code: def bench_cgs_uniform_neighbor_sample(
      gpubenchmark,
      remote_graph_objs,
      batch_size,
      fanout,
      with_replacement,
  ):
      (G, num_verts, uniform_neighbor_sample_func) = remote_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}
      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-service/pytest-based/bench_cgs_uniform_neighbor_sample.py
imports: import os
  from pathlib import Path
  import pytest
  import numpy
  from cugraph_service_client import CugraphServiceClient
  from cugraph_service_client.exceptions import CugraphServiceError
  from cugraph_service_client import RemoteGraph
  from cugraph_service_server.testing import utils
  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_cgs_uniform_neighbor_sample(gpubenchmark, remote_graph_objs, batch_size, fanout, with_replacement) without return types