code: def _calc_bc_full(
      G, Gnx, normalized, weight, endpoints, k, seed, result_dtype
  ):
      df = cugraph.betweenness_centrality(
          G,
          k=k,
          normalized=normalized,
          weight=weight,
          endpoints=endpoints,
          result_dtype=result_dtype,
      )
      assert (
          df["betweenness_centrality"].dtype == result_dtype
      ), "'betweenness_centrality' column has not the expected type"
      nx_bc = nx.betweenness_centrality(
          Gnx,
          k=k,
          normalized=normalized,
          weight=weight,
          endpoints=endpoints,
      )
  
      sorted_df = (
          df.sort_values("vertex")
          .rename(
              columns={"betweenness_centrality": "cu_bc"}, copy=False
          )
          .reset_index(drop=True)
      )
      _, nx_bc = zip(*sorted(nx_bc.items()))
      nx_df = cudf.DataFrame({"ref_bc": nx_bc})
  
      merged_sorted_df = cudf.concat(
          [sorted_df, nx_df], axis=1, sort=False
      )
  
      return merged_sorted_df
  
imports: import gc
  import pytest
  import random
  import numpy
  import networkx
  import cudf
  import cupy
  import cugraph
  from cugraph.datasets import karate_disjoint
  from cugraph.testing import utils, SMALL_DATASETS
label_desc: Use image node_id 5 for calling a global function with example usage: _calc_bc_full(G, Gnx, normalized, weight, endpoints, k, seed, result_dtype) and returns: merged_sorted_df