code: def _id_var(x: pd.Series[Any], drop: bool = False) -> list[int]:
      """
      Assign ids to items in x. If two items
      are the same, they get the same id.
  
      Parameters
      ----------
      x : array_like
          items to associate ids with
      drop : bool
          Whether to drop unused factor levels
      """
      if len(x) == 0:
          return []
  
      if array_kind.categorical(x):
          if drop:
              x = x.cat.remove_unused_categories()
              lst = list(x.cat.codes + 1)
          else:
              has_nan = any(
                  np.isnan(i) for i in x if isinstance(i, float)
              )
              if has_nan:
                  # NaNs are -1, we give them the highest code
                  nan_code = -1
                  new_nan_code = np.max(x.cat.codes) + 1
                  lst = [
                      val if val != nan_code else new_nan_code
                      for val in x
                  ]
              else:
                  lst = list(x.cat.codes + 1)
      else:
          try:
              levels = sorted(set(x))
          except TypeError:
              # x probably has NANs
              levels = multitype_sort(list(set(x)))
  
          lst = match(x, levels)
          lst = [item + 1 for item in lst]
  
      return lst
  
imports: from __future__ import annotations
  import inspect
  import itertools
  import warnings
  from collections import defaultdict
  from collections.abc import Iterable, Sequence
  from contextlib import suppress
  from typing import TYPE_CHECKING, cast, overload
  from warnings import warn
  import numpy
  import pandas
  from pandas.core.groupby import DataFrameGroupBy
  from ..exceptions import PlotnineError, PlotnineWarning
  from ..mapping import aes
label_desc: Use image node_id 9 for calling a global function with example usage: _id_var(x, drop) and returns: lst, list