code: def test_mg_uniform_neighbor_sample_ensure_no_duplicates(dask_client):
      # See issue #2760
      # This ensures that the starts are properly distributed
  
      df = cudf.DataFrame({"src": [6, 6, 6, 6], "dst": [7, 9, 10, 11]})
      df = df.astype("int32")
  
      dask_df = dask_cudf.from_cudf(df, npartitions=2)
  
      mg_G = cugraph.MultiGraph(directed=True)
      mg_G.from_dask_cudf_edgelist(
          dask_df, source="src", destination="dst", renumber=True
      )
  
      output_df = cugraph.dask.uniform_neighbor_sample(
          mg_G,
          cudf.Series([6]).astype("int32"),
          fanout_vals=[3],
          with_replacement=False,
      )
  
      assert len(output_df.compute()) == 3
  
imports: import gc
  import random
  import os
  import pytest
  import pandas
  import cupy
  import cudf
  import cugraph
  import dask_cudf
  import cugraph.dask
  from cugraph.testing import UNDIRECTED_DATASETS
  from cugraph.dask import uniform_neighbor_sample
  from cugraph.dask.common.mg_utils import is_single_gpu
  from cugraph.datasets import email_Eu_core, small_tree
  from pylibcugraph.testing.utils import gen_fixture_params_product
label_desc: Use image node_id 6 for calling a global function with example usage: test_mg_uniform_neighbor_sample_ensure_no_duplicates(dask_client) without return types