code: def validate_mode(mode: str) -> str:
      """Check the given mode for writing is valid."""
      if mode in ("w", "w+"):
          # 'w' in pandas equals 'overwrite' in Spark
          # '+' is meaningless for writing methods, but pandas just pass it as 'w'.
          mode = "overwrite"
      if mode in ("a", "a+"):
          # 'a' in pandas equals 'append' in Spark
          # '+' is meaningless for writing methods, but pandas just pass it as 'a'.
          mode = "append"
      if mode not in (
          "w",
          "a",
          "w+",
          "a+",
          "overwrite",
          "append",
          "ignore",
          "error",
          "errorifexists",
      ):
          raise ValueError(
              "The 'mode' parameter has to be amongst the following values: ",
              "['w', 'a', 'w+', 'a+', 'overwrite', 'append', 'ignore', 'error', 'errorifexists']",
          )
      return mode
  
imports: import functools
  from contextlib import contextmanager
  import os
  from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union, TYPE_CHECKING, cast, no_type_check, overload
  import warnings
  import pandas
  from pandas.api.types import is_list_like
  from pyspark.sql import functions, Column, DataFrame, SparkSession
  from pyspark.sql.types import DoubleType
  from pyspark.sql.utils import is_remote, get_dataframe_class
  from pyspark.errors import PySparkTypeError
  from pyspark import pandas
  from pyspark.pandas._typing import Axis, Label, Name, DataFrameOrSeries
  from pyspark.pandas.typedef.typehints import as_spark_type
label_desc: Use image node_id 17 for calling a global function with example usage: validate_mode(mode) and returns: mode