- update to 1.3.2:
  * All dataset fetchers now accept `data_home` as any object that
    implements the :class:`os.PathLike` interface, for instance,
    :class:`pathlib.Path`.
  * Fixes a bug in :class:`decomposition.KernelPCA` by forcing the
    output of the internal :class:`preprocessing.KernelCenterer` to
    be a default array. When the arpack solver is used, it expects
    an array with a `dtype` attribute.
  * Fixes a bug for metrics using `zero_division=np.nan`
    (e.g. :func:`~metrics.precision_score`) within a paralell loop
    (e.g. :func:`~model_selection.cross_val_score`) where the
    singleton for `np.nan` will be different in the sub-processes.
  * Do not leak data via non-initialized memory in decision tree
    pickle files and make the generation of those files
    deterministic.
  * Ridge models with `solver='sparse_cg'` may have slightly
    different results with scipy>=1.12, because of an underlying
    change in the scipy solver
  * The `set_output` API correctly works with list input.
  * :class:`calibration.CalibratedClassifierCV` can now handle
    models that produce large prediction scores.

- Skip another recalcitrant test on 32 bit.
  * We are in the process of introducing a new way to route metadata
    such as sample_weight throughout the codebase, which would
    affect how meta-estimators such as pipeline.Pipeline and
  * Originally hosted in the scikit-learn-contrib repository,
  * A new category encoding strategy preprocessing.TargetEncoder
    encodes the categories based on a shrunk estimate of the average
  * The classes tree.DecisionTreeClassifier and tree.DecisionTreeRegressor

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