This will always require a roll at the end, for a minimal gain

isinstance(..., int) does not recognize numpy.integers

Also remove maxsize logic

* fixed Einsum failing with repeated inputs

* Optimise the _ensure_not_equal function
Co-authored-by: Ricardo Vieira <28983449+ricardoV94@users.noreply.github.com>

* Fix einsum failing on repeated inputs

* Fix einsum failing with repeated inputs

* Added regression test for repeated inputs to the einsum function

* Fix for failing test
Co-authored-by: Ricardo Vieira <28983449+ricardoV94@users.noreply.github.com>

---------
Co-authored-by: Ricardo Vieira <28983449+ricardoV94@users.noreply.github.com>

Some poorly implemented BLAS operations don't handle them correctly

* Add transposed argument to `solve` and `solve_triangular`

* Expand test coverage for `Solve` and `SolveTriangular`

Started failing in 0.4.36: https://github.com/jax-ml/jax/issues/26888

Skip failing JAX test

Started failing in 0.4.36 and seems to be fixed in 0.5.1

Extends pre-existing rewrite to ravel multiple integer indices, and to place them consecutively. The following cases should now be supported without object mode:
* Advanced integer indexing (not mixed with basic or boolean indexing) that do not require broadcasting of indices
* Consecutive advanced integer indexing updating (set/inc) (not mixed with basic or boolean indexing) that do not require broadcasting of indices or y.

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