code: def test_blockwise_shufflesplit_rng():
      # Regression test for issue #380
      n_splits = 2
      splitter = dask_ml.model_selection.ShuffleSplit(
          n_splits=n_splits, random_state=0
      )
      gen = splitter.split(dX)
  
      train_indices = []
      test_indices = []
      for train_idx, test_idx in gen:
          train_indices.append(train_idx)
          test_indices.append(test_idx)
  
      assert not np.array_equal(train_indices[0], train_indices[1])
      assert not np.array_equal(test_indices[0], test_indices[1])
  
      # Test that splitting is reproducible
      n_splits = 2
      split1 = dask_ml.model_selection.ShuffleSplit(
          n_splits=n_splits, random_state=0
      )
      split2 = dask_ml.model_selection.ShuffleSplit(
          n_splits=n_splits, random_state=0
      )
  
      for (train_1, test_1), (train_2, test_2) in zip(
          split1.split(dX), split2.split(dX)
      ):
          da.utils.assert_eq(train_1, train_2)
          da.utils.assert_eq(test_1, test_2)
  
imports: import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import pytest
  from sklearn.datasets import fetch_20newsgroups, make_regression
  import dask_ml.model_selection
  from dask_ml._compat import DASK_2130
label_desc: Use image node_id 3 for calling a global function with example usage: test_blockwise_shufflesplit_rng() without return types