Remove inline from cython classes
Part of #33926 adding `-Winline` to the compiler options for me generates a 14 MB file with 157,000 lines of warnings about how functions aren't getting inlined.

I doubt that any of our Cython inline functions actually get inlined, but figured these are easiest to start with. The cython documentation itself states that class-level cdef functions have almost no chance of being inlined

https://cython.readthedocs.io/en/stable/src/userguide/pyrex_differences.html?highlight=inline#cdef-inline
FWIW gcc has an `-finline_functions` flag that would be enabled by default in our release builds. This would allow the compiler to decide for us what could be inline'd rather than having us try and say:

https://gcc.gnu.org/onlinedocs/gcc-12.1.0/gcc/Optimize-Options.html

But right now I think that gets clobbered by the amount of manually specified inlines we do. There's a mixed bag of errors but a lot are `param inline-unit-growth limit reached` which means the inlining would grow the compilation unit size too much Similar function for CPython discourages its use:

https://docs.python.org/3/c-api/intro.html#c.Py_ALWAYS_INLINE

```
Marking blindly a static inline function with Py_ALWAYS_INLINE can result in worse performances 
(due to increased code size for example). The compiler is usually smarter than the developer 
for the cost/benefit analysis.
``` Wasn't sure the best way to do a timeit so went a little more extreme, removed as many inline statements as possible (couldn't remove them from util.pxd) and ran the full test suite overnight. Results are below - I think any changes are spurious.

```sh
       before           after         ratio
     [82d271fe]       [9d9cca88]
     <main>           <remove-class-level-inline>
+     1.52±0.01ms         3.17±1ms     2.08  gil.ParallelRolling.time_rolling('mean')
+         130±3μs          194±1μs     1.49  index_cached_properties.IndexCache.time_is_monotonic_increasing('CategoricalIndex')
+         133±6μs        194±0.8μs     1.46  index_cached_properties.IndexCache.time_is_monotonic_decreasing('CategoricalIndex')
+       203±0.6μs          269±1μs     1.33  categoricals.Indexing.time_intersection
+     1.67±0.07ms       2.08±0.5ms     1.25  index_cached_properties.IndexCache.time_is_unique('MultiIndex')
+     12.5±0.04ms         15.6±4ms     1.24  rolling.Rank.time_rank('Series', 10, 'int', True, True, 'average')
+     1.41±0.01ms      1.73±0.01ms     1.23  timeseries.Lookup.time_lookup_and_cleanup
+      98.1±0.3μs        119±0.5μs     1.21  index_cached_properties.IndexCache.time_is_monotonic_decreasing('PeriodIndex')
+      98.2±0.4μs        119±0.5μs     1.21  index_cached_properties.IndexCache.time_is_monotonic_increasing('PeriodIndex')
+       100±0.1μs        121±0.1μs     1.21  index_cached_properties.IndexCache.time_is_monotonic_decreasing('DatetimeIndex')
+      99.3±0.7μs        120±0.4μs     1.21  index_cached_properties.IndexCache.time_is_monotonic_decreasing('TimedeltaIndex')
+        1.46±0ms       1.76±0.3ms     1.21  index_cached_properties.IndexCache.time_is_unique('Float64Index')
+     100.0±0.2μs        120±0.3μs     1.20  index_cached_properties.IndexCache.time_is_monotonic_increasing('DatetimeIndex')
+      99.5±0.5μs        120±0.4μs     1.20  index_cached_properties.IndexCache.time_is_monotonic_increasing('TimedeltaIndex')
+         949±5μs         1.11±0ms     1.17  rolling.ForwardWindowMethods.time_rolling('Series', 10, 'int', 'sum')
+        1.65±0ms      1.91±0.01ms     1.16  algorithms.Hashing.time_series_categorical
+        1.02±0ms      1.17±0.02ms     1.15  rolling.ForwardWindowMethods.time_rolling('Series', 10, 'float', 'mean')
+         983±4μs         1.13±0ms     1.15  rolling.ForwardWindowMethods.time_rolling('Series', 10, 'int', 'mean')
+         926±4μs         1.06±0ms     1.15  rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'float', 'sum')
+         929±2μs         1.07±0ms     1.15  rolling.ForwardWindowMethods.time_rolling('Series', 10, 'float', 'sum')
+         965±2μs      1.11±0.01ms     1.15  rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'int', 'sum')
+        1.01±0ms      1.15±0.01ms     1.14  rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'float', 'sum')
+     1.04±0.01ms      1.19±0.01ms     1.14  rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'int', 'sum')
+        1000±4μs         1.14±0ms     1.14  rolling.Methods.time_method('Series', ('rolling', {'window': 1000}), 'float', 'sum')
+         993±2μs      1.13±0.01ms     1.14  rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'int', 'mean')
+        1.01±0ms         1.15±0ms     1.14  rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'float', 'sum')
+     1.05±0.01ms      1.20±0.01ms     1.14  rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'int', 'sum')
+        1.09±0ms      1.24±0.01ms     1.14  rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'float', 'mean')
+     1.08±0.01ms      1.22±0.01ms     1.13  rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'int', 'mean')
+        1.08±0ms      1.22±0.01ms     1.13  rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'int', 'mean')
+        1.04±0ms         1.17±0ms     1.13  rolling.Methods.time_method('Series', ('rolling', {'window': 10}), 'int', 'sum')
+         791±6μs          892±6μs     1.13  rolling.Methods.time_method('Series', ('expanding', {}), 'float', 'sum')
+        1.01±0ms         1.14±0ms     1.13  rolling.Methods.time_method('Series', ('rolling', {'window': 10}), 'float', 'sum')
+     1.03±0.01ms         1.16±0ms     1.13  rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'float', 'mean')
+         823±5μs          926±5μs     1.13  rolling.Methods.time_method('Series', ('expanding', {}), 'int', 'sum')
+         837±3μs          940±3μs     1.12  rolling.Methods.time_method('Series', ('expanding', {}), 'int', 'mean')
+     1.04±0.01ms         1.17±0ms     1.12  rolling.Methods.time_method('Series', ('rolling', {'window': 1000}), 'int', 'sum')
+        1.11±0ms         1.24±0ms     1.12  rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'float', 'mean')
+     1.13±0.01ms      1.27±0.01ms     1.12  rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'int', 'sum')
+        1.09±0ms         1.22±0ms     1.12  rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'float', 'sum')
+         803±3μs          899±3μs     1.12  rolling.Methods.time_method('Series', ('expanding', {}), 'float', 'mean')
+        1.21±0ms      1.36±0.01ms     1.12  rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'int', 'mean')
+        1.09±0ms      1.22±0.01ms     1.12  rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'float', 'sum')
+        965±20μs      1.08±0.06ms     1.12  period.PeriodIndexConstructor.time_from_pydatetime('D', False)
+        1.32±0ms      1.48±0.01ms     1.12  rolling.VariableWindowMethods.time_method('Series', '50s', 'int', 'mean')
+        1.13±0ms         1.26±0ms     1.12  rolling.Methods.time_method('Series', ('rolling', {'window': 1000}), 'int', 'mean')
+     1.13±0.01ms      1.26±0.01ms     1.12  rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'int', 'sum')
+        1.10±0ms         1.23±0ms     1.12  rolling.Methods.time_method('Series', ('rolling', {'window': 1000}), 'float', 'mean')
+     1.11±0.01ms      1.24±0.01ms     1.12  rolling.Methods.time_method('Series', ('rolling', {'window': 10}), 'float', 'mean')
+         882±3μs          985±8μs     1.12  rolling.Methods.time_method('DataFrame', ('expanding', {}), 'float', 'sum')
+     1.18±0.01ms      1.32±0.01ms     1.12  rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'float', 'mean')
+     1.14±0.01ms         1.27±0ms     1.12  rolling.Methods.time_method('Series', ('rolling', {'window': 10}), 'int', 'mean')
+        1.19±0ms         1.33±0ms     1.11  rolling.VariableWindowMethods.time_method('Series', '1h', 'float', 'sum')
+        854±30ns        951±100ns     1.11  index_cached_properties.IndexCache.time_values('DatetimeIndex')
+        1.18±0ms         1.31±0ms     1.11  rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'float', 'mean')
+        1.22±0ms      1.36±0.01ms     1.11  rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'int', 'mean')
+     1.24±0.02ms      1.38±0.02ms     1.11  rolling.Methods.time_method('Series', ('rolling', {'window': 10}), 'float', 'count')
+        1.38±0ms         1.53±0ms     1.11  rolling.VariableWindowMethods.time_method('DataFrame', '1h', 'float', 'mean')
+        1.19±0ms         1.32±0ms     1.11  rolling.VariableWindowMethods.time_method('Series', '1d', 'float', 'sum')
+        1.67±0ms      1.85±0.01ms     1.11  rolling.ForwardWindowMethods.time_rolling('Series', 10, 'int', 'kurt')
+     1.34±0.02ms      1.48±0.01ms     1.11  rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'float', 'count')
+        1.24±0ms      1.37±0.01ms     1.11  rolling.VariableWindowMethods.time_method('Series', '50s', 'int', 'sum')
+        1.32±0ms      1.47±0.01ms     1.11  rolling.VariableWindowMethods.time_method('Series', '1h', 'int', 'mean')
+     1.23±0.01ms         1.36±0ms     1.11  rolling.VariableWindowMethods.time_method('Series', '1d', 'int', 'sum')
+        1.42±0ms         1.57±0ms     1.11  rolling.VariableWindowMethods.time_method('DataFrame', '1h', 'int', 'mean')
+         558±1μs          618±1μs     1.11  reindex.ReindexMethod.time_reindex_method('pad', <function period_range>)
+        1.30±0ms         1.43±0ms     1.11  rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'float', 'sum')
+         961±4μs         1.06±0ms     1.11  rolling.Methods.time_method('Series', ('expanding', {}), 'float', 'std')
+         553±2μs          612±1μs     1.11  reindex.ReindexMethod.time_reindex_method('pad', <function date_range>)
+     1.24±0.01ms      1.37±0.02ms     1.11  rolling.Methods.time_method('Series', ('rolling', {'window': 1000}), 'float', 'count')
+        1.29±0ms      1.43±0.01ms     1.10  rolling.VariableWindowMethods.time_method('Series', '50s', 'float', 'mean')
+         926±8μs         1.02±0ms     1.10  rolling.Methods.time_method('DataFrame', ('expanding', {}), 'int', 'mean')
+        1.28±0ms         1.41±0ms     1.10  rolling.VariableWindowMethods.time_method('Series', '1d', 'float', 'mean')
+     1.33±0.01ms         1.47±0ms     1.10  rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'int', 'sum')
+         894±1μs          984±2μs     1.10  rolling.Methods.time_method('DataFrame', ('expanding', {}), 'float', 'mean')
+         920±6μs      1.01±0.01ms     1.10  rolling.Methods.time_method('DataFrame', ('expanding', {}), 'int', 'sum')
+        1.31±0ms         1.44±0ms     1.10  rolling.VariableWindowMethods.time_method('Series', '1d', 'int', 'mean')
+     1.25±0.01ms         1.37±0ms     1.10  rolling.VariableWindowMethods.time_method('Series', '1h', 'int', 'sum')
+        1.34±0ms         1.47±0ms     1.10  rolling.VariableWindowMethods.time_method('DataFrame', '1h', 'int', 'sum')
-       181±0.3μs          165±1μs     0.91  index_cached_properties.IndexCache.time_is_monotonic_increasing('UInt64Index')
-       82.4±10ms       74.6±0.5ms     0.91  algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.float64'>, 1000000)
-     2.24±0.08ms         2.02±0ms     0.90  frame_methods.Fillna.time_frame_fillna(False, 'pad', 'float32')
-      1.45±0.5μs         1.31±0μs     0.90  tslibs.fields.TimeGetStartEndField.time_get_start_end_field(0, 'start', 'month', None, 12)
-        749±30μs         672±30μs     0.90  dtypes.SelectDtypes.time_select_dtype_string_exclude('Int8')
-        653±30μs         585±20μs     0.90  arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 5.0, <built-in function ge>)
-         473±1μs         421±10μs     0.89  dtypes.SelectDtypes.time_select_dtype_int_exclude('uint32')
-        454±20μs          401±5μs     0.88  dtypes.SelectDtypes.time_select_dtype_int_exclude('complex64')
-         454±1μs         400±10μs     0.88  dtypes.SelectDtypes.time_select_dtype_int_include(<class 'int'>)
-        11.1±1ms      9.77±0.03ms     0.88  arithmetic.FrameWithFrameWide.time_op_different_blocks(<built-in function gt>, (10000, 1000))
-     3.16±0.02ms       2.78±0.2ms     0.88  frame_methods.Fillna.time_frame_fillna(True, 'pad', 'float64')
-      3.00±0.1ms      2.58±0.01ms     0.86  frame_methods.Fillna.time_frame_fillna(True, 'pad', 'float32')
-      3.03±0.1ms      2.59±0.01ms     0.85  frame_methods.Fillna.time_frame_fillna(True, 'bfill', 'float32')
-      14.7±0.4ms       12.4±0.2ms     0.84  multiindex_object.SetOperations.time_operation('monotonic', 'int', 'intersection', None)
-        768±30μs         640±20μs     0.83  arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 2, <built-in function ne>)
-       703±100μs         577±20μs     0.82  arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function eq>)
-        762±60μs         615±30μs     0.81  arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 2, <built-in function ge>)
-         176±3μs        137±0.2μs     0.78  index_cached_properties.IndexCache.time_is_monotonic_increasing('Float64Index')
-      2.22±0.3μs       1.73±0.1μs     0.78  index_cached_properties.IndexCache.time_shape('CategoricalIndex')
-      1.77±0.3μs       1.37±0.1μs     0.77  index_cached_properties.IndexCache.time_values('CategoricalIndex')
-         178±3μs        137±0.3μs     0.77  index_cached_properties.IndexCache.time_is_monotonic_decreasing('Float64Index')
-       972±200μs         562±20μs     0.58  arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function lt>)

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE DECREASED.
```

As far as warnings go, this gets us down a few thousand, though there's still 150k+ lines of warnings. I don't know we will ever be able to use Winline since I think Cython uses it a lot.  mind double-checking the worst 2ish with timeit? does this change the build sizes (eg wheels)? Wasn't sure how to use timeit for the first benchmark since it measures parallel execution, but a subsequent run of just that yields no changes:

```sh
asv continuous -f 1.1 upstream/main HEAD -b gil.ParallelRolling.time_rolling

[100.00%] ··· gil.ParallelRolling.time_rolling                                                                                   ok
[100.00%] ··· ======== =============
               method               
              -------- -------------
               median     43.2±1ms  
                mean    1.53±0.03ms 
                min     2.24±0.01ms 
                max     2.24±0.02ms 
                var     2.00±0.03ms 
                skew    2.75±0.05ms 
                kurt    3.04±0.02ms 
                std     2.16±0.01ms 
              ======== =============


BENCHMARKS NOT SIGNIFICANTLY CHANGED.
```

Here's the next few entries on this branch:

```ipython
In [3]: import pandas as pd
   ...: N = 10**5
   ...: idx = pd.CategoricalIndex(range(N), range(N))
   ...: idx._cache = {}
   ...: %timeit idx.is_monotonic_increasing
   ...: %timeit idx.is_monotonic_decreasing
   ...: 
92.5 ns ± 0.651 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)
93.7 ns ± 0.81 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)
```

versus main

```sh
95.5 ns ± 0.888 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)
99.2 ns ± 0.836 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)
``` > does this change the build sizes (eg wheels)?

Looks to be 24kb larger on this branch than main thanks for double-checking.  this seems benign to me. thanks @WillAyd 