- Replace np.cast with np.asarray: in numpy 2.0,
  `np.cast[new_dtype](arr)` is deprecated.
  The literal replacement is `np.asarray(arr, dtype=new_dtype)`.

- Replace np.sctype2char and np.obj2sctype.
  Added try/except to handle change in behavior
  of `np.dtype`

- Replace np.find_common_type with np.result_type

Further changes to `TensorType`:

TensorType.dtype must be a string, so the code
has been changed from `self.dtype = np.dtype(dtype).type`,
where the right-hand side is of type `np.generic`, to
`self.dtype = str(np.dtype(dtype))`, where the right-hand
side is a string that satisfies:

`self.dtype == str(np.dtype(self.dtype))`

This doesn't change the behavior of `np.array(..., dtype=self.dtype)`
etc.

- replaced np.AxisError with np.exceptions.AxisError
- the `numpy.core` submodule has been renamed to `numpy._core`
- some parts of `numpy.core` have been moved to `numpy.lib.array_utils`

Except for `AxisError`, the updated imports are conditional on
the version of numpy, so the imports should work for numpy >= 1.26.

The conditional imports have been added to `npy_2_compat.py`, so the
imports elsewhere are unconditonal.

Also fixes bug in Scan L_op and Max R_op
Co-authored-by: Adrian Seyboldt <aseyboldt@users.noreply.github.com>

Also remove bad default values

Bumps [pypa/gh-action-pypi-publish](https://github.com/pypa/gh-action-pypi-publish) from 1.12.2 to 1.12.4.
- [Release notes](https://github.com/pypa/gh-action-pypi-publish/releases)
- [Commits](https://github.com/pypa/gh-action-pypi-publish/compare/v1.12.2...v1.12.4)

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

Also cleans up implementation and documentation

* Split and inverse

* PyTorch inline constants in dispatch to avoid graph breaks

Deprecate obscure ndim kwarg

A shortcut in the numpy implementation of einsum_path when there's nothing to optimize, creates a default path that can combine more than 2 operands. Our implementation only works with 2 or 1 operand operations at each step.

https://github.com/numpy/numpy/blob/cc5851e654bfd82a23f2758be4bd224be84fc1c3/numpy/_core/einsumfunc.py#L945-L951

Nothing clever was going on, unknown dims were simply faked as having length 8 for JAX polymorphism export

Internal API changed in numba 0.61

Existing benchmarks don't show any difference in performance

It replaces the old warning that does not actually apply by a more informative and actionable one.

This warning was for Ops that might use the alternative blas_headers, which rely on the Numpy C-API.

However, regular PyTensor user has not used this for a while. The only Op that would use C-code with this alternative headers is the GEMM Op which is not included in current rewrites. Instead Dot22 or Dot22Scalar are introduced, which refuse to generate C-code altogether if the blas flags are missing.

* Add 'overwrite_existing' flag to allow graph rewrites
and include appropriate testing

* Encapsulate test rewriters and use user-facing API

---------
Co-authored-by: Ricardo Vieira <ricardo.vieira1994@gmail.com>