code: def test_params_passed():
      # This makes sure that the "SuccessiveHalvingSearchCV params" key in
      # Hyperband.metadata["brackets"] is correct and they can be instantiated.
      est = ConstantFunction(value=0.4)
      params = {"value": np.linspace(0, 1)}
      params = {
          "aggressiveness": 3.5,
          "max_iter": 253,
          "random_state": 42,
          "scoring": False,
          "test_size": 0.212,
          "tol": 0,
      }
      params["patience"] = (
          params["max_iter"] // params["aggressiveness"]
      ) + 4
      hyperband = HyperbandSearchCV(est, params, **params)
  
      for k, v in params.items():
          assert getattr(hyperband, k) == v
  
      brackets = hyperband.metadata["brackets"]
      SHAs_params = [
          bracket["SuccessiveHalvingSearchCV params"]
          for bracket in brackets
      ]
  
      for SHA_params in SHAs_params:
          for k, v in params.items():
              if k == "random_state":
                  continue
              assert SHA_params[k] == v
      seeds = [SHA_params["random_state"] for SHA_params in SHAs_params]
      assert len(set(seeds)) == len(seeds)
  
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 2 for calling a global function with example usage: test_params_passed() without return types