code: def test_with_window_function(self):
      # SPARK-42168: a window function with same partition keys but differing key order
      ids = 2
      days = 100
      vals = 10000
      parts = 10
  
      id_df = self.spark.range(ids)
      day_df = self.spark.range(days).withColumnRenamed("id", "day")
      vals_df = self.spark.range(vals).withColumnRenamed("id", "value")
      df = id_df.join(day_df).join(vals_df)
  
      left_df = (
          df.withColumnRenamed("value", "left")
          .repartition(parts)
          .cache()
      )
      # SPARK-42132: this bug requires us to alias all columns from df here
      right_df = (
          df.select(
              col("id").alias("id"),
              col("day").alias("day"),
              col("value").alias("right"),
          )
          .repartition(parts)
          .cache()
      )
  
      # note the column order is different to the groupBy("id", "day") column order below
      window = Window.partitionBy("day", "id")
  
      left_grouped_df = left_df.groupBy("id", "day")
      right_grouped_df = right_df.withColumn(
          "day_sum", sum(col("day")).over(window)
      ).groupBy("id", "day")
  
      def cogroup(
          left: pd.DataFrame, right: pd.DataFrame
      ) -> pd.DataFrame:
          return pd.DataFrame(
              [
                  {
                      "id": left["id"][0]
                      if not left.empty
                      else (
                          right["id"][0] if not right.empty else None
                      ),
                      "day": left["day"][0]
                      if not left.empty
                      else (
                          right["day"][0] if not right.empty else None
                      ),
                      "lefts": len(left.index),
                      "rights": len(right.index),
                  }
              ]
          )
  
      df = left_grouped_df.cogroup(right_grouped_df).applyInPandas(
          cogroup,
          schema="id long, day long, lefts integer, rights integer",
      )
  
      actual = df.orderBy("id", "day").take(days)
      self.assertEqual(
          actual, [Row(0, day, vals, vals) for day in range(days)]
      )
  
imports: import unittest
  from typing import cast
  from pyspark.sql.functions import array, explode, col, lit, udf, pandas_udf, sum
  from pyspark.sql.types import ArrayType, DoubleType, LongType, StructType, StructField, YearMonthIntervalType, Row
  from pyspark.sql.window import Window
  from pyspark.errors import IllegalArgumentException, PythonException
  from pyspark.testing.sqlutils import ReusedSQLTestCase, have_pandas, have_pyarrow, pandas_requirement_message, pyarrow_requirement_message
  from pyspark.testing.utils import QuietTest
label_desc: Use image node_id 36 for calling the CogroupedApplyInPandasTestsMixin obj's underlying member method code with example usage: obj.test_with_window_function() and returns: pd