code: def runTest(self):
      document_assembler = (
          DocumentAssembler()
          .setInputCol("text")
          .setOutputCol("document")
      )
      sentence_detector = (
          SentenceDetector()
          .setInputCols(["document"])
          .setOutputCol("sentence")
      )
      tokenizer = (
          Tokenizer().setInputCols(["sentence"]).setOutputCol("token")
      )
      pos_tagger = (
          PerceptronModel.pretrained()
          .setInputCols(["token", "sentence"])
          .setOutputCol("pos")
      )
      chunker = (
          Chunker()
          .setInputCols(["sentence", "pos"])
          .setOutputCol("chunk")
          .setRegexParsers(["<DT>?<JJ>*<NN>+"])
      )
      glove = (
          WordEmbeddingsModel.pretrained()
          .setInputCols(["sentence", "token"])
          .setOutputCol("embeddings")
      )
      chunk_embeddings = (
          ChunkEmbeddings()
          .setInputCols(["chunk", "embeddings"])
          .setOutputCol("chunk_embeddings")
          .setPoolingStrategy("AVERAGE")
      )
  
      pipeline = Pipeline(
          stages=[
              document_assembler,
              sentence_detector,
              tokenizer,
              pos_tagger,
              chunker,
              glove,
              chunk_embeddings,
          ]
      )
  
      model = pipeline.fit(self.data)
      model.transform(self.data).show()
  
file_path: spark-nlp/python/test/annotator/embeddings/chunk_embeddings_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 ChunkEmbeddingsTestSpec obj's underlying member method code with example usage: obj.runTest() without return types