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	(xÚmSÍoÛ6oÐˊnKR·M·ac†î)âVv»5@°ÁȀ
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        _legacy_seeding(seed)

        Seed the generator in a backward compatible way. For modern
        applications, creating a new instance is preferable. Calling this
        overrides self._seed_seq

        Parameters
        ----------
        seed : {None, int, array_like}
            Random seed initializing the pseudo-random number generator.
            Can be an integer in [0, 2**32-1], array of integers in
            [0, 2**32-1], a `SeedSequence, or ``None``. If `seed`
            is ``None``, then fresh, unpredictable entropy will be pulled from
            the OS.

        Raises
        ------
        ValueError
            If seed values are out of range for the PRNG.
        
        jumped(jumps=1)

        Returns a new bit generator with the state jumped

        The state of the returned bit generator is jumped as-if
        2**(128 * jumps) random numbers have been generated.

        Parameters
        ----------
        jumps : integer, positive
            Number of times to jump the state of the bit generator returned

        Returns
        -------
        bit_generator : MT19937
            New instance of generator jumped iter times

        Notes
        -----
        The jump step is computed using a modified version of Matsumoto's
        implementation of Horner's method. The step polynomial is precomputed
        to perform 2**128 steps. The jumped state has been verified to match
        the state produced using Matsumoto's original code.

        References
        ----------
        .. [1] Matsumoto, M, Generating multiple disjoint streams of
           pseudorandom number sequences.  Accessed on: May 6, 2020.
           http://www.math.sci.hiroshima-u.ac.jp/m-mat/MT/JUMP/
        .. [2] Hiroshi Haramoto, Makoto Matsumoto, Takuji Nishimura, François
           Panneton, Pierre L'Ecuyer, "Efficient Jump Ahead for F2-Linear
           Random Number Generators", INFORMS JOURNAL ON COMPUTING, Vol. 20,
           No. 3, Summer 2008, pp. 385-390.
        @CAexactlyname '%U' is not definedMissing type objectnumpy/random/_mt19937.pyx_cython_3_2_4._common_types_metatype__setstate_cython__dtypeSeedSequence__name__ must be set to a string object%s() got multiple values for keyword argument '%U'__int__ returned non-int (type %.200s).  The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python.ndarraydoes not matchShared Cython type %.200s has the wrong size, try recompiling%.200s() %snumpy.random._mt19937.MT19937._legacy_seedingflexiblenumpy.core._multiarray_umathFailed to import '%.20s.decompress' - cannot initialise module strings. String compression was configured with the C macro 'CYTHON_COMPRESS_STRINGS=%d'.%.200s() keywords must be stringscomplexfloating_mt19937zlib__qualname__numpy.random._mt19937.MT19937.__reduce_cython__multiple bases have vtable conflict: '%.200s' and '%.200s'integerloader__package__numpy.random._mt19937<stringsource>__vectorcalloffset__func_doc__dict__at most%.200s() takes %.8s %zd positional argument%.1s (%zd given)floatingmodule was compiled against NumPy C-API version 0x%x (NumPy 1.20) but the running NumPy has C-API version 0x%x. Check the section C-API incompatibility at the Troubleshooting ImportError section at https://numpy.org/devdocs/user/troubleshooting-importerror.html#c-api-incompatibility for indications on how to solve this problem._legacy_seedingjumpedan integer is requirednumpy._core._multiarray_umath_ARRAY_API is NULL pointerstatesSeedlessSequence_ARRAY_APIFATAL: module compiled as unknown endiantakes no arguments__weaklistoffset__func_closureneeds an argument__file__parentsubmodule_search_locations%.200s() %s (%zd given)numpy.random._mt19937.MT19937func_dict__reduce_cython__NULL result without error in PyObject_Callnumpy.random._mt19937.MT19937.__init____loader___cython_3_2_4.cython_function_or_methodfunc_name__globals____name__flatiter__defaults__changes to cyfunction.__kwdefaults__ will not currently affect the values used in function callshasattr(): attribute name must be stringnumpy.random._mt19937.MT19937.jumpednumpy.random._mt19937.MT19937.state.__get__numbercython_runtimetakes no keyword arguments%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject
    MT19937(seed=None)

    Container for the Mersenne Twister pseudo-random number generator.

    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.

    Attributes
    ----------
    lock: threading.Lock
        Lock instance that is shared so that the same bit git generator can
        be used in multiple Generators without corrupting the state. Code that
        generates values from a bit generator should hold the bit generator's
        lock.

    Notes
    -----
    `MT19937` provides a capsule containing function pointers that produce
    doubles, and unsigned 32 and 64- bit integers [1]_. These are not
    directly consumable in Python and must be consumed by a `Generator`
    or similar object that supports low-level access.

    The Python stdlib module "random" also contains a Mersenne Twister
    pseudo-random number generator.

    **State and Seeding**

    The `MT19937` state vector consists of a 624-element array of
    32-bit unsigned integers plus a single integer value between 0 and 624
    that indexes the current position within the main array.

    The input seed is processed by `SeedSequence` to fill the whole state. The
    first element is reset such that only its most significant bit is set.

    **Parallel Features**

    The preferred way to use a BitGenerator in parallel applications is to use
    the `SeedSequence.spawn` method to obtain entropy values, and to use these
    to generate new BitGenerators:

    >>> from numpy.random import Generator, MT19937, SeedSequence
    >>> sg = SeedSequence(1234)
    >>> rg = [Generator(MT19937(s)) for s in sg.spawn(10)]

    Another method is to use `MT19937.jumped` which advances the state as-if
    :math:`2^{128}` random numbers have been generated ([1]_, [2]_). This
    allows the original sequence to be split so that distinct segments can be
    used in each worker process. All generators should be chained to ensure
    that the segments come from the same sequence.

    >>> from numpy.random import Generator, MT19937, SeedSequence
    >>> sg = SeedSequence(1234)
    >>> bit_generator = MT19937(sg)
    >>> rg = []
    >>> for _ in range(10):
    ...    rg.append(Generator(bit_generator))
    ...    # Chain the BitGenerators
    ...    bit_generator = bit_generator.jumped()

    **Compatibility Guarantee**

    `MT19937` makes a guarantee that a fixed seed will always produce
    the same random integer stream.

    References
    ----------
    .. [1] Hiroshi Haramoto, Makoto Matsumoto, and Pierre L'Ecuyer, "A Fast
        Jump Ahead Algorithm for Linear Recurrences in a Polynomial Space",
        Sequences and Their Applications - SETA, 290--298, 2008.
    .. [2] Hiroshi Haramoto, Makoto Matsumoto, Takuji Nishimura, François
        Panneton, Pierre L'Ecuyer, "Efficient Jump Ahead for F2-Linear
        Random Number Generators", INFORMS JOURNAL ON COMPUTING, Vol. 20,
        No. 3, Summer 2008, pp. 385-390.

    __init____path__invalid vtable found for imported typecharacterufuncmodule compiled against ABI version 0x%x but this version of numpy is 0x%xShared Cython type %.200s is not a type object__code____kwdefaults__ must be set to a dict objectBitGeneratornumpy.random._mt19937.MT19937.state.__set__Unable to initialize pickling for %.200sorigininit numpy.random._mt19937compile time Python version %d.%d of module '%.100s' %s runtime version %d.%d../numpy/__init__.cython-30.pxdcan't convert negative value to uint32_t__int__ returned non-int (type %.200s)numpy.random._mt19937.MT19937.__setstate_cython___is_coroutineinstance exception may not have a separate valuenumpy_ARRAY_API is not PyCapsule object_cython_3_2_4func_globalsfunc_code%.200s.%.200s is not a type objectcalling %R should have returned an instance of BaseException, not %Rnametakes exactly one argument__reduce____closure____qualname__ must be set to a string objectat leastvalue too large to convert to uint32_tunsignedintegernumpy.import_arraybuiltinsfunc_defaults__defaults__ must be set to a tuple objectextension type '%.200s' has no __dict__ slot, but base type '%.200s' has: either add 'cdef dict __dict__' to the extension type or add '__slots__ = [...]' to the base typetypedecompressunbound method %.200S() needs an argument__annotations__ must be set to a dict objectvalue too large to convert to intbroadcastFATAL: module compiled as little endian, but detected different endianness at runtimeModule '_mt19937' has already been imported. Re-initialisation is not supported.__doc__changes to cyfunction.__defaults__ will not currently affect the values used in function callsnumpy.random.bit_generator
        Get or set the PRNG state

        Returns
        -------
        state : dict
            Dictionary containing the information required to describe the
            state of the PRNG
        base class '%.200s' is not a heap typeInterpreter change detected - this module can only be loaded into one interpreter per process.__builtins____module__<cyfunction %U at %p>__kwdefaults__%s() got an unexpected keyword argument '%U' while calling a Python objectraise: exception class must be a subclass of BaseException__del__Bad call flags for CyFunctionCannot convert %.200s to %.200snumpy.random._mt19937.MT19937.jump_inplacesignedintegerinexactkeywords must be strings__annotations__genericþÿÿþÿÿþÿÿþÿÿ߰™߰™߰™߰™߰™€€€€c9ÞrĞpµ¶›'ˆåø#¨Y>Ø&Y"»ýçwR‹VçµHÕ(¿¹­°äªIHƒ‹’>Î9méÓ1a¯èòê	V„T3{LÁƒ|:‰¼½þg‚½
¿ÜGQæ}¦âtõþš	M3¸ÊÞÓðXýaU;p„Ø;€\ïB›³a·ß ÓþÖ[>_ÏwaAGéB„Ž«ϩŽÀ]X÷Ý`(…›,Ö÷ðü;»²}ã>ÊYæɁiÙ‡ޠs•QHRΔ;hwí¥Ís¼ü¼Vl•;ôޑ´¿Ä8”1˜…¢àjÊծ—BFž•\ç†T<%\ECÍØ̵sԂ‚DÍÊșß-F…¾Ö‰µˆR§lò´U˜ëp (=çÌJ&É¡x{ûð•—èîB€̫èQçg…Ì0š;‘^RøÓÒã>¡‰ÛL,}¨j:š^›#2‹‡íؤxwŒ¤fÀJ—UðB‚ Èé€ÞÖ@ϡLΒ(Ç1'„®hA`mî#ݲøϾ‡r¬ßQ—6¤ȋºÙ@XJ‚…¥§íÛ;õ—IºÏ¤ÃoÆÕ)Çò9­hƎM®µ©ò–óˆÅ»aNa,Ñ D³óaûë×.p;ò¦ÜËf·|•3(¾jC¢–ÐÀ oªá¸I¿Í×I¹E›ÈĤ
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