code: def benchmark_cugraph_graph_creation(
      scale, edgefactor, seed, directed, renumber
  ):
      """
      Entry point for the benchmark.
      """
      dask_df = generate_edgelist_rmat(
          scale=scale,
          edgefactor=edgefactor,
          seed=seed,
          unweighted=True,
          mg=True,
      )
      # We do below to remove the rmat memory overhead
      # which holds on to GPU memory
      dask_df = dask_df.map_partitions(
          lambda df: df.to_pandas()
      ).persist()
      dask_df = dask_df.map_partitions(cudf.from_pandas)
      dask_df = dask_df.astype("int64")
      dask_df = dask_df.reset_index(drop=True)
      input_memory = dask_df.memory_usage().sum().compute()
      num_input_edges = len(dask_df)
      print(
          f"Number of input edges = {num_input_edges:,}, directed = {directed}, renumber = {renumber}"
      )
      (G, g_creation_time), allocation_counts = construct_graph(
          dask_df, directed=directed, renumber=renumber
      )
      (
          input_to_peak_ratio,
          output_to_peak_ratio,
          input_memory_per_worker,
          peak_allocation_across_workers,
      ) = get_memory_statistics(
          allocation_counts=allocation_counts, input_memory=input_memory
      )
      print(f"Number of edges in final graph = {G.number_of_edges():,}")
      print("-" * 80)
      return (
          num_input_edges,
          input_to_peak_ratio,
          output_to_peak_ratio,
          input_memory_per_worker,
          peak_allocation_across_workers,
          g_creation_time,
      )
  
file_path: cugraph/benchmarks/cugraph/standalone/cugraph_graph_creation.py
imports: from cugraph.testing.mg_utils import generate_edgelist_rmat, get_allocation_counts_dask_persist, get_allocation_counts_dask_lazy, sizeof_fmt, get_peak_output_ratio_across_workers, restart_client
  from cugraph.testing.mg_utils import start_dask_client, stop_dask_client, enable_spilling
  from cugraph.structure.symmetrize import symmetrize_ddf
  import cugraph
  import cudf
  from time import sleep
  import pandas
  import time
label_desc: Use image node_id 4 for calling a global function with example usage: benchmark_cugraph_graph_creation(scale, edgefactor, seed, directed, renumber) and returns: num_input_edges,  input_to_peak_ratio,  output_to_peak_ratio,  input_memory_per_worker,  peak_allocation_across_workers,  g_creation_time