code: def _maybe_cache(
      arg: ArrayConvertible,
      format: str | None,
      cache: bool,
      convert_listlike: Callable,
  ) -> Series:
      """
      Create a cache of unique dates from an array of dates
  
      Parameters
      ----------
      arg : listlike, tuple, 1-d array, Series
      format : string
          Strftime format to parse time
      cache : bool
          True attempts to create a cache of converted values
      convert_listlike : function
          Conversion function to apply on dates
  
      Returns
      -------
      cache_array : Series
          Cache of converted, unique dates. Can be empty
      """
      from pandas import Series
  
      cache_array = Series(dtype=object)
  
      if cache:
          # Perform a quicker unique check
          if not should_cache(arg):
              return cache_array
  
          unique_dates = unique(arg)
          if len(unique_dates) < len(arg):
              cache_dates = convert_listlike(unique_dates, format)
              # GH#45319
              try:
                  cache_array = Series(
                      cache_dates, index=unique_dates, copy=False
                  )
              except OutOfBoundsDatetime:
                  return cache_array
              # GH#39882 and GH#35888 in case of None and NaT we get duplicates
              if not cache_array.index.is_unique:
                  cache_array = cache_array[
                      ~cache_array.index.duplicated()
                  ]
      return cache_array
  
imports: from __future__ import annotations
  from collections import abc
  from datetime import datetime
  from functools import partial
  from itertools import islice
  from typing import TYPE_CHECKING, Callable, Hashable, List, Tuple, TypedDict, Union, cast, overload
  import warnings
  import numpy
  from pandas._libs import lib, tslib
  from pandas._libs.tslibs import OutOfBoundsDatetime, Timedelta, Timestamp, astype_overflowsafe, get_unit_from_dtype, iNaT, is_supported_unit, nat_strings, parsing, timezones
  from pandas._libs.tslibs.conversion import precision_from_unit
  from pandas._libs.tslibs.parsing import DateParseError, guess_datetime_format
  from pandas._libs.tslibs.strptime import array_strptime
  from pandas._typing import AnyArrayLike, ArrayLike, DateTimeErrorChoices, npt
  from pandas.util._exceptions import find_stack_level
  from pandas.core.dtypes.common import ensure_object, is_datetime64_dtype, is_datetime64tz_dtype, is_float, is_integer, is_integer_dtype, is_list_like, is_numeric_dtype, is_scalar
  from pandas.core.dtypes.generic import ABCDataFrame, ABCSeries
  from pandas.core.dtypes.missing import notna
  from pandas.arrays import DatetimeArray, IntegerArray, PandasArray
  from pandas.core import algorithms
  from pandas.core.algorithms import unique
  from pandas.core.arrays.base import ExtensionArray
  from pandas.core.arrays.datetimes import maybe_convert_dtype, objects_to_datetime64ns, tz_to_dtype
  from pandas.core.construction import extract_array
  from pandas.core.indexes.base import Index
  from pandas.core.indexes.datetimes import DatetimeIndex
label_desc: Use image node_id 3 for calling a global function with example usage: _maybe_cache(arg, format, cache, convert_listlike) and returns: cache_array, cache_array, cache_array