code: def runTest(self):
      document_assembler = (
          DocumentAssembler()
          .setInputCol("text")
          .setOutputCol("document")
      )
      tokenizer = (
          Tokenizer().setInputCols(["document"]).setOutputCol("token")
      )
      entity_extractor = (
          TextMatcher()
          .setOutputCol("entity")
          .setEntities(
              path="file:///"
              + os.getcwd()
              + "/../src/test/resources/entity-extractor/test-chunks.txt"
          )
      )
      chunk2doc = (
          Chunk2Doc()
          .setInputCols(["entity"])
          .setOutputCol("entity_doc")
      )
      doc2chunk = (
          Doc2Chunk()
          .setInputCols(["entity_doc"])
          .setOutputCol("entity_rechunk")
      )
  
      pipeline = Pipeline(
          stages=[
              document_assembler,
              tokenizer,
              entity_extractor,
              chunk2doc,
              doc2chunk,
          ]
      )
  
      model = pipeline.fit(self.data)
      pipe_path = "file:///" + os.getcwd() + "/tmp_chunkdoc"
      model.write().overwrite().save(pipe_path)
      PipelineModel.load(pipe_path)
  
file_path: spark-nlp/python/test/annotator/chunk_doc_serializing_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 ChunkDocSerializingTestSpec obj's underlying member method code with example usage: obj.runTest() without return types