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_random.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

from __future__ import annotations

import random

from astroid import helpers
from astroid.context import InferenceContext
from astroid.exceptions import UseInferenceDefault
from astroid.inference_tip import inference_tip
from astroid.manager import AstroidManager
from astroid.nodes.node_classes import (
    Attribute,
    Call,
    Const,
    EvaluatedObject,
    List,
    Name,
    Set,
    Tuple,
)

ACCEPTED_ITERABLES_FOR_SAMPLE = (List, Set, Tuple)

def _clone_node_with_lineno(node, parent, lineno):
    if isinstance(node, EvaluatedObject):
        node = node.original
    cls = node.__class__
    other_fields = node._other_fields
    _astroid_fields = node._astroid_fields
    init_params = {"lineno": lineno, "col_offset": node.col_offset, "parent": parent}
    postinit_params = {param: getattr(node, param) for param in _astroid_fields}
    if other_fields:
        init_params.update({param: getattr(node, param) for param in other_fields})
    new_node = cls(**init_params)
    if hasattr(node, "postinit") and _astroid_fields:
        new_node.postinit(**postinit_params)
    return new_node

def infer_random_sample(node, context: InferenceContext | None = None):
    if len(node.args) != 2:
        raise UseInferenceDefault

inferred_length = helpers.safe_infer(node.args[1], context=context)
    if not isinstance(inferred_length, Const):
        raise UseInferenceDefault
    if not isinstance(inferred_length.value, int):
        raise UseInferenceDefault

inferred_sequence = helpers.safe_infer(node.args[0], context=context)
    if not inferred_sequence:
        raise UseInferenceDefault

if not isinstance(inferred_sequence, ACCEPTED_ITERABLES_FOR_SAMPLE):
        raise UseInferenceDefault

if inferred_length.value > len(inferred_sequence.elts):
        # In this case, this will raise a ValueError
        raise UseInferenceDefault

try:
        elts = random.sample(inferred_sequence.elts, inferred_length.value)
    except ValueError as exc:
        raise UseInferenceDefault from exc

new_node = List(lineno=node.lineno, col_offset=node.col_offset, parent=node.scope())
    new_elts = [
        _clone_node_with_lineno(elt, parent=new_node, lineno=new_node.lineno)
        for elt in elts
    ]
    new_node.postinit(new_elts)
    return iter((new_node,))

def _looks_like_random_sample(node) -> bool:
    func = node.func
    if isinstance(func, Attribute):
        return func.attrname == "sample"
    if isinstance(func, Name):
        return func.name == "sample"
    return False

AstroidManager().register_transform(
    Call, inference_tip(infer_random_sample), _looks_like_random_sample
)