code: def test_degree_centrality_multi_column(graph_file):
      dataset_path = graph_file.get_path()
      cu_M = utils.read_csv_file(dataset_path)
      cu_M.rename(columns={"0": "src_0", "1": "dst_0"}, inplace=True)
      cu_M["src_1"] = cu_M["src_0"] + 1000
      cu_M["dst_1"] = cu_M["dst_0"] + 1000
  
      G1 = cugraph.Graph(directed=True)
      G1.from_cudf_edgelist(
          cu_M,
          source=["src_0", "src_1"],
          destination=["dst_0", "dst_1"],
      )
  
      G2 = cugraph.Graph(directed=True)
      G2.from_cudf_edgelist(cu_M, source="src_0", destination="dst_0")
  
      k_df_exp = cugraph.degree_centrality(G2)
      k_df_exp = k_df_exp.sort_values("vertex").reset_index(drop=True)
  
      nstart = cudf.DataFrame()
      nstart["vertex_0"] = k_df_exp["vertex"]
      nstart["vertex_1"] = nstart["vertex_0"] + 1000
      nstart["values"] = k_df_exp["degree_centrality"]
  
      k_df_res = cugraph.degree_centrality(G1)
      k_df_res = k_df_res.sort_values("0_vertex").reset_index(drop=True)
      k_df_res.rename(columns={"0_vertex": "vertex"}, inplace=True)
  
      top_res = topKVertices(k_df_res, "degree_centrality", 10)
      top_exp = topKVertices(k_df_exp, "degree_centrality", 10)
  
      assert top_res.equals(top_exp)
  
file_path: cugraph/python/cugraph/cugraph/tests/centrality/test_degree_centrality.py
imports: import gc
  import pytest
  import networkx
  import cudf
  import cugraph
  from cugraph.testing import utils, UNDIRECTED_DATASETS
label_desc: Use image node_id 4 for calling a global function with example usage: test_degree_centrality_multi_column(graph_file) without return types