code: def test_blockwise_fusion_after_compute(c):
      # See: https://github.com/dask/dask/issues/7720
  
      pd = pytest.importorskip("pandas")
      dd = pytest.importorskip("dask.dataframe")
  
      # Simple sequence of Dask-Dataframe manipulations
      df = pd.DataFrame({"x": [1, 2, 3] * 5})
      series = dd.from_pandas(df, npartitions=2)["x"]
      result = series < 3
  
      # Trigger an optimization of the `series` graph
      # (which `result` depends on), then compute `result`.
      # This is essentially a test of `rewrite_blockwise`.
      series_len = len(series)
      assert series_len == 15
      assert df.x[result.compute()].sum() == 15
  
imports: from __future__ import annotations
  import pytest
  import asyncio
  import os
  import sys
  from functools import partial
  from operator import add
  from distributed import Client, SchedulerPlugin, WorkerPlugin
  from distributed.utils_test import cleanup
  from distributed.utils_test import client
  from distributed.utils_test import cluster, cluster_fixture, gen_cluster, loop, loop_in_thread, popen, varying
  import dask
  import dask.bag
  from dask import compute, delayed, persist
  from dask.base import compute_as_if_collection, get_scheduler
  from dask.blockwise import Blockwise
  from dask.delayed import Delayed
  from dask.distributed import futures_of, wait
  from dask.layers import ShuffleLayer, SimpleShuffleLayer
  from dask.utils import get_named_args, get_scheduler_lock, tmpdir, tmpfile
  from dask.utils_test import inc
label_desc: Use image node_id 20 for calling a global function with example usage: test_blockwise_fusion_after_compute(c) without return types