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        Get or set the PRNG state

        Returns
        -------
        state : dict
            Dictionary containing the information required to describe the
            state of the PRNG
        numpy.random._sfc64.SFC64.__init__base class '%.200s' is not a heap typenumpy.random._commonInterpreter change detected - this module can only be loaded into one interpreter per process.__builtins__<cyfunction %U at %p>__module____kwdefaults__%s() got an unexpected keyword argument '%U'raise: exception class must be a subclass of BaseException while calling a Python object__del__Bad call flags for CyFunctionCannot convert %.200s to %.200ssignedintegerinexactkeywords must be strings__annotations__
    SFC64(seed=None)

    BitGenerator for Chris Doty-Humphrey's Small Fast Chaotic PRNG.

    Parameters
    ----------
    seed : {None, int, array_like[ints], SeedSequence}, optional
        A seed to initialize the `BitGenerator`. If None, then fresh,
        unpredictable entropy will be pulled from the OS. If an ``int`` or
        ``array_like[ints]`` is passed, then it will be passed to
        `SeedSequence` to derive the initial `BitGenerator` state. One may also
        pass in a `SeedSequence` instance.

    Notes
    -----
    `SFC64` is a 256-bit implementation of Chris Doty-Humphrey's Small Fast
    Chaotic PRNG ([1]_). `SFC64` has a few different cycles that one might be
    on, depending on the seed; the expected period will be about
    :math:`2^{255}` ([2]_). `SFC64` incorporates a 64-bit counter which means
    that the absolute minimum cycle length is :math:`2^{64}` and that distinct
    seeds will not run into each other for at least :math:`2^{64}` iterations.

    `SFC64` provides a capsule containing function pointers that produce
    doubles, and unsigned 32 and 64- bit integers. These are not
    directly consumable in Python and must be consumed by a `Generator`
    or similar object that supports low-level access.

    **State and Seeding**

    The `SFC64` state vector consists of 4 unsigned 64-bit values. The last
    is a 64-bit counter that increments by 1 each iteration.

    The input seed is processed by `SeedSequence` to generate the first
    3 values, then the `SFC64` algorithm is iterated a small number of times
    to mix.

    **Compatibility Guarantee**

    `SFC64` makes a guarantee that a fixed seed will always produce the same
    random integer stream.

    References
    ----------
    .. [1] `"PractRand"
            <https://pracrand.sourceforge.net/RNG_engines.txt>`_
    .. [2] `"Random Invertible Mapping Statistics"
            <https://www.pcg-random.org/posts/random-invertible-mapping-statistics.html>`_
    generic__pyx_capi__numpy.random._sfc64<stringsource>__reduce____name___is_coroutineImportErrorSFC64SFC64.__reduce_cython__SFC64.__setstate_cython__TypeErrorValueError?*__all__asyncio.coroutinesbit_generator__class__cline_in_tracebackdisableemptyenableformatgcgenerate_stateget__getstate__has_uint32__import___initializingisenabled__main__npnumpy._core.multiarray failed to importnumpy._core.umath failed to import__pyx_state__pyx_vtable____reduce_ex__seedselfself._bitgen cannot be converted to a Python object for pickling__setstate____spec__state must be a dictstate must be for a {0} RNG__test__uint64uinteger__reduce_cython____setstate_cython__state__init__numpydtypeŸåà°ê­ÿ/áýÿÿêPãÿ/
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