golden hour

/opt/cloudlinux/venv/lib/python3.11/site-packages/astroid/brain

File/Folder	Size	Actions
__init__.py	0 B
__pycache__	-
brain_argparse.py	1.52 KB
brain_attrs.py	2.97 KB
brain_boto3.py	1012 B
brain_builtin_inference.py	33.45 KB
brain_collections.py	4.31 KB
brain_crypt.py	863 B
brain_ctypes.py	2.6 KB
brain_curses.py	3.4 KB
brain_dataclasses.py	21.57 KB
brain_dateutil.py	767 B
brain_fstrings.py	2.41 KB
brain_functools.py	5.85 KB
brain_gi.py	7.37 KB
brain_hashlib.py	2.75 KB
brain_http.py	10.39 KB
brain_hypothesis.py	1.69 KB
brain_io.py	1.49 KB
brain_mechanize.py	2.58 KB
brain_multiprocessing.py	3.14 KB
brain_namedtuple_enum.py	22.19 KB
brain_nose.py	2.27 KB
brain_numpy_core_einsumfunc.py	825 B
brain_numpy_core_fromnumeric.py	732 B
brain_numpy_core_function_base.py	1.27 KB
brain_numpy_core_multiarray.py	4.14 KB
brain_numpy_core_numeric.py	1.59 KB
brain_numpy_core_numerictypes.py	8.35 KB
brain_numpy_core_umath.py	4.78 KB
brain_numpy_ma.py	896 B
brain_numpy_ndarray.py	8.79 KB
brain_numpy_random_mtrand.py	3.36 KB
brain_numpy_utils.py	2.58 KB
brain_pathlib.py	1.51 KB
brain_pkg_resources.py	2.15 KB
brain_pytest.py	2.17 KB
brain_qt.py	2.74 KB
brain_random.py	2.82 KB
brain_re.py	2.8 KB
brain_regex.py	3.28 KB
brain_responses.py	1.82 KB
brain_scipy_signal.py	2.22 KB
brain_signal.py	3.79 KB
brain_six.py	7.42 KB
brain_sqlalchemy.py	1009 B
brain_ssl.py	6.4 KB
brain_subprocess.py	2.93 KB
brain_threading.py	870 B
brain_type.py	2.42 KB
brain_typing.py	13.98 KB
brain_unittest.py	1.12 KB
brain_uuid.py	667 B
helpers.py	908 B

Edit: brain_functools.py

# Licensed under the LGPL: https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html
# For details: https://github.com/PyCQA/astroid/blob/main/LICENSE
# Copyright (c) https://github.com/PyCQA/astroid/blob/main/CONTRIBUTORS.txt

"""Astroid hooks for understanding functools library module."""

from __future__ import annotations

from collections.abc import Iterator
from functools import partial
from itertools import chain

from astroid import BoundMethod, arguments, extract_node, helpers, nodes, objects
from astroid.context import InferenceContext
from astroid.exceptions import InferenceError, UseInferenceDefault
from astroid.inference_tip import inference_tip
from astroid.interpreter import objectmodel
from astroid.manager import AstroidManager
from astroid.nodes.node_classes import AssignName, Attribute, Call, Name
from astroid.nodes.scoped_nodes import FunctionDef
from astroid.util import UninferableBase

LRU_CACHE = "functools.lru_cache"

class LruWrappedModel(objectmodel.FunctionModel):
    """Special attribute model for functions decorated with functools.lru_cache.

The said decorators patches at decoration time some functions onto
    the decorated function.
    """

@property
    def attr___wrapped__(self):
        return self._instance

@property
    def attr_cache_info(self):
        cache_info = extract_node(
            """
        from functools import _CacheInfo
        _CacheInfo(0, 0, 0, 0)
        """
        )

class CacheInfoBoundMethod(BoundMethod):
            def infer_call_result(
                self, caller, context: InferenceContext | None = None
            ):
                yield helpers.safe_infer(cache_info)

return CacheInfoBoundMethod(proxy=self._instance, bound=self._instance)

@property
    def attr_cache_clear(self):
        node = extract_node("""def cache_clear(self): pass""")
        return BoundMethod(proxy=node, bound=self._instance.parent.scope())

def _transform_lru_cache(node, context: InferenceContext | None = None) -> None:
    # TODO: this is not ideal, since the node should be immutable,
    # but due to https://github.com/PyCQA/astroid/issues/354,
    # there's not much we can do now.
    # Replacing the node would work partially, because,
    # in pylint, the old node would still be available, leading
    # to spurious false positives.
    node.special_attributes = LruWrappedModel()(node)

def _functools_partial_inference(
    node: nodes.Call, context: InferenceContext | None = None
) -> Iterator[objects.PartialFunction]:
    call = arguments.CallSite.from_call(node, context=context)
    number_of_positional = len(call.positional_arguments)
    if number_of_positional < 1:
        raise UseInferenceDefault("functools.partial takes at least one argument")
    if number_of_positional == 1 and not call.keyword_arguments:
        raise UseInferenceDefault(
            "functools.partial needs at least to have some filled arguments"
        )

partial_function = call.positional_arguments[0]
    try:
        inferred_wrapped_function = next(partial_function.infer(context=context))
    except (InferenceError, StopIteration) as exc:
        raise UseInferenceDefault from exc
    if isinstance(inferred_wrapped_function, UninferableBase):
        raise UseInferenceDefault("Cannot infer the wrapped function")
    if not isinstance(inferred_wrapped_function, FunctionDef):
        raise UseInferenceDefault("The wrapped function is not a function")

# Determine if the passed keywords into the callsite are supported
    # by the wrapped function.
    if not inferred_wrapped_function.args:
        function_parameters = []
    else:
        function_parameters = chain(
            inferred_wrapped_function.args.args or (),
            inferred_wrapped_function.args.posonlyargs or (),
            inferred_wrapped_function.args.kwonlyargs or (),
        )
    parameter_names = {
        param.name for param in function_parameters if isinstance(param, AssignName)
    }
    if set(call.keyword_arguments) - parameter_names:
        raise UseInferenceDefault("wrapped function received unknown parameters")

partial_function = objects.PartialFunction(
        call,
        name=inferred_wrapped_function.name,
        lineno=inferred_wrapped_function.lineno,
        col_offset=inferred_wrapped_function.col_offset,
        parent=node.parent,
    )
    partial_function.postinit(
        args=inferred_wrapped_function.args,
        body=inferred_wrapped_function.body,
        decorators=inferred_wrapped_function.decorators,
        returns=inferred_wrapped_function.returns,
        type_comment_returns=inferred_wrapped_function.type_comment_returns,
        type_comment_args=inferred_wrapped_function.type_comment_args,
        doc_node=inferred_wrapped_function.doc_node,
    )
    return iter((partial_function,))

def _looks_like_lru_cache(node) -> bool:
    """Check if the given function node is decorated with lru_cache."""
    if not node.decorators:
        return False
    for decorator in node.decorators.nodes:
        if not isinstance(decorator, Call):
            continue
        if _looks_like_functools_member(decorator, "lru_cache"):
            return True
    return False

def _looks_like_functools_member(node, member) -> bool:
    """Check if the given Call node is a functools.partial call."""
    if isinstance(node.func, Name):
        return node.func.name == member
    if isinstance(node.func, Attribute):
        return (
            node.func.attrname == member
            and isinstance(node.func.expr, Name)
            and node.func.expr.name == "functools"
        )
    return False

_looks_like_partial = partial(_looks_like_functools_member, member="partial")

AstroidManager().register_transform(
    FunctionDef, _transform_lru_cache, _looks_like_lru_cache
)

AstroidManager().register_transform(
    Call,
    inference_tip(_functools_partial_inference),
    _looks_like_partial,
)