code: def test_edge_cut_clustering(graph_file, partitions):
      gc.collect()
  
      # read_weights_in_sp=True => value column dtype is float32
      G_edge = graph_file.get_graph(ignore_weights=True)
  
      # Get the edge_cut score for partitioning versus random assignment
      cu_vid, cu_score = cugraph_call(G_edge, partitions)
      rand_vid, rand_score = random_call(G_edge, partitions)
  
      # Assert that the partitioning has better edge_cut than the random
      # assignment
      dataset_name = graph_file.metadata["name"]
      print(
          "graph_file = ", dataset_name, ", partitions = ", partitions
      )
      print(cu_score, rand_score)
      assert cu_score < rand_score
  
file_path: cugraph/python/cugraph/cugraph/tests/community/test_balanced_cut.py
imports: import gc
  import random
  import pytest
  import networkx
  import pandas
  import cudf
  import cugraph
  from cugraph.testing import DEFAULT_DATASETS
label_desc: Use image node_id 3 for calling a global function with example usage: test_edge_cut_clustering(graph_file, partitions) without return types