code: def test_dask_mg_katz_centrality_nstart(dask_client, directed):
      input_data_path = (
          RAPIDS_DATASET_ROOT_DIR_PATH / "karate.csv"
      ).as_posix()
      print(f"dataset={input_data_path}")
      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"],
      )
  
      dg = cugraph.Graph(directed=True)
      dg.from_dask_cudf_edgelist(
          ddf, "src", "dst", store_transposed=True
      )
  
      mg_res = dcg.katz_centrality(dg, max_iter=50, tol=1e-6)
      mg_res = mg_res.compute()
  
      estimate = mg_res.copy()
      estimate = estimate.rename(
          columns={"vertex": "vertex", "katz_centrality": "values"}
      )
      estimate["values"] = 0.5
  
      mg_estimate_res = dcg.katz_centrality(
          dg, nstart=estimate, max_iter=50, tol=1e-6
      )
      mg_estimate_res = mg_estimate_res.compute()
  
      err = 0
      tol = 1.0e-05
      compare_res = mg_res.merge(
          mg_estimate_res, on="vertex", suffixes=["_dask", "_nstart"]
      )
  
      for i in range(len(compare_res)):
          diff = abs(
              compare_res["katz_centrality_dask"].iloc[i]
              - compare_res["katz_centrality_nstart"].iloc[i]
          )
          if diff > tol * 1.1:
              err = err + 1
      assert err == 0
  
file_path: cugraph/python/cugraph/cugraph/tests/centrality/test_katz_centrality_mg.py
imports: import gc
  import pytest
  import cudf
  import dask_cudf
  import cugraph
  import cugraph.dask
  from cugraph.dask.common.mg_utils import is_single_gpu
  from cugraph.testing.utils import RAPIDS_DATASET_ROOT_DIR_PATH
label_desc: Use image node_id 3 for calling a global function with example usage: test_dask_mg_katz_centrality_nstart(dask_client, directed) without return types