Merge branch 'main' into py3.14

Author: srittau

stdlib/ast.pyi

@@ -1,6 +1,8 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
+__all__ = ["clique_removal", "max_clique", "large_clique_size", "maximum_independent_set"]
+
 @_dispatchable
 def maximum_independent_set(G: Graph[_Node]): ...
 @_dispatchable

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

@@ -2,5 +2,7 @@ from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 from numpy.random import RandomState
 
+__all__ = ["average_clustering"]
+
 @_dispatchable
 def average_clustering(G: Graph[_Node], trials: int = 1000, seed: int | RandomState | None = None): ...

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

@@ -4,6 +4,8 @@ from collections.abc import Iterable
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
+__all__ = ["local_node_connectivity", "node_connectivity", "all_pairs_node_connectivity"]
+
 @_dispatchable
 def local_node_connectivity(G: Graph[_Node], source: _Node, target: _Node, cutoff: int | None = None): ...
 @_dispatchable

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

@@ -2,5 +2,7 @@ from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 from numpy.random import RandomState
 
+__all__ = ["diameter"]
+
 @_dispatchable
 def diameter(G: Graph[_Node], seed: int | RandomState | None = None): ...

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

@@ -1,6 +1,8 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
+__all__ = ["min_weighted_dominating_set", "min_edge_dominating_set"]
+
 @_dispatchable
 def min_weighted_dominating_set(G: Graph[_Node], weight: str | None = None): ...
 @_dispatchable

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

@@ -1,5 +1,7 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
+__all__ = ["k_components"]
+
 @_dispatchable
 def k_components(G: Graph[_Node], min_density: float = 0.95): ...

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

@@ -1,5 +1,7 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
+__all__ = ["min_maximal_matching"]
+
 @_dispatchable
 def min_maximal_matching(G: Graph[_Node]): ...

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

@@ -4,6 +4,8 @@ from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 from numpy.random import RandomState
 
+__all__ = ["randomized_partitioning", "one_exchange"]
+
 @_dispatchable
 def randomized_partitioning(
     G: Graph[_Node], seed: int | RandomState | None = None, p: float = 0.5, weight: str | None = None

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

@@ -1,5 +1,7 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
+__all__ = ["ramsey_R2"]
+
 @_dispatchable
 def ramsey_R2(G: Graph[_Node]): ...