code: def abc_graph():
      N = {
          "A": 2,  # 0, 1
          "B": 3,  # 2, 3, 4
          "C": 4,  # 5, 6, 7, 8
      }
  
      G = {
          # (0->2, 0->3, 1->3)
          ("A", "ab", "B"): [
              torch.tensor([0, 0, 1], dtype=torch.int64),
              torch.tensor([0, 1, 1], dtype=torch.int64),
          ],
          # (2->0, 2->1, 3->1, 4->0)
          ("B", "ba", "A"): [
              torch.tensor([0, 0, 1, 2], dtype=torch.int64),
              torch.tensor([0, 1, 1, 0], dtype=torch.int64),
          ],
          # (2->6, 2->8, 3->5, 3->7, 4->5, 4->8)
          ("B", "bc", "C"): [
              torch.tensor([0, 0, 1, 1, 2, 2], dtype=torch.int64),
              torch.tensor([1, 3, 0, 2, 0, 3], dtype=torch.int64),
          ],
      }
  
      F = FeatureStore()
      F.add_data(
          torch.tensor([3.2, 2.1], dtype=torch.float32),
          type_name="A",
          feat_name="prop1",
      )
  
      return F, G, N
  
imports: import os
  import pytest
  from dask_cuda.initialize import initialize
  from dask_cuda import LocalCUDACluster
  from dask.distributed import Client
  from cugraph.dask.comms import comms
  from cugraph.dask.common.mg_utils import get_visible_devices
  from cugraph.testing.mg_utils import stop_dask_client
  import torch
  import numpy
  from cugraph.gnn import FeatureStore
  from cugraph.datasets import karate
  import tempfile
label_desc: Use image node_id 7 for calling a global function with example usage: abc_graph() and returns: F,  G,  N