code: def check_column_order(self):
      # Helper function to set column names from a list
      def rename_pdf(pdf, names):
          pdf.rename(
              columns={
                  old: new for old, new in zip(pd_result.columns, names)
              },
              inplace=True,
          )
  
      df = self.data
      grouped_df = df.groupby("id")
      grouped_pdf = df.toPandas().groupby("id", as_index=False)
  
      # Function returns a pdf with required column names, but order could be arbitrary using dict
      def change_col_order(pdf):
          # Constructing a DataFrame from a dict should result in the same order,
          # but use OrderedDict to ensure the pdf column order is different than schema
          return pd.DataFrame.from_dict(
              OrderedDict(
                  [("id", pdf.id), ("u", pdf.v * 2), ("v", pdf.v)]
              )
          )
  
      ordered_udf = pandas_udf(
          change_col_order,
          "id long, v int, u int",
          PandasUDFType.GROUPED_MAP,
      )
  
      # The UDF result should assign columns by name from the pdf
      result = (
          grouped_df.apply(ordered_udf)
          .sort("id", "v")
          .select("id", "u", "v")
          .toPandas()
      )
      pd_result = grouped_pdf.apply(change_col_order)
      expected = pd_result.sort_values(["id", "v"]).reset_index(
          drop=True
      )
      assert_frame_equal(expected, result)
  
      # Function returns a pdf with positional columns, indexed by range
      def range_col_order(pdf):
          # Create a DataFrame with positional columns, fix types to long
          return pd.DataFrame(
              list(zip(pdf.id, pdf.v * 3, pdf.v)), dtype="int64"
          )
  
      range_udf = pandas_udf(
          range_col_order,
          "id long, u long, v long",
          PandasUDFType.GROUPED_MAP,
      )
  
      # The UDF result uses positional columns from the pdf
      result = (
          grouped_df.apply(range_udf)
          .sort("id", "v")
          .select("id", "u", "v")
          .toPandas()
      )
      pd_result = grouped_pdf.apply(range_col_order)
      rename_pdf(pd_result, ["id", "u", "v"])
      expected = pd_result.sort_values(["id", "v"]).reset_index(
          drop=True
      )
      assert_frame_equal(expected, result)
  
      # Function returns a pdf with columns indexed with integers
      def int_index(pdf):
          return pd.DataFrame(
              OrderedDict([(0, pdf.id), (1, pdf.v * 4), (2, pdf.v)])
          )
  
      int_index_udf = pandas_udf(
          int_index, "id long, u int, v int", PandasUDFType.GROUPED_MAP
      )
  
      # The UDF result should assign columns by position of integer index
      result = (
          grouped_df.apply(int_index_udf)
          .sort("id", "v")
          .select("id", "u", "v")
          .toPandas()
      )
      pd_result = grouped_pdf.apply(int_index)
      rename_pdf(pd_result, ["id", "u", "v"])
      expected = pd_result.sort_values(["id", "v"]).reset_index(
          drop=True
      )
      assert_frame_equal(expected, result)
  
      @pandas_udf("id long, v int", PandasUDFType.GROUPED_MAP)
      def column_name_typo(pdf):
          return pd.DataFrame({"iid": pdf.id, "v": pdf.v})
  
      @pandas_udf("id long, v decimal", PandasUDFType.GROUPED_MAP)
      def invalid_positional_types(pdf):
          return pd.DataFrame([(1, datetime.date(2020, 10, 5))])
  
      with self.sql_conf(
          {
              "spark.sql.execution.pandas.convertToArrowArraySafely": False
          }
      ):
          with self.assertRaisesRegex(
              PythonException,
              "Column names of the returned pandas.DataFrame do not match "
              "specified schema. Missing: id. Unexpected: iid.\n",
          ):
              grouped_df.apply(column_name_typo).collect()
          with self.assertRaisesRegex(
              Exception, "[D|d]ecimal.*got.*date"
          ):
              grouped_df.apply(invalid_positional_types).collect()
  
imports: import datetime
  import unittest
  from collections import OrderedDict
  from decimal import Decimal
  from typing import cast
  from pyspark.sql import Row
  from pyspark.sql.functions import array, explode, col, lit, udf, sum, pandas_udf, PandasUDFType, window
  from pyspark.sql.types import IntegerType, DoubleType, ArrayType, BinaryType, ByteType, LongType, DecimalType, ShortType, FloatType, StringType, BooleanType, StructType, StructField, NullType, MapType, YearMonthIntervalType
  from pyspark.errors import PythonException, PySparkTypeError
  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 34 for calling the GroupedApplyInPandasTestsMixin obj's underlying member method code with example usage: obj.check_column_order() and returns: pd, pd, pd, pd, pd