code: def _test_preprocessing(
      Preprocessor,
      dataset="iris",
      make_sparse=False,
      train_size_maximum=150,
  ):
      X_train, Y_train, X_test, Y_test = get_dataset(
          dataset=dataset,
          make_sparse=make_sparse,
          train_size_maximum=train_size_maximum,
      )
      original_X_train = X_train.copy()
      configuration_space = (
          Preprocessor.get_hyperparameter_search_space()
      )
      default_config = configuration_space.get_default_configuration()
  
      preprocessor = Preprocessor(random_state=0, **default_config)
  
      transformer = preprocessor.fit(X_train, Y_train)
      return transformer.transform(X_train), original_X_train
  
imports: import importlib
  import inspect
  import os
  import pkgutil
  import numpy
  import scipy.sparse
  import sklearn
  import sklearn.base
  import sklearn.datasets
  import unittest
label_desc: Use image node_id 6 for calling a global function with example usage: _test_preprocessing(Preprocessor, dataset, make_sparse, train_size_maximum) and returns: transformer,  original_X_train