code: def preprocess_last_window(
      last_window: Union[pd.Series, pd.DataFrame],
      return_values: bool = True,
  ) -> Tuple[np.ndarray, pd.Index]:
      """
      Return values and index of series separately. Index is overwritten
      according to the next rules:
  
      - If index is of type `DatetimeIndex` and has frequency, nothing is
      changed.
      - If index is of type `RangeIndex`, nothing is changed.
      - If index is of type `DatetimeIndex` but has no frequency, a
      `RangeIndex` is created.
      - If index is not of type `DatetimeIndex`, a `RangeIndex` is created.
  
      Parameters
      ----------
      last_window : pandas Series, pandas DataFrame
          Time series values.
      return_values : bool, default `True`
          If `True` return the values of `last_window` as numpy ndarray. This option
          is intended to avoid copying data when it is not necessary.
  
      Returns
      -------
      last_window_values : numpy ndarray
          Numpy array with values of `last_window`.
      last_window_index : pandas Index
          Index of `last_window` modified according to the rules.
  
      """
  
      if (
          isinstance(last_window.index, pd.DatetimeIndex)
          and last_window.index.freq is not None
      ):
          last_window_index = last_window.index
      elif isinstance(last_window.index, pd.RangeIndex):
          last_window_index = last_window.index
      elif (
          isinstance(last_window.index, pd.DatetimeIndex)
          and last_window.index.freq is None
      ):
          warnings.warn(
              (
                  "`last_window` has DatetimeIndex index but no frequency. "
                  "Index is overwritten with a RangeIndex of step 1."
              )
          )
          last_window_index = pd.RangeIndex(
              start=0, stop=len(last_window), step=1
          )
      else:
          warnings.warn(
              (
                  "`last_window` has no DatetimeIndex nor RangeIndex index. "
                  "Index is overwritten with a RangeIndex."
              )
          )
          last_window_index = pd.RangeIndex(
              start=0, stop=len(last_window), step=1
          )
  
      last_window_values = (
          last_window.to_numpy(copy=True) if return_values else None
      )
  
      return last_window_values, last_window_index
  
imports: from typing import Union, Any, Optional, Tuple, Callable
  import warnings
  import importlib
  import joblib
  import numpy
  import pandas
  import sklearn
  import sklearn.pipeline
  import sklearn.linear_model
  from sklearn.compose import ColumnTransformer
  from sklearn.exceptions import NotFittedError
  import inspect
  from copy import deepcopy
  import skforecast
  from ..exceptions import MissingValuesExogWarning
  from ..exceptions import DataTypeWarning
  from ..exceptions import IgnoredArgumentWarning
  from ..exceptions import SkforecastVersionWarning
label_desc: Use image node_id 12 for calling a global function with example usage: preprocess_last_window(last_window, return_values) and returns: last_window_values,  last_window_index