Fix KL divergence for zero true labels

[Kcstring]

Describe your change:

Fixes #12233.

kullback_leibler_divergence() now ignores positions where y_true is zero before applying np.log, matching the KL convention that zero-probability true labels contribute nothing to the sum. This avoids returning nan for inputs containing zero true labels without adding an epsilon or changing the behavior for non-zero labels.

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
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Local verification:

• python3 -m pytest machine_learning/loss_functions.py
• ruff check machine_learning/loss_functions.py
• ruff format --check machine_learning/loss_functions.py