FIX: Allow sparse data on TSNE

[david-cortes-intel]

Description

The docs say that TSNE does not support sparse inputs, and there are checks against them.

But it turns out that TSNE doesn't use the actual data - instead, it has some preliminary stages where it calculates distances or neighbors or PCA, and the results from those are then used to generate arrays that are passed to the actual TSNE algorithm. Those arrays are always dense, regardless of how the data comes in originally, so in way sklearnex has partial support for sparse data on TSNE.

This PR corrects the docs and removes the checks for sparsity.

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Checklist:

Completeness and readability

• 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.

[codecov]

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✅ All modified and coverable lines are covered by tests.

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[david-cortes-intel]

/azp run Nightly

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Azure Pipelines successfully started running 1 pipeline(s).

[Vika-F]

sklearn's test_t_sne.py doesn't have a case for sparse PCA initialization testing, only for sparse precomputed distances testing.
I think it worth adding the test on our side to test this branch.

[david-cortes-intel]

sklearn's test_t_sne.py doesn't have a case for sparse PCA initialization testing, only for sparse precomputed distances testing. I think it worth adding the test on our side to test this branch.

It does have a test like that as of 1.8: https://github.com/scikit-learn/scikit-learn/blob/bd0dd87f792ee88c8ec70248299aae779b51ac16/sklearn/manifold/tests/test_t_sne.py#L320

[david-cortes-intel]

/intelci: run