EFF Reduce the size of shared objects of the C-extensions generated by Cython

### Context

scikit-learn uses C-extensions in critical part of its implementations via Cython.

Each C-entension is build from one or several Cython translation unit (a `.pyx` file with a potential `.pxd` companion file). 

In scikit-learn, each C-extension build consists of a single Cython translation which is transpilled to a C or C++ translation unit, which is then compiled to a shared object file.

The resulting C or C++ translation unit contains the code translation from Cython to C and large preambule and epylogue of macros, functions, structs, global variables such as virtual tables, Python module definition, etc.

For instance, while the code of [`sklearn/utils/_heap.pyx`](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/utils/_heap.pyx) only consists of less than 100 lines for a single function, the resulting `sklearn/utils/heap.c` file consists of more than 3500 lines, most of being the preambule's and the epilogue's injected by Cython:

<details>
<summary> Content of the generated <code>sklearn/utils/heap.c</code> </summary>

```

▾ macros
   -CYTHON_ABI
   -CYTHON_ASSUME_SAFE_MACROS
   -CYTHON_ASSUME_SAFE_MACROS
   -CYTHON_ASSUME_SAFE_MACROS
   -CYTHON_ASSUME_SAFE_MACROS
   -CYTHON_AVOID_BORROWED_REFS
   -CYTHON_AVOID_BORROWED_REFS
   -CYTHON_AVOID_BORROWED_REFS
   -CYTHON_AVOID_BORROWED_REFS
   -CYTHON_COMPILING_IN_CPYTHON
   -CYTHON_COMPILING_IN_CPYTHON
   -CYTHON_COMPILING_IN_CPYTHON
   -CYTHON_COMPILING_IN_CPYTHON
   -CYTHON_COMPILING_IN_NOGIL
   -CYTHON_COMPILING_IN_NOGIL
   -CYTHON_COMPILING_IN_NOGIL
   -CYTHON_COMPILING_IN_NOGIL
   -CYTHON_COMPILING_IN_PYPY
   -CYTHON_COMPILING_IN_PYPY
   -CYTHON_COMPILING_IN_PYPY
   -CYTHON_COMPILING_IN_PYPY
   -CYTHON_COMPILING_IN_PYSTON
   -CYTHON_COMPILING_IN_PYSTON
   -CYTHON_COMPILING_IN_PYSTON
   -CYTHON_COMPILING_IN_PYSTON
   -CYTHON_FALLTHROUGH
   -CYTHON_FALLTHROUGH
   -CYTHON_FALLTHROUGH
   -CYTHON_FALLTHROUGH
   -CYTHON_FALLTHROUGH
   -CYTHON_FALLTHROUGH
   -CYTHON_FAST_PYCALL
   -CYTHON_FAST_PYCALL
   -CYTHON_FAST_PYCALL
   -CYTHON_FAST_PYCALL
   -CYTHON_FAST_PYCCALL
   -CYTHON_FAST_THREAD_STATE
   -CYTHON_FAST_THREAD_STATE
   -CYTHON_FAST_THREAD_STATE
   -CYTHON_FAST_THREAD_STATE
   -CYTHON_FAST_THREAD_STATE
   -CYTHON_FORMAT_SSIZE_T
   -CYTHON_FUTURE_DIVISION
   -CYTHON_HEX_VERSION
   -CYTHON_INLINE
   -CYTHON_INLINE
   -CYTHON_INLINE
   -CYTHON_INLINE
   -CYTHON_INLINE
   -CYTHON_MAYBE_UNUSED_VAR(x)
   -CYTHON_NCP_UNUSED
   -CYTHON_NCP_UNUSED
   -CYTHON_PEP393_ENABLED
   -CYTHON_PEP393_ENABLED
   -CYTHON_PEP489_MULTI_PHASE_INIT
   -CYTHON_PEP489_MULTI_PHASE_INIT
   -CYTHON_PEP489_MULTI_PHASE_INIT
   -CYTHON_PEP489_MULTI_PHASE_INIT
   -CYTHON_PEP489_MULTI_PHASE_INIT
   -CYTHON_REFNANNY
   -CYTHON_RESTRICT
   -CYTHON_RESTRICT
   -CYTHON_RESTRICT
   -CYTHON_RESTRICT
   -CYTHON_SMALL_CODE
   -CYTHON_SMALL_CODE
   -CYTHON_SMALL_CODE
   -CYTHON_UNPACK_METHODS
   -CYTHON_UNPACK_METHODS
   -CYTHON_UNPACK_METHODS
   -CYTHON_UNPACK_METHODS
   -CYTHON_UNUSED
   -CYTHON_UNUSED
   -CYTHON_UNUSED
   -CYTHON_UNUSED
   -CYTHON_UPDATE_DESCRIPTOR_DOC
   -CYTHON_UPDATE_DESCRIPTOR_DOC
   -CYTHON_UPDATE_DESCRIPTOR_DOC
   -CYTHON_USE_ASYNC_SLOTS
   -CYTHON_USE_ASYNC_SLOTS
   -CYTHON_USE_ASYNC_SLOTS
   -CYTHON_USE_ASYNC_SLOTS
   -CYTHON_USE_ASYNC_SLOTS
   -CYTHON_USE_ASYNC_SLOTS
   -CYTHON_USE_DICT_VERSIONS
   -CYTHON_USE_DICT_VERSIONS
   -CYTHON_USE_DICT_VERSIONS
   -CYTHON_USE_DICT_VERSIONS
   -CYTHON_USE_EXC_INFO_STACK
   -CYTHON_USE_EXC_INFO_STACK
   -CYTHON_USE_EXC_INFO_STACK
   -CYTHON_USE_EXC_INFO_STACK
   -CYTHON_USE_EXC_INFO_STACK
   -CYTHON_USE_PYLIST_INTERNALS
   -CYTHON_USE_PYLIST_INTERNALS
   -CYTHON_USE_PYLIST_INTERNALS
   -CYTHON_USE_PYLIST_INTERNALS
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   -CYTHON_USE_PYLONG_INTERNALS
   -CYTHON_USE_PYLONG_INTERNALS
   -CYTHON_USE_PYLONG_INTERNALS
   -CYTHON_USE_PYLONG_INTERNALS
   -CYTHON_USE_PYTYPE_LOOKUP
   -CYTHON_USE_PYTYPE_LOOKUP
   -CYTHON_USE_PYTYPE_LOOKUP
   -CYTHON_USE_PYTYPE_LOOKUP
   -CYTHON_USE_PYTYPE_LOOKUP
   -CYTHON_USE_TP_FINALIZE
   -CYTHON_USE_TP_FINALIZE
   -CYTHON_USE_TP_FINALIZE
   -CYTHON_USE_TP_FINALIZE
   -CYTHON_USE_TYPE_SLOTS
   -CYTHON_USE_TYPE_SLOTS
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   -CYTHON_USE_UNICODE_INTERNALS
   -CYTHON_USE_UNICODE_INTERNALS
   -CYTHON_USE_UNICODE_INTERNALS
   -CYTHON_USE_UNICODE_INTERNALS
   -CYTHON_USE_UNICODE_WRITER
   -CYTHON_USE_UNICODE_WRITER
   -CYTHON_USE_UNICODE_WRITER
   -CYTHON_USE_UNICODE_WRITER
   -CYTHON_USE_UNICODE_WRITER
   -CYTHON_WITHOUT_ASSERTIONS
   -DL_EXPORT(t)
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   -PY_LONG_LONG
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▾-__anonf7ac09720103 : enum
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▾-__anonf7ac09720208 : struct
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▾-__anonf7ac09720308 : struct
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```

</details>

### Problem

Currently the uncompressed size of scikit-learn is around 48.8MB, 20MB of which are shared object files. As reported by @rth in https://github.com/pyodide/pyodide/issues/4289, while shared object files are optimized for Emscripten quite heavily, they still accounts for most of the size of scikit-learn on this stack.

<details>
<summary>Extensions' shared object sizes on Linux</summary>

```bash
find . -name \*.so -exec du -h {} \; | sort -h --reverse
```

```
2,1M	./sklearn/_loss/_loss.cpython-312-x86_64-linux-gnu.so
712K	./sklearn/utils/sparsefuncs_fast.cpython-312-x86_64-linux-gnu.so
672K	./sklearn/neighbors/_kd_tree.cpython-312-x86_64-linux-gnu.so
672K	./sklearn/neighbors/_ball_tree.cpython-312-x86_64-linux-gnu.so
624K	./sklearn/tree/_tree.cpython-312-x86_64-linux-gnu.so
608K	./sklearn/metrics/_dist_metrics.cpython-312-x86_64-linux-gnu.so
520K	./sklearn/datasets/_svmlight_format_fast.cpython-312-x86_64-linux-gnu.so
504K	./sklearn/preprocessing/_target_encoder_fast.cpython-312-x86_64-linux-gnu.so
476K	./sklearn/svm/_libsvm.cpython-312-x86_64-linux-gnu.so
448K	./sklearn/svm/_libsvm_sparse.cpython-312-x86_64-linux-gnu.so
448K	./sklearn/metrics/_pairwise_distances_reduction/_middle_term_computer.cpython-312-x86_64-linux-gnu.so
440K	./sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.cpython-312-x86_64-linux-gnu.so
440K	./sklearn/linear_model/_cd_fast.cpython-312-x86_64-linux-gnu.so
404K	./sklearn/cluster/_k_means_elkan.cpython-312-x86_64-linux-gnu.so
396K	./sklearn/utils/_cython_blas.cpython-312-x86_64-linux-gnu.so
396K	./sklearn/cluster/_k_means_common.cpython-312-x86_64-linux-gnu.so
384K	./sklearn/preprocessing/_csr_polynomial_expansion.cpython-312-x86_64-linux-gnu.so
352K	./sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.cpython-312-x86_64-linux-gnu.so
340K	./sklearn/tree/_splitter.cpython-312-x86_64-linux-gnu.so
340K	./sklearn/cluster/_hdbscan/_tree.cpython-312-x86_64-linux-gnu.so
324K	./sklearn/metrics/_pairwise_distances_reduction/_argkmin.cpython-312-x86_64-linux-gnu.so
324K	./sklearn/linear_model/_sgd_fast.cpython-312-x86_64-linux-gnu.so
312K	./sklearn/tree/_criterion.cpython-312-x86_64-linux-gnu.so
312K	./sklearn/cluster/_k_means_lloyd.cpython-312-x86_64-linux-gnu.so
308K	./sklearn/ensemble/_hist_gradient_boosting/splitting.cpython-312-x86_64-linux-gnu.so
308K	./sklearn/cluster/_hdbscan/_reachability.cpython-312-x86_64-linux-gnu.so
304K	./sklearn/cluster/_hierarchical_fast.cpython-312-x86_64-linux-gnu.so
292K	./sklearn/metrics/_pairwise_distances_reduction/_base.cpython-312-x86_64-linux-gnu.so
280K	./sklearn/linear_model/_sag_fast.cpython-312-x86_64-linux-gnu.so
276K	./sklearn/ensemble/_hist_gradient_boosting/histogram.cpython-312-x86_64-linux-gnu.so
272K	./sklearn/svm/_liblinear.cpython-312-x86_64-linux-gnu.so
268K	./sklearn/utils/_seq_dataset.cpython-312-x86_64-linux-gnu.so
264K	./sklearn/neighbors/_quad_tree.cpython-312-x86_64-linux-gnu.so
260K	./sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors_classmode.cpython-312-x86_64-linux-gnu.so
256K	./sklearn/_isotonic.cpython-312-x86_64-linux-gnu.so
252K	./sklearn/cluster/_k_means_minibatch.cpython-312-x86_64-linux-gnu.so
248K	./sklearn/utils/_fast_dict.cpython-312-x86_64-linux-gnu.so
248K	./sklearn/tree/_utils.cpython-312-x86_64-linux-gnu.so
248K	./sklearn/metrics/_pairwise_fast.cpython-312-x86_64-linux-gnu.so
244K	./sklearn/decomposition/_online_lda_fast.cpython-312-x86_64-linux-gnu.so
240K	./sklearn/metrics/_pairwise_distances_reduction/_argkmin_classmode.cpython-312-x86_64-linux-gnu.so
232K	./sklearn/utils/_typedefs.cpython-312-x86_64-linux-gnu.so
232K	./sklearn/utils/_isfinite.cpython-312-x86_64-linux-gnu.so
220K	./sklearn/utils/arrayfuncs.cpython-312-x86_64-linux-gnu.so
220K	./sklearn/ensemble/_gradient_boosting.cpython-312-x86_64-linux-gnu.so
216K	./sklearn/ensemble/_hist_gradient_boosting/utils.cpython-312-x86_64-linux-gnu.so
212K	./sklearn/utils/_random.cpython-312-x86_64-linux-gnu.so
212K	./sklearn/utils/murmurhash.cpython-312-x86_64-linux-gnu.so
212K	./sklearn/ensemble/_hist_gradient_boosting/_predictor.cpython-312-x86_64-linux-gnu.so
212K	./sklearn/decomposition/_cdnmf_fast.cpython-312-x86_64-linux-gnu.so
212K	./sklearn/cluster/_hdbscan/_linkage.cpython-312-x86_64-linux-gnu.so
204K	./sklearn/metrics/cluster/_expected_mutual_info_fast.cpython-312-x86_64-linux-gnu.so
204K	./sklearn/manifold/_barnes_hut_tsne.cpython-312-x86_64-linux-gnu.so
192K	./sklearn/utils/_weight_vector.cpython-312-x86_64-linux-gnu.so
184K	./sklearn/manifold/_utils.cpython-312-x86_64-linux-gnu.so
184K	./sklearn/ensemble/_hist_gradient_boosting/_gradient_boosting.cpython-312-x86_64-linux-gnu.so
184K	./sklearn/cluster/_dbscan_inner.cpython-312-x86_64-linux-gnu.so
180K	./sklearn/ensemble/_hist_gradient_boosting/_bitset.cpython-312-x86_64-linux-gnu.so
180K	./sklearn/ensemble/_hist_gradient_boosting/_binning.cpython-312-x86_64-linux-gnu.so
128K	./sklearn/utils/_vector_sentinel.cpython-312-x86_64-linux-gnu.so
112K	./sklearn/ensemble/_hist_gradient_boosting/common.cpython-312-x86_64-linux-gnu.so
80K	    ./sklearn/feature_extraction/_hashing_fast.cpython-312-x86_64-linux-gnu.so
48K	    ./sklearn/utils/_openmp_helpers.cpython-312-x86_64-linux-gnu.so
32K	    ./sklearn/svm/_newrand.cpython-312-x86_64-linux-gnu.so
28K	    ./sklearn/utils/_sorting.cpython-312-x86_64-linux-gnu.so
28K	    ./sklearn/neighbors/_partition_nodes.cpython-312-x86_64-linux-gnu.so
24K	    ./sklearn/utils/_heap.cpython-312-x86_64-linux-gnu.so
24K	    ./sklearn/__check_build/_check_build.cpython-312-x86_64-linux-gnu.so
```

</details>

### Possible solutions

#### Strip all symbols and optimize for size

This can be done by adding `-Wl,--strip-all` to `extra_link_args` and `-Os -g0` to `extra_compile_args`.

In practice, it can significantly shrink shared object (up to nearly 50% size reduction):

<details>

<summary>Extensions' shared object sizes on Linux after striping all symbols and optimizing for size</summary>

```
find . -name \*.so -exec du -h {} \; | sort -h --reverse
```

```
1,2M	sklearn/_loss/_loss.cpython-312-x86_64-linux-gnu.so
560K	sklearn/tree/_tree.cpython-312-x86_64-linux-gnu.so
468K	sklearn/neighbors/_kd_tree.cpython-312-x86_64-linux-gnu.so
468K	sklearn/neighbors/_ball_tree.cpython-312-x86_64-linux-gnu.so
460K	sklearn/utils/sparsefuncs_fast.cpython-312-x86_64-linux-gnu.so
460K	sklearn/metrics/_dist_metrics.cpython-312-x86_64-linux-gnu.so
380K	sklearn/datasets/_svmlight_format_fast.cpython-312-x86_64-linux-gnu.so
376K	sklearn/svm/_libsvm.cpython-312-x86_64-linux-gnu.so
352K	sklearn/svm/_libsvm_sparse.cpython-312-x86_64-linux-gnu.so
336K	sklearn/tree/_splitter.cpython-312-x86_64-linux-gnu.so
320K	sklearn/preprocessing/_target_encoder_fast.cpython-312-x86_64-linux-gnu.so
320K	sklearn/metrics/_pairwise_distances_reduction/_middle_term_computer.cpython-312-x86_64-linux-gnu.so
316K	sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.cpython-312-x86_64-linux-gnu.so
312K	sklearn/utils/_cython_blas.cpython-312-x86_64-linux-gnu.so
308K	sklearn/linear_model/_cd_fast.cpython-312-x86_64-linux-gnu.so
300K	sklearn/tree/_criterion.cpython-312-x86_64-linux-gnu.so
280K	sklearn/preprocessing/_csr_polynomial_expansion.cpython-312-x86_64-linux-gnu.so
280K	sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.cpython-312-x86_64-linux-gnu.so
280K	sklearn/cluster/_k_means_elkan.cpython-312-x86_64-linux-gnu.so
276K	sklearn/cluster/_k_means_common.cpython-312-x86_64-linux-gnu.so
252K	sklearn/linear_model/_sgd_fast.cpython-312-x86_64-linux-gnu.so
248K	sklearn/cluster/_hdbscan/_tree.cpython-312-x86_64-linux-gnu.so
244K	sklearn/metrics/_pairwise_distances_reduction/_argkmin.cpython-312-x86_64-linux-gnu.so
240K	sklearn/tree/_utils.cpython-312-x86_64-linux-gnu.so
240K	sklearn/ensemble/_hist_gradient_boosting/splitting.cpython-312-x86_64-linux-gnu.so
236K	sklearn/cluster/_hierarchical_fast.cpython-312-x86_64-linux-gnu.so
236K	sklearn/cluster/_hdbscan/_reachability.cpython-312-x86_64-linux-gnu.so
232K	sklearn/svm/_liblinear.cpython-312-x86_64-linux-gnu.so
228K	sklearn/cluster/_k_means_lloyd.cpython-312-x86_64-linux-gnu.so
224K	sklearn/metrics/_pairwise_distances_reduction/_base.cpython-312-x86_64-linux-gnu.so
212K	sklearn/neighbors/_quad_tree.cpython-312-x86_64-linux-gnu.so
208K	sklearn/linear_model/_sag_fast.cpython-312-x86_64-linux-gnu.so
204K	sklearn/ensemble/_hist_gradient_boosting/histogram.cpython-312-x86_64-linux-gnu.so
200K	sklearn/utils/_seq_dataset.cpython-312-x86_64-linux-gnu.so
196K	sklearn/utils/_fast_dict.cpython-312-x86_64-linux-gnu.so
196K	sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors_classmode.cpython-312-x86_64-linux-gnu.so
196K	sklearn/_isotonic.cpython-312-x86_64-linux-gnu.so
196K	sklearn/decomposition/_online_lda_fast.cpython-312-x86_64-linux-gnu.so
196K	sklearn/cluster/_k_means_minibatch.cpython-312-x86_64-linux-gnu.so
192K	sklearn/utils/_isfinite.cpython-312-x86_64-linux-gnu.so
192K	sklearn/metrics/_pairwise_fast.cpython-312-x86_64-linux-gnu.so
192K	sklearn/metrics/_pairwise_distances_reduction/_argkmin_classmode.cpython-312-x86_64-linux-gnu.so
184K	sklearn/utils/_typedefs.cpython-312-x86_64-linux-gnu.so
180K	sklearn/utils/arrayfuncs.cpython-312-x86_64-linux-gnu.so
180K	sklearn/ensemble/_hist_gradient_boosting/utils.cpython-312-x86_64-linux-gnu.so
180K	sklearn/ensemble/_gradient_boosting.cpython-312-x86_64-linux-gnu.so
172K	sklearn/utils/_random.cpython-312-x86_64-linux-gnu.so
168K	sklearn/utils/murmurhash.cpython-312-x86_64-linux-gnu.so
168K	sklearn/ensemble/_hist_gradient_boosting/_predictor.cpython-312-x86_64-linux-gnu.so
168K	sklearn/decomposition/_cdnmf_fast.cpython-312-x86_64-linux-gnu.so
168K	sklearn/cluster/_hdbscan/_linkage.cpython-312-x86_64-linux-gnu.so
160K	sklearn/metrics/cluster/_expected_mutual_info_fast.cpython-312-x86_64-linux-gnu.so
160K	sklearn/manifold/_barnes_hut_tsne.cpython-312-x86_64-linux-gnu.so
152K	sklearn/utils/_weight_vector.cpython-312-x86_64-linux-gnu.so
152K	sklearn/manifold/_utils.cpython-312-x86_64-linux-gnu.so
152K	sklearn/ensemble/_hist_gradient_boosting/_gradient_boosting.cpython-312-x86_64-linux-gnu.so
152K	sklearn/cluster/_dbscan_inner.cpython-312-x86_64-linux-gnu.so
148K	sklearn/ensemble/_hist_gradient_boosting/_bitset.cpython-312-x86_64-linux-gnu.so
144K	sklearn/ensemble/_hist_gradient_boosting/_binning.cpython-312-x86_64-linux-gnu.so
112K	sklearn/utils/_vector_sentinel.cpython-312-x86_64-linux-gnu.so
96K	sklearn/ensemble/_hist_gradient_boosting/common.cpython-312-x86_64-linux-gnu.so
68K	sklearn/feature_extraction/_hashing_fast.cpython-312-x86_64-linux-gnu.so
44K	sklearn/utils/_openmp_helpers.cpython-312-x86_64-linux-gnu.so
32K	sklearn/svm/_newrand.cpython-312-x86_64-linux-gnu.so
28K	sklearn/utils/_sorting.cpython-312-x86_64-linux-gnu.so
28K	sklearn/neighbors/_partition_nodes.cpython-312-x86_64-linux-gnu.so
24K	sklearn/utils/_heap.cpython-312-x86_64-linux-gnu.so
24K	sklearn/__check_build/_check_build.cpython-312-x86_64-linux-gnu.so
```
</details>

#### Group several translation units within C extensions (and use interprocedural optimization)

So as to reuse duplicated symbols in shared objects and perform optimization over several translation units (such as inlining functions, etc.)

COMMENT:
There are nightly wheels on the Scientific Python channel: https://anaconda.org/scientific-python-nightly-wheels/cython

Labels: Build / CI, cython, C/C++