code: def transform(self, X):
      import scipy.sparse
      import sklearn.feature_selection
  
      # Because the pipeline guarantees that each feature is positive,
      # clip all values below zero to zero
      if self.score_func == sklearn.feature_selection.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()
      try:
          Xt = self.preprocessor.transform(X)
      except ValueError as e:
          if (
              "zero-size array to reduction operation maximum which has no "
              "identity" in e.message
          ):
              raise ValueError(
                  "%s removed all features." % self.__class__.__name__
              )
          else:
              raise e
  
      if Xt.shape[1] == 0:
          raise ValueError(
              "%s removed all features." % self.__class__.__name__
          )
      return Xt
  
file_path: auto-sklearn/autosklearn/pipeline/components/feature_preprocessing/select_rates_classification.py
imports: from typing import Optional
  from functools import partial
  from ConfigSpace import NotEqualsCondition
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, UniformFloatHyperparameter
  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 SelectClassificationRates obj's underlying member method code with example usage: obj.transform(X) and returns: Xt