Model Remediation is a library that provides solutions for machine learning practitioners working to create and train models in a way that reduces or eliminates user harm resulting from underlying performance biases.
I've written custom parsers and emitters for everything from docstrings to classes and functions. However, I recently came across an issue with the TensorFlow codebase: inconsistent use of Args: and Arguments: in its docstrings. It is easy enough to extend my parsers to support both variants, however it looks like Arguments: is wrong anyway, as per:
seano314
commented
3 years ago
I've written custom parsers and emitters for everything from docstrings to classes and functions. However, I recently came across an issue with the TensorFlow codebase: inconsistent use of
Args:andArguments:in its docstrings. It is easy enough to extend my parsers to support both variants, however it looks likeArguments:is wrong anyway, as per:https://google.github.io/styleguide/pyguide.html#doc-function-args @
ddccc0fhttps://chromium.googlesource.com/chromiumos/docs/+/master/styleguide/python.md#describing-arguments-in-docstrings @
9fc0fc0https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html @
c0ae8e3Therefore, only
Args:is valid. This PR replaces them throughout the codebase.PS: For related PRs, see tensorflow/tensorflow/pull/45420