import sys

import torch
from torch._C import _add_docstr, _fft  # type: ignore[attr-defined]
from torch._torch_docs import factory_common_args, common_args

__all__ = ['fft', 'ifft', 'fft2', 'ifft2', 'fftn', 'ifftn',
           'rfft', 'irfft', 'rfft2', 'irfft2', 'rfftn', 'irfftn',
           'hfft', 'ihfft', 'fftfreq', 'rfftfreq', 'fftshift', 'ifftshift',

            

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              import sys

import torch
from torch._C import _add_docstr, _fft  # type: ignore[attr-defined]
from torch._torch_docs import factory_common_args, common_args

__all__ = ['fft', 'ifft', 'fft2', 'ifft2', 'fftn', 'ifftn',
           'rfft', 'irfft', 'rfft2', 'irfft2', 'rfftn', 'irfftn',
           'hfft', 'ihfft', 'fftfreq', 'rfftfreq', 'fftshift', 'ifftshift',

            

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              from .onnx import *  # noqa: F403

            

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              from .onnx import *  # noqa: F403

            

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              from .converters import *  # noqa: F403

            

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              from .converters import *  # noqa: F403

            

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              from .autocast_mode import autocast

            

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              from .autocast_mode import autocast

            

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              from . import amp

            

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              from . import amp

            

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if __name__ == "__main__":
    from torch.testing._internal.common_utils import run_tests
    run_tests()

            

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              def load_tests(loader, standard_tests, pattern):
    """Load all tests from `test/pacakge/`
    """
    if pattern is None:
        # Use the default pattern if none is specified by the test loader.
        pattern = "test*.py"
    package_tests = loader.discover("package", pattern=pattern)
    standard_tests.addTests(package_tests)
    return standard_tests

            

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              from .tensor import *  # noqa: F403

            

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              from .tensor import *  # noqa: F403

            

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              from .linear import Linear

__all__ = [
    "Linear",
]

            

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              from .linear import Linear

__all__ = [
    "Linear",
]

            

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              from . import quantized

            

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              from . import quantized

            

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def add_docstr_all(method, docstr):
    for cls_name in storage_classes:
        cls = getattr(torch._C, cls_name)
        try:
            add_docstr(getattr(cls, method), docstr)
        except AttributeError:
            pass


            

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              ]


def add_docstr_all(method, docstr):
    for cls_name in storage_classes:
        cls = getattr(torch._C, cls_name)
        try:
            add_docstr(getattr(cls, method), docstr)
        except AttributeError:

            

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