code: def _get_SHA_params(SHA):
      """
      Parameters
      ----------
      SHA : SuccessiveHalvingSearchCV
  
      Returns
      -------
      params : dict
          Dictionary to re-create a SuccessiveHalvingSearchCV without the
          estimator or parameters
  
      Example
      -------
      >>> from sklearn.linear_model import SGDClassifier
      >>> model = SGDClassifier()
      >>> params = {"alpha": np.logspace(-1, 1)}
      >>> SHA = SuccessiveHalvingSearchCV(model, params, tol=0.1,
      ...                                 patience=True, random_state=42)
      >>> _get_SHA_params(SHA)
      {'aggressiveness': 3,
       'max_iter': 100,
       'n_initial_iter': 9,
       'n_initial_parameters': 10,
       'patience': True,
       'random_state': 42,
       'scoring': None,
       'test_size': None,
       'tol': 0.1}
  
      """
      return {
          k: v
          for k, v in SHA.get_params().items()
          if "estimator_" not in k
          and k != "parameters"
          and k != "estimator"
      }
  
file_path: dask-ml/dask_ml/model_selection/_hyperband.py
imports: from __future__ import division
  import asyncio
  import logging
  import math
  from typing import Any, Callable, Dict, Iterable, List, Tuple, Union
  from warnings import warn
  import numpy
  from sklearn.utils import check_random_state
  from ._incremental import BaseIncrementalSearchCV
  from ._successive_halving import SuccessiveHalvingSearchCV
label_desc: Use image node_id 3 for calling a global function with example usage: _get_SHA_params(SHA) and returns: unknown