Fix doubled words in docstrings and use raw strings in a markdown test

[marekl11]

Three docstrings with a repeated word: "are the the expected ones" in
test_chef_pipeline.py and "children data for for the node" in
views/internal.py. The third, in test_gcs_storage.py, read "Check that set
set a cache-control of private" - that one was missing a word rather than
repeating one, so it now reads "Check that we set a cache-control".

I also added a second small commit to this PR instead of opening another one:
in tests/utils/test_markdown.py three LaTeX strings (x\cdot y=z^2 and
\sum_{i=1}^{n} x_i = y) carried a # noqa W605 because \c and \s are not
Python escapes. They are raw strings now, so the suppressions are gone. Python
reads them as the same text - I compared every string constant in the module
before and after and they are identical - and flake8 is clean on the file.

AI usage

• Disclose: I used Claude Code to search for repeated words in
  docstrings and comments, to find the noqa W605 suppressions, and to apply
  the resulting small edits.
• Engage critically: I read each hit in context rather than taking
  the search at face value, and confirmed none of the three docstrings is
  asserted on by a test or used as a translation key. For the raw strings I
  checked that the parsed string values did not change.
• Edit: the third docstring hit needed rewriting rather than de-duplicating -
  "Check that set set a cache-control of private" is a missing word, not
  a repeated one, so it now reads "Check that we set a cache-control".
• Process sharing: the lesson worth passing on is that a
  doubled-word search returns three different kinds of hit - real
  duplicates, missing words, and deliberate repetition - so the
  candidates have to be read individually. Here one of three was not a
  plain duplicate.

Docstrings and test string literals only; no code paths or user-facing text change.

[learning-equality-bot]

👋 Hi @marekl11, thanks for contributing!

For the review process to begin, please verify that the following is satisfied:

• Contribution is aligned with our contributing guidelines

• Pull request description has correctly filled AI usage section & follows our AI guidance:

  AI guidance

  
  

  State explicitly whether you didn't use or used AI & how.

  If you used it, ensure that the PR is aligned with Using AI as well as our DEEP framework. DEEP asks you:

  • Disclose — Be open about when you've used AI for support.
  • Engage critically — Question what is generated. Review code for correctness and unnecessary complexity.
  • Edit — Review and refine AI output. Remove unnecessary code and verify it still works after your edits.
  • Process sharing — Explain how you used the AI so others can learn.

  
  

  Examples of good disclosures:

  "I used Claude Code to implement the component, prompting it to follow the pattern in ComponentX. I reviewed the generated code, removed unnecessary error handling, and verified the tests pass."

  "I brainstormed the approach with Gemini, then had it write failing tests for the feature. After reviewing the tests, I used Claude Code to generate the implementation. I refactored the output to reduce verbosity and ran the full test suite."

Also check that issue requirements are satisfied & you ran pre-commit locally.

Pull requests that don't follow the guidelines will be closed.

Reviewer assignment can take up to 2 weeks.