code: def davis_southern_women_graph():
      # fmt: off
      src_indices = cp.array(
          [
              0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3,
              3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 8, 8, 8,
              8, 9, 9, 9, 9, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12,
              12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 15, 15,
              16, 16, 17, 17, 18, 18, 18, 19, 19, 19, 20, 20, 20, 20, 20, 20, 21, 21,
              21, 21, 22, 22, 22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 23, 23, 23,
              24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 25, 25, 25, 25, 25, 25, 25,
              25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26,
              27, 27, 27, 27, 27, 28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 30, 30, 30,
              31, 31, 31,
          ],
          index_dtype,
      )
      dst_indices = cp.array(
          [
              18, 19, 20, 21, 22, 23, 25, 26, 18, 19, 20, 22, 23, 24, 25, 19, 20, 21,
              22, 23, 24, 25, 26, 18, 20, 21, 22, 23, 24, 25, 20, 21, 22, 24, 20, 22,
              23, 25, 22, 23, 24, 25, 23, 25, 26, 22, 24, 25, 26, 24, 25, 26, 29, 25,
              26, 27, 29, 25, 26, 27, 29, 30, 31, 24, 25, 26, 27, 29, 30, 31, 23, 24,
              26, 27, 28, 29, 30, 31, 24, 25, 27, 28, 29, 25, 26, 26, 28, 26, 28, 0, 1,
              3, 0, 1, 2, 0, 1, 2, 3, 4, 5, 0, 2, 3, 4, 0, 1, 2, 3, 4, 5, 6, 8, 0, 1,
              2, 3, 5, 6, 7, 13, 1, 2, 3, 4, 6, 8, 9, 12, 13, 14, 0, 1, 2, 3, 5, 6, 7,
              8, 9, 10, 11, 12, 14, 15, 0, 2, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 10,
              11, 12, 13, 14, 13, 14, 16, 17, 9, 10, 11, 12, 13, 14, 11, 12, 13, 11,
              12, 13,
          ],
          index_dtype,
      )
      bipartite = cp.array(
          [
              0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
              1, 1, 1, 1, 1, 1, 1, 1,
          ],
          np.int8,
      )
      women = [
          "Evelyn Jefferson", "Laura Mandeville", "Theresa Anderson", "Brenda Rogers",
          "Charlotte McDowd", "Frances Anderson", "Eleanor Nye", "Pearl Oglethorpe",
          "Ruth DeSand", "Verne Sanderson", "Myra Liddel", "Katherina Rogers",
          "Sylvia Avondale", "Nora Fayette", "Helen Lloyd", "Dorothy Murchison",
          "Olivia Carleton", "Flora Price",
      ]
      events = [
          "E1", "E2", "E3", "E4", "E5", "E6", "E7", "E8", "E9", "E10", "E11", "E12",
          "E13", "E14",
      ]
      # fmt: on
      return nxcg.Graph.from_coo(
          32,
          src_indices,
          dst_indices,
          node_values={"bipartite": bipartite},
          id_to_key=women + events,
          top=women,
          bottom=events,
      )
  
file_path: cugraph/python/nx-cugraph/nx_cugraph/generators/social.py
imports: import cupy
  import numpy
  import nx_cugraph
  from ..utils import index_dtype, networkx_algorithm
label_desc: Use image node_id 1 for calling a global function with example usage: davis_southern_women_graph() and returns: nxcg