class_docstr: Accessor object for categorical properties of the Series values.
  
  Examples
  --------
  >>> s.cat.categories  # doctest: +SKIP
  
  Notes
  -----
  Attributes that depend only on metadata are eager
  
  * categories
  * ordered
  
  Attributes depending on the entire dataset are lazy
  
  * codes
  * ...
  
  So `df.a.cat.categories` <=> `df.a._meta.cat.categories`
  So `df.a.cat.codes` <=> `df.a.map_partitions(lambda x: x.cat.codes)`
docstr: Ensure the categories in this series are unknown
docstr_tok: ["Ensure","the","categories","in","this","series","are","unknown"]
code: def as_unknown(self):
      """Ensure the categories in this series are unknown"""
      if not self.known:
          return self._series
      out = self._series.copy()
      out._meta = clear_known_categories(out._meta)
      return out
  
imports: from __future__ import annotations
  from collections import defaultdict
  from numbers import Integral
  import pandas
  from pandas.api.types import is_scalar
  from tlz import partition_all
  from dask.base import compute_as_if_collection, tokenize
  from dask.dataframe import methods
  from dask.dataframe.accessor import Accessor
  from dask.dataframe.dispatch import categorical_dtype, categorical_dtype_dispatch, is_categorical_dtype
  from dask.dataframe.utils import AttributeNotImplementedError, clear_known_categories, has_known_categories
  from dask.highlevelgraph import HighLevelGraph
label_desc: Use image node_id 3 for calling the CategoricalAccessor obj's underlying member method code with example usage: obj.as_unknown() and returns: out, self