code: def bench_edge_betweenness_centrality(
      benchmark, graph_obj, backend_wrapper, normalized, k
  ):
      G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper)
      result = benchmark.pedantic(
          target=backend_wrapper(nx.edge_betweenness_centrality),
          args=(G,),
          kwargs=dict(
              weight=None,
              normalized=normalized,
              k=k,
          ),
          rounds=rounds,
          iterations=iterations,
          warmup_rounds=warmup_rounds,
      )
      assert type(result) is dict
  
imports: import random
  import networkx
  import pandas
  import pytest
  from cugraph import datasets
  import nx_cugraph
label_desc: Use image node_id 10 for calling a global function with example usage: bench_edge_betweenness_centrality(benchmark, graph_obj, backend_wrapper, normalized, k) without return types