code: def test_random_state_no_seed_different_params():
      # Guarantees that specifying a random state relies in the successive
      # halving's having the same random state (and likewise for no random state)
  
      # This test is required because Hyperband wraps SHAs and the random state
      # needs to be passed correctly.
      values = scipy.stats.uniform(0, 1)
      max_iter = 9
      brackets = _get_hyperband_params(max_iter)
      kwargs = {"value": values}
  
      h1 = HyperbandSearchCV(
          ConstantFunction(), kwargs, max_iter=max_iter
      )
      h2 = HyperbandSearchCV(
          ConstantFunction(), kwargs, max_iter=max_iter
      )
      h1._get_SHAs(brackets)
      h2._get_SHAs(brackets)
      assert h1._SHA_seed != h2._SHA_seed
  
      h1 = HyperbandSearchCV(
          ConstantFunction(), kwargs, max_iter=9, random_state=0
      )
      h2 = HyperbandSearchCV(
          ConstantFunction(), kwargs, max_iter=9, random_state=0
      )
      h1._get_SHAs(brackets)
      h2._get_SHAs(brackets)
      assert h1._SHA_seed == h2._SHA_seed
  
file_path: dask-ml/tests/model_selection/test_hyperband.py
imports: import logging
  import math
  import warnings
  import numpy
  import pandas
  import pytest
  import scipy.stats
  from dask import array, dataframe
  from distributed.utils_test import captured_logger, cluster, gen_cluster, loop
  from sklearn.linear_model import SGDClassifier
  from dask_ml._compat import DISTRIBUTED_2_5_0, SK_LOG_LOSS
  from dask_ml.datasets import make_classification
  from dask_ml.model_selection import HyperbandSearchCV, IncrementalSearchCV, SuccessiveHalvingSearchCV
  from dask_ml.model_selection._hyperband import _get_hyperband_params
  from dask_ml.utils import ConstantFunction
  from dask_ml.wrappers import Incremental
label_desc: Use image node_id 3 for calling a global function with example usage: test_random_state_no_seed_different_params() without return types