code: def test_window_aggregate_process(self):
      data_stream = self.env.from_collection(
          [
              ("a", 1),
              ("a", 2),
              ("b", 3),
              ("a", 6),
              ("b", 8),
              ("b", 9),
              ("a", 15),
          ],
          type_info=Types.TUPLE([Types.STRING(), Types.INT()]),
      )  # type: DataStream
      watermark_strategy = WatermarkStrategy.for_monotonous_timestamps().with_timestamp_assigner(
          SecondColumnTimestampAssigner()
      )
  
      class MyAggregateFunction(AggregateFunction):
          def create_accumulator(self) -> Tuple[int, str]:
              return 0, ""
  
          def add(
              self, value: Tuple[str, int], accumulator: Tuple[int, str]
          ) -> Tuple[int, str]:
              return value[1] + accumulator[0], value[0]
  
          def get_result(self, accumulator: Tuple[str, int]):
              return accumulator[1], accumulator[0]
  
          def merge(
              self, acc_a: Tuple[int, str], acc_b: Tuple[int, str]
          ):
              return acc_a[0] + acc_b[0], acc_a[1]
  
      class MyProcessWindowFunction(ProcessWindowFunction):
          def process(
              self,
              key: str,
              context: ProcessWindowFunction.Context,
              elements: Iterable[Tuple[str, int]],
          ) -> Iterable[str]:
              agg_result = next(iter(elements))
              yield "key {} timestamp sum {}".format(
                  agg_result[0], agg_result[1]
              )
  
      data_stream.assign_timestamps_and_watermarks(
          watermark_strategy
      ).key_by(lambda x: x[0], key_type=Types.STRING()).window(
          EventTimeSessionWindows.with_gap(Time.milliseconds(2))
      ).aggregate(
          MyAggregateFunction(),
          window_function=MyProcessWindowFunction(),
          accumulator_type=Types.TUPLE([Types.INT(), Types.STRING()]),
          output_type=Types.STRING(),
      ).add_sink(
          self.test_sink
      )
  
      self.env.execute("test_window_aggregate_process")
      results = self.test_sink.get_results()
      expected = [
          "key a timestamp sum 15",
          "key a timestamp sum 3",
          "key a timestamp sum 6",
          "key b timestamp sum 17",
          "key b timestamp sum 3",
      ]
      self.assert_equals_sorted(expected, results)
  
imports: from typing import Iterable, Tuple, Dict
  from pyflink.common import Configuration
  from pyflink.common.time import Time
  from pyflink.common.typeinfo import Types
  from pyflink.common.watermark_strategy import WatermarkStrategy, TimestampAssigner
  from pyflink.datastream.data_stream import DataStream
  from pyflink.datastream.functions import ProcessWindowFunction, WindowFunction, AggregateFunction, ProcessAllWindowFunction
  from pyflink.datastream.output_tag import OutputTag
  from pyflink.datastream.window import TumblingEventTimeWindows, SlidingEventTimeWindows, EventTimeSessionWindows, CountSlidingWindowAssigner, SessionWindowTimeGapExtractor, CountWindow, PurgingTrigger, EventTimeTrigger, TimeWindow, GlobalWindows, CountTrigger
  from pyflink.datastream.tests.test_util import DataStreamTestSinkFunction
  from pyflink.java_gateway import get_gateway
  from pyflink.testing.test_case_utils import PyFlinkStreamingTestCase
  from pyflink.util.java_utils import get_j_env_configuration
label_desc: Use image node_id 14 for calling the WindowTests obj's underlying member method code with example usage: obj.test_window_aggregate_process() and returns: int,  str, unknown,  value, accumulator,  accumulator, unknown,  acc_a