llmeval-env/lib/python3.10/site-packages/networkx/utils/backends.py

"""
NetworkX utilizes a plugin-dispatch architecture, which means we can plug in and
out of backends with minimal code changes. A valid NetworkX backend specifies
`entry points <https://packaging.python.org/en/latest/specifications/entry-points>`_,
named ``networkx.backends`` and an optional ``networkx.backend_info`` when it is
installed (not imported). This allows NetworkX to dispatch (redirect) function calls
to the backend so the execution flows to the designated backend
implementation, similar to how plugging a charger into a socket redirects the
electricity to your phone. This design enhances flexibility and integration, making
NetworkX more adaptable and efficient. 

There are three main ways to use a backend after the package is installed.
You can set environment variables and run the exact same code you run for
NetworkX. You can use a keyword argument ``backend=...`` with the NetworkX
function. Or, you can convert the NetworkX Graph to a backend graph type and
call a NetworkX function supported by that backend. Environment variables
and backend keywords automatically convert your NetworkX Graph to the
backend type. Manually converting it yourself allows you to use that same
backend graph for more than one function call, reducing conversion time.

For example, you can set an environment variable before starting python to request
all dispatchable functions automatically dispatch to the given backend::

    bash> NETWORKX_AUTOMATIC_BACKENDS=cugraph python my_networkx_script.py

or you can specify the backend as a kwarg::

    nx.betweenness_centrality(G, k=10, backend="parallel")

or you can convert the NetworkX Graph object ``G`` into a Graph-like
object specific to the backend and then pass that in the NetworkX function::

    H = nx_parallel.ParallelGraph(G)
    nx.betweenness_centrality(H, k=10)

How it works: You might have seen the ``@nx._dispatchable`` decorator on
many of the NetworkX functions in the codebase. It decorates the function
with code that redirects execution to the function's backend implementation.
The code also manages any ``backend_kwargs`` you provide to the backend
version of the function. The code looks for the environment variable or
a ``backend`` keyword argument and if found, converts the input NetworkX
graph to the backend format before calling the backend's version of the
function. If no environment variable or backend keyword are found, the
dispatching code checks the input graph object for an attribute
called ``__networkx_backend__`` which tells it which backend provides this
graph type. That backend's version of the function is then called.
The backend system relies on Python ``entry_point`` system to signal
NetworkX that a backend is installed (even if not imported yet). Thus no
code needs to be changed between running with NetworkX and running with
a backend to NetworkX. The attribute ``__networkx_backend__`` holds a
string with the name of the ``entry_point``. If none of these options
are being used, the decorator code simply calls the NetworkX function
on the NetworkX graph as usual.

The NetworkX library does not need to know that a backend exists for it
to work. So long as the backend package creates the entry_point, and
provides the correct interface, it will be called when the user requests
it using one of the three approaches described above. Some backends have
been working with the NetworkX developers to ensure smooth operation.
They are the following::

- `graphblas <https://github.com/python-graphblas/graphblas-algorithms>`_
- `cugraph <https://github.com/rapidsai/cugraph/tree/branch-24.04/python/nx-cugraph>`_
- `parallel <https://github.com/networkx/nx-parallel>`_
- ``loopback`` is for testing purposes only and is not a real backend.

Note that the ``backend_name`` is e.g. ``parallel``, the package installed
is ``nx-parallel``, and we use ``nx_parallel`` while importing the package.

Creating a Custom backend
-------------------------

1.  To be a valid backend that is discoverable by NetworkX, your package must
    register an `entry-point <https://packaging.python.org/en/latest/specifications/entry-points/#entry-points>`_
    ``networkx.backends`` in the package's metadata, with a `key pointing to your
    dispatch object <https://packaging.python.org/en/latest/guides/creating-and-discovering-plugins/#using-package-metadata>`_ .
    For example, if you are using ``setuptools`` to manage your backend package,
    you can `add the following to your pyproject.toml file <https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`_::

        [project.entry-points."networkx.backends"]
        backend_name = "your_dispatcher_class"

    You can also add the ``backend_info`` entry-point. It points towards the ``get_info``
    function that returns all the backend information, which is then used to build the
    "Additional Backend Implementation" box at the end of algorithm's documentation
    page (e.g. `nx-cugraph's get_info function <https://github.com/rapidsai/cugraph/blob/branch-24.04/python/nx-cugraph/_nx_cugraph/__init__.py>`_)::

        [project.entry-points."networkx.backend_info"]
        backend_name = "your_get_info_function"

    Note that this would only work if your backend is a trusted backend of NetworkX,
    and is present in the `.circleci/config.yml` and
    `.github/workflows/deploy-docs.yml` files in the NetworkX repository.

2.  The backend must create an ``nx.Graph``-like object which contains an attribute
    ``__networkx_backend__`` with a value of the entry point name::

        class BackendGraph:
            __networkx_backend__ = "backend_name"
            ...


Testing the Custom backend
--------------------------

To test your custom backend, you can run the NetworkX test suite with your backend.
This also ensures that the custom backend is compatible with NetworkX's API.

Testing Environment Setup
~~~~~~~~~~~~~~~~~~~~~~~~~

To enable automatic testing with your custom backend, follow these steps:

1. Set Backend Environment Variables: 
    - ``NETWORKX_TEST_BACKEND`` : Setting this to your registered backend key will let
      the NetworkX's dispatch machinery automatically convert a regular NetworkX
      ``Graph``, ``DiGraph``, ``MultiGraph``, etc. to their backend equivalents, using
      ``your_dispatcher_class.convert_from_nx(G, ...)`` function.
    - ``NETWORKX_FALLBACK_TO_NX`` (default=False) : Setting this variable to `True` will
      instruct tests to use a NetworkX ``Graph`` for algorithms not implemented by your
      custom backend. Setting this to `False` will only run the tests for algorithms
      implemented by your custom backend and tests for other algorithms will ``xfail``.

2. Defining ``convert_from_nx`` and ``convert_to_nx`` methods:
    The arguments to ``convert_from_nx`` are:

    - ``G`` : NetworkX Graph
    - ``edge_attrs`` : dict, optional
        Dictionary mapping edge attributes to default values if missing in ``G``.
        If None, then no edge attributes will be converted and default may be 1.
    - ``node_attrs``: dict, optional
        Dictionary mapping node attributes to default values if missing in ``G``.
        If None, then no node attributes will be converted.
    - ``preserve_edge_attrs`` : bool
        Whether to preserve all edge attributes.
    - ``preserve_node_attrs`` : bool
        Whether to preserve all node attributes.
    - ``preserve_graph_attrs`` : bool
        Whether to preserve all graph attributes.
    - ``preserve_all_attrs`` : bool
        Whether to preserve all graph, node, and edge attributes.
    - ``name`` : str
        The name of the algorithm.
    - ``graph_name`` : str
        The name of the graph argument being converted.

Running Tests
~~~~~~~~~~~~~

You can invoke NetworkX tests for your custom backend with the following commands::

    NETWORKX_TEST_BACKEND=<backend_name>
    NETWORKX_FALLBACK_TO_NX=True # or False
    pytest --pyargs networkx

Conversions while running tests :

- Convert NetworkX graphs using ``<your_dispatcher_class>.convert_from_nx(G, ...)`` into
  the backend graph.
- Pass the backend graph objects to the backend implementation of the algorithm.
- Convert the result back to a form expected by NetworkX tests using 
  ``<your_dispatcher_class>.convert_to_nx(result, ...)``.

Notes
~~~~~

-   Dispatchable algorithms that are not implemented by the backend
    will cause a ``pytest.xfail``, giving some indication that not all
    tests are running, while avoiding causing an explicit failure.

-   If a backend only partially implements some algorithms, it can define
    a ``can_run(name, args, kwargs)`` function that returns True or False
    indicating whether it can run the algorithm with the given arguments.
    It may also return a string indicating why the algorithm can't be run;
    this string may be used in the future to give helpful info to the user.

-   A backend may also define ``should_run(name, args, kwargs)`` that is similar
    to ``can_run``, but answers whether the backend *should* be run (converting
    if necessary). Like ``can_run``, it receives the original arguments so it
    can decide whether it should be run by inspecting the arguments. ``can_run``
    runs before ``should_run``, so ``should_run`` may assume ``can_run`` is True.
    If not implemented by the backend, ``can_run`` and ``should_run`` are
    assumed to always return True if the backend implements the algorithm.

-   A special ``on_start_tests(items)`` function may be defined by the backend.
    It will be called with the list of NetworkX tests discovered. Each item
    is a test object that can be marked as xfail if the backend does not support
    the test using ``item.add_marker(pytest.mark.xfail(reason=...))``.

-   A backend graph instance may have a ``G.__networkx_cache__`` dict to enable
    caching, and care should be taken to clear the cache when appropriate.
"""

import inspect
import itertools
import os
import warnings
from functools import partial
from importlib.metadata import entry_points

import networkx as nx

from .decorators import argmap

__all__ = ["_dispatchable"]


def _do_nothing():
    """This does nothing at all, yet it helps turn `_dispatchable` into functions."""


def _get_backends(group, *, load_and_call=False):
    """
    Retrieve NetworkX ``backends`` and ``backend_info`` from the entry points.

    Parameters
    -----------
    group : str
        The entry_point to be retrieved.
    load_and_call : bool, optional
        If True, load and call the backend. Defaults to False.

    Returns
    --------
    dict
        A dictionary mapping backend names to their respective backend objects.

    Notes
    ------
    If a backend is defined more than once, a warning is issued.
    The `nx-loopback` backend is removed if it exists, as it is only available during testing.
    A warning is displayed if an error occurs while loading a backend.
    """
    items = entry_points(group=group)
    rv = {}
    for ep in items:
        if ep.name in rv:
            warnings.warn(
                f"networkx backend defined more than once: {ep.name}",
                RuntimeWarning,
                stacklevel=2,
            )
        elif load_and_call:
            try:
                rv[ep.name] = ep.load()()
            except Exception as exc:
                warnings.warn(
                    f"Error encountered when loading info for backend {ep.name}: {exc}",
                    RuntimeWarning,
                    stacklevel=2,
                )
        else:
            rv[ep.name] = ep
    rv.pop("nx-loopback", None)
    return rv


backends = _get_backends("networkx.backends")
backend_info = _get_backends("networkx.backend_info", load_and_call=True)

# We must import from config after defining `backends` above
from .configs import Config, config

# Get default configuration from environment variables at import time
config.backend_priority = [
    x.strip()
    for x in os.environ.get(
        "NETWORKX_BACKEND_PRIORITY",
        os.environ.get("NETWORKX_AUTOMATIC_BACKENDS", ""),
    ).split(",")
    if x.strip()
]
# Initialize default configuration for backends
config.backends = Config(
    **{
        backend: (
            cfg if isinstance(cfg := info["default_config"], Config) else Config(**cfg)
        )
        if "default_config" in info
        else Config()
        for backend, info in backend_info.items()
    }
)
type(config.backends).__doc__ = "All installed NetworkX backends and their configs."

# Load and cache backends on-demand
_loaded_backends = {}  # type: ignore[var-annotated]


def _always_run(name, args, kwargs):
    return True


def _load_backend(backend_name):
    if backend_name in _loaded_backends:
        return _loaded_backends[backend_name]
    rv = _loaded_backends[backend_name] = backends[backend_name].load()
    if not hasattr(rv, "can_run"):
        rv.can_run = _always_run
    if not hasattr(rv, "should_run"):
        rv.should_run = _always_run
    return rv


_registered_algorithms = {}


class _dispatchable:
    """Allow any of the following decorator forms:
    - @_dispatchable
    - @_dispatchable()
    - @_dispatchable(name="override_name")
    - @_dispatchable(graphs="graph")
    - @_dispatchable(edge_attrs="weight")
    - @_dispatchable(graphs={"G": 0, "H": 1}, edge_attrs={"weight": "default"})

    These class attributes are currently used to allow backends to run networkx tests.
    For example: `PYTHONPATH=. pytest --backend graphblas --fallback-to-nx`
    Future work: add configuration to control these.
    """

    _is_testing = False
    _fallback_to_nx = (
        os.environ.get("NETWORKX_FALLBACK_TO_NX", "true").strip().lower() == "true"
    )

    def __new__(
        cls,
        func=None,
        *,
        name=None,
        graphs="G",
        edge_attrs=None,
        node_attrs=None,
        preserve_edge_attrs=False,
        preserve_node_attrs=False,
        preserve_graph_attrs=False,
        preserve_all_attrs=False,
        mutates_input=False,
        returns_graph=False,
    ):
        """A decorator that makes certain input graph types dispatch to ``func``'s
        backend implementation.

        Usage can be any of the following decorator forms:
        - @_dispatchable
        - @_dispatchable()
        - @_dispatchable(name="override_name")
        - @_dispatchable(graphs="graph_var_name")
        - @_dispatchable(edge_attrs="weight")
        - @_dispatchable(graphs={"G": 0, "H": 1}, edge_attrs={"weight": "default"})
        with 0 and 1 giving the position in the signature function for graph objects.
        When edge_attrs is a dict, keys are keyword names and values are defaults.

        The class attributes are used to allow backends to run networkx tests.
        For example: `PYTHONPATH=. pytest --backend graphblas --fallback-to-nx`
        Future work: add configuration to control these.

        Parameters
        ----------
        func : callable, optional
            The function to be decorated. If ``func`` is not provided, returns a
            partial object that can be used to decorate a function later. If ``func``
            is provided, returns a new callable object that dispatches to a backend
            algorithm based on input graph types.

        name : str, optional
            The name of the algorithm to use for dispatching. If not provided,
            the name of ``func`` will be used. ``name`` is useful to avoid name
            conflicts, as all dispatched algorithms live in a single namespace.
            For example, ``tournament.is_strongly_connected`` had a name conflict
            with the standard ``nx.is_strongly_connected``, so we used
            ``@_dispatchable(name="tournament_is_strongly_connected")``.

        graphs : str or dict or None, default "G"
            If a string, the parameter name of the graph, which must be the first
            argument of the wrapped function. If more than one graph is required
            for the algorithm (or if the graph is not the first argument), provide
            a dict of parameter name to argument position for each graph argument.
            For example, ``@_dispatchable(graphs={"G": 0, "auxiliary?": 4})``
            indicates the 0th parameter ``G`` of the function is a required graph,
            and the 4th parameter ``auxiliary`` is an optional graph.
            To indicate an argument is a list of graphs, do e.g. ``"[graphs]"``.
            Use ``graphs=None`` if *no* arguments are NetworkX graphs such as for
            graph generators, readers, and conversion functions.

        edge_attrs : str or dict, optional
            ``edge_attrs`` holds information about edge attribute arguments
            and default values for those edge attributes.
            If a string, ``edge_attrs`` holds the function argument name that
            indicates a single edge attribute to include in the converted graph.
            The default value for this attribute is 1. To indicate that an argument
            is a list of attributes (all with default value 1), use e.g. ``"[attrs]"``.
            If a dict, ``edge_attrs`` holds a dict keyed by argument names, with
            values that are either the default value or, if a string, the argument
            name that indicates the default value.

        node_attrs : str or dict, optional
            Like ``edge_attrs``, but for node attributes.

        preserve_edge_attrs : bool or str or dict, optional
            For bool, whether to preserve all edge attributes.
            For str, the parameter name that may indicate (with ``True`` or a
            callable argument) whether all edge attributes should be preserved
            when converting.
            For dict of ``{graph_name: {attr: default}}``, indicate pre-determined
            edge attributes (and defaults) to preserve for input graphs.

        preserve_node_attrs : bool or str or dict, optional
            Like ``preserve_edge_attrs``, but for node attributes.

        preserve_graph_attrs : bool or set
            For bool, whether to preserve all graph attributes.
            For set, which input graph arguments to preserve graph attributes.

        preserve_all_attrs : bool
            Whether to preserve all edge, node and graph attributes.
            This overrides all the other preserve_*_attrs.

        mutates_input : bool or dict, default False
            For bool, whether the functions mutates an input graph argument.
            For dict of ``{arg_name: arg_pos}``, arguments that indicates whether an
            input graph will be mutated, and ``arg_name`` may begin with ``"not "``
            to negate the logic (for example, this is used by ``copy=`` arguments).
            By default, dispatching doesn't convert input graphs to a different
            backend for functions that mutate input graphs.

        returns_graph : bool, default False
            Whether the function can return or yield a graph object. By default,
            dispatching doesn't convert input graphs to a different backend for
            functions that return graphs.
        """
        if func is None:
            return partial(
                _dispatchable,
                name=name,
                graphs=graphs,
                edge_attrs=edge_attrs,
                node_attrs=node_attrs,
                preserve_edge_attrs=preserve_edge_attrs,
                preserve_node_attrs=preserve_node_attrs,
                preserve_graph_attrs=preserve_graph_attrs,
                preserve_all_attrs=preserve_all_attrs,
                mutates_input=mutates_input,
                returns_graph=returns_graph,
            )
        if isinstance(func, str):
            raise TypeError("'name' and 'graphs' must be passed by keyword") from None
        # If name not provided, use the name of the function
        if name is None:
            name = func.__name__

        self = object.__new__(cls)

        # standard function-wrapping stuff
        # __annotations__ not used
        self.__name__ = func.__name__
        # self.__doc__ = func.__doc__  # __doc__ handled as cached property
        self.__defaults__ = func.__defaults__
        # We "magically" add `backend=` keyword argument to allow backend to be specified
        if func.__kwdefaults__:
            self.__kwdefaults__ = {**func.__kwdefaults__, "backend": None}
        else:
            self.__kwdefaults__ = {"backend": None}
        self.__module__ = func.__module__
        self.__qualname__ = func.__qualname__
        self.__dict__.update(func.__dict__)
        self.__wrapped__ = func

        # Supplement docstring with backend info; compute and cache when needed
        self._orig_doc = func.__doc__
        self._cached_doc = None

        self.orig_func = func
        self.name = name
        self.edge_attrs = edge_attrs
        self.node_attrs = node_attrs
        self.preserve_edge_attrs = preserve_edge_attrs or preserve_all_attrs
        self.preserve_node_attrs = preserve_node_attrs or preserve_all_attrs
        self.preserve_graph_attrs = preserve_graph_attrs or preserve_all_attrs
        self.mutates_input = mutates_input
        # Keep `returns_graph` private for now, b/c we may extend info on return types
        self._returns_graph = returns_graph

        if edge_attrs is not None and not isinstance(edge_attrs, str | dict):
            raise TypeError(
                f"Bad type for edge_attrs: {type(edge_attrs)}. Expected str or dict."
            ) from None
        if node_attrs is not None and not isinstance(node_attrs, str | dict):
            raise TypeError(
                f"Bad type for node_attrs: {type(node_attrs)}. Expected str or dict."
            ) from None
        if not isinstance(self.preserve_edge_attrs, bool | str | dict):
            raise TypeError(
                f"Bad type for preserve_edge_attrs: {type(self.preserve_edge_attrs)}."
                " Expected bool, str, or dict."
            ) from None
        if not isinstance(self.preserve_node_attrs, bool | str | dict):
            raise TypeError(
                f"Bad type for preserve_node_attrs: {type(self.preserve_node_attrs)}."
                " Expected bool, str, or dict."
            ) from None
        if not isinstance(self.preserve_graph_attrs, bool | set):
            raise TypeError(
                f"Bad type for preserve_graph_attrs: {type(self.preserve_graph_attrs)}."
                " Expected bool or set."
            ) from None
        if not isinstance(self.mutates_input, bool | dict):
            raise TypeError(
                f"Bad type for mutates_input: {type(self.mutates_input)}."
                " Expected bool or dict."
            ) from None
        if not isinstance(self._returns_graph, bool):
            raise TypeError(
                f"Bad type for returns_graph: {type(self._returns_graph)}."
                " Expected bool."
            ) from None

        if isinstance(graphs, str):
            graphs = {graphs: 0}
        elif graphs is None:
            pass
        elif not isinstance(graphs, dict):
            raise TypeError(
                f"Bad type for graphs: {type(graphs)}. Expected str or dict."
            ) from None
        elif len(graphs) == 0:
            raise KeyError("'graphs' must contain at least one variable name") from None

        # This dict comprehension is complicated for better performance; equivalent shown below.
        self.optional_graphs = set()
        self.list_graphs = set()
        if graphs is None:
            self.graphs = {}
        else:
            self.graphs = {
                self.optional_graphs.add(val := k[:-1]) or val
                if (last := k[-1]) == "?"
                else self.list_graphs.add(val := k[1:-1]) or val
                if last == "]"
                else k: v
                for k, v in graphs.items()
            }
        # The above is equivalent to:
        # self.optional_graphs = {k[:-1] for k in graphs if k[-1] == "?"}
        # self.list_graphs = {k[1:-1] for k in graphs if k[-1] == "]"}
        # self.graphs = {k[:-1] if k[-1] == "?" else k: v for k, v in graphs.items()}

        # Compute and cache the signature on-demand
        self._sig = None

        # Which backends implement this function?
        self.backends = {
            backend
            for backend, info in backend_info.items()
            if "functions" in info and name in info["functions"]
        }

        if name in _registered_algorithms:
            raise KeyError(
                f"Algorithm already exists in dispatch registry: {name}"
            ) from None
        # Use the magic of `argmap` to turn `self` into a function. This does result
        # in small additional overhead compared to calling `_dispatchable` directly,
        # but `argmap` has the magical property that it can stack with other `argmap`
        # decorators "for free". Being a function is better for REPRs and type-checkers.
        self = argmap(_do_nothing)(self)
        _registered_algorithms[name] = self
        return self

    @property
    def __doc__(self):
        """If the cached documentation exists, it is returned.
        Otherwise, the documentation is generated using _make_doc() method,
        cached, and then returned."""

        if (rv := self._cached_doc) is not None:
            return rv
        rv = self._cached_doc = self._make_doc()
        return rv

    @__doc__.setter
    def __doc__(self, val):
        """Sets the original documentation to the given value and resets the
        cached documentation."""

        self._orig_doc = val
        self._cached_doc = None

    @property
    def __signature__(self):
        """Return the signature of the original function, with the addition of
        the `backend` and `backend_kwargs` parameters."""

        if self._sig is None:
            sig = inspect.signature(self.orig_func)
            # `backend` is now a reserved argument used by dispatching.
            # assert "backend" not in sig.parameters
            if not any(
                p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values()
            ):
                sig = sig.replace(
                    parameters=[
                        *sig.parameters.values(),
                        inspect.Parameter(
                            "backend", inspect.Parameter.KEYWORD_ONLY, default=None
                        ),
                        inspect.Parameter(
                            "backend_kwargs", inspect.Parameter.VAR_KEYWORD
                        ),
                    ]
                )
            else:
                *parameters, var_keyword = sig.parameters.values()
                sig = sig.replace(
                    parameters=[
                        *parameters,
                        inspect.Parameter(
                            "backend", inspect.Parameter.KEYWORD_ONLY, default=None
                        ),
                        var_keyword,
                    ]
                )
            self._sig = sig
        return self._sig

    def __call__(self, /, *args, backend=None, **kwargs):
        """Returns the result of the original function, or the backend function if
        the backend is specified and that backend implements `func`."""

        if not backends:
            # Fast path if no backends are installed
            return self.orig_func(*args, **kwargs)

        # Use `backend_name` in this function instead of `backend`
        backend_name = backend
        if backend_name is not None and backend_name not in backends:
            raise ImportError(f"Unable to load backend: {backend_name}")

        graphs_resolved = {}
        for gname, pos in self.graphs.items():
            if pos < len(args):
                if gname in kwargs:
                    raise TypeError(f"{self.name}() got multiple values for {gname!r}")
                val = args[pos]
            elif gname in kwargs:
                val = kwargs[gname]
            elif gname not in self.optional_graphs:
                raise TypeError(
                    f"{self.name}() missing required graph argument: {gname}"
                )
            else:
                continue
            if val is None:
                if gname not in self.optional_graphs:
                    raise TypeError(
                        f"{self.name}() required graph argument {gname!r} is None; must be a graph"
                    )
            else:
                graphs_resolved[gname] = val

        # Alternative to the above that does not check duplicated args or missing required graphs.
        # graphs_resolved = {
        #     val
        #     for gname, pos in self.graphs.items()
        #     if (val := args[pos] if pos < len(args) else kwargs.get(gname)) is not None
        # }

        # Check if any graph comes from a backend
        if self.list_graphs:
            # Make sure we don't lose values by consuming an iterator
            args = list(args)
            for gname in self.list_graphs & graphs_resolved.keys():
                val = list(graphs_resolved[gname])
                graphs_resolved[gname] = val
                if gname in kwargs:
                    kwargs[gname] = val
                else:
                    args[self.graphs[gname]] = val

            has_backends = any(
                hasattr(g, "__networkx_backend__")
                if gname not in self.list_graphs
                else any(hasattr(g2, "__networkx_backend__") for g2 in g)
                for gname, g in graphs_resolved.items()
            )
            if has_backends:
                graph_backend_names = {
                    getattr(g, "__networkx_backend__", "networkx")
                    for gname, g in graphs_resolved.items()
                    if gname not in self.list_graphs
                }
                for gname in self.list_graphs & graphs_resolved.keys():
                    graph_backend_names.update(
                        getattr(g, "__networkx_backend__", "networkx")
                        for g in graphs_resolved[gname]
                    )
        else:
            has_backends = any(
                hasattr(g, "__networkx_backend__") for g in graphs_resolved.values()
            )
            if has_backends:
                graph_backend_names = {
                    getattr(g, "__networkx_backend__", "networkx")
                    for g in graphs_resolved.values()
                }

        backend_priority = config.backend_priority
        if self._is_testing and backend_priority and backend_name is None:
            # Special path if we are running networkx tests with a backend.
            # This even runs for (and handles) functions that mutate input graphs.
            return self._convert_and_call_for_tests(
                backend_priority[0],
                args,
                kwargs,
                fallback_to_nx=self._fallback_to_nx,
            )

        if has_backends:
            # Dispatchable graphs found! Dispatch to backend function.
            # We don't handle calls with different backend graphs yet,
            # but we may be able to convert additional networkx graphs.
            backend_names = graph_backend_names - {"networkx"}
            if len(backend_names) != 1:
                # Future work: convert between backends and run if multiple backends found
                raise TypeError(
                    f"{self.name}() graphs must all be from the same backend, found {backend_names}"
                )
            [graph_backend_name] = backend_names
            if backend_name is not None and backend_name != graph_backend_name:
                # Future work: convert between backends to `backend_name` backend
                raise TypeError(
                    f"{self.name}() is unable to convert graph from backend {graph_backend_name!r} "
                    f"to the specified backend {backend_name!r}."
                )
            if graph_backend_name not in backends:
                raise ImportError(f"Unable to load backend: {graph_backend_name}")
            if (
                "networkx" in graph_backend_names
                and graph_backend_name not in backend_priority
            ):
                # Not configured to convert networkx graphs to this backend
                raise TypeError(
                    f"Unable to convert inputs and run {self.name}. "
                    f"{self.name}() has networkx and {graph_backend_name} graphs, but NetworkX is not "
                    f"configured to automatically convert graphs from networkx to {graph_backend_name}."
                )
            backend = _load_backend(graph_backend_name)
            if hasattr(backend, self.name):
                if "networkx" in graph_backend_names:
                    # We need to convert networkx graphs to backend graphs.
                    # There is currently no need to check `self.mutates_input` here.
                    return self._convert_and_call(
                        graph_backend_name,
                        args,
                        kwargs,
                        fallback_to_nx=self._fallback_to_nx,
                    )
                # All graphs are backend graphs--no need to convert!
                return getattr(backend, self.name)(*args, **kwargs)
            # Future work: try to convert and run with other backends in backend_priority
            raise nx.NetworkXNotImplemented(
                f"'{self.name}' not implemented by {graph_backend_name}"
            )

        # If backend was explicitly given by the user, so we need to use it no matter what
        if backend_name is not None:
            return self._convert_and_call(
                backend_name, args, kwargs, fallback_to_nx=False
            )

        # Only networkx graphs; try to convert and run with a backend with automatic
        # conversion, but don't do this by default for graph generators or loaders,
        # or if the functions mutates an input graph or returns a graph.
        # Only convert and run if `backend.should_run(...)` returns True.
        if (
            not self._returns_graph
            and (
                not self.mutates_input
                or isinstance(self.mutates_input, dict)
                # If `mutates_input` begins with "not ", then assume the argument is boolean,
                # otherwise treat it as a node or edge attribute if it's not None.
                and any(
                    not (
                        args[arg_pos]
                        if len(args) > arg_pos
                        else kwargs.get(arg_name[4:], True)
                    )
                    if arg_name.startswith("not ")
                    else (
                        args[arg_pos] if len(args) > arg_pos else kwargs.get(arg_name)
                    )
                    is not None
                    for arg_name, arg_pos in self.mutates_input.items()
                )
            )
        ):
            # Should we warn or log if we don't convert b/c the input will be mutated?
            for backend_name in backend_priority:
                if self._should_backend_run(backend_name, *args, **kwargs):
                    return self._convert_and_call(
                        backend_name,
                        args,
                        kwargs,
                        fallback_to_nx=self._fallback_to_nx,
                    )
        # Default: run with networkx on networkx inputs
        return self.orig_func(*args, **kwargs)

    def _can_backend_run(self, backend_name, /, *args, **kwargs):
        """Can the specified backend run this algorithm with these arguments?"""
        backend = _load_backend(backend_name)
        # `backend.can_run` and `backend.should_run` may return strings that describe
        # why they can't or shouldn't be run. We plan to use the strings in the future.
        return (
            hasattr(backend, self.name)
            and (can_run := backend.can_run(self.name, args, kwargs))
            and not isinstance(can_run, str)
        )

    def _should_backend_run(self, backend_name, /, *args, **kwargs):
        """Can/should the specified backend run this algorithm with these arguments?"""
        backend = _load_backend(backend_name)
        # `backend.can_run` and `backend.should_run` may return strings that describe
        # why they can't or shouldn't be run. We plan to use the strings in the future.
        return (
            hasattr(backend, self.name)
            and (can_run := backend.can_run(self.name, args, kwargs))
            and not isinstance(can_run, str)
            and (should_run := backend.should_run(self.name, args, kwargs))
            and not isinstance(should_run, str)
        )

    def _convert_arguments(self, backend_name, args, kwargs, *, use_cache):
        """Convert graph arguments to the specified backend.

        Returns
        -------
        args tuple and kwargs dict
        """
        bound = self.__signature__.bind(*args, **kwargs)
        bound.apply_defaults()
        if not self.graphs:
            bound_kwargs = bound.kwargs
            del bound_kwargs["backend"]
            return bound.args, bound_kwargs
        # Convert graphs into backend graph-like object
        # Include the edge and/or node labels if provided to the algorithm
        preserve_edge_attrs = self.preserve_edge_attrs
        edge_attrs = self.edge_attrs
        if preserve_edge_attrs is False:
            # e.g. `preserve_edge_attrs=False`
            pass
        elif preserve_edge_attrs is True:
            # e.g. `preserve_edge_attrs=True`
            edge_attrs = None
        elif isinstance(preserve_edge_attrs, str):
            if bound.arguments[preserve_edge_attrs] is True or callable(
                bound.arguments[preserve_edge_attrs]
            ):
                # e.g. `preserve_edge_attrs="attr"` and `func(attr=True)`
                # e.g. `preserve_edge_attrs="attr"` and `func(attr=myfunc)`
                preserve_edge_attrs = True
                edge_attrs = None
            elif bound.arguments[preserve_edge_attrs] is False and (
                isinstance(edge_attrs, str)
                and edge_attrs == preserve_edge_attrs
                or isinstance(edge_attrs, dict)
                and preserve_edge_attrs in edge_attrs
            ):
                # e.g. `preserve_edge_attrs="attr"` and `func(attr=False)`
                # Treat `False` argument as meaning "preserve_edge_data=False"
                # and not `False` as the edge attribute to use.
                preserve_edge_attrs = False
                edge_attrs = None
            else:
                # e.g. `preserve_edge_attrs="attr"` and `func(attr="weight")`
                preserve_edge_attrs = False
        # Else: e.g. `preserve_edge_attrs={"G": {"weight": 1}}`

        if edge_attrs is None:
            # May have been set to None above b/c all attributes are preserved
            pass
        elif isinstance(edge_attrs, str):
            if edge_attrs[0] == "[":
                # e.g. `edge_attrs="[edge_attributes]"` (argument of list of attributes)
                # e.g. `func(edge_attributes=["foo", "bar"])`
                edge_attrs = {
                    edge_attr: 1 for edge_attr in bound.arguments[edge_attrs[1:-1]]
                }
            elif callable(bound.arguments[edge_attrs]):
                # e.g. `edge_attrs="weight"` and `func(weight=myfunc)`
                preserve_edge_attrs = True
                edge_attrs = None
            elif bound.arguments[edge_attrs] is not None:
                # e.g. `edge_attrs="weight"` and `func(weight="foo")` (default of 1)
                edge_attrs = {bound.arguments[edge_attrs]: 1}
            elif self.name == "to_numpy_array" and hasattr(
                bound.arguments["dtype"], "names"
            ):
                # Custom handling: attributes may be obtained from `dtype`
                edge_attrs = {
                    edge_attr: 1 for edge_attr in bound.arguments["dtype"].names
                }
            else:
                # e.g. `edge_attrs="weight"` and `func(weight=None)`
                edge_attrs = None
        else:
            # e.g. `edge_attrs={"attr": "default"}` and `func(attr="foo", default=7)`
            # e.g. `edge_attrs={"attr": 0}` and `func(attr="foo")`
            edge_attrs = {
                edge_attr: bound.arguments.get(val, 1) if isinstance(val, str) else val
                for key, val in edge_attrs.items()
                if (edge_attr := bound.arguments[key]) is not None
            }

        preserve_node_attrs = self.preserve_node_attrs
        node_attrs = self.node_attrs
        if preserve_node_attrs is False:
            # e.g. `preserve_node_attrs=False`
            pass
        elif preserve_node_attrs is True:
            # e.g. `preserve_node_attrs=True`
            node_attrs = None
        elif isinstance(preserve_node_attrs, str):
            if bound.arguments[preserve_node_attrs] is True or callable(
                bound.arguments[preserve_node_attrs]
            ):
                # e.g. `preserve_node_attrs="attr"` and `func(attr=True)`
                # e.g. `preserve_node_attrs="attr"` and `func(attr=myfunc)`
                preserve_node_attrs = True
                node_attrs = None
            elif bound.arguments[preserve_node_attrs] is False and (
                isinstance(node_attrs, str)
                and node_attrs == preserve_node_attrs
                or isinstance(node_attrs, dict)
                and preserve_node_attrs in node_attrs
            ):
                # e.g. `preserve_node_attrs="attr"` and `func(attr=False)`
                # Treat `False` argument as meaning "preserve_node_data=False"
                # and not `False` as the node attribute to use. Is this used?
                preserve_node_attrs = False
                node_attrs = None
            else:
                # e.g. `preserve_node_attrs="attr"` and `func(attr="weight")`
                preserve_node_attrs = False
        # Else: e.g. `preserve_node_attrs={"G": {"pos": None}}`

        if node_attrs is None:
            # May have been set to None above b/c all attributes are preserved
            pass
        elif isinstance(node_attrs, str):
            if node_attrs[0] == "[":
                # e.g. `node_attrs="[node_attributes]"` (argument of list of attributes)
                # e.g. `func(node_attributes=["foo", "bar"])`
                node_attrs = {
                    node_attr: None for node_attr in bound.arguments[node_attrs[1:-1]]
                }
            elif callable(bound.arguments[node_attrs]):
                # e.g. `node_attrs="weight"` and `func(weight=myfunc)`
                preserve_node_attrs = True
                node_attrs = None
            elif bound.arguments[node_attrs] is not None:
                # e.g. `node_attrs="weight"` and `func(weight="foo")`
                node_attrs = {bound.arguments[node_attrs]: None}
            else:
                # e.g. `node_attrs="weight"` and `func(weight=None)`
                node_attrs = None
        else:
            # e.g. `node_attrs={"attr": "default"}` and `func(attr="foo", default=7)`
            # e.g. `node_attrs={"attr": 0}` and `func(attr="foo")`
            node_attrs = {
                node_attr: bound.arguments.get(val) if isinstance(val, str) else val
                for key, val in node_attrs.items()
                if (node_attr := bound.arguments[key]) is not None
            }

        preserve_graph_attrs = self.preserve_graph_attrs

        # It should be safe to assume that we either have networkx graphs or backend graphs.
        # Future work: allow conversions between backends.
        for gname in self.graphs:
            if gname in self.list_graphs:
                bound.arguments[gname] = [
                    self._convert_graph(
                        backend_name,
                        g,
                        edge_attrs=edge_attrs,
                        node_attrs=node_attrs,
                        preserve_edge_attrs=preserve_edge_attrs,
                        preserve_node_attrs=preserve_node_attrs,
                        preserve_graph_attrs=preserve_graph_attrs,
                        graph_name=gname,
                        use_cache=use_cache,
                    )
                    if getattr(g, "__networkx_backend__", "networkx") == "networkx"
                    else g
                    for g in bound.arguments[gname]
                ]
            else:
                graph = bound.arguments[gname]
                if graph is None:
                    if gname in self.optional_graphs:
                        continue
                    raise TypeError(
                        f"Missing required graph argument `{gname}` in {self.name} function"
                    )
                if isinstance(preserve_edge_attrs, dict):
                    preserve_edges = False
                    edges = preserve_edge_attrs.get(gname, edge_attrs)
                else:
                    preserve_edges = preserve_edge_attrs
                    edges = edge_attrs
                if isinstance(preserve_node_attrs, dict):
                    preserve_nodes = False
                    nodes = preserve_node_attrs.get(gname, node_attrs)
                else:
                    preserve_nodes = preserve_node_attrs
                    nodes = node_attrs
                if isinstance(preserve_graph_attrs, set):
                    preserve_graph = gname in preserve_graph_attrs
                else:
                    preserve_graph = preserve_graph_attrs
                if getattr(graph, "__networkx_backend__", "networkx") == "networkx":
                    bound.arguments[gname] = self._convert_graph(
                        backend_name,
                        graph,
                        edge_attrs=edges,
                        node_attrs=nodes,
                        preserve_edge_attrs=preserve_edges,
                        preserve_node_attrs=preserve_nodes,
                        preserve_graph_attrs=preserve_graph,
                        graph_name=gname,
                        use_cache=use_cache,
                    )
        bound_kwargs = bound.kwargs
        del bound_kwargs["backend"]
        return bound.args, bound_kwargs

    def _convert_graph(
        self,
        backend_name,
        graph,
        *,
        edge_attrs,
        node_attrs,
        preserve_edge_attrs,
        preserve_node_attrs,
        preserve_graph_attrs,
        graph_name,
        use_cache,
    ):
        if (
            use_cache
            and (nx_cache := getattr(graph, "__networkx_cache__", None)) is not None
        ):
            cache = nx_cache.setdefault("backends", {}).setdefault(backend_name, {})
            # edge_attrs: dict | None
            # node_attrs: dict | None
            # preserve_edge_attrs: bool (False if edge_attrs is not None)
            # preserve_node_attrs: bool (False if node_attrs is not None)
            # preserve_graph_attrs: bool
            key = edge_key, node_key, graph_key = (
                frozenset(edge_attrs.items())
                if edge_attrs is not None
                else preserve_edge_attrs,
                frozenset(node_attrs.items())
                if node_attrs is not None
                else preserve_node_attrs,
                preserve_graph_attrs,
            )
            if cache:
                warning_message = (
                    f"Using cached graph for {backend_name!r} backend in "
                    f"call to {self.name}.\n\nFor the cache to be consistent "
                    "(i.e., correct), the input graph must not have been "
                    "manually mutated since the cached graph was created. "
                    "Examples of manually mutating the graph data structures "
                    "resulting in an inconsistent cache include:\n\n"
                    "    >>> G[u][v][key] = val\n\n"
                    "and\n\n"
                    "    >>> for u, v, d in G.edges(data=True):\n"
                    "    ...     d[key] = val\n\n"
                    "Using methods such as `G.add_edge(u, v, weight=val)` "
                    "will correctly clear the cache to keep it consistent. "
                    "You may also use `G.__networkx_cache__.clear()` to "
                    "manually clear the cache, or set `G.__networkx_cache__` "
                    "to None to disable caching for G. Enable or disable "
                    "caching via `nx.config.cache_converted_graphs` config."
                )
                # Do a simple search for a cached graph with compatible data.
                # For example, if we need a single attribute, then it's okay
                # to use a cached graph that preserved all attributes.
                # This looks for an exact match first.
                for compat_key in itertools.product(
                    (edge_key, True) if edge_key is not True else (True,),
                    (node_key, True) if node_key is not True else (True,),
                    (graph_key, True) if graph_key is not True else (True,),
                ):
                    if (rv := cache.get(compat_key)) is not None:
                        warnings.warn(warning_message)
                        return rv
                if edge_key is not True and node_key is not True:
                    # Iterate over the items in `cache` to see if any are compatible.
                    # For example, if no edge attributes are needed, then a graph
                    # with any edge attribute will suffice. We use the same logic
                    # below (but switched) to clear unnecessary items from the cache.
                    # Use `list(cache.items())` to be thread-safe.
                    for (ekey, nkey, gkey), val in list(cache.items()):
                        if edge_key is False or ekey is True:
                            pass
                        elif (
                            edge_key is True
                            or ekey is False
                            or not edge_key.issubset(ekey)
                        ):
                            continue
                        if node_key is False or nkey is True:
                            pass
                        elif (
                            node_key is True
                            or nkey is False
                            or not node_key.issubset(nkey)
                        ):
                            continue
                        if graph_key and not gkey:
                            continue
                        warnings.warn(warning_message)
                        return val

        backend = _load_backend(backend_name)
        rv = backend.convert_from_nx(
            graph,
            edge_attrs=edge_attrs,
            node_attrs=node_attrs,
            preserve_edge_attrs=preserve_edge_attrs,
            preserve_node_attrs=preserve_node_attrs,
            preserve_graph_attrs=preserve_graph_attrs,
            name=self.name,
            graph_name=graph_name,
        )
        if use_cache and nx_cache is not None:
            # Remove old cached items that are no longer necessary since they
            # are dominated/subsumed/outdated by what was just calculated.
            # This uses the same logic as above, but with keys switched.
            cache[key] = rv  # Set at beginning to be thread-safe
            for cur_key in list(cache):
                if cur_key == key:
                    continue
                ekey, nkey, gkey = cur_key
                if ekey is False or edge_key is True:
                    pass
                elif ekey is True or edge_key is False or not ekey.issubset(edge_key):
                    continue
                if nkey is False or node_key is True:
                    pass
                elif nkey is True or node_key is False or not nkey.issubset(node_key):
                    continue
                if gkey and not graph_key:
                    continue
                cache.pop(cur_key, None)  # Use pop instead of del to be thread-safe

        return rv

    def _convert_and_call(self, backend_name, args, kwargs, *, fallback_to_nx=False):
        """Call this dispatchable function with a backend, converting graphs if necessary."""
        backend = _load_backend(backend_name)
        if not self._can_backend_run(backend_name, *args, **kwargs):
            if fallback_to_nx:
                return self.orig_func(*args, **kwargs)
            msg = f"'{self.name}' not implemented by {backend_name}"
            if hasattr(backend, self.name):
                msg += " with the given arguments"
            raise RuntimeError(msg)

        try:
            converted_args, converted_kwargs = self._convert_arguments(
                backend_name, args, kwargs, use_cache=config.cache_converted_graphs
            )
            result = getattr(backend, self.name)(*converted_args, **converted_kwargs)
        except (NotImplementedError, nx.NetworkXNotImplemented) as exc:
            if fallback_to_nx:
                return self.orig_func(*args, **kwargs)
            raise

        return result

    def _convert_and_call_for_tests(
        self, backend_name, args, kwargs, *, fallback_to_nx=False
    ):
        """Call this dispatchable function with a backend; for use with testing."""
        backend = _load_backend(backend_name)
        if not self._can_backend_run(backend_name, *args, **kwargs):
            if fallback_to_nx or not self.graphs:
                return self.orig_func(*args, **kwargs)

            import pytest

            msg = f"'{self.name}' not implemented by {backend_name}"
            if hasattr(backend, self.name):
                msg += " with the given arguments"
            pytest.xfail(msg)

        from collections.abc import Iterable, Iterator, Mapping
        from copy import copy
        from io import BufferedReader, BytesIO, StringIO, TextIOWrapper
        from itertools import tee
        from random import Random

        import numpy as np
        from numpy.random import Generator, RandomState
        from scipy.sparse import sparray

        # We sometimes compare the backend result to the original result,
        # so we need two sets of arguments. We tee iterators and copy
        # random state so that they may be used twice.
        if not args:
            args1 = args2 = args
        else:
            args1, args2 = zip(
                *(
                    (arg, copy(arg))
                    if isinstance(
                        arg, BytesIO | StringIO | Random | Generator | RandomState
                    )
                    else tee(arg)
                    if isinstance(arg, Iterator)
                    and not isinstance(arg, BufferedReader | TextIOWrapper)
                    else (arg, arg)
                    for arg in args
                )
            )
        if not kwargs:
            kwargs1 = kwargs2 = kwargs
        else:
            kwargs1, kwargs2 = zip(
                *(
                    ((k, v), (k, copy(v)))
                    if isinstance(
                        v, BytesIO | StringIO | Random | Generator | RandomState
                    )
                    else ((k, (teed := tee(v))[0]), (k, teed[1]))
                    if isinstance(v, Iterator)
                    and not isinstance(v, BufferedReader | TextIOWrapper)
                    else ((k, v), (k, v))
                    for k, v in kwargs.items()
                )
            )
            kwargs1 = dict(kwargs1)
            kwargs2 = dict(kwargs2)
        try:
            converted_args, converted_kwargs = self._convert_arguments(
                backend_name, args1, kwargs1, use_cache=False
            )
            result = getattr(backend, self.name)(*converted_args, **converted_kwargs)
        except (NotImplementedError, nx.NetworkXNotImplemented) as exc:
            if fallback_to_nx:
                return self.orig_func(*args2, **kwargs2)
            import pytest

            pytest.xfail(
                exc.args[0] if exc.args else f"{self.name} raised {type(exc).__name__}"
            )
        # Verify that `self._returns_graph` is correct. This compares the return type
        # to the type expected from `self._returns_graph`. This handles tuple and list
        # return types, but *does not* catch functions that yield graphs.
        if (
            self._returns_graph
            != (
                isinstance(result, nx.Graph)
                or hasattr(result, "__networkx_backend__")
                or isinstance(result, tuple | list)
                and any(
                    isinstance(x, nx.Graph) or hasattr(x, "__networkx_backend__")
                    for x in result
                )
            )
            and not (
                # May return Graph or None
                self.name in {"check_planarity", "check_planarity_recursive"}
                and any(x is None for x in result)
            )
            and not (
                # May return Graph or dict
                self.name in {"held_karp_ascent"}
                and any(isinstance(x, dict) for x in result)
            )
            and self.name
            not in {
                # yields graphs
                "all_triads",
                "general_k_edge_subgraphs",
                # yields graphs or arrays
                "nonisomorphic_trees",
            }
        ):
            raise RuntimeError(f"`returns_graph` is incorrect for {self.name}")

        def check_result(val, depth=0):
            if isinstance(val, np.number):
                raise RuntimeError(
                    f"{self.name} returned a numpy scalar {val} ({type(val)}, depth={depth})"
                )
            if isinstance(val, np.ndarray | sparray):
                return
            if isinstance(val, nx.Graph):
                check_result(val._node, depth=depth + 1)
                check_result(val._adj, depth=depth + 1)
                return
            if isinstance(val, Iterator):
                raise NotImplementedError
            if isinstance(val, Iterable) and not isinstance(val, str):
                for x in val:
                    check_result(x, depth=depth + 1)
            if isinstance(val, Mapping):
                for x in val.values():
                    check_result(x, depth=depth + 1)

        def check_iterator(it):
            for val in it:
                try:
                    check_result(val)
                except RuntimeError as exc:
                    raise RuntimeError(
                        f"{self.name} returned a numpy scalar {val} ({type(val)})"
                    ) from exc
                yield val

        if self.name in {"from_edgelist"}:
            # numpy scalars are explicitly given as values in some tests
            pass
        elif isinstance(result, Iterator):
            result = check_iterator(result)
        else:
            try:
                check_result(result)
            except RuntimeError as exc:
                raise RuntimeError(
                    f"{self.name} returned a numpy scalar {result} ({type(result)})"
                ) from exc
            check_result(result)

        if self.name in {
            "edmonds_karp",
            "barycenter",
            "contracted_edge",
            "contracted_nodes",
            "stochastic_graph",
            "relabel_nodes",
            "maximum_branching",
            "incremental_closeness_centrality",
            "minimal_branching",
            "minimum_spanning_arborescence",
            "recursive_simple_cycles",
            "connected_double_edge_swap",
        }:
            # Special-case algorithms that mutate input graphs
            bound = self.__signature__.bind(*converted_args, **converted_kwargs)
            bound.apply_defaults()
            bound2 = self.__signature__.bind(*args2, **kwargs2)
            bound2.apply_defaults()
            if self.name in {
                "minimal_branching",
                "minimum_spanning_arborescence",
                "recursive_simple_cycles",
                "connected_double_edge_swap",
            }:
                G1 = backend.convert_to_nx(bound.arguments["G"])
                G2 = bound2.arguments["G"]
                G2._adj = G1._adj
                nx._clear_cache(G2)
            elif self.name == "edmonds_karp":
                R1 = backend.convert_to_nx(bound.arguments["residual"])
                R2 = bound2.arguments["residual"]
                if R1 is not None and R2 is not None:
                    for k, v in R1.edges.items():
                        R2.edges[k]["flow"] = v["flow"]
                    R2.graph.update(R1.graph)
                    nx._clear_cache(R2)
            elif self.name == "barycenter" and bound.arguments["attr"] is not None:
                G1 = backend.convert_to_nx(bound.arguments["G"])
                G2 = bound2.arguments["G"]
                attr = bound.arguments["attr"]
                for k, v in G1.nodes.items():
                    G2.nodes[k][attr] = v[attr]
                nx._clear_cache(G2)
            elif (
                self.name in {"contracted_nodes", "contracted_edge"}
                and not bound.arguments["copy"]
            ):
                # Edges and nodes changed; node "contraction" and edge "weight" attrs
                G1 = backend.convert_to_nx(bound.arguments["G"])
                G2 = bound2.arguments["G"]
                G2.__dict__.update(G1.__dict__)
                nx._clear_cache(G2)
            elif self.name == "stochastic_graph" and not bound.arguments["copy"]:
                G1 = backend.convert_to_nx(bound.arguments["G"])
                G2 = bound2.arguments["G"]
                for k, v in G1.edges.items():
                    G2.edges[k]["weight"] = v["weight"]
                nx._clear_cache(G2)
            elif (
                self.name == "relabel_nodes"
                and not bound.arguments["copy"]
                or self.name in {"incremental_closeness_centrality"}
            ):
                G1 = backend.convert_to_nx(bound.arguments["G"])
                G2 = bound2.arguments["G"]
                if G1 is G2:
                    return G2
                G2._node.clear()
                G2._node.update(G1._node)
                G2._adj.clear()
                G2._adj.update(G1._adj)
                if hasattr(G1, "_pred") and hasattr(G2, "_pred"):
                    G2._pred.clear()
                    G2._pred.update(G1._pred)
                if hasattr(G1, "_succ") and hasattr(G2, "_succ"):
                    G2._succ.clear()
                    G2._succ.update(G1._succ)
                nx._clear_cache(G2)
                if self.name == "relabel_nodes":
                    return G2
            return backend.convert_to_nx(result)

        converted_result = backend.convert_to_nx(result)
        if isinstance(converted_result, nx.Graph) and self.name not in {
            "boykov_kolmogorov",
            "preflow_push",
            "quotient_graph",
            "shortest_augmenting_path",
            "spectral_graph_forge",
            # We don't handle tempfile.NamedTemporaryFile arguments
            "read_gml",
            "read_graph6",
            "read_sparse6",
            # We don't handle io.BufferedReader or io.TextIOWrapper arguments
            "bipartite_read_edgelist",
            "read_adjlist",
            "read_edgelist",
            "read_graphml",
            "read_multiline_adjlist",
            "read_pajek",
            "from_pydot",
            "pydot_read_dot",
            "agraph_read_dot",
            # graph comparison fails b/c of nan values
            "read_gexf",
        }:
            # For graph return types (e.g. generators), we compare that results are
            # the same between the backend and networkx, then return the original
            # networkx result so the iteration order will be consistent in tests.
            G = self.orig_func(*args2, **kwargs2)
            if not nx.utils.graphs_equal(G, converted_result):
                assert G.number_of_nodes() == converted_result.number_of_nodes()
                assert G.number_of_edges() == converted_result.number_of_edges()
                assert G.graph == converted_result.graph
                assert G.nodes == converted_result.nodes
                assert G.adj == converted_result.adj
                assert type(G) is type(converted_result)
                raise AssertionError("Graphs are not equal")
            return G
        return converted_result

    def _make_doc(self):
        """Generate the backends section at the end for functions having an alternate
        backend implementation(s) using the `backend_info` entry-point."""

        if not self.backends:
            return self._orig_doc
        lines = [
            "Backends",
            "--------",
        ]
        for backend in sorted(self.backends):
            info = backend_info[backend]
            if "short_summary" in info:
                lines.append(f"{backend} : {info['short_summary']}")
            else:
                lines.append(backend)
            if "functions" not in info or self.name not in info["functions"]:
                lines.append("")
                continue

            func_info = info["functions"][self.name]

            # Renaming extra_docstring to additional_docs
            if func_docs := (
                func_info.get("additional_docs") or func_info.get("extra_docstring")
            ):
                lines.extend(
                    f"  {line}" if line else line for line in func_docs.split("\n")
                )
                add_gap = True
            else:
                add_gap = False

            # Renaming extra_parameters to additional_parameters
            if extra_parameters := (
                func_info.get("extra_parameters")
                or func_info.get("additional_parameters")
            ):
                if add_gap:
                    lines.append("")
                lines.append("  Additional parameters:")
                for param in sorted(extra_parameters):
                    lines.append(f"    {param}")
                    if desc := extra_parameters[param]:
                        lines.append(f"      {desc}")
                    lines.append("")
            else:
                lines.append("")

            if func_url := func_info.get("url"):
                lines.append(f"[`Source <{func_url}>`_]")
                lines.append("")

        lines.pop()  # Remove last empty line
        to_add = "\n    ".join(lines)
        return f"{self._orig_doc.rstrip()}\n\n    {to_add}"

    def __reduce__(self):
        """Allow this object to be serialized with pickle.

        This uses the global registry `_registered_algorithms` to deserialize.
        """
        return _restore_dispatchable, (self.name,)


def _restore_dispatchable(name):
    return _registered_algorithms[name]


if os.environ.get("_NETWORKX_BUILDING_DOCS_"):
    # When building docs with Sphinx, use the original function with the
    # dispatched __doc__, b/c Sphinx renders normal Python functions better.
    # This doesn't show e.g. `*, backend=None, **backend_kwargs` in the
    # signatures, which is probably okay. It does allow the docstring to be
    # updated based on the installed backends.
    _orig_dispatchable = _dispatchable

    def _dispatchable(func=None, **kwargs):  # type: ignore[no-redef]
        if func is None:
            return partial(_dispatchable, **kwargs)
        dispatched_func = _orig_dispatchable(func, **kwargs)
        func.__doc__ = dispatched_func.__doc__
        return func

    _dispatchable.__doc__ = _orig_dispatchable.__new__.__doc__  # type: ignore[method-assign,assignment]
    _sig = inspect.signature(_orig_dispatchable.__new__)
    _dispatchable.__signature__ = _sig.replace(  # type: ignore[method-assign,assignment]
        parameters=[v for k, v in _sig.parameters.items() if k != "cls"]
    )