code: def transform(self, X):
      import scipy.sparse
  
      # Because the pipeline guarantees that each feature is positive,
      # clip all values below zero to zero
      if self.kernel == "chi2":
          if scipy.sparse.issparse(X):
              X.data[X.data < 0] = 0.0
          else:
              X[X < 0] = 0.0
  
      if self.preprocessor is None:
          raise NotImplementedError()
      return self.preprocessor.transform(X)
  
file_path: auto-sklearn/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py
imports: from typing import Optional
  from ConfigSpace.conditions import EqualsCondition, InCondition
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, UniformFloatHyperparameter, UniformIntegerHyperparameter
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
  from autosklearn.pipeline.constants import DENSE, INPUT, SIGNED_DATA, SPARSE, UNSIGNED_DATA
label_desc: Use image node_id 3 for calling the Nystroem obj's underlying member method code with example usage: obj.transform(X) and returns: self