code: def bench_cugraph_uniform_neighbor_sample(
      gpubenchmark, graph_objs, batch_size, fanout, with_replacement
  ):
      (G, num_verts, uniform_neighbor_sample_func) = graph_objs
  
      with TimerContext("computing sampling args"):
          uns_args = get_uniform_neighbor_sample_args(
              G, num_verts, _seed, batch_size, fanout, with_replacement
          )
      # print(f"\n{uns_args}")
      # FIXME: uniform_neighbor_sample cannot take a np.ndarray for start_list
      result = gpubenchmark(
          uniform_neighbor_sample_func,
          G,
          start_list=uns_args["start_list"],
          fanout_vals=uns_args["fanout"],
          with_replacement=uns_args["with_replacement"],
      )
      """
      dtmap = {"int32": 32 // 8, "int64": 64 // 8}
      if isinstance(result, tuple):
          dt = str(result[0].dtype)
          llen = len(result[0])
      else:
          dt = str(result.sources.dtype)
          llen = len(result.sources)
      print(f"\nresult list len: {llen} (x3), dtype={dt}, total bytes={3*llen*dtmap[dt]}")
      """
  
file_path: cugraph/benchmarks/cugraph/pytest-based/bench_cugraph_uniform_neighbor_sample.py
imports: import os
  import pytest
  import numpy
  import cupy
  from cugraph.testing.mg_utils import start_dask_client, stop_dask_client
  import cudf
  import dask_cudf
  import rmm
  from cugraph import MultiGraph, uniform_neighbor_sample
  from cugraph.generators import rmat
  from cugraph.experimental import datasets
  from cugraph.dask import uniform_neighbor_sample
  from cugraph_benchmarking import params
  from cugraph_benchmarking.timer import TimerContext
label_desc: Use image node_id 5 for calling a global function with example usage: bench_cugraph_uniform_neighbor_sample(gpubenchmark, graph_objs, batch_size, fanout, with_replacement) without return types