code: def test_louvain(graph_file):
      dataset_path = graph_file.get_path()
      M = utils.read_csv_for_nx(dataset_path)
      cu_parts, cu_mod = cugraph_call(graph_file, edgevals=True)
      nx_parts = networkx_call(M)
  
      # Calculating modularity scores for comparison
      Gnx = nx.from_pandas_edgelist(
          M,
          source="0",
          target="1",
          edge_attr="weight",
          create_using=nx.Graph(),
      )
  
      cu_parts = cu_parts.to_pandas()
      cu_map = dict(zip(cu_parts["vertex"], cu_parts["partition"]))
  
      assert set(nx_parts.keys()) == set(cu_map.keys())
  
      cu_mod_nx = community.modularity(cu_map, Gnx)
      nx_mod = community.modularity(nx_parts, Gnx)
  
      assert len(cu_parts) == len(nx_parts)
      assert cu_mod > (0.82 * nx_mod)
      assert abs(cu_mod - cu_mod_nx) < 0.0001
  
file_path: cugraph/python/cugraph/cugraph/tests/community/test_louvain.py
imports: import gc
  import time
  import pytest
  import networkx
  import cugraph
  import cupyx
  import cudf
  from cugraph.testing import utils, UNDIRECTED_DATASETS
  from cugraph.datasets import karate_asymmetric
label_desc: Use image node_id 4 for calling a global function with example usage: test_louvain(graph_file) without return types