fix(machine_learning): prevent log(0) and divide-by-zero warnings/NaN in kullback_leibler_divergence

[aoright]

Describe your change:

This PR fixes a bug where kullback_leibler_divergence raises RuntimeWarning and returns nan or inf under zero probabilities:

• Implements boolean masking to calculate the KL divergence contribution only for non-zero elements of y_true (since (0 \log 0 = 0) by definition).
• Clips predicted probabilities y_pred to a small value (e.g. 1e-15) to prevent division by zero or log of zero.
• Prevents generating RuntimeWarning warnings by calculating math operations only on valid masked elements.

• 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.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes Avoid log(0) in KL divergence #12233".