code: def test_group_aggregate_with_aux_group(self):
      t = self.t_env.from_elements(
          [
              (1, 2, 3),
              (3, 2, 3),
              (2, 1, 3),
              (1, 5, 4),
              (1, 8, 6),
              (2, 3, 4),
          ],
          DataTypes.ROW(
              [
                  DataTypes.FIELD("a", DataTypes.TINYINT()),
                  DataTypes.FIELD("b", DataTypes.SMALLINT()),
                  DataTypes.FIELD("c", DataTypes.INT()),
              ]
          ),
      )
  
      sink_table = generate_random_table_name()
      sink_table_ddl = f"""
          CREATE TABLE {sink_table}(a TINYINT, b INT, c FLOAT, d INT) WITH ('connector'='test-sink')
          """
      self.t_env.execute_sql(sink_table_ddl)
      self.t_env.get_config().get_configuration().set_string(
          "python.metric.enabled", "true"
      )
      self.t_env.get_config().set("python.metric.enabled", "true")
      t.group_by(t.a).select(
          t.a, (t.a + 1).alias("b"), (t.a + 2).alias("c")
      ).group_by(t.a, t.b).select(
          t.a, t.b, mean_udaf(t.b), call("max_add", t.b, t.c, 1)
      ).execute_insert(
          sink_table
      ).wait()
      actual = source_sink_utils.results()
      self.assert_equals(
          actual,
          ["+I[1, 2, 2.0, 6]", "+I[2, 3, 3.0, 8]", "+I[3, 4, 4.0, 10]"],
      )
  
imports: import uuid
  from pyflink.table.expressions import col, call, lit, row_interval
  from pyflink.table.types import DataTypes
  from pyflink.table.udf import udaf, udf, AggregateFunction
  from pyflink.testing import source_sink_utils
  from pyflink.testing.test_case_utils import PyFlinkBatchTableTestCase, PyFlinkStreamTableTestCase
label_desc: Use image node_id 5 for calling the BatchPandasUDAFITTests obj's underlying member method code with example usage: obj.test_group_aggregate_with_aux_group() without return types