- Update to 0.6.2
  * By default, we now use the MultiscaleMixture affinity model,
    enabling us to pass in a list of perplexities instead of a
    single perplexity value. This is fully backwards compatible.
  * Previously, perplexity values would be changed according to the
    dataset. E.g. we pass in perplexity=100 with N=150. Then
    TSNE.perplexity would be equal to 50. Instead, keep this value
    as is and add an effective_perplexity_ attribute (following the
    convention from scikit-learn, which puts in the corrected
    perplexity values.
  * Fix bug where interpolation grid was being prepared even when
    using BH optimization during transform.
  * Enable calling .transform with precomputed distances. In this
    case, the data matrix will be assumed to be a distance matrix.
  * Fix potential problem with clang-13, which actually does
    optimization with infinities using the -ffast-math flag
- Enable python310 build
- Skip a test in 32bit failing due to rounding errors

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