code: def test_same_homogeneousgraph_results(dask_client):
      single_gpu = False
      train_nid = th.tensor([1])
      # Create a heterograph with 3 node types and 3 edges types.
      dgl_g = dgl.graph(
          ([1, 2, 3, 4, 5, 6, 7, 8], [0, 0, 0, 0, 1, 1, 1, 1])
      )
      cugraph_gs = cugraph_storage_from_heterograph(
          dgl_g, single_gpu=single_gpu
      )
  
      dgl_output = sample_dgl_graphs(dgl_g, train_nid, [2])
      cugraph_output = sample_cugraph_dgl_graphs(
          cugraph_gs, train_nid, [2]
      )
  
      cugraph_output_nodes = (
          cugraph_output[0]["output_nodes"].cpu().numpy()
      )
      dgl_output_nodes = dgl_output[0]["output_nodes"].cpu().numpy()
      np.testing.assert_array_equal(
          cugraph_output_nodes, dgl_output_nodes
      )
      assert (
          dgl_output[0]["blocks"][0].num_dst_nodes()
          == cugraph_output[0]["blocks"][0].num_dst_nodes()
      )
      assert (
          dgl_output[0]["blocks"][0].num_edges()
          == cugraph_output[0]["blocks"][0].num_edges()
      )
  
file_path: cugraph/python/cugraph-dgl/tests/mg/test_dataloader.py
imports: import pytest
  import dgl
  import torch
  from cugraph_dgl import cugraph_storage_from_heterograph
  import tempfile
  import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: test_same_homogeneousgraph_results(dask_client) without return types