- Update to 1.4.1.post1
  ## Metadata Routing
  * Fix routing issue with ColumnTransformer when used inside
    another meta-estimator. #28188 by Adrin Jalali.
  * No error is raised when no metadata is passed to a
    metaestimator that includes a sub-estimator which doesn’t
    support metadata routing. #28256 by Adrin Jalali.
  * Fix multioutput.MultiOutputRegressor and
    multioutput.MultiOutputClassifier to work with estimators that
    don’t consume any metadata when metadata routing is enabled.
    #28240 by Adrin Jalali.
  ## DataFrame Support
  * Enhancement Fix Pandas and Polars dataframe are validated
    directly without ducktyping checks. #28195 by Thomas Fan.
  ## Changes impacting many modules
  * Efficiency Fix Partial revert of #28191 to avoid a performance
    regression for estimators relying on euclidean pairwise
    computation with sparse matrices. The impacted estimators are:
    - sklearn.metrics.pairwise_distances_argmin
    - sklearn.metrics.pairwise_distances_argmin_min
    - sklearn.cluster.AffinityPropagation
    - sklearn.cluster.Birch
    - sklearn.cluster.SpectralClustering
    - sklearn.neighbors.KNeighborsClassifier
    - sklearn.neighbors.KNeighborsRegressor
    - sklearn.neighbors.RadiusNeighborsClassifier
    - sklearn.neighbors.RadiusNeighborsRegressor
    - sklearn.neighbors.LocalOutlierFactor
    - sklearn.neighbors.NearestNeighbors
    - sklearn.manifold.Isomap
    - sklearn.manifold.TSNE
    - sklearn.manifold.trustworthiness
    - #28235 by Julien Jerphanion.
  * Fixes a bug for all scikit-learn transformers when using
    set_output with transform set to pandas or polars. The bug
    could lead to wrong naming of the columns of the returned
    dataframe. #28262 by Guillaume Lemaitre.
  * When users try to use a method in StackingClassifier,
    StackingClassifier, StackingClassifier, SelectFromModel, RFE,
    SelfTrainingClassifier, OneVsOneClassifier,
    OutputCodeClassifier or OneVsRestClassifier that their
    sub-estimators don’t implement, the AttributeError now reraises
    in the traceback. #28167 by Stefanie Senger.
- Release 1.4.0
  * HistGradientBoosting Natively Supports Categorical DTypes in
    DataFrames
  * Polars output in set_output
  * Missing value support for Random Forest
  * Add support for monotonic constraints in tree-based models
  * Enriched estimator displays
  * Metadata Routing Support
  * Improved memory and runtime efficiency for PCA on sparse data
  * Highlights and detailed changelog:
    * https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_4_0.html
    * https://scikit-learn.org/stable/whats_new/v1.4.html#release-notes-1-4
- Enable python312 test flavor, avoid testing it with the other
  flavors
- Prepare for python39 flavor drop

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