code: def bench_uniform_neighbor_sample(gpubenchmark, graph):
      uns = (
          dask_cugraph.uniform_neighbor_sample
          if is_graph_distributed(graph)
          else cugraph.uniform_neighbor_sample
      )
  
      seed = 42
      # FIXME: may need to provide number_of_vertices separately
      num_verts_in_graph = graph.number_of_vertices()
      len_start_list = max(int(num_verts_in_graph * 0.01), 2)
      srcs = graph.edgelist.edgelist_df["src"]
      frac = len_start_list / num_verts_in_graph
  
      start_list = srcs.sample(frac=frac, random_state=seed)
      # Attempt to automatically handle a dask Series
      if hasattr(start_list, "compute"):
          start_list = start_list.compute()
  
      fanout_vals = [5, 5, 5]
      gpubenchmark(
          uns, graph, start_list=start_list, fanout_vals=fanout_vals
      )
  
imports: import pytest
  import numpy
  import pytest_benchmark
  import rmm
  import dask_cudf
  from pylibcugraph.testing import gen_fixture_params_product
  import cugraph
  import cugraph.dask
  from cugraph.structure.number_map import NumberMap
  from cugraph.generators import rmat
  from cugraph.testing import utils, mg_utils
  from cugraph.utilities.utils import is_device_version_less_than
  from cugraph_benchmarking.params import directed_datasets, undirected_datasets, managed_memory, pool_allocator
label_desc: Use image node_id 28 for calling a global function with example usage: bench_uniform_neighbor_sample(gpubenchmark, graph) without return types