code: def _check_series_localize_timestamps(
      s: "PandasSeriesLike", timezone: str
  ) -> "PandasSeriesLike":
      """
      Convert timezone aware timestamps to timezone-naive in the specified timezone or local timezone.
  
      If the input series is not a timestamp series, then the same series is returned. If the input
      series is a timestamp series, then a converted series is returned.
  
      Parameters
      ----------
      s : pandas.Series
      timezone : str
          the timezone to convert. if None then use local timezone
  
      Returns
      -------
      pandas.Series
          `pandas.Series` that have been converted to tz-naive
      """
      from pyspark.sql.pandas.utils import (
          require_minimum_pandas_version,
      )
  
      require_minimum_pandas_version()
  
      import pandas as pd
  
      tz = timezone or _get_local_timezone()
      # TODO: handle nested timestamps, such as ArrayType(TimestampType())?
      if isinstance(s.dtype, pd.DatetimeTZDtype):
          return s.dt.tz_convert(tz).dt.tz_localize(None)
      else:
          return s
  
imports: import datetime
  import itertools
  from typing import Any, Callable, Iterable, List, Optional, Union, TYPE_CHECKING
  from pyspark.sql.types import cast, BooleanType, ByteType, ShortType, IntegerType, IntegralType, LongType, FloatType, DoubleType, DecimalType, StringType, BinaryType, DateType, TimestampType, TimestampNTZType, DayTimeIntervalType, ArrayType, MapType, StructType, StructField, NullType, DataType, UserDefinedType, _create_row
  from pyspark.errors import PySparkTypeError, UnsupportedOperationException, PySparkValueError
label_desc: Use image node_id 6 for calling a global function with example usage: _check_series_localize_timestamps(s, timezone) and returns: s, s