code: def check_cv(cv=3, y=None, classifier=False):
      
      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)
docs: Dask aware version of ``sklearn.model_selection.check_cv``
  
      Same as the scikit-learn version, but works if ``y`` is a dask object.