code: def xor(
      df1: PySparkDataFrame, df2: PySparkDataFrame
  ) -> PySparkDataFrame:
      colNames = df1.columns
  
      tmp_tag_col = verify_temp_column_name(df1, "__temporary_tag__")
      tmp_max_col = verify_temp_column_name(
          df1, "__temporary_max_tag__"
      )
      tmp_min_col = verify_temp_column_name(
          df1, "__temporary_min_tag__"
      )
  
      return (
          df1.withColumn(tmp_tag_col, F.lit(0))
          .union(df2.withColumn(tmp_tag_col, F.lit(1)))
          .groupBy(*colNames)
          .agg(
              F.min(tmp_tag_col).alias(tmp_min_col),
              F.max(tmp_tag_col).alias(tmp_max_col),
          )
          .where(F.col(tmp_min_col) == F.col(tmp_max_col))
          .select(*colNames)
      )
  
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 28 for calling a global function with example usage: xor(df1, df2) and returns: df1