code: def test_select_random_vertices(
      graph_file, random_state, num_vertices
  ):
      cu_M = utils.read_csv_file(graph_file)
  
      G = cugraph.Graph(directed=True)
      G.from_cudf_edgelist(
          cu_M, source="0", destination="1", edge_attr="2"
      )
  
      if num_vertices is None:
          # Select all vertices
          num_vertices = G.number_of_nodes()
  
      sampled_vertices = G.select_random_vertices(
          random_state, num_vertices
      )
  
      original_vertices_df = cudf.DataFrame()
      sampled_vertices_df = cudf.DataFrame()
  
      sampled_vertices_df["sampled_vertices"] = sampled_vertices
      original_vertices_df["original_vertices"] = G.nodes()
  
      join = sampled_vertices_df.merge(
          original_vertices_df,
          left_on="sampled_vertices",
          right_on="original_vertices",
      )
  
      assert len(join) == len(sampled_vertices)
  
imports: import gc
  import time
  import pytest
  import pandas
  import scipy
  import networkx
  import cupy
  import cudf
  import cugraph
  from cugraph.testing import utils
  from cudf.testing import assert_series_equal
  from cudf.testing.testing import assert_frame_equal
  import dask_cudf
  import cugraph.dask
  from dask.distributed import Client
  from dask_cuda import LocalCUDACluster
  from pylibcugraph import ResourceHandle
  from pylibcugraph import bfs
  from cugraph.dask.traversal.bfs import convert_to_cudf
  from cugraph.dask.common.mg_utils import is_single_gpu
label_desc: Use image node_id 32 for calling a global function with example usage: test_select_random_vertices(graph_file, random_state, num_vertices) without return types