code: def unique_combs(df: pd.DataFrame) -> pd.DataFrame:
      """
      Generate all possible combinations of the values in the columns
      """
  
      def _unique(s: pd.Series[Any]) -> npt.NDArray[Any] | pd.Index:
          if isinstance(s.dtype, pdtypes.CategoricalDtype):
              return s.cat.categories
          return s.unique()
  
      # List of unique values from every column
      lst = (_unique(x) for _, x in df.items())
      rows = list(itertools.product(*lst))
      _df = pd.DataFrame(rows, columns=df.columns)
  
      # preserve the column dtypes
      for col in df:
          t = df[col].dtype
          _df[col] = _df[col].astype(t, copy=False)
      return _df
  
imports: from __future__ import annotations
  import itertools
  import types
  import typing
  from copy import copy, deepcopy
  import numpy
  import pandas
  import pandas.api.types
  from .._utils import cross_join, match
  from ..exceptions import PlotnineError
  from ..scales.scales import Scales
  from .strips import Strips
label_desc: Use image node_id 2 for calling a global function with example usage: unique_combs(df) and returns: _df, s, s