code: def bench_get_vector_features(
      gpubenchmark, dask_client, n_rows, n_feats
  ):
      from cugraph.experimental import MGPropertyGraph
  
      df = cudf.DataFrame(
          {
              "src": cp.arange(0, n_rows, dtype=cp.int32),
              "dst": cp.arange(0, n_rows, dtype=cp.int32) + 1,
          }
      )
      for i in range(n_feats):
          df[f"feat_{i}"] = cp.ones(len(df), dtype=cp.int32)
      df = dask_cudf.from_cudf(df, npartitions=16)
  
      vector_properties = {
          "feat": [f"feat_{i}" for i in range(n_feats)]
      }
      pG = MGPropertyGraph()
      pG.add_edge_data(
          df,
          vertex_col_names=["src", "dst"],
          vector_properties=vector_properties,
      )
  
      def func(pG):
          df = pG.get_edge_data(edge_ids=cp.arange(0, 100_000))
          df = df.compute()
  
      gpubenchmark(func, pG)
  
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 34 for calling a global function with example usage: bench_get_vector_features(gpubenchmark, dask_client, n_rows, n_feats) without return types