code: def set_hyperparameters(
      self,
      configuration: Configuration,
      init_params: Optional[Dict[str, Any]] = None,
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
  ) -> "CoalescenseChoice":
      new_params = {}
  
      params = configuration.get_dictionary()
      choice = params["__choice__"]
      del params["__choice__"]
  
      for param, value in params.items():
          param = param.replace(choice, "").replace(":", "")
          new_params[param] = value
  
      if init_params is not None:
          for param, value in init_params.items():
              # These next two lines are different than in the base class -
              # they allow removing the categorical feature indicator array
              #  in order to not pass it to the no encoding
              if choice not in param:
                  continue
              param = param.replace(choice, "").replace(":", "")
              new_params[param] = value
  
      new_params["random_state"] = self.random_state
  
      self.new_params = new_params
      new_params["feat_type"] = feat_type
      self.choice = self.get_components()[choice](**new_params)
  
      return self
  
file_path: auto-sklearn/autosklearn/pipeline/components/data_preprocessing/minority_coalescense/__init__.py
imports: from typing import Any, Dict, Optional
  import os
  from collections import OrderedDict
  from ConfigSpace import Configuration
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter
  from sklearn.base import BaseEstimator
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE
  from ...base import AutoSklearnChoice, AutoSklearnPreprocessingAlgorithm, ThirdPartyComponents, _addons, find_components
label_desc: Use image node_id 3 for calling the CoalescenseChoice obj's underlying member method code with example usage: obj.set_hyperparameters(configuration, init_params, feat_type) and returns: self