code: def create_mg_graph(graph_data):
      """
      Create a graph instance based on the data to be loaded/generated.
      """
      G = MultiGraph(directed=True)
      # Assume strings are names of datasets in the datasets package
      scale = graph_data["scale"]
      num_edges = (2**scale) * graph_data["edgefactor"]
      seed = _seed
      edgelist_df = rmat(
          scale,
          num_edges,
          0.57,  # from Graph500
          0.19,  # from Graph500
          0.19,  # from Graph500
          seed,
          clip_and_flip=False,
          scramble_vertex_ids=False,  # FIXME: need to understand relevance of this
          create_using=None,  # None == return edgelist
          mg=True,
      )
      edgelist_df["weight"] = np.float32(1)
  
      G.from_dask_cudf_edgelist(
          edgelist_df,
          source="src",
          destination="dst",
          edge_attr="weight",
      )
      return G
  
file_path: cugraph/benchmarks/shared/build_cugraph_ucx/test_cugraph_sampling.py
imports: from dask_cuda import LocalCUDACluster
  from dask.distributed import Client
  import rmm
  import numpy
  from time import perf_counter_ns
  from cugraph.dask.comms import comms
  from cugraph.dask import uniform_neighbor_sample
  from cugraph import MultiGraph
  from cugraph.generators import rmat
label_desc: Use image node_id 2 for calling a global function with example usage: create_mg_graph(graph_data) and returns: G