy_imports import _lazy_import as _lazy_import
 from networkx.linalg import *
 from networkx.readwrite import *
 from networkx.relabel import *
-from networkx.utils.backends import _dispatch as _dispatch
+from networkx.utils import _clear_cache as _clear_cache, _dispatchable as _dispatchable, config as config
 
 from . import (
     algorithms as algorithms,

stubs/networkx/networkx/algorithms/approximation/clique.pyi

@@ -1,10 +1,10 @@
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def maximum_independent_set(G): ...
-@_dispatch
+@_dispatchable
 def max_clique(G): ...
-@_dispatch
+@_dispatchable
 def clique_removal(G): ...
-@_dispatch
+@_dispatchable
 def large_clique_size(G): ...

stubs/networkx/networkx/algorithms/approximation/clustering_coefficient.pyi

@@ -1,6 +1,6 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def average_clustering(G, trials: int = 1000, seed: Incomplete | None = None): ...

stubs/networkx/networkx/algorithms/approximation/connectivity.pyi

@@ -1,10 +1,10 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def local_node_connectivity(G, source, target, cutoff: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def node_connectivity(G, s: Incomplete | None = None, t: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def all_pairs_node_connectivity(G, nbunch: Incomplete | None = None, cutoff: Incomplete | None = None): ...

stubs/networkx/networkx/algorithms/approximation/distance_measures.pyi

@@ -1,6 +1,6 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def diameter(G, seed: Incomplete | None = None): ...

stubs/networkx/networkx/algorithms/approximation/dominating_set.pyi

@@ -1,8 +1,8 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def min_weighted_dominating_set(G, weight: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def min_edge_dominating_set(G): ...

stubs/networkx/networkx/algorithms/approximation/kcomponents.pyi

@@ -1,4 +1,4 @@
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def k_components(G, min_density: float = 0.95): ...

stubs/networkx/networkx/algorithms/approximation/matching.pyi

@@ -1,4 +1,4 @@
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def min_maximal_matching(G): ...

stubs/networkx/networkx/algorithms/approximation/maxcut.pyi

@@ -1,8 +1,8 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def randomized_partitioning(G, seed: Incomplete | None = None, p: float = 0.5, weight: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def one_exchange(G, initial_cut: Incomplete | None = None, seed: Incomplete | None = None, weight: Incomplete | None = None): ...

stubs/networkx/networkx/algorithms/approximation/ramsey.pyi

@@ -1,4 +1,4 @@
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def ramsey_R2(G): ...

stubs/networkx/networkx/algorithms/approximation/steinertree.pyi

@@ -1,8 +1,8 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def metric_closure(G, weight: str = "weight"): ...
-@_dispatch
+@_dispatchable
 def steiner_tree(G, terminal_nodes, weight: str = "weight", method: Incomplete | None = None): ...

stubs/networkx/networkx/algorithms/approximation/traveling_salesman.pyi

@@ -1,18 +1,18 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def christofides(G, weight: str = "weight", tree: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def traveling_salesman_problem(
-    G, weight: str = "weight", nodes: Incomplete | None = None, cycle: bool = True, method: Incomplete | None = None
+    G, weight: str = "weight", nodes: Incomplete | None = None, cycle: bool = True, method: Incomplete | None = None, **kwargs
 ): ...
-@_dispatch
+@_dispatchable
 def asadpour_atsp(G, weight: str = "weight", seed: Incomplete | None = None, source: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def greedy_tsp(G, weight: str = "weight", source: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def simulated_annealing_tsp(
     G,
     init_cycle,
@@ -26,7 +26,7 @@ def simulated_annealing_tsp(
     alpha: float = 0.01,
     seed: Incomplete | None = None,
 ): ...
-@_dispatch
+@_dispatchable
 def threshold_accepting_tsp(
     G,
     init_cycle,

stubs/networkx/networkx/algorithms/approximation/treewidth.pyi

@@ -1,12 +1,12 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
 __all__ = ["treewidth_min_degree", "treewidth_min_fill_in"]
 
-@_dispatch
+@_dispatchable
 def treewidth_min_degree(G): ...
-@_dispatch
+@_dispatchable
 def treewidth_min_fill_in(G): ...
 
 class MinDegreeHeuristic:

stubs/networkx/networkx/algorithms/approximation/vertex_cover.pyi

@@ -1,6 +1,6 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def min_weighted_vertex_cover(G, weight: Incomplete | None = None): ...

stubs/networkx/networkx/algorithms/assortativity/connectivity.pyi

@@ -1,8 +1,8 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def average_degree_connectivity(
     G, source: str = "in+out", target: str = "in+out", nodes: Incomplete | None = None, weight: Incomplete | None = None
 ): ...

stubs/networkx/networkx/algorithms/assortativity/correlation.pyi

@@ -1,16 +1,16 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def degree_assortativity_coefficient(
     G, x: str = "out", y: str = "in", weight: Incomplete | None = None, nodes: Incomplete | None = None
 ): ...
-@_dispatch
+@_dispatchable
 def degree_pearson_correlation_coefficient(
     G, x: str = "out", y: str = "in", weight: Incomplete | None = None, nodes: Incomplete | None = None
 ): ...
-@_dispatch
+@_dispatchable
 def attribute_assortativity_coefficient(G, attribute, nodes: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def numeric_assortativity_coefficient(G, attribute, nodes: Incomplete | None = None): ...

stubs/networkx/networkx/algorithms/assortativity/mixing.pyi

@@ -1,18 +1,18 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def attribute_mixing_dict(G, attribute, nodes: Incomplete | None = None, normalized: bool = False): ...
-@_dispatch
+@_dispatchable
 def attribute_mixing_matrix(
     G, attribute, nodes: Incomplete | None = None, mapping: Incomplete | None = None, normalized: bool = True
 ): ...
-@_dispatch
+@_dispatchable
 def degree_mixing_dict(
     G, x: str = "out", y: str = "in", weight: Incomplete | None = None, nodes: Incomplete | None = None, normalized: bool = False
 ): ...
-@_dispatch
+@_dispatchable
 def degree_mixing_matrix(
     G,
     x: str = "out",
@@ -22,5 +22,5 @@ def degree_mixing_matrix(
     normalized: bool = True,
     mapping: Incomplete | None = None,
 ): ...
-@_dispatch
+@_dispatchable
 def mixing_dict(xy, normalized: bool = False): ...

stubs/networkx/networkx/algorithms/assortativity/neighbor_degree.pyi

@@ -1,8 +1,8 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def average_neighbor_degree(
     G, source: str = "out", target: str = "out", nodes: Incomplete | None = None, weight: Incomplete | None = None
 ): ...

stubs/networkx/networkx/algorithms/assortativity/pairs.pyi

@@ -1,11 +1,11 @@
 from _typeshed import Incomplete
 from collections.abc import Generator
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def node_attribute_xy(G, attribute, nodes: Incomplete | None = None) -> Generator[Incomplete, None, None]: ...
-@_dispatch
+@_dispatchable
 def node_degree_xy(
     G, x: str = "out", y: str = "in", weight: Incomplete | None = None, nodes: Incomplete | None = None
 ) -> Generator[Incomplete, None, None]: ...

stubs/networkx/networkx/algorithms/asteroidal.pyi

@@ -1,6 +1,6 @@
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def find_asteroidal_triple(G): ...
-@_dispatch
+@_dispatchable
 def is_at_free(G): ...

stubs/networkx/networkx/algorithms/bipartite/basic.pyi

@@ -1,16 +1,16 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def color(G): ...
-@_dispatch
+@_dispatchable
 def is_bipartite(G): ...
-@_dispatch
+@_dispatchable
 def is_bipartite_node_set(G, nodes): ...
-@_dispatch
+@_dispatchable
 def sets(G, top_nodes: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def density(B, nodes): ...
-@_dispatch
+@_dispatchable
 def degrees(B, nodes, weight: Incomplete | None = None): ...

stubs/networkx/networkx/algorithms/bipartite/centrality.pyi

@@ -1,8 +1,8 @@
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def degree_centrality(G, nodes): ...
-@_dispatch
+@_dispatchable
 def betweenness_centrality(G, nodes): ...
-@_dispatch
+@_dispatchable
 def closeness_centrality(G, nodes, normalized: bool = True): ...

stubs/networkx/networkx/algorithms/bipartite/cluster.pyi

@@ -1,13 +1,13 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def latapy_clustering(G, nodes: Incomplete | None = None, mode: str = "dot"): ...
 
 clustering = latapy_clustering
 
-@_dispatch
+@_dispatchable
 def average_clustering(G, nodes: Incomplete | None = None, mode: str = "dot"): ...
-@_dispatch
+@_dispatchable
 def robins_alexander_clustering(G): ...

stubs/networkx/networkx/algorithms/bipartite/covering.pyi

@@ -1,6 +1,6 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def min_edge_cover(G, matching_algorithm: Incomplete | None = None): ...

stubs/networkx/networkx/algorithms/bipartite/edgelist.pyi

@@ -1,13 +1,13 @@
 from _typeshed import Incomplete
 from collections.abc import Generator
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def write_edgelist(G, path, comments: str = "#", delimiter: str = " ", data: bool = True, encoding: str = "utf-8") -> None: ...
-@_dispatch
+@_dispatchable
 def generate_edgelist(G, delimiter: str = " ", data: bool = True) -> Generator[Incomplete, None, None]: ...
-@_dispatch
+@_dispatchable
 def parse_edgelist(
     lines,
     comments: str = "#",
@@ -16,7 +16,7 @@ def parse_edgelist(
     nodetype: Incomplete | None = None,
     data: bool = True,
 ): ...
-@_dispatch
+@_dispatchable
 def read_edgelist(
     path,
     comments: str = "#",

stubs/networkx/networkx/algorithms/bipartite/generators.pyi

@@ -1,20 +1,20 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def complete_bipartite_graph(n1, n2, create_using: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def configuration_model(aseq, bseq, create_using: Incomplete | None = None, seed: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def havel_hakimi_graph(aseq, bseq, create_using: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def reverse_havel_hakimi_graph(aseq, bseq, create_using: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def alternating_havel_hakimi_graph(aseq, bseq, create_using: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def preferential_attachment_graph(aseq, p, create_using: Incomplete | None = None, seed: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def random_graph(n, m, p, seed: Incomplete | None = None, directed: bool = False): ...
-@_dispatch
+@_dispatchable
 def gnmk_random_graph(n, m, k, seed: Incomplete | None = None, directed: bool = False): ...

stubs/networkx/networkx/algorithms/bipartite/matching.pyi

@@ -1,15 +1,15 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def hopcroft_karp_matching(G, top_nodes: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def eppstein_matching(G, top_nodes: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def to_vertex_cover(G, matching, top_nodes: Incomplete | None = None): ...
 
 maximum_matching = hopcroft_karp_matching
 
-@_dispatch
+@_dispatchable
 def minimum_weight_full_matching(G, top_nodes: Incomplete | None = None, weight: str = "weight"): ...

stubs/networkx/networkx/algorithms/bipartite/matrix.pyi

@@ -1,8 +1,8 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def biadjacency_matrix(
     G,
     row_order,
@@ -11,5 +11,5 @@ def biadjacency_matrix(
     weight: str = "weight",
     format: str = "csr",
 ): ...
-@_dispatch
+@_dispatchable
 def from_biadjacency_matrix(A, create_using: Incomplete | None = None, edge_attribute: str = "weight"): ...

stubs/networkx/networkx/algorithms/bipartite/projection.pyi

@@ -1,14 +1,14 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def projected_graph(B, nodes, multigraph: bool = False): ...
-@_dispatch
+@_dispatchable
 def weighted_projected_graph(B, nodes, ratio: bool = False): ...
-@_dispatch
+@_dispatchable
 def collaboration_weighted_projected_graph(B, nodes): ...
-@_dispatch
+@_dispatchable
 def overlap_weighted_projected_graph(B, nodes, jaccard: bool = True): ...
-@_dispatch
+@_dispatchable
 def generic_weighted_projected_graph(B, nodes, weight_function: Incomplete | None = None): ...

stubs/networkx/networkx/algorithms/bipartite/redundancy.pyi

@@ -1,6 +1,6 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def node_redundancy(G, nodes: Incomplete | None = None): ...

stubs/networkx/networkx/algorithms/bipartite/spectral.pyi

@@ -1,6 +1,6 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def spectral_bipartivity(G, nodes: Incomplete | None = None, weight: str = "weight"): ...

stubs/networkx/networkx/algorithms/boundary.pyi

@@ -3,7 +3,7 @@ from collections.abc import Generator, Iterable
 from typing import Literal, TypeVar, overload
 
 from networkx.classes.graph import Graph, _Node
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
 _U = TypeVar("_U")
 
@@ -111,5 +111,5 @@ def edge_boundary(
     keys: Literal[True],
     default: _U | None = None,
 ) -> Generator[tuple[_Node, _Node, int, dict[str, _U]], None, None]: ...
-@_dispatch
+@_dispatchable
 def node_boundary(G: Graph[_Node], nbunch1: Iterable[_Node], nbunch2: Iterable[_Node] | None = None) -> set[_Node]: ...

stubs/networkx/networkx/algorithms/bridges.pyi

@@ -3,11 +3,11 @@ from collections.abc import Callable, Generator
 from typing import Literal, overload
 
 from networkx.classes.graph import Graph, _Node
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def bridges(G: Graph[_Node], root: _Node | None = None) -> Generator[_Node, None, None]: ...
-@_dispatch
+@_dispatchable
 def has_bridges(G: Graph[_Node], root: Incomplete | None = None) -> bool: ...
 @overload
 def local_bridges(

stubs/networkx/networkx/algorithms/broadcasting.pyi

@@ -0,0 +1,9 @@
+import networkx as nx
+from networkx.classes.graph import Graph, _Node
+
+__all__ = ["tree_broadcast_center", "tree_broadcast_time"]
+
+@nx._dispatchable
+def tree_broadcast_center(G: Graph[_Node]) -> tuple[int, set[_Node]]: ...
+@nx._dispatchable
+def tree_broadcast_time(G: Graph[_Node], node: int | None = None) -> int: ...

stubs/networkx/networkx/algorithms/centrality/betweenness.pyi

@@ -1,10 +1,10 @@
 from _typeshed import Incomplete
 
 from networkx.classes.graph import Graph, _Edge, _Node
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 from numpy.random import RandomState
 
-@_dispatch
+@_dispatchable
 def betweenness_centrality(
     G: Graph[_Node],
     k: int | None = None,
@@ -13,7 +13,7 @@ def betweenness_centrality(
     endpoints: bool = False,
     seed: int | RandomState | None = None,
 ) -> dict[_Node, float]: ...
-@_dispatch
+@_dispatchable
 def edge_betweenness_centrality(
     G: Graph[_Node], k: int | None = None, normalized: bool = True, weight: str | None = None, seed: Incomplete | None = None
 ) -> dict[_Edge[_Node], float]: ...

stubs/networkx/networkx/algorithms/centrality/betweenness_subset.pyi

@@ -1,13 +1,13 @@
 from collections.abc import Iterable
 
 from networkx.classes.graph import Graph, _Edge, _Node
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def betweenness_centrality_subset(
     G: Graph[_Node], sources: Iterable[_Node], targets: Iterable[_Node], normalized: bool = False, weight: str | None = None
 ) -> dict[_Node, float]: ...
-@_dispatch
+@_dispatchable
 def edge_betweenness_centrality_subset(
     G: Graph[_Node], sources: Iterable[_Node], targets: Iterable[_Node], normalized: bool = False, weight: str | None = None
 ) -> dict[_Edge[_Node], float]: ...

stubs/networkx/networkx/algorithms/centrality/closeness.pyi

@@ -1,13 +1,13 @@
 from _typeshed import SupportsKeysAndGetItem
 
 from networkx.classes.graph import Graph, _Edge, _Node
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def closeness_centrality(
     G: Graph[_Node], u: _Node | None = None, distance: str | None = None, wf_improved: bool = True
 ) -> dict[_Node, float]: ...
-@_dispatch
+@_dispatchable
 def incremental_closeness_centrality(
     G: Graph[_Node],
     edge: _Edge[_Node],

stubs/networkx/networkx/algorithms/centrality/current_flow_betweenness.pyi

@@ -1,8 +1,8 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def approximate_current_flow_betweenness_centrality(
     G,
     normalized: bool = True,
@@ -13,11 +13,11 @@ def approximate_current_flow_betweenness_centrality(
     kmax: int = 10000,
     seed: Incomplete | None = None,
 ): ...
-@_dispatch
+@_dispatchable
 def current_flow_betweenness_centrality(
     G, normalized: bool = True, weight: Incomplete | None = None, dtype=..., solver: str = "full"
 ): ...
-@_dispatch
+@_dispatchable
 def edge_current_flow_betweenness_centrality(
     G, normalized: bool = True, weight: Incomplete | None = None, dtype=..., solver: str = "full"
 ): ...

stubs/networkx/networkx/algorithms/centrality/current_flow_betweenness_subset.pyi

@@ -1,12 +1,12 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def current_flow_betweenness_centrality_subset(
     G, sources, targets, normalized: bool = True, weight: Incomplete | None = None, dtype=..., solver: str = "lu"
 ): ...
-@_dispatch
+@_dispatchable
 def edge_current_flow_betweenness_centrality_subset(
     G, sources, targets, normalized: bool = True, weight: Incomplete | None = None, dtype=..., solver: str = "lu"
 ): ...

stubs/networkx/networkx/algorithms/centrality/current_flow_closeness.pyi

@@ -1,8 +1,8 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def current_flow_closeness_centrality(G, weight: Incomplete | None = None, dtype=..., solver: str = "lu"): ...
 
 information_centrality = current_flow_closeness_centrality

stubs/networkx/networkx/algorithms/centrality/degree_alg.pyi

@@ -1,9 +1,9 @@
 from networkx.classes.graph import Graph, _Node
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def degree_centrality(G: Graph[_Node]) -> dict[_Node, float]: ...
-@_dispatch
+@_dispatchable
 def in_degree_centrality(G: Graph[_Node]) -> dict[_Node, float]: ...
-@_dispatch
+@_dispatchable
 def out_degree_centrality(G: Graph[_Node]) -> dict[_Node, float]: ...

stubs/networkx/networkx/algorithms/centrality/dispersion.pyi

@@ -1,7 +1,7 @@
 from networkx.classes.graph import Graph, _Node
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def dispersion(
     G: Graph[_Node],
     u: _Node | None = None,

stubs/networkx/networkx/algorithms/centrality/eigenvector.pyi

@@ -1,10 +1,10 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def eigenvector_centrality(
     G, max_iter: int = 100, tol: float = 1e-06, nstart: Incomplete | None = None, weight: Incomplete | None = None
 ): ...
-@_dispatch
+@_dispatchable
 def eigenvector_centrality_numpy(G, weight: Incomplete | None = None, max_iter: int = 50, tol: float = 0): ...

stubs/networkx/networkx/algorithms/centrality/flow_matrix.pyi

@@ -1,9 +1,9 @@
 from _typeshed import Incomplete
 from collections.abc import Generator
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def flow_matrix_row(G, weight: Incomplete | None = None, dtype=..., solver: str = "lu") -> Generator[Incomplete, None, None]: ...
 
 class InverseLaplacian:

stubs/networkx/networkx/algorithms/centrality/group.pyi

@@ -1,10 +1,10 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def group_betweenness_centrality(G, C, normalized: bool = True, weight: Incomplete | None = None, endpoints: bool = False): ...
-@_dispatch
+@_dispatchable
 def prominent_group(
     G,
     k,
@@ -14,11 +14,11 @@ def prominent_group(
     normalized: bool = True,
     greedy: bool = False,
 ): ...
-@_dispatch
+@_dispatchable
 def group_closeness_centrality(G, S, weight: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def group_degree_centrality(G, S): ...
-@_dispatch
+@_dispatchable
 def group_in_degree_centrality(G, S): ...
-@_dispatch
+@_dispatchable
 def group_out_degree_centrality(G, S): ...

stubs/networkx/networkx/algorithms/centrality/harmonic.pyi

@@ -1,8 +1,8 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def harmonic_centrality(
     G, nbunch: Incomplete | None = None, distance: Incomplete | None = None, sources: Incomplete | None = None
 ): ...

stubs/networkx/networkx/algorithms/centrality/katz.pyi

@@ -1,8 +1,8 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def katz_centrality(
     G,
     alpha: float = 0.1,
@@ -13,7 +13,7 @@ def katz_centrality(
     normalized: bool = True,
     weight: Incomplete | None = None,
 ): ...
-@_dispatch
+@_dispatchable
 def katz_centrality_numpy(
     G, alpha: float = 0.1, beta: float = 1.0, normalized: bool = True, weight: Incomplete | None = None
 ): ...

stubs/networkx/networkx/algorithms/centrality/laplacian.pyi

@@ -1,8 +1,8 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def laplacian_centrality(
     G,
     normalized: bool = True,

stubs/networkx/networkx/algorithms/centrality/load.pyi

@@ -1,15 +1,15 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
 __all__ = ["load_centrality", "edge_load_centrality"]
 
-@_dispatch
+@_dispatchable
 def newman_betweenness_centrality(
     G, v: Incomplete | None = None, cutoff: Incomplete | None = None, normalized: bool = True, weight: Incomplete | None = None
 ): ...
 
 load_centrality = newman_betweenness_centrality
 
-@_dispatch
+@_dispatchable
 def edge_load_centrality(G, cutoff: bool = False): ...

stubs/networkx/networkx/algorithms/centrality/percolation.pyi

@@ -1,8 +1,8 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def percolation_centrality(
     G, attribute: str = "percolation", states: Incomplete | None = None, weight: Incomplete | None = None
 ): ...

stubs/networkx/networkx/algorithms/centrality/reaching.pyi

@@ -1,10 +1,10 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def global_reaching_centrality(G, weight: Incomplete | None = None, normalized: bool = True): ...
-@_dispatch
+@_dispatchable
 def local_reaching_centrality(
     G, v, paths: Incomplete | None = None, weight: Incomplete | None = None, normalized: bool = True
 ): ...

stubs/networkx/networkx/algorithms/centrality/second_order.pyi

@@ -1,4 +1,4 @@
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def second_order_centrality(G): ...

stubs/networkx/networkx/algorithms/centrality/subgraph_alg.pyi

@@ -1,10 +1,10 @@
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def subgraph_centrality_exp(G): ...
-@_dispatch
+@_dispatchable
 def subgraph_centrality(G): ...
-@_dispatch
+@_dispatchable
 def communicability_betweenness_centrality(G): ...
-@_dispatch
+@_dispatchable
 def estrada_index(G): ...

stubs/networkx/networkx/algorithms/centrality/trophic.pyi

@@ -1,8 +1,8 @@
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def trophic_levels(G, weight: str = "weight"): ...
-@_dispatch
+@_dispatchable
 def trophic_differences(G, weight: str = "weight"): ...
-@_dispatch
+@_dispatchable
 def trophic_incoherence_parameter(G, weight: str = "weight", cannibalism: bool = False): ...

stubs/networkx/networkx/algorithms/centrality/voterank_alg.pyi

@@ -1,6 +1,6 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def voterank(G, number_of_nodes: Incomplete | None = None): ...

stubs/networkx/networkx/algorithms/chains.pyi

@@ -1,7 +1,7 @@
 from collections.abc import Generator
 
 from networkx.classes.graph import Graph, _Node
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def chain_decomposition(G: Graph[_Node], root: _Node | None = None) -> Generator[list[tuple[_Node, _Node]], None, None]: ...

stubs/networkx/networkx/algorithms/chordal.pyi

@@ -3,15 +3,15 @@ from collections.abc import Generator, Hashable
 
 from networkx.classes.graph import Graph, _Node
 from networkx.exception import NetworkXException
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
 class NetworkXTreewidthBoundExceeded(NetworkXException): ...
 
-@_dispatch
+@_dispatchable
 def is_chordal(G: Graph[Hashable]) -> bool: ...
-@_dispatch
+@_dispatchable
 def find_induced_nodes(G: Graph[_Node], s: _Node, t: _Node, treewidth_bound: float = sys.maxsize) -> set[_Node]: ...
-@_dispatch
+@_dispatchable
 def chordal_graph_cliques(G: Graph[_Node]) -> Generator[frozenset[_Node], None, None]: ...
-@_dispatch
+@_dispatchable
 def chordal_graph_treewidth(G: Graph[Hashable]) -> int: ...

stubs/networkx/networkx/algorithms/clique.pyi

@@ -3,17 +3,17 @@ from collections.abc import Generator, Iterable, Iterator, Sized
 from typing import overload
 
 from networkx.classes.graph import Graph, _Node
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def enumerate_all_cliques(G: Graph[_Node]) -> Generator[list[_Node], None, None]: ...
-@_dispatch
+@_dispatchable
 def find_cliques(G: Graph[_Node], nodes: SupportsGetItem[slice, _Node] | None = None) -> Generator[list[_Node], None, None]: ...
-@_dispatch
+@_dispatchable
 def find_cliques_recursive(G: Graph[_Node], nodes: SupportsGetItem[slice, _Node] | None = None) -> Iterator[list[_Node]]: ...
-@_dispatch
+@_dispatchable
 def make_max_clique_graph(G: Graph[_Node], create_using: type[Graph[_Node]] | None = None) -> Graph[_Node]: ...
-@_dispatch
+@_dispatchable
 def make_clique_bipartite(
     G: Graph[_Node], fpos: Unused = None, create_using: type[Graph[_Node]] | None = None, name: Unused = None
 ) -> Graph[_Node]: ...

stubs/networkx/networkx/algorithms/cluster.pyi

@@ -1,16 +1,16 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def triangles(G, nodes: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def average_clustering(G, nodes: Incomplete | None = None, weight: Incomplete | None = None, count_zeros: bool = True): ...
-@_dispatch
+@_dispatchable
 def clustering(G, nodes: Incomplete | None = None, weight: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def transitivity(G): ...
-@_dispatch
+@_dispatchable
 def square_clustering(G, nodes: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def generalized_degree(G, nodes: Incomplete | None = None): ...

stubs/networkx/networkx/algorithms/coloring/equitable_coloring.pyi

@@ -1,4 +1,4 @@
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def equitable_color(G, num_colors): ...

stubs/networkx/networkx/algorithms/coloring/greedy_coloring.pyi

@@ -1,7 +1,7 @@
 from _typeshed import Incomplete
 from collections.abc import Generator
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
 __all__ = [
     "greedy_color",
@@ -15,23 +15,23 @@ __all__ = [
     "strategy_smallest_last",
 ]
 
-@_dispatch
+@_dispatchable
 def strategy_largest_first(G, colors): ...
-@_dispatch
+@_dispatchable
 def strategy_random_sequential(G, colors, seed: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def strategy_smallest_last(G, colors): ...
-@_dispatch
+@_dispatchable
 def strategy_independent_set(G, colors) -> Generator[Incomplete, Incomplete, None]: ...
-@_dispatch
+@_dispatchable
 def strategy_connected_sequential_bfs(G, colors): ...
-@_dispatch
+@_dispatchable
 def strategy_connected_sequential_dfs(G, colors): ...
-@_dispatch
+@_dispatchable
 def strategy_connected_sequential(G, colors, traversal: str = "bfs") -> Generator[Incomplete, None, None]: ...
-@_dispatch
+@_dispatchable
 def strategy_saturation_largest_first(G, colors) -> Generator[Incomplete, None, Incomplete]: ...
-@_dispatch
+@_dispatchable
 def greedy_color(G, strategy: str = "largest_first", interchange: bool = False): ...
 
 class _Node:

stubs/networkx/networkx/algorithms/communicability_alg.pyi

@@ -1,6 +1,6 @@
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def communicability(G): ...
-@_dispatch
+@_dispatchable
 def communicability_exp(G): ...

stubs/networkx/networkx/algorithms/community/__init__.pyi

@@ -1,6 +1,7 @@
 from networkx.algorithms.community.asyn_fluid import *
 from networkx.algorithms.community.centrality import *
 from networkx.algorithms.community.community_utils import *
+from networkx.algorithms.community.divisive import *
 from networkx.algorithms.community.kclique import *
 from networkx.algorithms.community.kernighan_lin import *
 from networkx.algorithms.community.label_propagation import *

stubs/networkx/networkx/algorithms/community/asyn_fluid.pyi

@@ -1,6 +1,6 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def asyn_fluidc(G, k, max_iter: int = 100, seed: Incomplete | None = None): ...

stubs/networkx/networkx/algorithms/community/centrality.pyi

@@ -1,7 +1,7 @@
 from _typeshed import Incomplete
 from collections.abc import Generator
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def girvan_newman(G, most_valuable_edge: Incomplete | None = None) -> Generator[Incomplete, None, Incomplete]: ...

stubs/networkx/networkx/algorithms/community/community_utils.pyi

@@ -1,4 +1,4 @@
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def is_partition(G, communities): ...

stubs/networkx/networkx/algorithms/community/divisive.pyi

@@ -0,0 +1,10 @@
+from _typeshed import Incomplete
+
+import networkx as nx
+
+__all__ = ["edge_betweenness_partition", "edge_current_flow_betweenness_partition"]
+
+@nx._dispatchable
+def edge_betweenness_partition(G, number_of_sets: int, *, weight: Incomplete | None = None) -> list[Incomplete]: ...
+@nx._dispatchable
+def edge_current_flow_betweenness_partition(G, number_of_sets: int, *, weight: Incomplete | None = None) -> list[Incomplete]: ...

stubs/networkx/networkx/algorithms/community/kclique.pyi

@@ -1,7 +1,7 @@
 from _typeshed import Incomplete
 from collections.abc import Generator
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def k_clique_communities(G, k, cliques: Incomplete | None = None) -> Generator[Incomplete, None, None]: ...

stubs/networkx/networkx/algorithms/community/kernighan_lin.pyi

@@ -1,8 +1,8 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def kernighan_lin_bisection(
     G, partition: Incomplete | None = None, max_iter: int = 10, weight: str = "weight", seed: Incomplete | None = None
 ): ...

stubs/networkx/networkx/algorithms/community/label_propagation.pyi

@@ -1,11 +1,11 @@
 from _typeshed import Incomplete
 from collections.abc import Generator
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def asyn_lpa_communities(
     G, weight: Incomplete | None = None, seed: Incomplete | None = None
 ) -> Generator[Incomplete, Incomplete, None]: ...
-@_dispatch
+@_dispatchable
 def label_propagation_communities(G): ...

stubs/networkx/networkx/algorithms/community/louvain.pyi

@@ -1,13 +1,13 @@
 from _typeshed import Incomplete
 from collections.abc import Generator
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def louvain_communities(
     G, weight: str = "weight", resolution: float = 1, threshold: float = 1e-07, seed: Incomplete | None = None
 ): ...
-@_dispatch
+@_dispatchable
 def louvain_partitions(
     G, weight: str = "weight", resolution: float = 1, threshold: float = 1e-07, seed: Incomplete | None = None
 ) -> Generator[Incomplete, None, None]: ...

stubs/networkx/networkx/algorithms/community/lukes.pyi

@@ -1,6 +1,6 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def lukes_partitioning(G, max_size, node_weight: Incomplete | None = None, edge_weight: Incomplete | None = None): ...

stubs/networkx/networkx/algorithms/community/modularity_max.pyi

@@ -1,10 +1,10 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def greedy_modularity_communities(
     G, weight: Incomplete | None = None, resolution: float = 1, cutoff: int = 1, best_n: Incomplete | None = None
 ): ...
-@_dispatch
+@_dispatchable
 def naive_greedy_modularity_communities(G, resolution: float = 1, weight: Incomplete | None = None): ...

stubs/networkx/networkx/algorithms/community/quality.pyi

@@ -1,12 +1,12 @@
 from networkx.exception import NetworkXError
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
 __all__ = ["modularity", "partition_quality"]
 
 class NotAPartition(NetworkXError):
     def __init__(self, G, collection) -> None: ...
 
-@_dispatch
+@_dispatchable
 def modularity(G, communities, weight: str = "weight", resolution: float = 1): ...
-@_dispatch
+@_dispatchable
 def partition_quality(G, partition): ...

stubs/networkx/networkx/algorithms/components/attracting.pyi

@@ -1,11 +1,11 @@
 from _typeshed import Incomplete
 from collections.abc import Generator
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def attracting_components(G) -> Generator[Incomplete, None, None]: ...
-@_dispatch
+@_dispatchable
 def number_attracting_components(G): ...
-@_dispatch
+@_dispatchable
 def is_attracting_component(G): ...

stubs/networkx/networkx/algorithms/components/biconnected.pyi

@@ -1,13 +1,13 @@
 from _typeshed import Incomplete
 from collections.abc import Generator
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def is_biconnected(G): ...
-@_dispatch
+@_dispatchable
 def biconnected_component_edges(G) -> Generator[Incomplete, Incomplete, None]: ...
-@_dispatch
+@_dispatchable
 def biconnected_components(G) -> Generator[Incomplete, None, None]: ...
-@_dispatch
+@_dispatchable
 def articulation_points(G) -> Generator[Incomplete, None, None]: ...

stubs/networkx/networkx/algorithms/components/connected.pyi

@@ -1,13 +1,13 @@
 from _typeshed import Incomplete
 from collections.abc import Generator
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def connected_components(G) -> Generator[Incomplete, None, None]: ...
-@_dispatch
+@_dispatchable
 def number_connected_components(G): ...
-@_dispatch
+@_dispatchable
 def is_connected(G): ...
-@_dispatch
+@_dispatchable
 def node_connected_component(G, n): ...

stubs/networkx/networkx/algorithms/components/semiconnected.pyi

@@ -1,6 +1,6 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def is_semiconnected(G, topo_order: Incomplete | None = None): ...

stubs/networkx/networkx/algorithms/components/strongly_connected.pyi

@@ -2,17 +2,17 @@ from collections.abc import Generator, Hashable, Iterable
 
 from networkx.classes.digraph import DiGraph
 from networkx.classes.graph import Graph, _Node
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def strongly_connected_components(G: Graph[_Node]) -> Generator[set[_Node], None, None]: ...
-@_dispatch
+@_dispatchable
 def kosaraju_strongly_connected_components(G: Graph[_Node], source: _Node | None = None) -> Generator[set[_Node], None, None]: ...
-@_dispatch
+@_dispatchable
 def strongly_connected_components_recursive(G: Graph[_Node]) -> Generator[set[_Node], None, None]: ...
-@_dispatch
+@_dispatchable
 def number_strongly_connected_components(G: Graph[Hashable]) -> int: ...
-@_dispatch
+@_dispatchable
 def is_strongly_connected(G: Graph[Hashable]) -> bool: ...
-@_dispatch
+@_dispatchable
 def condensation(G: DiGraph[_Node], scc: Iterable[Iterable[_Node]] | None = None) -> DiGraph[int]: ...

stubs/networkx/networkx/algorithms/components/weakly_connected.pyi

@@ -1,11 +1,11 @@
 from collections.abc import Generator, Hashable
 
 from networkx.classes.graph import Graph, _Node
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def weakly_connected_components(G: Graph[_Node]) -> Generator[set[_Node], None, None]: ...
-@_dispatch
+@_dispatchable
 def number_weakly_connected_components(G: Graph[Hashable]) -> int: ...
-@_dispatch
+@_dispatchable
 def is_weakly_connected(G: Graph[Hashable]) -> bool: ...

stubs/networkx/networkx/algorithms/connectivity/connectivity.pyi

@@ -1,7 +1,7 @@
 from _typeshed import Incomplete
 
 from networkx.algorithms.flow import edmonds_karp
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
 __all__ = [
     "average_node_connectivity",
@@ -14,7 +14,7 @@ __all__ = [
 
 default_flow_func = edmonds_karp
 
-@_dispatch
+@_dispatchable
 def local_node_connectivity(
     G,
     s,
@@ -24,13 +24,13 @@ def local_node_connectivity(
     residual: Incomplete | None = None,
     cutoff: Incomplete | None = None,
 ): ...
-@_dispatch
+@_dispatchable
 def node_connectivity(G, s: Incomplete | None = None, t: Incomplete | None = None, flow_func: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def average_node_connectivity(G, flow_func: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def all_pairs_node_connectivity(G, nbunch: Incomplete | None = None, flow_func: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def local_edge_connectivity(
     G,
     s,
@@ -40,7 +40,7 @@ def local_edge_connectivity(
     residual: Incomplete | None = None,
     cutoff: Incomplete | None = None,
 ): ...
-@_dispatch
+@_dispatchable
 def edge_connectivity(
     G,
     s: Incomplete | None = None,

stubs/networkx/networkx/algorithms/connectivity/cuts.pyi

@@ -1,21 +1,21 @@
 from _typeshed import Incomplete
 
 from networkx.algorithms.flow import edmonds_karp
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
 __all__ = ["minimum_st_node_cut", "minimum_node_cut", "minimum_st_edge_cut", "minimum_edge_cut"]
 
 default_flow_func = edmonds_karp
 
-@_dispatch
+@_dispatchable
 def minimum_st_edge_cut(
     G, s, t, flow_func: Incomplete | None = None, auxiliary: Incomplete | None = None, residual: Incomplete | None = None
 ): ...
-@_dispatch
+@_dispatchable
 def minimum_st_node_cut(
     G, s, t, flow_func: Incomplete | None = None, auxiliary: Incomplete | None = None, residual: Incomplete | None = None
 ): ...
-@_dispatch
+@_dispatchable
 def minimum_node_cut(G, s: Incomplete | None = None, t: Incomplete | None = None, flow_func: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def minimum_edge_cut(G, s: Incomplete | None = None, t: Incomplete | None = None, flow_func: Incomplete | None = None): ...

stubs/networkx/networkx/algorithms/connectivity/disjoint_paths.pyi

@@ -2,13 +2,13 @@ from _typeshed import Incomplete
 from collections.abc import Generator
 
 from networkx.algorithms.flow import edmonds_karp
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
 __all__ = ["edge_disjoint_paths", "node_disjoint_paths"]
 
 default_flow_func = edmonds_karp
 
-@_dispatch
+@_dispatchable
 def edge_disjoint_paths(
     G,
     s,
@@ -18,7 +18,7 @@ def edge_disjoint_paths(
     auxiliary: Incomplete | None = None,
     residual: Incomplete | None = None,
 ) -> Generator[Incomplete, None, None]: ...
-@_dispatch
+@_dispatchable
 def node_disjoint_paths(
     G,
     s,

stubs/networkx/networkx/algorithms/connectivity/edge_augmentation.pyi

@@ -1,13 +1,13 @@
 from collections.abc import Generator, Hashable
 
 from networkx.classes.graph import Graph, _Node
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def is_k_edge_connected(G: Graph[Hashable], k: int): ...
-@_dispatch
+@_dispatchable
 def is_locally_k_edge_connected(G, s, t, k): ...
-@_dispatch
+@_dispatchable
 def k_edge_augmentation(
     G: Graph[_Node],
     k: int,

stubs/networkx/networkx/algorithms/connectivity/edge_kcomponents.pyi

@@ -1,13 +1,13 @@
 from _typeshed import Incomplete
 from collections.abc import Generator
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def k_edge_components(G, k): ...
-@_dispatch
+@_dispatchable
 def k_edge_subgraphs(G, k): ...
-@_dispatch
+@_dispatchable
 def bridge_components(G) -> Generator[Incomplete, Incomplete, None]: ...
 
 class EdgeComponentAuxGraph:

stubs/networkx/networkx/algorithms/connectivity/kcomponents.pyi

@@ -1,11 +1,11 @@
 from _typeshed import Incomplete
 
 from networkx.algorithms.flow import edmonds_karp
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
 __all__ = ["k_components"]
 
 default_flow_func = edmonds_karp
 
-@_dispatch
+@_dispatchable
 def k_components(G, flow_func: Incomplete | None = None): ...

stubs/networkx/networkx/algorithms/connectivity/kcutsets.pyi

@@ -2,11 +2,11 @@ from _typeshed import Incomplete
 from collections.abc import Generator
 
 from networkx.algorithms.flow import edmonds_karp
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
 __all__ = ["all_node_cuts"]
 
 default_flow_func = edmonds_karp
 
-@_dispatch
+@_dispatchable
 def all_node_cuts(G, k: Incomplete | None = None, flow_func: Incomplete | None = None) -> Generator[Incomplete, None, None]: ...

stubs/networkx/networkx/algorithms/connectivity/stoerwagner.pyi

@@ -1,4 +1,4 @@
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def stoer_wagner(G, weight: str = "weight", heap=...): ...

stubs/networkx/networkx/algorithms/connectivity/utils.pyi

@@ -1,6 +1,6 @@
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def build_auxiliary_node_connectivity(G): ...
-@_dispatch
+@_dispatchable
 def build_auxiliary_edge_connectivity(G): ...

stubs/networkx/networkx/algorithms/core.pyi

@@ -1,18 +1,18 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def core_number(G): ...
-@_dispatch
+@_dispatchable
 def k_core(G, k: Incomplete | None = None, core_number: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def k_shell(G, k: Incomplete | None = None, core_number: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def k_crust(G, k: Incomplete | None = None, core_number: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def k_corona(G, k, core_number: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def k_truss(G, k): ...
-@_dispatch
+@_dispatchable
 def onion_layers(G): ...

stubs/networkx/networkx/algorithms/covering.pyi

@@ -1,8 +1,8 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def min_edge_cover(G, matching_algorithm: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def is_edge_cover(G, cover): ...

stubs/networkx/networkx/algorithms/cuts.pyi

@@ -1,20 +1,20 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def cut_size(G, S, T: Incomplete | None = None, weight: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def volume(G, S, weight: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def normalized_cut_size(G, S, T: Incomplete | None = None, weight: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def conductance(G, S, T: Incomplete | None = None, weight: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def edge_expansion(G, S, T: Incomplete | None = None, weight: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def mixing_expansion(G, S, T: Incomplete | None = None, weight: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def node_expansion(G, S): ...
-@_dispatch
+@_dispatchable
 def boundary_expansion(G, S): ...

stubs/networkx/networkx/algorithms/cycles.pyi

@@ -1,11 +1,11 @@
 from _typeshed import Incomplete
 from collections.abc import Generator
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def cycle_basis(G, root: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def simple_cycles(G, length_bound: Incomplete | None = None) -> Generator[Incomplete, Incomplete, None]: ...
 
 class _NeighborhoodCache(dict[Incomplete, Incomplete]):
@@ -13,11 +13,11 @@ class _NeighborhoodCache(dict[Incomplete, Incomplete]):
     def __init__(self, G) -> None: ...
     def __missing__(self, v): ...
 
-@_dispatch
+@_dispatchable
 def chordless_cycles(G, length_bound: Incomplete | None = None) -> Generator[Incomplete, Incomplete, None]: ...
-@_dispatch
+@_dispatchable
 def recursive_simple_cycles(G): ...
-@_dispatch
+@_dispatchable
 def find_cycle(G, source: Incomplete | None = None, orientation: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def minimum_cycle_basis(G, weight: Incomplete | None = None): ...

stubs/networkx/networkx/algorithms/d_separation.pyi

@@ -1,8 +1,8 @@
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def d_separated(G, x, y, z): ...
-@_dispatch
+@_dispatchable
 def minimal_d_separator(G, u, v): ...
-@_dispatch
+@_dispatchable
 def is_minimal_d_separator(G, u, v, z): ...

stubs/networkx/networkx/algorithms/dag.pyi

@@ -3,39 +3,39 @@ from collections.abc import Callable, Generator, Iterable, Reversible
 
 from networkx.classes.digraph import DiGraph
 from networkx.classes.graph import Graph, _Node
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def descendants(G: Graph[_Node], source: _Node) -> set[_Node]: ...
-@_dispatch
+@_dispatchable
 def ancestors(G: Graph[_Node], source: _Node) -> set[_Node]: ...
-@_dispatch
+@_dispatchable
 def is_directed_acyclic_graph(G: Graph[_Node]) -> bool: ...
-@_dispatch
+@_dispatchable
 def topological_generations(G: DiGraph[_Node]) -> Generator[list[_Node], None, None]: ...
-@_dispatch
+@_dispatchable
 def topological_sort(G: DiGraph[_Node]) -> Generator[_Node, None, None]: ...
-@_dispatch
+@_dispatchable
 def lexicographical_topological_sort(
     G: DiGraph[_Node], key: Callable[[_Node], SupportsRichComparison] | None = None
 ) -> Generator[_Node, None, None]: ...
-@_dispatch
+@_dispatchable
 def all_topological_sorts(G: DiGraph[_Node]) -> Generator[list[_Node], None, None]: ...
-@_dispatch
+@_dispatchable
 def is_aperiodic(G: DiGraph[_Node]) -> bool: ...
-@_dispatch
+@_dispatchable
 def transitive_closure(G: Graph[_Node], reflexive: bool = False) -> Graph[_Node]: ...
-@_dispatch
+@_dispatchable
 def transitive_closure_dag(G: DiGraph[_Node], reflexive: bool = False) -> DiGraph[_Node]: ...
-@_dispatch
+@_dispatchable
 def transitive_reduction(G: DiGraph[_Node]) -> DiGraph[_Node]: ...
-@_dispatch
+@_dispatchable
 def antichains(G: DiGraph[_Node], topo_order: Reversible[_Node] | None = None) -> Generator[list[_Node], None, None]: ...
-@_dispatch
+@_dispatchable
 def dag_longest_path(
     G: DiGraph[_Node], weight: str = "weight", default_weight: int = 1, topo_order: Iterable[_Node] | None = None
 ) -> list[_Node]: ...
-@_dispatch
+@_dispatchable
 def dag_longest_path_length(G: DiGraph[_Node], weight: str = "weight", default_weight: int = 1) -> int: ...
-@_dispatch
+@_dispatchable
 def dag_to_branching(G: Graph[_Node]) -> Graph[_Node]: ...

stubs/networkx/networkx/algorithms/distance_measures.pyi

@@ -1,18 +1,18 @@
 from _typeshed import Incomplete
 
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def eccentricity(G, v: Incomplete | None = None, sp: Incomplete | None = None, weight: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def diameter(G, e: Incomplete | None = None, usebounds: bool = False, weight: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def periphery(G, e: Incomplete | None = None, usebounds: bool = False, weight: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def radius(G, e: Incomplete | None = None, usebounds: bool = False, weight: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def center(G, e: Incomplete | None = None, usebounds: bool = False, weight: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def barycenter(G, weight: Incomplete | None = None, attr: Incomplete | None = None, sp: Incomplete | None = None): ...
-@_dispatch
+@_dispatchable
 def resistance_distance(G, nodeA, nodeB, weight: Incomplete | None = None, invert_weight: bool = True): ...

stubs/networkx/networkx/algorithms/distance_regular.pyi

@@ -1,10 +1,10 @@
-from networkx.utils.backends import _dispatch
+from networkx.utils.backends import _dispatchable
 
-@_dispatch
+@_dispatchable
 def is_distance_regular(G): ...
-@_dispatch
+@_dispatchable
 def global_parameters(b, c): ...
-@_dispatch
+@_dispatchable
 def intersection_array(G): ...
-@_dispatch
+@_dispatchable
 def is_strongly_regular(G): ...