code: def net_MGPropertyGraph(dask_client):
      """
      Fixture which returns an instance of a PropertyGraph with vertex and edge
      data added from the netscience.csv dataset, parameterized for different
      DataFrame types.
      """
      from cugraph.experimental import MGPropertyGraph
  
      input_data_path = str(netscience.get_path())
      print(f"dataset={input_data_path}")
      chunksize = dcg.get_chunksize(input_data_path)
      ddf = dask_cudf.read_csv(
          input_data_path,
          chunksize=chunksize,
          delimiter=" ",
          names=["src", "dst", "value"],
          dtype=["int32", "int32", "float32"],
      )
  
      dpG = MGPropertyGraph()
      dpG.add_edge_data(ddf, ("src", "dst"))
      assert type_is_categorical(dpG)
      return dpG
  
imports: import gc
  import pytest
  import pandas
  import numpy
  import cudf
  import cugraph
  import dask_cudf
  import cupy
  import cugraph.dask
  from cupy.testing import assert_array_equal
  from cudf.testing import assert_frame_equal, assert_series_equal
  from pylibcugraph.testing.utils import gen_fixture_params_product
  from cugraph.dask.common.mg_utils import is_single_gpu
  from cugraph.datasets import cyber, netscience
label_desc: Use image node_id 9 for calling a global function with example usage: net_MGPropertyGraph(dask_client) and returns: dpG