code: def _bench_helper_scipy(
      gpubenchmark, N, attr_kind, create_using, method, fmt
  ):
      G = method(N, create_using=create_using)
      if attr_kind:
          for *_ids, edgedict in G.edges(data=True):
              edgedict["x"] = random.randint(0, 100000)
          gpubenchmark(
              nx.to_scipy_sparse_array, G, weight="x", format=fmt
          )
      else:
          gpubenchmark(
              nx.to_scipy_sparse_array, G, weight=None, format=fmt
          )
  
file_path: cugraph/python/nx-cugraph/nx_cugraph/tests/bench_convert.py
imports: import random
  import networkx
  import numpy
  import pytest
  import nx_cugraph
label_desc: Use image node_id 3 for calling a global function with example usage: _bench_helper_scipy(gpubenchmark, N, attr_kind, create_using, method, fmt) without return types