[networkx] Update to 3.6 (#15086)

stubs/networkx/METADATA.toml

@@ -1,4 +1,4 @@
-version = "3.5"
+version = "3.6"
 upstream_repository = "https://github.com/networkx/networkx"
 # requires a version of numpy with a `py.typed` file
 requires = ["numpy>=1.20"]

stubs/networkx/networkx/algorithms/__init__.pyi

@@ -102,6 +102,7 @@ from networkx.algorithms.mis import *
 from networkx.algorithms.moral import *
 from networkx.algorithms.non_randomness import *
 from networkx.algorithms.operators import *
+from networkx.algorithms.perfect_graph import *
 from networkx.algorithms.planar_drawing import *
 from networkx.algorithms.planarity import *
 from networkx.algorithms.polynomials import *

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

@@ -1,5 +1,6 @@
 from _typeshed import Incomplete
 from collections.abc import Iterable
+from typing_extensions import deprecated
 
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
@@ -7,6 +8,9 @@ from networkx.utils.backends import _dispatchable
 __all__ = ["metric_closure", "steiner_tree"]
 
 @_dispatchable
+@deprecated(
+    "`metric_closure` is deprecated and will be removed in NetworkX 3.8. Use `networkx.all_pairs_shortest_path_length` instead."
+)
 def metric_closure(G: Graph[_Node], weight="weight"): ...
 @_dispatchable
 def steiner_tree(G: Graph[_Node], terminal_nodes: Iterable[Incomplete], weight: str = "weight", method: str | None = None): ...

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

@@ -20,6 +20,8 @@ def approximate_current_flow_betweenness_centrality(
     epsilon: float = 0.5,
     kmax: int = 10000,
     seed: int | RandomState | None = None,
+    *,
+    sample_weight: float = 1,
 ) -> dict[Incomplete, float]: ...
 @_dispatchable
 def current_flow_betweenness_centrality(

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

@@ -6,9 +6,9 @@ from networkx.utils.backends import _dispatchable
 __all__ = ["subgraph_centrality_exp", "subgraph_centrality", "communicability_betweenness_centrality", "estrada_index"]
 
 @_dispatchable
-def subgraph_centrality_exp(G: Graph[_Node]) -> dict[Incomplete, float]: ...
+def subgraph_centrality_exp(G: Graph[_Node], *, normalized: bool = False) -> dict[Incomplete, float]: ...
 @_dispatchable
-def subgraph_centrality(G: Graph[_Node]) -> dict[Incomplete, float]: ...
+def subgraph_centrality(G: Graph[_Node], *, normalized: bool = False) -> dict[Incomplete, float]: ...
 @_dispatchable
 def communicability_betweenness_centrality(G: Graph[_Node]) -> dict[Incomplete, Incomplete]: ...
 @_dispatchable

stubs/networkx/networkx/algorithms/cluster.pyi

@@ -1,14 +1,24 @@
 from _typeshed import Incomplete
-from collections.abc import Iterable
+from collections.abc import Generator, Iterable
 
-from networkx.classes.graph import Graph, _Node
+from networkx.classes.graph import Graph, _NBunch, _Node
 from networkx.utils.backends import _dispatchable
 
-__all__ = ["triangles", "average_clustering", "clustering", "transitivity", "square_clustering", "generalized_degree"]
+__all__ = [
+    "triangles",
+    "all_triangles",
+    "average_clustering",
+    "clustering",
+    "transitivity",
+    "square_clustering",
+    "generalized_degree",
+]
 
 @_dispatchable
 def triangles(G: Graph[_Node], nodes=None) -> int | dict[Incomplete, int]: ...
 @_dispatchable
+def all_triangles(G: Graph[_Node], nbunch: _NBunch[_Node] = None) -> Generator[tuple[Incomplete, Incomplete, Incomplete]]: ...
+@_dispatchable
 def average_clustering(
     G: Graph[_Node], nodes: Iterable[_Node] | None = None, weight: str | None = None, count_zeros: bool = True
 ) -> float: ...

stubs/networkx/networkx/algorithms/dag.pyi

@@ -21,7 +21,6 @@ __all__ = [
     "dag_longest_path",
     "dag_longest_path_length",
     "dag_to_branching",
-    "compute_v_structures",
 ]
 
 @_dispatchable
@@ -61,5 +60,3 @@ def dag_longest_path(
 def dag_longest_path_length(G: DiGraph[_Node], weight: str | None = "weight", default_weight: int | None = 1) -> int: ...
 @_dispatchable
 def dag_to_branching(G: DiGraph[_Node]) -> DiGraph[_Node]: ...
-@_dispatchable
-def compute_v_structures(G: DiGraph[_Node]) -> Generator[tuple[Incomplete, Incomplete, Incomplete]]: ...

stubs/networkx/networkx/algorithms/distance_regular.pyi

@@ -9,8 +9,8 @@ __all__ = ["is_distance_regular", "is_strongly_regular", "intersection_array", "
 @_dispatchable
 def is_distance_regular(G: Graph[_Node]) -> bool: ...
 @_dispatchable
-def global_parameters(b, c) -> Generator[tuple[Incomplete, Incomplete, Incomplete]]: ...
+def global_parameters(b: list[Incomplete], c: list[Incomplete]) -> Generator[tuple[Incomplete, Incomplete, Incomplete]]: ...
 @_dispatchable
-def intersection_array(G: Graph[_Node]): ...
+def intersection_array(G: Graph[_Node]) -> tuple[list[Incomplete], list[Incomplete]]: ...
 @_dispatchable
 def is_strongly_regular(G: Graph[_Node]) -> bool: ...

stubs/networkx/networkx/algorithms/isomorphism/ismags.pyi

@@ -1,18 +1,31 @@
 from _typeshed import Incomplete
-from collections.abc import Generator
+from collections.abc import Callable, Generator, Hashable, Iterable
+from typing import Any
 
 __all__ = ["ISMAGS"]
 
+def are_all_equal(iterable: Iterable[Any]) -> bool: ...
+def make_partition(
+    items: Iterable[Hashable], test: Callable[[Hashable, Hashable], bool], check: bool = True
+) -> list[set[Incomplete]]: ...
+def node_to_part_ID_dict(partition: Iterable[Iterable[Incomplete]]) -> dict[Incomplete, int]: ...
+def color_degree_by_node(G, n_colors, e_colors): ...
+
+class EdgeLookup:
+    edge_dict: Incomplete
+    def __init__(self, edge_dict) -> None: ...
+    def __getitem__(self, edge): ...
+    def items(self): ...
+
 class ISMAGS:
     graph: Incomplete
     subgraph: Incomplete
-    node_equality: Incomplete
-    edge_equality: Incomplete
 
     def __init__(self, graph, subgraph, node_match=None, edge_match=None, cache=None) -> None: ...
+    def create_aligned_partitions(self, thing_matcher, sg_things, g_things): ...
     def find_isomorphisms(self, symmetry: bool = True) -> Generator[Incomplete, Incomplete, Incomplete]: ...
     def largest_common_subgraph(self, symmetry: bool = True) -> Generator[Incomplete, Incomplete, None]: ...
-    def analyze_symmetry(self, graph, node_partitions, edge_colors): ...
+    def analyze_subgraph_symmetry(self) -> dict[Hashable, set[Hashable]]: ...
     def is_isomorphic(self, symmetry: bool = False) -> bool: ...
     def subgraph_is_isomorphic(self, symmetry: bool = False) -> bool: ...
     def isomorphisms_iter(self, symmetry: bool = True) -> Generator[Incomplete, Incomplete, None]: ...

stubs/networkx/networkx/algorithms/non_randomness.pyi

@@ -4,4 +4,4 @@ from networkx.utils.backends import _dispatchable
 __all__ = ["non_randomness"]
 
 @_dispatchable
-def non_randomness(G: Graph[_Node], k: int | None = None, weight: str | None = "weight"): ...
+def non_randomness(G: Graph[_Node], k: int | None = None, weight: str | None = "weight") -> tuple[float, float]: ...

stubs/networkx/networkx/algorithms/perfect_graph.pyi

@@ -0,0 +1,7 @@
+from networkx.classes.graph import Graph, _Node
+from networkx.utils.backends import _dispatchable
+
+__all__ = ["is_perfect_graph"]
+
+@_dispatchable
+def is_perfect_graph(G: Graph[_Node]) -> bool: ...

stubs/networkx/networkx/algorithms/similarity.pyi

@@ -12,6 +12,7 @@ __all__ = [
     "optimize_edit_paths",
     "simrank_similarity",
     "panther_similarity",
+    "panther_vector_similarity",
     "generate_random_paths",
 ]
 
@@ -93,10 +94,25 @@ def panther_similarity(
     path_length: int = 5,
     c: float = 0.5,
     delta: float = 0.1,
-    eps=None,
+    eps: float | None = None,
     weight: str | None = "weight",
+    seed: int | RandomState | None = None,
 ) -> dict[bytes, bytes]: ...
 @_dispatchable
+def panther_vector_similarity(
+    G: Graph[_Node],
+    source: _Node,
+    *,
+    D: int = 10,
+    k: int = 5,
+    path_length: int = 5,
+    c: float = 0.5,
+    delta: float = 0.1,
+    eps: float | None = None,
+    weight: str | None = "weight",
+    seed: int | RandomState | None = None,
+) -> dict[Incomplete, float]: ...
+@_dispatchable
 def generate_random_paths(
     G: Graph[_Node],
     sample_size: int,

stubs/networkx/networkx/algorithms/threshold.pyi

@@ -37,4 +37,3 @@ def eigenvalues(creation_sequence): ...
 def random_threshold_sequence(n, p, seed: int | RandomState | None = None): ...
 def right_d_threshold_sequence(n: int, m: int) -> list[str]: ...
 def left_d_threshold_sequence(n: int, m: int) -> list[str]: ...
-def swap_d(cs, p_split=1.0, p_combine=1.0, seed: int | RandomState | None = None): ...

stubs/networkx/networkx/algorithms/tree/__init__.pyi

@@ -1,6 +1,7 @@
 from .branchings import *
 from .coding import *
 from .decomposition import *
+from .distance_measures import *
 from .mst import *
 from .operations import *
 from .recognition import *

stubs/networkx/networkx/algorithms/tree/distance_measures.pyi

@@ -0,0 +1,10 @@
+from _typeshed import Incomplete
+
+from networkx.classes.graph import Graph, _Node
+from networkx.utils.backends import _dispatchable
+
+__all__ = ["center", "centroid"]
+
+def center(G: Graph[_Node]) -> list[Incomplete]: ...
+@_dispatchable
+def centroid(G: Graph[_Node]) -> list[Incomplete]: ...

stubs/networkx/networkx/algorithms/wiener.pyi

@@ -1,7 +1,7 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
-__all__ = ["wiener_index", "schultz_index", "gutman_index"]
+__all__ = ["wiener_index", "schultz_index", "gutman_index", "hyper_wiener_index"]
 
 @_dispatchable
 def wiener_index(G: Graph[_Node], weight: str | None = None) -> float: ...
@@ -9,3 +9,5 @@ def wiener_index(G: Graph[_Node], weight: str | None = None) -> float: ...
 def schultz_index(G: Graph[_Node], weight=None) -> float: ...
 @_dispatchable
 def gutman_index(G: Graph[_Node], weight=None) -> float: ...
+@_dispatchable
+def hyper_wiener_index(G: Graph[_Node], weight=None) -> float: ...

stubs/networkx/networkx/classes/function.pyi

@@ -1,5 +1,5 @@
 from _typeshed import Incomplete, SupportsItems, SupportsKeysAndGetItem, Unused
-from collections.abc import Generator, Hashable, Iterable, Iterator
+from collections.abc import Callable, Generator, Hashable, Iterable, Iterator
 from typing import Literal, TypeVar, overload
 
 from networkx import _dispatchable
@@ -48,6 +48,7 @@ __all__ = [
     "number_of_selfloops",
     "path_weight",
     "is_path",
+    "describe",
 ]
 
 _U = TypeVar("_U")
@@ -171,3 +172,4 @@ def selfloop_edges(
 def number_of_selfloops(G: Graph[Hashable]) -> int: ...
 def is_path(G: Graph[_Node], path: Iterable[Incomplete]) -> bool: ...
 def path_weight(G: Graph[_Node], path, weight) -> int: ...
+def describe(G: Graph[_Node], describe_hook: Callable[[Graph[_Node]], dict[str, Incomplete]] | None = None) -> None: ...

stubs/networkx/networkx/classes/graph.pyi

@@ -41,6 +41,9 @@ class Graph(Collection[_Node]):
 
     def to_directed_class(self) -> type[DiGraph[_Node]]: ...
     def to_undirected_class(self) -> type[Graph[_Node]]: ...
+    # @_dispatchable adds `backend` argument, but this decorated is unsupported constructor type here
+    # and __init__() ignores this argument
+    def __new__(cls, incoming_graph_data: _Data[_Node] | None = None, *, backend=None, **attr: Any) -> Self: ...
     def __init__(self, incoming_graph_data: _Data[_Node] | None = None, **attr: Any) -> None: ...  # attr: key=value pairs
     @cached_property
     def adj(self) -> AdjacencyView[_Node, _Node, dict[str, Any]]: ...

stubs/networkx/networkx/classes/multigraph.pyi

@@ -1,7 +1,7 @@
 from collections.abc import Hashable
 from functools import cached_property
 from typing import Any, ClassVar, overload
-from typing_extensions import TypeAlias, TypeVar
+from typing_extensions import Self, TypeAlias, TypeVar
 
 from networkx.classes.coreviews import MultiAdjacencyView
 from networkx.classes.graph import Graph, _MapFactory, _Node
@@ -22,6 +22,9 @@ class MultiGraph(Graph[_Node]):
     edge_key_dict_factory: ClassVar[_MapFactory]
     def to_directed_class(self) -> type[MultiDiGraph[_Node]]: ...
     def to_undirected_class(self) -> type[MultiGraph[_Node]]: ...
+    # @_dispatchable adds `backend` argument, but this decorated is unsupported constructor type here
+    # and __init__() ignores this argument
+    def __new__(cls, incoming_graph_data=None, multigraph_input: bool | None = None, *, backend=None, **attr: Any) -> Self: ...
     def __init__(self, incoming_graph_data=None, multigraph_input: bool | None = None, **attr: Any) -> None: ...
     @cached_property
     def adj(self) -> MultiAdjacencyView[_Node, _Node, dict[str, Any]]: ...  # data can be any type

stubs/networkx/networkx/drawing/layout.pyi

@@ -141,7 +141,6 @@ def forceatlas2_layout(
     node_mass: Mapping[_Node, float] | None = None,
     node_size: Mapping[_Node, float] | None = None,
     weight: str | None = None,
-    dissuade_hubs: bool = False,
     linlog: bool = False,
     seed: Seed | None = None,
     dim: int = 2,

stubs/networkx/networkx/drawing/nx_pylab.pyi

@@ -110,6 +110,26 @@ def apply_matplotlib_colors(
     vmax: float | None = None,
     nodes: bool = True,
 ) -> None: ...
+
+class CurvedArrowTextBase:
+    arrow: FancyArrowPatch
+    label_pos: float
+    labels_horizontal: bool
+    ax: Axes
+    x: Incomplete
+    y: Incomplete
+    angle: Incomplete
+    def __init__(
+        self,
+        arrow: FancyArrowPatch,
+        *args,
+        label_pos: float = 0.5,
+        labels_horizontal: bool = False,
+        ax: Axes | None = None,
+        **kwargs,
+    ) -> None: ...
+    def draw(self, renderer) -> None: ...
+
 def display(
     G: _G,
     canvas: Axes | None = None,

stubs/networkx/networkx/generators/expanders.pyi

@@ -1,3 +1,5 @@
+from typing_extensions import deprecated
+
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
@@ -6,6 +8,7 @@ __all__ = [
     "chordal_cycle_graph",
     "paley_graph",
     "maybe_regular_expander",
+    "maybe_regular_expander_graph",
     "is_regular_expander",
     "random_regular_expander_graph",
 ]
@@ -17,7 +20,12 @@ def chordal_cycle_graph(p, create_using=None): ...
 @_dispatchable
 def paley_graph(p, create_using=None): ...
 @_dispatchable
-def maybe_regular_expander(n, d, *, create_using=None, max_tries=100, seed=None): ...
+def maybe_regular_expander_graph(n, d, *, create_using=None, max_tries: int = 100, seed=None): ...
+@deprecated(
+    "`maybe_regular_expander` is a deprecated alias for `maybe_regular_expander_graph`. "
+    "Use `maybe_regular_expander_graph` instead."
+)
+def maybe_regular_expander(n, d, *, create_using=None, max_tries: int = 100, seed=None): ...
 @_dispatchable
 def is_regular_expander(G: Graph[_Node], *, epsilon: float = 0) -> bool: ...
 @_dispatchable

stubs/networkx/networkx/generators/random_graphs.pyi

@@ -1,4 +1,5 @@
 from _typeshed import Incomplete
+from typing_extensions import deprecated
 
 from networkx.utils.backends import _dispatchable
 
@@ -20,6 +21,7 @@ __all__ = [
     "extended_barabasi_albert_graph",
     "powerlaw_cluster_graph",
     "random_lobster",
+    "random_lobster_graph",
     "random_shell_graph",
     "random_powerlaw_tree",
     "random_powerlaw_tree_sequence",
@@ -27,40 +29,43 @@ __all__ = [
 ]
 
 @_dispatchable
-def fast_gnp_random_graph(n, p, seed=None, directed: bool = False): ...
+def fast_gnp_random_graph(n, p, seed=None, directed: bool = False, *, create_using=None): ...
 @_dispatchable
-def gnp_random_graph(n, p, seed=None, directed: bool = False): ...
+def gnp_random_graph(n, p, seed=None, directed: bool = False, *, create_using=None): ...
 
 binomial_graph = gnp_random_graph
 erdos_renyi_graph = gnp_random_graph
 
 @_dispatchable
-def dense_gnm_random_graph(n, m, seed=None): ...
+def dense_gnm_random_graph(n, m, seed=None, *, create_using=None): ...
 @_dispatchable
-def gnm_random_graph(n, m, seed=None, directed: bool = False): ...
+def gnm_random_graph(n, m, seed=None, directed: bool = False, *, create_using=None): ...
 @_dispatchable
-def newman_watts_strogatz_graph(n, k, p, seed=None): ...
+def newman_watts_strogatz_graph(n, k, p, seed=None, *, create_using=None): ...
 @_dispatchable
-def watts_strogatz_graph(n, k, p, seed=None): ...
+def watts_strogatz_graph(n, k, p, seed=None, *, create_using=None): ...
 @_dispatchable
-def connected_watts_strogatz_graph(n, k, p, tries: int = 100, seed=None): ...
+def connected_watts_strogatz_graph(n, k, p, tries: int = 100, seed=None, *, create_using=None): ...
 @_dispatchable
-def random_regular_graph(d, n, seed=None): ...
+def random_regular_graph(d, n, seed=None, *, create_using=None): ...
 @_dispatchable
-def barabasi_albert_graph(n, m, seed=None, initial_graph=None) -> Graph[Incomplete]: ...
+def barabasi_albert_graph(n, m, seed=None, initial_graph=None, *, create_using=None) -> Graph[Incomplete]: ...
 @_dispatchable
-def dual_barabasi_albert_graph(n, m1, m2, p, seed=None, initial_graph=None) -> Graph[Incomplete]: ...
+def dual_barabasi_albert_graph(n, m1, m2, p, seed=None, initial_graph=None, *, create_using=None) -> Graph[Incomplete]: ...
 @_dispatchable
-def extended_barabasi_albert_graph(n, m, p, q, seed=None) -> Graph[Incomplete]: ...
+def extended_barabasi_albert_graph(n, m, p, q, seed=None, *, create_using=None) -> Graph[Incomplete]: ...
 @_dispatchable
-def powerlaw_cluster_graph(n, m, p, seed=None): ...
+def powerlaw_cluster_graph(n, m, p, seed=None, *, create_using=None): ...
 @_dispatchable
-def random_lobster(n, p1, p2, seed=None): ...
+def random_lobster_graph(n, p1, p2, seed=None, *, create_using=None): ...
 @_dispatchable
-def random_shell_graph(constructor, seed=None): ...
+@deprecated("`random_lobster` is a deprecated alias for `random_lobster_graph`. Use `random_lobster_graph` instead.")
+def random_lobster(n, p1, p2, seed=None, *, create_using=None): ...
 @_dispatchable
-def random_powerlaw_tree(n, gamma: float = 3, seed=None, tries: int = 100): ...
+def random_shell_graph(constructor, seed=None, *, create_using=None): ...
+@_dispatchable
+def random_powerlaw_tree(n, gamma: float = 3, seed=None, tries: int = 100, *, create_using=None): ...
 @_dispatchable
 def random_powerlaw_tree_sequence(n, gamma: float = 3, seed=None, tries: int = 100): ...
 @_dispatchable
-def random_kernel_graph(n, kernel_integral, kernel_root=None, seed=None): ...
+def random_kernel_graph(n, kernel_integral, kernel_root=None, seed=None, *, create_using=None): ...

stubs/networkx/networkx/generators/small.pyi

@@ -9,6 +9,7 @@ __all__ = [
     "diamond_graph",
     "dodecahedral_graph",
     "frucht_graph",
+    "generalized_petersen_graph",
     "heawood_graph",
     "hoffman_singleton_graph",
     "house_graph",
@@ -63,6 +64,8 @@ def pappus_graph(): ...
 @_dispatchable
 def petersen_graph(create_using=None): ...
 @_dispatchable
+def generalized_petersen_graph(n: int, k: int, *, create_using=None): ...
+@_dispatchable
 def sedgewick_maze_graph(create_using=None): ...
 @_dispatchable
 def tetrahedral_graph(create_using=None): ...

stubs/networkx/networkx/readwrite/json_graph/node_link.pyi

@@ -1,29 +1,8 @@
-from typing import overload
-from typing_extensions import deprecated
-
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 __all__ = ["node_link_data", "node_link_graph"]
 
-@overload
-@deprecated(
-    """\
-The `link` argument is deprecated and will be removed in version `3.6`.
-Use the `edges` keyword instead."""
-)
-def node_link_data(
-    G: Graph[_Node],
-    *,
-    link: str | None,
-    source: str = "source",
-    target: str = "target",
-    name: str = "id",
-    key: str = "key",
-    edges: str | None = None,
-    nodes: str = "nodes",
-): ...
-@overload
 def node_link_data(
     G: Graph[_Node],
     *,
@@ -31,7 +10,7 @@ def node_link_data(
     target: str = "target",
     name: str = "id",
     key: str = "key",
-    edges: str | None = None,
+    edges: str = "edges",
     nodes: str = "nodes",
 ): ...
 @_dispatchable
@@ -45,7 +24,6 @@ def node_link_graph(
     target: str = "target",
     name: str = "id",
     key: str = "key",
-    edges: str | None = None,
+    edges: str = "edges",
     nodes: str = "nodes",
-    link: str | None = None,
 ): ...

stubs/networkx/networkx/utils/misc.pyi

@@ -59,6 +59,6 @@ class PythonRandomInterface:
 
 def create_py_random_state(random_state: _RandomState = None): ...
 def nodes_equal(nodes1, nodes2) -> bool: ...
-def edges_equal(edges1, edges2) -> bool: ...
+def edges_equal(edges1, edges2, *, directed: bool = False) -> bool: ...
 def graphs_equal(graph1, graph2) -> bool: ...
 def _clear_cache(G: Graph[_Node]) -> None: ...

stubs/networkx/networkx/utils/random_sequence.pyi

@@ -1,5 +1,9 @@
+from _typeshed import Incomplete
+from collections.abc import Iterable
+
 __all__ = [
     "powerlaw_sequence",
+    "is_valid_tree_degree_sequence",
     "zipf_rv",
     "cumulative_distribution",
     "discrete_sequence",
@@ -8,6 +12,7 @@ __all__ = [
 ]
 
 def powerlaw_sequence(n, exponent: float = 2.0, seed=None): ...
+def is_valid_tree_degree_sequence(degree_sequence: Iterable[Incomplete]) -> tuple[bool, str]: ...
 def zipf_rv(alpha, xmin: int = 1, seed=None) -> int: ...
 def cumulative_distribution(distribution): ...
 def discrete_sequence(n, distribution=None, cdistribution=None, seed=None): ...