Use grad to compute jacobian when input shape is known to be (1,)

[jessegrabowski]

Description

Small optimization for pt.jacobian(x, *args). If x is known to be shape (1,), we should still use pt.grad rather than doing a scan.

Related Issue

• Closes #
• Related to #

Checklist

• Checked that the pre-commit linting/style checks pass
• Included tests that prove the fix is effective or that the new feature works
• Added necessary documentation (docstrings and/or example notebooks)
• If you are a pro: each commit corresponds to a relevant logical change

Type of change

• New feature / enhancement
• Bug fix
• Documentation
• Maintenance
• Other (please specify):

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📚 Documentation preview 📚: https://pytensor--1454.org.readthedocs.build/en/1454/

[ricardoV94]

PR title is imprecise, any array with statically known size 1 now uses this, so shape=(1, ..., 1)

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 82.12%. Comparing base (ff98ab8) to head (818b641).
Report is 1 commits behind head on main.

Additional details and impacted files

@@           Coverage Diff           @@
##             main    #1454   +/-   ##
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  Coverage   82.12%   82.12%           
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  Files         211      211           
  Lines       49757    49757           
  Branches     8819     8819           
=======================================
  Hits        40862    40862           
  Misses       6715     6715           
  Partials     2180     2180           

Files with missing lines	Coverage Δ
pytensor/gradient.py	78.55% <100.00%> (ø)

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[jessegrabowski]

PR title is imprecise, any array with statically known size 1 now uses this, so shape=(1, ..., 1)

No, because jacobian explicitly checks for ndim=1. So shape(1,1,1) will raise before we get to the change in this PR.

[ricardoV94]

PR title is imprecise, any array with statically known size 1 now uses this, so shape=(1, ..., 1)

No, because jacobian explicitly checks for ndim=1. So shape(1,1,1) will raise before we get to the change in this PR.

I think I'm my PR on vectorized I removed that constraint?