code: def test__create_homogeneous_cugraph_dgl_nn_sparse_graph():
      tensor_d = {
          "sources_range": 1,
          "destinations_range": 2,
          "sources": torch.as_tensor(
              [0, 0, 1, 1], dtype=torch.int64, device="cuda"
          ),
          "destinations": torch.as_tensor(
              [0, 0, 1, 2], dtype=torch.int64, device="cuda"
          ),
      }
  
      seednodes_range = 10
      sparse_graph = _create_homogeneous_cugraph_dgl_nn_sparse_graph(
          tensor_d, seednodes_range
      )
      assert sparse_graph.num_src_nodes() == 2
      assert sparse_graph.num_dst_nodes() == seednodes_range + 1
      assert isinstance(sparse_graph, cugraph_dgl.nn.SparseGraph)
  
file_path: cugraph/python/cugraph-dgl/tests/test_utils.py
imports: import cudf
  import cupy
  import numpy
  from cugraph_dgl.dataloading.utils.sampling_helpers import cast_to_tensor, _get_renumber_map, _split_tensor, _get_tensor_d_from_sampled_df, create_homogeneous_sampled_graphs_from_dataframe, _get_source_destination_range, _create_homogeneous_cugraph_dgl_nn_sparse_graph, create_homogeneous_sampled_graphs_from_dataframe_csc
  from cugraph.utilities.utils import import_optional
label_desc: Use image node_id 8 for calling a global function with example usage: test__create_homogeneous_cugraph_dgl_nn_sparse_graph() without return types