code: def cugraph_call(G, partitions):
      df = cugraph.spectralBalancedCutClustering(
          G, partitions, num_eigen_vects=partitions
      )
  
      score = cugraph.analyzeClustering_edge_cut(
          G, partitions, df, "vertex", "cluster"
      )
      return set(df["vertex"].to_numpy()), 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 1 for calling a global function with example usage: cugraph_call(G, partitions) and returns: set,  score