code: def test_run(self):
      document_assembler = (
          DocumentAssembler()
          .setInputCol("text")
          .setOutputCol("document")
      )
      sentence_detector = (
          SentenceDetector()
          .setInputCols(["document"])
          .setOutputCol("sentence")
      )
      tokenizer = (
          Tokenizer().setInputCols(["sentence"]).setOutputCol("token")
      )
      albert = self.tested_annotator
  
      pipeline = Pipeline(
          stages=[
              document_assembler,
              sentence_detector,
              tokenizer,
              albert,
          ]
      )
  
      model = pipeline.fit(self.data)
      model.transform(self.data).show()
  
file_path: spark-nlp/python/test/annotator/embeddings/albert_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 AlbertEmbeddingsTestSpec obj's underlying member method code with example usage: obj.test_run() without return types