code: def test_clean_text_preprocessing():
      test_text = [
          "This is the first document.",
          "This document is the second document.",
          "And this is the third one.",
          "Is this the first document?",
      ]
  
      input_data = InputData(
          features=np.array(test_text),
          target=np.array([0, 1, 1, 0]),
          idx=np.arange(0, len(test_text)),
          task=Task(TaskTypesEnum.classification),
          data_type=DataTypesEnum.text,
      )
  
      preprocessing_pipeline = Pipeline(PipelineNode("text_clean"))
      preprocessing_pipeline.fit(input_data)
  
      predicted_output = preprocessing_pipeline.predict(input_data)
      cleaned_text = predicted_output.predict
  
      assert len(test_text) == len(cleaned_text)
  
imports: import numpy
  from fedot.core.data.data import InputData
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum
label_desc: Use image node_id 1 for calling a global function with example usage: test_clean_text_preprocessing() without return types