code: def input_expected_output(input_combo):
      """
      This fixture returns the inputs and expected results from the Sorensen algo.
      (based on cuGraph Sorensen) which can be used for validation.
      """
  
      input_data_path = input_combo["graph_file"]
      directed = input_combo["directed"]
      has_vertex_pair = input_combo["has_vertex_pair"]
      is_weighted = input_combo["is_weighted"]
      G = utils.generate_cugraph_graph_from_file(
          input_data_path, directed=directed, edgevals=is_weighted
      )
      if has_vertex_pair:
          # Sample random vertices from the graph and compute the two_hop_neighbors
          # with those seeds
          k = random.randint(1, 10)
          seeds = random.sample(range(G.number_of_vertices()), k)
  
          vertex_pair = G.get_two_hop_neighbors(start_vertices=seeds)
      else:
          vertex_pair = None
  
      input_combo["vertex_pair"] = vertex_pair
      sg_cugraph_sorensen = cugraph.sorensen(
          G, input_combo["vertex_pair"], use_weight=is_weighted
      )
      # Save the results back to the input_combo dictionary to prevent redundant
      # cuGraph runs. Other tests using the input_combo fixture will look for
      # them, and if not present they will have to re-run the same cuGraph call.
  
      input_combo["sg_cugraph_results"] = sg_cugraph_sorensen
      chunksize = dcg.get_chunksize(input_data_path)
      ddf = dask_cudf.read_csv(
          input_data_path,
          chunksize=chunksize,
          delimiter=" ",
          names=["src", "dst", "value"],
          dtype=["int32", "int32", "float32"],
      )
  
      dg = cugraph.Graph(directed=directed)
      dg.from_dask_cudf_edgelist(
          ddf,
          source="src",
          destination="dst",
          edge_attr="value" if is_weighted else None,
          renumber=True,
          store_transposed=True,
      )
  
      input_combo["MGGraph"] = dg
  
      return input_combo
  
file_path: cugraph/python/cugraph/cugraph/tests/link_prediction/test_sorensen_mg.py
imports: import gc
  import random
  import pytest
  import cugraph
  import dask_cudf
  import cugraph.dask
  from cugraph.testing import utils
  from cugraph.dask.common.mg_utils import is_single_gpu
  from pylibcugraph.testing import gen_fixture_params_product
label_desc: Use image node_id 3 for calling a global function with example usage: input_expected_output(input_combo) and returns: input_combo