code: def convert_to_cudf(cp_paths, number_map=None, is_vertex_paths=False):
      """
      Creates cudf Series from cupy arrays from pylibcugraph wrapper
      """
  
      if is_vertex_paths and len(cp_paths) > 0:
          if number_map.implementation.numbered:
              df_ = cudf.DataFrame()
              df_["vertex_paths"] = cp_paths
              df_ = number_map.unrenumber(
                  df_, "vertex_paths", preserve_order=True
              ).compute()
              vertex_paths = cudf.Series(df_["vertex_paths"]).fillna(-1)
  
              return vertex_paths
  
      return cudf.Series(cp_paths)
  
file_path: cugraph/python/cugraph/cugraph/dask/sampling/random_walks.py
imports: from dask.distributed import wait, default_client
  import dask_cudf
  import cudf
  import operator
  from cugraph.dask.common.part_utils import persist_dask_df_equal_parts_per_worker
  from pylibcugraph import ResourceHandle
  from pylibcugraph import uniform_random_walks
  from cugraph.dask.comms import comms
label_desc: Use image node_id 1 for calling a global function with example usage: convert_to_cudf(cp_paths, number_map, is_vertex_paths) and returns: cudf, vertex_paths