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4080<0@0D0H0L0P0T0X0\0`0d0 h0!l0"p0#t0$x0%|0&€0'„0(ˆ0)Œ0*0+”0,˜0/œ00 01¤02¨03¬04°05´08¸09¼0;À0<Ä0=È0>Ì0?Ð0@Ô0AØ0CÜ0Dà0Eä0Fè0Gì0Kð0Lô0Mø0Nü0P1Q1R1S1T1U1W1X1Y 1Z$1](1^,1_01`41a81b<1c@1dD1eH1fL1gP1hT1iX1j\1k`1ld1mh1nl1op1pt1rx1s|1t€1u„1vˆ1xŒ1y1z”1{˜1|œ1} 1¤1‚¨1ƒ¬1…°1†´1ˆ¸1‰¼1ŠÀ1ŒÄ1È1Ì1‘Ð1’Ô1“Ø1”Ü1•à1–ä1—è1šì1›ð1œô1ø1Ÿü1 2¡2¢2£2¤2¥2¦2§2¨ 2©$2ª(2«,2¬02­42®4294967296name '%U' is not definedexactlycffi
    SeedSequence(entropy=None, *, spawn_key=(), pool_size=4)

    SeedSequence mixes sources of entropy in a reproducible way to set the
    initial state for independent and very probably non-overlapping
    BitGenerators.

    Once the SeedSequence is instantiated, you can call the `generate_state`
    method to get an appropriately sized seed. Calling `spawn(n) <spawn>` will
    create ``n`` SeedSequences that can be used to seed independent
    BitGenerators, i.e. for different threads.

    Parameters
    ----------
    entropy : {None, int, sequence[int]}, optional
        The entropy for creating a `SeedSequence`.
        All integer values must be non-negative.
    spawn_key : {(), sequence[int]}, optional
        An additional source of entropy based on the position of this
        `SeedSequence` in the tree of such objects created with the
        `SeedSequence.spawn` method. Typically, only `SeedSequence.spawn` will
        set this, and users will not.
    pool_size : {int}, optional
        Size of the pooled entropy to store. Default is 4 to give a 128-bit
        entropy pool. 8 (for 256 bits) is another reasonable choice if working
        with larger PRNGs, but there is very little to be gained by selecting
        another value.
    n_children_spawned : {int}, optional
        The number of children already spawned. Only pass this if
        reconstructing a `SeedSequence` from a serialized form.

    Notes
    -----

    Best practice for achieving reproducible bit streams is to use
    the default ``None`` for the initial entropy, and then use
    `SeedSequence.entropy` to log/pickle the `entropy` for reproducibility:

    >>> sq1 = np.random.SeedSequence()
    >>> sq1.entropy
    243799254704924441050048792905230269161  # random
    >>> sq2 = np.random.SeedSequence(sq1.entropy)
    >>> np.all(sq1.generate_state(10) == sq2.generate_state(10))
    True
    Missing type objectnumpy.random.bit_generator.SeedlessSeedSequencenumpy.random.bit_generator.SeedlessSeedSequence.__reduce_cython__numpy.random.bit_generator.BitGenerator.cffi.__get__Expected a dimension of size %zu, got %zutuplenumpy.random.bit_generator.__pyx_unpickle_SeedSequence%.200s() takes no arguments (%zd given)__name__ must be set to a string objectsetting function's dictionary to a non-dict%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.__%.4s__ returned non-%.4s (type %.200s)
        Get or set the PRNG state

        The base BitGenerator.state must be overridden by a subclass

        Returns
        -------
        state : dict
            Dictionary containing the information required to describe the
            state of the PRNG
        'long''unsigned long'00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899ndarrayPyObject *(bitgen_t *, PyObject *, Py_ssize_t, PyObject *)numpy.random.bit_generator._int_to_uint32_arraydoes not matchShared Cython type %.200s has the wrong size, try recompilingnumpy.random.bit_generator.BitGenerator.random_rawnumpy.random.bit_generator.BitGenerator.__init__'double''long double'strings are too large to concatpoolnumpy.random.bit_generator.SeedlessSeedSequence.__setstate_cython__flexibleprepare_ctypesC function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)numpy.core._multiarray_umath%.200s() keywords must be stringsnumpy.random.bit_generator.BitGenerator.__getstate__Buffer dtype mismatch, expected %s%s%s but got %scomplexfloatingmetaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its basesModule 'bit_generator' has already been imported. Re-initialisation is not supported.numpy.random.bit_generator.BitGenerator.state.__get__object of type 'NoneType' has no len()multiple bases have vtable conflict: '%.200s' and '%.200s'integer%.200s does not export expected C function %.200snumpy.random.bit_generator.ISeedSequence.generate_stateloader__package__func_docat most%.200s() takes %.8s %zd positional argument%.1s (%zd given)_ctypesnumpy.random.bit_generator.SeedSequence.generate_state
    A seed sequence for BitGenerators with no need for seed state.

    See Also
    --------
    SeedSequence, ISeedSequence
    floatingLEGACY_POISSON_LAM_MAXmodule 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.an integer is requirednumpy.random.bit_generator.BitGenerator._benchmark'Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'Out of bounds on buffer access (axis %d)'NoneType' object is not subscriptablePOISSON_LAM_MAXnumpy._core._multiarray_umath_ARRAY_API is NULL pointerinit numpy.random.bit_generatornumpy.random.bit_generator.BitGenerator.__setstate___seed_seqExpected a dimension of size %zu, got %dnumpy.random.bit_generator.SeedSequence.__repr__numpy.random.bit_generator.SeedSequence.__setstate_cython__%s.%s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObjectnumpy.random.bit_generator.BitGenerator.__reduce__Python objectnumpy.random.bit_generator.SeedSequence.pool_size.__get___ARRAY_APIFATAL: module compiled as unknown endiannumpy/random/bit_generator.cpython-312.so.p/numpy/random/bit_generator.pyx.cfunc_closure'complex double'a struct__mro_entries__ must return a tuple__file__parentsubmodule_search_locationsnpy_uint32%.200s object is not an iteratorfunc_dict'long long'Expected %s, got %.200snumpy.random.bit_generator.__pyx_unpickle_SeedSequence__set_stateNULL result without error in PyObject_Call__loader___cython_3_0_12func_name__globals__ctypes
        CFFI interface

        Returns
        -------
        interface : namedtuple
            Named tuple containing CFFI wrapper

            * state_address - Memory address of the state struct
            * state - pointer to the state struct
            * next_uint64 - function pointer to produce 64 bit integers
            * next_uint32 - function pointer to produce 32 bit integers
            * next_double - function pointer to produce doubles
            * bitgen - pointer to the bit generator struct
        'bool''short'__repr__numpy.random.bit_generator.__pyx_unpickle_SeedlessSeedSequence__set_statedoubleseed_seqlock'unsigned short''unsigned int''unsigned long long'Unexpected format string character: '%c'flatiterprepare_cffi__defaults__changes to cyfunction.__kwdefaults__ will not currently affect the values used in function callsnumpy.random.bit_generator.BitGeneratornumpy.random.bit_generator.BitGenerator.ctypes.__get__'float'a pointernumpy.random.bit_generator.SeedSequence.get_assembled_entropynumpy.random.bit_generator.SeedSequence.state.__get__intUnexpected end of format string, expected ')'numpy.random.bit_generator.SeedSequence.__reduce_cython__hasattr(): attribute name must be stringnumberC variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s)%s (%s:%d)cython_runtime%.200s() takes exactly one argument (%zd given)__init__'int'%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObjectbenchmarkcannot import name %Snumpy.random.bit_generator.__defaults____path__
        Get the seed sequence used to initialize the bit generator.

        .. versionadded:: 1.25.0

        Returns
        -------
        seed_seq : ISeedSequence
            The SeedSequence object used to initialize the BitGenerator.
            This is normally a `np.random.SeedSequence` instance.

        invalid vtable found for imported typecharacterufuncPyObject *(bitgen_t *)module compiled against ABI version 0x%x but this version of numpy is 0x%xnumpy.random.bit_generator.ISpawnableSeedSequence.spawnShared Cython type %.200s is not a type object__code__function's dictionary may not be deleted__kwdefaults__ must be set to a dict object'complex float'Unable to initialize pickling for %.200snumpy.random.bit_generator.SeedlessSequenceoriginbit_generator.pyx__init__.cython-30.pxdcompile time Python version %d.%d of module '%.100s' %s runtime version %d.%dPyObject *(bitgen_t *, PyObject *, PyObject *, PyObject *)%.200s() needs an argumentExpected %d dimension(s), got %dcan't convert negative value to uint32_tnumpy.random.bit_generator.__pyx_unpickle_SeedlessSeedSequence
    BitGenerator(seed=None)

    Base Class for generic BitGenerators, which provide a stream
    of random bits based on different algorithms. Must be overridden.

    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
        `~numpy.random.SeedSequence` to derive the initial `BitGenerator` state.
        One may also pass in a `SeedSequence` instance.
        All integer values must be non-negative.

    Attributes
    ----------
    lock : threading.Lock
        Lock instance that is shared so that the same BitGenerator 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.

    See Also
    --------
    SeedSequence
    instance exception may not have a separate valueBuffer acquisition: Expected '{' after 'T'Does not understand character buffer dtype format string ('%c')uint64_t_ARRAY_API is not PyCapsule objectfunc_globalsfunc_codecalling %R should have returned an instance of BaseException, not %R'complex long double'unparsable format string%.200s.%.200s is not a type objectname__orig_bases____closure____qualname__ must be set to a string objectat least_cffia stringExpected a comma in format string, got '%c'numpy.random.bit_generator.SeedSequenceunsignedintegerMAXSIZEnumpy.import_arraybuiltins%.200s() takes no keyword argumentsfunc_defaults__defaults__ must be set to a tuple objectnumpy.random.bit_generator.BitGenerator.spawncapsuleextension 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 typeItem size of buffer (%zd byte%s) does not match size of '%s' (%zd byte%s)typebit_generator_cython_3_0_12.cython_function_or_methodunbound method %.200S() needs an argument__annotations__ must be set to a dict objectnumpy.random.bit_generator.SeedSequence.mix_entropyendnumpy.random.bit_generator.SeedSequence.spawnnumpy.random.bit_generator.SeedSequence.n_children_spawned.__get__numpy.random.bit_generator.SeedlessSeedSequence.spawnbroadcast%.200s does not export expected C variable %.200sFATAL: module compiled as little endian, but detected different endianness at runtimebit_generator.pxdchanges to cyfunction.__defaults__ will not currently affect the values used in function callsbase class '%.200s' is not a heap typeBig-endian buffer not supported on little-endian compiler'unsigned char'Buffer dtype mismatch; next field is at offset %zd but %zd expectednumpy.random._commonInterpreter change detected - this module can only be loaded into one interpreter per process.__builtins__<cyfunction %U at %p>__kwdefaults__%s() got an unexpected keyword argument '%U'raise: exception class must be a subclass of BaseException__del__'signed char' while calling a Python objectBad call flags for CyFunction
        ctypes interface

        Returns
        -------
        interface : namedtuple
            Named tuple containing ctypes wrapper

            * state_address - Memory address of the state struct
            * state - pointer to the state struct
            * next_uint64 - function pointer to produce 64 bit integers
            * next_uint32 - function pointer to produce 32 bit integers
            * next_double - function pointer to produce doubles
            * bitgen - pointer to the bit generator struct
        Expected %d dimensions, got %dPython does not define a standard format string size for long double ('g')..Cannot convert %.200s to %.200s__debug__numpy.random.bit_generator.BitGenerator.state.__set__buffer dtype'char'numpy.random.bit_generator.SeedSequence.__init__numpy.random.bit_generator.SeedlessSeedSequence.generate_statesignedintegerinexactkeywords must be strings__annotations__Buffer has wrong number of dimensions (expected %d, got %d)Cannot handle repeated arrays in format stringjoin() result is too long for a Python stringgeneric__pyx_capi__numpy.random.bit_generator._coerce_to_uint32_array
BitGenerator base class and SeedSequence used to seed the BitGenerators.

SeedSequence is derived from Melissa E. O'Neill's C++11 `std::seed_seq`
implementation, as it has a lot of nice properties that we want.

https://gist.github.com/imneme/540829265469e673d045
https://www.pcg-random.org/posts/developing-a-seed_seq-alternative.html

The MIT License (MIT)

Copyright (c) 2015 Melissa E. O'Neill
Copyright (c) 2019 NumPy Developers

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
numpy.random.bit_generator<stringsource>__reduce____module____doc____name____qualname____dict___is_coroutine[0-9]+0xABC
    Abstract base class for seed sequences.

    ``BitGenerator`` implementations should treat any object that adheres to
    this interface as a seed sequence.

    See Also
    --------
    SeedSequence, SeedlessSeedSequence
    
    Abstract base class for seed sequences that can spawn.
    AssertionErrorBitGenerator.__getstate__BitGenerator.__reduce__BitGenerator.__setstate__BitGenerator._benchmark_BitGenerator__bit_generator_ctorBitGenerator is a base class and cannot be instantizedBitGenerator.random_rawBitGenerator.spawn Coerce an input to a uint32 array.

    If a `uint32` array, pass it through directly.
    If a non-negative integer, then break it up into `uint32` words, lowest
    bits first.
    If a string starting with "0x", then interpret as a hex integer, as above.
    If a string of decimal digits, interpret as a decimal integer, as above.
    If a sequence of ints or strings, interpret each element as above and
    concatenate.

    Note that the handling of `int64` or `uint64` arrays are not just
    straightforward views as `uint32` arrays. If an element is small enough to
    fit into a `uint32`, then it will only take up one `uint32` element in the
    output. This is to make sure that the interpretation of a sequence of
    integers is the same regardless of numpy's default integer type, which
    differs on different platforms.

    Parameters
    ----------
    x : int, str, sequence of int or str

    Returns
    -------
    seed_array : uint32 array

    Examples
    --------
    >>> import numpy as np
    >>> from numpy.random.bit_generator import _coerce_to_uint32_array
    >>> _coerce_to_uint32_array(12345)
    array([12345], dtype=uint32)
    >>> _coerce_to_uint32_array('12345')
    array([12345], dtype=uint32)
    >>> _coerce_to_uint32_array('0x12345')
    array([74565], dtype=uint32)
    >>> _coerce_to_uint32_array([12345, '67890'])
    array([12345, 67890], dtype=uint32)
    >>> _coerce_to_uint32_array(np.array([12345, 67890], dtype=np.uint32))
    array([12345, 67890], dtype=uint32)
    >>> _coerce_to_uint32_array(np.array([12345, 67890], dtype=np.int64))
    array([12345, 67890], dtype=uint32)
    >>> _coerce_to_uint32_array([12345, 0x10deadbeef, 67890, 0xdeadbeef])
    array([     12345, 3735928559,         16,      67890, 3735928559],
          dtype=uint32)
    >>> _coerce_to_uint32_array(1234567890123456789012345678901234567890)
    array([3460238034, 2898026390, 3235640248, 2697535605,          3],
          dtype=uint32)
    DECIMAL_REISeedSequenceISeedSequence.generate_stateISpawnableSeedSequenceISpawnableSeedSequence.spawnImportErrorIncompatible checksums (0x%x vs (0xe3b0c44, 0xda39a3e, 0xd41d8cd) = ())Incompatible checksums (0x%x vs (0x3eaa222, 0xc464c19, 0xf88304a) = (entropy, n_children_spawned, pool, pool_size, spawn_key))LockNotImplementedErrorNot implemented in base BitGeneratorPickleErrorSeedSequenceSeedSequence.__reduce_cython__SeedSequence.__setstate_cython__SeedSequence expects int or sequence of ints for entropy not {}SeedSequence.generate_stateSeedSequence.spawnSeedlessSeedSequenceSeedlessSeedSequence.__reduce_cython__SeedlessSeedSequence.__setstate_cython__SeedlessSeedSequence.generate_stateSeedlessSeedSequence.spawnSeedlessSequenceThe size of the entropy pool should be at least The underlying SeedSequence does not implement spawning.TypeErrorValueError,)
.*?(abcabstractmethod__all__arrarrayastypeasyncio.coroutinesbase__bit_generator_ctorcline_in_tracebackcnt_coerce_to_uint32_array (line 81)compileconcatenatecopycycledata_val_dictdisableenable    entropy=errstateexpected non-negative integerformatgchash_const/home/appveyor/projects/mobile-forge/build/cp312/numpy/2.2.2/numpy/random/bit_generator.pyxii_dstignore__import____init_subclass___initializingisenableditertools__main__match__metaclass__method__mro_entries__nn_children    n_children_spawned=n_words__new__npnumpy._core.multiarray failed to importnumpy._core.umath failed to importonly support uint32 or uint64out_dtypeoutputover_picklepickle    pool_size=__prepare____pyx_PickleError__pyx_checksum__pyx_result__pyx_state__pyx_type__pyx_vtable__randbitsrangere__reduce_ex__registersecretsseedseed must be integerseedless SeedSequences cannot generate stateselfseqs__set_name__size    spawn_key=__spec__src_cyclestartswithstate_seed_seqsubseqssupersys__test__threading<u4<u8uint32uint64unrecognized seed stringupdateuse_setstatevviewxzeros__getstate____setstate__spawn
        spawn(n_children)

        Create new independent child bit generators.

        See :ref:`seedsequence-spawn` for additional notes on spawning
        children.  Some bit generators also implement ``jumped``
        as a different approach for creating independent streams.

        .. versionadded:: 1.25.0

        Parameters
        ----------
        n_children : int

        Returns
        -------
        child_bit_generators : list of BitGenerators

        Raises
        ------
        TypeError
            When the underlying SeedSequence does not implement spawning.

        See Also
        --------
        random.Generator.spawn, random.SeedSequence.spawn :
            Equivalent method on the generator and seed sequence.

        random_raw
        random_raw(self, size=None)

        Return randoms as generated by the underlying BitGenerator

        Parameters
        ----------
        size : int or tuple of ints, optional
            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
            ``m * n * k`` samples are drawn.  Default is None, in which case a
            single value is returned.
        output : bool, optional
            Output values.  Used for performance testing since the generated
            values are not returned.

        Returns
        -------
        out : uint or ndarray
            Drawn samples.

        Notes
        -----
        This method directly exposes the raw underlying pseudo-random
        number generator. All values are returned as unsigned 64-bit
        values irrespective of the number of bits produced by the PRNG.

        See the class docstring for the number of bits returned.
        _benchmarkUsed in testssstateBitGeneratorgenerate_state
        generate_state(n_words, dtype=np.uint32)

        Return the requested number of words for PRNG seeding.

        A BitGenerator should call this method in its constructor with
        an appropriate `n_words` parameter to properly seed itself.

        Parameters
        ----------
        n_words : int
        dtype : np.uint32 or np.uint64, optional
            The size of each word. This should only be either `uint32` or
            `uint64`. Strings (`'uint32'`, `'uint64'`) are fine. Note that
            requesting `uint64` will draw twice as many bits as `uint32` for
            the same `n_words`. This is a convenience for `BitGenerator`\ s
            that express their states as `uint64` arrays.

        Returns
        -------
        state : uint32 or uint64 array, shape=(n_words,)
        
        spawn(n_children)

        Spawn a number of child `SeedSequence` s by extending the
        `spawn_key`.

        See :ref:`seedsequence-spawn` for additional notes on spawning
        children.

        Parameters
        ----------
        n_children : int

        Returns
        -------
        seqs : list of `SeedSequence` s

        See Also
        --------
        random.Generator.spawn, random.BitGenerator.spawn :
            Equivalent method on the generator and bit generator.

        __reduce_cython____setstate_cython__entropyspawn_keypool_sizen_children_spawnednumpydtype_int_to_uint32_array_coerce_to_uint32_array Coerce an input to a uint32 array.

    If a `uint32` array, pass it through directly.
    If a non-negative integer, then break it up into `uint32` words, lowest
    bits first.
    If a string starting with "0x", then interpret as a hex integer, as above.
    If a string of decimal digits, interpret as a decimal integer, as above.
    If a sequence of ints or strings, interpret each element as above and
    concatenate.

    Note that the handling of `int64` or `uint64` arrays are not just
    straightforward views as `uint32` arrays. If an element is small enough to
    fit into a `uint32`, then it will only take up one `uint32` element in the
    output. This is to make sure that the interpretation of a sequence of
    integers is the same regardless of numpy's default integer type, which
    differs on different platforms.

    Parameters
    ----------
    x : int, str, sequence of int or str

    Returns
    -------
    seed_array : uint32 array

    Examples
    --------
    >>> import numpy as np
    >>> from numpy.random.bit_generator import _coerce_to_uint32_array
    >>> _coerce_to_uint32_array(12345)
    array([12345], dtype=uint32)
    >>> _coerce_to_uint32_array('12345')
    array([12345], dtype=uint32)
    >>> _coerce_to_uint32_array('0x12345')
    array([74565], dtype=uint32)
    >>> _coerce_to_uint32_array([12345, '67890'])
    array([12345, 67890], dtype=uint32)
    >>> _coerce_to_uint32_array(np.array([12345, 67890], dtype=np.uint32))
    array([12345, 67890], dtype=uint32)
    >>> _coerce_to_uint32_array(np.array([12345, 67890], dtype=np.int64))
    array([12345, 67890], dtype=uint32)
    >>> _coerce_to_uint32_array([12345, 0x10deadbeef, 67890, 0xdeadbeef])
    array([     12345, 3735928559,         16,      67890, 3735928559],
          dtype=uint32)
    >>> _coerce_to_uint32_array(1234567890123456789012345678901234567890)
    array([3460238034, 2898026390, 3235640248, 2697535605,          3],
          dtype=uint32)
    
        generate_state(n_words, dtype=np.uint32)

        Return the requested number of words for PRNG seeding.

        A BitGenerator should call this method in its constructor with
        an appropriate `n_words` parameter to properly seed itself.

        Parameters
        ----------
        n_words : int
        dtype : np.uint32 or np.uint64, optional
            The size of each word. This should only be either `uint32` or
            `uint64`. Strings (`'uint32'`, `'uint64'`) are fine. Note that
            requesting `uint64` will draw twice as many bits as `uint32` for
            the same `n_words`. This is a convenience for `BitGenerator`\ s that
            express their states as `uint64` arrays.

        Returns
        -------
        state : uint32 or uint64 array, shape=(n_words,)
        
        spawn(n_children)

        Spawn a number of child `SeedSequence` s by extending the
        `spawn_key`.

        See :ref:`seedsequence-spawn` for additional notes on spawning
        children.

        Parameters
        ----------
        n_children : int

        Returns
        -------
        seqs : list of `SeedSequence` s
        __pyx_unpickle_SeedlessSeedSequence__pyx_unpickle_SeedSequenceŸåàBVêì¡
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