PEP 646: Add explicit section on endorsements (GH-2055)

Author: mrahtz

pep-0646.rst

@@ -1184,6 +1184,68 @@ Footnotes
    shape begins with 'time × batch', then ``videos_batch[1][0]`` would select the
    same frame.
 
+Endorsements
+============
+
+Variadic generics have a wide range of uses. For the fraction of that range
+involving numerical computing, how likely is it that relevant libraries will
+actually make use of the features proposed in this PEP?
+
+We reached out to a number of people with this question, and received the
+following endorsements.
+
+From **Stephan Hoyer**, member of the NumPy Steering Council:
+[#stephan-endorsement]_
+
+    I just wanted to thank Matthew & Pradeep for writing this PEP and for
+    clarifications to the broader context of PEP 646 for array typing in
+    https://github.com/python/peps/pull/1904.
+
+    As someone who is heavily involved in the Python numerical computing
+    community (e.g., NumPy, JAX, Xarray), but who is not so familiar with the
+    details of Python's type system, it is reassuring to see that a broad range
+    of use-cases related to type checking of named axes & shapes have been
+    considered, and could build upon the infrastructure in this PEP.
+
+    Type checking for shapes is something the NumPy community is very
+    interested in -- there are more thumbs up on the relevant issue on NumPy's
+    GitHub than any others (https://github.com/numpy/numpy/issues/7370) and we
+    recently added a "typing" module that is under active development.
+
+    It will certainly require experimentation to figure out the best ways to
+    use type checking for ndarrays, but this PEP looks like an excellent
+    foundation for such work.
+
+From **Bas van Beek**, who has worked on preliminary support for
+shape-generics in NumPy:
+
+    I very much share Stephan's opinion here and look forward to integrating the
+    new PEP 646 variadics into numpy.
+
+    In the context of numpy (and tensor typing general): the typing of array
+    shapes is a fairly complicated subject and the introduction of variadics
+    will likely play in big role in laying its foundation, as it allows for the
+    expression of both dimensioability as well as basic shape manipulation.
+
+    All in all, I'm very interested in where both PEP 646 and future PEPs will
+    take us and look forward to further developments.
+
+From **Dan Moldovan**, a Senior Software Engineer on the TensorFlow Dev Team
+and author of the TensorFlow RFC, `TensorFlow Canonical Type System`_: [#dan-endorsement]_
+
+    I'd be interested in using this the mechanisms defined in this PEP to define
+    rank-generic Tensor types in TensorFlow, which are important in specifying
+    ``tf.function`` signatures in a Pythonic way, using type annotations (rather
+    than the custom ``input_signature`` mechanism we have today - see this
+    issue: https://github.com/tensorflow/tensorflow/issues/31579). Variadic
+    generics are among the last few missing pieces to create an elegant set of
+    type definitions for tensors and shapes.
+
+(For the sake of transparency - we also reached out to folks from a third popular
+numerical computing library, PyTorch, but did *not* receive a statement of
+endorsement from them. Our understanding is that although they are interested
+in some of the same issues - e.g. static shape inference - they are currently
+focusing on enabling this through a DSL rather than the Python type system.)
 
 Acknowledgements
 ================
@@ -1193,7 +1255,7 @@ Thank you to **Alfonso Castaño**, **Antoine Pitrou**, **Bas v.B.**, **David Fos
 **Sergei Lebedev**, and **Vladimir Mikulik** for helpful feedback and suggestions on
 drafts of this PEP.
 
-Thank you especially to **Lucio** for suggesting the star syntax (which has made multiple aspects of this proposal much more concise and intuitive), and to **Stephan Hoyer** for his kind `endorsement`_ of the PEP on the python-dev mailing list.
+Thank you especially to **Lucio** for suggesting the star syntax (which has made multiple aspects of this proposal much more concise and intuitive), and to **Stephan Hoyer** and **Dan Moldovan** for their endorsements.
 
 Resources
 =========
@@ -1246,7 +1308,11 @@ References
 
 .. _this exercise: https://spinningup.openai.com/en/latest/spinningup/exercise2_2_soln.html
 
-.. _endorsement: https://mail.python.org/archives/list/[email protected]/message/UDM7Y6HLHQBKXQEBIBD5ZLB5XNPDZDXV/
+.. _TensorFlow Canonical Type System: https://github.com/tensorflow/community/pull/208
+
+.. [#stephan-endorsement] https://mail.python.org/archives/list/[email protected]/message/UDM7Y6HLHQBKXQEBIBD5ZLB5XNPDZDXV/
+
+.. [#dan-endorsement] https://mail.python.org/archives/list/[email protected]/message/HTCARTYYCHETAMHB6OVRNR5EW5T2CP4J/
 
 Copyright
 =========