code: def calc_random_walks(G):
      """
      compute random walks
  
      parameters
      ----------
      G : cuGraph.Graph or networkx.Graph
          The graph can be either directed (DiGraph) or undirected (Graph).
          Weights in the graph are ignored.
          Use weight parameter if weights need to be considered
          (currently not supported)
  
      Returns
      -------
      vertex_paths : cudf.Series or cudf.DataFrame
          Series containing the vertices of edges/paths in the random walk.
  
      edge_weight_paths: cudf.Series
          Series containing the edge weights of edges represented by the
          returned vertex_paths
  
      max_path_length : int
          The maximum path length
  
      start_vertices : list
          Roots for the random walks
  
      max_depth : int
      """
      k = random.randint(1, 4)
      random_walks_type = "uniform"
      max_depth = random.randint(2, 4)
  
      start_vertices = (
          G.nodes().compute().sample(k).reset_index(drop=True)
      )
  
      vertex_paths, edge_weights, max_path_length = dcg.random_walks(
          G, random_walks_type, start_vertices, max_depth
      )
  
      return (
          (vertex_paths, edge_weights, max_path_length),
          start_vertices,
          max_depth,
      )
  
file_path: cugraph/python/cugraph/cugraph/tests/sampling/test_random_walks_mg.py
imports: import gc
  import random
  import pytest
  import cugraph
  import dask_cudf
  import cugraph.dask
  from cugraph.testing import SMALL_DATASETS
  from cugraph.datasets import karate_asymmetric
  from pylibcugraph.testing.utils import gen_fixture_params_product
label_desc: Use image node_id 2 for calling a global function with example usage: calc_random_walks(G) and returns: vertex_paths,  edge_weights,  max_path_length,  start_vertices,  max_depth