Improve error message and add FAQ entry for stratified CV with Dataset (#1122) - #1152

Why

skorch's docs say you can pass a PyTorch or skorch Dataset directly to
.fit(). Doing so currently fails with a confusing error
(ValueError: Stratified CV requires explicitly passing a suitable y),
because the default validation split logic (ValidSplit, stratified by
default on NeuralNetClassifier) can't pull class labels out of a Dataset
object the way it can from raw arrays. Users hitting this had no indication
of what to do next (see the StackOverflow thread linked in the issue).

Per @BenjaminBossan's guidance on the issue, this fix does not guess y
from the dataset or silently disable stratification. It only makes the
existing error message and docs more actionable.

What changed

• ValidSplit.__call__ now detects when the failing dataset is a
  skorch.dataset.Dataset or torch.utils.data.TensorDataset and extends
  the error message to name the type and point to three workarounds:
  predefined_split(), train_split=ValidSplit(5, stratified=False), or
  train_split=None. Any other type still gets the original message,
  unchanged.
• Added a FAQ entry to docs/user/FAQ.rst covering this failure and the
  same three workarounds.
• Added a CHANGES.md entry under Unreleased.

Closes #1122

Acceptance criteria

• Tests added (skorch/tests/test_dataset.py: Dataset, TensorDataset,
  and generic array cases)
• All tests passing (python -m pytest: 1648 passed, 104 skipped,
  1 xfailed)
• Follows existing style and conventions in dataset.py and the test
  files
• No breaking changes. Behavior is unchanged except for the two newly
  detected dataset types, where the message is strictly more
  informative

Before and after

Before:

ValueError: Stratified CV requires explicitly passing a suitable y.

After:

ValueError: Stratified CV requires explicitly passing a suitable y. You
passed a skorch.dataset.Dataset as dataset, which skorch cannot introspect
to obtain labels for stratification. To resolve this, either wrap your
validation data with skorch.helper.predefined_split and pass it as
train_split, disable stratification with train_split=ValidSplit(5,
stratified=False), or disable the internal validation split entirely with
train_split=None.

Testing

• Ran the full test suite locally: 1648 passed, 104 skipped, 1 xfailed
• Updated the pre-existing test_net.py test that hard coded the old
  message text
• Manually reran the reproduction script from the issue and confirmed the
  new message
• Built the docs locally with sphinx-build to confirm the FAQ entry
  renders correctly

@BenjaminBossan, ready for review whenever you have a chance. Thank you for
scoping this out and for the mentorship on my first contribution here.