code: def test_dask_mg_pagerank(dask_client, directed):
      pandas.set_option("display.max_rows", 10000)
  
      input_data_path = (
          RAPIDS_DATASET_ROOT_DIR_PATH / "karate.csv"
      ).as_posix()
      chunksize = dcg.get_chunksize(input_data_path)
  
      ddf = dask_cudf.read_csv(
          input_data_path,
          chunksize=chunksize,
          delimiter=" ",
          names=["src", "dst", "value"],
          dtype=["int32", "int32", "float32"],
      )
  
      df = cudf.read_csv(
          input_data_path,
          delimiter=" ",
          names=["src", "dst", "value"],
          dtype=["int32", "int32", "float32"],
      )
  
      g = cugraph.Graph(directed=directed)
      g.from_cudf_edgelist(df, "src", "dst")
  
      dg = cugraph.Graph(directed=directed)
      dg.from_dask_cudf_edgelist(ddf, "src", "dst")
  
      expected_pr = cugraph.pagerank(g)
      result_pr = dcg.pagerank(dg).compute()
  
      err = 0
      tol = 1.0e-05
  
      assert len(expected_pr) == len(result_pr)
  
      compare_pr = expected_pr.merge(
          result_pr, on="vertex", suffixes=["_local", "_dask"]
      )
  
      for i in range(len(compare_pr)):
          diff = abs(
              compare_pr["pagerank_local"].iloc[i]
              - compare_pr["pagerank_dask"].iloc[i]
          )
          if diff > tol * 1.1:
              err = err + 1
      print("Mismatches:", err)
      assert err == 0
  
imports: import gc
  import pytest
  import pandas
  import numpy
  import dask
  import cudf
  import dask_cudf
  import cugraph.dask
  import cugraph
  from cugraph.testing import utils
  from cugraph.structure.number_map import NumberMap
  from cugraph.dask.common.mg_utils import is_single_gpu
  from cugraph.testing.utils import RAPIDS_DATASET_ROOT_DIR_PATH
  from cudf.testing import assert_frame_equal, assert_series_equal
label_desc: Use image node_id 4 for calling a global function with example usage: test_dask_mg_pagerank(dask_client, directed) without return types