code: def test_auto_preprocessing_mode():
      funcs = [
          data_with_only_categorical_features,
          data_with_too_much_nans,
          data_with_spaces_and_nans_in_features,
          data_with_nans_in_target_column,
          data_with_nans_in_multi_target,
      ]
  
      # Check for all datasets
      for data_generator in funcs:
          input_data = data_generator()
          single_processing = Fedot(
              problem="regression", use_auto_preprocessing=True
          )
          multi_processing = Fedot(
              problem="regression", use_auto_preprocessing=False
          )
  
          pipeline_single = single_processing.fit(
              input_data, predefined_model="auto"
          )
          pipeline_multi = multi_processing.fit(
              input_data, predefined_model="auto"
          )
  
          prediction_single = pipeline_single.predict(input_data)
          prediction_multi = pipeline_multi.predict(input_data)
  
          assert (
              prediction_single.features.shape
              == prediction_multi.features.shape
          )
          assert (
              prediction_single.features == prediction_single.features
          ).all()
          assert (
              prediction_single.predict == prediction_single.predict
          ).all()
  
imports: import numpy
  from fedot import Fedot
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.supplementary_data import SupplementaryData
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum
  from fedot.preprocessing.data_types import TYPE_TO_ID
label_desc: Use image node_id 14 for calling a global function with example usage: test_auto_preprocessing_mode() without return types