code: def truncated_tetrahedron_graph(create_using=None):
      graph_class, inplace = _create_using_class(create_using)
      if graph_class.is_directed():
          src_indices = cp.array(
              [0, 0, 0, 1, 1, 2, 3, 3, 4, 4, 5, 5, 6, 7, 8, 8, 9, 10],
              index_dtype,
          )
          dst_indices = cp.array(
              [
                  1,
                  2,
                  9,
                  2,
                  6,
                  3,
                  4,
                  11,
                  5,
                  11,
                  6,
                  7,
                  7,
                  8,
                  9,
                  10,
                  10,
                  11,
              ],
              index_dtype,
          )
      else:
          # fmt: off
          src_indices = cp.array(
              [
                  0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7,
                  7, 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11,
              ],
              index_dtype,
          )
          dst_indices = cp.array(
              [
                  1, 2, 9, 0, 2, 6, 0, 1, 3, 2, 4, 11, 3, 5, 11, 4, 6, 7, 1, 5, 7, 5,
                  6, 8, 7, 9, 10, 0, 8, 10, 8, 9, 11, 3, 4, 10,
              ],
              index_dtype,
          )
          # fmt: on
      G = graph_class.from_coo(
          12,
          src_indices,
          dst_indices,
          name="Truncated Tetrahedron Graph",
      )
      if inplace:
          return create_using._become(G)
      return G
  
imports: import cupy
  import networkx
  import nx_cugraph
  from ..utils import index_dtype, networkx_algorithm
  from ._utils import _IS_NX32_OR_LESS, _create_using_class
label_desc: Use image node_id 20 for calling a global function with example usage: truncated_tetrahedron_graph(create_using) and returns: G, create_using