diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/__init__.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/__init__.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..7ad2684aa79650e5349e97fabb961b477c53f937
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/__init__.pxd
@@ -0,0 +1,187 @@
+#####################################################################
+#
+# These are the Cython pxd files for (most of) the Python/C API.
+#
+# REFERENCE COUNTING:
+#
+#   JUST TO SCARE YOU:
+#   If you are going to use any of the Python/C API in your Cython
+#   program, you might be responsible for doing reference counting.
+#   Read http://docs.python.org/api/refcounts.html which is so
+#   important I've copied it below.
+#
+# For all the declaration below, whenever the Py_ function returns
+# a *new reference* to a PyObject*, the return type is "object".
+# When the function returns a borrowed reference, the return
+# type is PyObject*.  When Cython sees "object" as a return type
+# it doesn't increment the reference count.  When it sees PyObject*
+# in order to use the result you must explicitly cast to <object>,
+# and when you do that Cython increments the reference count whether
+# you want it to or not, forcing you to an explicit DECREF (or leak memory).
+# To avoid this we make the above convention.  Note, you can
+# always locally override this convention by putting something like
+#
+#     cdef extern from "Python.h":
+#         PyObject* PyNumber_Add(PyObject *o1, PyObject *o2)
+#
+# in your .pyx file or into a cimported .pxd file.  You just have to
+# use the one from the right (pxd-)namespace then.
+#
+# Cython automatically takes care of reference counting for anything
+# of type object.
+#
+## More precisely, I think the correct convention for
+## using the Python/C API from Cython is as follows.
+##
+## (1) Declare all input arguments as type "object".  This way no explicit
+##    <PyObject*> casting is needed, and moreover Cython doesn't generate
+##    any funny reference counting.
+## (2) Declare output as object if a new reference is returned.
+## (3) Declare output as PyObject* if a borrowed reference is returned.
+##
+## This way when you call objects, no cast is needed, and if the api
+## calls returns a new reference (which is about 95% of them), then
+## you can just assign to a variable of type object.  With borrowed
+## references if you do an explicit typecast to <object>, Cython generates an
+## INCREF and DECREF so you have to be careful.  However, you got a
+## borrowed reference in this case, so there's got to be another reference
+## to your object, so you're OK, as long as you relealize this
+## and use the result of an explicit cast to <object> as a borrowed
+## reference (and you can call Py_INCREF if you want to turn it
+## into another reference for some reason).
+#
+# "The reference count is important because today's computers have
+# a finite (and often severely limited) memory size; it counts how
+# many different places there are that have a reference to an
+# object. Such a place could be another object, or a global (or
+# static) C variable, or a local variable in some C function. When
+# an object's reference count becomes zero, the object is
+# deallocated. If it contains references to other objects, their
+# reference count is decremented. Those other objects may be
+# deallocated in turn, if this decrement makes their reference
+# count become zero, and so on. (There's an obvious problem with
+# objects that reference each other here; for now, the solution is
+# ``don't do that.'')
+#
+# Reference counts are always manipulated explicitly. The normal
+# way is to use the macro Py_INCREF() to increment an object's
+# reference count by one, and Py_DECREF() to decrement it by
+# one. The Py_DECREF() macro is considerably more complex than the
+# incref one, since it must check whether the reference count
+# becomes zero and then cause the object's deallocator to be
+# called. The deallocator is a function pointer contained in the
+# object's type structure. The type-specific deallocator takes
+# care of decrementing the reference counts for other objects
+# contained in the object if this is a compound object type, such
+# as a list, as well as performing any additional finalization
+# that's needed. There's no chance that the reference count can
+# overflow; at least as many bits are used to hold the reference
+# count as there are distinct memory locations in virtual memory
+# (assuming sizeof(long) >= sizeof(char*)). Thus, the reference
+# count increment is a simple operation.
+#
+# It is not necessary to increment an object's reference count for
+# every local variable that contains a pointer to an object. In
+# theory, the object's reference count goes up by one when the
+# variable is made to point to it and it goes down by one when the
+# variable goes out of scope. However, these two cancel each other
+# out, so at the end the reference count hasn't changed. The only
+# real reason to use the reference count is to prevent the object
+# from being deallocated as long as our variable is pointing to
+# it. If we know that there is at least one other reference to the
+# object that lives at least as long as our variable, there is no
+# need to increment the reference count temporarily. An important
+# situation where this arises is in objects that are passed as
+# arguments to C functions in an extension module that are called
+# from Python; the call mechanism guarantees to hold a reference
+# to every argument for the duration of the call.
+#
+# However, a common pitfall is to extract an object from a list
+# and hold on to it for a while without incrementing its reference
+# count. Some other operation might conceivably remove the object
+# from the list, decrementing its reference count and possible
+# deallocating it. The real danger is that innocent-looking
+# operations may invoke arbitrary Python code which could do this;
+# there is a code path which allows control to flow back to the
+# user from a Py_DECREF(), so almost any operation is potentially
+# dangerous.
+#
+# A safe approach is to always use the generic operations
+# (functions whose name begins with "PyObject_", "PyNumber_",
+# "PySequence_" or "PyMapping_"). These operations always
+# increment the reference count of the object they return. This
+# leaves the caller with the responsibility to call Py_DECREF()
+# when they are done with the result; this soon becomes second
+# nature.
+#
+# Now you should read http://docs.python.org/api/refcountDetails.html
+# just to be sure you understand what is going on.
+#
+#################################################################
+
+
+
+#################################################################
+# BIG FAT DEPRECATION WARNING
+#################################################################
+# Do NOT cimport any names directly from the cpython package,
+# despite of the star-imports below.  They will be removed at
+# some point.
+# Instead, use the correct sub-module to draw your cimports from.
+#
+# A direct cimport from the package will make your code depend on
+# all of the existing declarations. This may have side-effects
+# and reduces the portability of your code.
+#################################################################
+# START OF DEPRECATED SECTION
+#################################################################
+
+from cpython.version cimport *
+from cpython.ref cimport *
+from cpython.exc cimport *
+from cpython.module cimport *
+from cpython.mem cimport *
+from cpython.tuple cimport *
+from cpython.list cimport *
+from cpython.object cimport *
+from cpython.sequence cimport *
+from cpython.mapping cimport *
+from cpython.iterator cimport *
+from cpython.type cimport *
+from cpython.number cimport *
+from cpython.int cimport *
+from cpython.bool cimport *
+from cpython.long cimport *
+from cpython.float cimport *
+from cpython.complex cimport *
+from cpython.string cimport *
+from cpython.unicode cimport *
+from cpython.dict cimport *
+from cpython.instance cimport *
+from cpython.function cimport *
+from cpython.method cimport *
+from cpython.weakref cimport *
+from cpython.getargs cimport *
+from cpython.pythread cimport *
+from cpython.pystate cimport *
+
+# Python <= 2.x
+from cpython.cobject cimport *
+from cpython.oldbuffer cimport *
+
+# Python >= 2.4
+from cpython.set cimport *
+
+# Python >= 2.6
+from cpython.buffer cimport *
+from cpython.bytes cimport *
+
+# Python >= 3.0
+from cpython.pycapsule cimport *
+
+# Python >= 3.7
+from cpython.contextvars cimport *
+
+#################################################################
+# END OF DEPRECATED SECTION
+#################################################################
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/array.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/array.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..c64e972775b1aa3cdb3db7607d3d20aca799515f
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/array.pxd
@@ -0,0 +1,174 @@
+"""
+  array.pxd
+
+  Cython interface to Python's array.array module.
+
+  * 1D contiguous data view
+  * tools for fast array creation, maximum C-speed and handiness
+  * suitable as allround light weight auto-array within Cython code too
+
+  Usage:
+
+  >>> cimport array
+
+  Usage through Cython buffer interface (Py2.3+):
+
+    >>> def f(arg1, unsigned i, double dx)
+    ...     array.array[double] a = arg1
+    ...     a[i] += dx
+
+  Fast C-level new_array(_zeros), resize_array, copy_array, Py_SIZE(obj),
+  zero_array
+
+    cdef array.array[double] k = array.copy(d)
+    cdef array.array[double] n = array.array(d, Py_SIZE(d) * 2 )
+    cdef array.array[double] m = array.zeros_like(FLOAT_TEMPLATE)
+    array.resize(f, 200000)
+
+  Zero overhead with naked data pointer views by union:
+  _f, _d, _i, _c, _u, ...
+  => Original C array speed + Python dynamic memory management
+
+    cdef array.array a = inarray
+    if
+    a._d[2] += 0.66   # use as double array without extra casting
+
+    float *subview = vector._f + 10  # starting from 10th element
+    unsigned char *subview_buffer = vector._B + 4
+
+  Suitable as lightweight arrays intra Cython without speed penalty.
+  Replacement for C stack/malloc arrays; no trouble with refcounting,
+  mem.leaks; seamless Python compatibility, buffer() optional
+
+
+  last changes: 2009-05-15 rk
+              : 2009-12-06 bp
+              : 2012-05-02 andreasvc
+              : (see revision control)
+"""
+
+cdef extern from *:
+    """
+    #if CYTHON_COMPILING_IN_PYPY
+    #ifdef _MSC_VER
+    #pragma message ("This module uses CPython specific internals of 'array.array', which are not available in PyPy.")
+    #else
+    #warning This module uses CPython specific internals of 'array.array', which are not available in PyPy.
+    #endif
+    #endif
+    """
+
+from libc.string cimport memset, memcpy
+
+from cpython.object cimport Py_SIZE
+from cpython.ref cimport PyTypeObject, Py_TYPE
+from cpython.exc cimport PyErr_BadArgument
+from cpython.mem cimport PyObject_Malloc, PyObject_Free
+
+cdef extern from *:  # Hard-coded utility code hack.
+    ctypedef class array.array [object arrayobject]
+    ctypedef object GETF(array a, Py_ssize_t ix)
+    ctypedef object SETF(array a, Py_ssize_t ix, object o)
+    ctypedef struct arraydescr:  # [object arraydescr]:
+            char typecode
+            int itemsize
+            GETF getitem    # PyObject * (*getitem)(struct arrayobject *, Py_ssize_t);
+            SETF setitem    # int (*setitem)(struct arrayobject *, Py_ssize_t, PyObject *);
+
+    ctypedef union __data_union:
+        # views of ob_item:
+        float* as_floats        # direct float pointer access to buffer
+        double* as_doubles      # double ...
+        int*    as_ints
+        unsigned int *as_uints
+        unsigned char *as_uchars
+        signed char *as_schars
+        char *as_chars
+        unsigned long *as_ulongs
+        long *as_longs
+        unsigned long long *as_ulonglongs
+        long long *as_longlongs
+        short *as_shorts
+        unsigned short *as_ushorts
+        Py_UNICODE *as_pyunicodes
+        void *as_voidptr
+
+    ctypedef class array.array [object arrayobject]:
+        cdef __cythonbufferdefaults__ = {'ndim' : 1, 'mode':'c'}
+
+        cdef:
+            Py_ssize_t ob_size
+            arraydescr* ob_descr    # struct arraydescr *ob_descr;
+            __data_union data
+
+        def __getbuffer__(self, Py_buffer* info, int flags):
+            # This implementation of getbuffer is geared towards Cython
+            # requirements, and does not yet fulfill the PEP.
+            # In particular strided access is always provided regardless
+            # of flags
+            item_count = Py_SIZE(self)
+
+            info.suboffsets = NULL
+            info.buf = self.data.as_chars
+            info.readonly = 0
+            info.ndim = 1
+            info.itemsize = self.ob_descr.itemsize   # e.g. sizeof(float)
+            info.len = info.itemsize * item_count
+
+            info.shape = <Py_ssize_t*> PyObject_Malloc(sizeof(Py_ssize_t) + 2)
+            if not info.shape:
+                raise MemoryError()
+            info.shape[0] = item_count      # constant regardless of resizing
+            info.strides = &info.itemsize
+
+            info.format = <char*> (info.shape + 1)
+            info.format[0] = self.ob_descr.typecode
+            info.format[1] = 0
+            info.obj = self
+
+        def __releasebuffer__(self, Py_buffer* info):
+            PyObject_Free(info.shape)
+
+    array newarrayobject(PyTypeObject* type, Py_ssize_t size, arraydescr *descr)
+
+    # fast resize/realloc
+    # not suitable for small increments; reallocation 'to the point'
+    int resize(array self, Py_ssize_t n) except -1
+    # efficient for small increments (not in Py2.3-)
+    int resize_smart(array self, Py_ssize_t n) except -1
+
+
+cdef inline array clone(array template, Py_ssize_t length, bint zero):
+    """ fast creation of a new array, given a template array.
+    type will be same as template.
+    if zero is true, new array will be initialized with zeroes."""
+    cdef array op = newarrayobject(Py_TYPE(template), length, template.ob_descr)
+    if zero and op is not None:
+        memset(op.data.as_chars, 0, length * op.ob_descr.itemsize)
+    return op
+
+cdef inline array copy(array self):
+    """ make a copy of an array. """
+    cdef array op = newarrayobject(Py_TYPE(self), Py_SIZE(self), self.ob_descr)
+    memcpy(op.data.as_chars, self.data.as_chars, Py_SIZE(op) * op.ob_descr.itemsize)
+    return op
+
+cdef inline int extend_buffer(array self, char* stuff, Py_ssize_t n) except -1:
+    """ efficient appending of new stuff of same type
+    (e.g. of same array type)
+    n: number of elements (not number of bytes!) """
+    cdef Py_ssize_t itemsize = self.ob_descr.itemsize
+    cdef Py_ssize_t origsize = Py_SIZE(self)
+    resize_smart(self, origsize + n)
+    memcpy(self.data.as_chars + origsize * itemsize, stuff, n * itemsize)
+    return 0
+
+cdef inline int extend(array self, array other) except -1:
+    """ extend array with data from another array; types must match. """
+    if self.ob_descr.typecode != other.ob_descr.typecode:
+        PyErr_BadArgument()
+    return extend_buffer(self, other.data.as_chars, Py_SIZE(other))
+
+cdef inline void zero(array self) noexcept:
+    """ set all elements of array to zero. """
+    memset(self.data.as_chars, 0, Py_SIZE(self) * self.ob_descr.itemsize)
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/cellobject.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/cellobject.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..5e3dd3d63c4440693ee72baa02800807c7e49c3f
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/cellobject.pxd
@@ -0,0 +1,35 @@
+from .object cimport PyObject
+
+cdef extern from "Python.h":
+
+    ############################################################################
+    # Cell Objects
+    ############################################################################
+
+    bint PyCell_Check(object ob)
+    # Return true if ob is a cell object; ob must not be NULL.
+
+    object PyCell_New(PyObject* ob)
+    # Return value: New reference.
+    # Create and return a new cell object containing the value ob. The
+    # parameter may be NULL.
+
+    object PyCell_Get(object cell)
+    # Return value: New reference.
+    # Return the contents of the cell object cell.
+
+    object PyCell_GET(object cell)
+    # Return value: Borrowed reference.
+    # Return the contents of the cell object cell, but without checking that
+    # cell is non-NULL and is a cell object.
+
+    int PyCell_Set(object cell, PyObject* value) except? -1
+    # Set the contents of the cell object cell to value. This releases the
+    # reference to any current content of the cell. value may be NULL. cell
+    # must be non-NULL; if it is not a cell object, -1 will be returned. On
+    # success, 0 will be returned.
+
+    void PyCell_SET(object cell, PyObject* value)
+    # Sets the value of the cell object cell to value. No reference counts are
+    # adjusted, and no checks are made for safety; cell must be non-NULL and
+    # must be a cell object.
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/complex.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/complex.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..c0147547ce3db8ad6ba6b33e6310f30275e09abe
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/complex.pxd
@@ -0,0 +1,55 @@
+
+cdef extern from "Python.h":
+
+    ctypedef struct Py_complex:
+        double imag
+        double real
+
+    ############################################################################
+    # 7.2.5.2 Complex Numbers as Python Objects
+    ############################################################################
+
+    # PyComplexObject
+    # This subtype of PyObject represents a Python complex number object.
+
+    ctypedef class __builtin__.complex [object PyComplexObject]:
+        cdef Py_complex cval
+
+        @property
+        cdef inline double real(self) noexcept:
+            return self.cval.real
+
+        @property
+        cdef inline double imag(self) noexcept:
+            return self.cval.imag
+
+    # PyTypeObject PyComplex_Type
+    # This instance of PyTypeObject represents the Python complex
+    # number type. It is the same object as complex and
+    # types.ComplexType.
+
+    bint PyComplex_Check(object p)
+    # Return true if its argument is a PyComplexObject or a subtype of
+    # PyComplexObject.
+
+    bint PyComplex_CheckExact(object p)
+    # Return true if its argument is a PyComplexObject, but not a subtype of PyComplexObject.
+
+    object PyComplex_FromCComplex(Py_complex v)
+    # Return value: New reference.
+    # Create a new Python complex number object from a C Py_complex value.
+
+    object PyComplex_FromDoubles(double real, double imag)
+    # Return value: New reference.
+    # Return a new PyComplexObject object from real and imag.
+
+    double PyComplex_RealAsDouble(object op) except? -1
+    # Return the real part of op as a C double.
+
+    double PyComplex_ImagAsDouble(object op) except? -1
+    # Return the imaginary part of op as a C double.
+
+    Py_complex PyComplex_AsCComplex(object op)
+    # Return the Py_complex value of the complex number op.
+    #
+    # Returns (-1+0i) in case of an error
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/exc.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/exc.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..756342ad3af47c7d97301f91505383b2c3d432a7
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/exc.pxd
@@ -0,0 +1,263 @@
+from .object cimport PyObject
+
+cdef extern from "Python.h":
+
+    #####################################################################
+    # 3. Exception Handling
+    #####################################################################
+
+    # The functions described in this chapter will let you handle and
+    # raise Python exceptions. It is important to understand some of
+    # the basics of Python exception handling. It works somewhat like
+    # the Unix errno variable: there is a global indicator (per
+    # thread) of the last error that occurred. Most functions don't
+    # clear this on success, but will set it to indicate the cause of
+    # the error on failure. Most functions also return an error
+    # indicator, usually NULL if they are supposed to return a
+    # pointer, or -1 if they return an integer (exception: the
+    # PyArg_*() functions return 1 for success and 0 for failure).
+
+    # When a function must fail because some function it called
+    # failed, it generally doesn't set the error indicator; the
+    # function it called already set it. It is responsible for either
+    # handling the error and clearing the exception or returning after
+    # cleaning up any resources it holds (such as object references or
+    # memory allocations); it should not continue normally if it is
+    # not prepared to handle the error. If returning due to an error,
+    # it is important to indicate to the caller that an error has been
+    # set. If the error is not handled or carefully propagated,
+    # additional calls into the Python/C API may not behave as
+    # intended and may fail in mysterious ways.
+
+    # The error indicator consists of three Python objects
+    # corresponding to the Python variables sys.exc_type,
+    # sys.exc_value and sys.exc_traceback. API functions exist to
+    # interact with the error indicator in various ways. There is a
+    # separate error indicator for each thread.
+
+    void PyErr_Print()
+    # Print a standard traceback to sys.stderr and clear the error
+    # indicator. Call this function only when the error indicator is
+    # set. (Otherwise it will cause a fatal error!)
+
+    PyObject* PyErr_Occurred()
+    # Return value: Borrowed reference.
+    # Test whether the error indicator is set. If set, return the
+    # exception type (the first argument to the last call to one of
+    # the PyErr_Set*() functions or to PyErr_Restore()). If not set,
+    # return NULL. You do not own a reference to the return value, so
+    # you do not need to Py_DECREF() it. Note: Do not compare the
+    # return value to a specific exception; use
+    # PyErr_ExceptionMatches() instead, shown below. (The comparison
+    # could easily fail since the exception may be an instance instead
+    # of a class, in the case of a class exception, or it may be a
+    # subclass of the expected exception.)
+
+    bint PyErr_ExceptionMatches(object exc)
+    # Equivalent to "PyErr_GivenExceptionMatches(PyErr_Occurred(),
+    # exc)". This should only be called when an exception is actually
+    # set; a memory access violation will occur if no exception has
+    # been raised.
+
+    bint PyErr_GivenExceptionMatches(object given, object exc)
+    # Return true if the given exception matches the exception in
+    # exc. If exc is a class object, this also returns true when given
+    # is an instance of a subclass. If exc is a tuple, all exceptions
+    # in the tuple (and recursively in subtuples) are searched for a
+    # match. If given is NULL, a memory access violation will occur.
+
+    void PyErr_NormalizeException(PyObject** exc, PyObject** val, PyObject** tb)
+    # Under certain circumstances, the values returned by
+    # PyErr_Fetch() below can be ``unnormalized'', meaning that *exc
+    # is a class object but *val is not an instance of the same
+    # class. This function can be used to instantiate the class in
+    # that case. If the values are already normalized, nothing
+    # happens. The delayed normalization is implemented to improve
+    # performance.
+
+    void PyErr_Clear()
+    # Clear the error indicator. If the error indicator is not set, there is no effect.
+
+    void PyErr_Fetch(PyObject** ptype, PyObject** pvalue, PyObject** ptraceback)
+    # Retrieve the error indicator into three variables whose
+    # addresses are passed. If the error indicator is not set, set all
+    # three variables to NULL. If it is set, it will be cleared and
+    # you own a reference to each object retrieved. The value and
+    # traceback object may be NULL even when the type object is
+    # not. Note: This function is normally only used by code that
+    # needs to handle exceptions or by code that needs to save and
+    # restore the error indicator temporarily.
+
+    PyObject* PyErr_GetHandledException()
+    void PyErr_SetHandledException(PyObject* exc)
+    PyObject* PyErr_GetRaisedException()
+    void PyErr_SetRaisedException(PyObject* exc)
+
+    void PyErr_Restore(PyObject* type, PyObject* value, PyObject* traceback)
+    # Set the error indicator from the three objects. If the error
+    # indicator is already set, it is cleared first. If the objects
+    # are NULL, the error indicator is cleared. Do not pass a NULL
+    # type and non-NULL value or traceback. The exception type should
+    # be a class. Do not pass an invalid exception type or
+    # value. (Violating these rules will cause subtle problems later.)
+    # This call takes away a reference to each object: you must own a
+    # reference to each object before the call and after the call you
+    # no longer own these references. (If you don't understand this,
+    # don't use this function. I warned you.) Note: This function is
+    # normally only used by code that needs to save and restore the
+    # error indicator temporarily; use PyErr_Fetch() to save the
+    # current exception state.
+
+    void PyErr_SetString(object type, char *message)
+    # This is the most common way to set the error indicator. The
+    # first argument specifies the exception type; it is normally one
+    # of the standard exceptions, e.g. PyExc_RuntimeError. You need
+    # not increment its reference count. The second argument is an
+    # error message; it is converted to a string object.
+
+    void PyErr_SetObject(object type, object value)
+    # This function is similar to PyErr_SetString() but lets you
+    # specify an arbitrary Python object for the ``value'' of the
+    # exception.
+
+    PyObject* PyErr_Format(object exception, char *format, ...) except NULL
+    # Return value: Always NULL.
+    # This function sets the error indicator and returns
+    # NULL. exception should be a Python exception (class, not an
+    # instance). format should be a string, containing format codes,
+    # similar to printf(). The width.precision before a format code is
+    # parsed, but the width part is ignored.
+
+    void PyErr_SetNone(object type)
+    # This is a shorthand for "PyErr_SetObject(type, Py_None)".
+
+    int PyErr_BadArgument() except 0
+
+    # This is a shorthand for "PyErr_SetString(PyExc_TypeError,
+    # message)", where message indicates that a built-in operation was
+    # invoked with an illegal argument. It is mostly for internal use.
+
+    PyObject* PyErr_NoMemory() except NULL
+    # Return value: Always NULL.
+    # This is a shorthand for "PyErr_SetNone(PyExc_MemoryError)"; it
+    # returns NULL so an object allocation function can write "return
+    # PyErr_NoMemory();" when it runs out of memory.
+
+    PyObject* PyErr_SetFromErrno(object type) except NULL
+    # Return value: Always NULL.
+    # This is a convenience function to raise an exception when a C
+    # library function has returned an error and set the C variable
+    # errno. It constructs a tuple object whose first item is the
+    # integer errno value and whose second item is the corresponding
+    # error message (gotten from strerror()), and then calls
+    # "PyErr_SetObject(type, object)". On Unix, when the errno value
+    # is EINTR, indicating an interrupted system call, this calls
+    # PyErr_CheckSignals(), and if that set the error indicator,
+    # leaves it set to that. The function always returns NULL, so a
+    # wrapper function around a system call can write "return
+    # PyErr_SetFromErrno(type);" when the system call returns an
+    # error.
+
+    PyObject* PyErr_SetFromErrnoWithFilenameObject(object type, object filenameObject) except NULL
+    # Similar to PyErr_SetFromErrno(), with the additional behavior
+    # that if filenameObject is not NULL, it is passed to the
+    # constructor of type as a third parameter.
+    # In the case of OSError exception, this is used to define
+    # the filename attribute of the exception instance.
+
+    PyObject* PyErr_SetFromErrnoWithFilename(object type, char *filename) except NULL
+    # Return value: Always NULL.  Similar to PyErr_SetFromErrno(),
+    # with the additional behavior that if filename is not NULL, it is
+    # passed to the constructor of type as a third parameter. In the
+    # case of exceptions such as IOError and OSError, this is used to
+    # define the filename attribute of the exception instance.
+
+    PyObject* PyErr_SetFromWindowsErr(int ierr) except NULL
+    # Return value: Always NULL.  This is a convenience function to
+    # raise WindowsError. If called with ierr of 0, the error code
+    # returned by a call to GetLastError() is used instead. It calls
+    # the Win32 function FormatMessage() to retrieve the Windows
+    # description of error code given by ierr or GetLastError(), then
+    # it constructs a tuple object whose first item is the ierr value
+    # and whose second item is the corresponding error message (gotten
+    # from FormatMessage()), and then calls
+    # "PyErr_SetObject(PyExc_WindowsError, object)". This function
+    # always returns NULL. Availability: Windows.
+
+    PyObject* PyErr_SetExcFromWindowsErr(object type, int ierr) except NULL
+    # Return value: Always NULL.  Similar to
+    # PyErr_SetFromWindowsErr(), with an additional parameter
+    # specifying the exception type to be raised. Availability:
+    # Windows. New in version 2.3.
+
+    PyObject* PyErr_SetFromWindowsErrWithFilename(int ierr, char *filename) except NULL
+    # Return value: Always NULL.  Similar to
+    # PyErr_SetFromWindowsErr(), with the additional behavior that if
+    # filename is not NULL, it is passed to the constructor of
+    # WindowsError as a third parameter. Availability: Windows.
+
+    PyObject* PyErr_SetExcFromWindowsErrWithFilename(object type, int ierr, char *filename) except NULL
+    # Return value: Always NULL.
+    # Similar to PyErr_SetFromWindowsErrWithFilename(), with an
+    # additional parameter specifying the exception type to be
+    # raised. Availability: Windows.
+
+    void PyErr_BadInternalCall()
+    # This is a shorthand for "PyErr_SetString(PyExc_TypeError,
+    # message)", where message indicates that an internal operation
+    # (e.g. a Python/C API function) was invoked with an illegal
+    # argument. It is mostly for internal use.
+
+    int PyErr_WarnEx(object category, char *message, int stacklevel) except -1
+    # Issue a warning message. The category argument is a warning
+    # category (see below) or NULL; the message argument is a message
+    # string. stacklevel is a positive number giving a number of stack
+    # frames; the warning will be issued from the currently executing
+    # line of code in that stack frame. A stacklevel of 1 is the
+    # function calling PyErr_WarnEx(), 2 is the function above that,
+    # and so forth.
+
+    int PyErr_WarnExplicit(object category, char *message, char *filename, int lineno, char *module, object registry) except -1
+    # Issue a warning message with explicit control over all warning
+    # attributes. This is a straightforward wrapper around the Python
+    # function warnings.warn_explicit(), see there for more
+    # information. The module and registry arguments may be set to
+    # NULL to get the default effect described there.
+
+    int PyErr_CheckSignals() except -1
+    # This function interacts with Python's signal handling. It checks
+    # whether a signal has been sent to the processes and if so,
+    # invokes the corresponding signal handler. If the signal module
+    # is supported, this can invoke a signal handler written in
+    # Python. In all cases, the default effect for SIGINT is to raise
+    # the KeyboardInterrupt exception. If an exception is raised the
+    # error indicator is set and the function returns 1; otherwise the
+    # function returns 0. The error indicator may or may not be
+    # cleared if it was previously set.
+
+    void PyErr_SetInterrupt() nogil
+    # This function simulates the effect of a SIGINT signal arriving
+    # -- the next time PyErr_CheckSignals() is called,
+    # KeyboardInterrupt will be raised. It may be called without
+    # holding the interpreter lock.
+
+    int PyErr_SetInterruptEx(int signum)
+
+    object PyErr_NewException(char *name, object base, object dict)
+    # Return value: New reference.
+    # This utility function creates and returns a new exception
+    # object. The name argument must be the name of the new exception,
+    # a C string of the form module.class. The base and dict arguments
+    # are normally NULL. This creates a class object derived from
+    # Exception (accessible in C as PyExc_Exception).
+
+    void PyErr_WriteUnraisable(object obj)
+    # This utility function prints a warning message to sys.stderr
+    # when an exception has been set but it is impossible for the
+    # interpreter to actually raise the exception. It is used, for
+    # example, when an exception occurs in an __del__() method.
+    #
+    # The function is called with a single argument obj that
+    # identifies the context in which the unraisable exception
+    # occurred. The repr of obj will be printed in the warning
+    # message.
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/float.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/float.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..7c567a80fcc4268aa39a2c78beb56a80ef046f2c
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/float.pxd
@@ -0,0 +1,46 @@
+cdef extern from "Python.h":
+    """
+    #if PY_MAJOR_VERSION >= 3
+      #define __Pyx_PyFloat_FromString(obj)  PyFloat_FromString(obj)
+    #else
+      #define __Pyx_PyFloat_FromString(obj)  PyFloat_FromString(obj, NULL)
+    #endif
+    """
+
+    ############################################################################
+    # 7.2.3
+    ############################################################################
+    # PyFloatObject
+    #
+    # This subtype of PyObject represents a Python floating point object.
+
+    # PyTypeObject PyFloat_Type
+    #
+    # This instance of PyTypeObject represents the Python floating
+    # point type. This is the same object as float and
+    # types.FloatType.
+
+    bint PyFloat_Check(object p)
+    # Return true if its argument is a PyFloatObject or a subtype of
+    # PyFloatObject.
+
+    bint PyFloat_CheckExact(object p)
+    # Return true if its argument is a PyFloatObject, but not a
+    # subtype of PyFloatObject.
+
+    object PyFloat_FromString "__Pyx_PyFloat_FromString" (object str)
+    # Return value: New reference.
+    # Create a PyFloatObject object based on the string value in str,
+    # or NULL on failure. The pend argument is ignored. It remains
+    # only for backward compatibility.
+
+    object PyFloat_FromDouble(double v)
+    # Return value: New reference.
+    # Create a PyFloatObject object from v, or NULL on failure.
+
+    double PyFloat_AsDouble(object pyfloat) except? -1
+    # Return a C double representation of the contents of pyfloat.
+
+    double PyFloat_AS_DOUBLE(object pyfloat)
+    # Return a C double representation of the contents of pyfloat, but
+    # without error checking.
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/function.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/function.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..0002a3f6cbc426dc05dbdfce454da44483fbfa3e
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/function.pxd
@@ -0,0 +1,65 @@
+from .object cimport PyObject
+
+cdef extern from "Python.h":
+
+    ############################################################################
+    # 7.5.3 Function Objects
+    ############################################################################
+    # There are a few functions specific to Python functions.
+
+    # PyFunctionObject
+    #
+    # The C structure used for functions.
+
+    # PyTypeObject PyFunction_Type
+    #
+    # This is an instance of PyTypeObject and represents the Python
+    # function type. It is exposed to Python programmers as
+    # types.FunctionType.
+
+    bint PyFunction_Check(object o)
+    # Return true if o is a function object (has type
+    # PyFunction_Type). The parameter must not be NULL.
+
+    object PyFunction_New(object code, object globals)
+    # Return value: New reference.
+    # Return a new function object associated with the code object
+    # code. globals must be a dictionary with the global variables
+    # accessible to the function.
+    # The function's docstring, name and __module__ are retrieved from
+    # the code object, the argument defaults and closure are set to
+    # NULL.
+
+    PyObject* PyFunction_GetCode(object op) except? NULL
+    # Return value: Borrowed reference.
+    # Return the code object associated with the function object op.
+
+    PyObject* PyFunction_GetGlobals(object op) except? NULL
+    # Return value: Borrowed reference.
+    # Return the globals dictionary associated with the function object op.
+
+    PyObject* PyFunction_GetModule(object op) except? NULL
+    # Return value: Borrowed reference.
+    # Return the __module__ attribute of the function object op. This
+    # is normally a string containing the module name, but can be set
+    # to any other object by Python code.
+
+    PyObject* PyFunction_GetDefaults(object op) except? NULL
+    # Return value: Borrowed reference.
+    # Return the argument default values of the function object
+    # op. This can be a tuple of arguments or NULL.
+
+    int PyFunction_SetDefaults(object op, object defaults) except -1
+    # Set the argument default values for the function object
+    # op. defaults must be Py_None or a tuple.
+    # Raises SystemError and returns -1 on failure.
+
+    PyObject* PyFunction_GetClosure(object op) except? NULL
+    # Return value: Borrowed reference.
+    # Return the closure associated with the function object op. This
+    # can be NULL or a tuple of cell objects.
+
+    int PyFunction_SetClosure(object op, object closure) except -1
+    # Set the closure associated with the function object op. closure
+    # must be Py_None or a tuple of cell objects.
+    # Raises SystemError and returns -1 on failure.
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/int.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/int.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..50babff61514ad2fef33f4c1bad6e7393a113a38
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/int.pxd
@@ -0,0 +1,89 @@
+cdef extern from "Python.h":
+    ctypedef unsigned long long PY_LONG_LONG
+
+    ############################################################################
+    # Integer Objects
+    ############################################################################
+    # PyTypeObject PyInt_Type
+    # This instance of PyTypeObject represents the Python plain
+    # integer type. This is the same object as int and types.IntType.
+
+    bint PyInt_Check(object  o)
+    # Return true if o is of type PyInt_Type or a subtype of
+    # PyInt_Type.
+
+    bint PyInt_CheckExact(object  o)
+    # Return true if o is of type PyInt_Type, but not a subtype of
+    # PyInt_Type.
+
+    object PyInt_FromString(char *str, char **pend, int base)
+    # Return value: New reference.
+    # Return a new PyIntObject or PyLongObject based on the string
+    # value in str, which is interpreted according to the radix in
+    # base. If pend is non-NULL, *pend will point to the first
+    # character in str which follows the representation of the
+    # number. If base is 0, the radix will be determined based on the
+    # leading characters of str: if str starts with '0x' or '0X',
+    # radix 16 will be used; if str starts with '0', radix 8 will be
+    # used; otherwise radix 10 will be used. If base is not 0, it must
+    # be between 2 and 36, inclusive. Leading spaces are ignored. If
+    # there are no digits, ValueError will be raised. If the string
+    # represents a number too large to be contained within the
+    # machine's long int type and overflow warnings are being
+    # suppressed, a PyLongObject will be returned. If overflow
+    # warnings are not being suppressed, NULL will be returned in this
+    # case.
+
+    object PyInt_FromLong(long ival)
+    # Return value: New reference.
+    # Create a new integer object with a value of ival.
+    # The current implementation keeps an array of integer objects for
+    # all integers between -5 and 256, when you create an int in that
+    # range you actually just get back a reference to the existing
+    # object. So it should be possible to change the value of 1. I
+    # suspect the behaviour of Python in this case is undefined. :-)
+
+    object PyInt_FromSsize_t(Py_ssize_t ival)
+    # Return value: New reference.
+    # Create a new integer object with a value of ival. If the value
+    # is larger than LONG_MAX or smaller than LONG_MIN, a long integer
+    # object is returned.
+
+    object PyInt_FromSize_t(size_t ival)
+    # Return value: New reference.
+    # Create a new integer object with a value of ival. If the value
+    # exceeds LONG_MAX, a long integer object is returned.
+
+    long PyInt_AsLong(object io) except? -1
+    # Will first attempt to cast the object to a PyIntObject, if it is
+    # not already one, and then return its value. If there is an
+    # error, -1 is returned, and the caller should check
+    # PyErr_Occurred() to find out whether there was an error, or
+    # whether the value just happened to be -1.
+
+    long PyInt_AS_LONG(object io)
+    # Return the value of the object io. No error checking is performed.
+
+    unsigned long PyInt_AsUnsignedLongMask(object io) except? -1
+    # Will first attempt to cast the object to a PyIntObject or
+    # PyLongObject, if it is not already one, and then return its
+    # value as unsigned long. This function does not check for
+    # overflow.
+
+    PY_LONG_LONG PyInt_AsUnsignedLongLongMask(object io) except? -1
+    # Will first attempt to cast the object to a PyIntObject or
+    # PyLongObject, if it is not already one, and then return its
+    # value as unsigned long long, without checking for overflow.
+
+    Py_ssize_t PyInt_AsSsize_t(object io) except? -1
+    # Will first attempt to cast the object to a PyIntObject or
+    # PyLongObject, if it is not already one, and then return its
+    # value as Py_ssize_t.
+
+    long PyInt_GetMax()
+    # Return the system's idea of the largest integer it can handle
+    # (LONG_MAX, as defined in the system header files).
+
+    int PyInt_ClearFreeList()
+    # Clear the integer free list. Return the number of items that could not be freed.
+    # New in version 2.6.
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/iterobject.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/iterobject.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..a70aeccb097f3b8ff2b93d31e7752cbf3008dd88
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/iterobject.pxd
@@ -0,0 +1,24 @@
+cdef extern from "Python.h":
+
+    ###########################################################################
+    # Iterator Objects
+    ###########################################################################
+
+    bint PySeqIter_Check(object op)
+    # Return true if the type of op is PySeqIter_Type.
+
+    object PySeqIter_New(object seq)
+    # Return value: New reference.
+    # Return an iterator that works with a general sequence object, seq. The
+    # iteration ends when the sequence raises IndexError for the subscripting
+    # operation.
+
+    bint PyCallIter_Check(object op)
+    # Return true if the type of op is PyCallIter_Type.
+
+    object PyCallIter_New(object callable, object sentinel)
+    # Return value: New reference.
+    # Return a new iterator. The first parameter, callable, can be any Python
+    # callable object that can be called with no parameters; each call to it
+    # should return the next item in the iteration. When callable returns a
+    # value equal to sentinel, the iteration will be terminated.
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/list.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/list.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..1d0503c2c1afa11afea3138fac4b4b20d5178624
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/list.pxd
@@ -0,0 +1,92 @@
+from .object cimport PyObject
+
+cdef extern from "Python.h":
+
+    ############################################################################
+    # Lists
+    ############################################################################
+    list PyList_New(Py_ssize_t len)
+    # Return a new list of length len on success, or NULL on failure.
+    #
+    # Note: If length is greater than zero, the returned list object's
+    # items are set to NULL. Thus you cannot use abstract API
+    # functions such as PySequence_SetItem() or expose the object to
+    # Python code before setting all items to a real object with
+    # PyList_SetItem().
+
+    bint PyList_Check(object p)
+    # Return true if p is a list object or an instance of a subtype of
+    # the list type.
+
+    bint PyList_CheckExact(object p)
+    # Return true if p is a list object, but not an instance of a
+    # subtype of the list type.
+
+    Py_ssize_t PyList_Size(object list) except -1
+    # Return the length of the list object in list; this is equivalent
+    # to "len(list)" on a list object.
+
+    Py_ssize_t PyList_GET_SIZE(object list)
+    # Macro form of PyList_Size() without error checking.
+
+    PyObject* PyList_GetItem(object list, Py_ssize_t index) except NULL
+    # Return value: Borrowed reference.
+    # Return the object at position pos in the list pointed to by
+    # p. The position must be positive, indexing from the end of the
+    # list is not supported. If pos is out of bounds, return NULL and
+    # set an IndexError exception.
+
+    PyObject* PyList_GET_ITEM(object list, Py_ssize_t i)
+    # Return value: Borrowed reference.
+    # Macro form of PyList_GetItem() without error checking.
+
+    int PyList_SetItem(object list, Py_ssize_t index, object item) except -1
+    # Set the item at index index in list to item. Return 0 on success
+    # or -1 on failure.
+    #
+    # WARNING: This function _steals_ a reference to item and discards a
+    # reference to an item already in the list at the affected position.
+
+    void PyList_SET_ITEM(object list, Py_ssize_t i, object o)
+    # Macro form of PyList_SetItem() without error checking. This is
+    # normally only used to fill in new lists where there is no
+    # previous content.
+    #
+    # WARNING: This function _steals_ a reference to item, and, unlike
+    # PyList_SetItem(), does not discard a reference to any item that
+    # it being replaced; any reference in list at position i will be *leaked*.
+
+    int PyList_Insert(object list, Py_ssize_t index, object item) except -1
+    # Insert the item item into list list in front of index
+    # index. Return 0 if successful; return -1 and set an exception if
+    # unsuccessful. Analogous to list.insert(index, item).
+
+    int PyList_Append(object list, object item) except -1
+    # Append the object item at the end of list list. Return 0 if
+    # successful; return -1 and set an exception if
+    # unsuccessful. Analogous to list.append(item).
+
+    list PyList_GetSlice(object list, Py_ssize_t low, Py_ssize_t high)
+    # Return value: New reference.
+    # Return a list of the objects in list containing the objects
+    # between low and high. Return NULL and set an exception if
+    # unsuccessful. Analogous to list[low:high].
+
+    int PyList_SetSlice(object list, Py_ssize_t low, Py_ssize_t high, object itemlist) except -1
+    # Set the slice of list between low and high to the contents of
+    # itemlist. Analogous to list[low:high] = itemlist. The itemlist
+    # may be NULL, indicating the assignment of an empty list (slice
+    # deletion). Return 0 on success, -1 on failure.
+
+    int PyList_Sort(object list) except -1
+    # Sort the items of list in place. Return 0 on success, -1 on
+    # failure. This is equivalent to "list.sort()".
+
+    int PyList_Reverse(object list) except -1
+    # Reverse the items of list in place. Return 0 on success, -1 on
+    # failure. This is the equivalent of "list.reverse()".
+
+    tuple PyList_AsTuple(object list)
+    # Return value: New reference.
+    # Return a new tuple object containing the contents of list;
+    # equivalent to "tuple(list)".
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/long.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/long.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..f65cd0073ac43b4b7f560f76c85defbb4e4e9809
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/long.pxd
@@ -0,0 +1,149 @@
+
+cdef extern from "Python.h":
+    ctypedef long long PY_LONG_LONG
+    ctypedef unsigned long long uPY_LONG_LONG "unsigned PY_LONG_LONG"
+
+    ############################################################################
+    # 7.2.3 Long Integer Objects
+    ############################################################################
+
+    # PyLongObject
+    #
+    # This subtype of PyObject represents a Python long integer object.
+
+    # PyTypeObject PyLong_Type
+    #
+    # This instance of PyTypeObject represents the Python long integer
+    # type. This is the same object as long and types.LongType.
+
+    bint PyLong_Check(object p)
+    # Return true if its argument is a PyLongObject or a subtype of PyLongObject.
+
+    bint PyLong_CheckExact(object p)
+    # Return true if its argument is a PyLongObject, but not a subtype of PyLongObject.
+
+    object PyLong_FromLong(long v)
+    # Return value: New reference.
+    # Return a new PyLongObject object from v, or NULL on failure.
+
+    object PyLong_FromUnsignedLong(unsigned long v)
+    # Return value: New reference.
+    # Return a new PyLongObject object from a C unsigned long, or NULL on failure.
+
+    object PyLong_FromSsize_t(Py_ssize_t v)
+    # Return value: New reference.
+    # Return a new PyLongObject object from a C Py_ssize_t, or NULL on failure.)
+
+    object PyLong_FromSize_t(size_t v)
+    # Return value: New reference.
+    # Return a new PyLongObject object from a C size_t, or NULL on failure.
+
+    object PyLong_FromLongLong(PY_LONG_LONG v)
+    # Return value: New reference.
+    # Return a new PyLongObject object from a C long long, or NULL on failure.
+
+    object PyLong_FromUnsignedLongLong(uPY_LONG_LONG v)
+    # Return value: New reference.
+    # Return a new PyLongObject object from a C unsigned long long, or NULL on failure.
+
+    object PyLong_FromDouble(double v)
+    # Return value: New reference.
+    # Return a new PyLongObject object from the integer part of v, or NULL on failure.
+
+    object PyLong_FromString(char *str, char **pend, int base)
+    # Return value: New reference.
+    # Return a new PyLongObject based on the string value in str,
+    # which is interpreted according to the radix in base. If pend is
+    # non-NULL, *pend will point to the first character in str which
+    # follows the representation of the number. If base is 0, the
+    # radix will be determined based on the leading characters of str:
+    # if str starts with '0x' or '0X', radix 16 will be used; if str
+    # starts with '0', radix 8 will be used; otherwise radix 10 will
+    # be used. If base is not 0, it must be between 2 and 36,
+    # inclusive. Leading spaces are ignored. If there are no digits,
+    # ValueError will be raised.
+
+    object PyLong_FromUnicode(Py_UNICODE *u, Py_ssize_t length, int base)
+    # Return value: New reference.
+    # Convert a sequence of Unicode digits to a Python long integer
+    # value. The first parameter, u, points to the first character of
+    # the Unicode string, length gives the number of characters, and
+    # base is the radix for the conversion. The radix must be in the
+    # range [2, 36]; if it is out of range, ValueError will be
+    # raised.
+
+    # object PyLong_FromUnicodeObject(object u, int base)
+    # Convert a sequence of Unicode digits in the string u to a Python integer
+    # value. The Unicode string is first encoded to a byte string using
+    # PyUnicode_EncodeDecimal() and then converted using PyLong_FromString().
+    # New in version 3.3.
+
+    object PyLong_FromVoidPtr(void *p)
+    # Return value: New reference.
+    # Create a Python integer or long integer from the pointer p. The
+    # pointer value can be retrieved from the resulting value using
+    # PyLong_AsVoidPtr().  If the integer is larger than LONG_MAX, a
+    # positive long integer is returned.
+
+    long PyLong_AsLong(object pylong) except? -1
+    # Return a C long representation of the contents of pylong. If
+    # pylong is greater than LONG_MAX, an OverflowError is raised.
+
+    long PyLong_AsLongAndOverflow(object pylong, int *overflow) except? -1
+    # Return a C long representation of the contents of pylong. If pylong is
+    # greater than LONG_MAX or less than LONG_MIN, set *overflow to 1 or -1,
+    # respectively, and return -1; otherwise, set *overflow to 0. If any other
+    # exception occurs (for example a TypeError or MemoryError), then -1 will
+    # be returned and *overflow will be 0.
+    # New in version 2.7.
+
+    PY_LONG_LONG PyLong_AsLongLongAndOverflow(object pylong, int *overflow) except? -1
+    # Return a C long long representation of the contents of pylong. If pylong
+    # is greater than PY_LLONG_MAX or less than PY_LLONG_MIN, set *overflow to
+    # 1 or -1, respectively, and return -1; otherwise, set *overflow to 0. If
+    # any other exception occurs (for example a TypeError or MemoryError), then
+    # -1 will be returned and *overflow will be 0.
+    # New in version 2.7.
+
+    Py_ssize_t PyLong_AsSsize_t(object pylong) except? -1
+    # Return a C Py_ssize_t representation of the contents of pylong. If pylong
+    # is greater than PY_SSIZE_T_MAX, an OverflowError is raised and -1 will be
+    # returned.
+
+    unsigned long PyLong_AsUnsignedLong(object pylong) except? -1
+    # Return a C unsigned long representation of the contents of
+    # pylong. If pylong is greater than ULONG_MAX, an OverflowError is
+    # raised.
+
+    PY_LONG_LONG PyLong_AsLongLong(object pylong) except? -1
+    # Return a C long long from a Python long integer. If pylong
+    # cannot be represented as a long long, an OverflowError will be
+    # raised.
+
+    uPY_LONG_LONG PyLong_AsUnsignedLongLong(object pylong) except? -1
+    #unsigned PY_LONG_LONG PyLong_AsUnsignedLongLong(object pylong)
+    # Return a C unsigned long long from a Python long integer. If
+    # pylong cannot be represented as an unsigned long long, an
+    # OverflowError will be raised if the value is positive, or a
+    # TypeError will be raised if the value is negative.
+
+    unsigned long PyLong_AsUnsignedLongMask(object io) except? -1
+    # Return a C unsigned long from a Python long integer, without
+    # checking for overflow.
+
+    uPY_LONG_LONG PyLong_AsUnsignedLongLongMask(object io) except? -1
+    #unsigned PY_LONG_LONG PyLong_AsUnsignedLongLongMask(object io)
+    # Return a C unsigned long long from a Python long integer,
+    # without checking for overflow.
+
+    double PyLong_AsDouble(object pylong) except? -1.0
+    # Return a C double representation of the contents of pylong. If
+    # pylong cannot be approximately represented as a double, an
+    # OverflowError exception is raised and -1.0 will be returned.
+
+    void* PyLong_AsVoidPtr(object pylong) except? NULL
+    # Convert a Python integer or long integer pylong to a C void
+    # pointer. If pylong cannot be converted, an OverflowError will be
+    # raised. This is only assured to produce a usable void pointer
+    # for values created with PyLong_FromVoidPtr(). For values outside
+    # 0..LONG_MAX, both signed and unsigned integers are accepted.
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/mem.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/mem.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..236d111f6ff8d9b436c48eab131bcedc7a35848e
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/mem.pxd
@@ -0,0 +1,120 @@
+cdef extern from "Python.h":
+
+    #####################################################################
+    # 9.2 Memory Interface
+    #####################################################################
+    # You are definitely *supposed* to use these: "In most situations,
+    # however, it is recommended to allocate memory from the Python
+    # heap specifically because the latter is under control of the
+    # Python memory manager. For example, this is required when the
+    # interpreter is extended with new object types written in
+    # C. Another reason for using the Python heap is the desire to
+    # inform the Python memory manager about the memory needs of the
+    # extension module. Even when the requested memory is used
+    # exclusively for internal, highly-specific purposes, delegating
+    # all memory requests to the Python memory manager causes the
+    # interpreter to have a more accurate image of its memory
+    # footprint as a whole. Consequently, under certain circumstances,
+    # the Python memory manager may or may not trigger appropriate
+    # actions, like garbage collection, memory compaction or other
+    # preventive procedures. Note that by using the C library
+    # allocator as shown in the previous example, the allocated memory
+    # for the I/O buffer escapes completely the Python memory
+    # manager."
+
+    # The following function sets, modeled after the ANSI C standard,
+    # but specifying behavior when requesting zero bytes, are
+    # available for allocating and releasing memory from the Python
+    # heap:
+
+    void* PyMem_RawMalloc(size_t n) nogil
+    void* PyMem_Malloc(size_t n)
+    # Allocates n bytes and returns a pointer of type void* to the
+    # allocated memory, or NULL if the request fails. Requesting zero
+    # bytes returns a distinct non-NULL pointer if possible, as if
+    # PyMem_Malloc(1) had been called instead. The memory will not
+    # have been initialized in any way.
+
+    void* PyMem_RawCalloc(size_t nelem, size_t elsize) nogil
+    void* PyMem_Calloc(size_t nelem, size_t elsize)
+    # Allocates nelem elements each whose size in bytes is elsize and
+    # returns a pointer of type void* to the allocated memory, or NULL if
+    # the request fails. The memory is initialized to zeros. Requesting
+    # zero elements or elements of size zero bytes returns a distinct
+    # non-NULL pointer if possible, as if PyMem_Calloc(1, 1) had been
+    # called instead.
+
+    void* PyMem_RawRealloc(void *p, size_t n) nogil
+    void* PyMem_Realloc(void *p, size_t n)
+    # Resizes the memory block pointed to by p to n bytes. The
+    # contents will be unchanged to the minimum of the old and the new
+    # sizes. If p is NULL, the call is equivalent to PyMem_Malloc(n);
+    # else if n is equal to zero, the memory block is resized but is
+    # not freed, and the returned pointer is non-NULL. Unless p is
+    # NULL, it must have been returned by a previous call to
+    # PyMem_Malloc(), PyMem_Realloc(), or PyMem_Calloc().
+
+    void PyMem_RawFree(void *p) nogil
+    void PyMem_Free(void *p)
+    # Frees the memory block pointed to by p, which must have been
+    # returned by a previous call to PyMem_Malloc(), PyMem_Realloc(), or
+    # PyMem_Calloc(). Otherwise, or if PyMem_Free(p) has been called
+    # before, undefined behavior occurs. If p is NULL, no operation is
+    # performed.
+
+    # The following type-oriented macros are provided for
+    # convenience. Note that TYPE refers to any C type.
+
+    # TYPE* PyMem_New(TYPE, size_t n)
+    # Same as PyMem_Malloc(), but allocates (n * sizeof(TYPE)) bytes
+    # of memory. Returns a pointer cast to TYPE*. The memory will not
+    # have been initialized in any way.
+
+    # TYPE* PyMem_Resize(void *p, TYPE, size_t n)
+    # Same as PyMem_Realloc(), but the memory block is resized to (n *
+    # sizeof(TYPE)) bytes. Returns a pointer cast to TYPE*.
+
+    void PyMem_Del(void *p)
+    # Same as PyMem_Free().
+
+    # In addition, the following macro sets are provided for calling
+    # the Python memory allocator directly, without involving the C
+    # API functions listed above. However, note that their use does
+    # not preserve binary compatibility across Python versions and is
+    # therefore deprecated in extension modules.
+
+    # PyMem_MALLOC(), PyMem_REALLOC(), PyMem_FREE().
+    # PyMem_NEW(), PyMem_RESIZE(), PyMem_DEL().
+
+
+    #####################################################################
+    # Raw object memory interface
+    #####################################################################
+
+    # Functions to call the same malloc/realloc/free as used by Python's
+    # object allocator.  If WITH_PYMALLOC is enabled, these may differ from
+    # the platform malloc/realloc/free.  The Python object allocator is
+    # designed for fast, cache-conscious allocation of many "small" objects,
+    # and with low hidden memory overhead.
+    #
+    # PyObject_Malloc(0) returns a unique non-NULL pointer if possible.
+    #
+    # PyObject_Realloc(NULL, n) acts like PyObject_Malloc(n).
+    # PyObject_Realloc(p != NULL, 0) does not return  NULL, or free the memory
+    # at p.
+    #
+    # Returned pointers must be checked for NULL explicitly; no action is
+    # performed on failure other than to return NULL (no warning it printed, no
+    # exception is set, etc).
+    #
+    # For allocating objects, use PyObject_{New, NewVar} instead whenever
+    # possible.  The PyObject_{Malloc, Realloc, Free} family is exposed
+    # so that you can exploit Python's small-block allocator for non-object
+    # uses.  If you must use these routines to allocate object memory, make sure
+    # the object gets initialized via PyObject_{Init, InitVar} after obtaining
+    # the raw memory.
+
+    void* PyObject_Malloc(size_t size)
+    void* PyObject_Calloc(size_t nelem, size_t elsize)
+    void* PyObject_Realloc(void *ptr, size_t new_size)
+    void PyObject_Free(void *ptr)
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/memoryview.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/memoryview.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..83a84e6f91193e5b9a35f9f400e91f05f2c84e86
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/memoryview.pxd
@@ -0,0 +1,50 @@
+cdef extern from "Python.h":
+
+    ###########################################################################
+    # MemoryView Objects
+    ###########################################################################
+    # A memoryview object exposes the C level buffer interface as a Python
+    # object which can then be passed around like any other object
+
+    object PyMemoryView_FromObject(object obj)
+    # Return value: New reference.
+    # Create a memoryview object from an object that provides the buffer
+    # interface. If obj supports writable buffer exports, the memoryview object
+    # will be read/write, otherwise it may be either read-only or read/write at
+    # the discretion of the exporter.
+
+    object PyMemoryView_FromMemory(char *mem, Py_ssize_t size, int flags)
+    # Return value: New reference.
+    # Create a memoryview object using mem as the underlying buffer. flags can
+    # be one of PyBUF_READ or PyBUF_WRITE.
+    # New in version 3.3.
+
+    object PyMemoryView_FromBuffer(Py_buffer *view)
+    # Return value: New reference.
+    # Create a memoryview object wrapping the given buffer structure view. For
+    # simple byte buffers, PyMemoryView_FromMemory() is the preferred function.
+
+    object PyMemoryView_GetContiguous(object obj,
+                                      int buffertype,
+                                      char order)
+    # Return value: New reference.
+    # Create a memoryview object to a contiguous chunk of memory (in either ‘C’
+    # or ‘F’ortran order) from an object that defines the buffer interface. If
+    # memory is contiguous, the memoryview object points to the original
+    # memory. Otherwise, a copy is made and the memoryview points to a new
+    # bytes object.
+
+    bint PyMemoryView_Check(object obj)
+    # Return true if the object obj is a memoryview object. It is not currently
+    # allowed to create subclasses of memoryview.
+
+    Py_buffer *PyMemoryView_GET_BUFFER(object mview)
+    # Return a pointer to the memoryview’s private copy of the exporter’s
+    # buffer. mview must be a memoryview instance; this macro doesn’t check its
+    # type, you must do it yourself or you will risk crashes.
+
+    Py_buffer *PyMemoryView_GET_BASE(object mview)
+    # Return either a pointer to the exporting object that the memoryview is
+    # based on or NULL if the memoryview has been created by one of the
+    # functions PyMemoryView_FromMemory() or PyMemoryView_FromBuffer(). mview
+    # must be a memoryview instance.
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/object.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/object.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..41874159ce738ded59b7bf07c8a4779100538138
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/object.pxd
@@ -0,0 +1,440 @@
+from libc.stdio cimport FILE
+cimport cpython.type
+
+cdef extern from "Python.h":
+
+    ctypedef struct PyObject  # forward declaration
+
+    ctypedef object (*newfunc)(cpython.type.type, PyObject*, PyObject*)  # (type, args|NULL, kwargs|NULL)
+
+    ctypedef object (*unaryfunc)(object)
+    ctypedef object (*binaryfunc)(object, object)
+    ctypedef object (*ternaryfunc)(object, object, object)
+    ctypedef int (*inquiry)(object) except -1
+    ctypedef Py_ssize_t (*lenfunc)(object) except -1
+    ctypedef object (*ssizeargfunc)(object, Py_ssize_t)
+    ctypedef object (*ssizessizeargfunc)(object, Py_ssize_t, Py_ssize_t)
+    ctypedef int (*ssizeobjargproc)(object, Py_ssize_t, object) except -1
+    ctypedef int (*ssizessizeobjargproc)(object, Py_ssize_t, Py_ssize_t, object) except -1
+    ctypedef int (*objobjargproc)(object, object, object) except -1
+    ctypedef int (*objobjproc)(object, object) except -1
+
+    ctypedef Py_hash_t (*hashfunc)(object) except -1
+    ctypedef object (*reprfunc)(object)
+
+    ctypedef int (*cmpfunc)(object, object) except -2
+    ctypedef object (*richcmpfunc)(object, object, int)
+
+    # The following functions use 'PyObject*' as first argument instead of 'object' to prevent
+    # accidental reference counting when calling them during a garbage collection run.
+    ctypedef void (*destructor)(PyObject*)
+    ctypedef int (*visitproc)(PyObject*, void *) except -1
+    ctypedef int (*traverseproc)(PyObject*, visitproc, void*) except -1
+    ctypedef void (*freefunc)(void*)
+
+    ctypedef object (*descrgetfunc)(object, object, object)
+    ctypedef int (*descrsetfunc)(object, object, object) except -1
+
+    ctypedef object (*PyCFunction)(object, object)
+
+    ctypedef struct PyMethodDef:
+        const char* ml_name
+        PyCFunction ml_meth
+        int ml_flags
+        const char* ml_doc
+
+    ctypedef struct PyTypeObject:
+        const char* tp_name
+        const char* tp_doc
+        Py_ssize_t tp_basicsize
+        Py_ssize_t tp_itemsize
+        Py_ssize_t tp_dictoffset
+        unsigned long tp_flags
+
+        newfunc tp_new
+        destructor tp_dealloc
+        destructor tp_del
+        destructor tp_finalize
+        traverseproc tp_traverse
+        inquiry tp_clear
+        freefunc tp_free
+
+        ternaryfunc tp_call
+        hashfunc tp_hash
+        reprfunc tp_str
+        reprfunc tp_repr
+
+        cmpfunc tp_compare
+        richcmpfunc tp_richcompare
+
+        PyMethodDef* tp_methods
+
+        PyTypeObject* tp_base
+        PyObject* tp_dict
+
+        descrgetfunc tp_descr_get
+        descrsetfunc tp_descr_set
+
+        unsigned int tp_version_tag
+
+    ctypedef struct PyObject:
+        Py_ssize_t ob_refcnt
+        PyTypeObject *ob_type
+
+    cdef PyTypeObject *Py_TYPE(object)
+
+    void* PyObject_Malloc(size_t)
+    void* PyObject_Realloc(void *, size_t)
+    void PyObject_Free(void *)
+
+    #####################################################################
+    # 6.1 Object Protocol
+    #####################################################################
+    int PyObject_Print(object o, FILE *fp, int flags) except -1
+    # Print an object o, on file fp. Returns -1 on error. The flags
+    # argument is used to enable certain printing options. The only
+    # option currently supported is Py_PRINT_RAW; if given, the str()
+    # of the object is written instead of the repr().
+
+    bint PyObject_HasAttrString(object o, const char *attr_name)
+    # Returns 1 if o has the attribute attr_name, and 0
+    # otherwise. This is equivalent to the Python expression
+    # "hasattr(o, attr_name)". This function always succeeds.
+
+    object PyObject_GetAttrString(object o, const char *attr_name)
+    # Return value: New reference.  Retrieve an attribute named
+    # attr_name from object o. Returns the attribute value on success,
+    # or NULL on failure. This is the equivalent of the Python
+    # expression "o.attr_name".
+
+    bint PyObject_HasAttr(object o, object attr_name)
+    # Returns 1 if o has the attribute attr_name, and 0
+    # otherwise. This is equivalent to the Python expression
+    # "hasattr(o, attr_name)". This function always succeeds.
+
+    object PyObject_GetAttr(object o, object attr_name)
+    # Return value: New reference.  Retrieve an attribute named
+    # attr_name from object o. Returns the attribute value on success,
+    # or NULL on failure. This is the equivalent of the Python
+    # expression "o.attr_name".
+
+    object PyObject_GenericGetAttr(object o, object attr_name)
+
+    int PyObject_SetAttrString(object o, const char *attr_name, object v) except -1
+    # Set the value of the attribute named attr_name, for object o, to
+    # the value v. Returns -1 on failure. This is the equivalent of
+    # the Python statement "o.attr_name = v".
+
+    int PyObject_SetAttr(object o, object attr_name, object v) except -1
+    # Set the value of the attribute named attr_name, for object o, to
+    # the value v. Returns -1 on failure. This is the equivalent of
+    # the Python statement "o.attr_name = v".
+
+    int PyObject_GenericSetAttr(object o, object attr_name, object v) except -1
+
+    int PyObject_DelAttrString(object o, const char *attr_name) except -1
+    # Delete attribute named attr_name, for object o. Returns -1 on
+    # failure. This is the equivalent of the Python statement: "del
+    # o.attr_name".
+
+    int PyObject_DelAttr(object o, object attr_name) except -1
+    # Delete attribute named attr_name, for object o. Returns -1 on
+    # failure. This is the equivalent of the Python statement "del
+    # o.attr_name".
+
+    object PyObject_GenericGetDict(object o, void *context)
+    # Return value: New reference.
+    # A generic implementation for the getter of a __dict__ descriptor. It
+    # creates the dictionary if necessary.
+    # New in version 3.3.
+
+    int PyObject_GenericSetDict(object o, object value, void *context) except -1
+    # A generic implementation for the setter of a __dict__ descriptor. This
+    # implementation does not allow the dictionary to be deleted.
+    # New in version 3.3.
+
+    int Py_LT, Py_LE, Py_EQ, Py_NE, Py_GT, Py_GE
+
+    object PyObject_RichCompare(object o1, object o2, int opid)
+    # Return value: New reference.
+    # Compare the values of o1 and o2 using the operation specified by
+    # opid, which must be one of Py_LT, Py_LE, Py_EQ, Py_NE, Py_GT, or
+    # Py_GE, corresponding to <, <=, ==, !=, >, or >=
+    # respectively. This is the equivalent of the Python expression
+    # "o1 op o2", where op is the operator corresponding to
+    # opid. Returns the value of the comparison on success, or NULL on
+    # failure.
+
+    bint PyObject_RichCompareBool(object o1, object o2, int opid) except -1
+    # Compare the values of o1 and o2 using the operation specified by
+    # opid, which must be one of Py_LT, Py_LE, Py_EQ, Py_NE, Py_GT, or
+    # Py_GE, corresponding to <, <=, ==, !=, >, or >=
+    # respectively. Returns -1 on error, 0 if the result is false, 1
+    # otherwise. This is the equivalent of the Python expression "o1
+    # op o2", where op is the operator corresponding to opid.
+
+    int PyObject_Cmp(object o1, object o2, int *result) except -1
+    # Compare the values of o1 and o2 using a routine provided by o1,
+    # if one exists, otherwise with a routine provided by o2. The
+    # result of the comparison is returned in result. Returns -1 on
+    # failure. This is the equivalent of the Python statement "result
+    # = cmp(o1, o2)".
+
+    int PyObject_Compare(object o1, object o2) except *
+    # Compare the values of o1 and o2 using a routine provided by o1,
+    # if one exists, otherwise with a routine provided by o2. Returns
+    # the result of the comparison on success. On error, the value
+    # returned is undefined; use PyErr_Occurred() to detect an
+    # error. This is equivalent to the Python expression "cmp(o1,
+    # o2)".
+
+    object PyObject_Repr(object o)
+    # Return value: New reference.
+    # Compute a string representation of object o. Returns the string
+    # representation on success, NULL on failure. This is the
+    # equivalent of the Python expression "repr(o)". Called by the
+    # repr() built-in function and by reverse quotes.
+
+    object PyObject_Str(object o)
+    # Return value: New reference.
+    # Compute a string representation of object o. Returns the string
+    # representation on success, NULL on failure. This is the
+    # equivalent of the Python expression "str(o)". Called by the
+    # str() built-in function and by the print statement.
+
+    object PyObject_Bytes(object o)
+    # Return value: New reference.
+    # Compute a bytes representation of object o. Return NULL on
+    # failure and a bytes object on success. This is equivalent to
+    # the Python expression bytes(o), when o is not an integer.
+    # Unlike bytes(o), a TypeError is raised when o is an integer
+    # instead of a zero-initialized bytes object.
+
+    object PyObject_Unicode(object o)
+    # Return value: New reference.
+    # Compute a Unicode string representation of object o. Returns the
+    # Unicode string representation on success, NULL on failure. This
+    # is the equivalent of the Python expression "unicode(o)". Called
+    # by the unicode() built-in function.
+
+    bint PyObject_IsInstance(object inst, object cls) except -1
+    # Returns 1 if inst is an instance of the class cls or a subclass
+    # of cls, or 0 if not. On error, returns -1 and sets an
+    # exception. If cls is a type object rather than a class object,
+    # PyObject_IsInstance() returns 1 if inst is of type cls. If cls
+    # is a tuple, the check will be done against every entry in
+    # cls. The result will be 1 when at least one of the checks
+    # returns 1, otherwise it will be 0. If inst is not a class
+    # instance and cls is neither a type object, nor a class object,
+    # nor a tuple, inst must have a __class__ attribute -- the class
+    # relationship of the value of that attribute with cls will be
+    # used to determine the result of this function.
+
+    # Subclass determination is done in a fairly straightforward way,
+    # but includes a wrinkle that implementors of extensions to the
+    # class system may want to be aware of. If A and B are class
+    # objects, B is a subclass of A if it inherits from A either
+    # directly or indirectly. If either is not a class object, a more
+    # general mechanism is used to determine the class relationship of
+    # the two objects. When testing if B is a subclass of A, if A is
+    # B, PyObject_IsSubclass() returns true. If A and B are different
+    # objects, B's __bases__ attribute is searched in a depth-first
+    # fashion for A -- the presence of the __bases__ attribute is
+    # considered sufficient for this determination.
+
+    bint PyObject_IsSubclass(object derived, object cls) except -1
+    # Returns 1 if the class derived is identical to or derived from
+    # the class cls, otherwise returns 0. In case of an error, returns
+    # -1. If cls is a tuple, the check will be done against every
+    # entry in cls. The result will be 1 when at least one of the
+    # checks returns 1, otherwise it will be 0. If either derived or
+    # cls is not an actual class object (or tuple), this function uses
+    # the generic algorithm described above. New in version
+    # 2.1. Changed in version 2.3: Older versions of Python did not
+    # support a tuple as the second argument.
+
+    bint PyCallable_Check(object o)
+    # Determine if the object o is callable. Return 1 if the object is
+    # callable and 0 otherwise. This function always succeeds.
+
+    object PyObject_Call(object callable_object, object args, object kw)
+    # Return value: New reference.
+    # Call a callable Python object callable_object, with arguments
+    # given by the tuple args, and named arguments given by the
+    # dictionary kw. If no named arguments are needed, kw may be
+    # NULL. args must not be NULL, use an empty tuple if no arguments
+    # are needed. Returns the result of the call on success, or NULL
+    # on failure. This is the equivalent of the Python expression
+    # "apply(callable_object, args, kw)" or "callable_object(*args,
+    # **kw)".
+
+    object PyObject_CallObject(object callable_object, object args)
+    # Return value: New reference.
+    # Call a callable Python object callable_object, with arguments
+    # given by the tuple args. If no arguments are needed, then args
+    # may be NULL. Returns the result of the call on success, or NULL
+    # on failure. This is the equivalent of the Python expression
+    # "apply(callable_object, args)" or "callable_object(*args)".
+
+    object PyObject_CallFunction(object callable, char *format, ...)
+    # Return value: New reference.
+    # Call a callable Python object callable, with a variable number
+    # of C arguments. The C arguments are described using a
+    # Py_BuildValue() style format string. The format may be NULL,
+    # indicating that no arguments are provided. Returns the result of
+    # the call on success, or NULL on failure. This is the equivalent
+    # of the Python expression "apply(callable, args)" or
+    # "callable(*args)". Note that if you only pass object  args,
+    # PyObject_CallFunctionObjArgs is a faster alternative.
+
+    object PyObject_CallMethod(object o, char *method, char *format, ...)
+    # Return value: New reference.
+    # Call the method named method of object o with a variable number
+    # of C arguments. The C arguments are described by a
+    # Py_BuildValue() format string that should produce a tuple. The
+    # format may be NULL, indicating that no arguments are
+    # provided. Returns the result of the call on success, or NULL on
+    # failure. This is the equivalent of the Python expression
+    # "o.method(args)". Note that if you only pass object  args,
+    # PyObject_CallMethodObjArgs is a faster alternative.
+
+    #object PyObject_CallFunctionObjArgs(object callable, ..., NULL)
+    object PyObject_CallFunctionObjArgs(object callable, ...)
+    # Return value: New reference.
+    # Call a callable Python object callable, with a variable number
+    # of PyObject* arguments. The arguments are provided as a variable
+    # number of parameters followed by NULL. Returns the result of the
+    # call on success, or NULL on failure.
+
+    #PyObject* PyObject_CallMethodObjArgs(object o, object name, ..., NULL)
+    object PyObject_CallMethodObjArgs(object o, object name, ...)
+    # Return value: New reference.
+    # Calls a method of the object o, where the name of the method is
+    # given as a Python string object in name. It is called with a
+    # variable number of PyObject* arguments. The arguments are
+    # provided as a variable number of parameters followed by
+    # NULL. Returns the result of the call on success, or NULL on
+    # failure.
+
+    long PyObject_Hash(object o) except? -1
+    # Compute and return the hash value of an object o. On failure,
+    # return -1. This is the equivalent of the Python expression
+    # "hash(o)".
+
+    bint PyObject_IsTrue(object o) except -1
+    # Returns 1 if the object o is considered to be true, and 0
+    # otherwise. This is equivalent to the Python expression "not not
+    # o". On failure, return -1.
+
+    bint PyObject_Not(object o) except -1
+    # Returns 0 if the object o is considered to be true, and 1
+    # otherwise. This is equivalent to the Python expression "not
+    # o". On failure, return -1.
+
+    object PyObject_Type(object o)
+    # Return value: New reference.
+    # When o is non-NULL, returns a type object corresponding to the
+    # object type of object o. On failure, raises SystemError and
+    # returns NULL. This is equivalent to the Python expression
+    # type(o). This function increments the reference count of the
+    # return value. There's really no reason to use this function
+    # instead of the common expression o->ob_type, which returns a
+    # pointer of type PyTypeObject*, except when the incremented
+    # reference count is needed.
+
+    bint PyObject_TypeCheck(object o, PyTypeObject *type)
+    # Return true if the object o is of type type or a subtype of
+    # type. Both parameters must be non-NULL.
+
+    Py_ssize_t PyObject_Length(object o) except -1
+    Py_ssize_t PyObject_Size(object o) except -1
+    # Return the length of object o. If the object o provides either
+    # the sequence and mapping protocols, the sequence length is
+    # returned. On error, -1 is returned. This is the equivalent to
+    # the Python expression "len(o)".
+
+    Py_ssize_t PyObject_LengthHint(object o, Py_ssize_t default) except -1
+    # Return an estimated length for the object o. First try to return its
+    # actual length, then an estimate using __length_hint__(), and finally
+    # return the default value. On error, return -1. This is the equivalent to
+    # the Python expression "operator.length_hint(o, default)".
+    # New in version 3.4.
+
+    object PyObject_GetItem(object o, object key)
+    # Return value: New reference.
+    # Return element of o corresponding to the object key or NULL on
+    # failure. This is the equivalent of the Python expression
+    # "o[key]".
+
+    int PyObject_SetItem(object o, object key, object v) except -1
+    # Map the object key to the value v. Returns -1 on failure. This
+    # is the equivalent of the Python statement "o[key] = v".
+
+    int PyObject_DelItem(object o, object key) except -1
+    # Delete the mapping for key from o. Returns -1 on failure. This
+    # is the equivalent of the Python statement "del o[key]".
+
+    int PyObject_AsFileDescriptor(object o) except -1
+    # Derives a file-descriptor from a Python object. If the object is
+    # an integer or long integer, its value is returned. If not, the
+    # object's fileno() method is called if it exists; the method must
+    # return an integer or long integer, which is returned as the file
+    # descriptor value. Returns -1 on failure.
+
+    object PyObject_Dir(object o)
+    # Return value: New reference.
+    # This is equivalent to the Python expression "dir(o)", returning
+    # a (possibly empty) list of strings appropriate for the object
+    # argument, or NULL if there was an error. If the argument is
+    # NULL, this is like the Python "dir()", returning the names of
+    # the current locals; in this case, if no execution frame is
+    # active then NULL is returned but PyErr_Occurred() will return
+    # false.
+
+    object PyObject_GetIter(object o)
+    # Return value: New reference.
+    # This is equivalent to the Python expression "iter(o)". It
+    # returns a new iterator for the object argument, or the object
+    # itself if the object is already an iterator. Raises TypeError
+    # and returns NULL if the object cannot be iterated.
+
+    Py_ssize_t Py_SIZE(object o)
+
+    object PyObject_Format(object obj, object format_spec)
+    # Takes an arbitrary object and returns the result of calling
+    # obj.__format__(format_spec).
+    # Added in Py2.6
+
+    # Type flags (tp_flags of PyTypeObject)
+    long Py_TPFLAGS_HAVE_GETCHARBUFFER
+    long Py_TPFLAGS_HAVE_SEQUENCE_IN
+    long Py_TPFLAGS_HAVE_INPLACEOPS
+    long Py_TPFLAGS_CHECKTYPES
+    long Py_TPFLAGS_HAVE_RICHCOMPARE
+    long Py_TPFLAGS_HAVE_WEAKREFS
+    long Py_TPFLAGS_HAVE_ITER
+    long Py_TPFLAGS_HAVE_CLASS
+    long Py_TPFLAGS_HEAPTYPE
+    long Py_TPFLAGS_BASETYPE
+    long Py_TPFLAGS_READY
+    long Py_TPFLAGS_READYING
+    long Py_TPFLAGS_HAVE_GC
+    long Py_TPFLAGS_HAVE_STACKLESS_EXTENSION
+    long Py_TPFLAGS_HAVE_INDEX
+    long Py_TPFLAGS_HAVE_VERSION_TAG
+    long Py_TPFLAGS_VALID_VERSION_TAG
+    long Py_TPFLAGS_IS_ABSTRACT
+    long Py_TPFLAGS_HAVE_NEWBUFFER
+    long Py_TPFLAGS_INT_SUBCLASS
+    long Py_TPFLAGS_LONG_SUBCLASS
+    long Py_TPFLAGS_LIST_SUBCLASS
+    long Py_TPFLAGS_TUPLE_SUBCLASS
+    long Py_TPFLAGS_STRING_SUBCLASS
+    long Py_TPFLAGS_UNICODE_SUBCLASS
+    long Py_TPFLAGS_DICT_SUBCLASS
+    long Py_TPFLAGS_BASE_EXC_SUBCLASS
+    long Py_TPFLAGS_TYPE_SUBCLASS
+    long Py_TPFLAGS_DEFAULT_EXTERNAL
+    long Py_TPFLAGS_DEFAULT_CORE
+    long Py_TPFLAGS_DEFAULT
+    long Py_TPFLAGS_HAVE_FINALIZE
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/oldbuffer.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/oldbuffer.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..0222428ed48e1cb84cde849f83b36e3079089245
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/oldbuffer.pxd
@@ -0,0 +1,63 @@
+# Legacy Python 2 buffer interface.
+#
+# These functions are no longer available in Python 3, use the new
+# buffer interface instead.
+
+cdef extern from "Python.h":
+    cdef enum _:
+        Py_END_OF_BUFFER
+    #    This constant may be passed as the size parameter to
+    #    PyBuffer_FromObject() or PyBuffer_FromReadWriteObject(). It
+    #    indicates that the new PyBufferObject should refer to base object
+    #    from the specified offset to the end of its exported
+    #    buffer. Using this enables the caller to avoid querying the base
+    #    object for its length.
+
+    bint PyBuffer_Check(object p)
+    #    Return true if the argument has type PyBuffer_Type.
+
+    object PyBuffer_FromObject(object base, Py_ssize_t offset, Py_ssize_t size)
+    #    Return value: New reference.
+    #
+    #    Return a new read-only buffer object. This raises TypeError if
+    #    base doesn't support the read-only buffer protocol or doesn't
+    #    provide exactly one buffer segment, or it raises ValueError if
+    #    offset is less than zero. The buffer will hold a reference to the
+    #    base object, and the buffer's contents will refer to the base
+    #    object's buffer interface, starting as position offset and
+    #    extending for size bytes. If size is Py_END_OF_BUFFER, then the
+    #    new buffer's contents extend to the length of the base object's
+    #    exported buffer data.
+
+    object PyBuffer_FromReadWriteObject(object base, Py_ssize_t offset, Py_ssize_t size)
+    #    Return value: New reference.
+    #
+    #    Return a new writable buffer object. Parameters and exceptions
+    #    are similar to those for PyBuffer_FromObject(). If the base
+    #    object does not export the writeable buffer protocol, then
+    #    TypeError is raised.
+
+    object PyBuffer_FromMemory(void *ptr, Py_ssize_t size)
+    #    Return value: New reference.
+    #
+    #    Return a new read-only buffer object that reads from a specified
+    #    location in memory, with a specified size. The caller is
+    #    responsible for ensuring that the memory buffer, passed in as
+    #    ptr, is not deallocated while the returned buffer object
+    #    exists. Raises ValueError if size is less than zero. Note that
+    #    Py_END_OF_BUFFER may not be passed for the size parameter;
+    #    ValueError will be raised in that case.
+
+    object PyBuffer_FromReadWriteMemory(void *ptr, Py_ssize_t size)
+    #    Return value: New reference.
+    #
+    #    Similar to PyBuffer_FromMemory(), but the returned buffer is
+    #    writable.
+
+    object PyBuffer_New(Py_ssize_t size)
+    #    Return value: New reference.
+    #
+    #    Return a new writable buffer object that maintains its own memory
+    #    buffer of size bytes. ValueError is returned if size is not zero
+    #    or positive. Note that the memory buffer (as returned by
+    #    PyObject_AsWriteBuffer()) is not specifically aligned.
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/pycapsule.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/pycapsule.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..1c21b370b3c2939361ff071798a6bcd4fdf82473
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/pycapsule.pxd
@@ -0,0 +1,143 @@
+
+# available since Python 2.7!
+
+
+cdef extern from "Python.h":
+
+    ctypedef struct PyCapsule_Type
+    # This subtype of PyObject represents an opaque value, useful for
+    # C extension modules who need to pass an opaque value (as a void*
+    # pointer) through Python code to other C code. It is often used
+    # to make a C function pointer defined in one module available to
+    # other modules, so the regular import mechanism can be used to
+    # access C APIs defined in dynamically loaded modules.
+
+
+    ctypedef void (*PyCapsule_Destructor)(object o) noexcept
+    # The type of a destructor callback for a capsule.
+    #
+    # See PyCapsule_New() for the semantics of PyCapsule_Destructor
+    # callbacks.
+
+
+    bint PyCapsule_CheckExact(object o)
+    # Return true if its argument is a PyCapsule.
+
+
+    object PyCapsule_New(void *pointer, const char *name,
+                         PyCapsule_Destructor destructor)
+    # Return value: New reference.
+    #
+    # Create a PyCapsule encapsulating the pointer. The pointer
+    # argument may not be NULL.
+    #
+    # On failure, set an exception and return NULL.
+    #
+    # The name string may either be NULL or a pointer to a valid C
+    # string. If non-NULL, this string must outlive the
+    # capsule. (Though it is permitted to free it inside the
+    # destructor.)
+    #
+    # If the destructor argument is not NULL, it will be called with
+    # the capsule as its argument when it is destroyed.
+    #
+    # If this capsule will be stored as an attribute of a module, the
+    # name should be specified as modulename.attributename. This will
+    # enable other modules to import the capsule using
+    # PyCapsule_Import().
+
+
+    void* PyCapsule_GetPointer(object capsule, const char *name) except? NULL
+    # Retrieve the pointer stored in the capsule. On failure, set an
+    # exception and return NULL.
+    #
+    # The name parameter must compare exactly to the name stored in
+    # the capsule. If the name stored in the capsule is NULL, the name
+    # passed in must also be NULL. Python uses the C function strcmp()
+    # to compare capsule names.
+
+
+    PyCapsule_Destructor PyCapsule_GetDestructor(object capsule) except? NULL
+    # Return the current destructor stored in the capsule. On failure,
+    # set an exception and return NULL.
+    #
+    # It is legal for a capsule to have a NULL destructor. This makes
+    # a NULL return code somewhat ambiguous; use PyCapsule_IsValid()
+    # or PyErr_Occurred() to disambiguate.
+
+
+    const char* PyCapsule_GetName(object capsule) except? NULL
+    # Return the current name stored in the capsule. On failure, set
+    # an exception and return NULL.
+    #
+    # It is legal for a capsule to have a NULL name. This makes a NULL
+    # return code somewhat ambiguous; use PyCapsule_IsValid() or
+    # PyErr_Occurred() to disambiguate.
+
+
+    void* PyCapsule_GetContext(object capsule) except? NULL
+    # Return the current context stored in the capsule. On failure,
+    # set an exception and return NULL.
+    #
+    # It is legal for a capsule to have a NULL context. This makes a
+    # NULL return code somewhat ambiguous; use PyCapsule_IsValid() or
+    # PyErr_Occurred() to disambiguate.
+
+
+    bint PyCapsule_IsValid(object capsule, const char *name)
+    # Determines whether or not capsule is a valid capsule. A valid
+    # capsule is non-NULL, passes PyCapsule_CheckExact(), has a
+    # non-NULL pointer stored in it, and its internal name matches the
+    # name parameter. (See PyCapsule_GetPointer() for information on
+    # how capsule names are compared.)
+    #
+    # In other words, if PyCapsule_IsValid() returns a true value,
+    # calls to any of the accessors (any function starting with
+    # PyCapsule_Get()) are guaranteed to succeed.
+    #
+    # Return a nonzero value if the object is valid and matches the
+    # name passed in. Return 0 otherwise. This function will not fail.
+
+
+    int PyCapsule_SetPointer(object capsule, void *pointer) except -1
+    # Set the void pointer inside capsule to pointer. The pointer may
+    # not be NULL.
+    #
+    # Return 0 on success. Return nonzero and set an exception on
+    # failure.
+
+
+    int PyCapsule_SetDestructor(object capsule, PyCapsule_Destructor destructor) except -1
+    # Set the destructor inside capsule to destructor.
+    #
+    # Return 0 on success. Return nonzero and set an exception on
+    # failure.
+
+
+    int PyCapsule_SetName(object capsule, const char *name) except -1
+    # Set the name inside capsule to name. If non-NULL, the name must
+    # outlive the capsule. If the previous name stored in the capsule
+    # was not NULL, no attempt is made to free it.
+    #
+    # Return 0 on success. Return nonzero and set an exception on
+    # failure.
+
+
+    int PyCapsule_SetContext(object capsule, void *context) except -1
+    # Set the context pointer inside capsule to context.  Return 0 on
+    # success. Return nonzero and set an exception on failure.
+
+
+    void* PyCapsule_Import(const char *name, int no_block) except? NULL
+    # Import a pointer to a C object from a capsule attribute in a
+    # module. The name parameter should specify the full name to the
+    # attribute, as in module.attribute. The name stored in the
+    # capsule must match this string exactly. If no_block is true,
+    # import the module without blocking (using
+    # PyImport_ImportModuleNoBlock()). If no_block is false, import
+    # the module conventionally (using PyImport_ImportModule()).
+    #
+    # Return the capsule’s internal pointer on success. On failure,
+    # set an exception and return NULL. However, if PyCapsule_Import()
+    # failed to import the module, and no_block was true, no exception
+    # is set.
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/pyport.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/pyport.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..fec59c9c8c4944c7c8ddec39d0e047bb0a0461bc
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/pyport.pxd
@@ -0,0 +1,8 @@
+cdef extern from "Python.h":
+    ctypedef int int32_t
+    ctypedef int int64_t
+    ctypedef unsigned int uint32_t
+    ctypedef unsigned int uint64_t
+
+    const Py_ssize_t PY_SSIZE_T_MIN
+    const Py_ssize_t PY_SSIZE_T_MAX
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/pystate.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/pystate.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..ee8856b20abe758e6a454eb76f97867ecfb9a111
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/pystate.pxd
@@ -0,0 +1,95 @@
+# Thread and interpreter state structures and their interfaces
+
+from .object cimport PyObject
+
+cdef extern from "Python.h":
+
+    # We make these an opaque types. If the user wants specific attributes,
+    # they can be declared manually.
+
+    ctypedef long PY_INT64_T  # FIXME: Py2.7+, not defined here but used here
+
+    ctypedef struct PyInterpreterState:
+        pass
+
+    ctypedef struct PyThreadState:
+        pass
+
+    ctypedef struct PyFrameObject:
+        pass
+
+    # This is not actually a struct, but make sure it can never be coerced to
+    # an int or used in arithmetic expressions
+    ctypedef struct PyGILState_STATE:
+        pass
+
+    # The type of the trace function registered using PyEval_SetProfile() and
+    # PyEval_SetTrace().
+    # Py_tracefunc return -1 when raising an exception, or 0 for success.
+    ctypedef int (*Py_tracefunc)(PyObject *, PyFrameObject *, int, PyObject *)
+
+    # The following values are used for 'what' for tracefunc functions
+    enum:
+        PyTrace_CALL
+        PyTrace_EXCEPTION
+        PyTrace_LINE
+        PyTrace_RETURN
+        PyTrace_C_CALL
+        PyTrace_C_EXCEPTION
+        PyTrace_C_RETURN
+
+
+    PyInterpreterState * PyInterpreterState_New()
+    void PyInterpreterState_Clear(PyInterpreterState *)
+    void PyInterpreterState_Delete(PyInterpreterState *)
+    PY_INT64_T PyInterpreterState_GetID(PyInterpreterState *)
+
+    PyThreadState * PyThreadState_New(PyInterpreterState *)
+    void PyThreadState_Clear(PyThreadState *)
+    void PyThreadState_Delete(PyThreadState *)
+
+    PyThreadState * PyThreadState_Get()
+    PyThreadState * PyThreadState_Swap(PyThreadState *)  # NOTE: DO NOT USE IN CYTHON CODE !
+    PyObject * PyThreadState_GetDict()
+    int PyThreadState_SetAsyncExc(long, PyObject *)
+
+    # Ensure that the current thread is ready to call the Python
+    # C API, regardless of the current state of Python, or of its
+    # thread lock.  This may be called as many times as desired
+    # by a thread so long as each call is matched with a call to
+    # PyGILState_Release().  In general, other thread-state APIs may
+    # be used between _Ensure() and _Release() calls, so long as the
+    # thread-state is restored to its previous state before the Release().
+    # For example, normal use of the Py_BEGIN_ALLOW_THREADS/
+    # Py_END_ALLOW_THREADS macros are acceptable.
+
+    # The return value is an opaque "handle" to the thread state when
+    # PyGILState_Ensure() was called, and must be passed to
+    # PyGILState_Release() to ensure Python is left in the same state. Even
+    # though recursive calls are allowed, these handles can *not* be shared -
+    # each unique call to PyGILState_Ensure must save the handle for its
+    # call to PyGILState_Release.
+
+    # When the function returns, the current thread will hold the GIL.
+
+    # Failure is a fatal error.
+    PyGILState_STATE PyGILState_Ensure()
+
+    # Release any resources previously acquired.  After this call, Python's
+    # state will be the same as it was prior to the corresponding
+    # PyGILState_Ensure() call (but generally this state will be unknown to
+    # the caller, hence the use of the GILState API.)
+
+    # Every call to PyGILState_Ensure must be matched by a call to
+    # PyGILState_Release on the same thread.
+    void PyGILState_Release(PyGILState_STATE)
+
+    # Return 1 if the current thread holds the GIL and 0 otherwise.
+    int PyGILState_Check()
+
+    # Routines for advanced debuggers, requested by David Beazley.
+    # Don't use unless you know what you are doing!
+    PyInterpreterState * PyInterpreterState_Head()
+    PyInterpreterState * PyInterpreterState_Next(PyInterpreterState *)
+    PyThreadState * PyInterpreterState_ThreadHead(PyInterpreterState *)
+    PyThreadState * PyThreadState_Next(PyThreadState *)
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/sequence.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/sequence.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..e50e4c495ea598e1e2b8ece8a8447337354ada93
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/sequence.pxd
@@ -0,0 +1,134 @@
+from .object cimport PyObject
+
+cdef extern from "Python.h":
+
+    ############################################################################
+    # 6.3 Sequence Protocol
+    ############################################################################
+
+    bint PySequence_Check(object o)
+    # Return 1 if the object provides sequence protocol, and 0
+    # otherwise. This function always succeeds.
+
+    Py_ssize_t PySequence_Size(object o) except -1
+    # Returns the number of objects in sequence o on success, and -1
+    # on failure. For objects that do not provide sequence protocol,
+    # this is equivalent to the Python expression "len(o)".
+
+    Py_ssize_t PySequence_Length(object o) except -1
+    # Alternate name for PySequence_Size().
+
+    object PySequence_Concat(object o1, object o2)
+    # Return value: New reference.
+    # Return the concatenation of o1 and o2 on success, and NULL on
+    # failure. This is the equivalent of the Python expression "o1 +
+    # o2".
+
+    object PySequence_Repeat(object o, Py_ssize_t count)
+    # Return value: New reference.
+    # Return the result of repeating sequence object o count times, or
+    # NULL on failure. This is the equivalent of the Python expression
+    # "o * count".
+
+    object PySequence_InPlaceConcat(object o1, object o2)
+    # Return value: New reference.
+    # Return the concatenation of o1 and o2 on success, and NULL on
+    # failure. The operation is done in-place when o1 supports
+    # it. This is the equivalent of the Python expression "o1 += o2".
+
+    object PySequence_InPlaceRepeat(object o, Py_ssize_t count)
+    # Return value: New reference.
+    # Return the result of repeating sequence object o count times, or
+    # NULL on failure. The operation is done in-place when o supports
+    # it. This is the equivalent of the Python expression "o *=
+    # count".
+
+    object PySequence_GetItem(object o, Py_ssize_t i)
+    # Return value: New reference.
+    # Return the ith element of o, or NULL on failure. This is the
+    # equivalent of the Python expression "o[i]".
+
+    object PySequence_GetSlice(object o, Py_ssize_t i1, Py_ssize_t i2)
+    # Return value: New reference.
+    # Return the slice of sequence object o between i1 and i2, or NULL
+    # on failure. This is the equivalent of the Python expression
+    # "o[i1:i2]".
+
+    int PySequence_SetItem(object o, Py_ssize_t i, object v) except -1
+    # Assign object v to the ith element of o. Returns -1 on
+    # failure. This is the equivalent of the Python statement "o[i] =
+    # v". This function does not steal a reference to v.
+
+    int PySequence_DelItem(object o, Py_ssize_t i) except -1
+    # Delete the ith element of object o. Returns -1 on failure. This
+    # is the equivalent of the Python statement "del o[i]".
+
+    int PySequence_SetSlice(object o, Py_ssize_t i1, Py_ssize_t i2, object v) except -1
+    # Assign the sequence object v to the slice in sequence object o
+    # from i1 to i2. This is the equivalent of the Python statement
+    # "o[i1:i2] = v".
+
+    int PySequence_DelSlice(object o, Py_ssize_t i1, Py_ssize_t i2) except -1
+    # Delete the slice in sequence object o from i1 to i2. Returns -1
+    # on failure. This is the equivalent of the Python statement "del
+    # o[i1:i2]".
+
+    int PySequence_Count(object o, object value) except -1
+    # Return the number of occurrences of value in o, that is, return
+    # the number of keys for which o[key] == value. On failure, return
+    # -1. This is equivalent to the Python expression
+    # "o.count(value)".
+
+    int PySequence_Contains(object o, object value) except -1
+    # Determine if o contains value. If an item in o is equal to
+    # value, return 1, otherwise return 0. On error, return -1. This
+    # is equivalent to the Python expression "value in o".
+
+    Py_ssize_t PySequence_Index(object o, object value) except -1
+    # Return the first index i for which o[i] == value. On error,
+    # return -1. This is equivalent to the Python expression
+    # "o.index(value)".
+
+    object PySequence_List(object o)
+    # Return value: New reference.
+    # Return a list object with the same contents as the arbitrary
+    # sequence o. The returned list is guaranteed to be new.
+
+    object PySequence_Tuple(object o)
+    # Return value: New reference.
+    # Return a tuple object with the same contents as the arbitrary
+    # sequence o or NULL on failure. If o is a tuple, a new reference
+    # will be returned, otherwise a tuple will be constructed with the
+    # appropriate contents. This is equivalent to the Python
+    # expression "tuple(o)".
+
+    object PySequence_Fast(object o, char *m)
+    # Return value: New reference.
+    # Returns the sequence o as a tuple, unless it is already a tuple
+    # or list, in which case o is returned. Use
+    # PySequence_Fast_GET_ITEM() to access the members of the
+    # result. Returns NULL on failure. If the object is not a
+    # sequence, raises TypeError with m as the message text.
+
+    PyObject* PySequence_Fast_GET_ITEM(object o, Py_ssize_t i)
+    # Return value: Borrowed reference.
+    # Return the ith element of o, assuming that o was returned by
+    # PySequence_Fast(), o is not NULL, and that i is within bounds.
+
+    PyObject** PySequence_Fast_ITEMS(object o)
+    # Return the underlying array of PyObject pointers. Assumes that o
+    # was returned by PySequence_Fast() and o is not NULL.
+
+    object PySequence_ITEM(object o, Py_ssize_t i)
+    # Return value: New reference.
+    # Return the ith element of o or NULL on failure. Macro form of
+    # PySequence_GetItem() but without checking that
+    # PySequence_Check(o) is true and without adjustment for negative
+    # indices.
+
+    Py_ssize_t PySequence_Fast_GET_SIZE(object o)
+    # Returns the length of o, assuming that o was returned by
+    # PySequence_Fast() and that o is not NULL. The size can also be
+    # gotten by calling PySequence_Size() on o, but
+    # PySequence_Fast_GET_SIZE() is faster because it can assume o is
+    # a list or tuple.
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/type.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/type.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..928a748cd3e245818bde517f494e2283aac97354
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/type.pxd
@@ -0,0 +1,53 @@
+
+cdef extern from "Python.h":
+    # The C structure of the objects used to describe built-in types.
+
+    ############################################################################
+    # 7.1.1 Type Objects
+    ############################################################################
+
+    ctypedef class __builtin__.type [object PyTypeObject]:
+        pass
+
+    # PyObject* PyType_Type
+    # This is the type object for type objects; it is the same object
+    # as type and types.TypeType in the Python layer.
+
+    bint PyType_Check(object o)
+    # Return true if the object o is a type object, including
+    # instances of types derived from the standard type object. Return
+    # false in all other cases.
+
+    bint PyType_CheckExact(object o)
+    # Return true if the object o is a type object, but not a subtype
+    # of the standard type object. Return false in all other
+    # cases.
+
+    void PyType_Modified(type type)
+    # Invalidate the internal lookup cache for the type and all of its
+    # subtypes. This function must be called after any manual modification
+    # of the attributes or base classes of the type.
+
+    bint PyType_HasFeature(object o, int feature)
+    # Return true if the type object o sets the feature feature. Type
+    # features are denoted by single bit flags.
+
+    bint PyType_IS_GC(object o)
+    # Return true if the type object includes support for the cycle
+    # detector; this tests the type flag Py_TPFLAGS_HAVE_GC.
+
+    bint PyType_IsSubtype(type a, type b)
+    # Return true if a is a subtype of b.
+
+    object PyType_GenericAlloc(object type, Py_ssize_t nitems)
+    # Return value: New reference.
+
+    object PyType_GenericNew(type type, object args, object kwds)
+    # Return value: New reference.
+
+    bint PyType_Ready(type type) except -1
+    # Finalize a type object. This should be called on all type
+    # objects to finish their initialization. This function is
+    # responsible for adding inherited slots from a type's base
+    # class. Return 0 on success, or return -1 and sets an exception
+    # on error.
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/version.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/version.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..ce31b249cf0a86ee14da4e05203b089f7d98f9c4
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/cpython/version.pxd
@@ -0,0 +1,32 @@
+# Python version constants
+#
+# It's better to evaluate these at runtime (i.e. C compile time) using
+#
+#      if PY_MAJOR_VERSION >= 3:
+#           do_stuff_in_Py3_0_and_later()
+#      if PY_VERSION_HEX >= 0x02070000:
+#           do_stuff_in_Py2_7_and_later()
+#
+# than using the IF/DEF statements, which are evaluated at Cython
+# compile time.  This will keep your C code portable.
+
+
+cdef extern from *:
+    # the complete version, e.g. 0x010502B2 == 1.5.2b2
+    int PY_VERSION_HEX
+
+    # the individual sections as plain numbers
+    int PY_MAJOR_VERSION
+    int PY_MINOR_VERSION
+    int PY_MICRO_VERSION
+    int PY_RELEASE_LEVEL
+    int PY_RELEASE_SERIAL
+
+    # Note: PY_RELEASE_LEVEL is one of
+    #    0xA (alpha)
+    #    0xB (beta)
+    #    0xC (release candidate)
+    #    0xF (final)
+
+    char PY_VERSION[]
+    char PY_PATCHLEVEL_REVISION[]
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/__init__.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/__init__.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..111ea25c2f27d2feeec82db94dee6954bce27976
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/__init__.pxd
@@ -0,0 +1,4 @@
+cdef extern from *:
+    ctypedef bint bool
+    ctypedef void* nullptr_t
+    nullptr_t nullptr
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/algorithm.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/algorithm.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..b800c569ccf98788ae4e7add08b4a7191a4978d7
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/algorithm.pxd
@@ -0,0 +1,320 @@
+from libcpp cimport bool
+from libcpp.utility cimport pair
+from libc.stddef import ptrdiff_t
+
+
+cdef extern from "<algorithm>" namespace "std" nogil:
+    # Non-modifying sequence operations
+    bool all_of[Iter, Pred](Iter first, Iter last, Pred pred) except +
+    bool all_of[ExecutionPolicy, Iter, Pred](ExecutionPolicy&& policy, Iter first, Iter last, Pred pred) except +
+    bool any_of[Iter, Pred](Iter first, Iter last, Pred pred) except +
+    bool any_of[ExecutionPolicy, Iter, Pred](ExecutionPolicy&& policy, Iter first, Iter last, Pred pred) except +
+    bool none_of[Iter, Pred](Iter first, Iter last, Pred pred) except +
+    bool none_of[ExecutionPolicy, Iter, Pred](ExecutionPolicy&& policy, Iter first, Iter last, Pred pred) except +
+
+    void for_each[Iter, UnaryFunction](Iter first, Iter last, UnaryFunction f) except +  # actually returns f
+    void for_each[ExecutionPolicy, Iter, UnaryFunction](ExecutionPolicy&& policy, Iter first, Iter last, UnaryFunction f) except +  # actually returns f
+
+    ptrdiff_t count[Iter, T](Iter first, Iter last, const T& value) except +
+    ptrdiff_t count[ExecutionPolicy, Iter, T](ExecutionPolicy&& policy, Iter first, Iter last, const T& value) except +
+    ptrdiff_t count_if[Iter, Pred](Iter first, Iter last, Pred pred) except +
+    ptrdiff_t count_if[ExecutionPolicy, Iter, Pred](ExecutionPolicy&& policy, Iter first, Iter last, Pred pred) except +
+
+    pair[Iter1, Iter2] mismatch[Iter1, Iter2](
+        Iter1 first1, Iter1 last1, Iter2 first2) except +  # other overloads are tricky
+    pair[Iter1, Iter2] mismatch[ExecutionPolicy, Iter1, Iter2](
+        ExecutionPolicy&& policy, Iter1 first1, Iter1 last1, Iter2 first2) except +
+
+    Iter find[Iter, T](Iter first, Iter last, const T& value) except +
+    Iter find[ExecutionPolicy, Iter, T](ExecutionPolicy&& policy, Iter first, Iter last, const T& value) except +
+
+    Iter find_if[Iter, Pred](Iter first, Iter last, Pred pred) except +
+    Iter find_if[ExecutionPolicy, Iter, Pred](ExecutionPolicy&& policy, Iter first, Iter last, Pred pred) except +
+    Iter find_if_not[Iter, Pred](Iter first, Iter last, Pred pred) except +
+    Iter find_if_not[ExecutionPolicy, Iter, Pred](ExecutionPolicy&& policy, Iter first, Iter last, Pred pred) except +
+
+    Iter1 find_end[Iter1, Iter2](Iter1 first1, Iter1 last1, Iter2 first2, Iter2 last2) except +
+    Iter1 find_end[ExecutionPolicy, Iter1, Iter2](ExecutionPolicy&& policy, Iter1 first1, Iter1 last1, Iter2 first2, Iter2 last2) except +
+    Iter1 find_end[Iter1, Iter2, BinaryPred](
+        Iter1 first1, Iter1 last1, Iter2 first2, Iter2 last2, BinaryPred pred) except +
+    Iter1 find_end[ExecutionPolicy, Iter1, Iter2, BinaryPred](
+        ExecutionPolicy&& policy, Iter1 first1, Iter1 last1, Iter2 first2, Iter2 last2, BinaryPred pred) except +
+
+
+    Iter1 find_first_of[Iter1, Iter2](Iter1 first1, Iter1 last1, Iter2 first2, Iter2 last2) except +
+    Iter1 find_first_of[Iter1, Iter2, BinaryPred](
+        Iter1 first1, Iter1 last1, Iter2 first2, Iter2 last2, BinaryPred pred) except +
+    Iter1 find_first_of[ExecutionPolicy, Iter1, Iter2](ExecutionPolicy&& policy, Iter1 first1, Iter1 last1, Iter2 first2, Iter2 last2) except +
+    Iter1 find_first_of[ExecutionPolicy, Iter1, Iter2, BinaryPred](
+        ExecutionPolicy&& policy, Iter1 first1, Iter1 last1, Iter2 first2, Iter2 last2, BinaryPred pred) except +
+
+    Iter adjacent_find[Iter](Iter first, Iter last) except +
+    Iter adjacent_find[ExecutionPolicy, Iter](ExecutionPolicy&& policy, Iter first, Iter last) except +
+    Iter adjacent_find[Iter, BinaryPred](Iter first, Iter last, BinaryPred pred) except +
+    Iter adjacent_find[ExecutionPolicy, Iter, BinaryPred](ExecutionPolicy&& policy, Iter first, Iter last, BinaryPred pred) except +
+
+    Iter1 search[Iter1, Iter2](Iter1 first1, Iter1 last1, Iter2 first2, Iter2 last2) except +
+    Iter1 search[ExecutionPolicy, Iter1, Iter2](ExecutionPolicy&& policy, Iter1 first1, Iter1 last1, Iter2 first2, Iter2 last2) except +
+    Iter1 search[Iter1, Iter2, BinaryPred](
+        Iter1 first1, Iter1 last1, Iter2 first2, Iter2 last2, BinaryPred pred) except +
+    Iter1 search[ExecutionPolicy, Iter1, Iter2, BinaryPred](
+        ExecutionPolicy&& policy, Iter1 first1, Iter1 last1, Iter2 first2, Iter2 last2, BinaryPred pred) except +
+    Iter search_n[Iter, Size, T](Iter first1, Iter last1, Size count, const T& value) except +
+    Iter search_n[ExecutionPolicy, Iter, Size, T](ExecutionPolicy&& policy, Iter first1, Iter last1, Size count, const T& value) except +
+    Iter search_n[Iter, Size, T, BinaryPred](
+        Iter first1, Iter last1, Size count, const T& value, BinaryPred pred) except +
+    Iter search_n[ExecutionPolicy, Iter, Size, T, BinaryPred](
+        ExecutionPolicy&& policy, Iter first1, Iter last1, Size count, const T& value, BinaryPred pred) except +
+
+    # Modifying sequence operations
+    OutputIt copy[InputIt, OutputIt](InputIt first, InputIt last, OutputIt d_first) except +
+    OutputIt copy[ExecutionPolicy, InputIt, OutputIt](ExecutionPolicy&& policy, InputIt first, InputIt last, OutputIt d_first) except +
+    OutputIt copy_if[InputIt, OutputIt, Pred](InputIt first, InputIt last, OutputIt d_first, Pred pred) except +
+    OutputIt copy_if[ExecutionPolicy, InputIt, OutputIt, Pred](ExecutionPolicy&& policy, InputIt first, InputIt last, OutputIt d_first, Pred pred) except +
+    OutputIt copy_n[InputIt, Size, OutputIt](InputIt first, Size count, OutputIt result) except +
+    OutputIt copy_n[ExecutionPolicy, InputIt, Size, OutputIt](ExecutionPolicy&& policy, InputIt first, Size count, OutputIt result) except +
+    Iter2 copy_backward[Iter1, Iter2](Iter1 first, Iter1 last, Iter2 d_last) except +
+    Iter2 copy_backward[ExecutionPolicy, Iter1, Iter2](ExecutionPolicy&& policy, Iter1 first, Iter1 last, Iter2 d_last) except +
+
+    OutputIt move[InputIt, OutputIt](InputIt first, InputIt last, OutputIt d_first) except +
+    OutputIt move[ExecutionPolicy, InputIt, OutputIt](ExecutionPolicy&& policy, InputIt first, InputIt last, OutputIt d_first) except +
+    Iter2 move_backward[Iter1, Iter2](Iter1 first, Iter1 last, Iter2 d_last) except +
+    Iter2 move_backward[ExecutionPolicy, Iter1, Iter2](ExecutionPolicy&& policy, Iter1 first, Iter1 last, Iter2 d_last) except +
+
+    void fill[Iter, T](Iter first, Iter last, const T& value) except +
+    void fill[ExecutionPolicy, Iter, T](ExecutionPolicy&& policy, Iter first, Iter last, const T& value) except +
+    Iter fill_n[Iter, Size, T](Iter first, Size count, const T& value) except +
+    Iter fill_n[ExecutionPolicy, Iter, Size, T](ExecutionPolicy&& policy, Iter first, Size count, const T& value) except +
+
+    OutputIt transform[InputIt, OutputIt, UnaryOp](
+        InputIt first1, InputIt last1, OutputIt d_first, UnaryOp unary_op) except +
+
+    # This overload is ambiguous with the next one. We just let C++ disambiguate from the arguments
+    # OutputIt transform[ExecutionPolicy, InputIt, OutputIt, UnaryOp](
+    #     ExecutionPolicy&& policy, InputIt first1, InputIt last1, OutputIt d_first, UnaryOp unary_op) except +
+
+    OutputIt transform[InputIt1, InputIt2, OutputIt, BinaryOp](
+        InputIt1 first1, InputIt1 last1, InputIt2 first2, OutputIt d_first, BinaryOp binary_op) except +
+
+    OutputIt transform[ExecutionPolicy, InputIt1, InputIt2, OutputIt, BinaryOp](
+        ExecutionPolicy&& policy, InputIt1 first1, InputIt1 last1, InputIt2 first2, OutputIt d_first, BinaryOp binary_op) except +
+
+    void generate[Iter, Generator](Iter first, Iter last, Generator g) except +
+    void generate[ExecutionPolicy, Iter, Generator](ExecutionPolicy&& policy, Iter first, Iter last, Generator g) except +
+    void generate_n[Iter, Size, Generator](Iter first, Size count, Generator g) except +
+    void generate_n[ExecutionPolicy, Iter, Size, Generator](ExecutionPolicy&& policy, Iter first, Size count, Generator g) except +
+
+    Iter remove[Iter, T](Iter first, Iter last, const T& value) except +
+    Iter remove[ExecutionPolicy, Iter, T](ExecutionPolicy&& policy, Iter first, Iter last, const T& value) except +
+    Iter remove_if[Iter, UnaryPred](Iter first, Iter last, UnaryPred pred) except +
+    Iter remove_if[ExecutionPolicy, Iter, UnaryPred](ExecutionPolicy&& policy, Iter first, Iter last, UnaryPred pred) except +
+    OutputIt remove_copy[InputIt, OutputIt, T](InputIt first, InputIt last, OutputIt d_first, const T& value) except +
+    OutputIt remove_copy[ExecutionPolicy, InputIt, OutputIt, T](ExecutionPolicy&& policy, InputIt first, InputIt last, OutputIt d_first, const T& value) except +
+    OutputIt remove_copy_if[InputIt, OutputIt, UnaryPred](
+        InputIt first, InputIt last, OutputIt d_first, UnaryPred pred) except +
+    OutputIt remove_copy_if[ExecutionPolicy, InputIt, OutputIt, UnaryPred](
+        ExecutionPolicy&& policy, InputIt first, InputIt last, OutputIt d_first, UnaryPred pred) except +
+
+    void replace[Iter, T](Iter first, Iter last, const T& old_value, const T& new_value) except +
+    void replace[ExecutionPolicy, Iter, T](ExecutionPolicy&& policy, Iter first, Iter last, const T& old_value, const T& new_value) except +
+    void replace_if[Iter, UnaryPred, T](Iter first, Iter last, UnaryPred pred, const T& new_value) except +
+    OutputIt replace_copy[InputIt, OutputIt, T](
+        InputIt first, InputIt last, OutputIt d_first, const T& old_value, const T& new_value) except +
+    void replace_if[ExecutionPolicy, Iter, UnaryPred, T](ExecutionPolicy&& policy, Iter first, Iter last, UnaryPred pred, const T& new_value) except +
+
+    OutputIt replace_copy[ExecutionPolicy, InputIt, OutputIt, T](
+        ExecutionPolicy&& policy, InputIt first, InputIt last, OutputIt d_first, const T& old_value, const T& new_value) except +
+    OutputIt replace_copy_if[InputIt, OutputIt, UnaryPred, T](
+        InputIt first, InputIt last, OutputIt d_first, UnaryPred pred, const T& new_value) except +
+    OutputIt replace_copy_if[ExecutionPolicy, InputIt, OutputIt, UnaryPred, T](
+        ExecutionPolicy&& policy, InputIt first, InputIt last, OutputIt d_first, UnaryPred pred, const T& new_value) except +
+
+    void swap[T](T& a, T& b) except +  # array overload also works
+    Iter2 swap_ranges[Iter1, Iter2](Iter1 first1, Iter1 last1, Iter2 first2) except +
+    void iter_swap[Iter](Iter a, Iter b) except +
+
+    void reverse[Iter](Iter first, Iter last) except +
+    void reverse[ExecutionPolicy, Iter](ExecutionPolicy&& policy, Iter first, Iter last) except +
+    OutputIt reverse_copy[InputIt, OutputIt](InputIt first, InputIt last, OutputIt d_first) except +
+    OutputIt reverse_copy[ExecutionPolicy, InputIt, OutputIt](ExecutionPolicy&& policy, InputIt first, InputIt last, OutputIt d_first) except +
+
+    Iter rotate[Iter](Iter first, Iter n_first, Iter last) except +
+    Iter rotate[ExecutionPolicy, Iter](ExecutionPolicy&& policy, Iter first, Iter n_first, Iter last) except +
+    OutputIt rotate_copy[InputIt, OutputIt](InputIt first, InputIt n_first, InputIt last, OutputIt d_first) except +
+    OutputIt rotate_copy[ExecutionPolicy, InputIt, OutputIt](ExecutionPolicy&& policy, InputIt first, InputIt n_first, InputIt last, OutputIt d_first) except +
+
+    Iter unique[Iter](Iter first, Iter last) except +
+    Iter unique[ExecutionPolicy, Iter](ExecutionPolicy&& policy, Iter first, Iter last) except +
+    Iter unique[Iter, BinaryPred](Iter first, Iter last, BinaryPred p) except +
+    Iter unique[ExecutionPolicy, Iter, BinaryPred](ExecutionPolicy&& policy, Iter first, Iter last, BinaryPred p) except +
+    OutputIt unique_copy[InputIt, OutputIt](InputIt first, InputIt last, OutputIt d_first) except +
+    OutputIt unique_copy[ExecutionPolicy, InputIt, OutputIt](ExecutionPolicy&& policy, InputIt first, InputIt last, OutputIt d_first) except +
+    OutputIt unique_copy[InputIt, OutputIt, BinaryPred](
+        InputIt first, InputIt last, OutputIt d_first, BinaryPred pred) except +
+    OutputIt unique_copy[ExecutionPolicy, InputIt, OutputIt, BinaryPred](
+        ExecutionPolicy&& policy, InputIt first, InputIt last, OutputIt d_first, BinaryPred pred) except +
+
+    SampleIt sample[PopulationIt, SampleIt, Distance, URBG](PopulationIt first, PopulationIt last, SampleIt out, Distance n, URBG&& g) except +
+
+    # Partitioning operations
+    bool is_partitioned[Iter, Pred](Iter first, Iter last, Pred p) except +
+    bool is_partitioned[ExecutionPolicy, Iter, Pred](ExecutionPolicy&& policy, Iter first, Iter last, Pred p) except +
+    Iter partition[Iter, Pred](Iter first, Iter last, Pred p) except +
+    Iter partition[ExecutionPolicy, Iter, Pred](ExecutionPolicy&& policy, Iter first, Iter last, Pred p) except +
+    pair[OutputIt1, OutputIt2] partition_copy[InputIt, OutputIt1, OutputIt2, Pred](
+        InputIt first, InputIt last, OutputIt1 d_first_true, OutputIt2 d_first_false, Pred p) except +
+    pair[OutputIt1, OutputIt2] partition_copy[ExecutionPolicy, InputIt, OutputIt1, OutputIt2, Pred](
+        ExecutionPolicy&& policy, InputIt first, InputIt last, OutputIt1 d_first_true, OutputIt2 d_first_false, Pred p) except +
+
+    Iter stable_partition[Iter, Pred](Iter first, Iter last, Pred p) except +
+    Iter stable_partition[ExecutionPolicy, Iter, Pred](ExecutionPolicy&& policy, Iter first, Iter last, Pred p) except +
+    Iter partition_point[Iter, Pred](Iter first, Iter last, Pred p) except +
+    Iter partition_point[ExecutionPolicy, Iter, Pred](ExecutionPolicy&& policy, Iter first, Iter last, Pred p) except +
+
+    # Sorting operations
+    bool is_sorted[Iter](Iter first, Iter last) except +
+    bool is_sorted[ExecutionPolicy, Iter](ExecutionPolicy&& policy, Iter first, Iter last) except +
+    bool is_sorted[Iter, Compare](Iter first, Iter last, Compare comp) except +
+    bool is_sorted[ExecutionPolicy, Iter, Compare](ExecutionPolicy&& policy, Iter first, Iter last, Compare comp) except +
+
+    Iter is_sorted_until[Iter](Iter first, Iter last) except +
+    Iter is_sorted_until[ExecutionPolicy, Iter](ExecutionPolicy&& policy, Iter first, Iter last) except +
+    Iter is_sorted_until[Iter, Compare](Iter first, Iter last, Compare comp) except +
+    Iter is_sorted_until[ExecutionPolicy, Iter, Compare](ExecutionPolicy&& policy, Iter first, Iter last, Compare comp) except +
+
+    void sort[Iter](Iter first, Iter last) except +
+    void sort[ExecutionPolicy, Iter](ExecutionPolicy&& policy, Iter first, Iter last) except +
+    void sort[Iter, Compare](Iter first, Iter last, Compare comp) except +
+    void sort[ExecutionPolicy, Iter, Compare](ExecutionPolicy&& policy, Iter first, Iter last, Compare comp) except +
+
+    void partial_sort[Iter](Iter first, Iter middle, Iter last) except +
+    void partial_sort[ExecutionPolicy, Iter](ExecutionPolicy&& policy, Iter first, Iter middle, Iter last) except +
+    void partial_sort[Iter, Compare](Iter first, Iter middle, Iter last, Compare comp) except +
+    void partial_sort[ExecutionPolicy, Iter, Compare](ExecutionPolicy&& policy, Iter first, Iter middle, Iter last, Compare comp) except +
+
+    OutputIt partial_sort_copy[InputIt, OutputIt](
+        InputIt first, InputIt last, OutputIt d_first, OutputIt d_last) except +
+    OutputIt partial_sort_copy[ExecutionPolicy, InputIt, OutputIt](
+        ExecutionPolicy&& policy, InputIt first, InputIt last, OutputIt d_first, OutputIt d_last) except +
+    OutputIt partial_sort_copy[InputIt, OutputIt, Compare](
+        InputIt first, InputIt last, OutputIt d_first, OutputIt d_last, Compare comp) except +
+    OutputIt partial_sort_copy[ExecutionPolicy, InputIt, OutputIt, Compare](
+        ExecutionPolicy&& policy, InputIt first, InputIt last, OutputIt d_first, OutputIt d_last, Compare comp) except +
+
+    void stable_sort[Iter](Iter first, Iter last) except +
+    void stable_sort[ExecutionPolicy, Iter](ExecutionPolicy&& policy, Iter first, Iter last) except +
+    void stable_sort[Iter, Compare](Iter first, Iter last, Compare comp) except +
+    void stable_sort[ExecutionPolicy, Iter, Compare](ExecutionPolicy&& policy, Iter first, Iter last, Compare comp) except +
+
+    void nth_element[Iter](Iter first, Iter nth, Iter last) except +
+    void nth_element[ExecutionPolicy, Iter](ExecutionPolicy&& policy, Iter first, Iter nth, Iter last) except +
+    void nth_element[Iter, Compare](Iter first, Iter nth, Iter last, Compare comp) except +
+    void nth_element[ExecutionPolicy, Iter, Compare](ExecutionPolicy&& policy, Iter first, Iter nth, Iter last, Compare comp) except +
+
+    # Binary search operations (on sorted ranges)
+    Iter lower_bound[Iter, T](Iter first, Iter last, const T& value) except +
+    Iter lower_bound[ExecutionPolicy, Iter, T](ExecutionPolicy&& policy, Iter first, Iter last, const T& value) except +
+    Iter lower_bound[Iter, T, Compare](Iter first, Iter last, const T& value, Compare comp) except +
+    Iter lower_bound[ExecutionPolicy, Iter, T, Compare](ExecutionPolicy&& policy, Iter first, Iter last, const T& value, Compare comp) except +
+
+    Iter upper_bound[Iter, T](Iter first, Iter last, const T& value) except +
+    Iter upper_bound[ExecutionPolicy, Iter, T](ExecutionPolicy&& policy, Iter first, Iter last, const T& value) except +
+    Iter upper_bound[Iter, T, Compare](Iter first, Iter last, const T& value, Compare comp) except +
+    Iter upper_bound[ExecutionPolicy, Iter, T, Compare](ExecutionPolicy&& policy, Iter first, Iter last, const T& value, Compare comp) except +
+
+    bool binary_search[Iter, T](Iter first, Iter last, const T& value) except +
+    bool binary_search[ExecutionPolicy, Iter, T](ExecutionPolicy&& policy, Iter first, Iter last, const T& value) except +
+    bool binary_search[Iter, T, Compare](Iter first, Iter last, const T& value, Compare comp) except +
+    bool binary_search[ExecutionPolicy, Iter, T, Compare](ExecutionPolicy&& policy, Iter first, Iter last, const T& value, Compare comp) except +
+
+    # Other operations on sorted ranges
+    OutputIt merge[InputIt1, InputIt2, OutputIt](
+        InputIt1 first1, InputIt1 last1, InputIt2 first2, InputIt2 last2, OutputIt out) except +
+    OutputIt merge[InputIt1, InputIt2, OutputIt, Compare](
+        InputIt1 first1, InputIt1 last1, InputIt2 first2, InputIt2 last2, OutputIt out, Compare comp) except +
+
+    void inplace_merge[BidirIt](BidirIt first, BidirIt middle, BidirIt last) except +
+    void inplace_merge[BidirIt, Compare](BidirIt first, BidirIt middle, BidirIt last, Compare comp) except +
+
+    # Set operations (on sorted ranges)
+    bool includes[InputIt1, InputIt2](
+        InputIt1 first1, InputIt1 last1, InputIt2 first2, InputIt2 last2) except +
+
+    bool includes[InputIt1, InputIt2, Compare](
+        InputIt1 first1, InputIt1 last1, InputIt2 first2, InputIt2 last2, Compare comp) except +
+
+    OutputIt set_difference[InputIt1, InputIt2, OutputIt](
+        InputIt1 first1, InputIt1 last1, InputIt2 first2, InputIt2 last2, OutputIt out) except +
+
+    OutputIt set_difference[InputIt1, InputIt2, OutputIt, Compare](
+        InputIt1 first1, InputIt1 last1, InputIt2 first2, InputIt2 last2,
+        OutputIt out, Compare comp) except +
+
+    OutputIt set_intersection[InputIt1, InputIt2, OutputIt](
+        InputIt1 first1, InputIt1 last1, InputIt2 first2, InputIt2 last2, OutputIt out) except +
+
+    OutputIt set_intersection[InputIt1, InputIt2, OutputIt, Compare](
+        InputIt1 first1, InputIt1 last1, InputIt2 first2, InputIt2 last2, OutputIt out, Compare comp) except +
+
+    OutputIt set_symmetric_difference[InputIt1, InputIt2, OutputIt](
+        InputIt1 first1, InputIt1 last1, InputIt2 first2, InputIt2 last2, OutputIt out) except +
+
+    OutputIt set_symmetric_difference[InputIt1, InputIt2, OutputIt, Compare](
+        InputIt1 first1, InputIt1 last1, InputIt2 first2, InputIt2 last2, OutputIt out, Compare comp) except +
+
+    OutputIt set_union[InputIt1, InputIt2, OutputIt](
+        InputIt1 first1, InputIt1 last1, InputIt2 first2, InputIt2 last2, OutputIt out) except +
+
+    OutputIt set_union[InputIt1, InputIt2, OutputIt, Compare](
+        InputIt1 first1, InputIt1 last1, InputIt2 first2, InputIt2 last2, OutputIt out, Compare comp) except +
+
+    # Heap operations
+    void make_heap[Iter](Iter first, Iter last) except +
+    void make_heap[Iter, Compare](Iter first, Iter last, Compare comp) except +
+
+    void push_heap[Iter](Iter first, Iter last) except +
+    void push_heap[Iter, Compare](Iter first, Iter last, Compare comp) except +
+
+    void pop_heap[Iter](Iter first, Iter last) except +
+    void pop_heap[Iter, Compare](Iter first, Iter last, Compare comp) except +
+
+    void sort_heap[Iter](Iter first, Iter last) except +
+    void sort_heap[Iter, Compare](Iter first, Iter last, Compare comp) except +
+
+    # Minimum/maximum operations
+    Iter min_element[Iter](Iter first, Iter last) except +
+    Iter min_element[Iter, Compare](Iter first, Iter last, Compare comp) except +
+    Iter min_element[ExecutionPolicy, Iter](ExecutionPolicy&& policy, Iter first, Iter last) except +
+    Iter max_element[Iter](Iter first, Iter last) except +
+    Iter max_element[Iter, Compare](Iter first, Iter last, Compare comp) except +
+    Iter max_element[ExecutionPolicy, Iter](ExecutionPolicy&& policy, Iter first, Iter last) except +
+    pair[T, T] minmax[T](const T& a, const T& b) except +
+    pair[T, T] minmax[T, Compare](const T& a, const T& b, Compare comp) except +
+    pair[Iter, Iter] minmax_element[Iter](Iter first, Iter last) except +
+    pair[Iter, Iter] minmax_element[Iter, Compare](Iter first, Iter last, Compare comp) except +
+    pair[Iter, Iter] minmax_element[ExecutionPolicy, Iter](ExecutionPolicy&& policy, Iter first, Iter last) except +
+    const T& clamp[T](const T& v, const T& lo, const T& hi) except +
+    const T& clamp[T, Compare](const T& v, const T& lo, const T& hi, Compare comp) except +
+
+    # Comparison operations
+    bool equal[InputIt1, InputIt2](InputIt1 first1, InputIt1 last1, InputIt2 first2) except +
+    bool equal[InputIt1, InputIt2, BinPred](InputIt1 first1, InputIt1 last1, InputIt2 first2, BinPred pred) except +
+    # ambiguous with previous overload
+    #bool equal[InputIt1, InputIt2](InputIt1 first1, InputIt1 last1, InputIt2 first2, InputIt2 last2) except +
+    bool equal[InputIt1, InputIt2, BinPred](InputIt1 first1, InputIt1 last1, InputIt2 first2, InputIt2 last2, BinPred pred) except +
+
+    bool lexicographical_compare[InputIt1, InputIt2](InputIt1 first1, InputIt1 last1, InputIt2 first2, InputIt2 last2) except +
+    # ambiguous with next overload
+    #bool lexicographical_compare[InputIt1, InputIt2, ExecutionPolicy](ExecutionPolicy&& policy, InputIt1 first1, InputIt1 last1, InputIt2 first2, InputIt2 last2) except +
+    bool lexicographical_compare[InputIt1, InputIt2, Compare](InputIt1 first1, InputIt1 last1, InputIt2 first2, InputIt2 last2, Compare comp) except +
+
+    # Permutation operations
+    bool is_permutation[ForwardIt1, ForwardIt2](ForwardIt1 first1, ForwardIt1 last1, ForwardIt2 first2) except +
+    bool is_permutation[ForwardIt1, ForwardIt2, BinaryPred](ForwardIt1 first1, ForwardIt1 last1, ForwardIt2 first2, BinaryPred p) except +
+    # ambiguous with previous overload
+    #bool is_permutation[ForwardIt1, ForwardIt2](ForwardIt1 first1, ForwardIt1 last1, ForwardIt2 first2, ForwardIt2 last2) except +
+    bool is_permutation[ForwardIt1, ForwardIt2, BinaryPred](ForwardIt1 first1, ForwardIt1 last1, ForwardIt2 first2, ForwardIt2 last2, BinaryPred p) except +
+    bool next_permutation[BidirIt](BidirIt first, BidirIt last) except +
+    bool next_permutation[BidirIt, Compare](BidirIt first, BidirIt last, Compare comp) except +
+    bool prev_permutation[BidirIt](BidirIt first, BidirIt last) except +
+    bool prev_permutation[BidirIt, Compare](BidirIt first, BidirIt last, Compare comp) except +
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/any.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/any.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..7c0d000cdbb963e1c6f89b334f78334c9ab4f9ed
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/any.pxd
@@ -0,0 +1,16 @@
+from libcpp cimport bool
+from libcpp.typeinfo cimport type_info
+
+cdef extern from "<any>" namespace "std" nogil:
+    cdef cppclass any:
+        any()
+        any(any&) except +
+        void reset()
+        bool has_value()
+        type_info& type()
+        T& emplace[T](...) except +
+        void swap(any&)
+        any& operator=(any&) except +
+        any& operator=[U](U&) except +
+
+    cdef T any_cast[T](any&) except +
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/complex.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/complex.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..9d8a700ea00f900def1a2b47f8c79d516d5d202b
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/complex.pxd
@@ -0,0 +1,106 @@
+# Note: add integer versions of the functions?
+
+cdef extern from "<complex>" namespace "std" nogil:
+    cdef cppclass complex[T]:
+        complex() except +
+        complex(T, T) except +
+        complex(complex[T]&) except +
+        # How to make the converting constructor, i.e. convert complex[double]
+        # to complex[float]?
+
+        complex[T] operator+(complex[T]&, complex[T]&)
+        complex[T] operator+(complex[T]&, T&)
+        complex[T] operator+(T&, complex[T]&)
+
+        complex[T] operator-(complex[T]&, complex[T]&)
+        complex[T] operator-(complex[T]&, T&)
+        complex[T] operator-(T&, complex[T]&)
+
+        complex[T] operator*(complex[T]&, complex[T]&)
+        complex[T] operator*(complex[T]&, T&)
+        complex[T] operator*(T&, complex[T]&)
+
+        complex[T] operator/(complex[T]&, complex[T]&)
+        complex[T] operator/(complex[T]&, T&)
+        complex[T] operator/(T&, complex[T]&)
+
+        complex[T] operator+()
+        complex[T] operator-()
+
+        bint operator==(complex[T]&, complex[T]&)
+        bint operator==(complex[T]&, T&)
+        bint operator==(T&, complex[T]&)
+        bint operator!=(complex[T]&, complex[T]&)
+        bint operator!=(complex[T]&, T&)
+        bint operator!=(T&, complex[T]&)
+
+        # Access real part
+        T real()
+        void real(T)
+
+        # Access imaginary part
+        T imag()
+        void imag(T)
+
+    # Return real part
+    T real[T](complex[T]&)
+    long double real(long double)
+    double real(double)
+    float real(float)
+
+    # Return imaginary part
+    T imag[T](complex[T]&)
+    long double imag(long double)
+    double imag(double)
+    float imag(float)
+
+    T abs[T](complex[T]&)
+
+    T arg[T](complex[T]&)
+    long double arg(long double)
+    double arg(double)
+    float arg(float)
+
+    T norm[T](complex[T])
+    long double norm(long double)
+    double norm(double)
+    float norm(float)
+
+    complex[T] conj[T](complex[T]&)
+    complex[long double] conj(long double)
+    complex[double] conj(double)
+    complex[float] conj(float)
+
+    complex[T] proj[T](complex[T])
+    complex[long double] proj(long double)
+    complex[double] proj(double)
+    complex[float] proj(float)
+
+    complex[T] polar[T](T&, T&)
+    complex[T] polar[T](T&)
+
+    complex[T] exp[T](complex[T]&)
+    complex[T] log[T](complex[T]&)
+    complex[T] log10[T](complex[T]&)
+
+    complex[T] pow[T](complex[T]&, complex[T]&)
+    complex[T] pow[T](complex[T]&, T&)
+    complex[T] pow[T](T&, complex[T]&)
+    # There are some promotion versions too
+
+    complex[T] sqrt[T](complex[T]&)
+
+    complex[T] sin[T](complex[T]&)
+    complex[T] cos[T](complex[T]&)
+    complex[T] tan[T](complex[T]&)
+    complex[T] asin[T](complex[T]&)
+    complex[T] acos[T](complex[T]&)
+    complex[T] atan[T](complex[T]&)
+
+    complex[T] sinh[T](complex[T]&)
+    complex[T] cosh[T](complex[T]&)
+    complex[T] tanh[T](complex[T]&)
+
+    complex[T] asinh[T](complex[T]&)
+    complex[T] acosh[T](complex[T]&)
+    complex[T] atanh[T](complex[T]&)
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/execution.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/execution.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..eb92e3404f6ba337a1003139b9f24b55ffa2ee66
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/execution.pxd
@@ -0,0 +1,15 @@
+
+cdef extern from "<execution>" namespace "std::execution" nogil:
+    cdef cppclass sequenced_policy:
+        pass
+    cdef cppclass parallel_policy:
+        pass
+    cdef cppclass parallel_unsequenced_policy:
+        pass
+    cdef cppclass unsequenced_policy:
+        pass
+
+    const sequenced_policy seq "std::execution::seq"
+    const parallel_policy par "std::execution::par"
+    const parallel_unsequenced_policy par_unseq "std::execution::par_unseq"
+    const unsequenced_policy unseq "std::execution::unseq"
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/functional.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/functional.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..596ea90da0198c44bb5fd4807ff62bebf7480371
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/functional.pxd
@@ -0,0 +1,26 @@
+from libcpp cimport bool
+
+cdef extern from "<functional>" namespace "std" nogil:
+    cdef cppclass function[T]:
+        function() except +
+        function(T*) except +
+        function(function&) except +
+        function(void*) except +
+
+        function operator=(T*)
+        function operator=(function&)
+        function operator=(void*)
+        function operator=[U](U)
+
+        bool operator bool()
+
+    # Comparisons
+    cdef cppclass greater[T=*]:
+        # https://github.com/cython/cython/issues/3193
+        greater() except +
+        bool operator()(const T& lhs, const T& rhs) except +
+
+    cdef cppclass reference_wrapper[T]:
+        reference_wrapper()
+        reference_wrapper(T)
+        T& get() const
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/optional.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/optional.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..9b0b07a6d2bae85a7dde810b4981b6536479cd31
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/optional.pxd
@@ -0,0 +1,34 @@
+from libcpp cimport bool
+
+cdef extern from "<optional>" namespace "std" nogil:
+    cdef cppclass nullopt_t:
+        nullopt_t()
+
+    cdef nullopt_t nullopt
+
+    cdef cppclass optional[T]:
+        ctypedef T value_type
+        optional()
+        optional(nullopt_t)
+        optional(optional&) except +
+        optional(T&) except +
+        bool has_value()
+        T& value() except +
+        T& value_or[U](U& default_value)
+        void swap(optional&)
+        void reset()
+        T& emplace(...)
+        T& operator*()
+        #T* operator->() # Not Supported
+        optional& operator=(optional&)
+        optional& operator=[U](U&)
+        bool operator bool()
+        bool operator!()
+        bool operator==[U](optional&, U&)
+        bool operator!=[U](optional&, U&)
+        bool operator<[U](optional&, U&)
+        bool operator>[U](optional&, U&)
+        bool operator<=[U](optional&, U&)
+        bool operator>=[U](optional&, U&)
+
+    optional[T] make_optional[T](...) except +
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/queue.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/queue.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..578cbd91599d805d1508157be3a50984d8e0a8e5
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/queue.pxd
@@ -0,0 +1,25 @@
+cdef extern from "<queue>" namespace "std" nogil:
+    cdef cppclass queue[T]:
+        queue() except +
+        queue(queue&) except +
+        #queue(Container&)
+        T& back()
+        bint empty()
+        T& front()
+        void pop()
+        void push(T&)
+        size_t size()
+        # C++11 methods
+        void swap(queue&)
+
+    cdef cppclass priority_queue[T]:
+        priority_queue() except +
+        priority_queue(priority_queue&) except +
+        #priority_queue(Container&)
+        bint empty()
+        void pop()
+        void push(T&)
+        size_t size()
+        T& top()
+        # C++11 methods
+        void swap(priority_queue&)
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/stack.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/stack.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..f92240f66699cb384ce36771d0da9a90c04cd1a3
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/stack.pxd
@@ -0,0 +1,11 @@
+cdef extern from "<stack>" namespace "std" nogil:
+    cdef cppclass stack[T]:
+        ctypedef T value_type
+        stack() except +
+        stack(stack&) except +
+        #stack(Container&)
+        bint empty()
+        void pop()
+        void push(T&) except +
+        size_t size()
+        T& top()
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/string.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/string.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..566c748f5f62ac6c0f49940abdaef8bf85ec5118
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/string.pxd
@@ -0,0 +1,333 @@
+
+# deprecated cimport for backwards compatibility:
+from libc.string cimport const_char
+
+cdef extern from "<string>" namespace "std::string" nogil:
+    const size_t npos
+
+cdef extern from "<string>" namespace "std" nogil:
+    cdef cppclass string:
+        ctypedef char value_type
+
+        # these should really be allocator_type.size_type and
+        # allocator_type.difference_type to be true to the C++ definition
+        # but cython doesn't support deferred access on template arguments
+        ctypedef size_t size_type
+        ctypedef ptrdiff_t difference_type
+
+        cppclass const_iterator
+        cppclass iterator:
+            iterator() except +
+            iterator(iterator&) except +
+            value_type& operator*()
+            iterator operator++()
+            iterator operator--()
+            iterator operator++(int)
+            iterator operator--(int)
+            iterator operator+(size_type)
+            iterator operator-(size_type)
+            difference_type operator-(iterator)
+            difference_type operator-(const_iterator)
+            bint operator==(iterator)
+            bint operator==(const_iterator)
+            bint operator!=(iterator)
+            bint operator!=(const_iterator)
+            bint operator<(iterator)
+            bint operator<(const_iterator)
+            bint operator>(iterator)
+            bint operator>(const_iterator)
+            bint operator<=(iterator)
+            bint operator<=(const_iterator)
+            bint operator>=(iterator)
+            bint operator>=(const_iterator)
+        cppclass const_iterator:
+            const_iterator() except +
+            const_iterator(iterator&) except +
+            const_iterator(const_iterator&) except +
+            operator=(iterator&) except +
+            const value_type& operator*()
+            const_iterator operator++()
+            const_iterator operator--()
+            const_iterator operator++(int)
+            const_iterator operator--(int)
+            const_iterator operator+(size_type)
+            const_iterator operator-(size_type)
+            difference_type operator-(iterator)
+            difference_type operator-(const_iterator)
+            bint operator==(iterator)
+            bint operator==(const_iterator)
+            bint operator!=(iterator)
+            bint operator!=(const_iterator)
+            bint operator<(iterator)
+            bint operator<(const_iterator)
+            bint operator>(iterator)
+            bint operator>(const_iterator)
+            bint operator<=(iterator)
+            bint operator<=(const_iterator)
+            bint operator>=(iterator)
+            bint operator>=(const_iterator)
+
+        cppclass const_reverse_iterator
+        cppclass reverse_iterator:
+            reverse_iterator() except +
+            reverse_iterator(reverse_iterator&) except +
+            value_type& operator*()
+            reverse_iterator operator++()
+            reverse_iterator operator--()
+            reverse_iterator operator++(int)
+            reverse_iterator operator--(int)
+            reverse_iterator operator+(size_type)
+            reverse_iterator operator-(size_type)
+            difference_type operator-(iterator)
+            difference_type operator-(const_iterator)
+            bint operator==(reverse_iterator)
+            bint operator==(const_reverse_iterator)
+            bint operator!=(reverse_iterator)
+            bint operator!=(const_reverse_iterator)
+            bint operator<(reverse_iterator)
+            bint operator<(const_reverse_iterator)
+            bint operator>(reverse_iterator)
+            bint operator>(const_reverse_iterator)
+            bint operator<=(reverse_iterator)
+            bint operator<=(const_reverse_iterator)
+            bint operator>=(reverse_iterator)
+            bint operator>=(const_reverse_iterator)
+        cppclass const_reverse_iterator:
+            const_reverse_iterator() except +
+            const_reverse_iterator(reverse_iterator&) except +
+            operator=(reverse_iterator&) except +
+            const value_type& operator*()
+            const_reverse_iterator operator++()
+            const_reverse_iterator operator--()
+            const_reverse_iterator operator++(int)
+            const_reverse_iterator operator--(int)
+            const_reverse_iterator operator+(size_type)
+            const_reverse_iterator operator-(size_type)
+            difference_type operator-(iterator)
+            difference_type operator-(const_iterator)
+            bint operator==(reverse_iterator)
+            bint operator==(const_reverse_iterator)
+            bint operator!=(reverse_iterator)
+            bint operator!=(const_reverse_iterator)
+            bint operator<(reverse_iterator)
+            bint operator<(const_reverse_iterator)
+            bint operator>(reverse_iterator)
+            bint operator>(const_reverse_iterator)
+            bint operator<=(reverse_iterator)
+            bint operator<=(const_reverse_iterator)
+            bint operator>=(reverse_iterator)
+            bint operator>=(const_reverse_iterator)
+
+        string() except +
+        string(const string& s) except +
+        string(const string& s, size_t pos) except +
+        string(const string& s, size_t pos, size_t len) except +
+        string(const char* s) except +
+        string(const char* s, size_t n) except +
+        string(size_t n, char c) except +
+        string(iterator first, iterator last) except +
+
+        iterator begin()
+        const_iterator const_begin "begin"()
+        const_iterator cbegin()
+        iterator end()
+        const_iterator const_end "end"()
+        const_iterator cend()
+        reverse_iterator rbegin()
+        const_reverse_iterator const_rbegin "rbegin"()
+        const_reverse_iterator crbegin()
+        reverse_iterator rend()
+        const_reverse_iterator const_rend "rend"()
+        const_reverse_iterator crend()
+
+        const char* c_str()
+        const char* data()
+        size_t size()
+        size_t max_size()
+        size_t length()
+        void resize(size_t) except +
+        void resize(size_t, char) except +
+        void shrink_to_fit() except +
+        void swap(string& other)
+        size_t capacity()
+        void reserve(size_t) except +
+        void clear()
+        bint empty()
+
+        iterator erase(iterator first, iterator last)
+        iterator erase(iterator p)
+        iterator erase(const_iterator first, const_iterator last)
+        iterator erase(const_iterator p)
+        string& erase(size_t pos, size_t len) except +
+        string& erase(size_t pos) except +
+        string& erase() except +
+
+        char& at(size_t pos) except +
+        char& operator[](size_t pos)
+        char& front()
+        char& back()
+        int compare(const string& s)
+        int compare(size_t pos, size_t len, const string& s) except +
+        int compare(size_t pos, size_t len, const string& s, size_t subpos, size_t sublen) except +
+        int compare(const char* s) except +
+        int compare(size_t pos, size_t len, const char* s) except +
+        int compare(size_t pos, size_t len, const char* s , size_t n) except +
+
+        string& append(const string& s) except +
+        string& append(const string& s, size_t subpos, size_t sublen) except +
+        string& append(const char* s) except +
+        string& append(const char* s, size_t n) except +
+        string& append(size_t n, char c) except +
+
+        void push_back(char c) except +
+        void pop_back()
+
+        string& assign(const string& s) except +
+        string& assign(const string& s, size_t subpos, size_t sublen) except +
+        string& assign(const char* s, size_t n) except +
+        string& assign(const char* s) except +
+        string& assign(size_t n, char c) except +
+
+        string& insert(size_t pos, const string& s, size_t subpos, size_t sublen) except +
+        string& insert(size_t pos, const string& s) except +
+        string& insert(size_t pos, const char* s, size_t n) except +
+        string& insert(size_t pos, const char* s) except +
+        string& insert(size_t pos, size_t n, char c) except +
+        void insert(iterator p, size_t n, char c) except +
+        iterator insert(iterator p, char c) except +
+
+        string& replace(size_t pos, size_t len, const string& str) except +
+        string& replace(iterator i1, iterator i2, const string& str) except +
+        string& replace(size_t pos, size_t len, const string& str, size_t subpos, size_t sublen) except +
+        string& replace(size_t pos, size_t len, const char* s) except +
+        string& replace(iterator i1, iterator i2, const char* s) except +
+        string& replace(size_t pos, size_t len, const char* s, size_t n) except +
+        string& replace(iterator i1, iterator i2, const char* s, size_t n) except +
+        string& replace(size_t pos, size_t len, size_t n, char c) except +
+        string& replace(iterator i1, iterator i2, size_t n, char c) except +
+
+        size_t copy(char* s, size_t len, size_t pos) except +
+        size_t copy(char* s, size_t len) except +
+
+        size_t find(const string& s, size_t pos)
+        size_t find(const string& s)
+        size_t find(const char* s, size_t pos, size_t n)
+        size_t find(const char* s, size_t pos)
+        size_t find(const char* s)
+        size_t find(char c, size_t pos)
+        size_t find(char c)
+
+        size_t rfind(const string&, size_t pos)
+        size_t rfind(const string&)
+        size_t rfind(const char* s, size_t pos, size_t n)
+        size_t rfind(const char* s, size_t pos)
+        size_t rfind(const char* s)
+        size_t rfind(char c, size_t pos)
+        size_t rfind(char c)
+
+        size_t find_first_of(const string&, size_t pos)
+        size_t find_first_of(const string&)
+        size_t find_first_of(const char* s, size_t pos, size_t n)
+        size_t find_first_of(const char* s, size_t pos)
+        size_t find_first_of(const char* s)
+        size_t find_first_of(char c, size_t pos)
+        size_t find_first_of(char c)
+
+        size_t find_first_not_of(const string& s, size_t pos)
+        size_t find_first_not_of(const string& s)
+        size_t find_first_not_of(const char* s, size_t pos, size_t n)
+        size_t find_first_not_of(const char* s, size_t pos)
+        size_t find_first_not_of(const char*)
+        size_t find_first_not_of(char c, size_t pos)
+        size_t find_first_not_of(char c)
+
+        size_t find_last_of(const string& s, size_t pos)
+        size_t find_last_of(const string& s)
+        size_t find_last_of(const char* s, size_t pos, size_t n)
+        size_t find_last_of(const char* s, size_t pos)
+        size_t find_last_of(const char* s)
+        size_t find_last_of(char c, size_t pos)
+        size_t find_last_of(char c)
+
+        size_t find_last_not_of(const string& s, size_t pos)
+        size_t find_last_not_of(const string& s)
+        size_t find_last_not_of(const char* s, size_t pos, size_t n)
+        size_t find_last_not_of(const char* s, size_t pos)
+        size_t find_last_not_of(const char* s)
+        size_t find_last_not_of(char c, size_t pos)
+        size_t find_last_not_of(char c)
+
+        string substr(size_t pos, size_t len) except +
+        string substr(size_t pos) except +
+        string substr()
+
+        # C++20
+        bint starts_with(char c) except +
+        bint starts_with(const char* s)
+        bint ends_with(char c) except +
+        bint ends_with(const char* s)
+        # C++23
+        bint contains(char c) except +
+        bint contains(const char* s)
+
+        #string& operator= (const string&)
+        #string& operator= (const char*)
+        #string& operator= (char)
+
+        string operator+ (const string&) except +
+        string operator+ (const char*) except +
+
+        bint operator==(const string&)
+        bint operator==(const char*)
+
+        bint operator!= (const string&)
+        bint operator!= (const char*)
+
+        bint operator< (const string&)
+        bint operator< (const char*)
+
+        bint operator> (const string&)
+        bint operator> (const char*)
+
+        bint operator<= (const string&)
+        bint operator<= (const char*)
+
+        bint operator>= (const string&)
+        bint operator>= (const char*)
+
+
+    string to_string(int val) except +
+    string to_string(long val) except +
+    string to_string(long long val) except +
+    string to_string(unsigned val) except +
+    string to_string(size_t val) except +
+    string to_string(ssize_t val) except +
+    string to_string(unsigned long val) except +
+    string to_string(unsigned long long val) except +
+    string to_string(float val) except +
+    string to_string(double val) except +
+    string to_string(long double val) except +
+
+    int stoi(const string& s, size_t* idx, int base) except +
+    int stoi(const string& s, size_t* idx) except +
+    int stoi(const string& s) except +
+    long stol(const string& s, size_t* idx, int base) except +
+    long stol(const string& s, size_t* idx) except +
+    long stol(const string& s) except +
+    long long stoll(const string& s, size_t* idx, int base) except +
+    long long stoll(const string& s, size_t* idx) except +
+    long long stoll(const string& s) except +
+
+    unsigned long stoul(const string& s, size_t* idx, int base) except +
+    unsigned long stoul(const string& s, size_t* idx) except +
+    unsigned long stoul(const string& s) except +
+    unsigned long long stoull(const string& s, size_t* idx, int base) except +
+    unsigned long long stoull(const string& s, size_t* idx) except +
+    unsigned long long stoull(const string& s) except +
+
+    float stof(const string& s, size_t* idx) except +
+    float stof(const string& s) except +
+    double stod(const string& s, size_t* idx) except +
+    double stod(const string& s) except +
+    long double stold(const string& s, size_t* idx) except +
+    long double stold(const string& s) except +
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/unordered_set.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/unordered_set.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..6aae890d93015b836783888e5f58d7aaf109038f
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/libcpp/unordered_set.pxd
@@ -0,0 +1,152 @@
+from .utility cimport pair
+
+cdef extern from "<unordered_set>" namespace "std" nogil:
+    cdef cppclass unordered_set[T,HASH=*,PRED=*,ALLOCATOR=*]:
+        ctypedef T value_type
+
+        # these should really be allocator_type.size_type and
+        # allocator_type.difference_type to be true to the C++ definition
+        # but cython doesn't support deferred access on template arguments
+        ctypedef size_t size_type
+        ctypedef ptrdiff_t difference_type
+
+        cppclass const_iterator
+        cppclass iterator:
+            iterator() except +
+            iterator(iterator&) except +
+            value_type& operator*()
+            iterator operator++()
+            iterator operator--()
+            iterator operator++(int)
+            iterator operator--(int)
+            bint operator==(iterator)
+            bint operator==(const_iterator)
+            bint operator!=(iterator)
+            bint operator!=(const_iterator)
+        cppclass const_iterator:
+            const_iterator() except +
+            const_iterator(iterator&) except +
+            operator=(iterator&) except +
+            const value_type& operator*()
+            const_iterator operator++()
+            const_iterator operator--()
+            const_iterator operator++(int)
+            const_iterator operator--(int)
+            bint operator==(iterator)
+            bint operator==(const_iterator)
+            bint operator!=(iterator)
+            bint operator!=(const_iterator)
+
+        unordered_set() except +
+        unordered_set(unordered_set&) except +
+        #unordered_set& operator=(unordered_set&)
+        bint operator==(unordered_set&, unordered_set&)
+        bint operator!=(unordered_set&, unordered_set&)
+        iterator begin()
+        const_iterator const_begin "begin"()
+        const_iterator cbegin()
+        void clear()
+        size_t count(const T&)
+        bint empty()
+        iterator end()
+        const_iterator const_end "end"()
+        const_iterator cend()
+        pair[iterator, iterator] equal_range(const T&)
+        pair[const_iterator, const_iterator] const_equal_range "equal_range"(const T&)
+        iterator erase(iterator)
+        iterator const_erase "erase"(const_iterator)
+        iterator erase(const_iterator, const_iterator)
+        size_t erase(const T&)
+        iterator find(const T&)
+        const_iterator const_find "find"(const T&)
+        pair[iterator, bint] insert(const T&) except +
+        iterator insert(const_iterator, const T&) except +
+        void insert[InputIt](InputIt, InputIt) except +
+        size_t max_size()
+        size_t size()
+        void swap(unordered_set&)
+        #value_compare value_comp()
+        void max_load_factor(float)
+        float max_load_factor()
+        float load_factor()
+        void rehash(size_t)
+        void reserve(size_t)
+        size_t bucket_count()
+        size_t max_bucket_count()
+        size_t bucket_size(size_t)
+        size_t bucket(const T&)
+        # C++20
+        bint contains(const T&)
+
+    cdef cppclass unordered_multiset[T,HASH=*,PRED=*,ALLOCATOR=*]:
+        ctypedef T value_type
+
+        # these should really be allocator_type.size_type and
+        # allocator_type.difference_type to be true to the C++ definition
+        # but cython doesn't support deferred access on template arguments
+        ctypedef size_t size_type
+        ctypedef ptrdiff_t difference_type
+
+        cppclass const_iterator
+        cppclass iterator:
+            iterator() except +
+            iterator(iterator&) except +
+            value_type& operator*()
+            iterator operator++()
+            iterator operator++(int)
+            bint operator==(iterator)
+            bint operator==(const_iterator)
+            bint operator!=(iterator)
+            bint operator!=(const_iterator)
+        cppclass const_iterator:
+            const_iterator() except +
+            const_iterator(iterator&) except +
+            operator=(iterator&) except +
+            const value_type& operator*()
+            const_iterator operator++()
+            const_iterator operator++(int)
+            bint operator==(iterator)
+            bint operator==(const_iterator)
+            bint operator!=(iterator)
+            bint operator!=(const_iterator)
+
+        unordered_multiset() except +
+        unordered_multiset(unordered_multiset&) except +
+        #unordered_multiset& operator=(unordered_multiset&)
+        bint operator==(unordered_multiset&, unordered_multiset&)
+        bint operator!=(unordered_multiset&, unordered_multiset&)
+        iterator begin()
+        const_iterator const_begin "begin"()
+        const_iterator cbegin()
+        void clear()
+        size_t count(const T&)
+        bint empty()
+        iterator end()
+        const_iterator const_end "end"()
+        const_iterator cend()
+        pair[iterator, iterator] equal_range(const T&)
+        pair[const_iterator, const_iterator] const_equal_range "equal_range"(const T&)
+        iterator erase(iterator)
+        iterator const_erase "erase"(const_iterator)
+        iterator erase(const_iterator, const_iterator)
+        size_t erase(const T&)
+        iterator find(const T&)
+        const_iterator const_find "find"(const T&)
+        iterator insert(const T&) except +
+        iterator insert(const_iterator, const T&) except +
+        void insert[InputIt](InputIt, InputIt) except +
+        size_t max_size()
+        size_t size()
+        void swap(unordered_multiset&)
+        #value_compare value_comp()
+        void max_load_factor(float)
+        float max_load_factor()
+        float load_factor()
+        void rehash(size_t)
+        void reserve(size_t)
+        size_t bucket_count()
+        size_t max_bucket_count()
+        size_t bucket_size(size_t)
+        size_t bucket(const T&)
+        # C++20
+        bint contains(const T&)
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/numpy/__init__.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/numpy/__init__.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..228aee4cff89203e4190bcdcb8331f3a2118537d
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/numpy/__init__.pxd
@@ -0,0 +1,1057 @@
+# NumPy static imports for Cython
+#
+# NOTE: Do not make incompatible local changes to this file without contacting the NumPy project.
+# This file is maintained by the NumPy project at
+# https://github.com/numpy/numpy/tree/master/numpy
+#
+# If any of the PyArray_* functions are called, import_array must be
+# called first.  This is done automatically by Cython 3.0+ if a call
+# is not detected inside of the module.
+#
+# Author: Dag Sverre Seljebotn
+#
+
+from cpython.ref cimport Py_INCREF
+from cpython.object cimport PyObject, PyTypeObject, PyObject_TypeCheck
+cimport libc.stdio as stdio
+
+
+cdef extern from *:
+    # Leave a marker that the NumPy declarations came from Cython and not from NumPy itself.
+    # See https://github.com/cython/cython/issues/3573
+    """
+    /* Using NumPy API declarations from "Cython/Includes/numpy/" */
+    """
+
+
+cdef extern from "Python.h":
+    ctypedef Py_ssize_t Py_intptr_t
+
+cdef extern from "numpy/arrayobject.h":
+    ctypedef Py_intptr_t npy_intp
+    ctypedef size_t npy_uintp
+
+    cdef enum NPY_TYPES:
+        NPY_BOOL
+        NPY_BYTE
+        NPY_UBYTE
+        NPY_SHORT
+        NPY_USHORT
+        NPY_INT
+        NPY_UINT
+        NPY_LONG
+        NPY_ULONG
+        NPY_LONGLONG
+        NPY_ULONGLONG
+        NPY_FLOAT
+        NPY_DOUBLE
+        NPY_LONGDOUBLE
+        NPY_CFLOAT
+        NPY_CDOUBLE
+        NPY_CLONGDOUBLE
+        NPY_OBJECT
+        NPY_STRING
+        NPY_UNICODE
+        NPY_VOID
+        NPY_DATETIME
+        NPY_TIMEDELTA
+        NPY_NTYPES
+        NPY_NOTYPE
+
+        NPY_INT8
+        NPY_INT16
+        NPY_INT32
+        NPY_INT64
+        NPY_INT128
+        NPY_INT256
+        NPY_UINT8
+        NPY_UINT16
+        NPY_UINT32
+        NPY_UINT64
+        NPY_UINT128
+        NPY_UINT256
+        NPY_FLOAT16
+        NPY_FLOAT32
+        NPY_FLOAT64
+        NPY_FLOAT80
+        NPY_FLOAT96
+        NPY_FLOAT128
+        NPY_FLOAT256
+        NPY_COMPLEX32
+        NPY_COMPLEX64
+        NPY_COMPLEX128
+        NPY_COMPLEX160
+        NPY_COMPLEX192
+        NPY_COMPLEX256
+        NPY_COMPLEX512
+
+        NPY_INTP
+
+    ctypedef enum NPY_ORDER:
+        NPY_ANYORDER
+        NPY_CORDER
+        NPY_FORTRANORDER
+        NPY_KEEPORDER
+
+    ctypedef enum NPY_CASTING:
+        NPY_NO_CASTING
+        NPY_EQUIV_CASTING
+        NPY_SAFE_CASTING
+        NPY_SAME_KIND_CASTING
+        NPY_UNSAFE_CASTING
+
+    ctypedef enum NPY_CLIPMODE:
+        NPY_CLIP
+        NPY_WRAP
+        NPY_RAISE
+
+    ctypedef enum NPY_SCALARKIND:
+        NPY_NOSCALAR,
+        NPY_BOOL_SCALAR,
+        NPY_INTPOS_SCALAR,
+        NPY_INTNEG_SCALAR,
+        NPY_FLOAT_SCALAR,
+        NPY_COMPLEX_SCALAR,
+        NPY_OBJECT_SCALAR
+
+    ctypedef enum NPY_SORTKIND:
+        NPY_QUICKSORT
+        NPY_HEAPSORT
+        NPY_MERGESORT
+
+    ctypedef enum NPY_SEARCHSIDE:
+        NPY_SEARCHLEFT
+        NPY_SEARCHRIGHT
+
+    enum:
+        # DEPRECATED since NumPy 1.7 ! Do not use in new code!
+        NPY_C_CONTIGUOUS
+        NPY_F_CONTIGUOUS
+        NPY_CONTIGUOUS
+        NPY_FORTRAN
+        NPY_OWNDATA
+        NPY_FORCECAST
+        NPY_ENSURECOPY
+        NPY_ENSUREARRAY
+        NPY_ELEMENTSTRIDES
+        NPY_ALIGNED
+        NPY_NOTSWAPPED
+        NPY_WRITEABLE
+        NPY_UPDATEIFCOPY
+        NPY_ARR_HAS_DESCR
+
+        NPY_BEHAVED
+        NPY_BEHAVED_NS
+        NPY_CARRAY
+        NPY_CARRAY_RO
+        NPY_FARRAY
+        NPY_FARRAY_RO
+        NPY_DEFAULT
+
+        NPY_IN_ARRAY
+        NPY_OUT_ARRAY
+        NPY_INOUT_ARRAY
+        NPY_IN_FARRAY
+        NPY_OUT_FARRAY
+        NPY_INOUT_FARRAY
+
+        NPY_UPDATE_ALL
+
+    enum:
+        # Added in NumPy 1.7 to replace the deprecated enums above.
+        NPY_ARRAY_C_CONTIGUOUS
+        NPY_ARRAY_F_CONTIGUOUS
+        NPY_ARRAY_OWNDATA
+        NPY_ARRAY_FORCECAST
+        NPY_ARRAY_ENSURECOPY
+        NPY_ARRAY_ENSUREARRAY
+        NPY_ARRAY_ELEMENTSTRIDES
+        NPY_ARRAY_ALIGNED
+        NPY_ARRAY_NOTSWAPPED
+        NPY_ARRAY_WRITEABLE
+        NPY_ARRAY_UPDATEIFCOPY
+
+        NPY_ARRAY_BEHAVED
+        NPY_ARRAY_BEHAVED_NS
+        NPY_ARRAY_CARRAY
+        NPY_ARRAY_CARRAY_RO
+        NPY_ARRAY_FARRAY
+        NPY_ARRAY_FARRAY_RO
+        NPY_ARRAY_DEFAULT
+
+        NPY_ARRAY_IN_ARRAY
+        NPY_ARRAY_OUT_ARRAY
+        NPY_ARRAY_INOUT_ARRAY
+        NPY_ARRAY_IN_FARRAY
+        NPY_ARRAY_OUT_FARRAY
+        NPY_ARRAY_INOUT_FARRAY
+
+        NPY_ARRAY_UPDATE_ALL
+
+    cdef enum:
+        NPY_MAXDIMS
+
+    npy_intp NPY_MAX_ELSIZE
+
+    ctypedef void (*PyArray_VectorUnaryFunc)(void *, void *, npy_intp, void *,  void *)
+
+    ctypedef struct PyArray_ArrayDescr:
+        # shape is a tuple, but Cython doesn't support "tuple shape"
+        # inside a non-PyObject declaration, so we have to declare it
+        # as just a PyObject*.
+        PyObject* shape
+
+    ctypedef struct PyArray_Descr:
+        pass
+
+    ctypedef class numpy.dtype [object PyArray_Descr, check_size ignore]:
+        # Use PyDataType_* macros when possible, however there are no macros
+        # for accessing some of the fields, so some are defined.
+        cdef PyTypeObject* typeobj
+        cdef char kind
+        cdef char type
+        # Numpy sometimes mutates this without warning (e.g. it'll
+        # sometimes change "|" to "<" in shared dtype objects on
+        # little-endian machines). If this matters to you, use
+        # PyArray_IsNativeByteOrder(dtype.byteorder) instead of
+        # directly accessing this field.
+        cdef char byteorder
+        cdef char flags
+        cdef int type_num
+        cdef int itemsize "elsize"
+        cdef int alignment
+        cdef object fields
+        cdef tuple names
+        # Use PyDataType_HASSUBARRAY to test whether this field is
+        # valid (the pointer can be NULL). Most users should access
+        # this field via the inline helper method PyDataType_SHAPE.
+        cdef PyArray_ArrayDescr* subarray
+
+    ctypedef class numpy.flatiter [object PyArrayIterObject, check_size ignore]:
+        # Use through macros
+        pass
+
+    ctypedef class numpy.broadcast [object PyArrayMultiIterObject, check_size ignore]:
+        # Use through macros
+        pass
+
+    ctypedef struct PyArrayObject:
+        # For use in situations where ndarray can't replace PyArrayObject*,
+        # like PyArrayObject**.
+        pass
+
+    ctypedef class numpy.ndarray [object PyArrayObject, check_size ignore]:
+        cdef __cythonbufferdefaults__ = {"mode": "strided"}
+
+        # NOTE: no field declarations since direct access is deprecated since NumPy 1.7
+        # Instead, we use properties that map to the corresponding C-API functions.
+
+        @property
+        cdef inline PyObject* base(self) nogil:
+            """Returns a borrowed reference to the object owning the data/memory.
+            """
+            return PyArray_BASE(self)
+
+        @property
+        cdef inline dtype descr(self):
+            """Returns an owned reference to the dtype of the array.
+            """
+            return <dtype>PyArray_DESCR(self)
+
+        @property
+        cdef inline int ndim(self) nogil:
+            """Returns the number of dimensions in the array.
+            """
+            return PyArray_NDIM(self)
+
+        @property
+        cdef inline npy_intp *shape(self) nogil:
+            """Returns a pointer to the dimensions/shape of the array.
+            The number of elements matches the number of dimensions of the array (ndim).
+            Can return NULL for 0-dimensional arrays.
+            """
+            return PyArray_DIMS(self)
+
+        @property
+        cdef inline npy_intp *strides(self) nogil:
+            """Returns a pointer to the strides of the array.
+            The number of elements matches the number of dimensions of the array (ndim).
+            """
+            return PyArray_STRIDES(self)
+
+        @property
+        cdef inline npy_intp size(self) nogil:
+            """Returns the total size (in number of elements) of the array.
+            """
+            return PyArray_SIZE(self)
+
+        @property
+        cdef inline char* data(self) nogil:
+            """The pointer to the data buffer as a char*.
+            This is provided for legacy reasons to avoid direct struct field access.
+            For new code that needs this access, you probably want to cast the result
+            of `PyArray_DATA()` instead, which returns a 'void*'.
+            """
+            return PyArray_BYTES(self)
+
+    ctypedef unsigned char      npy_bool
+
+    ctypedef signed char      npy_byte
+    ctypedef signed short     npy_short
+    ctypedef signed int       npy_int
+    ctypedef signed long      npy_long
+    ctypedef signed long long npy_longlong
+
+    ctypedef unsigned char      npy_ubyte
+    ctypedef unsigned short     npy_ushort
+    ctypedef unsigned int       npy_uint
+    ctypedef unsigned long      npy_ulong
+    ctypedef unsigned long long npy_ulonglong
+
+    ctypedef float        npy_float
+    ctypedef double       npy_double
+    ctypedef long double  npy_longdouble
+
+    ctypedef signed char        npy_int8
+    ctypedef signed short       npy_int16
+    ctypedef signed int         npy_int32
+    ctypedef signed long long   npy_int64
+    ctypedef signed long long   npy_int96
+    ctypedef signed long long   npy_int128
+
+    ctypedef unsigned char      npy_uint8
+    ctypedef unsigned short     npy_uint16
+    ctypedef unsigned int       npy_uint32
+    ctypedef unsigned long long npy_uint64
+    ctypedef unsigned long long npy_uint96
+    ctypedef unsigned long long npy_uint128
+
+    ctypedef float        npy_float32
+    ctypedef double       npy_float64
+    ctypedef long double  npy_float80
+    ctypedef long double  npy_float96
+    ctypedef long double  npy_float128
+
+    ctypedef struct npy_cfloat:
+        double real
+        double imag
+
+    ctypedef struct npy_cdouble:
+        double real
+        double imag
+
+    ctypedef struct npy_clongdouble:
+        long double real
+        long double imag
+
+    ctypedef struct npy_complex64:
+        float real
+        float imag
+
+    ctypedef struct npy_complex128:
+        double real
+        double imag
+
+    ctypedef struct npy_complex160:
+        long double real
+        long double imag
+
+    ctypedef struct npy_complex192:
+        long double real
+        long double imag
+
+    ctypedef struct npy_complex256:
+        long double real
+        long double imag
+
+    ctypedef struct PyArray_Dims:
+        npy_intp *ptr
+        int len
+
+    int _import_array() except -1
+    # A second definition so _import_array isn't marked as used when we use it here.
+    # Do not use - subject to change any time.
+    int __pyx_import_array "_import_array"() except -1
+
+    #
+    # Macros from ndarrayobject.h
+    #
+    bint PyArray_CHKFLAGS(ndarray m, int flags) nogil
+    bint PyArray_IS_C_CONTIGUOUS(ndarray arr) nogil
+    bint PyArray_IS_F_CONTIGUOUS(ndarray arr) nogil
+    bint PyArray_ISCONTIGUOUS(ndarray m) nogil
+    bint PyArray_ISWRITEABLE(ndarray m) nogil
+    bint PyArray_ISALIGNED(ndarray m) nogil
+
+    int PyArray_NDIM(ndarray) nogil
+    bint PyArray_ISONESEGMENT(ndarray) nogil
+    bint PyArray_ISFORTRAN(ndarray) nogil
+    int PyArray_FORTRANIF(ndarray) nogil
+
+    void* PyArray_DATA(ndarray) nogil
+    char* PyArray_BYTES(ndarray) nogil
+
+    npy_intp* PyArray_DIMS(ndarray) nogil
+    npy_intp* PyArray_STRIDES(ndarray) nogil
+    npy_intp PyArray_DIM(ndarray, size_t) nogil
+    npy_intp PyArray_STRIDE(ndarray, size_t) nogil
+
+    PyObject *PyArray_BASE(ndarray) nogil  # returns borrowed reference!
+    PyArray_Descr *PyArray_DESCR(ndarray) nogil  # returns borrowed reference to dtype!
+    PyArray_Descr *PyArray_DTYPE(ndarray) nogil  # returns borrowed reference to dtype! NP 1.7+ alias for descr.
+    int PyArray_FLAGS(ndarray) nogil
+    void PyArray_CLEARFLAGS(ndarray, int flags) nogil  # Added in NumPy 1.7
+    void PyArray_ENABLEFLAGS(ndarray, int flags) nogil  # Added in NumPy 1.7
+    npy_intp PyArray_ITEMSIZE(ndarray) nogil
+    int PyArray_TYPE(ndarray arr) nogil
+
+    object PyArray_GETITEM(ndarray arr, void *itemptr)
+    int PyArray_SETITEM(ndarray arr, void *itemptr, object obj)
+
+    bint PyTypeNum_ISBOOL(int) nogil
+    bint PyTypeNum_ISUNSIGNED(int) nogil
+    bint PyTypeNum_ISSIGNED(int) nogil
+    bint PyTypeNum_ISINTEGER(int) nogil
+    bint PyTypeNum_ISFLOAT(int) nogil
+    bint PyTypeNum_ISNUMBER(int) nogil
+    bint PyTypeNum_ISSTRING(int) nogil
+    bint PyTypeNum_ISCOMPLEX(int) nogil
+    bint PyTypeNum_ISPYTHON(int) nogil
+    bint PyTypeNum_ISFLEXIBLE(int) nogil
+    bint PyTypeNum_ISUSERDEF(int) nogil
+    bint PyTypeNum_ISEXTENDED(int) nogil
+    bint PyTypeNum_ISOBJECT(int) nogil
+
+    bint PyDataType_ISBOOL(dtype) nogil
+    bint PyDataType_ISUNSIGNED(dtype) nogil
+    bint PyDataType_ISSIGNED(dtype) nogil
+    bint PyDataType_ISINTEGER(dtype) nogil
+    bint PyDataType_ISFLOAT(dtype) nogil
+    bint PyDataType_ISNUMBER(dtype) nogil
+    bint PyDataType_ISSTRING(dtype) nogil
+    bint PyDataType_ISCOMPLEX(dtype) nogil
+    bint PyDataType_ISPYTHON(dtype) nogil
+    bint PyDataType_ISFLEXIBLE(dtype) nogil
+    bint PyDataType_ISUSERDEF(dtype) nogil
+    bint PyDataType_ISEXTENDED(dtype) nogil
+    bint PyDataType_ISOBJECT(dtype) nogil
+    bint PyDataType_HASFIELDS(dtype) nogil
+    bint PyDataType_HASSUBARRAY(dtype) nogil
+
+    bint PyArray_ISBOOL(ndarray) nogil
+    bint PyArray_ISUNSIGNED(ndarray) nogil
+    bint PyArray_ISSIGNED(ndarray) nogil
+    bint PyArray_ISINTEGER(ndarray) nogil
+    bint PyArray_ISFLOAT(ndarray) nogil
+    bint PyArray_ISNUMBER(ndarray) nogil
+    bint PyArray_ISSTRING(ndarray) nogil
+    bint PyArray_ISCOMPLEX(ndarray) nogil
+    bint PyArray_ISPYTHON(ndarray) nogil
+    bint PyArray_ISFLEXIBLE(ndarray) nogil
+    bint PyArray_ISUSERDEF(ndarray) nogil
+    bint PyArray_ISEXTENDED(ndarray) nogil
+    bint PyArray_ISOBJECT(ndarray) nogil
+    bint PyArray_HASFIELDS(ndarray) nogil
+
+    bint PyArray_ISVARIABLE(ndarray) nogil
+
+    bint PyArray_SAFEALIGNEDCOPY(ndarray) nogil
+    bint PyArray_ISNBO(char) nogil              # works on ndarray.byteorder
+    bint PyArray_IsNativeByteOrder(char) nogil  # works on ndarray.byteorder
+    bint PyArray_ISNOTSWAPPED(ndarray) nogil
+    bint PyArray_ISBYTESWAPPED(ndarray) nogil
+
+    bint PyArray_FLAGSWAP(ndarray, int) nogil
+
+    bint PyArray_ISCARRAY(ndarray) nogil
+    bint PyArray_ISCARRAY_RO(ndarray) nogil
+    bint PyArray_ISFARRAY(ndarray) nogil
+    bint PyArray_ISFARRAY_RO(ndarray) nogil
+    bint PyArray_ISBEHAVED(ndarray) nogil
+    bint PyArray_ISBEHAVED_RO(ndarray) nogil
+
+
+    bint PyDataType_ISNOTSWAPPED(dtype) nogil
+    bint PyDataType_ISBYTESWAPPED(dtype) nogil
+
+    bint PyArray_DescrCheck(object)
+
+    bint PyArray_Check(object)
+    bint PyArray_CheckExact(object)
+
+    # Cannot be supported due to out arg:
+    # bint PyArray_HasArrayInterfaceType(object, dtype, object, object&)
+    # bint PyArray_HasArrayInterface(op, out)
+
+
+    bint PyArray_IsZeroDim(object)
+    # Cannot be supported due to ## ## in macro:
+    # bint PyArray_IsScalar(object, verbatim work)
+    bint PyArray_CheckScalar(object)
+    bint PyArray_IsPythonNumber(object)
+    bint PyArray_IsPythonScalar(object)
+    bint PyArray_IsAnyScalar(object)
+    bint PyArray_CheckAnyScalar(object)
+
+    ndarray PyArray_GETCONTIGUOUS(ndarray)
+    bint PyArray_SAMESHAPE(ndarray, ndarray) nogil
+    npy_intp PyArray_SIZE(ndarray) nogil
+    npy_intp PyArray_NBYTES(ndarray) nogil
+
+    object PyArray_FROM_O(object)
+    object PyArray_FROM_OF(object m, int flags)
+    object PyArray_FROM_OT(object m, int type)
+    object PyArray_FROM_OTF(object m, int type, int flags)
+    object PyArray_FROMANY(object m, int type, int min, int max, int flags)
+    object PyArray_ZEROS(int nd, npy_intp* dims, int type, int fortran)
+    object PyArray_EMPTY(int nd, npy_intp* dims, int type, int fortran)
+    void PyArray_FILLWBYTE(object, int val)
+    npy_intp PyArray_REFCOUNT(object)
+    object PyArray_ContiguousFromAny(op, int, int min_depth, int max_depth)
+    unsigned char PyArray_EquivArrTypes(ndarray a1, ndarray a2)
+    bint PyArray_EquivByteorders(int b1, int b2) nogil
+    object PyArray_SimpleNew(int nd, npy_intp* dims, int typenum)
+    object PyArray_SimpleNewFromData(int nd, npy_intp* dims, int typenum, void* data)
+    #object PyArray_SimpleNewFromDescr(int nd, npy_intp* dims, dtype descr)
+    object PyArray_ToScalar(void* data, ndarray arr)
+
+    void* PyArray_GETPTR1(ndarray m, npy_intp i) nogil
+    void* PyArray_GETPTR2(ndarray m, npy_intp i, npy_intp j) nogil
+    void* PyArray_GETPTR3(ndarray m, npy_intp i, npy_intp j, npy_intp k) nogil
+    void* PyArray_GETPTR4(ndarray m, npy_intp i, npy_intp j, npy_intp k, npy_intp l) nogil
+
+    void PyArray_XDECREF_ERR(ndarray)
+    # Cannot be supported due to out arg
+    # void PyArray_DESCR_REPLACE(descr)
+
+
+    object PyArray_Copy(ndarray)
+    object PyArray_FromObject(object op, int type, int min_depth, int max_depth)
+    object PyArray_ContiguousFromObject(object op, int type, int min_depth, int max_depth)
+    object PyArray_CopyFromObject(object op, int type, int min_depth, int max_depth)
+
+    object PyArray_Cast(ndarray mp, int type_num)
+    object PyArray_Take(ndarray ap, object items, int axis)
+    object PyArray_Put(ndarray ap, object items, object values)
+
+    void PyArray_ITER_RESET(flatiter it) nogil
+    void PyArray_ITER_NEXT(flatiter it) nogil
+    void PyArray_ITER_GOTO(flatiter it, npy_intp* destination) nogil
+    void PyArray_ITER_GOTO1D(flatiter it, npy_intp ind) nogil
+    void* PyArray_ITER_DATA(flatiter it) nogil
+    bint PyArray_ITER_NOTDONE(flatiter it) nogil
+
+    void PyArray_MultiIter_RESET(broadcast multi) nogil
+    void PyArray_MultiIter_NEXT(broadcast multi) nogil
+    void PyArray_MultiIter_GOTO(broadcast multi, npy_intp dest) nogil
+    void PyArray_MultiIter_GOTO1D(broadcast multi, npy_intp ind) nogil
+    void* PyArray_MultiIter_DATA(broadcast multi, npy_intp i) nogil
+    void PyArray_MultiIter_NEXTi(broadcast multi, npy_intp i) nogil
+    bint PyArray_MultiIter_NOTDONE(broadcast multi) nogil
+
+    # Functions from __multiarray_api.h
+
+    # Functions taking dtype and returning object/ndarray are disabled
+    # for now as they steal dtype references. I'm conservative and disable
+    # more than is probably needed until it can be checked further.
+    int PyArray_SetNumericOps        (object)
+    object PyArray_GetNumericOps ()
+    int PyArray_INCREF (ndarray)
+    int PyArray_XDECREF (ndarray)
+    void PyArray_SetStringFunction (object, int)
+    dtype PyArray_DescrFromType (int)
+    object PyArray_TypeObjectFromType (int)
+    char * PyArray_Zero (ndarray)
+    char * PyArray_One (ndarray)
+    #object PyArray_CastToType (ndarray, dtype, int)
+    int PyArray_CastTo (ndarray, ndarray)
+    int PyArray_CastAnyTo (ndarray, ndarray)
+    int PyArray_CanCastSafely (int, int)
+    npy_bool PyArray_CanCastTo (dtype, dtype)
+    int PyArray_ObjectType (object, int)
+    dtype PyArray_DescrFromObject (object, dtype)
+    #ndarray* PyArray_ConvertToCommonType (object, int *)
+    dtype PyArray_DescrFromScalar (object)
+    dtype PyArray_DescrFromTypeObject (object)
+    npy_intp PyArray_Size (object)
+    #object PyArray_Scalar (void *, dtype, object)
+    #object PyArray_FromScalar (object, dtype)
+    void PyArray_ScalarAsCtype (object, void *)
+    #int PyArray_CastScalarToCtype (object, void *, dtype)
+    #int PyArray_CastScalarDirect (object, dtype, void *, int)
+    object PyArray_ScalarFromObject (object)
+    #PyArray_VectorUnaryFunc * PyArray_GetCastFunc (dtype, int)
+    object PyArray_FromDims (int, int *, int)
+    #object PyArray_FromDimsAndDataAndDescr (int, int *, dtype, char *)
+    #object PyArray_FromAny (object, dtype, int, int, int, object)
+    object PyArray_EnsureArray (object)
+    object PyArray_EnsureAnyArray (object)
+    #object PyArray_FromFile (stdio.FILE *, dtype, npy_intp, char *)
+    #object PyArray_FromString (char *, npy_intp, dtype, npy_intp, char *)
+    #object PyArray_FromBuffer (object, dtype, npy_intp, npy_intp)
+    #object PyArray_FromIter (object, dtype, npy_intp)
+    object PyArray_Return (ndarray)
+    #object PyArray_GetField (ndarray, dtype, int)
+    #int PyArray_SetField (ndarray, dtype, int, object)
+    object PyArray_Byteswap (ndarray, npy_bool)
+    object PyArray_Resize (ndarray, PyArray_Dims *, int, NPY_ORDER)
+    int PyArray_MoveInto (ndarray, ndarray)
+    int PyArray_CopyInto (ndarray, ndarray)
+    int PyArray_CopyAnyInto (ndarray, ndarray)
+    int PyArray_CopyObject (ndarray, object)
+    object PyArray_NewCopy (ndarray, NPY_ORDER)
+    object PyArray_ToList (ndarray)
+    object PyArray_ToString (ndarray, NPY_ORDER)
+    int PyArray_ToFile (ndarray, stdio.FILE *, char *, char *)
+    int PyArray_Dump (object, object, int)
+    object PyArray_Dumps (object, int)
+    int PyArray_ValidType (int)
+    void PyArray_UpdateFlags (ndarray, int)
+    object PyArray_New (type, int, npy_intp *, int, npy_intp *, void *, int, int, object)
+    #object PyArray_NewFromDescr (type, dtype, int, npy_intp *, npy_intp *, void *, int, object)
+    #dtype PyArray_DescrNew (dtype)
+    dtype PyArray_DescrNewFromType (int)
+    double PyArray_GetPriority (object, double)
+    object PyArray_IterNew (object)
+    object PyArray_MultiIterNew (int, ...)
+
+    int PyArray_PyIntAsInt (object)
+    npy_intp PyArray_PyIntAsIntp (object)
+    int PyArray_Broadcast (broadcast)
+    void PyArray_FillObjectArray (ndarray, object)
+    int PyArray_FillWithScalar (ndarray, object)
+    npy_bool PyArray_CheckStrides (int, int, npy_intp, npy_intp, npy_intp *, npy_intp *)
+    dtype PyArray_DescrNewByteorder (dtype, char)
+    object PyArray_IterAllButAxis (object, int *)
+    #object PyArray_CheckFromAny (object, dtype, int, int, int, object)
+    #object PyArray_FromArray (ndarray, dtype, int)
+    object PyArray_FromInterface (object)
+    object PyArray_FromStructInterface (object)
+    #object PyArray_FromArrayAttr (object, dtype, object)
+    #NPY_SCALARKIND PyArray_ScalarKind (int, ndarray*)
+    int PyArray_CanCoerceScalar (int, int, NPY_SCALARKIND)
+    object PyArray_NewFlagsObject (object)
+    npy_bool PyArray_CanCastScalar (type, type)
+    #int PyArray_CompareUCS4 (npy_ucs4 *, npy_ucs4 *, register size_t)
+    int PyArray_RemoveSmallest (broadcast)
+    int PyArray_ElementStrides (object)
+    void PyArray_Item_INCREF (char *, dtype)
+    void PyArray_Item_XDECREF (char *, dtype)
+    object PyArray_FieldNames (object)
+    object PyArray_Transpose (ndarray, PyArray_Dims *)
+    object PyArray_TakeFrom (ndarray, object, int, ndarray, NPY_CLIPMODE)
+    object PyArray_PutTo (ndarray, object, object, NPY_CLIPMODE)
+    object PyArray_PutMask (ndarray, object, object)
+    object PyArray_Repeat (ndarray, object, int)
+    object PyArray_Choose (ndarray, object, ndarray, NPY_CLIPMODE)
+    int PyArray_Sort (ndarray, int, NPY_SORTKIND)
+    object PyArray_ArgSort (ndarray, int, NPY_SORTKIND)
+    object PyArray_SearchSorted (ndarray, object, NPY_SEARCHSIDE, PyObject *)
+    object PyArray_ArgMax (ndarray, int, ndarray)
+    object PyArray_ArgMin (ndarray, int, ndarray)
+    object PyArray_Reshape (ndarray, object)
+    object PyArray_Newshape (ndarray, PyArray_Dims *, NPY_ORDER)
+    object PyArray_Squeeze (ndarray)
+    #object PyArray_View (ndarray, dtype, type)
+    object PyArray_SwapAxes (ndarray, int, int)
+    object PyArray_Max (ndarray, int, ndarray)
+    object PyArray_Min (ndarray, int, ndarray)
+    object PyArray_Ptp (ndarray, int, ndarray)
+    object PyArray_Mean (ndarray, int, int, ndarray)
+    object PyArray_Trace (ndarray, int, int, int, int, ndarray)
+    object PyArray_Diagonal (ndarray, int, int, int)
+    object PyArray_Clip (ndarray, object, object, ndarray)
+    object PyArray_Conjugate (ndarray, ndarray)
+    object PyArray_Nonzero (ndarray)
+    object PyArray_Std (ndarray, int, int, ndarray, int)
+    object PyArray_Sum (ndarray, int, int, ndarray)
+    object PyArray_CumSum (ndarray, int, int, ndarray)
+    object PyArray_Prod (ndarray, int, int, ndarray)
+    object PyArray_CumProd (ndarray, int, int, ndarray)
+    object PyArray_All (ndarray, int, ndarray)
+    object PyArray_Any (ndarray, int, ndarray)
+    object PyArray_Compress (ndarray, object, int, ndarray)
+    object PyArray_Flatten (ndarray, NPY_ORDER)
+    object PyArray_Ravel (ndarray, NPY_ORDER)
+    npy_intp PyArray_MultiplyList (npy_intp *, int)
+    int PyArray_MultiplyIntList (int *, int)
+    void * PyArray_GetPtr (ndarray, npy_intp*)
+    int PyArray_CompareLists (npy_intp *, npy_intp *, int)
+    #int PyArray_AsCArray (object*, void *, npy_intp *, int, dtype)
+    #int PyArray_As1D (object*, char **, int *, int)
+    #int PyArray_As2D (object*, char ***, int *, int *, int)
+    int PyArray_Free (object, void *)
+    #int PyArray_Converter (object, object*)
+    int PyArray_IntpFromSequence (object, npy_intp *, int)
+    object PyArray_Concatenate (object, int)
+    object PyArray_InnerProduct (object, object)
+    object PyArray_MatrixProduct (object, object)
+    object PyArray_CopyAndTranspose (object)
+    object PyArray_Correlate (object, object, int)
+    int PyArray_TypestrConvert (int, int)
+    #int PyArray_DescrConverter (object, dtype*)
+    #int PyArray_DescrConverter2 (object, dtype*)
+    int PyArray_IntpConverter (object, PyArray_Dims *)
+    #int PyArray_BufferConverter (object, chunk)
+    int PyArray_AxisConverter (object, int *)
+    int PyArray_BoolConverter (object, npy_bool *)
+    int PyArray_ByteorderConverter (object, char *)
+    int PyArray_OrderConverter (object, NPY_ORDER *)
+    unsigned char PyArray_EquivTypes (dtype, dtype)
+    #object PyArray_Zeros (int, npy_intp *, dtype, int)
+    #object PyArray_Empty (int, npy_intp *, dtype, int)
+    object PyArray_Where (object, object, object)
+    object PyArray_Arange (double, double, double, int)
+    #object PyArray_ArangeObj (object, object, object, dtype)
+    int PyArray_SortkindConverter (object, NPY_SORTKIND *)
+    object PyArray_LexSort (object, int)
+    object PyArray_Round (ndarray, int, ndarray)
+    unsigned char PyArray_EquivTypenums (int, int)
+    int PyArray_RegisterDataType (dtype)
+    int PyArray_RegisterCastFunc (dtype, int, PyArray_VectorUnaryFunc *)
+    int PyArray_RegisterCanCast (dtype, int, NPY_SCALARKIND)
+    #void PyArray_InitArrFuncs (PyArray_ArrFuncs *)
+    object PyArray_IntTupleFromIntp (int, npy_intp *)
+    int PyArray_TypeNumFromName (char *)
+    int PyArray_ClipmodeConverter (object, NPY_CLIPMODE *)
+    #int PyArray_OutputConverter (object, ndarray*)
+    object PyArray_BroadcastToShape (object, npy_intp *, int)
+    void _PyArray_SigintHandler (int)
+    void* _PyArray_GetSigintBuf ()
+    #int PyArray_DescrAlignConverter (object, dtype*)
+    #int PyArray_DescrAlignConverter2 (object, dtype*)
+    int PyArray_SearchsideConverter (object, void *)
+    object PyArray_CheckAxis (ndarray, int *, int)
+    npy_intp PyArray_OverflowMultiplyList (npy_intp *, int)
+    int PyArray_CompareString (char *, char *, size_t)
+    int PyArray_SetBaseObject(ndarray, base)  # NOTE: steals a reference to base! Use "set_array_base()" instead.
+
+
+# Typedefs that matches the runtime dtype objects in
+# the numpy module.
+
+# The ones that are commented out needs an IFDEF function
+# in Cython to enable them only on the right systems.
+
+ctypedef npy_int8       int8_t
+ctypedef npy_int16      int16_t
+ctypedef npy_int32      int32_t
+ctypedef npy_int64      int64_t
+#ctypedef npy_int96      int96_t
+#ctypedef npy_int128     int128_t
+
+ctypedef npy_uint8      uint8_t
+ctypedef npy_uint16     uint16_t
+ctypedef npy_uint32     uint32_t
+ctypedef npy_uint64     uint64_t
+#ctypedef npy_uint96     uint96_t
+#ctypedef npy_uint128    uint128_t
+
+ctypedef npy_float32    float32_t
+ctypedef npy_float64    float64_t
+#ctypedef npy_float80    float80_t
+#ctypedef npy_float128   float128_t
+
+ctypedef float complex  complex64_t
+ctypedef double complex complex128_t
+
+# The int types are mapped a bit surprising --
+# numpy.int corresponds to 'l' and numpy.long to 'q'
+ctypedef npy_long       int_t
+ctypedef npy_longlong   longlong_t
+
+ctypedef npy_ulong      uint_t
+ctypedef npy_ulonglong  ulonglong_t
+
+ctypedef npy_intp       intp_t
+ctypedef npy_uintp      uintp_t
+
+ctypedef npy_double     float_t
+ctypedef npy_double     double_t
+ctypedef npy_longdouble longdouble_t
+
+ctypedef npy_cfloat      cfloat_t
+ctypedef npy_cdouble     cdouble_t
+ctypedef npy_clongdouble clongdouble_t
+
+ctypedef npy_cdouble     complex_t
+
+cdef inline object PyArray_MultiIterNew1(a):
+    return PyArray_MultiIterNew(1, <void*>a)
+
+cdef inline object PyArray_MultiIterNew2(a, b):
+    return PyArray_MultiIterNew(2, <void*>a, <void*>b)
+
+cdef inline object PyArray_MultiIterNew3(a, b, c):
+    return PyArray_MultiIterNew(3, <void*>a, <void*>b, <void*> c)
+
+cdef inline object PyArray_MultiIterNew4(a, b, c, d):
+    return PyArray_MultiIterNew(4, <void*>a, <void*>b, <void*>c, <void*> d)
+
+cdef inline object PyArray_MultiIterNew5(a, b, c, d, e):
+    return PyArray_MultiIterNew(5, <void*>a, <void*>b, <void*>c, <void*> d, <void*> e)
+
+cdef inline tuple PyDataType_SHAPE(dtype d):
+    if PyDataType_HASSUBARRAY(d):
+        return <tuple>d.subarray.shape
+    else:
+        return ()
+
+
+cdef extern from "numpy/ndarrayobject.h":
+    PyTypeObject PyTimedeltaArrType_Type
+    PyTypeObject PyDatetimeArrType_Type
+    ctypedef int64_t npy_timedelta
+    ctypedef int64_t npy_datetime
+
+cdef extern from "numpy/ndarraytypes.h":
+    ctypedef struct PyArray_DatetimeMetaData:
+        NPY_DATETIMEUNIT base
+        int64_t num
+
+cdef extern from "numpy/arrayscalars.h":
+
+    # abstract types
+    ctypedef class numpy.generic [object PyObject]:
+        pass
+    ctypedef class numpy.number [object PyObject]:
+        pass
+    ctypedef class numpy.integer [object PyObject]:
+        pass
+    ctypedef class numpy.signedinteger [object PyObject]:
+        pass
+    ctypedef class numpy.unsignedinteger [object PyObject]:
+        pass
+    ctypedef class numpy.inexact [object PyObject]:
+        pass
+    ctypedef class numpy.floating [object PyObject]:
+        pass
+    ctypedef class numpy.complexfloating [object PyObject]:
+        pass
+    ctypedef class numpy.flexible [object PyObject]:
+        pass
+    ctypedef class numpy.character [object PyObject]:
+        pass
+
+    ctypedef struct PyDatetimeScalarObject:
+        # PyObject_HEAD
+        npy_datetime obval
+        PyArray_DatetimeMetaData obmeta
+
+    ctypedef struct PyTimedeltaScalarObject:
+        # PyObject_HEAD
+        npy_timedelta obval
+        PyArray_DatetimeMetaData obmeta
+
+    ctypedef enum NPY_DATETIMEUNIT:
+        NPY_FR_Y
+        NPY_FR_M
+        NPY_FR_W
+        NPY_FR_D
+        NPY_FR_B
+        NPY_FR_h
+        NPY_FR_m
+        NPY_FR_s
+        NPY_FR_ms
+        NPY_FR_us
+        NPY_FR_ns
+        NPY_FR_ps
+        NPY_FR_fs
+        NPY_FR_as
+
+
+#
+# ufunc API
+#
+
+cdef extern from "numpy/ufuncobject.h":
+
+    ctypedef void (*PyUFuncGenericFunction) (char **, npy_intp *, npy_intp *, void *)
+
+    ctypedef class numpy.ufunc [object PyUFuncObject, check_size ignore]:
+        cdef:
+            int nin, nout, nargs
+            int identity
+            PyUFuncGenericFunction *functions
+            void **data
+            int ntypes
+            int check_return
+            char *name
+            char *types
+            char *doc
+            void *ptr
+            PyObject *obj
+            PyObject *userloops
+
+    cdef enum:
+        PyUFunc_Zero
+        PyUFunc_One
+        PyUFunc_None
+        UFUNC_ERR_IGNORE
+        UFUNC_ERR_WARN
+        UFUNC_ERR_RAISE
+        UFUNC_ERR_CALL
+        UFUNC_ERR_PRINT
+        UFUNC_ERR_LOG
+        UFUNC_MASK_DIVIDEBYZERO
+        UFUNC_MASK_OVERFLOW
+        UFUNC_MASK_UNDERFLOW
+        UFUNC_MASK_INVALID
+        UFUNC_SHIFT_DIVIDEBYZERO
+        UFUNC_SHIFT_OVERFLOW
+        UFUNC_SHIFT_UNDERFLOW
+        UFUNC_SHIFT_INVALID
+        UFUNC_FPE_DIVIDEBYZERO
+        UFUNC_FPE_OVERFLOW
+        UFUNC_FPE_UNDERFLOW
+        UFUNC_FPE_INVALID
+        UFUNC_ERR_DEFAULT
+        UFUNC_ERR_DEFAULT2
+
+    object PyUFunc_FromFuncAndData(PyUFuncGenericFunction *,
+          void **, char *, int, int, int, int, char *, char *, int)
+    int PyUFunc_RegisterLoopForType(ufunc, int,
+                                    PyUFuncGenericFunction, int *, void *)
+    int PyUFunc_GenericFunction \
+        (ufunc, PyObject *, PyObject *, PyArrayObject **)
+    void PyUFunc_f_f_As_d_d \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_d_d \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_f_f \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_g_g \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_F_F_As_D_D \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_F_F \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_D_D \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_G_G \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_O_O \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_ff_f_As_dd_d \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_ff_f \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_dd_d \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_gg_g \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_FF_F_As_DD_D \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_DD_D \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_FF_F \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_GG_G \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_OO_O \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_O_O_method \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_OO_O_method \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_On_Om \
+         (char **, npy_intp *, npy_intp *, void *)
+    int PyUFunc_GetPyValues \
+        (char *, int *, int *, PyObject **)
+    int PyUFunc_checkfperr \
+           (int, PyObject *, int *)
+    void PyUFunc_clearfperr()
+    int PyUFunc_getfperr()
+    int PyUFunc_handlefperr \
+        (int, PyObject *, int, int *)
+    int PyUFunc_ReplaceLoopBySignature \
+        (ufunc, PyUFuncGenericFunction, int *, PyUFuncGenericFunction *)
+    object PyUFunc_FromFuncAndDataAndSignature \
+             (PyUFuncGenericFunction *, void **, char *, int, int, int,
+              int, char *, char *, int, char *)
+
+    int _import_umath() except -1
+
+cdef inline void set_array_base(ndarray arr, object base):
+    Py_INCREF(base) # important to do this before stealing the reference below!
+    PyArray_SetBaseObject(arr, base)
+
+cdef inline object get_array_base(ndarray arr):
+    base = PyArray_BASE(arr)
+    if base is NULL:
+        return None
+    return <object>base
+
+# Versions of the import_* functions which are more suitable for
+# Cython code.
+cdef inline int import_array() except -1:
+    try:
+        __pyx_import_array()
+    except Exception:
+        raise ImportError("numpy.core.multiarray failed to import")
+
+cdef inline int import_umath() except -1:
+    try:
+        _import_umath()
+    except Exception:
+        raise ImportError("numpy.core.umath failed to import")
+
+cdef inline int import_ufunc() except -1:
+    try:
+        _import_umath()
+    except Exception:
+        raise ImportError("numpy.core.umath failed to import")
+
+
+cdef inline bint is_timedelta64_object(object obj):
+    """
+    Cython equivalent of `isinstance(obj, np.timedelta64)`
+
+    Parameters
+    ----------
+    obj : object
+
+    Returns
+    -------
+    bool
+    """
+    return PyObject_TypeCheck(obj, &PyTimedeltaArrType_Type)
+
+
+cdef inline bint is_datetime64_object(object obj):
+    """
+    Cython equivalent of `isinstance(obj, np.datetime64)`
+
+    Parameters
+    ----------
+    obj : object
+
+    Returns
+    -------
+    bool
+    """
+    return PyObject_TypeCheck(obj, &PyDatetimeArrType_Type)
+
+
+cdef inline npy_datetime get_datetime64_value(object obj) nogil:
+    """
+    returns the int64 value underlying scalar numpy datetime64 object
+
+    Note that to interpret this as a datetime, the corresponding unit is
+    also needed.  That can be found using `get_datetime64_unit`.
+    """
+    return (<PyDatetimeScalarObject*>obj).obval
+
+
+cdef inline npy_timedelta get_timedelta64_value(object obj) nogil:
+    """
+    returns the int64 value underlying scalar numpy timedelta64 object
+    """
+    return (<PyTimedeltaScalarObject*>obj).obval
+
+
+cdef inline NPY_DATETIMEUNIT get_datetime64_unit(object obj) nogil:
+    """
+    returns the unit part of the dtype for a numpy datetime64 object.
+    """
+    return <NPY_DATETIMEUNIT>(<PyDatetimeScalarObject*>obj).obmeta.base
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/numpy/math.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/numpy/math.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..c16df1c51a5543ea963d44f30d2027414b6cdf40
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/numpy/math.pxd
@@ -0,0 +1,133 @@
+# NumPy math library
+#
+# This exports the functionality of the NumPy core math library, aka npymath,
+# which provides implementations of C99 math functions and macros for system
+# with a C89 library (such as MSVC). npymath is available with NumPy >=1.3,
+# although some functions will require later versions. The spacing function is
+# not in C99, but comes from Fortran.
+#
+# On the Cython side, the npymath functions are available without the "npy_"
+# prefix that they have in C, to make this is a drop-in replacement for
+# libc.math. The same is true for the constants, where possible.
+#
+# See the NumPy documentation for linking instructions.
+#
+# Complex number support and NumPy 2.0 half-precision functions are currently
+# not exported.
+#
+# Author: Lars Buitinck
+
+cdef extern from "numpy/npy_math.h" nogil:
+    # Floating-point classification
+    long double NAN "NPY_NAN"
+    long double INFINITY "NPY_INFINITY"
+    long double PZERO "NPY_PZERO"        # positive zero
+    long double NZERO "NPY_NZERO"        # negative zero
+
+    # These four are actually macros and work on any floating-point type.
+    int isinf "npy_isinf"(long double)  # -1 / 0 / 1
+    bint isfinite "npy_isfinite"(long double)
+    bint isnan "npy_isnan"(long double)
+    bint signbit "npy_signbit"(long double)
+
+    # Math constants
+    long double E "NPY_E"
+    long double LOG2E "NPY_LOG2E"       # ln(e) / ln(2)
+    long double LOG10E "NPY_LOG10E"     # ln(e) / ln(10)
+    long double LOGE2 "NPY_LOGE2"       # ln(2)
+    long double LOGE10 "NPY_LOGE10"     # ln(10)
+    long double PI "NPY_PI"
+    long double PI_2 "NPY_PI_2"         # pi / 2
+    long double PI_4 "NPY_PI_4"         # pi / 4
+    long double NPY_1_PI                # 1 / pi; NPY_ because of ident syntax
+    long double NPY_2_PI                # 2 / pi
+    long double EULER "NPY_EULER"       # Euler constant (gamma, 0.57721)
+
+    # Low-level floating point manipulation (NumPy >=1.4)
+    float copysignf "npy_copysignf"(float, float)
+    float nextafterf "npy_nextafterf"(float x, float y)
+    float spacingf "npy_spacingf"(float x)
+    double copysign "npy_copysign"(double, double)
+    double nextafter "npy_nextafter"(double x, double y)
+    double spacing "npy_spacing"(double x)
+    long double copysignl "npy_copysignl"(long double, long double)
+    long double nextafterl "npy_nextafterl"(long double x, long double y)
+    long double spacingl "npy_spacingl"(long double x)
+
+    # Float C99 functions
+    float sinf "npy_sinf"(float x)
+    float cosf "npy_cosf"(float x)
+    float tanf "npy_tanf"(float x)
+    float sinhf "npy_sinhf"(float x)
+    float coshf "npy_coshf"(float x)
+    float tanhf "npy_tanhf"(float x)
+    float fabsf "npy_fabsf"(float x)
+    float floorf "npy_floorf"(float x)
+    float ceilf "npy_ceilf"(float x)
+    float rintf "npy_rintf"(float x)
+    float sqrtf "npy_sqrtf"(float x)
+    float log10f "npy_log10f"(float x)
+    float logf "npy_logf"(float x)
+    float expf "npy_expf"(float x)
+    float expm1f "npy_expm1f"(float x)
+    float asinf "npy_asinf"(float x)
+    float acosf "npy_acosf"(float x)
+    float atanf "npy_atanf"(float x)
+    float asinhf "npy_asinhf"(float x)
+    float acoshf "npy_acoshf"(float x)
+    float atanhf "npy_atanhf"(float x)
+    float log1pf "npy_log1pf"(float x)
+    float exp2f "npy_exp2f"(float x)
+    float log2f "npy_log2f"(float x)
+    float atan2f "npy_atan2f"(float x, float y)
+    float hypotf "npy_hypotf"(float x, float y)
+    float powf "npy_powf"(float x, float y)
+    float fmodf "npy_fmodf"(float x, float y)
+    float modff "npy_modff"(float x, float* y)
+
+    # Long double C99 functions
+    long double sinl "npy_sinl"(long double x)
+    long double cosl "npy_cosl"(long double x)
+    long double tanl "npy_tanl"(long double x)
+    long double sinhl "npy_sinhl"(long double x)
+    long double coshl "npy_coshl"(long double x)
+    long double tanhl "npy_tanhl"(long double x)
+    long double fabsl "npy_fabsl"(long double x)
+    long double floorl "npy_floorl"(long double x)
+    long double ceill "npy_ceill"(long double x)
+    long double rintl "npy_rintl"(long double x)
+    long double sqrtl "npy_sqrtl"(long double x)
+    long double log10l "npy_log10l"(long double x)
+    long double logl "npy_logl"(long double x)
+    long double expl "npy_expl"(long double x)
+    long double expm1l "npy_expm1l"(long double x)
+    long double asinl "npy_asinl"(long double x)
+    long double acosl "npy_acosl"(long double x)
+    long double atanl "npy_atanl"(long double x)
+    long double asinhl "npy_asinhl"(long double x)
+    long double acoshl "npy_acoshl"(long double x)
+    long double atanhl "npy_atanhl"(long double x)
+    long double log1pl "npy_log1pl"(long double x)
+    long double exp2l "npy_exp2l"(long double x)
+    long double log2l "npy_log2l"(long double x)
+    long double atan2l "npy_atan2l"(long double x, long double y)
+    long double hypotl "npy_hypotl"(long double x, long double y)
+    long double powl "npy_powl"(long double x, long double y)
+    long double fmodl "npy_fmodl"(long double x, long double y)
+    long double modfl "npy_modfl"(long double x, long double* y)
+
+    # NumPy extensions
+    float deg2radf "npy_deg2radf"(float x)
+    float rad2degf "npy_rad2degf"(float x)
+    float logaddexpf "npy_logaddexpf"(float x, float y)
+    float logaddexp2f "npy_logaddexp2f"(float x, float y)
+
+    double deg2rad "npy_deg2rad"(double x)
+    double rad2deg "npy_rad2deg"(double x)
+    double logaddexp "npy_logaddexp"(double x, double y)
+    double logaddexp2 "npy_logaddexp2"(double x, double y)
+
+    long double deg2radl "npy_deg2radl"(long double x)
+    long double rad2degl "npy_rad2degl"(long double x)
+    long double logaddexpl "npy_logaddexpl"(long double x, long double y)
+    long double logaddexp2l "npy_logaddexp2l"(long double x, long double y)
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/dlfcn.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/dlfcn.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..bf61997f341f92f124fde3487b2a0616da63decf
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/dlfcn.pxd
@@ -0,0 +1,14 @@
+# POSIX dynamic linking/loading interface.
+# https://pubs.opengroup.org/onlinepubs/9699919799/basedefs/dlfcn.h.html
+
+cdef extern from "<dlfcn.h>" nogil:
+    void *dlopen(const char *, int)
+    char *dlerror()
+    void *dlsym(void *, const char *)
+    int dlclose(void *)
+
+    enum:
+        RTLD_LAZY
+        RTLD_NOW
+        RTLD_GLOBAL
+        RTLD_LOCAL
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/ioctl.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/ioctl.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..dacbc307f3f19b92d84bd655ae3eaa92f9385f6f
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/ioctl.pxd
@@ -0,0 +1,4 @@
+cdef extern from "<sys/ioctl.h>" nogil:
+    enum: FIONBIO
+
+    int ioctl(int fd, int request, ...)
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/resource.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/resource.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..b9628c66bdf68488e285b24550d4079bcf22aa01
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/resource.pxd
@@ -0,0 +1,57 @@
+# https://pubs.opengroup.org/onlinepubs/9699919799/basedefs/sys_resource.h.html
+# https://man7.org/linux/man-pages/man2/getrusage.2.html
+
+from posix.time  cimport timeval
+from posix.types cimport id_t
+
+cdef extern from "<sys/resource.h>" nogil:
+
+    enum: PRIO_PROCESS
+    enum: PRIO_PGRP
+    enum: PRIO_USER
+
+    enum: RLIM_INFINITY
+    enum: RLIM_SAVED_MAX
+    enum: RLIM_SAVED_CUR
+
+    enum: RUSAGE_SELF
+    enum: RUSAGE_CHILDREN
+
+    enum: RLIMIT_CORE
+    enum: RLIMIT_CPU
+    enum: RLIMIT_DATA
+    enum: RLIMIT_FSIZE
+    enum: RLIMIT_NOFILE
+    enum: RLIMIT_STACK
+    enum: RLIMIT_AS
+
+    ctypedef unsigned long rlim_t
+
+    cdef struct rlimit:
+        rlim_t rlim_cur
+        rlim_t rlim_max
+
+    cdef struct rusage:
+        timeval ru_utime
+        timeval ru_stime
+        # Linux-specific
+        long    ru_maxrss
+        long    ru_ixrss
+        long    ru_idrss
+        long    ru_isrss
+        long    ru_minflt
+        long    ru_majflt
+        long    ru_nswap
+        long    ru_inblock
+        long    ru_oublock
+        long    ru_msgsnd
+        long    ru_msgrcv
+        long    ru_nsignals
+        long    ru_nvcsw
+        long    ru_nivcsw
+
+    int  getpriority(int, id_t)
+    int  getrlimit(int, rlimit *)
+    int  getrusage(int, rusage *)
+    int  setpriority(int, id_t, int)
+    int  setrlimit(int, const rlimit *)
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/select.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/select.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..803c492d40fe95bde9a31ed7c68ac9f665d39651
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/select.pxd
@@ -0,0 +1,21 @@
+# https://pubs.opengroup.org/onlinepubs/9699919799/basedefs/sys_select.h.html
+
+from .types cimport sigset_t
+from .time cimport timeval, timespec
+
+cdef extern from "<sys/select.h>" nogil:
+    ctypedef struct fd_set:
+        pass
+
+    int FD_SETSIZE
+    void FD_SET(int, fd_set*)
+    void FD_CLR(int, fd_set*)
+    bint FD_ISSET(int, fd_set*)
+    void FD_ZERO(fd_set*)
+
+    int select(int nfds, fd_set *readfds, fd_set *writefds,
+        fd_set *exceptfds, timeval *timeout)
+
+    int pselect(int nfds, fd_set *readfds, fd_set *writefds,
+        fd_set *exceptfds, const timespec *timeout,
+        const sigset_t *sigmask)
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/stat.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/stat.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..9247423f84e1a7690b2677c906c009be0c1c574d
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/stat.pxd
@@ -0,0 +1,98 @@
+# https://pubs.opengroup.org/onlinepubs/009695399/basedefs/sys/stat.h.html
+# https://pubs.opengroup.org/onlinepubs/9699919799/basedefs/sys_stat.h.html
+
+from posix.types cimport (blkcnt_t, blksize_t, dev_t, gid_t, ino_t, mode_t,
+                          nlink_t, off_t, time_t, uid_t)
+from posix.time cimport timespec
+
+
+cdef extern from "<sys/stat.h>" nogil:
+    cdef struct struct_stat "stat":
+        dev_t   st_dev
+        ino_t   st_ino
+        mode_t  st_mode
+        nlink_t st_nlink
+        uid_t   st_uid
+        gid_t   st_gid
+        dev_t   st_rdev
+        off_t   st_size
+        blksize_t st_blksize
+        blkcnt_t st_blocks
+        # POSIX.1-2001
+        time_t  st_atime
+        time_t  st_mtime
+        time_t  st_ctime
+        # POSIX.1-2008
+        timespec st_atim
+        timespec st_mtim
+        timespec st_ctim
+
+        # st_birthtime exists on *BSD and OS X.
+        # Under Linux, defining it here does not hurt. Compilation under Linux
+        # will only (and rightfully) fail when attempting to use the field.
+        time_t  st_birthtime
+
+# POSIX prescribes including both <sys/stat.h> and <unistd.h> for these
+cdef extern from "<unistd.h>" nogil:
+    int chmod(const char *, mode_t)
+    int fchmod(int, mode_t)
+    int fchmodat(int, const char *, mode_t, int flags)
+
+    int stat(const char *, struct_stat *)
+    int lstat(const char *, struct_stat *)
+    int fstat(int, struct_stat *)
+    int fstatat(int, const char *, struct_stat *, int flags)
+
+    int mkdir(const char *, mode_t)
+    int mkdirat(int, const char *, mode_t)
+    int mkfifo(const char *, mode_t)
+    int mkfifoat(int, const char *, mode_t)
+    int mknod(const char *, mode_t, dev_t)
+    int mknodat(int, const char *, mode_t, dev_t)
+
+    int futimens(int, const timespec *)
+    int utimensat(int, const char *, const timespec *, int flags)
+
+    # Macros for st_mode
+    mode_t S_ISREG(mode_t)
+    mode_t S_ISDIR(mode_t)
+    mode_t S_ISCHR(mode_t)
+    mode_t S_ISBLK(mode_t)
+    mode_t S_ISFIFO(mode_t)
+    mode_t S_ISLNK(mode_t)
+    mode_t S_ISSOCK(mode_t)
+
+    mode_t S_IFMT
+    mode_t S_IFREG
+    mode_t S_IFDIR
+    mode_t S_IFCHR
+    mode_t S_IFBLK
+    mode_t S_IFIFO
+    mode_t S_IFLNK
+    mode_t S_IFSOCK
+
+    # Permissions
+    mode_t S_ISUID
+    mode_t S_ISGID
+    mode_t S_ISVTX
+
+    mode_t S_IRWXU
+    mode_t S_IRUSR
+    mode_t S_IWUSR
+    mode_t S_IXUSR
+
+    mode_t S_IRWXG
+    mode_t S_IRGRP
+    mode_t S_IWGRP
+    mode_t S_IXGRP
+
+    mode_t S_IRWXO
+    mode_t S_IROTH
+    mode_t S_IWOTH
+    mode_t S_IXOTH
+
+    # test file types
+    bint S_TYPEISMQ(struct_stat *buf)
+    bint S_TYPEISSEM(struct_stat *buf)
+    bint S_TYPEISSHM(struct_stat *buf)
+    bint S_TYPEISTMO(struct_stat *buf)
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/strings.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/strings.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..6ee48491eb833da21e5aefe1a8efe521c132cbf4
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/strings.pxd
@@ -0,0 +1,9 @@
+cdef extern from "<strings.h>" nogil:
+    int    bcmp(const void *, const void *, size_t)
+    void   bcopy(const void *, void *, size_t)
+    void   bzero(void *, size_t)
+    int    ffs(int)
+    char   *index(const char *, int)
+    char   *rindex(const char *, int)
+    int    strcasecmp(const char *, const char *)
+    int    strncasecmp(const char *, const char *, size_t)
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/uio.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/uio.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..d9971bd4a06bce03952fa3f4bdbf1ee4fa06d811
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/uio.pxd
@@ -0,0 +1,26 @@
+# https://pubs.opengroup.org/onlinepubs/9699919799/basedefs/sys_uio.h.html
+
+from posix.types cimport off_t
+
+
+cdef extern from "<sys/uio.h>" nogil:
+
+    cdef struct iovec:
+        void  *iov_base
+        size_t iov_len
+
+    ssize_t readv (int fd, const iovec *iov, int iovcnt)
+    ssize_t writev(int fd, const iovec *iov, int iovcnt)
+
+    # Linux-specific, https://man7.org/linux/man-pages/man2/readv.2.html
+    ssize_t preadv (int fd, const iovec *iov, int iovcnt, off_t offset)
+    ssize_t pwritev(int fd, const iovec *iov, int iovcnt, off_t offset)
+
+    enum: RWF_DSYNC
+    enum: RWF_HIPRI
+    enum: RWF_SYNC
+    enum: RWF_NOWAIT
+    enum: RWF_APPEND
+
+    ssize_t preadv2 (int fd, const iovec *iov, int iovcnt, off_t offset, int flags)
+    ssize_t pwritev2(int fd, const iovec *iov, int iovcnt, off_t offset, int flags)
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/unistd.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/unistd.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..1afeca385456877c2d6ef0f59660f57012e81201
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/unistd.pxd
@@ -0,0 +1,271 @@
+# http://www.opengroup.org/onlinepubs/009695399/basedefs/unistd.h.html
+
+from posix.types cimport gid_t, pid_t, off_t, uid_t
+
+cdef extern from "<unistd.h>" nogil:
+
+    #:NULL
+
+    enum: R_OK
+    enum: W_OK
+    enum: X_OK
+    enum: F_OK
+
+    enum: _CS_PATH
+    enum: _CS_POSIX_V6_ILP32_OFF32_CFLAGS
+    enum: _CS_POSIX_V6_ILP32_OFF32_LDFLAGS
+    enum: _CS_POSIX_V6_ILP32_OFF32_LIBS
+    enum: _CS_POSIX_V6_ILP32_OFFBIG_CFLAGS
+    enum: _CS_POSIX_V6_ILP32_OFFBIG_LDFLAGS
+    enum: _CS_POSIX_V6_ILP32_OFFBIG_LIBS
+    enum: _CS_POSIX_V6_LP64_OFF64_CFLAGS
+    enum: _CS_POSIX_V6_LP64_OFF64_LDFLAGS
+    enum: _CS_POSIX_V6_LP64_OFF64_LIBS
+    enum: _CS_POSIX_V6_LPBIG_OFFBIG_CFLAGS
+    enum: _CS_POSIX_V6_LPBIG_OFFBIG_LDFLAGS
+    enum: _CS_POSIX_V6_LPBIG_OFFBIG_LIBS
+    enum: _CS_POSIX_V6_WIDTH_RESTRICTED_ENVS
+
+    enum: SEEK_SET
+    enum: SEEK_CUR
+    enum: SEEK_END
+
+    enum: F_LOCK
+    enum: F_TEST
+    enum: F_TLOCK
+    enum: F_ULOCK
+
+    enum: _PC_2_SYMLINKS
+    enum: _PC_ALLOC_SIZE_MIN
+    enum: _PC_ASYNC_IO
+    enum: _PC_CHOWN_RESTRICTED
+    enum: _PC_FILESIZEBITS
+    enum: _PC_LINK_MAX
+    enum: _PC_MAX_CANON
+    enum: _PC_MAX_INPUT
+    enum: _PC_NAME_MAX
+    enum: _PC_NO_TRUNC
+    enum: _PC_PATH_MAX
+    enum: _PC_PIPE_BUF
+    enum: _PC_PRIO_IO
+    enum: _PC_REC_INCR_XFER_SIZE
+    enum: _PC_REC_MIN_XFER_SIZE
+    enum: _PC_REC_XFER_ALIGN
+    enum: _PC_SYMLINK_MAX
+    enum: _PC_SYNC_IO
+    enum: _PC_VDISABLE
+
+    enum: _SC_2_C_BIND
+    enum: _SC_2_C_DEV
+    enum: _SC_2_CHAR_TERM
+    enum: _SC_2_FORT_DEV
+    enum: _SC_2_FORT_RUN
+    enum: _SC_2_LOCALEDEF
+    enum: _SC_2_PBS
+    enum: _SC_2_PBS_ACCOUNTING
+    enum: _SC_2_PBS_CHECKPOINT
+    enum: _SC_2_PBS_LOCATE
+    enum: _SC_2_PBS_MESSAGE
+    enum: _SC_2_PBS_TRACK
+    enum: _SC_2_SW_DEV
+    enum: _SC_2_UPE
+    enum: _SC_2_VERSION
+    enum: _SC_ADVISORY_INFO
+    enum: _SC_AIO_LISTIO_MAX
+    enum: _SC_AIO_MAX
+    enum: _SC_AIO_PRIO_DELTA_MAX
+    enum: _SC_ARG_MAX
+    enum: _SC_ASYNCHRONOUS_IO
+    enum: _SC_ATEXIT_MAX
+    enum: _SC_BARRIERS
+    enum: _SC_BC_BASE_MAX
+    enum: _SC_BC_DIM_MAX
+    enum: _SC_BC_SCALE_MAX
+    enum: _SC_BC_STRING_MAX
+    enum: _SC_CHILD_MAX
+    enum: _SC_CLK_TCK
+    enum: _SC_CLOCK_SELECTION
+    enum: _SC_COLL_WEIGHTS_MAX
+    enum: _SC_CPUTIME
+    enum: _SC_DELAYTIMER_MAX
+    enum: _SC_EXPR_NEST_MAX
+    enum: _SC_FSYNC
+    enum: _SC_GETGR_R_SIZE_MAX
+    enum: _SC_GETPW_R_SIZE_MAX
+    enum: _SC_HOST_NAME_MAX
+    enum: _SC_IOV_MAX
+    enum: _SC_IPV6
+    enum: _SC_JOB_CONTROL
+    enum: _SC_LINE_MAX
+    enum: _SC_LOGIN_NAME_MAX
+    enum: _SC_MAPPED_FILES
+    enum: _SC_MEMLOCK
+    enum: _SC_MEMLOCK_RANGE
+    enum: _SC_MEMORY_PROTECTION
+    enum: _SC_MESSAGE_PASSING
+    enum: _SC_MONOTONIC_CLOCK
+    enum: _SC_MQ_OPEN_MAX
+    enum: _SC_MQ_PRIO_MAX
+    enum: _SC_NGROUPS_MAX
+    enum: _SC_OPEN_MAX
+    enum: _SC_PAGE_SIZE
+    enum: _SC_PAGESIZE
+    enum: _SC_PRIORITIZED_IO
+    enum: _SC_PRIORITY_SCHEDULING
+    enum: _SC_RAW_SOCKETS
+    enum: _SC_RE_DUP_MAX
+    enum: _SC_READER_WRITER_LOCKS
+    enum: _SC_REALTIME_SIGNALS
+    enum: _SC_REGEXP
+    enum: _SC_RTSIG_MAX
+    enum: _SC_SAVED_IDS
+    enum: _SC_SEM_NSEMS_MAX
+    enum: _SC_SEM_VALUE_MAX
+    enum: _SC_SEMAPHORES
+    enum: _SC_SHARED_MEMORY_OBJECTS
+    enum: _SC_SHELL
+    enum: _SC_SIGQUEUE_MAX
+    enum: _SC_SPAWN
+    enum: _SC_SPIN_LOCKS
+    enum: _SC_SPORADIC_SERVER
+    enum: _SC_SS_REPL_MAX
+    enum: _SC_STREAM_MAX
+    enum: _SC_SYMLOOP_MAX
+    enum: _SC_SYNCHRONIZED_IO
+    enum: _SC_THREAD_ATTR_STACKADDR
+    enum: _SC_THREAD_ATTR_STACKSIZE
+    enum: _SC_THREAD_CPUTIME
+    enum: _SC_THREAD_DESTRUCTOR_ITERATIONS
+    enum: _SC_THREAD_KEYS_MAX
+    enum: _SC_THREAD_PRIO_INHERIT
+    enum: _SC_THREAD_PRIO_PROTECT
+    enum: _SC_THREAD_PRIORITY_SCHEDULING
+    enum: _SC_THREAD_PROCESS_SHARED
+    enum: _SC_THREAD_SAFE_FUNCTIONS
+    enum: _SC_THREAD_SPORADIC_SERVER
+    enum: _SC_THREAD_STACK_MIN
+    enum: _SC_THREAD_THREADS_MAX
+    enum: _SC_THREADS
+    enum: _SC_TIMEOUTS
+    enum: _SC_TIMER_MAX
+    enum: _SC_TIMERS
+    enum: _SC_TRACE
+    enum: _SC_TRACE_EVENT_FILTER
+    enum: _SC_TRACE_EVENT_NAME_MAX
+    enum: _SC_TRACE_INHERIT
+    enum: _SC_TRACE_LOG
+    enum: _SC_TRACE_NAME_MAX
+    enum: _SC_TRACE_SYS_MAX
+    enum: _SC_TRACE_USER_EVENT_MAX
+    enum: _SC_TTY_NAME_MAX
+    enum: _SC_TYPED_MEMORY_OBJECTS
+    enum: _SC_TZNAME_MAX
+    enum: _SC_V6_ILP32_OFF32
+    enum: _SC_V6_ILP32_OFFBIG
+    enum: _SC_V6_LP64_OFF64
+    enum: _SC_V6_LPBIG_OFFBIG
+    enum: _SC_VERSION
+    enum: _SC_XBS5_ILP32_OFF32
+    enum: _SC_XBS5_ILP32_OFFBIG
+    enum: _SC_XBS5_LP64_OFF64
+    enum: _SC_XBS5_LPBIG_OFFBIG
+    enum: _SC_XOPEN_CRYPT
+    enum: _SC_XOPEN_ENH_I18N
+    enum: _SC_XOPEN_LEGACY
+    enum: _SC_XOPEN_REALTIME
+    enum: _SC_XOPEN_REALTIME_THREADS
+    enum: _SC_XOPEN_SHM
+    enum: _SC_XOPEN_STREAMS
+    enum: _SC_XOPEN_UNIX
+    enum: _SC_XOPEN_VERSION
+
+    enum: STDIN_FILENO	#0
+    enum: STDOUT_FILENO	#1
+    enum: STDERR_FILENO	#2
+
+    ctypedef unsigned useconds_t
+
+    int          access(const char *, int)
+    unsigned     alarm(unsigned)
+    int          chdir(const char *)
+    int          chown(const char *, uid_t, gid_t)
+    int          close(int)
+    size_t       confstr(int, char *, size_t)
+    char        *crypt(const char *, const char *)
+    char        *ctermid(char *)
+    int          dup(int)
+    int          dup2(int, int)
+    void         encrypt(char[64], int)
+    int          execl(const char *, const char *, ...)
+    int          execle(const char *, const char *, ...)
+    int          execlp(const char *, const char *, ...)
+    int          execv(const char *, char *[])
+    int          execve(const char *, char *[], char *[])
+    int          execvp(const char *, char *[])
+    void        _exit(int)
+    int          fchown(int, uid_t, gid_t)
+    int          fchdir(int)
+    int          fdatasync(int)
+    pid_t        fork()
+    long         fpathconf(int, int)
+    int          fsync(int)
+    int          ftruncate(int, off_t)
+    char        *getcwd(char *, size_t)
+    gid_t        getegid()
+    uid_t        geteuid()
+    gid_t        getgid()
+    int          getgroups(int, gid_t [])
+    long         gethostid()
+    int          gethostname(char *, size_t)
+    char        *getlogin()
+    int          getlogin_r(char *, size_t)
+    int          getopt(int, char * [], const char *)
+    pid_t        getpgid(pid_t)
+    pid_t        getpgrp()
+    pid_t        getpid()
+    pid_t        getppid()
+    pid_t        getsid(pid_t)
+    uid_t        getuid()
+    char        *getwd(char *)
+    int          isatty(int)
+    int          lchown(const char *, uid_t, gid_t)
+    int          link(const char *, const char *)
+    int          lockf(int, int, off_t)
+    off_t        lseek(int, off_t, int)
+    int          nice(int)
+    long         pathconf(char *, int)
+    int          pause()
+    int          pipe(int [2])
+    ssize_t      pread(int, void *, size_t, off_t)
+    ssize_t      pwrite(int, const void *, size_t, off_t)
+    ssize_t      read(int, void *, size_t)
+    ssize_t      readlink(const char *, char *, size_t)
+    int          rmdir(const char *)
+    int          setegid(gid_t)
+    int          seteuid(uid_t)
+    int          setgid(gid_t)
+    int          setpgid(pid_t, pid_t)
+    pid_t        setpgrp()
+    int          setregid(gid_t, gid_t)
+    int          setreuid(uid_t, uid_t)
+    pid_t        setsid()
+    int          setuid(uid_t)
+    unsigned     sleep(unsigned)
+    void         swab(const void *, void *, ssize_t)
+    int          symlink(const char *, const char *)
+    void         sync()
+    long         sysconf(int)
+    pid_t        tcgetpgrp(int)
+    int          tcsetpgrp(int, pid_t)
+    int          truncate(const char *, off_t)
+    char        *ttyname(int)
+    int          ttyname_r(int, char *, size_t)
+    useconds_t   ualarm(useconds_t, useconds_t)
+    int          unlink(const char *)
+    int          usleep(useconds_t)
+    pid_t        vfork()
+    ssize_t      write(int, const void *, size_t)
+    char         *optarg
+    int          optind
+    int          opterr
+    int          optopt
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/wait.pxd b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/wait.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..f30be06df2cc70818f09c73108ef87b3de052dbb
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/Cython/Includes/posix/wait.pxd
@@ -0,0 +1,38 @@
+# https://pubs.opengroup.org/onlinepubs/9699919799/basedefs/sys_wait.h.html
+
+from posix.types cimport pid_t, id_t
+from posix.signal cimport siginfo_t
+from posix.resource cimport rusage
+
+cdef extern from "<sys/wait.h>" nogil:
+    enum: WNOHANG
+    enum: WUNTRACED
+    enum: WCONTINUED
+    enum: WEXITED
+    enum: WSTOPPED
+    enum: WNOWAIT
+
+    int WEXITSTATUS(int status)
+    int WIFCONTINUED(int status)
+    int WIFEXITED(int status)
+    int WIFSIGNALED(int status)
+    int WIFSTOPPED(int status)
+    int WSTOPSIG(int status)
+    int WTERMSIG(int status)
+
+    ctypedef int idtype_t
+    enum: P_ALL             # idtype_t values
+    enum: P_PID
+    enum: P_PGID
+
+    pid_t wait(int *stat_loc)
+    pid_t waitpid(pid_t pid, int *status, int options)
+    int waitid(idtype_t idtype, id_t id, siginfo_t *infop, int options)
+
+# wait3 was in POSIX until 2008 while wait4 was never standardized.
+# Even so, these calls are in almost every Unix, always in sys/wait.h.
+# Hence, posix.wait is the least surprising place to declare them for Cython.
+# libc may require _XXX_SOURCE to be defined at C-compile time to provide them.
+
+    pid_t wait3(int *status, int options, rusage *rusage)
+    pid_t wait4(pid_t pid, int *status, int options, rusage *rusage)
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/assortativity/neighbor_degree.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/assortativity/neighbor_degree.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8980da766f1e63e06990b35a3b403df5486cd50
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/assortativity/neighbor_degree.py
@@ -0,0 +1,160 @@
+import networkx as nx
+
+__all__ = ["average_neighbor_degree"]
+
+
+@nx._dispatch(edge_attrs="weight")
+def average_neighbor_degree(G, source="out", target="out", nodes=None, weight=None):
+    r"""Returns the average degree of the neighborhood of each node.
+
+    In an undirected graph, the neighborhood `N(i)` of node `i` contains the
+    nodes that are connected to `i` by an edge.
+
+    For directed graphs, `N(i)` is defined according to the parameter `source`:
+
+        - if source is 'in', then `N(i)` consists of predecessors of node `i`.
+        - if source is 'out', then `N(i)` consists of successors of node `i`.
+        - if source is 'in+out', then `N(i)` is both predecessors and successors.
+
+    The average neighborhood degree of a node `i` is
+
+    .. math::
+
+        k_{nn,i} = \frac{1}{|N(i)|} \sum_{j \in N(i)} k_j
+
+    where `N(i)` are the neighbors of node `i` and `k_j` is
+    the degree of node `j` which belongs to `N(i)`. For weighted
+    graphs, an analogous measure can be defined [1]_,
+
+    .. math::
+
+        k_{nn,i}^{w} = \frac{1}{s_i} \sum_{j \in N(i)} w_{ij} k_j
+
+    where `s_i` is the weighted degree of node `i`, `w_{ij}`
+    is the weight of the edge that links `i` and `j` and
+    `N(i)` are the neighbors of node `i`.
+
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : string ("in"|"out"|"in+out"), optional (default="out")
+       Directed graphs only.
+       Use "in"- or "out"-neighbors of source node.
+
+    target : string ("in"|"out"|"in+out"), optional (default="out")
+       Directed graphs only.
+       Use "in"- or "out"-degree for target node.
+
+    nodes : list or iterable, optional (default=G.nodes)
+        Compute neighbor degree only for specified nodes.
+
+    weight : string or None, optional (default=None)
+       The edge attribute that holds the numerical value used as a weight.
+       If None, then each edge has weight 1.
+
+    Returns
+    -------
+    d: dict
+       A dictionary keyed by node to the average degree of its neighbors.
+
+    Raises
+    ------
+    NetworkXError
+        If either `source` or `target` are not one of 'in', 'out', or 'in+out'.
+        If either `source` or `target` is passed for an undirected graph.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> G.edges[0, 1]["weight"] = 5
+    >>> G.edges[2, 3]["weight"] = 3
+
+    >>> nx.average_neighbor_degree(G)
+    {0: 2.0, 1: 1.5, 2: 1.5, 3: 2.0}
+    >>> nx.average_neighbor_degree(G, weight="weight")
+    {0: 2.0, 1: 1.1666666666666667, 2: 1.25, 3: 2.0}
+
+    >>> G = nx.DiGraph()
+    >>> nx.add_path(G, [0, 1, 2, 3])
+    >>> nx.average_neighbor_degree(G, source="in", target="in")
+    {0: 0.0, 1: 0.0, 2: 1.0, 3: 1.0}
+
+    >>> nx.average_neighbor_degree(G, source="out", target="out")
+    {0: 1.0, 1: 1.0, 2: 0.0, 3: 0.0}
+
+    See Also
+    --------
+    average_degree_connectivity
+
+    References
+    ----------
+    .. [1] A. Barrat, M. Barthélemy, R. Pastor-Satorras, and A. Vespignani,
+       "The architecture of complex weighted networks".
+       PNAS 101 (11): 3747–3752 (2004).
+    """
+    if G.is_directed():
+        if source == "in":
+            source_degree = G.in_degree
+        elif source == "out":
+            source_degree = G.out_degree
+        elif source == "in+out":
+            source_degree = G.degree
+        else:
+            raise nx.NetworkXError(
+                f"source argument {source} must be 'in', 'out' or 'in+out'"
+            )
+
+        if target == "in":
+            target_degree = G.in_degree
+        elif target == "out":
+            target_degree = G.out_degree
+        elif target == "in+out":
+            target_degree = G.degree
+        else:
+            raise nx.NetworkXError(
+                f"target argument {target} must be 'in', 'out' or 'in+out'"
+            )
+    else:
+        if source != "out" or target != "out":
+            raise nx.NetworkXError(
+                f"source and target arguments are only supported for directed graphs"
+            )
+        source_degree = target_degree = G.degree
+
+    # precompute target degrees -- should *not* be weighted degree
+    t_deg = dict(target_degree())
+
+    # Set up both predecessor and successor neighbor dicts leaving empty if not needed
+    G_P = G_S = {n: {} for n in G}
+    if G.is_directed():
+        # "in" or "in+out" cases: G_P contains predecessors
+        if "in" in source:
+            G_P = G.pred
+        # "out" or "in+out" cases: G_S contains successors
+        if "out" in source:
+            G_S = G.succ
+    else:
+        # undirected leave G_P empty but G_S is the adjacency
+        G_S = G.adj
+
+    # Main loop: Compute average degree of neighbors
+    avg = {}
+    for n, deg in source_degree(nodes, weight=weight):
+        # handle degree zero average
+        if deg == 0:
+            avg[n] = 0.0
+            continue
+
+        # we sum over both G_P and G_S, but one of the two is usually empty.
+        if weight is None:
+            avg[n] = (
+                sum(t_deg[nbr] for nbr in G_S[n]) + sum(t_deg[nbr] for nbr in G_P[n])
+            ) / deg
+        else:
+            avg[n] = (
+                sum(dd.get(weight, 1) * t_deg[nbr] for nbr, dd in G_S[n].items())
+                + sum(dd.get(weight, 1) * t_deg[nbr] for nbr, dd in G_P[n].items())
+            ) / deg
+    return avg
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@@ -0,0 +1,505 @@
+"""
+Equitable coloring of graphs with bounded degree.
+"""
+
+from collections import defaultdict
+
+import networkx as nx
+
+__all__ = ["equitable_color"]
+
+
+@nx._dispatch
+def is_coloring(G, coloring):
+    """Determine if the coloring is a valid coloring for the graph G."""
+    # Verify that the coloring is valid.
+    return all(coloring[s] != coloring[d] for s, d in G.edges)
+
+
+@nx._dispatch
+def is_equitable(G, coloring, num_colors=None):
+    """Determines if the coloring is valid and equitable for the graph G."""
+
+    if not is_coloring(G, coloring):
+        return False
+
+    # Verify whether it is equitable.
+    color_set_size = defaultdict(int)
+    for color in coloring.values():
+        color_set_size[color] += 1
+
+    if num_colors is not None:
+        for color in range(num_colors):
+            if color not in color_set_size:
+                # These colors do not have any vertices attached to them.
+                color_set_size[color] = 0
+
+    # If there are more than 2 distinct values, the coloring cannot be equitable
+    all_set_sizes = set(color_set_size.values())
+    if len(all_set_sizes) == 0 and num_colors is None:  # Was an empty graph
+        return True
+    elif len(all_set_sizes) == 1:
+        return True
+    elif len(all_set_sizes) == 2:
+        a, b = list(all_set_sizes)
+        return abs(a - b) <= 1
+    else:  # len(all_set_sizes) > 2:
+        return False
+
+
+def make_C_from_F(F):
+    C = defaultdict(list)
+    for node, color in F.items():
+        C[color].append(node)
+
+    return C
+
+
+def make_N_from_L_C(L, C):
+    nodes = L.keys()
+    colors = C.keys()
+    return {
+        (node, color): sum(1 for v in L[node] if v in C[color])
+        for node in nodes
+        for color in colors
+    }
+
+
+def make_H_from_C_N(C, N):
+    return {
+        (c1, c2): sum(1 for node in C[c1] if N[(node, c2)] == 0) for c1 in C for c2 in C
+    }
+
+
+def change_color(u, X, Y, N, H, F, C, L):
+    """Change the color of 'u' from X to Y and update N, H, F, C."""
+    assert F[u] == X and X != Y
+
+    # Change the class of 'u' from X to Y
+    F[u] = Y
+
+    for k in C:
+        # 'u' witnesses an edge from k -> Y instead of from k -> X now.
+        if N[u, k] == 0:
+            H[(X, k)] -= 1
+            H[(Y, k)] += 1
+
+    for v in L[u]:
+        # 'v' has lost a neighbor in X and gained one in Y
+        N[(v, X)] -= 1
+        N[(v, Y)] += 1
+
+        if N[(v, X)] == 0:
+            # 'v' witnesses F[v] -> X
+            H[(F[v], X)] += 1
+
+        if N[(v, Y)] == 1:
+            # 'v' no longer witnesses F[v] -> Y
+            H[(F[v], Y)] -= 1
+
+    C[X].remove(u)
+    C[Y].append(u)
+
+
+def move_witnesses(src_color, dst_color, N, H, F, C, T_cal, L):
+    """Move witness along a path from src_color to dst_color."""
+    X = src_color
+    while X != dst_color:
+        Y = T_cal[X]
+        # Move _any_ witness from X to Y = T_cal[X]
+        w = next(x for x in C[X] if N[(x, Y)] == 0)
+        change_color(w, X, Y, N=N, H=H, F=F, C=C, L=L)
+        X = Y
+
+
+@nx._dispatch
+def pad_graph(G, num_colors):
+    """Add a disconnected complete clique K_p such that the number of nodes in
+    the graph becomes a multiple of `num_colors`.
+
+    Assumes that the graph's nodes are labelled using integers.
+
+    Returns the number of nodes with each color.
+    """
+
+    n_ = len(G)
+    r = num_colors - 1
+
+    # Ensure that the number of nodes in G is a multiple of (r + 1)
+    s = n_ // (r + 1)
+    if n_ != s * (r + 1):
+        p = (r + 1) - n_ % (r + 1)
+        s += 1
+
+        # Complete graph K_p between (imaginary) nodes [n_, ... , n_ + p]
+        K = nx.relabel_nodes(nx.complete_graph(p), {idx: idx + n_ for idx in range(p)})
+        G.add_edges_from(K.edges)
+
+    return s
+
+
+def procedure_P(V_minus, V_plus, N, H, F, C, L, excluded_colors=None):
+    """Procedure P as described in the paper."""
+
+    if excluded_colors is None:
+        excluded_colors = set()
+
+    A_cal = set()
+    T_cal = {}
+    R_cal = []
+
+    # BFS to determine A_cal, i.e. colors reachable from V-
+    reachable = [V_minus]
+    marked = set(reachable)
+    idx = 0
+
+    while idx < len(reachable):
+        pop = reachable[idx]
+        idx += 1
+
+        A_cal.add(pop)
+        R_cal.append(pop)
+
+        # TODO: Checking whether a color has been visited can be made faster by
+        # using a look-up table instead of testing for membership in a set by a
+        # logarithmic factor.
+        next_layer = []
+        for k in C:
+            if (
+                H[(k, pop)] > 0
+                and k not in A_cal
+                and k not in excluded_colors
+                and k not in marked
+            ):
+                next_layer.append(k)
+
+        for dst in next_layer:
+            # Record that `dst` can reach `pop`
+            T_cal[dst] = pop
+
+        marked.update(next_layer)
+        reachable.extend(next_layer)
+
+    # Variables for the algorithm
+    b = len(C) - len(A_cal)
+
+    if V_plus in A_cal:
+        # Easy case: V+ is in A_cal
+        # Move one node from V+ to V- using T_cal to find the parents.
+        move_witnesses(V_plus, V_minus, N=N, H=H, F=F, C=C, T_cal=T_cal, L=L)
+    else:
+        # If there is a solo edge, we can resolve the situation by
+        # moving witnesses from B to A, making G[A] equitable and then
+        # recursively balancing G[B - w] with a different V_minus and
+        # but the same V_plus.
+
+        A_0 = set()
+        A_cal_0 = set()
+        num_terminal_sets_found = 0
+        made_equitable = False
+
+        for W_1 in R_cal[::-1]:
+            for v in C[W_1]:
+                X = None
+
+                for U in C:
+                    if N[(v, U)] == 0 and U in A_cal and U != W_1:
+                        X = U
+
+                # v does not witness an edge in H[A_cal]
+                if X is None:
+                    continue
+
+                for U in C:
+                    # Note: Departing from the paper here.
+                    if N[(v, U)] >= 1 and U not in A_cal:
+                        X_prime = U
+                        w = v
+
+                        try:
+                            # Finding the solo neighbor of w in X_prime
+                            y = next(
+                                node
+                                for node in L[w]
+                                if F[node] == X_prime and N[(node, W_1)] == 1
+                            )
+                        except StopIteration:
+                            pass
+                        else:
+                            W = W_1
+
+                            # Move w from W to X, now X has one extra node.
+                            change_color(w, W, X, N=N, H=H, F=F, C=C, L=L)
+
+                            # Move witness from X to V_minus, making the coloring
+                            # equitable.
+                            move_witnesses(
+                                src_color=X,
+                                dst_color=V_minus,
+                                N=N,
+                                H=H,
+                                F=F,
+                                C=C,
+                                T_cal=T_cal,
+                                L=L,
+                            )
+
+                            # Move y from X_prime to W, making W the correct size.
+                            change_color(y, X_prime, W, N=N, H=H, F=F, C=C, L=L)
+
+                            # Then call the procedure on G[B - y]
+                            procedure_P(
+                                V_minus=X_prime,
+                                V_plus=V_plus,
+                                N=N,
+                                H=H,
+                                C=C,
+                                F=F,
+                                L=L,
+                                excluded_colors=excluded_colors.union(A_cal),
+                            )
+                            made_equitable = True
+                            break
+
+                if made_equitable:
+                    break
+            else:
+                # No node in W_1 was found such that
+                # it had a solo-neighbor.
+                A_cal_0.add(W_1)
+                A_0.update(C[W_1])
+                num_terminal_sets_found += 1
+
+            if num_terminal_sets_found == b:
+                # Otherwise, construct the maximal independent set and find
+                # a pair of z_1, z_2 as in Case II.
+
+                # BFS to determine B_cal': the set of colors reachable from V+
+                B_cal_prime = set()
+                T_cal_prime = {}
+
+                reachable = [V_plus]
+                marked = set(reachable)
+                idx = 0
+                while idx < len(reachable):
+                    pop = reachable[idx]
+                    idx += 1
+
+                    B_cal_prime.add(pop)
+
+                    # No need to check for excluded_colors here because
+                    # they only exclude colors from A_cal
+                    next_layer = [
+                        k
+                        for k in C
+                        if H[(pop, k)] > 0 and k not in B_cal_prime and k not in marked
+                    ]
+
+                    for dst in next_layer:
+                        T_cal_prime[pop] = dst
+
+                    marked.update(next_layer)
+                    reachable.extend(next_layer)
+
+                # Construct the independent set of G[B']
+                I_set = set()
+                I_covered = set()
+                W_covering = {}
+
+                B_prime = [node for k in B_cal_prime for node in C[k]]
+
+                # Add the nodes in V_plus to I first.
+                for z in C[V_plus] + B_prime:
+                    if z in I_covered or F[z] not in B_cal_prime:
+                        continue
+
+                    I_set.add(z)
+                    I_covered.add(z)
+                    I_covered.update(list(L[z]))
+
+                    for w in L[z]:
+                        if F[w] in A_cal_0 and N[(z, F[w])] == 1:
+                            if w not in W_covering:
+                                W_covering[w] = z
+                            else:
+                                # Found z1, z2 which have the same solo
+                                # neighbor in some W
+                                z_1 = W_covering[w]
+                                # z_2 = z
+
+                                Z = F[z_1]
+                                W = F[w]
+
+                                # shift nodes along W, V-
+                                move_witnesses(
+                                    W, V_minus, N=N, H=H, F=F, C=C, T_cal=T_cal, L=L
+                                )
+
+                                # shift nodes along V+ to Z
+                                move_witnesses(
+                                    V_plus,
+                                    Z,
+                                    N=N,
+                                    H=H,
+                                    F=F,
+                                    C=C,
+                                    T_cal=T_cal_prime,
+                                    L=L,
+                                )
+
+                                # change color of z_1 to W
+                                change_color(z_1, Z, W, N=N, H=H, F=F, C=C, L=L)
+
+                                # change color of w to some color in B_cal
+                                W_plus = next(
+                                    k for k in C if N[(w, k)] == 0 and k not in A_cal
+                                )
+                                change_color(w, W, W_plus, N=N, H=H, F=F, C=C, L=L)
+
+                                # recurse with G[B \cup W*]
+                                excluded_colors.update(
+                                    [k for k in C if k != W and k not in B_cal_prime]
+                                )
+                                procedure_P(
+                                    V_minus=W,
+                                    V_plus=W_plus,
+                                    N=N,
+                                    H=H,
+                                    C=C,
+                                    F=F,
+                                    L=L,
+                                    excluded_colors=excluded_colors,
+                                )
+
+                                made_equitable = True
+                                break
+
+                    if made_equitable:
+                        break
+                else:
+                    assert False, (
+                        "Must find a w which is the solo neighbor "
+                        "of two vertices in B_cal_prime."
+                    )
+
+            if made_equitable:
+                break
+
+
+@nx._dispatch
+def equitable_color(G, num_colors):
+    """Provides an equitable coloring for nodes of `G`.
+
+    Attempts to color a graph using `num_colors` colors, where no neighbors of
+    a node can have same color as the node itself and the number of nodes with
+    each color differ by at most 1. `num_colors` must be greater than the
+    maximum degree of `G`. The algorithm is described in [1]_ and has
+    complexity O(num_colors * n**2).
+
+    Parameters
+    ----------
+    G : networkX graph
+       The nodes of this graph will be colored.
+
+    num_colors : number of colors to use
+       This number must be at least one more than the maximum degree of nodes
+       in the graph.
+
+    Returns
+    -------
+    A dictionary with keys representing nodes and values representing
+    corresponding coloring.
+
+    Examples
+    --------
+    >>> G = nx.cycle_graph(4)
+    >>> nx.coloring.equitable_color(G, num_colors=3)  # doctest: +SKIP
+    {0: 2, 1: 1, 2: 2, 3: 0}
+
+    Raises
+    ------
+    NetworkXAlgorithmError
+        If `num_colors` is not at least the maximum degree of the graph `G`
+
+    References
+    ----------
+    .. [1] Kierstead, H. A., Kostochka, A. V., Mydlarz, M., & Szemerédi, E.
+        (2010). A fast algorithm for equitable coloring. Combinatorica, 30(2),
+        217-224.
+    """
+
+    # Map nodes to integers for simplicity later.
+    nodes_to_int = {}
+    int_to_nodes = {}
+
+    for idx, node in enumerate(G.nodes):
+        nodes_to_int[node] = idx
+        int_to_nodes[idx] = node
+
+    G = nx.relabel_nodes(G, nodes_to_int, copy=True)
+
+    # Basic graph statistics and sanity check.
+    if len(G.nodes) > 0:
+        r_ = max(G.degree(node) for node in G.nodes)
+    else:
+        r_ = 0
+
+    if r_ >= num_colors:
+        raise nx.NetworkXAlgorithmError(
+            f"Graph has maximum degree {r_}, needs "
+            f"{r_ + 1} (> {num_colors}) colors for guaranteed coloring."
+        )
+
+    # Ensure that the number of nodes in G is a multiple of (r + 1)
+    pad_graph(G, num_colors)
+
+    # Starting the algorithm.
+    # L = {node: list(G.neighbors(node)) for node in G.nodes}
+    L_ = {node: [] for node in G.nodes}
+
+    # Arbitrary equitable allocation of colors to nodes.
+    F = {node: idx % num_colors for idx, node in enumerate(G.nodes)}
+
+    C = make_C_from_F(F)
+
+    # The neighborhood is empty initially.
+    N = make_N_from_L_C(L_, C)
+
+    # Currently all nodes witness all edges.
+    H = make_H_from_C_N(C, N)
+
+    # Start of algorithm.
+    edges_seen = set()
+
+    for u in sorted(G.nodes):
+        for v in sorted(G.neighbors(u)):
+            # Do not double count edges if (v, u) has already been seen.
+            if (v, u) in edges_seen:
+                continue
+
+            edges_seen.add((u, v))
+
+            L_[u].append(v)
+            L_[v].append(u)
+
+            N[(u, F[v])] += 1
+            N[(v, F[u])] += 1
+
+            if F[u] != F[v]:
+                # Were 'u' and 'v' witnesses for F[u] -> F[v] or F[v] -> F[u]?
+                if N[(u, F[v])] == 1:
+                    H[F[u], F[v]] -= 1  # u cannot witness an edge between F[u], F[v]
+
+                if N[(v, F[u])] == 1:
+                    H[F[v], F[u]] -= 1  # v cannot witness an edge between F[v], F[u]
+
+        if N[(u, F[u])] != 0:
+            # Find the first color where 'u' does not have any neighbors.
+            Y = next(k for k in C if N[(u, k)] == 0)
+            X = F[u]
+            change_color(u, X, Y, N=N, H=H, F=F, C=C, L=L_)
+
+            # Procedure P
+            procedure_P(V_minus=X, V_plus=Y, N=N, H=H, F=F, C=C, L=L_)
+
+    return {int_to_nodes[x]: F[x] for x in int_to_nodes}
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diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/coloring/tests/test_coloring.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/coloring/tests/test_coloring.py
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--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/coloring/tests/test_coloring.py
@@ -0,0 +1,865 @@
+"""Greedy coloring test suite.
+
+"""
+
+import itertools
+
+import pytest
+
+import networkx as nx
+
+is_coloring = nx.algorithms.coloring.equitable_coloring.is_coloring
+is_equitable = nx.algorithms.coloring.equitable_coloring.is_equitable
+
+
+ALL_STRATEGIES = [
+    "largest_first",
+    "random_sequential",
+    "smallest_last",
+    "independent_set",
+    "connected_sequential_bfs",
+    "connected_sequential_dfs",
+    "connected_sequential",
+    "saturation_largest_first",
+    "DSATUR",
+]
+
+# List of strategies where interchange=True results in an error
+INTERCHANGE_INVALID = ["independent_set", "saturation_largest_first", "DSATUR"]
+
+
+class TestColoring:
+    def test_basic_cases(self):
+        def check_basic_case(graph_func, n_nodes, strategy, interchange):
+            graph = graph_func()
+            coloring = nx.coloring.greedy_color(
+                graph, strategy=strategy, interchange=interchange
+            )
+            assert verify_length(coloring, n_nodes)
+            assert verify_coloring(graph, coloring)
+
+        for graph_func, n_nodes in BASIC_TEST_CASES.items():
+            for interchange in [True, False]:
+                for strategy in ALL_STRATEGIES:
+                    check_basic_case(graph_func, n_nodes, strategy, False)
+                    if strategy not in INTERCHANGE_INVALID:
+                        check_basic_case(graph_func, n_nodes, strategy, True)
+
+    def test_special_cases(self):
+        def check_special_case(strategy, graph_func, interchange, colors):
+            graph = graph_func()
+            coloring = nx.coloring.greedy_color(
+                graph, strategy=strategy, interchange=interchange
+            )
+            if not hasattr(colors, "__len__"):
+                colors = [colors]
+            assert any(verify_length(coloring, n_colors) for n_colors in colors)
+            assert verify_coloring(graph, coloring)
+
+        for strategy, arglist in SPECIAL_TEST_CASES.items():
+            for args in arglist:
+                check_special_case(strategy, args[0], args[1], args[2])
+
+    def test_interchange_invalid(self):
+        graph = one_node_graph()
+        for strategy in INTERCHANGE_INVALID:
+            pytest.raises(
+                nx.NetworkXPointlessConcept,
+                nx.coloring.greedy_color,
+                graph,
+                strategy=strategy,
+                interchange=True,
+            )
+
+    def test_bad_inputs(self):
+        graph = one_node_graph()
+        pytest.raises(
+            nx.NetworkXError,
+            nx.coloring.greedy_color,
+            graph,
+            strategy="invalid strategy",
+        )
+
+    def test_strategy_as_function(self):
+        graph = lf_shc()
+        colors_1 = nx.coloring.greedy_color(graph, "largest_first")
+        colors_2 = nx.coloring.greedy_color(graph, nx.coloring.strategy_largest_first)
+        assert colors_1 == colors_2
+
+    def test_seed_argument(self):
+        graph = lf_shc()
+        rs = nx.coloring.strategy_random_sequential
+        c1 = nx.coloring.greedy_color(graph, lambda g, c: rs(g, c, seed=1))
+        for u, v in graph.edges:
+            assert c1[u] != c1[v]
+
+    def test_is_coloring(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (1, 2)])
+        coloring = {0: 0, 1: 1, 2: 0}
+        assert is_coloring(G, coloring)
+
+        coloring[0] = 1
+        assert not is_coloring(G, coloring)
+        assert not is_equitable(G, coloring)
+
+    def test_is_equitable(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (1, 2)])
+        coloring = {0: 0, 1: 1, 2: 0}
+        assert is_equitable(G, coloring)
+
+        G.add_edges_from([(2, 3), (2, 4), (2, 5)])
+        coloring[3] = 1
+        coloring[4] = 1
+        coloring[5] = 1
+        assert is_coloring(G, coloring)
+        assert not is_equitable(G, coloring)
+
+    def test_num_colors(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (0, 3)])
+        pytest.raises(nx.NetworkXAlgorithmError, nx.coloring.equitable_color, G, 2)
+
+    def test_equitable_color(self):
+        G = nx.fast_gnp_random_graph(n=10, p=0.2, seed=42)
+        coloring = nx.coloring.equitable_color(G, max_degree(G) + 1)
+        assert is_equitable(G, coloring)
+
+    def test_equitable_color_empty(self):
+        G = nx.empty_graph()
+        coloring = nx.coloring.equitable_color(G, max_degree(G) + 1)
+        assert is_equitable(G, coloring)
+
+    def test_equitable_color_large(self):
+        G = nx.fast_gnp_random_graph(100, 0.1, seed=42)
+        coloring = nx.coloring.equitable_color(G, max_degree(G) + 1)
+        assert is_equitable(G, coloring, num_colors=max_degree(G) + 1)
+
+    def test_case_V_plus_not_in_A_cal(self):
+        # Hand crafted case to avoid the easy case.
+        L = {
+            0: [2, 5],
+            1: [3, 4],
+            2: [0, 8],
+            3: [1, 7],
+            4: [1, 6],
+            5: [0, 6],
+            6: [4, 5],
+            7: [3],
+            8: [2],
+        }
+
+        F = {
+            # Color 0
+            0: 0,
+            1: 0,
+            # Color 1
+            2: 1,
+            3: 1,
+            4: 1,
+            5: 1,
+            # Color 2
+            6: 2,
+            7: 2,
+            8: 2,
+        }
+
+        C = nx.algorithms.coloring.equitable_coloring.make_C_from_F(F)
+        N = nx.algorithms.coloring.equitable_coloring.make_N_from_L_C(L, C)
+        H = nx.algorithms.coloring.equitable_coloring.make_H_from_C_N(C, N)
+
+        nx.algorithms.coloring.equitable_coloring.procedure_P(
+            V_minus=0, V_plus=1, N=N, H=H, F=F, C=C, L=L
+        )
+        check_state(L=L, N=N, H=H, F=F, C=C)
+
+    def test_cast_no_solo(self):
+        L = {
+            0: [8, 9],
+            1: [10, 11],
+            2: [8],
+            3: [9],
+            4: [10, 11],
+            5: [8],
+            6: [9],
+            7: [10, 11],
+            8: [0, 2, 5],
+            9: [0, 3, 6],
+            10: [1, 4, 7],
+            11: [1, 4, 7],
+        }
+
+        F = {0: 0, 1: 0, 2: 2, 3: 2, 4: 2, 5: 3, 6: 3, 7: 3, 8: 1, 9: 1, 10: 1, 11: 1}
+
+        C = nx.algorithms.coloring.equitable_coloring.make_C_from_F(F)
+        N = nx.algorithms.coloring.equitable_coloring.make_N_from_L_C(L, C)
+        H = nx.algorithms.coloring.equitable_coloring.make_H_from_C_N(C, N)
+
+        nx.algorithms.coloring.equitable_coloring.procedure_P(
+            V_minus=0, V_plus=1, N=N, H=H, F=F, C=C, L=L
+        )
+        check_state(L=L, N=N, H=H, F=F, C=C)
+
+    def test_hard_prob(self):
+        # Tests for two levels of recursion.
+        num_colors, s = 5, 5
+
+        G = nx.Graph()
+        G.add_edges_from(
+            [
+                (0, 10),
+                (0, 11),
+                (0, 12),
+                (0, 23),
+                (10, 4),
+                (10, 9),
+                (10, 20),
+                (11, 4),
+                (11, 8),
+                (11, 16),
+                (12, 9),
+                (12, 22),
+                (12, 23),
+                (23, 7),
+                (1, 17),
+                (1, 18),
+                (1, 19),
+                (1, 24),
+                (17, 5),
+                (17, 13),
+                (17, 22),
+                (18, 5),
+                (19, 5),
+                (19, 6),
+                (19, 8),
+                (24, 7),
+                (24, 16),
+                (2, 4),
+                (2, 13),
+                (2, 14),
+                (2, 15),
+                (4, 6),
+                (13, 5),
+                (13, 21),
+                (14, 6),
+                (14, 15),
+                (15, 6),
+                (15, 21),
+                (3, 16),
+                (3, 20),
+                (3, 21),
+                (3, 22),
+                (16, 8),
+                (20, 8),
+                (21, 9),
+                (22, 7),
+            ]
+        )
+        F = {node: node // s for node in range(num_colors * s)}
+        F[s - 1] = num_colors - 1
+
+        params = make_params_from_graph(G=G, F=F)
+
+        nx.algorithms.coloring.equitable_coloring.procedure_P(
+            V_minus=0, V_plus=num_colors - 1, **params
+        )
+        check_state(**params)
+
+    def test_hardest_prob(self):
+        # Tests for two levels of recursion.
+        num_colors, s = 10, 4
+
+        G = nx.Graph()
+        G.add_edges_from(
+            [
+                (0, 19),
+                (0, 24),
+                (0, 29),
+                (0, 30),
+                (0, 35),
+                (19, 3),
+                (19, 7),
+                (19, 9),
+                (19, 15),
+                (19, 21),
+                (19, 24),
+                (19, 30),
+                (19, 38),
+                (24, 5),
+                (24, 11),
+                (24, 13),
+                (24, 20),
+                (24, 30),
+                (24, 37),
+                (24, 38),
+                (29, 6),
+                (29, 10),
+                (29, 13),
+                (29, 15),
+                (29, 16),
+                (29, 17),
+                (29, 20),
+                (29, 26),
+                (30, 6),
+                (30, 10),
+                (30, 15),
+                (30, 22),
+                (30, 23),
+                (30, 39),
+                (35, 6),
+                (35, 9),
+                (35, 14),
+                (35, 18),
+                (35, 22),
+                (35, 23),
+                (35, 25),
+                (35, 27),
+                (1, 20),
+                (1, 26),
+                (1, 31),
+                (1, 34),
+                (1, 38),
+                (20, 4),
+                (20, 8),
+                (20, 14),
+                (20, 18),
+                (20, 28),
+                (20, 33),
+                (26, 7),
+                (26, 10),
+                (26, 14),
+                (26, 18),
+                (26, 21),
+                (26, 32),
+                (26, 39),
+                (31, 5),
+                (31, 8),
+                (31, 13),
+                (31, 16),
+                (31, 17),
+                (31, 21),
+                (31, 25),
+                (31, 27),
+                (34, 7),
+                (34, 8),
+                (34, 13),
+                (34, 18),
+                (34, 22),
+                (34, 23),
+                (34, 25),
+                (34, 27),
+                (38, 4),
+                (38, 9),
+                (38, 12),
+                (38, 14),
+                (38, 21),
+                (38, 27),
+                (2, 3),
+                (2, 18),
+                (2, 21),
+                (2, 28),
+                (2, 32),
+                (2, 33),
+                (2, 36),
+                (2, 37),
+                (2, 39),
+                (3, 5),
+                (3, 9),
+                (3, 13),
+                (3, 22),
+                (3, 23),
+                (3, 25),
+                (3, 27),
+                (18, 6),
+                (18, 11),
+                (18, 15),
+                (18, 39),
+                (21, 4),
+                (21, 10),
+                (21, 14),
+                (21, 36),
+                (28, 6),
+                (28, 10),
+                (28, 14),
+                (28, 16),
+                (28, 17),
+                (28, 25),
+                (28, 27),
+                (32, 5),
+                (32, 10),
+                (32, 12),
+                (32, 16),
+                (32, 17),
+                (32, 22),
+                (32, 23),
+                (33, 7),
+                (33, 10),
+                (33, 12),
+                (33, 16),
+                (33, 17),
+                (33, 25),
+                (33, 27),
+                (36, 5),
+                (36, 8),
+                (36, 15),
+                (36, 16),
+                (36, 17),
+                (36, 25),
+                (36, 27),
+                (37, 5),
+                (37, 11),
+                (37, 15),
+                (37, 16),
+                (37, 17),
+                (37, 22),
+                (37, 23),
+                (39, 7),
+                (39, 8),
+                (39, 15),
+                (39, 22),
+                (39, 23),
+            ]
+        )
+        F = {node: node // s for node in range(num_colors * s)}
+        F[s - 1] = num_colors - 1  # V- = 0, V+ = num_colors - 1
+
+        params = make_params_from_graph(G=G, F=F)
+
+        nx.algorithms.coloring.equitable_coloring.procedure_P(
+            V_minus=0, V_plus=num_colors - 1, **params
+        )
+        check_state(**params)
+
+    def test_strategy_saturation_largest_first(self):
+        def color_remaining_nodes(
+            G,
+            colored_nodes,
+            full_color_assignment=None,
+            nodes_to_add_between_calls=1,
+        ):
+            color_assignments = []
+            aux_colored_nodes = colored_nodes.copy()
+
+            node_iterator = nx.algorithms.coloring.greedy_coloring.strategy_saturation_largest_first(
+                G, aux_colored_nodes
+            )
+
+            for u in node_iterator:
+                # Set to keep track of colors of neighbours
+                neighbour_colors = {
+                    aux_colored_nodes[v] for v in G[u] if v in aux_colored_nodes
+                }
+                # Find the first unused color.
+                for color in itertools.count():
+                    if color not in neighbour_colors:
+                        break
+                aux_colored_nodes[u] = color
+                color_assignments.append((u, color))
+
+                # Color nodes between iterations
+                for i in range(nodes_to_add_between_calls - 1):
+                    if not len(color_assignments) + len(colored_nodes) >= len(
+                        full_color_assignment
+                    ):
+                        full_color_assignment_node, color = full_color_assignment[
+                            len(color_assignments) + len(colored_nodes)
+                        ]
+
+                        # Assign the new color to the current node.
+                        aux_colored_nodes[full_color_assignment_node] = color
+                        color_assignments.append((full_color_assignment_node, color))
+
+            return color_assignments, aux_colored_nodes
+
+        for G, _, _ in SPECIAL_TEST_CASES["saturation_largest_first"]:
+            G = G()
+
+            # Check that function still works when nodes are colored between iterations
+            for nodes_to_add_between_calls in range(1, 5):
+                # Get a full color assignment, (including the order in which nodes were colored)
+                colored_nodes = {}
+                full_color_assignment, full_colored_nodes = color_remaining_nodes(
+                    G, colored_nodes
+                )
+
+                # For each node in the color assignment, add it to colored_nodes and re-run the function
+                for ind, (node, color) in enumerate(full_color_assignment):
+                    colored_nodes[node] = color
+
+                    (
+                        partial_color_assignment,
+                        partial_colored_nodes,
+                    ) = color_remaining_nodes(
+                        G,
+                        colored_nodes,
+                        full_color_assignment=full_color_assignment,
+                        nodes_to_add_between_calls=nodes_to_add_between_calls,
+                    )
+
+                    # Check that the color assignment and order of remaining nodes are the same
+                    assert full_color_assignment[ind + 1 :] == partial_color_assignment
+                    assert full_colored_nodes == partial_colored_nodes
+
+
+#  ############################  Utility functions ############################
+def verify_coloring(graph, coloring):
+    for node in graph.nodes():
+        if node not in coloring:
+            return False
+
+        color = coloring[node]
+        for neighbor in graph.neighbors(node):
+            if coloring[neighbor] == color:
+                return False
+
+    return True
+
+
+def verify_length(coloring, expected):
+    coloring = dict_to_sets(coloring)
+    return len(coloring) == expected
+
+
+def dict_to_sets(colors):
+    if len(colors) == 0:
+        return []
+
+    k = max(colors.values()) + 1
+    sets = [set() for _ in range(k)]
+
+    for node, color in colors.items():
+        sets[color].add(node)
+
+    return sets
+
+
+#  ############################  Graph Generation ############################
+
+
+def empty_graph():
+    return nx.Graph()
+
+
+def one_node_graph():
+    graph = nx.Graph()
+    graph.add_nodes_from([1])
+    return graph
+
+
+def two_node_graph():
+    graph = nx.Graph()
+    graph.add_nodes_from([1, 2])
+    graph.add_edges_from([(1, 2)])
+    return graph
+
+
+def three_node_clique():
+    graph = nx.Graph()
+    graph.add_nodes_from([1, 2, 3])
+    graph.add_edges_from([(1, 2), (1, 3), (2, 3)])
+    return graph
+
+
+def disconnected():
+    graph = nx.Graph()
+    graph.add_edges_from([(1, 2), (2, 3), (4, 5), (5, 6)])
+    return graph
+
+
+def rs_shc():
+    graph = nx.Graph()
+    graph.add_nodes_from([1, 2, 3, 4])
+    graph.add_edges_from([(1, 2), (2, 3), (3, 4)])
+    return graph
+
+
+def slf_shc():
+    graph = nx.Graph()
+    graph.add_nodes_from([1, 2, 3, 4, 5, 6, 7])
+    graph.add_edges_from(
+        [(1, 2), (1, 5), (1, 6), (2, 3), (2, 7), (3, 4), (3, 7), (4, 5), (4, 6), (5, 6)]
+    )
+    return graph
+
+
+def slf_hc():
+    graph = nx.Graph()
+    graph.add_nodes_from([1, 2, 3, 4, 5, 6, 7, 8])
+    graph.add_edges_from(
+        [
+            (1, 2),
+            (1, 3),
+            (1, 4),
+            (1, 5),
+            (2, 3),
+            (2, 4),
+            (2, 6),
+            (5, 7),
+            (5, 8),
+            (6, 7),
+            (6, 8),
+            (7, 8),
+        ]
+    )
+    return graph
+
+
+def lf_shc():
+    graph = nx.Graph()
+    graph.add_nodes_from([1, 2, 3, 4, 5, 6])
+    graph.add_edges_from([(6, 1), (1, 4), (4, 3), (3, 2), (2, 5)])
+    return graph
+
+
+def lf_hc():
+    graph = nx.Graph()
+    graph.add_nodes_from([1, 2, 3, 4, 5, 6, 7])
+    graph.add_edges_from(
+        [
+            (1, 7),
+            (1, 6),
+            (1, 3),
+            (1, 4),
+            (7, 2),
+            (2, 6),
+            (2, 3),
+            (2, 5),
+            (5, 3),
+            (5, 4),
+            (4, 3),
+        ]
+    )
+    return graph
+
+
+def sl_shc():
+    graph = nx.Graph()
+    graph.add_nodes_from([1, 2, 3, 4, 5, 6])
+    graph.add_edges_from(
+        [(1, 2), (1, 3), (2, 3), (1, 4), (2, 5), (3, 6), (4, 5), (4, 6), (5, 6)]
+    )
+    return graph
+
+
+def sl_hc():
+    graph = nx.Graph()
+    graph.add_nodes_from([1, 2, 3, 4, 5, 6, 7, 8])
+    graph.add_edges_from(
+        [
+            (1, 2),
+            (1, 3),
+            (1, 5),
+            (1, 7),
+            (2, 3),
+            (2, 4),
+            (2, 8),
+            (8, 4),
+            (8, 6),
+            (8, 7),
+            (7, 5),
+            (7, 6),
+            (3, 4),
+            (4, 6),
+            (6, 5),
+            (5, 3),
+        ]
+    )
+    return graph
+
+
+def gis_shc():
+    graph = nx.Graph()
+    graph.add_nodes_from([1, 2, 3, 4])
+    graph.add_edges_from([(1, 2), (2, 3), (3, 4)])
+    return graph
+
+
+def gis_hc():
+    graph = nx.Graph()
+    graph.add_nodes_from([1, 2, 3, 4, 5, 6])
+    graph.add_edges_from([(1, 5), (2, 5), (3, 6), (4, 6), (5, 6)])
+    return graph
+
+
+def cs_shc():
+    graph = nx.Graph()
+    graph.add_nodes_from([1, 2, 3, 4, 5])
+    graph.add_edges_from([(1, 2), (1, 5), (2, 3), (2, 4), (2, 5), (3, 4), (4, 5)])
+    return graph
+
+
+def rsi_shc():
+    graph = nx.Graph()
+    graph.add_nodes_from([1, 2, 3, 4, 5, 6])
+    graph.add_edges_from(
+        [(1, 2), (1, 5), (1, 6), (2, 3), (3, 4), (4, 5), (4, 6), (5, 6)]
+    )
+    return graph
+
+
+def lfi_shc():
+    graph = nx.Graph()
+    graph.add_nodes_from([1, 2, 3, 4, 5, 6, 7])
+    graph.add_edges_from(
+        [(1, 2), (1, 5), (1, 6), (2, 3), (2, 7), (3, 4), (3, 7), (4, 5), (4, 6), (5, 6)]
+    )
+    return graph
+
+
+def lfi_hc():
+    graph = nx.Graph()
+    graph.add_nodes_from([1, 2, 3, 4, 5, 6, 7, 8, 9])
+    graph.add_edges_from(
+        [
+            (1, 2),
+            (1, 5),
+            (1, 6),
+            (1, 7),
+            (2, 3),
+            (2, 8),
+            (2, 9),
+            (3, 4),
+            (3, 8),
+            (3, 9),
+            (4, 5),
+            (4, 6),
+            (4, 7),
+            (5, 6),
+        ]
+    )
+    return graph
+
+
+def sli_shc():
+    graph = nx.Graph()
+    graph.add_nodes_from([1, 2, 3, 4, 5, 6, 7])
+    graph.add_edges_from(
+        [
+            (1, 2),
+            (1, 3),
+            (1, 5),
+            (1, 7),
+            (2, 3),
+            (2, 6),
+            (3, 4),
+            (4, 5),
+            (4, 6),
+            (5, 7),
+            (6, 7),
+        ]
+    )
+    return graph
+
+
+def sli_hc():
+    graph = nx.Graph()
+    graph.add_nodes_from([1, 2, 3, 4, 5, 6, 7, 8, 9])
+    graph.add_edges_from(
+        [
+            (1, 2),
+            (1, 3),
+            (1, 4),
+            (1, 5),
+            (2, 3),
+            (2, 7),
+            (2, 8),
+            (2, 9),
+            (3, 6),
+            (3, 7),
+            (3, 9),
+            (4, 5),
+            (4, 6),
+            (4, 8),
+            (4, 9),
+            (5, 6),
+            (5, 7),
+            (5, 8),
+            (6, 7),
+            (6, 9),
+            (7, 8),
+            (8, 9),
+        ]
+    )
+    return graph
+
+
+# --------------------------------------------------------------------------
+# Basic tests for all strategies
+# For each basic graph function, specify the number of expected colors.
+BASIC_TEST_CASES = {
+    empty_graph: 0,
+    one_node_graph: 1,
+    two_node_graph: 2,
+    disconnected: 2,
+    three_node_clique: 3,
+}
+
+
+# --------------------------------------------------------------------------
+# Special test cases. Each strategy has a list of tuples of the form
+# (graph function, interchange, valid # of colors)
+SPECIAL_TEST_CASES = {
+    "random_sequential": [
+        (rs_shc, False, (2, 3)),
+        (rs_shc, True, 2),
+        (rsi_shc, True, (3, 4)),
+    ],
+    "saturation_largest_first": [(slf_shc, False, (3, 4)), (slf_hc, False, 4)],
+    "largest_first": [
+        (lf_shc, False, (2, 3)),
+        (lf_hc, False, 4),
+        (lf_shc, True, 2),
+        (lf_hc, True, 3),
+        (lfi_shc, True, (3, 4)),
+        (lfi_hc, True, 4),
+    ],
+    "smallest_last": [
+        (sl_shc, False, (3, 4)),
+        (sl_hc, False, 5),
+        (sl_shc, True, 3),
+        (sl_hc, True, 4),
+        (sli_shc, True, (3, 4)),
+        (sli_hc, True, 5),
+    ],
+    "independent_set": [(gis_shc, False, (2, 3)), (gis_hc, False, 3)],
+    "connected_sequential": [(cs_shc, False, (3, 4)), (cs_shc, True, 3)],
+    "connected_sequential_dfs": [(cs_shc, False, (3, 4))],
+}
+
+
+# --------------------------------------------------------------------------
+# Helper functions to test
+# (graph function, interchange, valid # of colors)
+
+
+def check_state(L, N, H, F, C):
+    s = len(C[0])
+    num_colors = len(C.keys())
+
+    assert all(u in L[v] for u in L for v in L[u])
+    assert all(F[u] != F[v] for u in L for v in L[u])
+    assert all(len(L[u]) < num_colors for u in L)
+    assert all(len(C[x]) == s for x in C)
+    assert all(H[(c1, c2)] >= 0 for c1 in C for c2 in C)
+    assert all(N[(u, F[u])] == 0 for u in F)
+
+
+def max_degree(G):
+    """Get the maximum degree of any node in G."""
+    return max(G.degree(node) for node in G.nodes) if len(G.nodes) > 0 else 0
+
+
+def make_params_from_graph(G, F):
+    """Returns {N, L, H, C} from the given graph."""
+    num_nodes = len(G)
+    L = {u: [] for u in range(num_nodes)}
+    for u, v in G.edges:
+        L[u].append(v)
+        L[v].append(u)
+
+    C = nx.algorithms.coloring.equitable_coloring.make_C_from_F(F)
+    N = nx.algorithms.coloring.equitable_coloring.make_N_from_L_C(L, C)
+    H = nx.algorithms.coloring.equitable_coloring.make_H_from_C_N(C, N)
+
+    return {"N": N, "F": F, "C": C, "H": H, "L": L}
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+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/connectivity/cuts.py
@@ -0,0 +1,615 @@
+"""
+Flow based cut algorithms
+"""
+import itertools
+
+import networkx as nx
+
+# Define the default maximum flow function to use in all flow based
+# cut algorithms.
+from networkx.algorithms.flow import build_residual_network, edmonds_karp
+
+default_flow_func = edmonds_karp
+
+from .utils import build_auxiliary_edge_connectivity, build_auxiliary_node_connectivity
+
+__all__ = [
+    "minimum_st_node_cut",
+    "minimum_node_cut",
+    "minimum_st_edge_cut",
+    "minimum_edge_cut",
+]
+
+
+@nx._dispatch(
+    graphs={"G": 0, "auxiliary?": 4, "residual?": 5},
+    preserve_edge_attrs={
+        "auxiliary": {"capacity": float("inf")},
+        "residual": {"capacity": float("inf")},
+    },
+    preserve_graph_attrs={"auxiliary", "residual"},
+)
+def minimum_st_edge_cut(G, s, t, flow_func=None, auxiliary=None, residual=None):
+    """Returns the edges of the cut-set of a minimum (s, t)-cut.
+
+    This function returns the set of edges of minimum cardinality that,
+    if removed, would destroy all paths among source and target in G.
+    Edge weights are not considered. See :meth:`minimum_cut` for
+    computing minimum cuts considering edge weights.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    s : node
+        Source node for the flow.
+
+    t : node
+        Sink node for the flow.
+
+    auxiliary : NetworkX DiGraph
+        Auxiliary digraph to compute flow based node connectivity. It has
+        to have a graph attribute called mapping with a dictionary mapping
+        node names in G and in the auxiliary digraph. If provided
+        it will be reused instead of recreated. Default value: None.
+
+    flow_func : function
+        A function for computing the maximum flow among a pair of nodes.
+        The function has to accept at least three parameters: a Digraph,
+        a source node, and a target node. And return a residual network
+        that follows NetworkX conventions (see :meth:`maximum_flow` for
+        details). If flow_func is None, the default maximum flow function
+        (:meth:`edmonds_karp`) is used. See :meth:`node_connectivity` for
+        details. The choice of the default function may change from version
+        to version and should not be relied on. Default value: None.
+
+    residual : NetworkX DiGraph
+        Residual network to compute maximum flow. If provided it will be
+        reused instead of recreated. Default value: None.
+
+    Returns
+    -------
+    cutset : set
+        Set of edges that, if removed from the graph, will disconnect it.
+
+    See also
+    --------
+    :meth:`minimum_cut`
+    :meth:`minimum_node_cut`
+    :meth:`minimum_edge_cut`
+    :meth:`stoer_wagner`
+    :meth:`node_connectivity`
+    :meth:`edge_connectivity`
+    :meth:`maximum_flow`
+    :meth:`edmonds_karp`
+    :meth:`preflow_push`
+    :meth:`shortest_augmenting_path`
+
+    Examples
+    --------
+    This function is not imported in the base NetworkX namespace, so you
+    have to explicitly import it from the connectivity package:
+
+    >>> from networkx.algorithms.connectivity import minimum_st_edge_cut
+
+    We use in this example the platonic icosahedral graph, which has edge
+    connectivity 5.
+
+    >>> G = nx.icosahedral_graph()
+    >>> len(minimum_st_edge_cut(G, 0, 6))
+    5
+
+    If you need to compute local edge cuts on several pairs of
+    nodes in the same graph, it is recommended that you reuse the
+    data structures that NetworkX uses in the computation: the
+    auxiliary digraph for edge connectivity, and the residual
+    network for the underlying maximum flow computation.
+
+    Example of how to compute local edge cuts among all pairs of
+    nodes of the platonic icosahedral graph reusing the data
+    structures.
+
+    >>> import itertools
+    >>> # You also have to explicitly import the function for
+    >>> # building the auxiliary digraph from the connectivity package
+    >>> from networkx.algorithms.connectivity import build_auxiliary_edge_connectivity
+    >>> H = build_auxiliary_edge_connectivity(G)
+    >>> # And the function for building the residual network from the
+    >>> # flow package
+    >>> from networkx.algorithms.flow import build_residual_network
+    >>> # Note that the auxiliary digraph has an edge attribute named capacity
+    >>> R = build_residual_network(H, "capacity")
+    >>> result = dict.fromkeys(G, dict())
+    >>> # Reuse the auxiliary digraph and the residual network by passing them
+    >>> # as parameters
+    >>> for u, v in itertools.combinations(G, 2):
+    ...     k = len(minimum_st_edge_cut(G, u, v, auxiliary=H, residual=R))
+    ...     result[u][v] = k
+    >>> all(result[u][v] == 5 for u, v in itertools.combinations(G, 2))
+    True
+
+    You can also use alternative flow algorithms for computing edge
+    cuts. For instance, in dense networks the algorithm
+    :meth:`shortest_augmenting_path` will usually perform better than
+    the default :meth:`edmonds_karp` which is faster for sparse
+    networks with highly skewed degree distributions. Alternative flow
+    functions have to be explicitly imported from the flow package.
+
+    >>> from networkx.algorithms.flow import shortest_augmenting_path
+    >>> len(minimum_st_edge_cut(G, 0, 6, flow_func=shortest_augmenting_path))
+    5
+
+    """
+    if flow_func is None:
+        flow_func = default_flow_func
+
+    if auxiliary is None:
+        H = build_auxiliary_edge_connectivity(G)
+    else:
+        H = auxiliary
+
+    kwargs = {"capacity": "capacity", "flow_func": flow_func, "residual": residual}
+
+    cut_value, partition = nx.minimum_cut(H, s, t, **kwargs)
+    reachable, non_reachable = partition
+    # Any edge in the original graph linking the two sets in the
+    # partition is part of the edge cutset
+    cutset = set()
+    for u, nbrs in ((n, G[n]) for n in reachable):
+        cutset.update((u, v) for v in nbrs if v in non_reachable)
+
+    return cutset
+
+
+@nx._dispatch(
+    graphs={"G": 0, "auxiliary?": 4, "residual?": 5},
+    preserve_edge_attrs={"residual": {"capacity": float("inf")}},
+    preserve_node_attrs={"auxiliary": {"id": None}},
+    preserve_graph_attrs={"auxiliary", "residual"},
+)
+def minimum_st_node_cut(G, s, t, flow_func=None, auxiliary=None, residual=None):
+    r"""Returns a set of nodes of minimum cardinality that disconnect source
+    from target in G.
+
+    This function returns the set of nodes of minimum cardinality that,
+    if removed, would destroy all paths among source and target in G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    s : node
+        Source node.
+
+    t : node
+        Target node.
+
+    flow_func : function
+        A function for computing the maximum flow among a pair of nodes.
+        The function has to accept at least three parameters: a Digraph,
+        a source node, and a target node. And return a residual network
+        that follows NetworkX conventions (see :meth:`maximum_flow` for
+        details). If flow_func is None, the default maximum flow function
+        (:meth:`edmonds_karp`) is used. See below for details. The choice
+        of the default function may change from version to version and
+        should not be relied on. Default value: None.
+
+    auxiliary : NetworkX DiGraph
+        Auxiliary digraph to compute flow based node connectivity. It has
+        to have a graph attribute called mapping with a dictionary mapping
+        node names in G and in the auxiliary digraph. If provided
+        it will be reused instead of recreated. Default value: None.
+
+    residual : NetworkX DiGraph
+        Residual network to compute maximum flow. If provided it will be
+        reused instead of recreated. Default value: None.
+
+    Returns
+    -------
+    cutset : set
+        Set of nodes that, if removed, would destroy all paths between
+        source and target in G.
+
+    Examples
+    --------
+    This function is not imported in the base NetworkX namespace, so you
+    have to explicitly import it from the connectivity package:
+
+    >>> from networkx.algorithms.connectivity import minimum_st_node_cut
+
+    We use in this example the platonic icosahedral graph, which has node
+    connectivity 5.
+
+    >>> G = nx.icosahedral_graph()
+    >>> len(minimum_st_node_cut(G, 0, 6))
+    5
+
+    If you need to compute local st cuts between several pairs of
+    nodes in the same graph, it is recommended that you reuse the
+    data structures that NetworkX uses in the computation: the
+    auxiliary digraph for node connectivity and node cuts, and the
+    residual network for the underlying maximum flow computation.
+
+    Example of how to compute local st node cuts reusing the data
+    structures:
+
+    >>> # You also have to explicitly import the function for
+    >>> # building the auxiliary digraph from the connectivity package
+    >>> from networkx.algorithms.connectivity import build_auxiliary_node_connectivity
+    >>> H = build_auxiliary_node_connectivity(G)
+    >>> # And the function for building the residual network from the
+    >>> # flow package
+    >>> from networkx.algorithms.flow import build_residual_network
+    >>> # Note that the auxiliary digraph has an edge attribute named capacity
+    >>> R = build_residual_network(H, "capacity")
+    >>> # Reuse the auxiliary digraph and the residual network by passing them
+    >>> # as parameters
+    >>> len(minimum_st_node_cut(G, 0, 6, auxiliary=H, residual=R))
+    5
+
+    You can also use alternative flow algorithms for computing minimum st
+    node cuts. For instance, in dense networks the algorithm
+    :meth:`shortest_augmenting_path` will usually perform better than
+    the default :meth:`edmonds_karp` which is faster for sparse
+    networks with highly skewed degree distributions. Alternative flow
+    functions have to be explicitly imported from the flow package.
+
+    >>> from networkx.algorithms.flow import shortest_augmenting_path
+    >>> len(minimum_st_node_cut(G, 0, 6, flow_func=shortest_augmenting_path))
+    5
+
+    Notes
+    -----
+    This is a flow based implementation of minimum node cut. The algorithm
+    is based in solving a number of maximum flow computations to determine
+    the capacity of the minimum cut on an auxiliary directed network that
+    corresponds to the minimum node cut of G. It handles both directed
+    and undirected graphs. This implementation is based on algorithm 11
+    in [1]_.
+
+    See also
+    --------
+    :meth:`minimum_node_cut`
+    :meth:`minimum_edge_cut`
+    :meth:`stoer_wagner`
+    :meth:`node_connectivity`
+    :meth:`edge_connectivity`
+    :meth:`maximum_flow`
+    :meth:`edmonds_karp`
+    :meth:`preflow_push`
+    :meth:`shortest_augmenting_path`
+
+    References
+    ----------
+    .. [1] Abdol-Hossein Esfahanian. Connectivity Algorithms.
+        http://www.cse.msu.edu/~cse835/Papers/Graph_connectivity_revised.pdf
+
+    """
+    if auxiliary is None:
+        H = build_auxiliary_node_connectivity(G)
+    else:
+        H = auxiliary
+
+    mapping = H.graph.get("mapping", None)
+    if mapping is None:
+        raise nx.NetworkXError("Invalid auxiliary digraph.")
+    if G.has_edge(s, t) or G.has_edge(t, s):
+        return {}
+    kwargs = {"flow_func": flow_func, "residual": residual, "auxiliary": H}
+
+    # The edge cut in the auxiliary digraph corresponds to the node cut in the
+    # original graph.
+    edge_cut = minimum_st_edge_cut(H, f"{mapping[s]}B", f"{mapping[t]}A", **kwargs)
+    # Each node in the original graph maps to two nodes of the auxiliary graph
+    node_cut = {H.nodes[node]["id"] for edge in edge_cut for node in edge}
+    return node_cut - {s, t}
+
+
+@nx._dispatch
+def minimum_node_cut(G, s=None, t=None, flow_func=None):
+    r"""Returns a set of nodes of minimum cardinality that disconnects G.
+
+    If source and target nodes are provided, this function returns the
+    set of nodes of minimum cardinality that, if removed, would destroy
+    all paths among source and target in G. If not, it returns a set
+    of nodes of minimum cardinality that disconnects G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    s : node
+        Source node. Optional. Default value: None.
+
+    t : node
+        Target node. Optional. Default value: None.
+
+    flow_func : function
+        A function for computing the maximum flow among a pair of nodes.
+        The function has to accept at least three parameters: a Digraph,
+        a source node, and a target node. And return a residual network
+        that follows NetworkX conventions (see :meth:`maximum_flow` for
+        details). If flow_func is None, the default maximum flow function
+        (:meth:`edmonds_karp`) is used. See below for details. The
+        choice of the default function may change from version
+        to version and should not be relied on. Default value: None.
+
+    Returns
+    -------
+    cutset : set
+        Set of nodes that, if removed, would disconnect G. If source
+        and target nodes are provided, the set contains the nodes that
+        if removed, would destroy all paths between source and target.
+
+    Examples
+    --------
+    >>> # Platonic icosahedral graph has node connectivity 5
+    >>> G = nx.icosahedral_graph()
+    >>> node_cut = nx.minimum_node_cut(G)
+    >>> len(node_cut)
+    5
+
+    You can use alternative flow algorithms for the underlying maximum
+    flow computation. In dense networks the algorithm
+    :meth:`shortest_augmenting_path` will usually perform better
+    than the default :meth:`edmonds_karp`, which is faster for
+    sparse networks with highly skewed degree distributions. Alternative
+    flow functions have to be explicitly imported from the flow package.
+
+    >>> from networkx.algorithms.flow import shortest_augmenting_path
+    >>> node_cut == nx.minimum_node_cut(G, flow_func=shortest_augmenting_path)
+    True
+
+    If you specify a pair of nodes (source and target) as parameters,
+    this function returns a local st node cut.
+
+    >>> len(nx.minimum_node_cut(G, 3, 7))
+    5
+
+    If you need to perform several local st cuts among different
+    pairs of nodes on the same graph, it is recommended that you reuse
+    the data structures used in the maximum flow computations. See
+    :meth:`minimum_st_node_cut` for details.
+
+    Notes
+    -----
+    This is a flow based implementation of minimum node cut. The algorithm
+    is based in solving a number of maximum flow computations to determine
+    the capacity of the minimum cut on an auxiliary directed network that
+    corresponds to the minimum node cut of G. It handles both directed
+    and undirected graphs. This implementation is based on algorithm 11
+    in [1]_.
+
+    See also
+    --------
+    :meth:`minimum_st_node_cut`
+    :meth:`minimum_cut`
+    :meth:`minimum_edge_cut`
+    :meth:`stoer_wagner`
+    :meth:`node_connectivity`
+    :meth:`edge_connectivity`
+    :meth:`maximum_flow`
+    :meth:`edmonds_karp`
+    :meth:`preflow_push`
+    :meth:`shortest_augmenting_path`
+
+    References
+    ----------
+    .. [1] Abdol-Hossein Esfahanian. Connectivity Algorithms.
+        http://www.cse.msu.edu/~cse835/Papers/Graph_connectivity_revised.pdf
+
+    """
+    if (s is not None and t is None) or (s is None and t is not None):
+        raise nx.NetworkXError("Both source and target must be specified.")
+
+    # Local minimum node cut.
+    if s is not None and t is not None:
+        if s not in G:
+            raise nx.NetworkXError(f"node {s} not in graph")
+        if t not in G:
+            raise nx.NetworkXError(f"node {t} not in graph")
+        return minimum_st_node_cut(G, s, t, flow_func=flow_func)
+
+    # Global minimum node cut.
+    # Analog to the algorithm 11 for global node connectivity in [1].
+    if G.is_directed():
+        if not nx.is_weakly_connected(G):
+            raise nx.NetworkXError("Input graph is not connected")
+        iter_func = itertools.permutations
+
+        def neighbors(v):
+            return itertools.chain.from_iterable([G.predecessors(v), G.successors(v)])
+
+    else:
+        if not nx.is_connected(G):
+            raise nx.NetworkXError("Input graph is not connected")
+        iter_func = itertools.combinations
+        neighbors = G.neighbors
+
+    # Reuse the auxiliary digraph and the residual network.
+    H = build_auxiliary_node_connectivity(G)
+    R = build_residual_network(H, "capacity")
+    kwargs = {"flow_func": flow_func, "auxiliary": H, "residual": R}
+
+    # Choose a node with minimum degree.
+    v = min(G, key=G.degree)
+    # Initial node cutset is all neighbors of the node with minimum degree.
+    min_cut = set(G[v])
+    # Compute st node cuts between v and all its non-neighbors nodes in G.
+    for w in set(G) - set(neighbors(v)) - {v}:
+        this_cut = minimum_st_node_cut(G, v, w, **kwargs)
+        if len(min_cut) >= len(this_cut):
+            min_cut = this_cut
+    # Also for non adjacent pairs of neighbors of v.
+    for x, y in iter_func(neighbors(v), 2):
+        if y in G[x]:
+            continue
+        this_cut = minimum_st_node_cut(G, x, y, **kwargs)
+        if len(min_cut) >= len(this_cut):
+            min_cut = this_cut
+
+    return min_cut
+
+
+@nx._dispatch
+def minimum_edge_cut(G, s=None, t=None, flow_func=None):
+    r"""Returns a set of edges of minimum cardinality that disconnects G.
+
+    If source and target nodes are provided, this function returns the
+    set of edges of minimum cardinality that, if removed, would break
+    all paths among source and target in G. If not, it returns a set of
+    edges of minimum cardinality that disconnects G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    s : node
+        Source node. Optional. Default value: None.
+
+    t : node
+        Target node. Optional. Default value: None.
+
+    flow_func : function
+        A function for computing the maximum flow among a pair of nodes.
+        The function has to accept at least three parameters: a Digraph,
+        a source node, and a target node. And return a residual network
+        that follows NetworkX conventions (see :meth:`maximum_flow` for
+        details). If flow_func is None, the default maximum flow function
+        (:meth:`edmonds_karp`) is used. See below for details. The
+        choice of the default function may change from version
+        to version and should not be relied on. Default value: None.
+
+    Returns
+    -------
+    cutset : set
+        Set of edges that, if removed, would disconnect G. If source
+        and target nodes are provided, the set contains the edges that
+        if removed, would destroy all paths between source and target.
+
+    Examples
+    --------
+    >>> # Platonic icosahedral graph has edge connectivity 5
+    >>> G = nx.icosahedral_graph()
+    >>> len(nx.minimum_edge_cut(G))
+    5
+
+    You can use alternative flow algorithms for the underlying
+    maximum flow computation. In dense networks the algorithm
+    :meth:`shortest_augmenting_path` will usually perform better
+    than the default :meth:`edmonds_karp`, which is faster for
+    sparse networks with highly skewed degree distributions.
+    Alternative flow functions have to be explicitly imported
+    from the flow package.
+
+    >>> from networkx.algorithms.flow import shortest_augmenting_path
+    >>> len(nx.minimum_edge_cut(G, flow_func=shortest_augmenting_path))
+    5
+
+    If you specify a pair of nodes (source and target) as parameters,
+    this function returns the value of local edge connectivity.
+
+    >>> nx.edge_connectivity(G, 3, 7)
+    5
+
+    If you need to perform several local computations among different
+    pairs of nodes on the same graph, it is recommended that you reuse
+    the data structures used in the maximum flow computations. See
+    :meth:`local_edge_connectivity` for details.
+
+    Notes
+    -----
+    This is a flow based implementation of minimum edge cut. For
+    undirected graphs the algorithm works by finding a 'small' dominating
+    set of nodes of G (see algorithm 7 in [1]_) and computing the maximum
+    flow between an arbitrary node in the dominating set and the rest of
+    nodes in it. This is an implementation of algorithm 6 in [1]_. For
+    directed graphs, the algorithm does n calls to the max flow function.
+    The function raises an error if the directed graph is not weakly
+    connected and returns an empty set if it is weakly connected.
+    It is an implementation of algorithm 8 in [1]_.
+
+    See also
+    --------
+    :meth:`minimum_st_edge_cut`
+    :meth:`minimum_node_cut`
+    :meth:`stoer_wagner`
+    :meth:`node_connectivity`
+    :meth:`edge_connectivity`
+    :meth:`maximum_flow`
+    :meth:`edmonds_karp`
+    :meth:`preflow_push`
+    :meth:`shortest_augmenting_path`
+
+    References
+    ----------
+    .. [1] Abdol-Hossein Esfahanian. Connectivity Algorithms.
+        http://www.cse.msu.edu/~cse835/Papers/Graph_connectivity_revised.pdf
+
+    """
+    if (s is not None and t is None) or (s is None and t is not None):
+        raise nx.NetworkXError("Both source and target must be specified.")
+
+    # reuse auxiliary digraph and residual network
+    H = build_auxiliary_edge_connectivity(G)
+    R = build_residual_network(H, "capacity")
+    kwargs = {"flow_func": flow_func, "residual": R, "auxiliary": H}
+
+    # Local minimum edge cut if s and t are not None
+    if s is not None and t is not None:
+        if s not in G:
+            raise nx.NetworkXError(f"node {s} not in graph")
+        if t not in G:
+            raise nx.NetworkXError(f"node {t} not in graph")
+        return minimum_st_edge_cut(H, s, t, **kwargs)
+
+    # Global minimum edge cut
+    # Analog to the algorithm for global edge connectivity
+    if G.is_directed():
+        # Based on algorithm 8 in [1]
+        if not nx.is_weakly_connected(G):
+            raise nx.NetworkXError("Input graph is not connected")
+
+        # Initial cutset is all edges of a node with minimum degree
+        node = min(G, key=G.degree)
+        min_cut = set(G.edges(node))
+        nodes = list(G)
+        n = len(nodes)
+        for i in range(n):
+            try:
+                this_cut = minimum_st_edge_cut(H, nodes[i], nodes[i + 1], **kwargs)
+                if len(this_cut) <= len(min_cut):
+                    min_cut = this_cut
+            except IndexError:  # Last node!
+                this_cut = minimum_st_edge_cut(H, nodes[i], nodes[0], **kwargs)
+                if len(this_cut) <= len(min_cut):
+                    min_cut = this_cut
+
+        return min_cut
+
+    else:  # undirected
+        # Based on algorithm 6 in [1]
+        if not nx.is_connected(G):
+            raise nx.NetworkXError("Input graph is not connected")
+
+        # Initial cutset is all edges of a node with minimum degree
+        node = min(G, key=G.degree)
+        min_cut = set(G.edges(node))
+        # A dominating set is \lambda-covering
+        # We need a dominating set with at least two nodes
+        for node in G:
+            D = nx.dominating_set(G, start_with=node)
+            v = D.pop()
+            if D:
+                break
+        else:
+            # in complete graphs the dominating set will always be of one node
+            # thus we return min_cut, which now contains the edges of a node
+            # with minimum degree
+            return min_cut
+        for w in D:
+            this_cut = minimum_st_edge_cut(H, v, w, **kwargs)
+            if len(this_cut) <= len(min_cut):
+                min_cut = this_cut
+
+        return min_cut
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/connectivity/tests/test_disjoint_paths.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/connectivity/tests/test_disjoint_paths.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c0fad9f5ca474a6b547a399f8f284f7ff6e33a4
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/connectivity/tests/test_disjoint_paths.py
@@ -0,0 +1,249 @@
+import pytest
+
+import networkx as nx
+from networkx.algorithms import flow
+from networkx.utils import pairwise
+
+flow_funcs = [
+    flow.boykov_kolmogorov,
+    flow.edmonds_karp,
+    flow.dinitz,
+    flow.preflow_push,
+    flow.shortest_augmenting_path,
+]
+
+
+def is_path(G, path):
+    return all(v in G[u] for u, v in pairwise(path))
+
+
+def are_edge_disjoint_paths(G, paths):
+    if not paths:
+        return False
+    for path in paths:
+        assert is_path(G, path)
+    paths_edges = [list(pairwise(p)) for p in paths]
+    num_of_edges = sum(len(e) for e in paths_edges)
+    num_unique_edges = len(set.union(*[set(es) for es in paths_edges]))
+    if num_of_edges == num_unique_edges:
+        return True
+    return False
+
+
+def are_node_disjoint_paths(G, paths):
+    if not paths:
+        return False
+    for path in paths:
+        assert is_path(G, path)
+    # first and last nodes are source and target
+    st = {paths[0][0], paths[0][-1]}
+    num_of_nodes = len([n for path in paths for n in path if n not in st])
+    num_unique_nodes = len({n for path in paths for n in path if n not in st})
+    if num_of_nodes == num_unique_nodes:
+        return True
+    return False
+
+
+def test_graph_from_pr_2053():
+    G = nx.Graph()
+    G.add_edges_from(
+        [
+            ("A", "B"),
+            ("A", "D"),
+            ("A", "F"),
+            ("A", "G"),
+            ("B", "C"),
+            ("B", "D"),
+            ("B", "G"),
+            ("C", "D"),
+            ("C", "E"),
+            ("C", "Z"),
+            ("D", "E"),
+            ("D", "F"),
+            ("E", "F"),
+            ("E", "Z"),
+            ("F", "Z"),
+            ("G", "Z"),
+        ]
+    )
+    for flow_func in flow_funcs:
+        kwargs = {"flow_func": flow_func}
+        errmsg = f"Assertion failed in function: {flow_func.__name__}"
+        # edge disjoint paths
+        edge_paths = list(nx.edge_disjoint_paths(G, "A", "Z", **kwargs))
+        assert are_edge_disjoint_paths(G, edge_paths), errmsg
+        assert nx.edge_connectivity(G, "A", "Z") == len(edge_paths), errmsg
+        # node disjoint paths
+        node_paths = list(nx.node_disjoint_paths(G, "A", "Z", **kwargs))
+        assert are_node_disjoint_paths(G, node_paths), errmsg
+        assert nx.node_connectivity(G, "A", "Z") == len(node_paths), errmsg
+
+
+def test_florentine_families():
+    G = nx.florentine_families_graph()
+    for flow_func in flow_funcs:
+        kwargs = {"flow_func": flow_func}
+        errmsg = f"Assertion failed in function: {flow_func.__name__}"
+        # edge disjoint paths
+        edge_dpaths = list(nx.edge_disjoint_paths(G, "Medici", "Strozzi", **kwargs))
+        assert are_edge_disjoint_paths(G, edge_dpaths), errmsg
+        assert nx.edge_connectivity(G, "Medici", "Strozzi") == len(edge_dpaths), errmsg
+        # node disjoint paths
+        node_dpaths = list(nx.node_disjoint_paths(G, "Medici", "Strozzi", **kwargs))
+        assert are_node_disjoint_paths(G, node_dpaths), errmsg
+        assert nx.node_connectivity(G, "Medici", "Strozzi") == len(node_dpaths), errmsg
+
+
+def test_karate():
+    G = nx.karate_club_graph()
+    for flow_func in flow_funcs:
+        kwargs = {"flow_func": flow_func}
+        errmsg = f"Assertion failed in function: {flow_func.__name__}"
+        # edge disjoint paths
+        edge_dpaths = list(nx.edge_disjoint_paths(G, 0, 33, **kwargs))
+        assert are_edge_disjoint_paths(G, edge_dpaths), errmsg
+        assert nx.edge_connectivity(G, 0, 33) == len(edge_dpaths), errmsg
+        # node disjoint paths
+        node_dpaths = list(nx.node_disjoint_paths(G, 0, 33, **kwargs))
+        assert are_node_disjoint_paths(G, node_dpaths), errmsg
+        assert nx.node_connectivity(G, 0, 33) == len(node_dpaths), errmsg
+
+
+def test_petersen_disjoint_paths():
+    G = nx.petersen_graph()
+    for flow_func in flow_funcs:
+        kwargs = {"flow_func": flow_func}
+        errmsg = f"Assertion failed in function: {flow_func.__name__}"
+        # edge disjoint paths
+        edge_dpaths = list(nx.edge_disjoint_paths(G, 0, 6, **kwargs))
+        assert are_edge_disjoint_paths(G, edge_dpaths), errmsg
+        assert 3 == len(edge_dpaths), errmsg
+        # node disjoint paths
+        node_dpaths = list(nx.node_disjoint_paths(G, 0, 6, **kwargs))
+        assert are_node_disjoint_paths(G, node_dpaths), errmsg
+        assert 3 == len(node_dpaths), errmsg
+
+
+def test_octahedral_disjoint_paths():
+    G = nx.octahedral_graph()
+    for flow_func in flow_funcs:
+        kwargs = {"flow_func": flow_func}
+        errmsg = f"Assertion failed in function: {flow_func.__name__}"
+        # edge disjoint paths
+        edge_dpaths = list(nx.edge_disjoint_paths(G, 0, 5, **kwargs))
+        assert are_edge_disjoint_paths(G, edge_dpaths), errmsg
+        assert 4 == len(edge_dpaths), errmsg
+        # node disjoint paths
+        node_dpaths = list(nx.node_disjoint_paths(G, 0, 5, **kwargs))
+        assert are_node_disjoint_paths(G, node_dpaths), errmsg
+        assert 4 == len(node_dpaths), errmsg
+
+
+def test_icosahedral_disjoint_paths():
+    G = nx.icosahedral_graph()
+    for flow_func in flow_funcs:
+        kwargs = {"flow_func": flow_func}
+        errmsg = f"Assertion failed in function: {flow_func.__name__}"
+        # edge disjoint paths
+        edge_dpaths = list(nx.edge_disjoint_paths(G, 0, 6, **kwargs))
+        assert are_edge_disjoint_paths(G, edge_dpaths), errmsg
+        assert 5 == len(edge_dpaths), errmsg
+        # node disjoint paths
+        node_dpaths = list(nx.node_disjoint_paths(G, 0, 6, **kwargs))
+        assert are_node_disjoint_paths(G, node_dpaths), errmsg
+        assert 5 == len(node_dpaths), errmsg
+
+
+def test_cutoff_disjoint_paths():
+    G = nx.icosahedral_graph()
+    for flow_func in flow_funcs:
+        kwargs = {"flow_func": flow_func}
+        errmsg = f"Assertion failed in function: {flow_func.__name__}"
+        for cutoff in [2, 4]:
+            kwargs["cutoff"] = cutoff
+            # edge disjoint paths
+            edge_dpaths = list(nx.edge_disjoint_paths(G, 0, 6, **kwargs))
+            assert are_edge_disjoint_paths(G, edge_dpaths), errmsg
+            assert cutoff == len(edge_dpaths), errmsg
+            # node disjoint paths
+            node_dpaths = list(nx.node_disjoint_paths(G, 0, 6, **kwargs))
+            assert are_node_disjoint_paths(G, node_dpaths), errmsg
+            assert cutoff == len(node_dpaths), errmsg
+
+
+def test_missing_source_edge_paths():
+    with pytest.raises(nx.NetworkXError):
+        G = nx.path_graph(4)
+        list(nx.edge_disjoint_paths(G, 10, 1))
+
+
+def test_missing_source_node_paths():
+    with pytest.raises(nx.NetworkXError):
+        G = nx.path_graph(4)
+        list(nx.node_disjoint_paths(G, 10, 1))
+
+
+def test_missing_target_edge_paths():
+    with pytest.raises(nx.NetworkXError):
+        G = nx.path_graph(4)
+        list(nx.edge_disjoint_paths(G, 1, 10))
+
+
+def test_missing_target_node_paths():
+    with pytest.raises(nx.NetworkXError):
+        G = nx.path_graph(4)
+        list(nx.node_disjoint_paths(G, 1, 10))
+
+
+def test_not_weakly_connected_edges():
+    with pytest.raises(nx.NetworkXNoPath):
+        G = nx.DiGraph()
+        nx.add_path(G, [1, 2, 3])
+        nx.add_path(G, [4, 5])
+        list(nx.edge_disjoint_paths(G, 1, 5))
+
+
+def test_not_weakly_connected_nodes():
+    with pytest.raises(nx.NetworkXNoPath):
+        G = nx.DiGraph()
+        nx.add_path(G, [1, 2, 3])
+        nx.add_path(G, [4, 5])
+        list(nx.node_disjoint_paths(G, 1, 5))
+
+
+def test_not_connected_edges():
+    with pytest.raises(nx.NetworkXNoPath):
+        G = nx.Graph()
+        nx.add_path(G, [1, 2, 3])
+        nx.add_path(G, [4, 5])
+        list(nx.edge_disjoint_paths(G, 1, 5))
+
+
+def test_not_connected_nodes():
+    with pytest.raises(nx.NetworkXNoPath):
+        G = nx.Graph()
+        nx.add_path(G, [1, 2, 3])
+        nx.add_path(G, [4, 5])
+        list(nx.node_disjoint_paths(G, 1, 5))
+
+
+def test_isolated_edges():
+    with pytest.raises(nx.NetworkXNoPath):
+        G = nx.Graph()
+        G.add_node(1)
+        nx.add_path(G, [4, 5])
+        list(nx.edge_disjoint_paths(G, 1, 5))
+
+
+def test_isolated_nodes():
+    with pytest.raises(nx.NetworkXNoPath):
+        G = nx.Graph()
+        G.add_node(1)
+        nx.add_path(G, [4, 5])
+        list(nx.node_disjoint_paths(G, 1, 5))
+
+
+def test_invalid_auxiliary():
+    with pytest.raises(nx.NetworkXError):
+        G = nx.complete_graph(5)
+        list(nx.node_disjoint_paths(G, 0, 3, auxiliary=G))
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/connectivity/utils.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/connectivity/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf6860a208f0f11b50d2950e8462f1c5649b4243
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/connectivity/utils.py
@@ -0,0 +1,87 @@
+"""
+Utilities for connectivity package
+"""
+import networkx as nx
+
+__all__ = ["build_auxiliary_node_connectivity", "build_auxiliary_edge_connectivity"]
+
+
+@nx._dispatch
+def build_auxiliary_node_connectivity(G):
+    r"""Creates a directed graph D from an undirected graph G to compute flow
+    based node connectivity.
+
+    For an undirected graph G having `n` nodes and `m` edges we derive a
+    directed graph D with `2n` nodes and `2m+n` arcs by replacing each
+    original node `v` with two nodes `vA`, `vB` linked by an (internal)
+    arc in D. Then for each edge (`u`, `v`) in G we add two arcs (`uB`, `vA`)
+    and (`vB`, `uA`) in D. Finally we set the attribute capacity = 1 for each
+    arc in D [1]_.
+
+    For a directed graph having `n` nodes and `m` arcs we derive a
+    directed graph D with `2n` nodes and `m+n` arcs by replacing each
+    original node `v` with two nodes `vA`, `vB` linked by an (internal)
+    arc (`vA`, `vB`) in D. Then for each arc (`u`, `v`) in G we add one
+    arc (`uB`, `vA`) in D. Finally we set the attribute capacity = 1 for
+    each arc in D.
+
+    A dictionary with a mapping between nodes in the original graph and the
+    auxiliary digraph is stored as a graph attribute: D.graph['mapping'].
+
+    References
+    ----------
+    .. [1] Kammer, Frank and Hanjo Taubig. Graph Connectivity. in Brandes and
+        Erlebach, 'Network Analysis: Methodological Foundations', Lecture
+        Notes in Computer Science, Volume 3418, Springer-Verlag, 2005.
+        https://doi.org/10.1007/978-3-540-31955-9_7
+
+    """
+    directed = G.is_directed()
+
+    mapping = {}
+    H = nx.DiGraph()
+
+    for i, node in enumerate(G):
+        mapping[node] = i
+        H.add_node(f"{i}A", id=node)
+        H.add_node(f"{i}B", id=node)
+        H.add_edge(f"{i}A", f"{i}B", capacity=1)
+
+    edges = []
+    for source, target in G.edges():
+        edges.append((f"{mapping[source]}B", f"{mapping[target]}A"))
+        if not directed:
+            edges.append((f"{mapping[target]}B", f"{mapping[source]}A"))
+    H.add_edges_from(edges, capacity=1)
+
+    # Store mapping as graph attribute
+    H.graph["mapping"] = mapping
+    return H
+
+
+@nx._dispatch
+def build_auxiliary_edge_connectivity(G):
+    """Auxiliary digraph for computing flow based edge connectivity
+
+    If the input graph is undirected, we replace each edge (`u`,`v`) with
+    two reciprocal arcs (`u`, `v`) and (`v`, `u`) and then we set the attribute
+    'capacity' for each arc to 1. If the input graph is directed we simply
+    add the 'capacity' attribute. Part of algorithm 1 in [1]_ .
+
+    References
+    ----------
+    .. [1] Abdol-Hossein Esfahanian. Connectivity Algorithms. (this is a
+        chapter, look for the reference of the book).
+        http://www.cse.msu.edu/~cse835/Papers/Graph_connectivity_revised.pdf
+    """
+    if G.is_directed():
+        H = nx.DiGraph()
+        H.add_nodes_from(G.nodes())
+        H.add_edges_from(G.edges(), capacity=1)
+        return H
+    else:
+        H = nx.DiGraph()
+        H.add_nodes_from(G.nodes())
+        for source, target in G.edges():
+            H.add_edges_from([(source, target), (target, source)], capacity=1)
+        return H
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diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/generic.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/generic.py
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+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/generic.py
@@ -0,0 +1,719 @@
+"""
+Compute the shortest paths and path lengths between nodes in the graph.
+
+These algorithms work with undirected and directed graphs.
+
+"""
+import warnings
+
+import networkx as nx
+
+__all__ = [
+    "shortest_path",
+    "all_shortest_paths",
+    "single_source_all_shortest_paths",
+    "all_pairs_all_shortest_paths",
+    "shortest_path_length",
+    "average_shortest_path_length",
+    "has_path",
+]
+
+
+@nx._dispatch
+def has_path(G, source, target):
+    """Returns *True* if *G* has a path from *source* to *target*.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+       Starting node for path
+
+    target : node
+       Ending node for path
+    """
+    try:
+        nx.shortest_path(G, source, target)
+    except nx.NetworkXNoPath:
+        return False
+    return True
+
+
+@nx._dispatch(edge_attrs="weight")
+def shortest_path(G, source=None, target=None, weight=None, method="dijkstra"):
+    """Compute shortest paths in the graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node, optional
+        Starting node for path. If not specified, compute shortest
+        paths for each possible starting node.
+
+    target : node, optional
+        Ending node for path. If not specified, compute shortest
+        paths to all possible nodes.
+
+    weight : None, string or function, optional (default = None)
+        If None, every edge has weight/distance/cost 1.
+        If a string, use this edge attribute as the edge weight.
+        Any edge attribute not present defaults to 1.
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly
+        three positional arguments: the two endpoints of an edge and
+        the dictionary of edge attributes for that edge.
+        The function must return a number.
+
+    method : string, optional (default = 'dijkstra')
+        The algorithm to use to compute the path.
+        Supported options: 'dijkstra', 'bellman-ford'.
+        Other inputs produce a ValueError.
+        If `weight` is None, unweighted graph methods are used, and this
+        suggestion is ignored.
+
+    Returns
+    -------
+    path: list or dictionary
+        All returned paths include both the source and target in the path.
+
+        If the source and target are both specified, return a single list
+        of nodes in a shortest path from the source to the target.
+
+        If only the source is specified, return a dictionary keyed by
+        targets with a list of nodes in a shortest path from the source
+        to one of the targets.
+
+        If only the target is specified, return a dictionary keyed by
+        sources with a list of nodes in a shortest path from one of the
+        sources to the target.
+
+        If neither the source nor target are specified return a dictionary
+        of dictionaries with path[source][target]=[list of nodes in path].
+
+    Raises
+    ------
+    NodeNotFound
+        If `source` is not in `G`.
+
+    ValueError
+        If `method` is not among the supported options.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> print(nx.shortest_path(G, source=0, target=4))
+    [0, 1, 2, 3, 4]
+    >>> p = nx.shortest_path(G, source=0)  # target not specified
+    >>> p[3] # shortest path from source=0 to target=3
+    [0, 1, 2, 3]
+    >>> p = nx.shortest_path(G, target=4)  # source not specified
+    >>> p[1] # shortest path from source=1 to target=4
+    [1, 2, 3, 4]
+    >>> p = nx.shortest_path(G)  # source, target not specified
+    >>> p[2][4] # shortest path from source=2 to target=4
+    [2, 3, 4]
+
+    Notes
+    -----
+    There may be more than one shortest path between a source and target.
+    This returns only one of them.
+
+    See Also
+    --------
+    all_pairs_shortest_path
+    all_pairs_dijkstra_path
+    all_pairs_bellman_ford_path
+    single_source_shortest_path
+    single_source_dijkstra_path
+    single_source_bellman_ford_path
+    """
+    if method not in ("dijkstra", "bellman-ford"):
+        # so we don't need to check in each branch later
+        raise ValueError(f"method not supported: {method}")
+    method = "unweighted" if weight is None else method
+    if source is None:
+        if target is None:
+            msg = "shortest_path for all_pairs will return an iterator in v3.3"
+            warnings.warn(msg, DeprecationWarning)
+
+            # Find paths between all pairs.
+            if method == "unweighted":
+                paths = dict(nx.all_pairs_shortest_path(G))
+            elif method == "dijkstra":
+                paths = dict(nx.all_pairs_dijkstra_path(G, weight=weight))
+            else:  # method == 'bellman-ford':
+                paths = dict(nx.all_pairs_bellman_ford_path(G, weight=weight))
+        else:
+            # Find paths from all nodes co-accessible to the target.
+            if G.is_directed():
+                G = G.reverse(copy=False)
+            if method == "unweighted":
+                paths = nx.single_source_shortest_path(G, target)
+            elif method == "dijkstra":
+                paths = nx.single_source_dijkstra_path(G, target, weight=weight)
+            else:  # method == 'bellman-ford':
+                paths = nx.single_source_bellman_ford_path(G, target, weight=weight)
+            # Now flip the paths so they go from a source to the target.
+            for target in paths:
+                paths[target] = list(reversed(paths[target]))
+    else:
+        if target is None:
+            # Find paths to all nodes accessible from the source.
+            if method == "unweighted":
+                paths = nx.single_source_shortest_path(G, source)
+            elif method == "dijkstra":
+                paths = nx.single_source_dijkstra_path(G, source, weight=weight)
+            else:  # method == 'bellman-ford':
+                paths = nx.single_source_bellman_ford_path(G, source, weight=weight)
+        else:
+            # Find shortest source-target path.
+            if method == "unweighted":
+                paths = nx.bidirectional_shortest_path(G, source, target)
+            elif method == "dijkstra":
+                _, paths = nx.bidirectional_dijkstra(G, source, target, weight)
+            else:  # method == 'bellman-ford':
+                paths = nx.bellman_ford_path(G, source, target, weight)
+    return paths
+
+
+@nx._dispatch(edge_attrs="weight")
+def shortest_path_length(G, source=None, target=None, weight=None, method="dijkstra"):
+    """Compute shortest path lengths in the graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node, optional
+        Starting node for path.
+        If not specified, compute shortest path lengths using all nodes as
+        source nodes.
+
+    target : node, optional
+        Ending node for path.
+        If not specified, compute shortest path lengths using all nodes as
+        target nodes.
+
+    weight : None, string or function, optional (default = None)
+        If None, every edge has weight/distance/cost 1.
+        If a string, use this edge attribute as the edge weight.
+        Any edge attribute not present defaults to 1.
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly
+        three positional arguments: the two endpoints of an edge and
+        the dictionary of edge attributes for that edge.
+        The function must return a number.
+
+    method : string, optional (default = 'dijkstra')
+        The algorithm to use to compute the path length.
+        Supported options: 'dijkstra', 'bellman-ford'.
+        Other inputs produce a ValueError.
+        If `weight` is None, unweighted graph methods are used, and this
+        suggestion is ignored.
+
+    Returns
+    -------
+    length: int or iterator
+        If the source and target are both specified, return the length of
+        the shortest path from the source to the target.
+
+        If only the source is specified, return a dict keyed by target
+        to the shortest path length from the source to that target.
+
+        If only the target is specified, return a dict keyed by source
+        to the shortest path length from that source to the target.
+
+        If neither the source nor target are specified, return an iterator
+        over (source, dictionary) where dictionary is keyed by target to
+        shortest path length from source to that target.
+
+    Raises
+    ------
+    NodeNotFound
+        If `source` is not in `G`.
+
+    NetworkXNoPath
+        If no path exists between source and target.
+
+    ValueError
+        If `method` is not among the supported options.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> nx.shortest_path_length(G, source=0, target=4)
+    4
+    >>> p = nx.shortest_path_length(G, source=0)  # target not specified
+    >>> p[4]
+    4
+    >>> p = nx.shortest_path_length(G, target=4)  # source not specified
+    >>> p[0]
+    4
+    >>> p = dict(nx.shortest_path_length(G))  # source,target not specified
+    >>> p[0][4]
+    4
+
+    Notes
+    -----
+    The length of the path is always 1 less than the number of nodes involved
+    in the path since the length measures the number of edges followed.
+
+    For digraphs this returns the shortest directed path length. To find path
+    lengths in the reverse direction use G.reverse(copy=False) first to flip
+    the edge orientation.
+
+    See Also
+    --------
+    all_pairs_shortest_path_length
+    all_pairs_dijkstra_path_length
+    all_pairs_bellman_ford_path_length
+    single_source_shortest_path_length
+    single_source_dijkstra_path_length
+    single_source_bellman_ford_path_length
+    """
+    if method not in ("dijkstra", "bellman-ford"):
+        # so we don't need to check in each branch later
+        raise ValueError(f"method not supported: {method}")
+    method = "unweighted" if weight is None else method
+    if source is None:
+        if target is None:
+            # Find paths between all pairs.
+            if method == "unweighted":
+                paths = nx.all_pairs_shortest_path_length(G)
+            elif method == "dijkstra":
+                paths = nx.all_pairs_dijkstra_path_length(G, weight=weight)
+            else:  # method == 'bellman-ford':
+                paths = nx.all_pairs_bellman_ford_path_length(G, weight=weight)
+        else:
+            # Find paths from all nodes co-accessible to the target.
+            if G.is_directed():
+                G = G.reverse(copy=False)
+            if method == "unweighted":
+                path_length = nx.single_source_shortest_path_length
+                paths = path_length(G, target)
+            elif method == "dijkstra":
+                path_length = nx.single_source_dijkstra_path_length
+                paths = path_length(G, target, weight=weight)
+            else:  # method == 'bellman-ford':
+                path_length = nx.single_source_bellman_ford_path_length
+                paths = path_length(G, target, weight=weight)
+    else:
+        if target is None:
+            # Find paths to all nodes accessible from the source.
+            if method == "unweighted":
+                paths = nx.single_source_shortest_path_length(G, source)
+            elif method == "dijkstra":
+                path_length = nx.single_source_dijkstra_path_length
+                paths = path_length(G, source, weight=weight)
+            else:  # method == 'bellman-ford':
+                path_length = nx.single_source_bellman_ford_path_length
+                paths = path_length(G, source, weight=weight)
+        else:
+            # Find shortest source-target path.
+            if method == "unweighted":
+                p = nx.bidirectional_shortest_path(G, source, target)
+                paths = len(p) - 1
+            elif method == "dijkstra":
+                paths = nx.dijkstra_path_length(G, source, target, weight)
+            else:  # method == 'bellman-ford':
+                paths = nx.bellman_ford_path_length(G, source, target, weight)
+    return paths
+
+
+@nx._dispatch(edge_attrs="weight")
+def average_shortest_path_length(G, weight=None, method=None):
+    r"""Returns the average shortest path length.
+
+    The average shortest path length is
+
+    .. math::
+
+       a =\sum_{\substack{s,t \in V \\ s\neq t}} \frac{d(s, t)}{n(n-1)}
+
+    where `V` is the set of nodes in `G`,
+    `d(s, t)` is the shortest path from `s` to `t`,
+    and `n` is the number of nodes in `G`.
+
+    .. versionchanged:: 3.0
+       An exception is raised for directed graphs that are not strongly
+       connected.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    weight : None, string or function, optional (default = None)
+        If None, every edge has weight/distance/cost 1.
+        If a string, use this edge attribute as the edge weight.
+        Any edge attribute not present defaults to 1.
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly
+        three positional arguments: the two endpoints of an edge and
+        the dictionary of edge attributes for that edge.
+        The function must return a number.
+
+    method : string, optional (default = 'unweighted' or 'dijkstra')
+        The algorithm to use to compute the path lengths.
+        Supported options are 'unweighted', 'dijkstra', 'bellman-ford',
+        'floyd-warshall' and 'floyd-warshall-numpy'.
+        Other method values produce a ValueError.
+        The default method is 'unweighted' if `weight` is None,
+        otherwise the default method is 'dijkstra'.
+
+    Raises
+    ------
+    NetworkXPointlessConcept
+        If `G` is the null graph (that is, the graph on zero nodes).
+
+    NetworkXError
+        If `G` is not connected (or not strongly connected, in the case
+        of a directed graph).
+
+    ValueError
+        If `method` is not among the supported options.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> nx.average_shortest_path_length(G)
+    2.0
+
+    For disconnected graphs, you can compute the average shortest path
+    length for each component
+
+    >>> G = nx.Graph([(1, 2), (3, 4)])
+    >>> for C in (G.subgraph(c).copy() for c in nx.connected_components(G)):
+    ...     print(nx.average_shortest_path_length(C))
+    1.0
+    1.0
+
+    """
+    single_source_methods = ["unweighted", "dijkstra", "bellman-ford"]
+    all_pairs_methods = ["floyd-warshall", "floyd-warshall-numpy"]
+    supported_methods = single_source_methods + all_pairs_methods
+
+    if method is None:
+        method = "unweighted" if weight is None else "dijkstra"
+    if method not in supported_methods:
+        raise ValueError(f"method not supported: {method}")
+
+    n = len(G)
+    # For the special case of the null graph, raise an exception, since
+    # there are no paths in the null graph.
+    if n == 0:
+        msg = (
+            "the null graph has no paths, thus there is no average "
+            "shortest path length"
+        )
+        raise nx.NetworkXPointlessConcept(msg)
+    # For the special case of the trivial graph, return zero immediately.
+    if n == 1:
+        return 0
+    # Shortest path length is undefined if the graph is not strongly connected.
+    if G.is_directed() and not nx.is_strongly_connected(G):
+        raise nx.NetworkXError("Graph is not strongly connected.")
+    # Shortest path length is undefined if the graph is not connected.
+    if not G.is_directed() and not nx.is_connected(G):
+        raise nx.NetworkXError("Graph is not connected.")
+
+    # Compute all-pairs shortest paths.
+    def path_length(v):
+        if method == "unweighted":
+            return nx.single_source_shortest_path_length(G, v)
+        elif method == "dijkstra":
+            return nx.single_source_dijkstra_path_length(G, v, weight=weight)
+        elif method == "bellman-ford":
+            return nx.single_source_bellman_ford_path_length(G, v, weight=weight)
+
+    if method in single_source_methods:
+        # Sum the distances for each (ordered) pair of source and target node.
+        s = sum(l for u in G for l in path_length(u).values())
+    else:
+        if method == "floyd-warshall":
+            all_pairs = nx.floyd_warshall(G, weight=weight)
+            s = sum(sum(t.values()) for t in all_pairs.values())
+        elif method == "floyd-warshall-numpy":
+            s = nx.floyd_warshall_numpy(G, weight=weight).sum()
+    return s / (n * (n - 1))
+
+
+@nx._dispatch(edge_attrs="weight")
+def all_shortest_paths(G, source, target, weight=None, method="dijkstra"):
+    """Compute all shortest simple paths in the graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+       Starting node for path.
+
+    target : node
+       Ending node for path.
+
+    weight : None, string or function, optional (default = None)
+        If None, every edge has weight/distance/cost 1.
+        If a string, use this edge attribute as the edge weight.
+        Any edge attribute not present defaults to 1.
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly
+        three positional arguments: the two endpoints of an edge and
+        the dictionary of edge attributes for that edge.
+        The function must return a number.
+
+    method : string, optional (default = 'dijkstra')
+       The algorithm to use to compute the path lengths.
+       Supported options: 'dijkstra', 'bellman-ford'.
+       Other inputs produce a ValueError.
+       If `weight` is None, unweighted graph methods are used, and this
+       suggestion is ignored.
+
+    Returns
+    -------
+    paths : generator of lists
+        A generator of all paths between source and target.
+
+    Raises
+    ------
+    ValueError
+        If `method` is not among the supported options.
+
+    NetworkXNoPath
+        If `target` cannot be reached from `source`.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> nx.add_path(G, [0, 1, 2])
+    >>> nx.add_path(G, [0, 10, 2])
+    >>> print([p for p in nx.all_shortest_paths(G, source=0, target=2)])
+    [[0, 1, 2], [0, 10, 2]]
+
+    Notes
+    -----
+    There may be many shortest paths between the source and target.  If G
+    contains zero-weight cycles, this function will not produce all shortest
+    paths because doing so would produce infinitely many paths of unbounded
+    length -- instead, we only produce the shortest simple paths.
+
+    See Also
+    --------
+    shortest_path
+    single_source_shortest_path
+    all_pairs_shortest_path
+    """
+    method = "unweighted" if weight is None else method
+    if method == "unweighted":
+        pred = nx.predecessor(G, source)
+    elif method == "dijkstra":
+        pred, dist = nx.dijkstra_predecessor_and_distance(G, source, weight=weight)
+    elif method == "bellman-ford":
+        pred, dist = nx.bellman_ford_predecessor_and_distance(G, source, weight=weight)
+    else:
+        raise ValueError(f"method not supported: {method}")
+
+    return _build_paths_from_predecessors({source}, target, pred)
+
+
+@nx._dispatch(edge_attrs="weight")
+def single_source_all_shortest_paths(G, source, weight=None, method="dijkstra"):
+    """Compute all shortest simple paths from the given source in the graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+       Starting node for path.
+
+    weight : None, string or function, optional (default = None)
+        If None, every edge has weight/distance/cost 1.
+        If a string, use this edge attribute as the edge weight.
+        Any edge attribute not present defaults to 1.
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly
+        three positional arguments: the two endpoints of an edge and
+        the dictionary of edge attributes for that edge.
+        The function must return a number.
+
+    method : string, optional (default = 'dijkstra')
+       The algorithm to use to compute the path lengths.
+       Supported options: 'dijkstra', 'bellman-ford'.
+       Other inputs produce a ValueError.
+       If `weight` is None, unweighted graph methods are used, and this
+       suggestion is ignored.
+
+    Returns
+    -------
+    paths : generator of dictionary
+        A generator of all paths between source and all nodes in the graph.
+
+    Raises
+    ------
+    ValueError
+        If `method` is not among the supported options.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> nx.add_path(G, [0, 1, 2, 3, 0])
+    >>> dict(nx.single_source_all_shortest_paths(G, source=0))
+    {0: [[0]], 1: [[0, 1]], 2: [[0, 1, 2], [0, 3, 2]], 3: [[0, 3]]}
+
+    Notes
+    -----
+    There may be many shortest paths between the source and target.  If G
+    contains zero-weight cycles, this function will not produce all shortest
+    paths because doing so would produce infinitely many paths of unbounded
+    length -- instead, we only produce the shortest simple paths.
+
+    See Also
+    --------
+    shortest_path
+    all_shortest_paths
+    single_source_shortest_path
+    all_pairs_shortest_path
+    all_pairs_all_shortest_paths
+    """
+    method = "unweighted" if weight is None else method
+    if method == "unweighted":
+        pred = nx.predecessor(G, source)
+    elif method == "dijkstra":
+        pred, dist = nx.dijkstra_predecessor_and_distance(G, source, weight=weight)
+    elif method == "bellman-ford":
+        pred, dist = nx.bellman_ford_predecessor_and_distance(G, source, weight=weight)
+    else:
+        raise ValueError(f"method not supported: {method}")
+    for n in G:
+        try:
+            yield n, list(_build_paths_from_predecessors({source}, n, pred))
+        except nx.NetworkXNoPath:
+            pass
+
+
+@nx._dispatch(edge_attrs="weight")
+def all_pairs_all_shortest_paths(G, weight=None, method="dijkstra"):
+    """Compute all shortest paths between all nodes.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    weight : None, string or function, optional (default = None)
+        If None, every edge has weight/distance/cost 1.
+        If a string, use this edge attribute as the edge weight.
+        Any edge attribute not present defaults to 1.
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly
+        three positional arguments: the two endpoints of an edge and
+        the dictionary of edge attributes for that edge.
+        The function must return a number.
+
+    method : string, optional (default = 'dijkstra')
+       The algorithm to use to compute the path lengths.
+       Supported options: 'dijkstra', 'bellman-ford'.
+       Other inputs produce a ValueError.
+       If `weight` is None, unweighted graph methods are used, and this
+       suggestion is ignored.
+
+    Returns
+    -------
+    paths : generator of dictionary
+        Dictionary of arrays, keyed by source and target, of all shortest paths.
+
+    Raises
+    ------
+    ValueError
+        If `method` is not among the supported options.
+
+    Examples
+    --------
+    >>> G = nx.cycle_graph(4)
+    >>> dict(nx.all_pairs_all_shortest_paths(G))[0][2]
+    [[0, 1, 2], [0, 3, 2]]
+    >>> dict(nx.all_pairs_all_shortest_paths(G))[0][3]
+    [[0, 3]]
+
+    Notes
+    -----
+    There may be multiple shortest paths with equal lengths. Unlike
+    all_pairs_shortest_path, this method returns all shortest paths.
+
+    See Also
+    --------
+    all_pairs_shortest_path
+    single_source_all_shortest_paths
+    """
+    for n in G:
+        yield n, dict(
+            single_source_all_shortest_paths(G, n, weight=weight, method=method)
+        )
+
+
+def _build_paths_from_predecessors(sources, target, pred):
+    """Compute all simple paths to target, given the predecessors found in
+    pred, terminating when any source in sources is found.
+
+    Parameters
+    ----------
+    sources : set
+       Starting nodes for path.
+
+    target : node
+       Ending node for path.
+
+    pred : dict
+       A dictionary of predecessor lists, keyed by node
+
+    Returns
+    -------
+    paths : generator of lists
+        A generator of all paths between source and target.
+
+    Raises
+    ------
+    NetworkXNoPath
+        If `target` cannot be reached from `source`.
+
+    Notes
+    -----
+    There may be many paths between the sources and target.  If there are
+    cycles among the predecessors, this function will not produce all
+    possible paths because doing so would produce infinitely many paths
+    of unbounded length -- instead, we only produce simple paths.
+
+    See Also
+    --------
+    shortest_path
+    single_source_shortest_path
+    all_pairs_shortest_path
+    all_shortest_paths
+    bellman_ford_path
+    """
+    if target not in pred:
+        raise nx.NetworkXNoPath(f"Target {target} cannot be reached from given sources")
+
+    seen = {target}
+    stack = [[target, 0]]
+    top = 0
+    while top >= 0:
+        node, i = stack[top]
+        if node in sources:
+            yield [p for p, n in reversed(stack[: top + 1])]
+        if len(pred[node]) > i:
+            stack[top][1] = i + 1
+            next = pred[node][i]
+            if next in seen:
+                continue
+            else:
+                seen.add(next)
+            top += 1
+            if top == len(stack):
+                stack.append([next, 0])
+            else:
+                stack[top][:] = [next, 0]
+        else:
+            seen.discard(node)
+            top -= 1
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/drawing/__pycache__/__init__.cpython-311.pyc b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/drawing/__pycache__/__init__.cpython-311.pyc
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diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/drawing/layout.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/drawing/layout.py
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index 0000000000000000000000000000000000000000..fa120d670748a67cb536f48dbba20081669b96a4
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/drawing/layout.py
@@ -0,0 +1,1297 @@
+"""
+******
+Layout
+******
+
+Node positioning algorithms for graph drawing.
+
+For `random_layout()` the possible resulting shape
+is a square of side [0, scale] (default: [0, 1])
+Changing `center` shifts the layout by that amount.
+
+For the other layout routines, the extent is
+[center - scale, center + scale] (default: [-1, 1]).
+
+Warning: Most layout routines have only been tested in 2-dimensions.
+
+"""
+import networkx as nx
+from networkx.utils import np_random_state
+
+__all__ = [
+    "bipartite_layout",
+    "circular_layout",
+    "kamada_kawai_layout",
+    "random_layout",
+    "rescale_layout",
+    "rescale_layout_dict",
+    "shell_layout",
+    "spring_layout",
+    "spectral_layout",
+    "planar_layout",
+    "fruchterman_reingold_layout",
+    "spiral_layout",
+    "multipartite_layout",
+    "arf_layout",
+]
+
+
+def _process_params(G, center, dim):
+    # Some boilerplate code.
+    import numpy as np
+
+    if not isinstance(G, nx.Graph):
+        empty_graph = nx.Graph()
+        empty_graph.add_nodes_from(G)
+        G = empty_graph
+
+    if center is None:
+        center = np.zeros(dim)
+    else:
+        center = np.asarray(center)
+
+    if len(center) != dim:
+        msg = "length of center coordinates must match dimension of layout"
+        raise ValueError(msg)
+
+    return G, center
+
+
+@np_random_state(3)
+def random_layout(G, center=None, dim=2, seed=None):
+    """Position nodes uniformly at random in the unit square.
+
+    For every node, a position is generated by choosing each of dim
+    coordinates uniformly at random on the interval [0.0, 1.0).
+
+    NumPy (http://scipy.org) is required for this function.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    dim : int
+        Dimension of layout.
+
+    seed : int, RandomState instance or None  optional (default=None)
+        Set the random state for deterministic node layouts.
+        If int, `seed` is the seed used by the random number generator,
+        if numpy.random.RandomState instance, `seed` is the random
+        number generator,
+        if None, the random number generator is the RandomState instance used
+        by numpy.random.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Examples
+    --------
+    >>> G = nx.lollipop_graph(4, 3)
+    >>> pos = nx.random_layout(G)
+
+    """
+    import numpy as np
+
+    G, center = _process_params(G, center, dim)
+    pos = seed.rand(len(G), dim) + center
+    pos = pos.astype(np.float32)
+    pos = dict(zip(G, pos))
+
+    return pos
+
+
+def circular_layout(G, scale=1, center=None, dim=2):
+    # dim=2 only
+    """Position nodes on a circle.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    dim : int
+        Dimension of layout.
+        If dim>2, the remaining dimensions are set to zero
+        in the returned positions.
+        If dim<2, a ValueError is raised.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Raises
+    ------
+    ValueError
+        If dim < 2
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> pos = nx.circular_layout(G)
+
+    Notes
+    -----
+    This algorithm currently only works in two dimensions and does not
+    try to minimize edge crossings.
+
+    """
+    import numpy as np
+
+    if dim < 2:
+        raise ValueError("cannot handle dimensions < 2")
+
+    G, center = _process_params(G, center, dim)
+
+    paddims = max(0, (dim - 2))
+
+    if len(G) == 0:
+        pos = {}
+    elif len(G) == 1:
+        pos = {nx.utils.arbitrary_element(G): center}
+    else:
+        # Discard the extra angle since it matches 0 radians.
+        theta = np.linspace(0, 1, len(G) + 1)[:-1] * 2 * np.pi
+        theta = theta.astype(np.float32)
+        pos = np.column_stack(
+            [np.cos(theta), np.sin(theta), np.zeros((len(G), paddims))]
+        )
+        pos = rescale_layout(pos, scale=scale) + center
+        pos = dict(zip(G, pos))
+
+    return pos
+
+
+def shell_layout(G, nlist=None, rotate=None, scale=1, center=None, dim=2):
+    """Position nodes in concentric circles.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    nlist : list of lists
+       List of node lists for each shell.
+
+    rotate : angle in radians (default=pi/len(nlist))
+       Angle by which to rotate the starting position of each shell
+       relative to the starting position of the previous shell.
+       To recreate behavior before v2.5 use rotate=0.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    dim : int
+        Dimension of layout, currently only dim=2 is supported.
+        Other dimension values result in a ValueError.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Raises
+    ------
+    ValueError
+        If dim != 2
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> shells = [[0], [1, 2, 3]]
+    >>> pos = nx.shell_layout(G, shells)
+
+    Notes
+    -----
+    This algorithm currently only works in two dimensions and does not
+    try to minimize edge crossings.
+
+    """
+    import numpy as np
+
+    if dim != 2:
+        raise ValueError("can only handle 2 dimensions")
+
+    G, center = _process_params(G, center, dim)
+
+    if len(G) == 0:
+        return {}
+    if len(G) == 1:
+        return {nx.utils.arbitrary_element(G): center}
+
+    if nlist is None:
+        # draw the whole graph in one shell
+        nlist = [list(G)]
+
+    radius_bump = scale / len(nlist)
+
+    if len(nlist[0]) == 1:
+        # single node at center
+        radius = 0.0
+    else:
+        # else start at r=1
+        radius = radius_bump
+
+    if rotate is None:
+        rotate = np.pi / len(nlist)
+    first_theta = rotate
+    npos = {}
+    for nodes in nlist:
+        # Discard the last angle (endpoint=False) since 2*pi matches 0 radians
+        theta = (
+            np.linspace(0, 2 * np.pi, len(nodes), endpoint=False, dtype=np.float32)
+            + first_theta
+        )
+        pos = radius * np.column_stack([np.cos(theta), np.sin(theta)]) + center
+        npos.update(zip(nodes, pos))
+        radius += radius_bump
+        first_theta += rotate
+
+    return npos
+
+
+def bipartite_layout(
+    G, nodes, align="vertical", scale=1, center=None, aspect_ratio=4 / 3
+):
+    """Position nodes in two straight lines.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    nodes : list or container
+        Nodes in one node set of the bipartite graph.
+        This set will be placed on left or top.
+
+    align : string (default='vertical')
+        The alignment of nodes. Vertical or horizontal.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    aspect_ratio : number (default=4/3):
+        The ratio of the width to the height of the layout.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node.
+
+    Examples
+    --------
+    >>> G = nx.bipartite.gnmk_random_graph(3, 5, 10, seed=123)
+    >>> top = nx.bipartite.sets(G)[0]
+    >>> pos = nx.bipartite_layout(G, top)
+
+    Notes
+    -----
+    This algorithm currently only works in two dimensions and does not
+    try to minimize edge crossings.
+
+    """
+
+    import numpy as np
+
+    if align not in ("vertical", "horizontal"):
+        msg = "align must be either vertical or horizontal."
+        raise ValueError(msg)
+
+    G, center = _process_params(G, center=center, dim=2)
+    if len(G) == 0:
+        return {}
+
+    height = 1
+    width = aspect_ratio * height
+    offset = (width / 2, height / 2)
+
+    top = dict.fromkeys(nodes)
+    bottom = [v for v in G if v not in top]
+    nodes = list(top) + bottom
+
+    left_xs = np.repeat(0, len(top))
+    right_xs = np.repeat(width, len(bottom))
+    left_ys = np.linspace(0, height, len(top))
+    right_ys = np.linspace(0, height, len(bottom))
+
+    top_pos = np.column_stack([left_xs, left_ys]) - offset
+    bottom_pos = np.column_stack([right_xs, right_ys]) - offset
+
+    pos = np.concatenate([top_pos, bottom_pos])
+    pos = rescale_layout(pos, scale=scale) + center
+    if align == "horizontal":
+        pos = pos[:, ::-1]  # swap x and y coords
+    pos = dict(zip(nodes, pos))
+    return pos
+
+
+@np_random_state(10)
+def spring_layout(
+    G,
+    k=None,
+    pos=None,
+    fixed=None,
+    iterations=50,
+    threshold=1e-4,
+    weight="weight",
+    scale=1,
+    center=None,
+    dim=2,
+    seed=None,
+):
+    """Position nodes using Fruchterman-Reingold force-directed algorithm.
+
+    The algorithm simulates a force-directed representation of the network
+    treating edges as springs holding nodes close, while treating nodes
+    as repelling objects, sometimes called an anti-gravity force.
+    Simulation continues until the positions are close to an equilibrium.
+
+    There are some hard-coded values: minimal distance between
+    nodes (0.01) and "temperature" of 0.1 to ensure nodes don't fly away.
+    During the simulation, `k` helps determine the distance between nodes,
+    though `scale` and `center` determine the size and place after
+    rescaling occurs at the end of the simulation.
+
+    Fixing some nodes doesn't allow them to move in the simulation.
+    It also turns off the rescaling feature at the simulation's end.
+    In addition, setting `scale` to `None` turns off rescaling.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    k : float (default=None)
+        Optimal distance between nodes.  If None the distance is set to
+        1/sqrt(n) where n is the number of nodes.  Increase this value
+        to move nodes farther apart.
+
+    pos : dict or None  optional (default=None)
+        Initial positions for nodes as a dictionary with node as keys
+        and values as a coordinate list or tuple.  If None, then use
+        random initial positions.
+
+    fixed : list or None  optional (default=None)
+        Nodes to keep fixed at initial position.
+        Nodes not in ``G.nodes`` are ignored.
+        ValueError raised if `fixed` specified and `pos` not.
+
+    iterations : int  optional (default=50)
+        Maximum number of iterations taken
+
+    threshold: float optional (default = 1e-4)
+        Threshold for relative error in node position changes.
+        The iteration stops if the error is below this threshold.
+
+    weight : string or None   optional (default='weight')
+        The edge attribute that holds the numerical value used for
+        the edge weight.  Larger means a stronger attractive force.
+        If None, then all edge weights are 1.
+
+    scale : number or None (default: 1)
+        Scale factor for positions. Not used unless `fixed is None`.
+        If scale is None, no rescaling is performed.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+        Not used unless `fixed is None`.
+
+    dim : int
+        Dimension of layout.
+
+    seed : int, RandomState instance or None  optional (default=None)
+        Set the random state for deterministic node layouts.
+        If int, `seed` is the seed used by the random number generator,
+        if numpy.random.RandomState instance, `seed` is the random
+        number generator,
+        if None, the random number generator is the RandomState instance used
+        by numpy.random.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> pos = nx.spring_layout(G)
+
+    # The same using longer but equivalent function name
+    >>> pos = nx.fruchterman_reingold_layout(G)
+    """
+    import numpy as np
+
+    G, center = _process_params(G, center, dim)
+
+    if fixed is not None:
+        if pos is None:
+            raise ValueError("nodes are fixed without positions given")
+        for node in fixed:
+            if node not in pos:
+                raise ValueError("nodes are fixed without positions given")
+        nfixed = {node: i for i, node in enumerate(G)}
+        fixed = np.asarray([nfixed[node] for node in fixed if node in nfixed])
+
+    if pos is not None:
+        # Determine size of existing domain to adjust initial positions
+        dom_size = max(coord for pos_tup in pos.values() for coord in pos_tup)
+        if dom_size == 0:
+            dom_size = 1
+        pos_arr = seed.rand(len(G), dim) * dom_size + center
+
+        for i, n in enumerate(G):
+            if n in pos:
+                pos_arr[i] = np.asarray(pos[n])
+    else:
+        pos_arr = None
+        dom_size = 1
+
+    if len(G) == 0:
+        return {}
+    if len(G) == 1:
+        return {nx.utils.arbitrary_element(G.nodes()): center}
+
+    try:
+        # Sparse matrix
+        if len(G) < 500:  # sparse solver for large graphs
+            raise ValueError
+        A = nx.to_scipy_sparse_array(G, weight=weight, dtype="f")
+        if k is None and fixed is not None:
+            # We must adjust k by domain size for layouts not near 1x1
+            nnodes, _ = A.shape
+            k = dom_size / np.sqrt(nnodes)
+        pos = _sparse_fruchterman_reingold(
+            A, k, pos_arr, fixed, iterations, threshold, dim, seed
+        )
+    except ValueError:
+        A = nx.to_numpy_array(G, weight=weight)
+        if k is None and fixed is not None:
+            # We must adjust k by domain size for layouts not near 1x1
+            nnodes, _ = A.shape
+            k = dom_size / np.sqrt(nnodes)
+        pos = _fruchterman_reingold(
+            A, k, pos_arr, fixed, iterations, threshold, dim, seed
+        )
+    if fixed is None and scale is not None:
+        pos = rescale_layout(pos, scale=scale) + center
+    pos = dict(zip(G, pos))
+    return pos
+
+
+fruchterman_reingold_layout = spring_layout
+
+
+@np_random_state(7)
+def _fruchterman_reingold(
+    A, k=None, pos=None, fixed=None, iterations=50, threshold=1e-4, dim=2, seed=None
+):
+    # Position nodes in adjacency matrix A using Fruchterman-Reingold
+    # Entry point for NetworkX graph is fruchterman_reingold_layout()
+    import numpy as np
+
+    try:
+        nnodes, _ = A.shape
+    except AttributeError as err:
+        msg = "fruchterman_reingold() takes an adjacency matrix as input"
+        raise nx.NetworkXError(msg) from err
+
+    if pos is None:
+        # random initial positions
+        pos = np.asarray(seed.rand(nnodes, dim), dtype=A.dtype)
+    else:
+        # make sure positions are of same type as matrix
+        pos = pos.astype(A.dtype)
+
+    # optimal distance between nodes
+    if k is None:
+        k = np.sqrt(1.0 / nnodes)
+    # the initial "temperature"  is about .1 of domain area (=1x1)
+    # this is the largest step allowed in the dynamics.
+    # We need to calculate this in case our fixed positions force our domain
+    # to be much bigger than 1x1
+    t = max(max(pos.T[0]) - min(pos.T[0]), max(pos.T[1]) - min(pos.T[1])) * 0.1
+    # simple cooling scheme.
+    # linearly step down by dt on each iteration so last iteration is size dt.
+    dt = t / (iterations + 1)
+    delta = np.zeros((pos.shape[0], pos.shape[0], pos.shape[1]), dtype=A.dtype)
+    # the inscrutable (but fast) version
+    # this is still O(V^2)
+    # could use multilevel methods to speed this up significantly
+    for iteration in range(iterations):
+        # matrix of difference between points
+        delta = pos[:, np.newaxis, :] - pos[np.newaxis, :, :]
+        # distance between points
+        distance = np.linalg.norm(delta, axis=-1)
+        # enforce minimum distance of 0.01
+        np.clip(distance, 0.01, None, out=distance)
+        # displacement "force"
+        displacement = np.einsum(
+            "ijk,ij->ik", delta, (k * k / distance**2 - A * distance / k)
+        )
+        # update positions
+        length = np.linalg.norm(displacement, axis=-1)
+        length = np.where(length < 0.01, 0.1, length)
+        delta_pos = np.einsum("ij,i->ij", displacement, t / length)
+        if fixed is not None:
+            # don't change positions of fixed nodes
+            delta_pos[fixed] = 0.0
+        pos += delta_pos
+        # cool temperature
+        t -= dt
+        if (np.linalg.norm(delta_pos) / nnodes) < threshold:
+            break
+    return pos
+
+
+@np_random_state(7)
+def _sparse_fruchterman_reingold(
+    A, k=None, pos=None, fixed=None, iterations=50, threshold=1e-4, dim=2, seed=None
+):
+    # Position nodes in adjacency matrix A using Fruchterman-Reingold
+    # Entry point for NetworkX graph is fruchterman_reingold_layout()
+    # Sparse version
+    import numpy as np
+    import scipy as sp
+
+    try:
+        nnodes, _ = A.shape
+    except AttributeError as err:
+        msg = "fruchterman_reingold() takes an adjacency matrix as input"
+        raise nx.NetworkXError(msg) from err
+    # make sure we have a LIst of Lists representation
+    try:
+        A = A.tolil()
+    except AttributeError:
+        A = (sp.sparse.coo_array(A)).tolil()
+
+    if pos is None:
+        # random initial positions
+        pos = np.asarray(seed.rand(nnodes, dim), dtype=A.dtype)
+    else:
+        # make sure positions are of same type as matrix
+        pos = pos.astype(A.dtype)
+
+    # no fixed nodes
+    if fixed is None:
+        fixed = []
+
+    # optimal distance between nodes
+    if k is None:
+        k = np.sqrt(1.0 / nnodes)
+    # the initial "temperature"  is about .1 of domain area (=1x1)
+    # this is the largest step allowed in the dynamics.
+    t = max(max(pos.T[0]) - min(pos.T[0]), max(pos.T[1]) - min(pos.T[1])) * 0.1
+    # simple cooling scheme.
+    # linearly step down by dt on each iteration so last iteration is size dt.
+    dt = t / (iterations + 1)
+
+    displacement = np.zeros((dim, nnodes))
+    for iteration in range(iterations):
+        displacement *= 0
+        # loop over rows
+        for i in range(A.shape[0]):
+            if i in fixed:
+                continue
+            # difference between this row's node position and all others
+            delta = (pos[i] - pos).T
+            # distance between points
+            distance = np.sqrt((delta**2).sum(axis=0))
+            # enforce minimum distance of 0.01
+            distance = np.where(distance < 0.01, 0.01, distance)
+            # the adjacency matrix row
+            Ai = A.getrowview(i).toarray()  # TODO: revisit w/ sparse 1D container
+            # displacement "force"
+            displacement[:, i] += (
+                delta * (k * k / distance**2 - Ai * distance / k)
+            ).sum(axis=1)
+        # update positions
+        length = np.sqrt((displacement**2).sum(axis=0))
+        length = np.where(length < 0.01, 0.1, length)
+        delta_pos = (displacement * t / length).T
+        pos += delta_pos
+        # cool temperature
+        t -= dt
+        if (np.linalg.norm(delta_pos) / nnodes) < threshold:
+            break
+    return pos
+
+
+def kamada_kawai_layout(
+    G, dist=None, pos=None, weight="weight", scale=1, center=None, dim=2
+):
+    """Position nodes using Kamada-Kawai path-length cost-function.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    dist : dict (default=None)
+        A two-level dictionary of optimal distances between nodes,
+        indexed by source and destination node.
+        If None, the distance is computed using shortest_path_length().
+
+    pos : dict or None  optional (default=None)
+        Initial positions for nodes as a dictionary with node as keys
+        and values as a coordinate list or tuple.  If None, then use
+        circular_layout() for dim >= 2 and a linear layout for dim == 1.
+
+    weight : string or None   optional (default='weight')
+        The edge attribute that holds the numerical value used for
+        the edge weight.  If None, then all edge weights are 1.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    dim : int
+        Dimension of layout.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> pos = nx.kamada_kawai_layout(G)
+    """
+    import numpy as np
+
+    G, center = _process_params(G, center, dim)
+    nNodes = len(G)
+    if nNodes == 0:
+        return {}
+
+    if dist is None:
+        dist = dict(nx.shortest_path_length(G, weight=weight))
+    dist_mtx = 1e6 * np.ones((nNodes, nNodes))
+    for row, nr in enumerate(G):
+        if nr not in dist:
+            continue
+        rdist = dist[nr]
+        for col, nc in enumerate(G):
+            if nc not in rdist:
+                continue
+            dist_mtx[row][col] = rdist[nc]
+
+    if pos is None:
+        if dim >= 3:
+            pos = random_layout(G, dim=dim)
+        elif dim == 2:
+            pos = circular_layout(G, dim=dim)
+        else:
+            pos = dict(zip(G, np.linspace(0, 1, len(G))))
+    pos_arr = np.array([pos[n] for n in G])
+
+    pos = _kamada_kawai_solve(dist_mtx, pos_arr, dim)
+
+    pos = rescale_layout(pos, scale=scale) + center
+    return dict(zip(G, pos))
+
+
+def _kamada_kawai_solve(dist_mtx, pos_arr, dim):
+    # Anneal node locations based on the Kamada-Kawai cost-function,
+    # using the supplied matrix of preferred inter-node distances,
+    # and starting locations.
+
+    import numpy as np
+    import scipy as sp
+
+    meanwt = 1e-3
+    costargs = (np, 1 / (dist_mtx + np.eye(dist_mtx.shape[0]) * 1e-3), meanwt, dim)
+
+    optresult = sp.optimize.minimize(
+        _kamada_kawai_costfn,
+        pos_arr.ravel(),
+        method="L-BFGS-B",
+        args=costargs,
+        jac=True,
+    )
+
+    return optresult.x.reshape((-1, dim))
+
+
+def _kamada_kawai_costfn(pos_vec, np, invdist, meanweight, dim):
+    # Cost-function and gradient for Kamada-Kawai layout algorithm
+    nNodes = invdist.shape[0]
+    pos_arr = pos_vec.reshape((nNodes, dim))
+
+    delta = pos_arr[:, np.newaxis, :] - pos_arr[np.newaxis, :, :]
+    nodesep = np.linalg.norm(delta, axis=-1)
+    direction = np.einsum("ijk,ij->ijk", delta, 1 / (nodesep + np.eye(nNodes) * 1e-3))
+
+    offset = nodesep * invdist - 1.0
+    offset[np.diag_indices(nNodes)] = 0
+
+    cost = 0.5 * np.sum(offset**2)
+    grad = np.einsum("ij,ij,ijk->ik", invdist, offset, direction) - np.einsum(
+        "ij,ij,ijk->jk", invdist, offset, direction
+    )
+
+    # Additional parabolic term to encourage mean position to be near origin:
+    sumpos = np.sum(pos_arr, axis=0)
+    cost += 0.5 * meanweight * np.sum(sumpos**2)
+    grad += meanweight * sumpos
+
+    return (cost, grad.ravel())
+
+
+def spectral_layout(G, weight="weight", scale=1, center=None, dim=2):
+    """Position nodes using the eigenvectors of the graph Laplacian.
+
+    Using the unnormalized Laplacian, the layout shows possible clusters of
+    nodes which are an approximation of the ratio cut. If dim is the number of
+    dimensions then the positions are the entries of the dim eigenvectors
+    corresponding to the ascending eigenvalues starting from the second one.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    weight : string or None   optional (default='weight')
+        The edge attribute that holds the numerical value used for
+        the edge weight.  If None, then all edge weights are 1.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    dim : int
+        Dimension of layout.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> pos = nx.spectral_layout(G)
+
+    Notes
+    -----
+    Directed graphs will be considered as undirected graphs when
+    positioning the nodes.
+
+    For larger graphs (>500 nodes) this will use the SciPy sparse
+    eigenvalue solver (ARPACK).
+    """
+    # handle some special cases that break the eigensolvers
+    import numpy as np
+
+    G, center = _process_params(G, center, dim)
+
+    if len(G) <= 2:
+        if len(G) == 0:
+            pos = np.array([])
+        elif len(G) == 1:
+            pos = np.array([center])
+        else:
+            pos = np.array([np.zeros(dim), np.array(center) * 2.0])
+        return dict(zip(G, pos))
+    try:
+        # Sparse matrix
+        if len(G) < 500:  # dense solver is faster for small graphs
+            raise ValueError
+        A = nx.to_scipy_sparse_array(G, weight=weight, dtype="d")
+        # Symmetrize directed graphs
+        if G.is_directed():
+            A = A + np.transpose(A)
+        pos = _sparse_spectral(A, dim)
+    except (ImportError, ValueError):
+        # Dense matrix
+        A = nx.to_numpy_array(G, weight=weight)
+        # Symmetrize directed graphs
+        if G.is_directed():
+            A += A.T
+        pos = _spectral(A, dim)
+
+    pos = rescale_layout(pos, scale=scale) + center
+    pos = dict(zip(G, pos))
+    return pos
+
+
+def _spectral(A, dim=2):
+    # Input adjacency matrix A
+    # Uses dense eigenvalue solver from numpy
+    import numpy as np
+
+    try:
+        nnodes, _ = A.shape
+    except AttributeError as err:
+        msg = "spectral() takes an adjacency matrix as input"
+        raise nx.NetworkXError(msg) from err
+
+    # form Laplacian matrix where D is diagonal of degrees
+    D = np.identity(nnodes, dtype=A.dtype) * np.sum(A, axis=1)
+    L = D - A
+
+    eigenvalues, eigenvectors = np.linalg.eig(L)
+    # sort and keep smallest nonzero
+    index = np.argsort(eigenvalues)[1 : dim + 1]  # 0 index is zero eigenvalue
+    return np.real(eigenvectors[:, index])
+
+
+def _sparse_spectral(A, dim=2):
+    # Input adjacency matrix A
+    # Uses sparse eigenvalue solver from scipy
+    # Could use multilevel methods here, see Koren "On spectral graph drawing"
+    import numpy as np
+    import scipy as sp
+
+    try:
+        nnodes, _ = A.shape
+    except AttributeError as err:
+        msg = "sparse_spectral() takes an adjacency matrix as input"
+        raise nx.NetworkXError(msg) from err
+
+    # form Laplacian matrix
+    # TODO: Rm csr_array wrapper in favor of spdiags array constructor when available
+    D = sp.sparse.csr_array(sp.sparse.spdiags(A.sum(axis=1), 0, nnodes, nnodes))
+    L = D - A
+
+    k = dim + 1
+    # number of Lanczos vectors for ARPACK solver.What is the right scaling?
+    ncv = max(2 * k + 1, int(np.sqrt(nnodes)))
+    # return smallest k eigenvalues and eigenvectors
+    eigenvalues, eigenvectors = sp.sparse.linalg.eigsh(L, k, which="SM", ncv=ncv)
+    index = np.argsort(eigenvalues)[1:k]  # 0 index is zero eigenvalue
+    return np.real(eigenvectors[:, index])
+
+
+def planar_layout(G, scale=1, center=None, dim=2):
+    """Position nodes without edge intersections.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G. If G is of type
+        nx.PlanarEmbedding, the positions are selected accordingly.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    dim : int
+        Dimension of layout.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Raises
+    ------
+    NetworkXException
+        If G is not planar
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> pos = nx.planar_layout(G)
+    """
+    import numpy as np
+
+    if dim != 2:
+        raise ValueError("can only handle 2 dimensions")
+
+    G, center = _process_params(G, center, dim)
+
+    if len(G) == 0:
+        return {}
+
+    if isinstance(G, nx.PlanarEmbedding):
+        embedding = G
+    else:
+        is_planar, embedding = nx.check_planarity(G)
+        if not is_planar:
+            raise nx.NetworkXException("G is not planar.")
+    pos = nx.combinatorial_embedding_to_pos(embedding)
+    node_list = list(embedding)
+    pos = np.row_stack([pos[x] for x in node_list])
+    pos = pos.astype(np.float64)
+    pos = rescale_layout(pos, scale=scale) + center
+    return dict(zip(node_list, pos))
+
+
+def spiral_layout(G, scale=1, center=None, dim=2, resolution=0.35, equidistant=False):
+    """Position nodes in a spiral layout.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+    scale : number (default: 1)
+        Scale factor for positions.
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+    dim : int, default=2
+        Dimension of layout, currently only dim=2 is supported.
+        Other dimension values result in a ValueError.
+    resolution : float, default=0.35
+        The compactness of the spiral layout returned.
+        Lower values result in more compressed spiral layouts.
+    equidistant : bool, default=False
+        If True, nodes will be positioned equidistant from each other
+        by decreasing angle further from center.
+        If False, nodes will be positioned at equal angles
+        from each other by increasing separation further from center.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Raises
+    ------
+    ValueError
+        If dim != 2
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> pos = nx.spiral_layout(G)
+    >>> nx.draw(G, pos=pos)
+
+    Notes
+    -----
+    This algorithm currently only works in two dimensions.
+
+    """
+    import numpy as np
+
+    if dim != 2:
+        raise ValueError("can only handle 2 dimensions")
+
+    G, center = _process_params(G, center, dim)
+
+    if len(G) == 0:
+        return {}
+    if len(G) == 1:
+        return {nx.utils.arbitrary_element(G): center}
+
+    pos = []
+    if equidistant:
+        chord = 1
+        step = 0.5
+        theta = resolution
+        theta += chord / (step * theta)
+        for _ in range(len(G)):
+            r = step * theta
+            theta += chord / r
+            pos.append([np.cos(theta) * r, np.sin(theta) * r])
+
+    else:
+        dist = np.arange(len(G), dtype=float)
+        angle = resolution * dist
+        pos = np.transpose(dist * np.array([np.cos(angle), np.sin(angle)]))
+
+    pos = rescale_layout(np.array(pos), scale=scale) + center
+
+    pos = dict(zip(G, pos))
+
+    return pos
+
+
+def multipartite_layout(G, subset_key="subset", align="vertical", scale=1, center=None):
+    """Position nodes in layers of straight lines.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    subset_key : string (default='subset')
+        Key of node data to be used as layer subset.
+
+    align : string (default='vertical')
+        The alignment of nodes. Vertical or horizontal.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node.
+
+    Examples
+    --------
+    >>> G = nx.complete_multipartite_graph(28, 16, 10)
+    >>> pos = nx.multipartite_layout(G)
+
+    Notes
+    -----
+    This algorithm currently only works in two dimensions and does not
+    try to minimize edge crossings.
+
+    Network does not need to be a complete multipartite graph. As long as nodes
+    have subset_key data, they will be placed in the corresponding layers.
+
+    """
+    import numpy as np
+
+    if align not in ("vertical", "horizontal"):
+        msg = "align must be either vertical or horizontal."
+        raise ValueError(msg)
+
+    G, center = _process_params(G, center=center, dim=2)
+    if len(G) == 0:
+        return {}
+
+    layers = {}
+    for v, data in G.nodes(data=True):
+        try:
+            layer = data[subset_key]
+        except KeyError:
+            msg = "all nodes must have subset_key (default='subset') as data"
+            raise ValueError(msg)
+        layers[layer] = [v] + layers.get(layer, [])
+
+    # Sort by layer, if possible
+    try:
+        layers = sorted(layers.items())
+    except TypeError:
+        layers = list(layers.items())
+
+    pos = None
+    nodes = []
+    width = len(layers)
+    for i, (_, layer) in enumerate(layers):
+        height = len(layer)
+        xs = np.repeat(i, height)
+        ys = np.arange(0, height, dtype=float)
+        offset = ((width - 1) / 2, (height - 1) / 2)
+        layer_pos = np.column_stack([xs, ys]) - offset
+        if pos is None:
+            pos = layer_pos
+        else:
+            pos = np.concatenate([pos, layer_pos])
+        nodes.extend(layer)
+    pos = rescale_layout(pos, scale=scale) + center
+    if align == "horizontal":
+        pos = pos[:, ::-1]  # swap x and y coords
+    pos = dict(zip(nodes, pos))
+    return pos
+
+
+def arf_layout(
+    G,
+    pos=None,
+    scaling=1,
+    a=1.1,
+    etol=1e-6,
+    dt=1e-3,
+    max_iter=1000,
+):
+    """Arf layout for networkx
+
+    The attractive and repulsive forces (arf) layout [1]
+    improves the spring layout in three ways. First, it
+    prevents congestion of highly connected nodes due to
+    strong forcing between nodes. Second, it utilizes the
+    layout space more effectively by preventing large gaps
+    that spring layout tends to create. Lastly, the arf
+    layout represents symmetries in the layout better than
+    the default spring layout.
+
+    Parameters
+    ----------
+    G : nx.Graph or nx.DiGraph
+        Networkx graph.
+    pos : dict
+        Initial  position of  the nodes.  If set  to None  a
+        random layout will be used.
+    scaling : float
+        Scales the radius of the circular layout space.
+    a : float
+        Strength of springs between connected nodes. Should be larger than 1. The greater a, the clearer the separation ofunconnected sub clusters.
+    etol : float
+        Gradient sum of spring forces must be larger than `etol` before successful termination.
+    dt : float
+        Time step for force differential equation simulations.
+    max_iter : int
+        Max iterations before termination of the algorithm.
+
+    References
+    .. [1] "Self-Organization Applied to Dynamic Network Layout", M. Geipel,
+            International Journal of Modern Physics C, 2007, Vol 18, No 10, pp. 1537-1549.
+            https://doi.org/10.1142/S0129183107011558 https://arxiv.org/abs/0704.1748
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node.
+
+    Examples
+    --------
+    >>> G = nx.grid_graph((5, 5))
+    >>> pos = nx.arf_layout(G)
+
+    """
+    import warnings
+
+    import numpy as np
+
+    if a <= 1:
+        msg = "The parameter a should be larger than 1"
+        raise ValueError(msg)
+
+    pos_tmp = nx.random_layout(G)
+    if pos is None:
+        pos = pos_tmp
+    else:
+        for node in G.nodes():
+            if node not in pos:
+                pos[node] = pos_tmp[node].copy()
+
+    # Initialize spring constant matrix
+    N = len(G)
+    # No nodes no computation
+    if N == 0:
+        return pos
+
+    # init force of springs
+    K = np.ones((N, N)) - np.eye(N)
+    node_order = {node: i for i, node in enumerate(G)}
+    for x, y in G.edges():
+        if x != y:
+            idx, jdx = (node_order[i] for i in (x, y))
+            K[idx, jdx] = a
+
+    # vectorize values
+    p = np.asarray(list(pos.values()))
+
+    # equation 10 in [1]
+    rho = scaling * np.sqrt(N)
+
+    # looping variables
+    error = etol + 1
+    n_iter = 0
+    while error > etol:
+        diff = p[:, np.newaxis] - p[np.newaxis]
+        A = np.linalg.norm(diff, axis=-1)[..., np.newaxis]
+        # attraction_force - repulsions force
+        # suppress nans due to division; caused by diagonal set to zero.
+        # Does not affect the computation due to nansum
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            change = K[..., np.newaxis] * diff - rho / A * diff
+        change = np.nansum(change, axis=0)
+        p += change * dt
+
+        error = np.linalg.norm(change, axis=-1).sum()
+        if n_iter > max_iter:
+            break
+        n_iter += 1
+    return dict(zip(G.nodes(), p))
+
+
+def rescale_layout(pos, scale=1):
+    """Returns scaled position array to (-scale, scale) in all axes.
+
+    The function acts on NumPy arrays which hold position information.
+    Each position is one row of the array. The dimension of the space
+    equals the number of columns. Each coordinate in one column.
+
+    To rescale, the mean (center) is subtracted from each axis separately.
+    Then all values are scaled so that the largest magnitude value
+    from all axes equals `scale` (thus, the aspect ratio is preserved).
+    The resulting NumPy Array is returned (order of rows unchanged).
+
+    Parameters
+    ----------
+    pos : numpy array
+        positions to be scaled. Each row is a position.
+
+    scale : number (default: 1)
+        The size of the resulting extent in all directions.
+
+    Returns
+    -------
+    pos : numpy array
+        scaled positions. Each row is a position.
+
+    See Also
+    --------
+    rescale_layout_dict
+    """
+    import numpy as np
+
+    # Find max length over all dimensions
+    pos -= pos.mean(axis=0)
+    lim = np.abs(pos).max()  # max coordinate for all axes
+    # rescale to (-scale, scale) in all directions, preserves aspect
+    if lim > 0:
+        pos *= scale / lim
+    return pos
+
+
+def rescale_layout_dict(pos, scale=1):
+    """Return a dictionary of scaled positions keyed by node
+
+    Parameters
+    ----------
+    pos : A dictionary of positions keyed by node
+
+    scale : number (default: 1)
+        The size of the resulting extent in all directions.
+
+    Returns
+    -------
+    pos : A dictionary of positions keyed by node
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> pos = {0: np.array((0, 0)), 1: np.array((1, 1)), 2: np.array((0.5, 0.5))}
+    >>> nx.rescale_layout_dict(pos)
+    {0: array([-1., -1.]), 1: array([1., 1.]), 2: array([0., 0.])}
+
+    >>> pos = {0: np.array((0, 0)), 1: np.array((-1, 1)), 2: np.array((-0.5, 0.5))}
+    >>> nx.rescale_layout_dict(pos, scale=2)
+    {0: array([ 2., -2.]), 1: array([-2.,  2.]), 2: array([0., 0.])}
+
+    See Also
+    --------
+    rescale_layout
+    """
+    import numpy as np
+
+    if not pos:  # empty_graph
+        return {}
+    pos_v = np.array(list(pos.values()))
+    pos_v = rescale_layout(pos_v, scale=scale)
+    return dict(zip(pos, pos_v))
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/drawing/nx_latex.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/drawing/nx_latex.py
new file mode 100644
index 0000000000000000000000000000000000000000..6312f71505e79bf5a2e2d9979274ba2a08b2e32b
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/drawing/nx_latex.py
@@ -0,0 +1,571 @@
+r"""
+*****
+LaTeX
+*****
+
+Export NetworkX graphs in LaTeX format using the TikZ library within TeX/LaTeX.
+Usually, you will want the drawing to appear in a figure environment so
+you use ``to_latex(G, caption="A caption")``. If you want the raw
+drawing commands without a figure environment use :func:`to_latex_raw`.
+And if you want to write to a file instead of just returning the latex
+code as a string, use ``write_latex(G, "filename.tex", caption="A caption")``.
+
+To construct a figure with subfigures for each graph to be shown, provide
+``to_latex`` or ``write_latex`` a list of graphs, a list of subcaptions,
+and a number of rows of subfigures inside the figure.
+
+To be able to refer to the figures or subfigures in latex using ``\\ref``,
+the keyword ``latex_label`` is available for figures and `sub_labels` for
+a list of labels, one for each subfigure.
+
+We intend to eventually provide an interface to the TikZ Graph
+features which include e.g. layout algorithms.
+
+Let us know via github what you'd like to see available, or better yet
+give us some code to do it, or even better make a github pull request
+to add the feature.
+
+The TikZ approach
+=================
+Drawing options can be stored on the graph as node/edge attributes, or
+can be provided as dicts keyed by node/edge to a string of the options
+for that node/edge. Similarly a label can be shown for each node/edge
+by specifying the labels as graph node/edge attributes or by providing
+a dict keyed by node/edge to the text to be written for that node/edge.
+
+Options for the tikzpicture environment (e.g. "[scale=2]") can be provided
+via a keyword argument. Similarly default node and edge options can be
+provided through keywords arguments. The default node options are applied
+to the single TikZ "path" that draws all nodes (and no edges). The default edge
+options are applied to a TikZ "scope" which contains a path for each edge.
+
+Examples
+========
+>>> G = nx.path_graph(3)
+>>> nx.write_latex(G, "just_my_figure.tex", as_document=True)
+>>> nx.write_latex(G, "my_figure.tex", caption="A path graph", latex_label="fig1")
+>>> latex_code = nx.to_latex(G)  # a string rather than a file
+
+You can change many features of the nodes and edges.
+
+>>> G = nx.path_graph(4, create_using=nx.DiGraph)
+>>> pos = {n: (n, n) for n in G}  # nodes set on a line
+
+>>> G.nodes[0]["style"] = "blue"
+>>> G.nodes[2]["style"] = "line width=3,draw"
+>>> G.nodes[3]["label"] = "Stop"
+>>> G.edges[(0, 1)]["label"] = "1st Step"
+>>> G.edges[(0, 1)]["label_opts"] = "near start"
+>>> G.edges[(1, 2)]["style"] = "line width=3"
+>>> G.edges[(1, 2)]["label"] = "2nd Step"
+>>> G.edges[(2, 3)]["style"] = "green"
+>>> G.edges[(2, 3)]["label"] = "3rd Step"
+>>> G.edges[(2, 3)]["label_opts"] = "near end"
+
+>>> nx.write_latex(G, "latex_graph.tex", pos=pos, as_document=True)
+
+Then compile the LaTeX using something like ``pdflatex latex_graph.tex``
+and view the pdf file created: ``latex_graph.pdf``.
+
+If you want **subfigures** each containing one graph, you can input a list of graphs.
+
+>>> H1 = nx.path_graph(4)
+>>> H2 = nx.complete_graph(4)
+>>> H3 = nx.path_graph(8)
+>>> H4 = nx.complete_graph(8)
+>>> graphs = [H1, H2, H3, H4]
+>>> caps = ["Path 4", "Complete graph 4", "Path 8", "Complete graph 8"]
+>>> lbls = ["fig2a", "fig2b", "fig2c", "fig2d"]
+>>> nx.write_latex(graphs, "subfigs.tex", n_rows=2, sub_captions=caps, sub_labels=lbls)
+>>> latex_code = nx.to_latex(graphs, n_rows=2, sub_captions=caps, sub_labels=lbls)
+
+>>> node_color = {0: "red", 1: "orange", 2: "blue", 3: "gray!90"}
+>>> edge_width = {e: "line width=1.5" for e in H3.edges}
+>>> pos = nx.circular_layout(H3)
+>>> latex_code = nx.to_latex(H3, pos, node_options=node_color, edge_options=edge_width)
+>>> print(latex_code)
+\documentclass{report}
+\usepackage{tikz}
+\usepackage{subcaption}
+<BLANKLINE>
+\begin{document}
+\begin{figure}
+  \begin{tikzpicture}
+      \draw
+        (1.0, 0.0) node[red] (0){0}
+        (0.707, 0.707) node[orange] (1){1}
+        (-0.0, 1.0) node[blue] (2){2}
+        (-0.707, 0.707) node[gray!90] (3){3}
+        (-1.0, -0.0) node (4){4}
+        (-0.707, -0.707) node (5){5}
+        (0.0, -1.0) node (6){6}
+        (0.707, -0.707) node (7){7};
+      \begin{scope}[-]
+        \draw[line width=1.5] (0) to (1);
+        \draw[line width=1.5] (1) to (2);
+        \draw[line width=1.5] (2) to (3);
+        \draw[line width=1.5] (3) to (4);
+        \draw[line width=1.5] (4) to (5);
+        \draw[line width=1.5] (5) to (6);
+        \draw[line width=1.5] (6) to (7);
+      \end{scope}
+    \end{tikzpicture}
+\end{figure}
+\end{document}
+
+Notes
+-----
+If you want to change the preamble/postamble of the figure/document/subfigure
+environment, use the keyword arguments: `figure_wrapper`, `document_wrapper`,
+`subfigure_wrapper`. The default values are stored in private variables
+e.g. ``nx.nx_layout._DOCUMENT_WRAPPER``
+
+References
+----------
+TikZ:          https://tikz.dev/
+
+TikZ options details:   https://tikz.dev/tikz-actions
+"""
+import numbers
+import os
+
+import networkx as nx
+
+__all__ = [
+    "to_latex_raw",
+    "to_latex",
+    "write_latex",
+]
+
+
+@nx.utils.not_implemented_for("multigraph")
+def to_latex_raw(
+    G,
+    pos="pos",
+    tikz_options="",
+    default_node_options="",
+    node_options="node_options",
+    node_label="label",
+    default_edge_options="",
+    edge_options="edge_options",
+    edge_label="label",
+    edge_label_options="edge_label_options",
+):
+    """Return a string of the LaTeX/TikZ code to draw `G`
+
+    This function produces just the code for the tikzpicture
+    without any enclosing environment.
+
+    Parameters
+    ==========
+    G : NetworkX graph
+        The NetworkX graph to be drawn
+    pos : string or dict (default "pos")
+        The name of the node attribute on `G` that holds the position of each node.
+        Positions can be sequences of length 2 with numbers for (x,y) coordinates.
+        They can also be strings to denote positions in TikZ style, such as (x, y)
+        or (angle:radius).
+        If a dict, it should be keyed by node to a position.
+        If an empty dict, a circular layout is computed by TikZ.
+    tikz_options : string
+        The tikzpicture options description defining the options for the picture.
+        Often large scale options like `[scale=2]`.
+    default_node_options : string
+        The draw options for a path of nodes. Individual node options override these.
+    node_options : string or dict
+        The name of the node attribute on `G` that holds the options for each node.
+        Or a dict keyed by node to a string holding the options for that node.
+    node_label : string or dict
+        The name of the node attribute on `G` that holds the node label (text)
+        displayed for each node. If the attribute is "" or not present, the node
+        itself is drawn as a string. LaTeX processing such as ``"$A_1$"`` is allowed.
+        Or a dict keyed by node to a string holding the label for that node.
+    default_edge_options : string
+        The options for the scope drawing all edges. The default is "[-]" for
+        undirected graphs and "[->]" for directed graphs.
+    edge_options : string or dict
+        The name of the edge attribute on `G` that holds the options for each edge.
+        If the edge is a self-loop and ``"loop" not in edge_options`` the option
+        "loop," is added to the options for the self-loop edge. Hence you can
+        use "[loop above]" explicitly, but the default is "[loop]".
+        Or a dict keyed by edge to a string holding the options for that edge.
+    edge_label : string or dict
+        The name of the edge attribute on `G` that holds the edge label (text)
+        displayed for each edge. If the attribute is "" or not present, no edge
+        label is drawn.
+        Or a dict keyed by edge to a string holding the label for that edge.
+    edge_label_options : string or dict
+        The name of the edge attribute on `G` that holds the label options for
+        each edge. For example, "[sloped,above,blue]". The default is no options.
+        Or a dict keyed by edge to a string holding the label options for that edge.
+
+    Returns
+    =======
+    latex_code : string
+       The text string which draws the desired graph(s) when compiled by LaTeX.
+
+    See Also
+    ========
+    to_latex
+    write_latex
+    """
+    i4 = "\n    "
+    i8 = "\n        "
+
+    # set up position dict
+    # TODO allow pos to be None and use a nice TikZ default
+    if not isinstance(pos, dict):
+        pos = nx.get_node_attributes(G, pos)
+    if not pos:
+        # circular layout with radius 2
+        pos = {n: f"({round(360.0 * i / len(G), 3)}:2)" for i, n in enumerate(G)}
+    for node in G:
+        if node not in pos:
+            raise nx.NetworkXError(f"node {node} has no specified pos {pos}")
+        posnode = pos[node]
+        if not isinstance(posnode, str):
+            try:
+                posx, posy = posnode
+                pos[node] = f"({round(posx, 3)}, {round(posy, 3)})"
+            except (TypeError, ValueError):
+                msg = f"position pos[{node}] is not 2-tuple or a string: {posnode}"
+                raise nx.NetworkXError(msg)
+
+    # set up all the dicts
+    if not isinstance(node_options, dict):
+        node_options = nx.get_node_attributes(G, node_options)
+    if not isinstance(node_label, dict):
+        node_label = nx.get_node_attributes(G, node_label)
+    if not isinstance(edge_options, dict):
+        edge_options = nx.get_edge_attributes(G, edge_options)
+    if not isinstance(edge_label, dict):
+        edge_label = nx.get_edge_attributes(G, edge_label)
+    if not isinstance(edge_label_options, dict):
+        edge_label_options = nx.get_edge_attributes(G, edge_label_options)
+
+    # process default options (add brackets or not)
+    topts = "" if tikz_options == "" else f"[{tikz_options.strip('[]')}]"
+    defn = "" if default_node_options == "" else f"[{default_node_options.strip('[]')}]"
+    linestyle = f"{'->' if G.is_directed() else '-'}"
+    if default_edge_options == "":
+        defe = "[" + linestyle + "]"
+    elif "-" in default_edge_options:
+        defe = default_edge_options
+    else:
+        defe = f"[{linestyle},{default_edge_options.strip('[]')}]"
+
+    # Construct the string line by line
+    result = "  \\begin{tikzpicture}" + topts
+    result += i4 + "  \\draw" + defn
+    # load the nodes
+    for n in G:
+        # node options goes inside square brackets
+        nopts = f"[{node_options[n].strip('[]')}]" if n in node_options else ""
+        # node text goes inside curly brackets {}
+        ntext = f"{{{node_label[n]}}}" if n in node_label else f"{{{n}}}"
+
+        result += i8 + f"{pos[n]} node{nopts} ({n}){ntext}"
+    result += ";\n"
+
+    # load the edges
+    result += "      \\begin{scope}" + defe
+    for edge in G.edges:
+        u, v = edge[:2]
+        e_opts = f"{edge_options[edge]}".strip("[]") if edge in edge_options else ""
+        # add loop options for selfloops if not present
+        if u == v and "loop" not in e_opts:
+            e_opts = "loop," + e_opts
+        e_opts = f"[{e_opts}]" if e_opts != "" else ""
+        # TODO -- handle bending of multiedges
+
+        els = edge_label_options[edge] if edge in edge_label_options else ""
+        # edge label options goes inside square brackets []
+        els = f"[{els.strip('[]')}]"
+        # edge text is drawn using the TikZ node command inside curly brackets {}
+        e_label = f" node{els} {{{edge_label[edge]}}}" if edge in edge_label else ""
+
+        result += i8 + f"\\draw{e_opts} ({u}) to{e_label} ({v});"
+
+    result += "\n      \\end{scope}\n    \\end{tikzpicture}\n"
+    return result
+
+
+_DOC_WRAPPER_TIKZ = r"""\documentclass{{report}}
+\usepackage{{tikz}}
+\usepackage{{subcaption}}
+
+\begin{{document}}
+{content}
+\end{{document}}"""
+
+
+_FIG_WRAPPER = r"""\begin{{figure}}
+{content}{caption}{label}
+\end{{figure}}"""
+
+
+_SUBFIG_WRAPPER = r"""  \begin{{subfigure}}{{{size}\textwidth}}
+{content}{caption}{label}
+  \end{{subfigure}}"""
+
+
+def to_latex(
+    Gbunch,
+    pos="pos",
+    tikz_options="",
+    default_node_options="",
+    node_options="node_options",
+    node_label="node_label",
+    default_edge_options="",
+    edge_options="edge_options",
+    edge_label="edge_label",
+    edge_label_options="edge_label_options",
+    caption="",
+    latex_label="",
+    sub_captions=None,
+    sub_labels=None,
+    n_rows=1,
+    as_document=True,
+    document_wrapper=_DOC_WRAPPER_TIKZ,
+    figure_wrapper=_FIG_WRAPPER,
+    subfigure_wrapper=_SUBFIG_WRAPPER,
+):
+    """Return latex code to draw the graph(s) in `Gbunch`
+
+    The TikZ drawing utility in LaTeX is used to draw the graph(s).
+    If `Gbunch` is a graph, it is drawn in a figure environment.
+    If `Gbunch` is an iterable of graphs, each is drawn in a subfigure environment
+    within a single figure environment.
+
+    If `as_document` is True, the figure is wrapped inside a document environment
+    so that the resulting string is ready to be compiled by LaTeX. Otherwise,
+    the string is ready for inclusion in a larger tex document using ``\\include``
+    or ``\\input`` statements.
+
+    Parameters
+    ==========
+    Gbunch : NetworkX graph or iterable of NetworkX graphs
+        The NetworkX graph to be drawn or an iterable of graphs
+        to be drawn inside subfigures of a single figure.
+    pos : string or list of strings
+        The name of the node attribute on `G` that holds the position of each node.
+        Positions can be sequences of length 2 with numbers for (x,y) coordinates.
+        They can also be strings to denote positions in TikZ style, such as (x, y)
+        or (angle:radius).
+        If a dict, it should be keyed by node to a position.
+        If an empty dict, a circular layout is computed by TikZ.
+        If you are drawing many graphs in subfigures, use a list of position dicts.
+    tikz_options : string
+        The tikzpicture options description defining the options for the picture.
+        Often large scale options like `[scale=2]`.
+    default_node_options : string
+        The draw options for a path of nodes. Individual node options override these.
+    node_options : string or dict
+        The name of the node attribute on `G` that holds the options for each node.
+        Or a dict keyed by node to a string holding the options for that node.
+    node_label : string or dict
+        The name of the node attribute on `G` that holds the node label (text)
+        displayed for each node. If the attribute is "" or not present, the node
+        itself is drawn as a string. LaTeX processing such as ``"$A_1$"`` is allowed.
+        Or a dict keyed by node to a string holding the label for that node.
+    default_edge_options : string
+        The options for the scope drawing all edges. The default is "[-]" for
+        undirected graphs and "[->]" for directed graphs.
+    edge_options : string or dict
+        The name of the edge attribute on `G` that holds the options for each edge.
+        If the edge is a self-loop and ``"loop" not in edge_options`` the option
+        "loop," is added to the options for the self-loop edge. Hence you can
+        use "[loop above]" explicitly, but the default is "[loop]".
+        Or a dict keyed by edge to a string holding the options for that edge.
+    edge_label : string or dict
+        The name of the edge attribute on `G` that holds the edge label (text)
+        displayed for each edge. If the attribute is "" or not present, no edge
+        label is drawn.
+        Or a dict keyed by edge to a string holding the label for that edge.
+    edge_label_options : string or dict
+        The name of the edge attribute on `G` that holds the label options for
+        each edge. For example, "[sloped,above,blue]". The default is no options.
+        Or a dict keyed by edge to a string holding the label options for that edge.
+    caption : string
+        The caption string for the figure environment
+    latex_label : string
+        The latex label used for the figure for easy referral from the main text
+    sub_captions : list of strings
+        The sub_caption string for each subfigure in the figure
+    sub_latex_labels : list of strings
+        The latex label for each subfigure in the figure
+    n_rows : int
+        The number of rows of subfigures to arrange for multiple graphs
+    as_document : bool
+        Whether to wrap the latex code in a document environment for compiling
+    document_wrapper : formatted text string with variable ``content``.
+        This text is called to evaluate the content embedded in a document
+        environment with a preamble setting up TikZ.
+    figure_wrapper : formatted text string
+        This text is evaluated with variables ``content``, ``caption`` and ``label``.
+        It wraps the content and if a caption is provided, adds the latex code for
+        that caption, and if a label is provided, adds the latex code for a label.
+    subfigure_wrapper : formatted text string
+        This text evaluate variables ``size``, ``content``, ``caption`` and ``label``.
+        It wraps the content and if a caption is provided, adds the latex code for
+        that caption, and if a label is provided, adds the latex code for a label.
+        The size is the vertical size of each row of subfigures as a fraction.
+
+    Returns
+    =======
+    latex_code : string
+        The text string which draws the desired graph(s) when compiled by LaTeX.
+
+    See Also
+    ========
+    write_latex
+    to_latex_raw
+    """
+    if hasattr(Gbunch, "adj"):
+        raw = to_latex_raw(
+            Gbunch,
+            pos,
+            tikz_options,
+            default_node_options,
+            node_options,
+            node_label,
+            default_edge_options,
+            edge_options,
+            edge_label,
+            edge_label_options,
+        )
+    else:  # iterator of graphs
+        sbf = subfigure_wrapper
+        size = 1 / n_rows
+
+        N = len(Gbunch)
+        if isinstance(pos, (str, dict)):
+            pos = [pos] * N
+        if sub_captions is None:
+            sub_captions = [""] * N
+        if sub_labels is None:
+            sub_labels = [""] * N
+        if not (len(Gbunch) == len(pos) == len(sub_captions) == len(sub_labels)):
+            raise nx.NetworkXError(
+                "length of Gbunch, sub_captions and sub_figures must agree"
+            )
+
+        raw = ""
+        for G, pos, subcap, sublbl in zip(Gbunch, pos, sub_captions, sub_labels):
+            subraw = to_latex_raw(
+                G,
+                pos,
+                tikz_options,
+                default_node_options,
+                node_options,
+                node_label,
+                default_edge_options,
+                edge_options,
+                edge_label,
+                edge_label_options,
+            )
+            cap = f"    \\caption{{{subcap}}}" if subcap else ""
+            lbl = f"\\label{{{sublbl}}}" if sublbl else ""
+            raw += sbf.format(size=size, content=subraw, caption=cap, label=lbl)
+            raw += "\n"
+
+    # put raw latex code into a figure environment and optionally into a document
+    raw = raw[:-1]
+    cap = f"\n  \\caption{{{caption}}}" if caption else ""
+    lbl = f"\\label{{{latex_label}}}" if latex_label else ""
+    fig = figure_wrapper.format(content=raw, caption=cap, label=lbl)
+    if as_document:
+        return document_wrapper.format(content=fig)
+    return fig
+
+
+@nx.utils.open_file(1, mode="w")
+def write_latex(Gbunch, path, **options):
+    """Write the latex code to draw the graph(s) onto `path`.
+
+    This convenience function creates the latex drawing code as a string
+    and writes that to a file ready to be compiled when `as_document` is True
+    or ready to be ``import`` ed or ``include`` ed into your main LaTeX document.
+
+    The `path` argument can be a string filename or a file handle to write to.
+
+    Parameters
+    ----------
+    Gbunch : NetworkX graph or iterable of NetworkX graphs
+        If Gbunch is a graph, it is drawn in a figure environment.
+        If Gbunch is an iterable of graphs, each is drawn in a subfigure
+        environment within a single figure environment.
+    path : filename
+        Filename or file handle to write to
+    options : dict
+        By default, TikZ is used with options: (others are ignored)::
+
+            pos : string or dict or list
+                The name of the node attribute on `G` that holds the position of each node.
+                Positions can be sequences of length 2 with numbers for (x,y) coordinates.
+                They can also be strings to denote positions in TikZ style, such as (x, y)
+                or (angle:radius).
+                If a dict, it should be keyed by node to a position.
+                If an empty dict, a circular layout is computed by TikZ.
+                If you are drawing many graphs in subfigures, use a list of position dicts.
+            tikz_options : string
+                The tikzpicture options description defining the options for the picture.
+                Often large scale options like `[scale=2]`.
+            default_node_options : string
+                The draw options for a path of nodes. Individual node options override these.
+            node_options : string or dict
+                The name of the node attribute on `G` that holds the options for each node.
+                Or a dict keyed by node to a string holding the options for that node.
+            node_label : string or dict
+                The name of the node attribute on `G` that holds the node label (text)
+                displayed for each node. If the attribute is "" or not present, the node
+                itself is drawn as a string. LaTeX processing such as ``"$A_1$"`` is allowed.
+                Or a dict keyed by node to a string holding the label for that node.
+            default_edge_options : string
+                The options for the scope drawing all edges. The default is "[-]" for
+                undirected graphs and "[->]" for directed graphs.
+            edge_options : string or dict
+                The name of the edge attribute on `G` that holds the options for each edge.
+                If the edge is a self-loop and ``"loop" not in edge_options`` the option
+                "loop," is added to the options for the self-loop edge. Hence you can
+                use "[loop above]" explicitly, but the default is "[loop]".
+                Or a dict keyed by edge to a string holding the options for that edge.
+            edge_label : string or dict
+                The name of the edge attribute on `G` that holds the edge label (text)
+                displayed for each edge. If the attribute is "" or not present, no edge
+                label is drawn.
+                Or a dict keyed by edge to a string holding the label for that edge.
+            edge_label_options : string or dict
+                The name of the edge attribute on `G` that holds the label options for
+                each edge. For example, "[sloped,above,blue]". The default is no options.
+                Or a dict keyed by edge to a string holding the label options for that edge.
+            caption : string
+                The caption string for the figure environment
+            latex_label : string
+                The latex label used for the figure for easy referral from the main text
+            sub_captions : list of strings
+                The sub_caption string for each subfigure in the figure
+            sub_latex_labels : list of strings
+                The latex label for each subfigure in the figure
+            n_rows : int
+                The number of rows of subfigures to arrange for multiple graphs
+            as_document : bool
+                Whether to wrap the latex code in a document environment for compiling
+            document_wrapper : formatted text string with variable ``content``.
+                This text is called to evaluate the content embedded in a document
+                environment with a preamble setting up the TikZ syntax.
+            figure_wrapper : formatted text string
+                This text is evaluated with variables ``content``, ``caption`` and ``label``.
+                It wraps the content and if a caption is provided, adds the latex code for
+                that caption, and if a label is provided, adds the latex code for a label.
+            subfigure_wrapper : formatted text string
+                This text evaluate variables ``size``, ``content``, ``caption`` and ``label``.
+                It wraps the content and if a caption is provided, adds the latex code for
+                that caption, and if a label is provided, adds the latex code for a label.
+                The size is the vertical size of each row of subfigures as a fraction.
+
+    See Also
+    ========
+    to_latex
+    """
+    path.write(to_latex(Gbunch, **options))
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/drawing/tests/test_pydot.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/drawing/tests/test_pydot.py
new file mode 100644
index 0000000000000000000000000000000000000000..a58aa1647c983fffe79cf915fccb107f1863b2ea
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/drawing/tests/test_pydot.py
@@ -0,0 +1,190 @@
+"""Unit tests for pydot drawing functions."""
+import os
+import tempfile
+from io import StringIO
+
+import pytest
+
+import networkx as nx
+from networkx.utils import graphs_equal
+
+pydot = pytest.importorskip("pydot")
+
+
+@pytest.mark.xfail
+class TestPydot:
+    def pydot_checks(self, G, prog):
+        """
+        Validate :mod:`pydot`-based usage of the passed NetworkX graph with the
+        passed basename of an external GraphViz command (e.g., `dot`, `neato`).
+        """
+
+        # Set the name of this graph to... "G". Failing to do so will
+        # subsequently trip an assertion expecting this name.
+        G.graph["name"] = "G"
+
+        # Add arbitrary nodes and edges to the passed empty graph.
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "C"), ("A", "D")])
+        G.add_node("E")
+
+        # Validate layout of this graph with the passed GraphViz command.
+        graph_layout = nx.nx_pydot.pydot_layout(G, prog=prog)
+        assert isinstance(graph_layout, dict)
+
+        # Convert this graph into a "pydot.Dot" instance.
+        P = nx.nx_pydot.to_pydot(G)
+
+        # Convert this "pydot.Dot" instance back into a graph of the same type.
+        G2 = G.__class__(nx.nx_pydot.from_pydot(P))
+
+        # Validate the original and resulting graphs to be the same.
+        assert graphs_equal(G, G2)
+
+        fd, fname = tempfile.mkstemp()
+
+        # Serialize this "pydot.Dot" instance to a temporary file in dot format
+        P.write_raw(fname)
+
+        # Deserialize a list of new "pydot.Dot" instances back from this file.
+        Pin_list = pydot.graph_from_dot_file(path=fname, encoding="utf-8")
+
+        # Validate this file to contain only one graph.
+        assert len(Pin_list) == 1
+
+        # The single "pydot.Dot" instance deserialized from this file.
+        Pin = Pin_list[0]
+
+        # Sorted list of all nodes in the original "pydot.Dot" instance.
+        n1 = sorted(p.get_name() for p in P.get_node_list())
+
+        # Sorted list of all nodes in the deserialized "pydot.Dot" instance.
+        n2 = sorted(p.get_name() for p in Pin.get_node_list())
+
+        # Validate these instances to contain the same nodes.
+        assert n1 == n2
+
+        # Sorted list of all edges in the original "pydot.Dot" instance.
+        e1 = sorted((e.get_source(), e.get_destination()) for e in P.get_edge_list())
+
+        # Sorted list of all edges in the original "pydot.Dot" instance.
+        e2 = sorted((e.get_source(), e.get_destination()) for e in Pin.get_edge_list())
+
+        # Validate these instances to contain the same edges.
+        assert e1 == e2
+
+        # Deserialize a new graph of the same type back from this file.
+        Hin = nx.nx_pydot.read_dot(fname)
+        Hin = G.__class__(Hin)
+
+        # Validate the original and resulting graphs to be the same.
+        assert graphs_equal(G, Hin)
+
+        os.close(fd)
+        os.unlink(fname)
+
+    def test_undirected(self):
+        self.pydot_checks(nx.Graph(), prog="neato")
+
+    def test_directed(self):
+        self.pydot_checks(nx.DiGraph(), prog="dot")
+
+    def test_read_write(self):
+        G = nx.MultiGraph()
+        G.graph["name"] = "G"
+        G.add_edge("1", "2", key="0")  # read assumes strings
+        fh = StringIO()
+        nx.nx_pydot.write_dot(G, fh)
+        fh.seek(0)
+        H = nx.nx_pydot.read_dot(fh)
+        assert graphs_equal(G, H)
+
+
+def test_pydot_issue_258():
+    G = nx.Graph([("Example:A", 1)])
+    with pytest.raises(ValueError):
+        nx.nx_pydot.to_pydot(G)
+    with pytest.raises(ValueError):
+        nx.nx_pydot.pydot_layout(G)
+
+    G = nx.Graph()
+    G.add_node("1.2", style="filled", fillcolor="red:yellow")
+    with pytest.raises(ValueError):
+        nx.nx_pydot.to_pydot(G)
+    G.remove_node("1.2")
+    G.add_node("1.2", style="filled", fillcolor='"red:yellow"')
+    assert (
+        G.nodes.data() == nx.nx_pydot.from_pydot(nx.nx_pydot.to_pydot(G)).nodes.data()
+    )
+
+    G = nx.DiGraph()
+    G.add_edge("1", "2", foo="bar:1")
+    with pytest.raises(ValueError):
+        nx.nx_pydot.to_pydot(G)
+    G = nx.DiGraph()
+    G.add_edge("1", "2", foo='"bar:1"')
+    assert G["1"]["2"] == nx.nx_pydot.from_pydot(nx.nx_pydot.to_pydot(G))["1"]["2"]
+
+    G = nx.MultiGraph()
+    G.add_edge("1", "2", foo="b:1")
+    G.add_edge("1", "2", bar="foo:foo")
+    with pytest.raises(ValueError):
+        nx.nx_pydot.to_pydot(G)
+    G = nx.MultiGraph()
+    G.add_edge("1", "2", foo='"b:1"')
+    G.add_edge("1", "2", bar='"foo:foo"')
+    # Keys as integers aren't preserved in the conversion. They are read as strings.
+    assert [attr for _, _, attr in G.edges.data()] == [
+        attr
+        for _, _, attr in nx.nx_pydot.from_pydot(nx.nx_pydot.to_pydot(G)).edges.data()
+    ]
+
+    G = nx.Graph()
+    G.add_edge("1", "2")
+    G["1"]["2"]["f:oo"] = "bar"
+    with pytest.raises(ValueError):
+        nx.nx_pydot.to_pydot(G)
+    G = nx.Graph()
+    G.add_edge("1", "2")
+    G["1"]["2"]['"f:oo"'] = "bar"
+    assert G["1"]["2"] == nx.nx_pydot.from_pydot(nx.nx_pydot.to_pydot(G))["1"]["2"]
+
+    G = nx.Graph([('"Example:A"', 1)])
+    layout = nx.nx_pydot.pydot_layout(G)
+    assert isinstance(layout, dict)
+
+
+@pytest.mark.parametrize(
+    "graph_type", [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph]
+)
+def test_hashable_pydot(graph_type):
+    # gh-5790
+    G = graph_type()
+    G.add_edge("5", frozenset([1]), t='"Example:A"', l=False)
+    G.add_edge("1", 2, w=True, t=("node1",), l=frozenset(["node1"]))
+    G.add_edge("node", (3, 3), w="string")
+
+    assert [
+        {"t": '"Example:A"', "l": "False"},
+        {"w": "True", "t": "('node1',)", "l": "frozenset({'node1'})"},
+        {"w": "string"},
+    ] == [
+        attr
+        for _, _, attr in nx.nx_pydot.from_pydot(nx.nx_pydot.to_pydot(G)).edges.data()
+    ]
+
+    assert {str(i) for i in G.nodes()} == set(
+        nx.nx_pydot.from_pydot(nx.nx_pydot.to_pydot(G)).nodes
+    )
+
+
+def test_pydot_numerical_name():
+    G = nx.Graph()
+    G.add_edges_from([("A", "B"), (0, 1)])
+    graph_layout = nx.nx_pydot.pydot_layout(G, prog="dot")
+    assert isinstance(graph_layout, dict)
+    assert "0" not in graph_layout
+    assert 0 in graph_layout
+    assert "1" not in graph_layout
+    assert 1 in graph_layout
+    assert "A" in graph_layout
+    assert "B" in graph_layout
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diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/utils/decorators.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/utils/decorators.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc15882da72891a855b727c117b9125d196588c2
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/utils/decorators.py
@@ -0,0 +1,1270 @@
+import bz2
+import collections
+import gzip
+import inspect
+import itertools
+import re
+import warnings
+from collections import defaultdict
+from contextlib import contextmanager
+from functools import wraps
+from inspect import Parameter, signature
+from os.path import splitext
+from pathlib import Path
+
+import networkx as nx
+from networkx.utils import create_py_random_state, create_random_state
+
+__all__ = [
+    "not_implemented_for",
+    "open_file",
+    "nodes_or_number",
+    "np_random_state",
+    "py_random_state",
+    "argmap",
+    "deprecate_positional_args",
+]
+
+
+def not_implemented_for(*graph_types):
+    """Decorator to mark algorithms as not implemented
+
+    Parameters
+    ----------
+    graph_types : container of strings
+        Entries must be one of "directed", "undirected", "multigraph", or "graph".
+
+    Returns
+    -------
+    _require : function
+        The decorated function.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+    If any of the packages cannot be imported
+
+    Notes
+    -----
+    Multiple types are joined logically with "and".
+    For "or" use multiple @not_implemented_for() lines.
+
+    Examples
+    --------
+    Decorate functions like this::
+
+       @not_implemented_for("directed")
+       def sp_function(G):
+           pass
+
+       # rule out MultiDiGraph
+       @not_implemented_for("directed","multigraph")
+       def sp_np_function(G):
+           pass
+
+       # rule out all except DiGraph
+       @not_implemented_for("undirected")
+       @not_implemented_for("multigraph")
+       def sp_np_function(G):
+           pass
+    """
+    if ("directed" in graph_types) and ("undirected" in graph_types):
+        raise ValueError("Function not implemented on directed AND undirected graphs?")
+    if ("multigraph" in graph_types) and ("graph" in graph_types):
+        raise ValueError("Function not implemented on graph AND multigraphs?")
+    if not set(graph_types) < {"directed", "undirected", "multigraph", "graph"}:
+        raise KeyError(
+            "use one or more of directed, undirected, multigraph, graph.  "
+            f"You used {graph_types}"
+        )
+
+    # 3-way logic: True if "directed" input, False if "undirected" input, else None
+    dval = ("directed" in graph_types) or "undirected" not in graph_types and None
+    mval = ("multigraph" in graph_types) or "graph" not in graph_types and None
+    errmsg = f"not implemented for {' '.join(graph_types)} type"
+
+    def _not_implemented_for(g):
+        if (mval is None or mval == g.is_multigraph()) and (
+            dval is None or dval == g.is_directed()
+        ):
+            raise nx.NetworkXNotImplemented(errmsg)
+
+        return g
+
+    return argmap(_not_implemented_for, 0)
+
+
+# To handle new extensions, define a function accepting a `path` and `mode`.
+# Then add the extension to _dispatch_dict.
+fopeners = {
+    ".gz": gzip.open,
+    ".gzip": gzip.open,
+    ".bz2": bz2.BZ2File,
+}
+_dispatch_dict = defaultdict(lambda: open, **fopeners)
+
+
+def open_file(path_arg, mode="r"):
+    """Decorator to ensure clean opening and closing of files.
+
+    Parameters
+    ----------
+    path_arg : string or int
+        Name or index of the argument that is a path.
+
+    mode : str
+        String for opening mode.
+
+    Returns
+    -------
+    _open_file : function
+        Function which cleanly executes the io.
+
+    Examples
+    --------
+    Decorate functions like this::
+
+       @open_file(0,"r")
+       def read_function(pathname):
+           pass
+
+       @open_file(1,"w")
+       def write_function(G, pathname):
+           pass
+
+       @open_file(1,"w")
+       def write_function(G, pathname="graph.dot"):
+           pass
+
+       @open_file("pathname","w")
+       def write_function(G, pathname="graph.dot"):
+           pass
+
+       @open_file("path", "w+")
+       def another_function(arg, **kwargs):
+           path = kwargs["path"]
+           pass
+
+    Notes
+    -----
+    Note that this decorator solves the problem when a path argument is
+    specified as a string, but it does not handle the situation when the
+    function wants to accept a default of None (and then handle it).
+
+    Here is an example of how to handle this case::
+
+      @open_file("path")
+      def some_function(arg1, arg2, path=None):
+         if path is None:
+             fobj = tempfile.NamedTemporaryFile(delete=False)
+         else:
+             # `path` could have been a string or file object or something
+             # similar. In any event, the decorator has given us a file object
+             # and it will close it for us, if it should.
+             fobj = path
+
+         try:
+             fobj.write("blah")
+         finally:
+             if path is None:
+                 fobj.close()
+
+    Normally, we'd want to use "with" to ensure that fobj gets closed.
+    However, the decorator will make `path` a file object for us,
+    and using "with" would undesirably close that file object.
+    Instead, we use a try block, as shown above.
+    When we exit the function, fobj will be closed, if it should be, by the decorator.
+    """
+
+    def _open_file(path):
+        # Now we have the path_arg. There are two types of input to consider:
+        #   1) string representing a path that should be opened
+        #   2) an already opened file object
+        if isinstance(path, str):
+            ext = splitext(path)[1]
+        elif isinstance(path, Path):
+            # path is a pathlib reference to a filename
+            ext = path.suffix
+            path = str(path)
+        else:
+            # could be None, or a file handle, in which case the algorithm will deal with it
+            return path, lambda: None
+
+        fobj = _dispatch_dict[ext](path, mode=mode)
+        return fobj, lambda: fobj.close()
+
+    return argmap(_open_file, path_arg, try_finally=True)
+
+
+def nodes_or_number(which_args):
+    """Decorator to allow number of nodes or container of nodes.
+
+    With this decorator, the specified argument can be either a number or a container
+    of nodes. If it is a number, the nodes used are `range(n)`.
+    This allows `nx.complete_graph(50)` in place of `nx.complete_graph(list(range(50)))`.
+    And it also allows `nx.complete_graph(any_list_of_nodes)`.
+
+    Parameters
+    ----------
+    which_args : string or int or sequence of strings or ints
+        If string, the name of the argument to be treated.
+        If int, the index of the argument to be treated.
+        If more than one node argument is allowed, can be a list of locations.
+
+    Returns
+    -------
+    _nodes_or_numbers : function
+        Function which replaces int args with ranges.
+
+    Examples
+    --------
+    Decorate functions like this::
+
+       @nodes_or_number("nodes")
+       def empty_graph(nodes):
+           # nodes is converted to a list of nodes
+
+       @nodes_or_number(0)
+       def empty_graph(nodes):
+           # nodes is converted to a list of nodes
+
+       @nodes_or_number(["m1", "m2"])
+       def grid_2d_graph(m1, m2, periodic=False):
+           # m1 and m2 are each converted to a list of nodes
+
+       @nodes_or_number([0, 1])
+       def grid_2d_graph(m1, m2, periodic=False):
+           # m1 and m2 are each converted to a list of nodes
+
+       @nodes_or_number(1)
+       def full_rary_tree(r, n)
+           # presumably r is a number. It is not handled by this decorator.
+           # n is converted to a list of nodes
+    """
+
+    def _nodes_or_number(n):
+        try:
+            nodes = list(range(n))
+        except TypeError:
+            nodes = tuple(n)
+        else:
+            if n < 0:
+                raise nx.NetworkXError(f"Negative number of nodes not valid: {n}")
+        return (n, nodes)
+
+    try:
+        iter_wa = iter(which_args)
+    except TypeError:
+        iter_wa = (which_args,)
+
+    return argmap(_nodes_or_number, *iter_wa)
+
+
+def np_random_state(random_state_argument):
+    """Decorator to generate a `numpy.random.RandomState` instance.
+
+    The decorator processes the argument indicated by `random_state_argument`
+    using :func:`nx.utils.create_random_state`.
+    The argument value can be a seed (integer), or a `numpy.random.RandomState`
+    instance or (`None` or `numpy.random`). The latter options use the glocal
+    random number generator used by `numpy.random`.
+    The result is a `numpy.random.RandomState` instance.
+
+    Parameters
+    ----------
+    random_state_argument : string or int
+        The name or index of the argument to be converted
+        to a `numpy.random.RandomState` instance.
+
+    Returns
+    -------
+    _random_state : function
+        Function whose random_state keyword argument is a RandomState instance.
+
+    Examples
+    --------
+    Decorate functions like this::
+
+       @np_random_state("seed")
+       def random_float(seed=None):
+           return seed.rand()
+
+       @np_random_state(0)
+       def random_float(rng=None):
+           return rng.rand()
+
+       @np_random_state(1)
+       def random_array(dims, random_state=1):
+           return random_state.rand(*dims)
+
+    See Also
+    --------
+    py_random_state
+    """
+    return argmap(create_random_state, random_state_argument)
+
+
+def py_random_state(random_state_argument):
+    """Decorator to generate a random.Random instance (or equiv).
+
+    The decorator processes the argument indicated by `random_state_argument`
+    using :func:`nx.utils.create_py_random_state`.
+    The argument value can be a seed (integer), or a random number generator::
+
+        If int, return a random.Random instance set with seed=int.
+        If random.Random instance, return it.
+        If None or the `random` package, return the global random number
+        generator used by `random`.
+        If np.random package, return the global numpy random number
+        generator wrapped in a PythonRandomInterface class.
+        If np.random.RandomState instance, return it wrapped in
+        PythonRandomInterface
+        If a PythonRandomInterface instance, return it
+
+    Parameters
+    ----------
+    random_state_argument : string or int
+        The name of the argument or the index of the argument in args that is
+        to be converted to the random.Random instance or numpy.random.RandomState
+        instance that mimics basic methods of random.Random.
+
+    Returns
+    -------
+    _random_state : function
+        Function whose random_state_argument is converted to a Random instance.
+
+    Examples
+    --------
+    Decorate functions like this::
+
+       @py_random_state("random_state")
+       def random_float(random_state=None):
+           return random_state.rand()
+
+       @py_random_state(0)
+       def random_float(rng=None):
+           return rng.rand()
+
+       @py_random_state(1)
+       def random_array(dims, seed=12345):
+           return seed.rand(*dims)
+
+    See Also
+    --------
+    np_random_state
+    """
+
+    return argmap(create_py_random_state, random_state_argument)
+
+
+class argmap:
+    """A decorator to apply a map to arguments before calling the function
+
+    This class provides a decorator that maps (transforms) arguments of the function
+    before the function is called. Thus for example, we have similar code
+    in many functions to determine whether an argument is the number of nodes
+    to be created, or a list of nodes to be handled. The decorator provides
+    the code to accept either -- transforming the indicated argument into a
+    list of nodes before the actual function is called.
+
+    This decorator class allows us to process single or multiple arguments.
+    The arguments to be processed can be specified by string, naming the argument,
+    or by index, specifying the item in the args list.
+
+    Parameters
+    ----------
+    func : callable
+        The function to apply to arguments
+
+    *args : iterable of (int, str or tuple)
+        A list of parameters, specified either as strings (their names), ints
+        (numerical indices) or tuples, which may contain ints, strings, and
+        (recursively) tuples. Each indicates which parameters the decorator
+        should map. Tuples indicate that the map function takes (and returns)
+        multiple parameters in the same order and nested structure as indicated
+        here.
+
+    try_finally : bool (default: False)
+        When True, wrap the function call in a try-finally block with code
+        for the finally block created by `func`. This is used when the map
+        function constructs an object (like a file handle) that requires
+        post-processing (like closing).
+
+        Note: try_finally decorators cannot be used to decorate generator
+        functions.
+
+    Examples
+    --------
+    Most of these examples use `@argmap(...)` to apply the decorator to
+    the function defined on the next line.
+    In the NetworkX codebase however, `argmap` is used within a function to
+    construct a decorator. That is, the decorator defines a mapping function
+    and then uses `argmap` to build and return a decorated function.
+    A simple example is a decorator that specifies which currency to report money.
+    The decorator (named `convert_to`) would be used like::
+
+        @convert_to("US_Dollars", "income")
+        def show_me_the_money(name, income):
+            print(f"{name} : {income}")
+
+    And the code to create the decorator might be::
+
+        def convert_to(currency, which_arg):
+            def _convert(amount):
+                if amount.currency != currency:
+                    amount = amount.to_currency(currency)
+                return amount
+            return argmap(_convert, which_arg)
+
+    Despite this common idiom for argmap, most of the following examples
+    use the `@argmap(...)` idiom to save space.
+
+    Here's an example use of argmap to sum the elements of two of the functions
+    arguments. The decorated function::
+
+        @argmap(sum, "xlist", "zlist")
+        def foo(xlist, y, zlist):
+            return xlist - y + zlist
+
+    is syntactic sugar for::
+
+        def foo(xlist, y, zlist):
+            x = sum(xlist)
+            z = sum(zlist)
+            return x - y + z
+
+    and is equivalent to (using argument indexes)::
+
+        @argmap(sum, "xlist", 2)
+        def foo(xlist, y, zlist):
+            return xlist - y + zlist
+
+    or::
+
+        @argmap(sum, "zlist", 0)
+        def foo(xlist, y, zlist):
+            return xlist - y + zlist
+
+    Transforming functions can be applied to multiple arguments, such as::
+
+        def swap(x, y):
+            return y, x
+
+        # the 2-tuple tells argmap that the map `swap` has 2 inputs/outputs.
+        @argmap(swap, ("a", "b")):
+        def foo(a, b, c):
+            return a / b * c
+
+    is equivalent to::
+
+        def foo(a, b, c):
+            a, b = swap(a, b)
+            return a / b * c
+
+    More generally, the applied arguments can be nested tuples of strings or ints.
+    The syntax `@argmap(some_func, ("a", ("b", "c")))` would expect `some_func` to
+    accept 2 inputs with the second expected to be a 2-tuple. It should then return
+    2 outputs with the second a 2-tuple. The returns values would replace input "a"
+    "b" and "c" respectively. Similarly for `@argmap(some_func, (0, ("b", 2)))`.
+
+    Also, note that an index larger than the number of named parameters is allowed
+    for variadic functions. For example::
+
+        def double(a):
+            return 2 * a
+
+        @argmap(double, 3)
+        def overflow(a, *args):
+            return a, args
+
+        print(overflow(1, 2, 3, 4, 5, 6))  # output is 1, (2, 3, 8, 5, 6)
+
+    **Try Finally**
+
+    Additionally, this `argmap` class can be used to create a decorator that
+    initiates a try...finally block. The decorator must be written to return
+    both the transformed argument and a closing function.
+    This feature was included to enable the `open_file` decorator which might
+    need to close the file or not depending on whether it had to open that file.
+    This feature uses the keyword-only `try_finally` argument to `@argmap`.
+
+    For example this map opens a file and then makes sure it is closed::
+
+        def open_file(fn):
+            f = open(fn)
+            return f, lambda: f.close()
+
+    The decorator applies that to the function `foo`::
+
+        @argmap(open_file, "file", try_finally=True)
+        def foo(file):
+            print(file.read())
+
+    is syntactic sugar for::
+
+        def foo(file):
+            file, close_file = open_file(file)
+            try:
+                print(file.read())
+            finally:
+                close_file()
+
+    and is equivalent to (using indexes)::
+
+        @argmap(open_file, 0, try_finally=True)
+        def foo(file):
+            print(file.read())
+
+    Here's an example of the try_finally feature used to create a decorator::
+
+        def my_closing_decorator(which_arg):
+            def _opener(path):
+                if path is None:
+                    path = open(path)
+                    fclose = path.close
+                else:
+                    # assume `path` handles the closing
+                    fclose = lambda: None
+                return path, fclose
+            return argmap(_opener, which_arg, try_finally=True)
+
+    which can then be used as::
+
+        @my_closing_decorator("file")
+        def fancy_reader(file=None):
+            # this code doesn't need to worry about closing the file
+            print(file.read())
+
+    Decorators with try_finally = True cannot be used with generator functions,
+    because the `finally` block is evaluated before the generator is exhausted::
+
+        @argmap(open_file, "file", try_finally=True)
+        def file_to_lines(file):
+            for line in file.readlines():
+                yield line
+
+    is equivalent to::
+
+        def file_to_lines_wrapped(file):
+            for line in file.readlines():
+                yield line
+
+        def file_to_lines_wrapper(file):
+            try:
+                file = open_file(file)
+                return file_to_lines_wrapped(file)
+            finally:
+                file.close()
+
+    which behaves similarly to::
+
+        def file_to_lines_whoops(file):
+            file = open_file(file)
+            file.close()
+            for line in file.readlines():
+                yield line
+
+    because the `finally` block of `file_to_lines_wrapper` is executed before
+    the caller has a chance to exhaust the iterator.
+
+    Notes
+    -----
+    An object of this class is callable and intended to be used when
+    defining a decorator. Generally, a decorator takes a function as input
+    and constructs a function as output. Specifically, an `argmap` object
+    returns the input function decorated/wrapped so that specified arguments
+    are mapped (transformed) to new values before the decorated function is called.
+
+    As an overview, the argmap object returns a new function with all the
+    dunder values of the original function (like `__doc__`, `__name__`, etc).
+    Code for this decorated function is built based on the original function's
+    signature. It starts by mapping the input arguments to potentially new
+    values. Then it calls the decorated function with these new values in place
+    of the indicated arguments that have been mapped. The return value of the
+    original function is then returned. This new function is the function that
+    is actually called by the user.
+
+    Three additional features are provided.
+        1) The code is lazily compiled. That is, the new function is returned
+        as an object without the code compiled, but with all information
+        needed so it can be compiled upon it's first invocation. This saves
+        time on import at the cost of additional time on the first call of
+        the function. Subsequent calls are then just as fast as normal.
+
+        2) If the "try_finally" keyword-only argument is True, a try block
+        follows each mapped argument, matched on the other side of the wrapped
+        call, by a finally block closing that mapping.  We expect func to return
+        a 2-tuple: the mapped value and a function to be called in the finally
+        clause.  This feature was included so the `open_file` decorator could
+        provide a file handle to the decorated function and close the file handle
+        after the function call. It even keeps track of whether to close the file
+        handle or not based on whether it had to open the file or the input was
+        already open. So, the decorated function does not need to include any
+        code to open or close files.
+
+        3) The maps applied can process multiple arguments. For example,
+        you could swap two arguments using a mapping, or transform
+        them to their sum and their difference. This was included to allow
+        a decorator in the `quality.py` module that checks that an input
+        `partition` is a valid partition of the nodes of the input graph `G`.
+        In this example, the map has inputs `(G, partition)`. After checking
+        for a valid partition, the map either raises an exception or leaves
+        the inputs unchanged. Thus many functions that make this check can
+        use the decorator rather than copy the checking code into each function.
+        More complicated nested argument structures are described below.
+
+    The remaining notes describe the code structure and methods for this
+    class in broad terms to aid in understanding how to use it.
+
+    Instantiating an `argmap` object simply stores the mapping function and
+    the input identifiers of which arguments to map. The resulting decorator
+    is ready to use this map to decorate any function. Calling that object
+    (`argmap.__call__`, but usually done via `@my_decorator`) a lazily
+    compiled thin wrapper of the decorated function is constructed,
+    wrapped with the necessary function dunder attributes like `__doc__`
+    and `__name__`. That thinly wrapped function is returned as the
+    decorated function. When that decorated function is called, the thin
+    wrapper of code calls `argmap._lazy_compile` which compiles the decorated
+    function (using `argmap.compile`) and replaces the code of the thin
+    wrapper with the newly compiled code. This saves the compilation step
+    every import of networkx, at the cost of compiling upon the first call
+    to the decorated function.
+
+    When the decorated function is compiled, the code is recursively assembled
+    using the `argmap.assemble` method. The recursive nature is needed in
+    case of nested decorators. The result of the assembly is a number of
+    useful objects.
+
+      sig : the function signature of the original decorated function as
+          constructed by :func:`argmap.signature`. This is constructed
+          using `inspect.signature` but enhanced with attribute
+          strings `sig_def` and `sig_call`, and other information
+          specific to mapping arguments of this function.
+          This information is used to construct a string of code defining
+          the new decorated function.
+
+      wrapped_name : a unique internally used name constructed by argmap
+          for the decorated function.
+
+      functions : a dict of the functions used inside the code of this
+          decorated function, to be used as `globals` in `exec`.
+          This dict is recursively updated to allow for nested decorating.
+
+      mapblock : code (as a list of strings) to map the incoming argument
+          values to their mapped values.
+
+      finallys : code (as a list of strings) to provide the possibly nested
+          set of finally clauses if needed.
+
+      mutable_args : a bool indicating whether the `sig.args` tuple should be
+          converted to a list so mutation can occur.
+
+    After this recursive assembly process, the `argmap.compile` method
+    constructs code (as strings) to convert the tuple `sig.args` to a list
+    if needed. It joins the defining code with appropriate indents and
+    compiles the result.  Finally, this code is evaluated and the original
+    wrapper's implementation is replaced with the compiled version (see
+    `argmap._lazy_compile` for more details).
+
+    Other `argmap` methods include `_name` and `_count` which allow internally
+    generated names to be unique within a python session.
+    The methods `_flatten` and `_indent` process the nested lists of strings
+    into properly indented python code ready to be compiled.
+
+    More complicated nested tuples of arguments also allowed though
+    usually not used. For the simple 2 argument case, the argmap
+    input ("a", "b") implies the mapping function will take 2 arguments
+    and return a 2-tuple of mapped values. A more complicated example
+    with argmap input `("a", ("b", "c"))` requires the mapping function
+    take 2 inputs, with the second being a 2-tuple. It then must output
+    the 3 mapped values in the same nested structure `(newa, (newb, newc))`.
+    This level of generality is not often needed, but was convenient
+    to implement when handling the multiple arguments.
+
+    See Also
+    --------
+    not_implemented_for
+    open_file
+    nodes_or_number
+    random_state
+    py_random_state
+    networkx.community.quality.require_partition
+    require_partition
+
+    """
+
+    def __init__(self, func, *args, try_finally=False):
+        self._func = func
+        self._args = args
+        self._finally = try_finally
+
+    @staticmethod
+    def _lazy_compile(func):
+        """Compile the source of a wrapped function
+
+        Assemble and compile the decorated function, and intrusively replace its
+        code with the compiled version's.  The thinly wrapped function becomes
+        the decorated function.
+
+        Parameters
+        ----------
+        func : callable
+            A function returned by argmap.__call__ which is in the process
+            of being called for the first time.
+
+        Returns
+        -------
+        func : callable
+            The same function, with a new __code__ object.
+
+        Notes
+        -----
+        It was observed in NetworkX issue #4732 [1] that the import time of
+        NetworkX was significantly bloated by the use of decorators: over half
+        of the import time was being spent decorating functions.  This was
+        somewhat improved by a change made to the `decorator` library, at the
+        cost of a relatively heavy-weight call to `inspect.Signature.bind`
+        for each call to the decorated function.
+
+        The workaround we arrived at is to do minimal work at the time of
+        decoration.  When the decorated function is called for the first time,
+        we compile a function with the same function signature as the wrapped
+        function.  The resulting decorated function is faster than one made by
+        the `decorator` library, so that the overhead of the first call is
+        'paid off' after a small number of calls.
+
+        References
+        ----------
+
+        [1] https://github.com/networkx/networkx/issues/4732
+
+        """
+        real_func = func.__argmap__.compile(func.__wrapped__)
+        func.__code__ = real_func.__code__
+        func.__globals__.update(real_func.__globals__)
+        func.__dict__.update(real_func.__dict__)
+        return func
+
+    def __call__(self, f):
+        """Construct a lazily decorated wrapper of f.
+
+        The decorated function will be compiled when it is called for the first time,
+        and it will replace its own __code__ object so subsequent calls are fast.
+
+        Parameters
+        ----------
+        f : callable
+            A function to be decorated.
+
+        Returns
+        -------
+        func : callable
+            The decorated function.
+
+        See Also
+        --------
+        argmap._lazy_compile
+        """
+
+        def func(*args, __wrapper=None, **kwargs):
+            return argmap._lazy_compile(__wrapper)(*args, **kwargs)
+
+        # standard function-wrapping stuff
+        func.__name__ = f.__name__
+        func.__doc__ = f.__doc__
+        func.__defaults__ = f.__defaults__
+        func.__kwdefaults__.update(f.__kwdefaults__ or {})
+        func.__module__ = f.__module__
+        func.__qualname__ = f.__qualname__
+        func.__dict__.update(f.__dict__)
+        func.__wrapped__ = f
+
+        # now that we've wrapped f, we may have picked up some __dict__ or
+        # __kwdefaults__ items that were set by a previous argmap.  Thus, we set
+        # these values after those update() calls.
+
+        # If we attempt to access func from within itself, that happens through
+        # a closure -- which trips an error when we replace func.__code__.  The
+        # standard workaround for functions which can't see themselves is to use
+        # a Y-combinator, as we do here.
+        func.__kwdefaults__["_argmap__wrapper"] = func
+
+        # this self-reference is here because functools.wraps preserves
+        # everything in __dict__, and we don't want to mistake a non-argmap
+        # wrapper for an argmap wrapper
+        func.__self__ = func
+
+        # this is used to variously call self.assemble and self.compile
+        func.__argmap__ = self
+
+        if hasattr(f, "__argmap__"):
+            func.__is_generator = f.__is_generator
+        else:
+            func.__is_generator = inspect.isgeneratorfunction(f)
+
+        if self._finally and func.__is_generator:
+            raise nx.NetworkXError("argmap cannot decorate generators with try_finally")
+
+        return func
+
+    __count = 0
+
+    @classmethod
+    def _count(cls):
+        """Maintain a globally-unique identifier for function names and "file" names
+
+        Note that this counter is a class method reporting a class variable
+        so the count is unique within a Python session. It could differ from
+        session to session for a specific decorator depending on the order
+        that the decorators are created. But that doesn't disrupt `argmap`.
+
+        This is used in two places: to construct unique variable names
+        in the `_name` method and to construct unique fictitious filenames
+        in the `_compile` method.
+
+        Returns
+        -------
+        count : int
+            An integer unique to this Python session (simply counts from zero)
+        """
+        cls.__count += 1
+        return cls.__count
+
+    _bad_chars = re.compile("[^a-zA-Z0-9_]")
+
+    @classmethod
+    def _name(cls, f):
+        """Mangle the name of a function to be unique but somewhat human-readable
+
+        The names are unique within a Python session and set using `_count`.
+
+        Parameters
+        ----------
+        f : str or object
+
+        Returns
+        -------
+        name : str
+            The mangled version of `f.__name__` (if `f.__name__` exists) or `f`
+
+        """
+        f = f.__name__ if hasattr(f, "__name__") else f
+        fname = re.sub(cls._bad_chars, "_", f)
+        return f"argmap_{fname}_{cls._count()}"
+
+    def compile(self, f):
+        """Compile the decorated function.
+
+        Called once for a given decorated function -- collects the code from all
+        argmap decorators in the stack, and compiles the decorated function.
+
+        Much of the work done here uses the `assemble` method to allow recursive
+        treatment of multiple argmap decorators on a single decorated function.
+        That flattens the argmap decorators, collects the source code to construct
+        a single decorated function, then compiles/executes/returns that function.
+
+        The source code for the decorated function is stored as an attribute
+        `_code` on the function object itself.
+
+        Note that Python's `compile` function requires a filename, but this
+        code is constructed without a file, so a fictitious filename is used
+        to describe where the function comes from. The name is something like:
+        "argmap compilation 4".
+
+        Parameters
+        ----------
+        f : callable
+            The function to be decorated
+
+        Returns
+        -------
+        func : callable
+            The decorated file
+
+        """
+        sig, wrapped_name, functions, mapblock, finallys, mutable_args = self.assemble(
+            f
+        )
+
+        call = f"{sig.call_sig.format(wrapped_name)}#"
+        mut_args = f"{sig.args} = list({sig.args})" if mutable_args else ""
+        body = argmap._indent(sig.def_sig, mut_args, mapblock, call, finallys)
+        code = "\n".join(body)
+
+        locl = {}
+        globl = dict(functions.values())
+        filename = f"{self.__class__} compilation {self._count()}"
+        compiled = compile(code, filename, "exec")
+        exec(compiled, globl, locl)
+        func = locl[sig.name]
+        func._code = code
+        return func
+
+    def assemble(self, f):
+        """Collects components of the source for the decorated function wrapping f.
+
+        If `f` has multiple argmap decorators, we recursively assemble the stack of
+        decorators into a single flattened function.
+
+        This method is part of the `compile` method's process yet separated
+        from that method to allow recursive processing. The outputs are
+        strings, dictionaries and lists that collect needed info to
+        flatten any nested argmap-decoration.
+
+        Parameters
+        ----------
+        f : callable
+            The function to be decorated.  If f is argmapped, we assemble it.
+
+        Returns
+        -------
+        sig : argmap.Signature
+            The function signature as an `argmap.Signature` object.
+        wrapped_name : str
+            The mangled name used to represent the wrapped function in the code
+            being assembled.
+        functions : dict
+            A dictionary mapping id(g) -> (mangled_name(g), g) for functions g
+            referred to in the code being assembled. These need to be present
+            in the ``globals`` scope of ``exec`` when defining the decorated
+            function.
+        mapblock : list of lists and/or strings
+            Code that implements mapping of parameters including any try blocks
+            if needed. This code will precede the decorated function call.
+        finallys : list of lists and/or strings
+            Code that implements the finally blocks to post-process the
+            arguments (usually close any files if needed) after the
+            decorated function is called.
+        mutable_args : bool
+            True if the decorator needs to modify positional arguments
+            via their indices. The compile method then turns the argument
+            tuple into a list so that the arguments can be modified.
+        """
+
+        # first, we check if f is already argmapped -- if that's the case,
+        # build up the function recursively.
+        # > mapblock is generally a list of function calls of the sort
+        #     arg = func(arg)
+        # in addition to some try-blocks if needed.
+        # > finallys is a recursive list of finally blocks of the sort
+        #         finally:
+        #             close_func_1()
+        #     finally:
+        #         close_func_2()
+        # > functions is a dict of functions used in the scope of our decorated
+        # function. It will be used to construct globals used in compilation.
+        # We make functions[id(f)] = name_of_f, f to ensure that a given
+        # function is stored and named exactly once even if called by
+        # nested decorators.
+        if hasattr(f, "__argmap__") and f.__self__ is f:
+            (
+                sig,
+                wrapped_name,
+                functions,
+                mapblock,
+                finallys,
+                mutable_args,
+            ) = f.__argmap__.assemble(f.__wrapped__)
+            functions = dict(functions)  # shallow-copy just in case
+        else:
+            sig = self.signature(f)
+            wrapped_name = self._name(f)
+            mapblock, finallys = [], []
+            functions = {id(f): (wrapped_name, f)}
+            mutable_args = False
+
+        if id(self._func) in functions:
+            fname, _ = functions[id(self._func)]
+        else:
+            fname, _ = functions[id(self._func)] = self._name(self._func), self._func
+
+        # this is a bit complicated -- we can call functions with a variety of
+        # nested arguments, so long as their input and output are tuples with
+        # the same nested structure. e.g. ("a", "b") maps arguments a and b.
+        # A more complicated nesting like (0, (3, 4)) maps arguments 0, 3, 4
+        # expecting the mapping to output new values in the same nested shape.
+        # The ability to argmap multiple arguments was necessary for
+        # the decorator `nx.algorithms.community.quality.require_partition`, and
+        # while we're not taking full advantage of the ability to handle
+        # multiply-nested tuples, it was convenient to implement this in
+        # generality because the recursive call to `get_name` is necessary in
+        # any case.
+        applied = set()
+
+        def get_name(arg, first=True):
+            nonlocal mutable_args
+            if isinstance(arg, tuple):
+                name = ", ".join(get_name(x, False) for x in arg)
+                return name if first else f"({name})"
+            if arg in applied:
+                raise nx.NetworkXError(f"argument {arg} is specified multiple times")
+            applied.add(arg)
+            if arg in sig.names:
+                return sig.names[arg]
+            elif isinstance(arg, str):
+                if sig.kwargs is None:
+                    raise nx.NetworkXError(
+                        f"name {arg} is not a named parameter and this function doesn't have kwargs"
+                    )
+                return f"{sig.kwargs}[{arg!r}]"
+            else:
+                if sig.args is None:
+                    raise nx.NetworkXError(
+                        f"index {arg} not a parameter index and this function doesn't have args"
+                    )
+                mutable_args = True
+                return f"{sig.args}[{arg - sig.n_positional}]"
+
+        if self._finally:
+            # here's where we handle try_finally decorators.  Such a decorator
+            # returns a mapped argument and a function to be called in a
+            # finally block.  This feature was required by the open_file
+            # decorator.  The below generates the code
+            #
+            # name, final = func(name)                   #<--append to mapblock
+            # try:                                       #<--append to mapblock
+            #     ... more argmapping and try blocks
+            #     return WRAPPED_FUNCTION(...)
+            #     ... more finally blocks
+            # finally:                                   #<--prepend to finallys
+            #     final()                                #<--prepend to finallys
+            #
+            for a in self._args:
+                name = get_name(a)
+                final = self._name(name)
+                mapblock.append(f"{name}, {final} = {fname}({name})")
+                mapblock.append("try:")
+                finallys = ["finally:", f"{final}()#", "#", finallys]
+        else:
+            mapblock.extend(
+                f"{name} = {fname}({name})" for name in map(get_name, self._args)
+            )
+
+        return sig, wrapped_name, functions, mapblock, finallys, mutable_args
+
+    @classmethod
+    def signature(cls, f):
+        r"""Construct a Signature object describing `f`
+
+        Compute a Signature so that we can write a function wrapping f with
+        the same signature and call-type.
+
+        Parameters
+        ----------
+        f : callable
+            A function to be decorated
+
+        Returns
+        -------
+        sig : argmap.Signature
+            The Signature of f
+
+        Notes
+        -----
+        The Signature is a namedtuple with names:
+
+            name : a unique version of the name of the decorated function
+            signature : the inspect.signature of the decorated function
+            def_sig : a string used as code to define the new function
+            call_sig : a string used as code to call the decorated function
+            names : a dict keyed by argument name and index to the argument's name
+            n_positional : the number of positional arguments in the signature
+            args : the name of the VAR_POSITIONAL argument if any, i.e. \*theseargs
+            kwargs : the name of the VAR_KEYWORDS argument if any, i.e. \*\*kwargs
+
+        These named attributes of the signature are used in `assemble` and `compile`
+        to construct a string of source code for the decorated function.
+
+        """
+        sig = inspect.signature(f, follow_wrapped=False)
+        def_sig = []
+        call_sig = []
+        names = {}
+
+        kind = None
+        args = None
+        kwargs = None
+        npos = 0
+        for i, param in enumerate(sig.parameters.values()):
+            # parameters can be position-only, keyword-or-position, keyword-only
+            # in any combination, but only in the order as above.  we do edge
+            # detection to add the appropriate punctuation
+            prev = kind
+            kind = param.kind
+            if prev == param.POSITIONAL_ONLY != kind:
+                # the last token was position-only, but this one isn't
+                def_sig.append("/")
+            if prev != param.KEYWORD_ONLY == kind != param.VAR_POSITIONAL:
+                # param is the first keyword-only arg and isn't starred
+                def_sig.append("*")
+
+            # star arguments as appropriate
+            if kind == param.VAR_POSITIONAL:
+                name = "*" + param.name
+                args = param.name
+                count = 0
+            elif kind == param.VAR_KEYWORD:
+                name = "**" + param.name
+                kwargs = param.name
+                count = 0
+            else:
+                names[i] = names[param.name] = param.name
+                name = param.name
+                count = 1
+
+            # assign to keyword-only args in the function call
+            if kind == param.KEYWORD_ONLY:
+                call_sig.append(f"{name} = {name}")
+            else:
+                npos += count
+                call_sig.append(name)
+
+            def_sig.append(name)
+
+        fname = cls._name(f)
+        def_sig = f'def {fname}({", ".join(def_sig)}):'
+
+        call_sig = f"return {{}}({', '.join(call_sig)})"
+
+        return cls.Signature(fname, sig, def_sig, call_sig, names, npos, args, kwargs)
+
+    Signature = collections.namedtuple(
+        "Signature",
+        [
+            "name",
+            "signature",
+            "def_sig",
+            "call_sig",
+            "names",
+            "n_positional",
+            "args",
+            "kwargs",
+        ],
+    )
+
+    @staticmethod
+    def _flatten(nestlist, visited):
+        """flattens a recursive list of lists that doesn't have cyclic references
+
+        Parameters
+        ----------
+        nestlist : iterable
+            A recursive list of objects to be flattened into a single iterable
+
+        visited : set
+            A set of object ids which have been walked -- initialize with an
+            empty set
+
+        Yields
+        ------
+        Non-list objects contained in nestlist
+
+        """
+        for thing in nestlist:
+            if isinstance(thing, list):
+                if id(thing) in visited:
+                    raise ValueError("A cycle was found in nestlist.  Be a tree.")
+                else:
+                    visited.add(id(thing))
+                yield from argmap._flatten(thing, visited)
+            else:
+                yield thing
+
+    _tabs = " " * 64
+
+    @staticmethod
+    def _indent(*lines):
+        """Indent list of code lines to make executable Python code
+
+        Indents a tree-recursive list of strings, following the rule that one
+        space is added to the tab after a line that ends in a colon, and one is
+        removed after a line that ends in an hashmark.
+
+        Parameters
+        ----------
+        *lines : lists and/or strings
+            A recursive list of strings to be assembled into properly indented
+            code.
+
+        Returns
+        -------
+        code : str
+
+        Examples
+        --------
+
+            argmap._indent(*["try:", "try:", "pass#", "finally:", "pass#", "#",
+                             "finally:", "pass#"])
+
+        renders to
+
+            '''try:
+             try:
+              pass#
+             finally:
+              pass#
+             #
+            finally:
+             pass#'''
+        """
+        depth = 0
+        for line in argmap._flatten(lines, set()):
+            yield f"{argmap._tabs[:depth]}{line}"
+            depth += (line[-1:] == ":") - (line[-1:] == "#")
+
+
+# Vendored in from https://github.com/scikit-learn/scikit-learn/blob/8ed0270b99344cee9bb253cbfa1d986561ea6cd7/sklearn/utils/validation.py#L37C1-L90C44
+def deprecate_positional_args(func=None, *, version):
+    """Decorator for methods that issues warnings for positional arguments.
+
+    Using the keyword-only argument syntax in pep 3102, arguments after the
+    * will issue a warning when passed as a positional argument.
+
+    Parameters
+    ----------
+    func : callable, default=None
+        Function to check arguments on.
+    version : callable, default="1.3"
+        The version when positional arguments will result in error.
+    """
+
+    def _inner_deprecate_positional_args(f):
+        sig = signature(f)
+        kwonly_args = []
+        all_args = []
+
+        for name, param in sig.parameters.items():
+            if param.kind == Parameter.POSITIONAL_OR_KEYWORD:
+                all_args.append(name)
+            elif param.kind == Parameter.KEYWORD_ONLY:
+                kwonly_args.append(name)
+
+        @wraps(f)
+        def inner_f(*args, **kwargs):
+            extra_args = len(args) - len(all_args)
+            if extra_args <= 0:
+                return f(*args, **kwargs)
+
+            # extra_args > 0
+            args_msg = [
+                f"{name}={arg}"
+                for name, arg in zip(kwonly_args[:extra_args], args[-extra_args:])
+            ]
+            args_msg = ", ".join(args_msg)
+            warnings.warn(
+                (
+                    f"Pass {args_msg} as keyword args. From NetworkX version "
+                    f"{version} passing these as positional arguments "
+                    "will result in an error"
+                ),
+                FutureWarning,
+            )
+            kwargs.update(zip(sig.parameters, args))
+            return f(**kwargs)
+
+        return inner_f
+
+    if func is not None:
+        return _inner_deprecate_positional_args(func)
+
+    return _inner_deprecate_positional_args
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/utils/random_sequence.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/utils/random_sequence.py
new file mode 100644
index 0000000000000000000000000000000000000000..20a7b5e0a7fcc426ed9840f8bed2abf500e357e5
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/utils/random_sequence.py
@@ -0,0 +1,164 @@
+"""
+Utilities for generating random numbers, random sequences, and
+random selections.
+"""
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = [
+    "powerlaw_sequence",
+    "zipf_rv",
+    "cumulative_distribution",
+    "discrete_sequence",
+    "random_weighted_sample",
+    "weighted_choice",
+]
+
+
+# The same helpers for choosing random sequences from distributions
+# uses Python's random module
+# https://docs.python.org/3/library/random.html
+
+
+@py_random_state(2)
+def powerlaw_sequence(n, exponent=2.0, seed=None):
+    """
+    Return sample sequence of length n from a power law distribution.
+    """
+    return [seed.paretovariate(exponent - 1) for i in range(n)]
+
+
+@py_random_state(2)
+def zipf_rv(alpha, xmin=1, seed=None):
+    r"""Returns a random value chosen from the Zipf distribution.
+
+    The return value is an integer drawn from the probability distribution
+
+    .. math::
+
+        p(x)=\frac{x^{-\alpha}}{\zeta(\alpha, x_{\min})},
+
+    where $\zeta(\alpha, x_{\min})$ is the Hurwitz zeta function.
+
+    Parameters
+    ----------
+    alpha : float
+      Exponent value of the distribution
+    xmin : int
+      Minimum value
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    x : int
+      Random value from Zipf distribution
+
+    Raises
+    ------
+    ValueError:
+      If xmin < 1 or
+      If alpha <= 1
+
+    Notes
+    -----
+    The rejection algorithm generates random values for a the power-law
+    distribution in uniformly bounded expected time dependent on
+    parameters.  See [1]_ for details on its operation.
+
+    Examples
+    --------
+    >>> nx.utils.zipf_rv(alpha=2, xmin=3, seed=42)
+    8
+
+    References
+    ----------
+    .. [1] Luc Devroye, Non-Uniform Random Variate Generation,
+       Springer-Verlag, New York, 1986.
+    """
+    if xmin < 1:
+        raise ValueError("xmin < 1")
+    if alpha <= 1:
+        raise ValueError("a <= 1.0")
+    a1 = alpha - 1.0
+    b = 2**a1
+    while True:
+        u = 1.0 - seed.random()  # u in (0,1]
+        v = seed.random()  # v in [0,1)
+        x = int(xmin * u ** -(1.0 / a1))
+        t = (1.0 + (1.0 / x)) ** a1
+        if v * x * (t - 1.0) / (b - 1.0) <= t / b:
+            break
+    return x
+
+
+def cumulative_distribution(distribution):
+    """Returns normalized cumulative distribution from discrete distribution."""
+
+    cdf = [0.0]
+    psum = sum(distribution)
+    for i in range(len(distribution)):
+        cdf.append(cdf[i] + distribution[i] / psum)
+    return cdf
+
+
+@py_random_state(3)
+def discrete_sequence(n, distribution=None, cdistribution=None, seed=None):
+    """
+    Return sample sequence of length n from a given discrete distribution
+    or discrete cumulative distribution.
+
+    One of the following must be specified.
+
+    distribution = histogram of values, will be normalized
+
+    cdistribution = normalized discrete cumulative distribution
+
+    """
+    import bisect
+
+    if cdistribution is not None:
+        cdf = cdistribution
+    elif distribution is not None:
+        cdf = cumulative_distribution(distribution)
+    else:
+        raise nx.NetworkXError(
+            "discrete_sequence: distribution or cdistribution missing"
+        )
+
+    # get a uniform random number
+    inputseq = [seed.random() for i in range(n)]
+
+    # choose from CDF
+    seq = [bisect.bisect_left(cdf, s) - 1 for s in inputseq]
+    return seq
+
+
+@py_random_state(2)
+def random_weighted_sample(mapping, k, seed=None):
+    """Returns k items without replacement from a weighted sample.
+
+    The input is a dictionary of items with weights as values.
+    """
+    if k > len(mapping):
+        raise ValueError("sample larger than population")
+    sample = set()
+    while len(sample) < k:
+        sample.add(weighted_choice(mapping, seed))
+    return list(sample)
+
+
+@py_random_state(1)
+def weighted_choice(mapping, seed=None):
+    """Returns a single element from a weighted sample.
+
+    The input is a dictionary of items with weights as values.
+    """
+    # use roulette method
+    rnd = seed.random() * sum(mapping.values())
+    for k, w in mapping.items():
+        rnd -= w
+        if rnd < 0:
+            return k
diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/utils/tests/test__init.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/utils/tests/test__init.py
new file mode 100644
index 0000000000000000000000000000000000000000..ecbcce36df7cd37781dd45879f63f7d6f55e5567
--- /dev/null
+++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/networkx/utils/tests/test__init.py
@@ -0,0 +1,11 @@
+import pytest
+
+
+def test_utils_namespace():
+    """Ensure objects are not unintentionally exposed in utils namespace."""
+    with pytest.raises(ImportError):
+        from networkx.utils import nx
+    with pytest.raises(ImportError):
+        from networkx.utils import sys
+    with pytest.raises(ImportError):
+        from networkx.utils import defaultdict, deque