typing: un-ignore parameter-already-assigned

[deerred643-star]

Un-ignores parameter-already-assigned as part of the ty gradual-typing baseline.

ty check --exclude-scripts --error parameter-already-assigned on a synced Python 3.14 env reported exactly one diagnostic, in machine_learning/k_means_clust.py:

error[parameter-already-assigned]: Multiple values provided for parameter `s` of bound method `Axes.scatter`
   --> machine_learning/k_means_clust.py:163:69
    |
163 |         centroids[:, 0], centroids[:, 1], centroids[:, 2], c="red", s=100, marker="x"
    |                                                                     ^^^^^

plt.axes(projection="3d") is annotated as returning the 2D Axes, whose scatter signature is (x, y, s=None, c=None, ...), so the third positional argument binds to s and collides with s=100. At runtime the object is an Axes3D, whose signature is (xs, ys, zs=0, zdir="z", s=20, c=None, ...), so the call is valid: this is a false positive caused by the imprecise static return type of plt.axes, not a latent TypeError.

The fix passes the third coordinate explicitly as zs=, which is a positional-or-keyword parameter of Axes3D.scatter. This keeps the runtime behaviour identical (verified by rendering the plot under the Agg backend before and after and comparing offsets, marker sizes, face colours and marker paths — byte-for-byte identical), and avoids Any, cast, new imports, and suppression comments.

With the diagnostic count at zero, rules.parameter-already-assigned = "ignore" is removed from [tool.ty]. No other rule severity is touched.

Validation

• ty check --exclude-scripts: Found 5 -> Found 4 diagnostics; parameter-already-assigned: 1 -> 0. The 4 remaining diagnostics are pre-existing (redundant-condition x2, invalid-assignment x2) and untouched.
• Full run including PEP 723 scripts: Found 50 -> Found 49; parameter-already-assigned: 1 -> 0.
• ruff check machine_learning/k_means_clust.py — all checks passed
• ruff format --check machine_learning/k_means_clust.py — already formatted
• ruff check pyproject.toml — all checks passed
• pytest --doctest-modules machine_learning/k_means_clust.py — 5 passed

Part of #15187

[algorithms-keeper]

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