Clarification of output array type when metrics accept multiclass/multioutput

Clarification of how we should handle array output type when a metric outputs several values (i.e. accepts multiclass or multioutput input).

The issue was summarised succinctly in https://github.com/scikit-learn/scikit-learn/pull/30439#issuecomment-2532238196:

> Not sure what should be the output namespace / device in case we output an array, e.g. roc_auc_score with average=None on multiclass problems...

Currently all regression/classification metrics that support array API and multiclass or multioutput, all output an array in the same namespace and device as the input (checked code and manually). Summary of these metrics :

### Regression metrics

Returns array in same namespace/device:
* [explained_variance_score](https://scikit-learn.org/dev/modules/generated/sklearn.metrics.explained_variance_score.html#sklearn.metrics.explained_variance_score)
* [r2_score](https://scikit-learn.org/dev/modules/generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score) 
* [mean_absolute_error](https://scikit-learn.org/dev/modules/generated/sklearn.metrics.mean_absolute_error.html#sklearn.metrics.mean_absolute_error)
* [mean_absolute_percentage_error](https://scikit-learn.org/dev/modules/generated/sklearn.metrics.mean_absolute_percentage_error.html#sklearn.metrics.mean_absolute_percentage_error)
* [mean_pinball_loss](https://scikit-learn.org/dev/modules/generated/sklearn.metrics.mean_pinball_loss.html#sklearn.metrics.mean_pinball_loss)
* [mean_squared_error](https://scikit-learn.org/dev/modules/generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error)
* [mean_squared_log_error](https://scikit-learn.org/dev/modules/generated/sklearn.metrics.mean_squared_log_error.html#sklearn.metrics.mean_squared_log_error)
* [root_mean_squared_error](https://scikit-learn.org/dev/modules/generated/sklearn.metrics.root_mean_squared_error.html#sklearn.metrics.root_mean_squared_error)
* [root_mean_squared_log_error](https://scikit-learn.org/dev/modules/generated/sklearn.metrics.root_mean_squared_log_error.html#sklearn.metrics.root_mean_squared_log_error)

### Classification metrics
Returns array in same namespace/device:

* [precision_recall_fscore_support](https://scikit-learn.org/dev/modules/generated/sklearn.metrics.precision_recall_fscore_support.html#sklearn.metrics.precision_recall_fscore_support) and family ([f1_score](https://scikit-learn.org/dev/modules/generated/sklearn.metrics.f1_score.html#sklearn.metrics.f1_score), [fbeta_score](https://scikit-learn.org/dev/modules/generated/sklearn.metrics.fbeta_score.html#sklearn.metrics.fbeta_score), [precision_score](https://scikit-learn.org/dev/modules/generated/sklearn.metrics.precision_score.html#sklearn.metrics.precision_score), [recall_score](https://scikit-learn.org/dev/modules/generated/sklearn.metrics.recall_score.html#sklearn.metrics.recall_score))

Looking at the metrics code, if we wanted to support a list of scalars, we'd generally have to do extra processing to convert an array (often output of an xp.** function) to a list of scalars.

Once we arrive at a consensus we should update the array API documentation and update the `check_array_api_metric` in tests such that when the output is array/list - we check that the output type etc is correct.

cc @ogrisel @betatim

COMMENT:
It seems a lot of the plus ones for having Numpy arrays as output comes from the matplotlib/pandas use-case. While in general converting to Numpy is hard and annoying (dlpack doesn't quite work yet, our `_convert_to_numpy` is a hack, etc), if you are only using one library for your code it is a well known thing (cupy users know how to covnert cupy to numpy, pytorch users know how to convert pytorch) and my guess is that most users who use an array library that is not Numpy use just one in their code. They aren't running their notebook with cupy, numpy and pytorch arrays as input - they are probably sticking to one of these per notebook/script/application.

This means the cost for doing the conversion is smaller.

This means that I am now tending towards "don't surprise users" -> input and output namespace match.

Labels: Needs Decision, Array API