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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_numpy_core_multiarray.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 numpy.core.multiarray module."""

import functools

from astroid.brain.brain_numpy_utils import infer_numpy_member, looks_like_numpy_member
from astroid.brain.helpers import register_module_extender
from astroid.builder import parse
from astroid.inference_tip import inference_tip
from astroid.manager import AstroidManager
from astroid.nodes.node_classes import Attribute, Name

def numpy_core_multiarray_transform():
    return parse(
        """
    # different functions defined in multiarray.py
    def inner(a, b):
        return numpy.ndarray([0, 0])

def vdot(a, b):
        return numpy.ndarray([0, 0])
        """
    )

register_module_extender(
    AstroidManager(), "numpy.core.multiarray", numpy_core_multiarray_transform
)

METHODS_TO_BE_INFERRED = {
    "array": """def array(object, dtype=None, copy=True, order='K', subok=False, ndmin=0):
            return numpy.ndarray([0, 0])""",
    "dot": """def dot(a, b, out=None):
            return numpy.ndarray([0, 0])""",
    "empty_like": """def empty_like(a, dtype=None, order='K', subok=True):
            return numpy.ndarray((0, 0))""",
    "concatenate": """def concatenate(arrays, axis=None, out=None):
            return numpy.ndarray((0, 0))""",
    "where": """def where(condition, x=None, y=None):
            return numpy.ndarray([0, 0])""",
    "empty": """def empty(shape, dtype=float, order='C'):
            return numpy.ndarray([0, 0])""",
    "bincount": """def bincount(x, weights=None, minlength=0):
            return numpy.ndarray([0, 0])""",
    "busday_count": """def busday_count(
        begindates, enddates, weekmask='1111100', holidays=[], busdaycal=None, out=None
    ):
        return numpy.ndarray([0, 0])""",
    "busday_offset": """def busday_offset(
        dates, offsets, roll='raise', weekmask='1111100', holidays=None,
        busdaycal=None, out=None
    ):
        return numpy.ndarray([0, 0])""",
    "can_cast": """def can_cast(from_, to, casting='safe'):
            return True""",
    "copyto": """def copyto(dst, src, casting='same_kind', where=True):
            return None""",
    "datetime_as_string": """def datetime_as_string(arr, unit=None, timezone='naive', casting='same_kind'):
            return numpy.ndarray([0, 0])""",
    "is_busday": """def is_busday(dates, weekmask='1111100', holidays=None, busdaycal=None, out=None):
            return numpy.ndarray([0, 0])""",
    "lexsort": """def lexsort(keys, axis=-1):
            return numpy.ndarray([0, 0])""",
    "may_share_memory": """def may_share_memory(a, b, max_work=None):
            return True""",
    # Not yet available because dtype is not yet present in those brains
    #     "min_scalar_type": """def min_scalar_type(a):
    #             return numpy.dtype('int16')""",
    "packbits": """def packbits(a, axis=None, bitorder='big'):
            return numpy.ndarray([0, 0])""",
    # Not yet available because dtype is not yet present in those brains
    #     "result_type": """def result_type(*arrays_and_dtypes):
    #             return numpy.dtype('int16')""",
    "shares_memory": """def shares_memory(a, b, max_work=None):
            return True""",
    "unpackbits": """def unpackbits(a, axis=None, count=None, bitorder='big'):
            return numpy.ndarray([0, 0])""",
    "unravel_index": """def unravel_index(indices, shape, order='C'):
            return (numpy.ndarray([0, 0]),)""",
    "zeros": """def zeros(shape, dtype=float, order='C'):
            return numpy.ndarray([0, 0])""",
}

for method_name, function_src in METHODS_TO_BE_INFERRED.items():
    inference_function = functools.partial(infer_numpy_member, function_src)
    AstroidManager().register_transform(
        Attribute,
        inference_tip(inference_function),
        functools.partial(looks_like_numpy_member, method_name),
    )
    AstroidManager().register_transform(
        Name,
        inference_tip(inference_function),
        functools.partial(looks_like_numpy_member, method_name),
    )

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Editing: brain_numpy_core_multiarray.py

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