code: def test_sorensen(gpubenchmark, read_csv, use_weight):
      M_cu, M, graph_file = read_csv
      cu_src, cu_dst, cu_coeff = cugraph_call(
          gpubenchmark, graph_file, input_df=M_cu, use_weight=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
  
imports: import gc
  import pytest
  import networkx
  import cudf
  import cugraph
  from cugraph.testing import utils, UNDIRECTED_DATASETS
  from cugraph.datasets import netscience
  from cudf.testing import assert_series_equal
  from cudf.testing.testing import assert_frame_equal
label_desc: Use image node_id 6 for calling a global function with example usage: test_sorensen(gpubenchmark, read_csv, use_weight) without return types