Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 2.22.0 to 2.23.0.
- [Release notes](https://github.com/pypa/cibuildwheel/releases)
- [Changelog](https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md)
- [Commits](https://github.com/pypa/cibuildwheel/compare/v2.22.0...v2.23.0)

---
updated-dependencies:
- dependency-name: pypa/cibuildwheel
  dependency-type: direct:production
  update-type: version-update:semver-minor
...
Signed-off-by: dependabot[bot] <support@github.com>

JAX needs no special handling because it excludes inplace rewrites.

Introduced in e593b0ac due to a bug when inputs had zero-strides.

The bug can be fixed just by removing a block that assumed some `full`/`broadcasting` behavior by the operation, but this is not happening with DimShuffle.

This was caused by 223ee154, which used the generic `PyArray_IntpConverter` to convert the shape numpy vector into a simple C-array for the Reshape operation. There seems to be no need for this change as the strides were correctly used

Profiling suggests the previous changes caused a 7.5x slowdown. The benchmark detects only a 2.3x slowdown due to the PyTensor call overhead.

* Added print_shape option to debugprint and simplify __str__ logic in TensorType
* Add print_memory_map option to debugprint to enable destroy and view maps

For more context, see the discussion in:
https://github.com/conda-forge/pytensor-suite-feedstock/pull/133#issuecomment-2673831166

* Fix bug in solve_triangular when `overwrite_b = True`

* Add regression test

pytest-sphinx is not compatible with Python 3.13: https://github.com/twmr/pytest-sphinx/issues/67

We want to test numpy 1.26.x on `test_math.py` but test_math.py was being excluded in python 3.10, and numpy 1.26.x was being excluded in the non python3.10 job.

<https://conda-forge.org/news/2024/08/14/sunsetting-pypy/>

Notably changes the behavior of `__bool__` to always raise. Before there was a hack based on whether a variable had been compared to something before.

Co-authored-by: Adv <adhvaithhundi.221ds003@nitk.edu.in>

Remaining tests now run on latest numpy,
except for Numba jobs, which need numpy 2.1.0

Also added ruff numpy2 transition rule.

I split this test up to test uint64 separately, since
this is the case discussed in Issue #770. I also added
a test for the exact example used in that issue.

The uint dtypes with lower precision should pass.

The uint64 case started passing for me locally on Mac OSX,
but still fails on CI. I'm not sure why this is, but at
least the test will be more specific now if it fails in the future.

Due to changes in numpy conversion rules (NEP 50),
overflows are not ignored; in particular, negating
a unsigned int causes an overflow error.

The test for `neg` has been changed to check that
this error is raised.

In numpy 2.0, if axis=None, then np.unique
does not flatten the inverse indices returned
if return_inverse=True

A helper function has been added to npy_2_compat.py
to mimic the output of `np.unique` from version of
numpy before 2.0