code: def random_call(G, partitions):
      random.seed(0)
      num_verts = G.number_of_vertices()
  
      score = 0.0
      for repeat in range(20):
          assignment = []
          for i in range(num_verts):
              assignment.append(random.randint(0, partitions - 1))
  
          assign_cu = cudf.DataFrame(assignment, columns=["cluster"])
          assign_cu["vertex"] = assign_cu.index
          assign_cu = assign_cu.astype("int32")
  
          score += cugraph.analyzeClustering_edge_cut(
              G, partitions, assign_cu
          )
  
      return set(range(num_verts)), (score / 10.0)
  
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 2 for calling a global function with example usage: random_call(G, partitions) and returns: set,  unknown