code: def edge_betweenness_centrality(
      G, k=None, normalized=True, weight=None, seed=None
  ):
      """`weight` parameter is not yet supported, and RNG with seed may be different."""
      if weight is not None:
          raise NotImplementedError(
              "Weighted implementation of betweenness centrality not currently supported"
          )
      seed = _seed_to_int(seed)
      G = _to_graph(G, weight)
      (
          src_ids,
          dst_ids,
          values,
          _edge_ids,
      ) = plc.edge_betweenness_centrality(
          resource_handle=plc.ResourceHandle(),
          graph=G._get_plc_graph(),
          k=k,
          random_state=seed,
          normalized=normalized,
          do_expensive_check=False,
      )
      if not G.is_directed():
          mask = src_ids <= dst_ids
          src_ids = src_ids[mask]
          dst_ids = dst_ids[mask]
          values = 2 * values[mask]
      return G._edgearrays_to_dict(src_ids, dst_ids, values)
  
imports: import pylibcugraph
  from nx_cugraph.convert import _to_graph
  from nx_cugraph.utils import _seed_to_int, networkx_algorithm
label_desc: Use image node_id 3 for calling a global function with example usage: edge_betweenness_centrality(G, k, normalized, weight, seed) and returns: G