[stubsabot] Bump networkx to 3.3

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): ...