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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def check_cv(cv=3, y=None, classifier=False):
 """Dask aware version of ``sklearn.model_selection.check_cv`` Same as the scikit-learn version, but works if ``y`` is a dask object. """

    if cv is None:
        cv = 3

    # If ``cv`` is not an integer, the scikit-learn implementation doesn't
    # touch the ``y`` object, so passing on a dask object is fine
    if not is_dask_collection(y) or not isinstance(cv, numbers.Integral):
        return model_selection.check_cv(cv, y, classifier)

    if classifier:
        # ``y`` is a dask object. We need to compute the target type
        target_type = delayed(type_of_target, pure=True)(y).compute()
        if target_type in ("binary", "multiclass"):
            return StratifiedKFold(cv)
    return KFold(cv)