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from _typeshed import Incomplete
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import numpy as np
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from numpy.lib._function_base_impl import average
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from numpy.lib._index_tricks_impl import AxisConcatenator
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from .core import MaskedArray, dot
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__all__ = [
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    "apply_along_axis",
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    "apply_over_axes",
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    "atleast_1d",
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    "atleast_2d",
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    "atleast_3d",
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    "average",
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    "clump_masked",
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    "clump_unmasked",
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    "column_stack",
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    "compress_cols",
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    "compress_nd",
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    "compress_rowcols",
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    "compress_rows",
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    "corrcoef",
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    "count_masked",
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    "cov",
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    "diagflat",
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    "dot",
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    "dstack",
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    "ediff1d",
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    "flatnotmasked_contiguous",
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    "flatnotmasked_edges",
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    "hsplit",
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    "hstack",
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    "in1d",
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    "intersect1d",
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    "isin",
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    "mask_cols",
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    "mask_rowcols",
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    "mask_rows",
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    "masked_all",
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    "masked_all_like",
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    "median",
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    "mr_",
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    "ndenumerate",
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    "notmasked_contiguous",
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    "notmasked_edges",
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    "polyfit",
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    "row_stack",
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    "setdiff1d",
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    "setxor1d",
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    "stack",
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    "union1d",
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    "unique",
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    "vander",
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    "vstack",
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]
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def count_masked(arr, axis=...): ...
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def masked_all(shape, dtype=...): ...
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def masked_all_like(arr): ...
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class _fromnxfunction:
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    __name__: Incomplete
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    __doc__: Incomplete
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    def __init__(self, funcname) -> None: ...
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    def getdoc(self): ...
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    def __call__(self, *args, **params): ...
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class _fromnxfunction_single(_fromnxfunction):
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    def __call__(self, x, *args, **params): ...
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class _fromnxfunction_seq(_fromnxfunction):
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    def __call__(self, x, *args, **params): ...
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class _fromnxfunction_allargs(_fromnxfunction):
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    def __call__(self, *args, **params): ...
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atleast_1d: _fromnxfunction_allargs
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atleast_2d: _fromnxfunction_allargs
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atleast_3d: _fromnxfunction_allargs
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vstack: _fromnxfunction_seq
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row_stack: _fromnxfunction_seq
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hstack: _fromnxfunction_seq
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column_stack: _fromnxfunction_seq
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dstack: _fromnxfunction_seq
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stack: _fromnxfunction_seq
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hsplit: _fromnxfunction_single
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diagflat: _fromnxfunction_single
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def apply_along_axis(func1d, axis, arr, *args, **kwargs): ...
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def apply_over_axes(func, a, axes): ...
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def median(a, axis=..., out=..., overwrite_input=..., keepdims=...): ...
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def compress_nd(x, axis=...): ...
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def compress_rowcols(x, axis=...): ...
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def compress_rows(a): ...
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def compress_cols(a): ...
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def mask_rows(a, axis=...): ...
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def mask_cols(a, axis=...): ...
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def ediff1d(arr, to_end=..., to_begin=...): ...
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def unique(ar1, return_index=..., return_inverse=...): ...
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def intersect1d(ar1, ar2, assume_unique=...): ...
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def setxor1d(ar1, ar2, assume_unique=...): ...
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def in1d(ar1, ar2, assume_unique=..., invert=...): ...
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def isin(element, test_elements, assume_unique=..., invert=...): ...
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def union1d(ar1, ar2): ...
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def setdiff1d(ar1, ar2, assume_unique=...): ...
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def cov(x, y=..., rowvar=..., bias=..., allow_masked=..., ddof=...): ...
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def corrcoef(x, y=..., rowvar=..., bias=..., allow_masked=..., ddof=...): ...
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class MAxisConcatenator(AxisConcatenator):
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    @staticmethod
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    def concatenate(arrays: Incomplete, axis: int = 0) -> Incomplete: ...  # type: ignore[override]  # pyright: ignore[reportIncompatibleMethodOverride]
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    @classmethod
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    def makemat(cls, arr: Incomplete) -> Incomplete: ...  # type: ignore[override]  # pyright: ignore[reportIncompatibleVariableOverride]
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class mr_class(MAxisConcatenator):
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    def __init__(self) -> None: ...
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mr_: mr_class
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def ndenumerate(a, compressed=...): ...
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def flatnotmasked_edges(a): ...
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def notmasked_edges(a, axis=...): ...
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def flatnotmasked_contiguous(a): ...
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def notmasked_contiguous(a, axis=...): ...
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def clump_unmasked(a): ...
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def clump_masked(a): ...
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def vander(x, n=...): ...
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def polyfit(x, y, deg, rcond=..., full=..., w=..., cov=...): ...
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#
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def mask_rowcols(a: Incomplete, axis: Incomplete | None = None) -> MaskedArray[Incomplete, np.dtype[Incomplete]]: ...
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