code: def __init__(
      self,
      n_clusters,
      affinity,
      linkage,
      pooling_func,
      random_state=None,
  ):
      self.n_clusters = n_clusters
      self.affinity = affinity
      self.linkage = linkage
      self.pooling_func = pooling_func
      self.random_state = random_state
  
      self.pooling_func_mapping = dict(
          mean=np.mean, median=np.median, max=np.max
      )
  
file_path: auto-sklearn/autosklearn/pipeline/components/feature_preprocessing/feature_agglomeration.py
imports: from typing import Optional
  import numpy
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.forbidden import ForbiddenAndConjunction, ForbiddenEqualsClause, ForbiddenInClause
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, UniformIntegerHyperparameter
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
  from autosklearn.pipeline.constants import DENSE, INPUT, UNSIGNED_DATA
label_desc: Use image node_id 1 to create a new FeatureAgglomeration object from inherited base classes: AutoSklearnPreprocessingAlgorithm with example: obj = FeatureAgglomeration(n_clusters, affinity, linkage, pooling_func, random_state)