code: def sample_pyg_hetero_data():
      torch.manual_seed(12345)
      raw_data_dict = {
          "v0": torch.randn(6, 3),
          "v1": torch.randn(7, 2),
          "v2": torch.randn(5, 4),
          ("v2", "e0", "v1"): torch.tensor(
              [[0, 2, 2, 4, 4], [4, 3, 6, 0, 1]]
          ),
          ("v1", "e1", "v1"): torch.tensor(
              [[0, 2, 2, 2, 3, 5, 5], [4, 0, 4, 5, 3, 0, 1]]
          ),
          ("v0", "e2", "v0"): torch.tensor(
              [[0, 2, 2, 3, 5, 5], [1, 1, 5, 1, 1, 2]]
          ),
          ("v1", "e3", "v2"): torch.tensor(
              [[0, 1, 1, 2, 4, 5, 6], [1, 2, 3, 1, 2, 2, 2]]
          ),
          ("v0", "e4", "v2"): torch.tensor(
              [[1, 1, 3, 3, 4, 4], [1, 4, 1, 4, 0, 3]]
          ),
      }
  
      # create a nested dictionary to facilitate PyG's HeteroData construction
      hetero_data_dict = {}
      for key, value in raw_data_dict.items():
          if isinstance(key, tuple):
              hetero_data_dict[key] = {"edge_index": value}
          else:
              hetero_data_dict[key] = {"x": value}
  
      return hetero_data_dict
  
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 10 for calling a global function with example usage: sample_pyg_hetero_data() and returns: hetero_data_dict