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Merge remote-tracking branch 'cpython/main' into pythongh-89240 
2 parents b9113f5 + 6fbaba5 commit a3eb90b

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‎.gitattributes‎

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@@ -75,6 +75,7 @@ Include/internal/pycore_opcode.h generated
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Include/internal/pycore_opcode_metadata.h generated
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Include/internal/pycore_*_generated.h generated
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Include/opcode.h generated
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Include/opcode_ids.h generated
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Include/token.h generated
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Lib/_opcode_metadata.py generated
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Lib/keyword.py generated

‎Doc/c-api/buffer.rst‎

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@@ -161,10 +161,14 @@ a buffer, see :c:func:`PyObject_GetBuffer`.
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If it is ``0``, :c:member:`~Py_buffer.buf` points to a single item representing
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a scalar. In this case, :c:member:`~Py_buffer.shape`, :c:member:`~Py_buffer.strides`
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and :c:member:`~Py_buffer.suboffsets` MUST be ``NULL``.
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The maximum number of dimensions is given by :c:macro:`PyBUF_MAX_NDIM`.
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The macro :c:macro:`PyBUF_MAX_NDIM` limits the maximum number of dimensions
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to 64. Exporters MUST respect this limit, consumers of multi-dimensional
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buffers SHOULD be able to handle up to :c:macro:`PyBUF_MAX_NDIM` dimensions.
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.. :c:macro:: PyBUF_MAX_NDIM
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The maximum number of dimensions the memory represents.
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Exporters MUST respect this limit, consumers of multi-dimensional
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buffers SHOULD be able to handle up to :c:macro:`!PyBUF_MAX_NDIM` dimensions.
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Currently set to 64.
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.. c:member:: Py_ssize_t *shape
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‎Doc/c-api/typeobj.rst‎

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+------------------------------------------------+-----------------------------------+-------------------+---+---+---+---+
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| :c:member:`~PyTypeObject.tp_vectorcall` | :c:type:`vectorcallfunc` | | | | | |
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+------------------------------------------------+-----------------------------------+-------------------+---+---+---+---+
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| [:c:member:`~PyTypeObject.tp_watched`] | char | | | | | |
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| [:c:member:`~PyTypeObject.tp_watched`] | unsigned char | | | | | |
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+------------------------------------------------+-----------------------------------+-------------------+---+---+---+---+
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.. [#slots]
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.. versionadded:: 3.9 (the field exists since 3.8 but it's only used since 3.9)
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.. c:member:: char PyTypeObject.tp_watched
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.. c:member:: unsigned char PyTypeObject.tp_watched
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Internal. Do not use.
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‎Doc/includes/typestruct.h‎

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vectorcallfunc tp_vectorcall;
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/* bitset of which type-watchers care about this type */
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char tp_watched;
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unsigned char tp_watched;
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} PyTypeObject;

‎Doc/library/kde_example.png‎

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‎Doc/library/logging.rst‎

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@@ -397,21 +397,39 @@ have specific values relative to the predefined levels. If you define a level
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with the same numeric value, it overwrites the predefined value; the predefined
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name is lost.
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+-----------------------+---------------+
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| Level | Numeric value |
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+=======================+===============+
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| .. py:data:: CRITICAL | 50 |
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+-----------------------+---------------+
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| .. py:data:: ERROR | 40 |
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+-----------------------+---------------+
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| .. py:data:: WARNING | 30 |
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+-----------------------+---------------+
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| .. py:data:: INFO | 20 |
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+-----------------------+---------------+
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| .. py:data:: DEBUG | 10 |
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+-----------------------+---------------+
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| .. py:data:: NOTSET | 0 |
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+-----------------------+---------------+
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+-----------------------+---------------+-------------------------------------+
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| Level | Numeric value | What it means / When to use it |
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+=======================+===============+=====================================+
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| .. py:data:: NOTSET | 0 | When set on a logger, indicates that|
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| | | ancestor loggers are to be consulted|
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| | | to determine the effective level. |
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| | | If that still resolves to |
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| | | :const:`!NOTSET`, then all events |
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| | | are logged. When set on a handler, |
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| | | all events are handled. |
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+-----------------------+---------------+-------------------------------------+
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| .. py:data:: DEBUG | 10 | Detailed information, typically only|
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| | | of interest to a developer trying to|
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| | | diagnose a problem. |
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+-----------------------+---------------+-------------------------------------+
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| .. py:data:: INFO | 20 | Confirmation that things are working|
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| | | as expected. |
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+-----------------------+---------------+-------------------------------------+
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| .. py:data:: WARNING | 30 | An indication that something |
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| | | unexpected happened, or that a |
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| | | problem might occur in the near |
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| | | future (e.g. 'disk space low'). The |
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| | | software is still working as |
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| | | expected. |
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+-----------------------+---------------+-------------------------------------+
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| .. py:data:: ERROR | 40 | Due to a more serious problem, the |
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| | | software has not been able to |
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| | | perform some function. |
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+-----------------------+---------------+-------------------------------------+
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| .. py:data:: CRITICAL | 50 | A serious error, indicating that the|
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| | | program itself may be unable to |
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| | | continue running. |
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+-----------------------+---------------+-------------------------------------+
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.. _handler:

‎Doc/library/statistics.rst‎

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:class:`NormalDist` Examples and Recipes
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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Classic probability problems
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****************************
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:class:`NormalDist` readily solves classic probability problems.
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For example, given `historical data for SAT exams
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>>> list(map(round, sat.quantiles(n=10)))
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[810, 896, 958, 1011, 1060, 1109, 1162, 1224, 1310]
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Monte Carlo inputs for simulations
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**********************************
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To estimate the distribution for a model than isn't easy to solve
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analytically, :class:`NormalDist` can generate input samples for a `Monte
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Carlo simulation <https://en.wikipedia.org/wiki/Monte_Carlo_method>`_:
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>>> quantiles(map(model, X, Y, Z)) # doctest: +SKIP
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[1.4591308524824727, 1.8035946855390597, 2.175091447274739]
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Approximating binomial distributions
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************************************
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Normal distributions can be used to approximate `Binomial
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distributions <https://mathworld.wolfram.com/BinomialDistribution.html>`_
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when the sample size is large and when the probability of a successful
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>>> mean(trial() <= k for i in range(10_000))
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Naive bayesian classifier
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*************************
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Normal distributions commonly arise in machine learning problems.
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Wikipedia has a `nice example of a Naive Bayesian Classifier
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'female'
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Kernel density estimation
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*************************
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It is possible to estimate a continuous probability density function
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from a fixed number of discrete samples.
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The basic idea is to smooth the data using `a kernel function such as a
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normal distribution, triangular distribution, or uniform distribution
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<https://en.wikipedia.org/wiki/Kernel_(statistics)#Kernel_functions_in_common_use>`_.
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The degree of smoothing is controlled by a single
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parameter, ``h``, representing the variance of the kernel function.
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.. testcode::
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import math
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def kde_normal(sample, h):
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"Create a continous probability density function from a sample."
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# Smooth the sample with a normal distribution of variance h.
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kernel_h = NormalDist(0.0, math.sqrt(h)).pdf
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n = len(sample)
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def pdf(x):
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return sum(kernel_h(x - x_i) for x_i in sample) / n
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return pdf
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`Wikipedia has an example
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<https://en.wikipedia.org/wiki/Kernel_density_estimation#Example>`_
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where we can use the ``kde_normal()`` recipe to generate and plot
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a probability density function estimated from a small sample:
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.. doctest::
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>>> sample = [-2.1, -1.3, -0.4, 1.9, 5.1, 6.2]
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>>> f_hat = kde_normal(sample, h=2.25)
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>>> xarr = [i/100 for i in range(-750, 1100)]
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>>> yarr = [f_hat(x) for x in xarr]
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The points in ``xarr`` and ``yarr`` can be used to make a PDF plot:
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.. image:: kde_example.png
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:alt: Scatter plot of the estimated probability density function.
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..
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# This modelines must appear within the last ten lines of the file.
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kate: indent-width 3; remove-trailing-space on; replace-tabs on; encoding utf-8;

‎Doc/whatsnew/3.12.rst‎

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1,13-1,17: FSTRING_MIDDLE ' end'
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1,17-1,18: FSTRING_END '"'
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Additionally, there may be some minor behavioral changes as a consecuence of the
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Additionally, there may be some minor behavioral changes as a consequence of the
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changes required to support :pep:`701`. Some of these changes include:
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* The ``type`` attribute of the tokens emitted when tokenizing some invalid Python

‎Include/cpython/object.h‎

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vectorcallfunc tp_vectorcall;
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/* bitset of which type-watchers care about this type */
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char tp_watched;
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unsigned char tp_watched;
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};
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/* This struct is used by the specializer

‎Include/internal/pycore_object.h‎

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extern PyObject* _PyType_GetDocFromInternalDoc(const char *, const char *);
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extern PyObject* _PyType_GetTextSignatureFromInternalDoc(const char *, const char *);
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extern PyObject* _PyType_GetTextSignatureFromInternalDoc(const char *, const char *, int);
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extern int _PyObject_InitializeDict(PyObject *obj);
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int _PyObject_InitInlineValues(PyObject *obj, PyTypeObject *tp);

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