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>

Also allow arbitrary capitalization of the modes.

Also make linker and optimizer non-mutable config as the mode is cached after using them for the first time.

Accidentally introduced in c655b028

Also move tests to the rewriting test file

* Add trusted publishing

* Don't persist credentials in pypi.yml

Ref: <https://woodruffw.github.io/zizmor/audits/#artipacked>

* Don't attempt to publish from forks

* Include more comments

* Don't persist credentials in pypi.yml

Ref: <https://woodruffw.github.io/zizmor/audits/#artipacked>

* Don't persist credentials

This is an insecure default on GitHub that increases the chances of credential leakage.
<https://unit42.paloaltonetworks.com/github-repo-artifacts-leak-tokens/>

* Add zizmor exception for RTD pull_request_target trigger

* Create zizmor workflow

1. Use actual Solve Op to infer output dtype as CholSolve outputs a different dtype than basic Solve in Scipy==1.15

2. Tweaked test related to https://github.com/pymc-devs/pytensor/issues/1152

3. Tweak tolerage

Fixes bug in `local_add_neg_to_sub` reported in https://github.com/pymc-devs/pytensor/issues/584

* Add example gallery

* Add example gallery

* Update rtd environment

* Fix typo in `generate_gallery.py`

* Add graph rewriting tutorial to examples

* Add default gallery image

* Fix default image path

* Add default image

* Add scan tutorial example

* Add header links to github/colab/binder

Add ablog with tags, authors, and categories

Modify example notebooks

* Update license and package name in footer

Delete placeholder notebook

* Delete docs for `sandbox` module

* rename `pytensor.tensor.var._tensor_py_operators` -> `pytensor.tensor.variable._tensor_py_operators`

* Fix underline lengths

* Remove reference to depreciated functions

* Add end of file newline

* Remove references to sandbox

* Try adding sitemap and robots.txt

* pre-commit

* Add cross-references and fix formatting

* Fix cross-reference

* add interpolate.py

* Add jax dispatch for `searchsorted`

* Import user-facing functions in `tensor.__init__`

* Add for loop based scalar loop

* Pass all loop tests

* Fetch constants from op

* Add while loop test

* Fix while loop and nasty stack over dtypes

* Disable compile here based on CI result

* Fix mypy signature

* Remove unnecessary torch stack

* Only call .cpu when necessary

* Recursive false for torch compiler

* Add elemwise test

* Late import torch

* Do iteration instead of vmap for elemwise

* Clean up and add description

* Add unit test to verify iteration

* Refactor to ravel method

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

* Fix comment

* Remove extra return

* Update test

* Add single carry test

* Remove compiler disable

* Better name

* Lint

* Better docstring

* Pr comments

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

This can only be done when the output of infer_shape of the core_op depends only on the input shapes, and not their values.

This Op does not really fit the CAReduce API, as it requires an extra bit of information (number of elements in the axis) during the loop. A better solution will be a fused Elemwise+CAReduce