Implement generalized gamma distribution

[kylejcaron]

Thank you for opening a PR!

This PR implements an Op for a Generalized Gamma Random Variable with the parameterization GG(alpha, p, lambd). This corresponds to scipy in the following mapping: scipy.stats.gengamma(a=alpha/p, c=p, scale=lambd).

This goes along with the following PyMC PR to implement the Generalized Gamma distribution

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[codecov]

Codecov Report

Merging #1059 (7fa673c) into main (8794f48) will increase coverage by 0.00%.
The diff coverage is 100.00%.

❗ Current head 7fa673c differs from pull request most recent head d9ede7d. Consider uploading reports for the commit d9ede7d to get more accurate results

@@           Coverage Diff           @@
##             main    #1059   +/-   ##
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  Coverage   79.25%   79.25%           
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  Files         152      152           
  Lines       47875    47887   +12     
  Branches    10906    10907    +1     
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+ Hits        37942    37954   +12     
  Misses       7436     7436           
  Partials     2497     2497           

Impacted Files	Coverage Δ
aesara/tensor/random/basic.py	98.92% <100.00%> (+0.02%)	⬆️

[brandonwillard]

Looks good, although the commits need to be squashed/rebased before merging (because there's a merge commit in this branch).

[kylejcaron]

Looks good, although the commits need to be squashed/rebased before merging (because there's a merge commit in this branch).

just rebased and squashed! Any advice on how to coordinate this PR with the corresponding PyMC PR?

[brandonwillard]

Any advice on how to coordinate this PR with the corresponding PyMC PR?

You can increment the required Aesara version in your PyMC PR; that will at least guarantee that your changes will only apply to a version of Aesara with these changes.