code: def test_pagerank(sg_transposed_graph_objs):
      from pylibcugraph import pagerank
  
      (g, resource_handle, ds_name) = sg_transposed_graph_objs
      (expected_verts, expected_pageranks) = _test_data[ds_name]
  
      precomputed_vertex_out_weight_sums = None
      do_expensive_check = False
      precomputed_vertex_out_weight_vertices = None
      precomputed_vertex_out_weight_sums = None
      initial_guess_vertices = None
      initial_guess_values = None
  
      result = pagerank(
          resource_handle,
          g,
          precomputed_vertex_out_weight_vertices,
          precomputed_vertex_out_weight_sums,
          initial_guess_vertices,
          initial_guess_values,
          _alpha,
          _epsilon,
          _max_iterations,
          do_expensive_check,
      )
  
      num_expected_verts = len(expected_verts)
      (actual_verts, actual_pageranks) = result
  
      # Do a simple check using the vertices as array indices.  First, ensure
      # the test data vertices start from 0 with no gaps.
      assert sum(range(num_expected_verts)) == sum(expected_verts)
  
      assert actual_verts.dtype == expected_verts.dtype
      assert actual_pageranks.dtype == expected_pageranks.dtype
  
      actual_pageranks = actual_pageranks.tolist()
      actual_verts = actual_verts.tolist()
      expected_pageranks = expected_pageranks.tolist()
  
      for i in range(num_expected_verts):
          assert actual_pageranks[i] == pytest.approx(
              expected_pageranks[actual_verts[i]], 1e-4
          ), f"actual != expected for result at index {i}"
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_pagerank(sg_transposed_graph_objs) without return types