code: def runTest(self):
      document_assembler = (
          DocumentAssembler()
          .setInputCol("text")
          .setOutputCol("document")
      )
      tokenizer = (
          Tokenizer().setInputCols(["document"]).setOutputCol("token")
      )
      entity_extractor = (
          TextMatcher()
          .setInputCols(["document", "token"])
          .setOutputCol("entity")
          .setEntities(
              path="file:///"
              + os.getcwd()
              + "/../src/test/resources/entity-extractor/test-chunks.txt"
          )
      )
      chunk_tokenizer = (
          ChunkTokenizer()
          .setInputCols(["entity"])
          .setOutputCol("chunk_token")
      )
  
      pipeline = Pipeline(
          stages=[
              document_assembler,
              tokenizer,
              entity_extractor,
              chunk_tokenizer,
          ]
      )
  
      data = self.session.createDataFrame(
          [
              [
                  "Hello world, my name is Michael, I am an artist and I work at Benezar"
              ],
              [
                  "Robert, an engineer from Farendell, graduated last year. The other one, Lucas, graduated last week."
              ],
          ]
      ).toDF("text")
  
      pipeline.fit(data).transform(data).show()
  
file_path: spark-nlp/python/test/annotator/token/chunk_tokenizer_test.py
imports: import os
  import unittest
  import pytest
  from sparknlp.annotator import *
  from sparknlp.base import *
  from test.util import SparkContextForTest
label_desc: Use image node_id 2 for calling the ChunkTokenizerTestSpec obj's underlying member method code with example usage: obj.runTest() without return types