code: def h2oai_q8(ds: Dataset) -> Dataset:
      def accumulate_block(
          agg: Tuple[float, float], block: Block
      ) -> Tuple[float, float]:
          column = block["v3"]
          top_k_indices = pac.top_k_unstable(column, k=2)
          top_k_result = pac.take(column, top_k_indices).to_pylist()
          top_k_result.extend([float("-inf")] * (2 - len(top_k_result)))
          top_k_result = (top_k_result[0], top_k_result[1])
          return merge(agg, top_k_result)
  
      def merge(
          agg1: Tuple[float, float],
          agg2: Tuple[float, float],
      ) -> Tuple[float, float]:
          if agg1[0] >= agg2[0]:
              value1 = agg1[0]
              value2 = max(agg1[1], agg2[0])
          else:
              value1 = agg2[0]
              value2 = max(agg1[0], agg2[1])
          return (value1, value2)
  
      class Top2(_AggregateOnKeyBase):
          def __init__(self, on):
              self._set_key_fn(on)
              super().__init__(
                  init=lambda _: (float("-inf"), float("-inf")),
                  merge=merge,
                  accumulate_block=accumulate_block,
                  name=(f"top2({str(on)})"),
              )
  
      return ds.groupby("id6").aggregate(Top2("v3"))
  
file_path: ray/release/nightly_tests/dataset/aggregate_benchmark.py
imports: from typing import Tuple
  import ray
  from ray.data.aggregate import Max, Mean, Min, Sum
  from ray.data.aggregate._aggregate import _AggregateOnKeyBase
  from ray.data.block import Block
  from ray.data.dataset import Dataset
  import pyarrow.compute
  from benchmark import Benchmark
label_desc: Use image node_id 9 for calling a global function with example usage: h2oai_q8(ds) and returns: ds, merge, value1,  value2