code: def test_jaccard_edgevals(gpubenchmark, graph_file, use_weight):
      dataset_path = netscience.get_path()
      M = utils.read_csv_for_nx(dataset_path)
      M_cu = utils.read_csv_file(dataset_path)
      cu_src, cu_dst, cu_coeff = cugraph_call(
          gpubenchmark, netscience, input_df=M_cu, use_weight=use_weight
      )
      if not use_weight:
          nx_src, nx_dst, nx_coeff = networkx_call(M)
  
          # Calculating mismatch
          err = 0
          tol = 1.0e-06
  
          assert len(cu_coeff) == len(nx_coeff)
          for i in range(len(cu_coeff)):
              if abs(cu_coeff[i] - nx_coeff[i]) > tol * 1.1:
                  err += 1
  
          print("Mismatches:  %d" % err)
          assert err == 0
      else:
          # FIXME: compare results against resultset api
          pass
  
imports: import gc
  import pytest
  import networkx
  import cudf
  import cugraph
  from cugraph.datasets import netscience
  from cugraph.testing import utils, UNDIRECTED_DATASETS
  from cudf.testing import assert_series_equal
  from cudf.testing.testing import assert_frame_equal
label_desc: Use image node_id 9 for calling a global function with example usage: test_jaccard_edgevals(gpubenchmark, graph_file, use_weight) without return types