code: def multi_edge_multi_vertex_graph_1():
      G = {
          ("brown", "horse", "brown"): [
              torch.tensor([0, 0]),
              torch.tensor([1, 2]),
          ],
          ("brown", "tortoise", "black"): [
              torch.tensor([1, 1, 2]),
              torch.tensor([1, 0, 1]),
          ],
          ("brown", "mongoose", "black"): [
              torch.tensor([2, 1]),
              torch.tensor([0, 1]),
          ],
          ("black", "cow", "brown"): [
              torch.tensor([0, 0]),
              torch.tensor([1, 2]),
          ],
          ("black", "snake", "black"): [
              torch.tensor([1]),
              torch.tensor([0]),
          ],
      }
  
      N = {"brown": 3, "black": 2}
  
      F = FeatureStore()
      F.add_data(
          torch.tensor([100, 200, 300]),
          type_name="brown",
          feat_name="prop1",
      )
  
      F.add_data(
          torch.tensor([400, 500]), type_name="black", feat_name="prop1"
      )
  
      F.add_data(
          torch.tensor([5, 4, 3]), type_name="brown", feat_name="prop2"
      )
  
      F.add_data(
          torch.tensor([2, 1]), type_name="black", 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 5 for calling a global function with example usage: multi_edge_multi_vertex_graph_1() and returns: F,  G,  N