code: def multi_edge_graph_1():
      G = {
          ("vt1", "pig", "vt1"): [
              torch.tensor([0, 2, 3, 1]),
              torch.tensor([1, 3, 1, 4]),
          ],
          ("vt1", "dog", "vt1"): [
              torch.tensor([0, 3, 4]),
              torch.tensor([2, 2, 3]),
          ],
          ("vt1", "cat", "vt1"): [
              torch.tensor([1, 2, 2]),
              torch.tensor([4, 3, 4]),
          ],
      }
  
      N = {"vt1": 5}
  
      F = FeatureStore()
      F.add_data(
          torch.tensor([100, 200, 300, 400, 500]),
          type_name="vt1",
          feat_name="prop1",
      )
  
      F.add_data(
          torch.tensor([5, 4, 3, 2, 1]),
          type_name="vt1",
          feat_name="prop2",
      )
  
      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 4 for calling a global function with example usage: multi_edge_graph_1() and returns: F,  G,  N