[maintenance] remove unused imports

[icfaust]

Description

While waiting on CI jobs, abuse PyRight to simplify things. These imports aren't used and can be removed.

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Checklist:

Completeness and readability

• I have commented my code, particularly in hard-to-understand areas.
• I have updated the documentation to reflect the changes or created a separate PR with updates and provided its number in the description, if necessary.
• Git commit message contains an appropriate signed-off-by string (see CONTRIBUTING.md for details).
• I have resolved any merge conflicts that might occur with the base branch.

Testing

• I have run it locally and tested the changes extensively.
• All CI jobs are green or I have provided justification why they aren't.
• I have extended testing suite if new functionality was introduced in this PR.

Performance

• I have measured performance for affected algorithms using scikit-learn_bench and provided at least a summary table with measured data, if performance change is expected.
• I have provided justification why performance and/or quality metrics have changed or why changes are not expected.
• I have extended the benchmarking suite and provided a corresponding scikit-learn_bench PR if new measurable functionality was introduced in this PR.

[david-cortes-intel]

/intelci: run

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.

Flag	Coverage Δ
azure	80.40% <100.00%> (-0.04%)	⬇️
github	82.00% <100.00%> (-0.04%)	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

Files with missing lines	Coverage Δ
onedal/covariance/incremental_covariance.py	93.47% <ø> (-0.40%)	⬇️
onedal/datatypes/_dlpack.py	69.56% <ø> (-1.27%)	⬇️
onedal/decomposition/incremental_pca.py	96.66% <ø> (-0.11%)	⬇️
onedal/decomposition/pca.py	95.91% <ø> (-0.17%)	⬇️
sklearnex/covariance/incremental_covariance.py	79.41% <ø> (-0.13%)	⬇️
sklearnex/decomposition/pca.py	93.56% <ø> (-0.03%)	⬇️
sklearnex/ensemble/_forest.py	79.70% <100.00%> (ø)
sklearnex/linear_model/linear.py	83.33% <ø> (-0.12%)	⬇️
sklearnex/linear_model/logistic_regression.py	57.24% <100.00%> (ø)
sklearnex/neighbors/common.py	92.44% <ø> (-0.05%)	⬇️

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