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

Editing: 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
)

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Editing: brain_random.py

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