code: def test_random_state(seed_type):
      for i in range(10):
          seed_n = np.random.randint(0, int(1e9))
          if seed_type == "int":
              seed = seed_n
          if seed_type == "cupy":
              seed = cp.random.RandomState(seed=seed_n)
          if seed_type == "numpy":
              seed = np.random.RandomState(seed=seed_n)
          X = cudf.DataFrame({"x": range(100)})
          y = cudf.Series(([0] * (100 // 2)) + ([1] * (100 // 2)))
  
          X_train, X_test, y_train, y_test = train_test_split(
              X, y, random_state=seed
          )
  
          if seed_type == "cupy":
              seed = cp.random.RandomState(seed=seed_n)
          if seed_type == "numpy":
              seed = np.random.RandomState(seed=seed_n)
  
          X_train2, X_test2, y_train2, y_test2 = train_test_split(
              X, y, random_state=seed
          )
  
          assert X_train.equals(X_train2)
          assert X_test.equals(X_test2)
          assert y_train.equals(y_train2)
          assert y_test.equals(y_test2)
  
imports: from cuml.internals.safe_imports import gpu_only_import_from
  from cuml.datasets import make_classification
  from cuml.model_selection import train_test_split
  import pytest
  from cuml.internals.safe_imports import cpu_only_import
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 6 for calling a global function with example usage: test_random_state(seed_type) without return types