code: def runTest(self):
      document_assembler = (
          DocumentAssembler()
          .setInputCol("text")
          .setOutputCol("document")
      )
      sentence_detector = (
          SentenceDetector()
          .setInputCols(["document"])
          .setOutputCol("sentence")
      )
      tokenizer = (
          Tokenizer().setInputCols(["sentence"]).setOutputCol("token")
      )
      albert = (
          BertEmbeddings.loadSavedModel(
              os.getcwd() + "/../src/test/resources/tf-hub-bert/model",
              SparkContextForTest.spark,
          )
          .setInputCols(["sentence", "token"])
          .setOutputCol("embeddings")
      )
  
      pipeline = Pipeline(
          stages=[
              document_assembler,
              sentence_detector,
              tokenizer,
              albert,
          ]
      )
  
      model = pipeline.fit(self.data)
      model.write().overwrite().save("./tmp_bert_pipeline_model")
      model.transform(self.data).show()
  
file_path: spark-nlp/python/test/annotator/embeddings/bert_embeddings_test.py
imports: import os
  import unittest
  import pytest
  from sparknlp.annotator import *
  from sparknlp.base import *
  from test.annotator.common.has_max_sentence_length_test import HasMaxSentenceLengthTests
  from test.util import SparkContextForTest
label_desc: Use image node_id 2 for calling the BertEmbeddingsLoadSavedModelTestSpec obj's underlying member method code with example usage: obj.runTest() without return types